Compare commits

..

1 Commits

Author SHA1 Message Date
teknium1
f984cc335b feat: enhance auxiliary model configuration and environment variable handling
- Added support for auxiliary model overrides in the configuration, allowing users to specify providers and models for vision and web extraction tasks.
- Updated the CLI configuration example to include new auxiliary model settings.
- Enhanced the environment variable mapping in the CLI to accommodate auxiliary model configurations.
- Improved the resolution logic for auxiliary clients to support task-specific provider overrides.
- Updated relevant documentation and comments for clarity on the new features and their usage.
2026-03-07 08:52:06 -08:00
898 changed files with 13126 additions and 182137 deletions

View File

@@ -24,14 +24,10 @@ GLM_API_KEY=
# =============================================================================
# LLM PROVIDER (Kimi / Moonshot)
# =============================================================================
# Kimi Code provides access to Moonshot AI coding models (kimi-k2.5, etc.)
# Get your key at: https://platform.kimi.ai (Kimi Code console)
# Keys prefixed sk-kimi- use the Kimi Code API (api.kimi.com) by default.
# Legacy keys from platform.moonshot.ai need KIMI_BASE_URL override below.
# Kimi/Moonshot provides access to Moonshot AI coding models
# Get your key at: https://platform.moonshot.ai
KIMI_API_KEY=
# KIMI_BASE_URL=https://api.kimi.com/coding/v1 # Default for sk-kimi- keys
# KIMI_BASE_URL=https://api.moonshot.ai/v1 # For legacy Moonshot keys
# KIMI_BASE_URL=https://api.moonshot.cn/v1 # For Moonshot China keys
# KIMI_BASE_URL=https://api.moonshot.ai/v1 # Override default base URL
# =============================================================================
# LLM PROVIDER (MiniMax)
@@ -45,34 +41,17 @@ MINIMAX_API_KEY=
MINIMAX_CN_API_KEY=
# MINIMAX_CN_BASE_URL=https://api.minimaxi.com/v1 # Override default base URL
# =============================================================================
# LLM PROVIDER (OpenCode Zen)
# =============================================================================
# OpenCode Zen provides curated, tested models (GPT, Claude, Gemini, MiniMax, GLM, Kimi)
# Pay-as-you-go pricing. Get your key at: https://opencode.ai/auth
OPENCODE_ZEN_API_KEY=
# OPENCODE_ZEN_BASE_URL=https://opencode.ai/zen/v1 # Override default base URL
# =============================================================================
# LLM PROVIDER (OpenCode Go)
# =============================================================================
# OpenCode Go provides access to open models (GLM-5, Kimi K2.5, MiniMax M2.5)
# $10/month subscription. Get your key at: https://opencode.ai/auth
OPENCODE_GO_API_KEY=
# OPENCODE_GO_BASE_URL=https://opencode.ai/zen/go/v1 # Override default base URL
# =============================================================================
# TOOL API KEYS
# =============================================================================
# Parallel API Key - AI-native web search and extract
# Get at: https://parallel.ai
PARALLEL_API_KEY=
# Firecrawl API Key - Web search, extract, and crawl
# Get at: https://firecrawl.dev/
FIRECRAWL_API_KEY=
# Nous Research API Key - Vision analysis and multi-model reasoning
# Get at: https://inference-api.nousresearch.com/
NOUS_API_KEY=
# FAL.ai API Key - Image generation
# Get at: https://fal.ai/
@@ -222,18 +201,6 @@ VOICE_TOOLS_OPENAI_KEY=
# WHATSAPP_ENABLED=false
# WHATSAPP_ALLOWED_USERS=15551234567
# Email (IMAP/SMTP — send and receive emails as Hermes)
# For Gmail: enable 2FA → create App Password at https://myaccount.google.com/apppasswords
# EMAIL_ADDRESS=hermes@gmail.com
# EMAIL_PASSWORD=xxxx xxxx xxxx xxxx
# EMAIL_IMAP_HOST=imap.gmail.com
# EMAIL_IMAP_PORT=993
# EMAIL_SMTP_HOST=smtp.gmail.com
# EMAIL_SMTP_PORT=587
# EMAIL_POLL_INTERVAL=15
# EMAIL_ALLOWED_USERS=your@email.com
# EMAIL_HOME_ADDRESS=your@email.com
# Gateway-wide: allow ALL users without an allowlist (default: false = deny)
# Only set to true if you intentionally want open access.
# GATEWAY_ALLOW_ALL_USERS=false
@@ -296,27 +263,3 @@ WANDB_API_KEY=
# GITHUB_APP_ID=
# GITHUB_APP_PRIVATE_KEY_PATH=
# GITHUB_APP_INSTALLATION_ID=
# Groq API key (free tier — used for Whisper STT in voice mode)
# GROQ_API_KEY=
# =============================================================================
# STT PROVIDER SELECTION
# =============================================================================
# Default STT provider is "local" (faster-whisper) — runs on your machine, no API key needed.
# Install with: pip install faster-whisper
# Model downloads automatically on first use (~150 MB for "base").
# To use cloud providers instead, set GROQ_API_KEY or VOICE_TOOLS_OPENAI_KEY above.
# Provider priority: local > groq > openai
# Configure in config.yaml: stt.provider: local | groq | openai
# =============================================================================
# STT ADVANCED OVERRIDES (optional)
# =============================================================================
# Override default STT models per provider (normally set via stt.model in config.yaml)
# STT_GROQ_MODEL=whisper-large-v3-turbo
# STT_OPENAI_MODEL=whisper-1
# Override STT provider endpoints (for proxies or self-hosted instances)
# GROQ_BASE_URL=https://api.groq.com/openai/v1
# STT_OPENAI_BASE_URL=https://api.openai.com/v1

View File

@@ -1,39 +0,0 @@
name: Docs Site Checks
on:
pull_request:
paths:
- 'website/**'
- '.github/workflows/docs-site-checks.yml'
workflow_dispatch:
jobs:
docs-site-checks:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: 20
cache: npm
cache-dependency-path: website/package-lock.json
- name: Install website dependencies
run: npm ci
working-directory: website
- uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install ascii-guard
run: python -m pip install ascii-guard
- name: Lint docs diagrams
run: npm run lint:diagrams
working-directory: website
- name: Build Docusaurus
run: npm run build
working-directory: website

View File

@@ -34,7 +34,7 @@ jobs:
- name: Run tests
run: |
source .venv/bin/activate
python -m pytest tests/ -q --ignore=tests/integration --tb=short -n auto
python -m pytest tests/ -q --ignore=tests/integration --tb=short
env:
# Ensure tests don't accidentally call real APIs
OPENROUTER_API_KEY: ""

105
.gitignore vendored
View File

@@ -1,55 +1,50 @@
/venv/
/_pycache/
*.pyc*
__pycache__/
.venv/
.vscode/
.env
.env.local
.env.development.local
.env.test.local
.env.production.local
.env.development
.env.test
export*
__pycache__/model_tools.cpython-310.pyc
__pycache__/web_tools.cpython-310.pyc
logs/
data/
.pytest_cache/
tmp/
temp_vision_images/
hermes-*/*
examples/
tests/quick_test_dataset.jsonl
tests/sample_dataset.jsonl
run_datagen_kimik2-thinking.sh
run_datagen_megascience_glm4-6.sh
run_datagen_sonnet.sh
source-data/*
run_datagen_megascience_glm4-6.sh
data/*
node_modules/
browser-use/
agent-browser/
# Private keys
*.ppk
*.pem
privvy*
images/
__pycache__/
hermes_agent.egg-info/
wandb/
testlogs
# CLI config (may contain sensitive SSH paths)
cli-config.yaml
# Skills Hub state (lives in ~/.hermes/skills/.hub/ at runtime, but just in case)
skills/.hub/
ignored/
.worktrees/
environments/benchmarks/evals/
# Release script temp files
.release_notes.md
/venv/
/_pycache/
*.pyc*
__pycache__/
.venv/
.vscode/
.env
.env.local
.env.development.local
.env.test.local
.env.production.local
.env.development
.env.test
export*
__pycache__/model_tools.cpython-310.pyc
__pycache__/web_tools.cpython-310.pyc
logs/
data/
.pytest_cache/
tmp/
temp_vision_images/
hermes-*/*
examples/
tests/quick_test_dataset.jsonl
tests/sample_dataset.jsonl
run_datagen_kimik2-thinking.sh
run_datagen_megascience_glm4-6.sh
run_datagen_sonnet.sh
source-data/*
run_datagen_megascience_glm4-6.sh
data/*
node_modules/
browser-use/
agent-browser/
# Private keys
*.ppk
*.pem
privvy*
images/
__pycache__/
hermes_agent.egg-info/
wandb/
testlogs
# CLI config (may contain sensitive SSH paths)
cli-config.yaml
# Skills Hub state (lives in ~/.hermes/skills/.hub/ at runtime, but just in case)
skills/.hub/
ignored/

View File

@@ -1,291 +0,0 @@
# OpenAI-Compatible API Server for Hermes Agent
## Motivation
Every major chat frontend (Open WebUI 126k★, LobeChat 73k★, LibreChat 34k★,
AnythingLLM 56k★, NextChat 87k★, ChatBox 39k★, Jan 26k★, HF Chat-UI 8k★,
big-AGI 7k★) connects to backends via the OpenAI-compatible REST API with
SSE streaming. By exposing this endpoint, hermes-agent becomes instantly
usable as a backend for all of them — no custom adapters needed.
## What It Enables
```
┌──────────────────┐
│ Open WebUI │──┐
│ LobeChat │ │ POST /v1/chat/completions
│ LibreChat │ ├──► Authorization: Bearer <key> ┌─────────────────┐
│ AnythingLLM │ │ {"messages": [...]} │ hermes-agent │
│ NextChat │ │ │ gateway │
│ Any OAI client │──┘ ◄── SSE streaming response │ (API server) │
└──────────────────┘ └─────────────────┘
```
A user would:
1. Set `API_SERVER_ENABLED=true` in `~/.hermes/.env`
2. Run `hermes gateway` (API server starts alongside Telegram/Discord/etc.)
3. Point Open WebUI (or any frontend) at `http://localhost:8642/v1`
4. Chat with hermes-agent through any OpenAI-compatible UI
## Endpoints
| Method | Path | Purpose |
|--------|------|---------|
| POST | `/v1/chat/completions` | Chat with the agent (streaming + non-streaming) |
| GET | `/v1/models` | List available "models" (returns hermes-agent as a model) |
| GET | `/health` | Health check |
## Architecture
### Option A: Gateway Platform Adapter (recommended)
Create `gateway/platforms/api_server.py` as a new platform adapter that
extends `BasePlatformAdapter`. This is the cleanest approach because:
- Reuses all gateway infrastructure (session management, auth, context building)
- Runs in the same async loop as other adapters
- Gets message handling, interrupt support, and session persistence for free
- Follows the established pattern (like Telegram, Discord, etc.)
- Uses `aiohttp.web` (already a dependency) for the HTTP server
The adapter would start an `aiohttp.web.Application` server in `connect()`
and route incoming HTTP requests through the standard `handle_message()` pipeline.
### Option B: Standalone Component
A separate HTTP server class in `gateway/api_server.py` that creates its own
AIAgent instances directly. Simpler but duplicates session/auth logic.
**Recommendation: Option A** — fits the existing architecture, less code to
maintain, gets all gateway features for free.
## Request/Response Format
### Chat Completions (non-streaming)
```
POST /v1/chat/completions
Authorization: Bearer hermes-api-key-here
Content-Type: application/json
{
"model": "hermes-agent",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What files are in the current directory?"}
],
"stream": false,
"temperature": 0.7
}
```
Response:
```json
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"created": 1710000000,
"model": "hermes-agent",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "Here are the files in the current directory:\n..."
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 50,
"completion_tokens": 200,
"total_tokens": 250
}
}
```
### Chat Completions (streaming)
Same request with `"stream": true`. Response is SSE:
```
data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}
data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"Here "},"finish_reason":null}]}
data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"are "},"finish_reason":null}]}
data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
data: [DONE]
```
### Models List
```
GET /v1/models
Authorization: Bearer hermes-api-key-here
```
Response:
```json
{
"object": "list",
"data": [{
"id": "hermes-agent",
"object": "model",
"created": 1710000000,
"owned_by": "hermes-agent"
}]
}
```
## Key Design Decisions
### 1. Session Management
The OpenAI API is stateless — each request includes the full conversation.
But hermes-agent sessions have persistent state (memory, skills, tool context).
**Approach: Hybrid**
- Default: Stateless. Each request is independent. The `messages` array IS
the conversation. No session persistence between requests.
- Opt-in persistent sessions via `X-Session-ID` header. When provided, the
server maintains session state across requests (conversation history,
memory context, tool state). This enables richer agent behavior.
- The session ID also enables interrupt support — a subsequent request with
the same session ID while one is running triggers an interrupt.
### 2. Streaming
The agent's `run_conversation()` is synchronous and returns the full response.
For real SSE streaming, we need to emit chunks as they're generated.
**Phase 1 (MVP):** Run agent in a thread, return the complete response as
a single SSE chunk + `[DONE]`. This works with all frontends — they just see
a fast single-chunk response. Not true streaming but functional.
**Phase 2:** Add a response callback to AIAgent that emits text chunks as the
LLM generates them. The API server captures these via a queue and streams them
as SSE events. This gives real token-by-token streaming.
**Phase 3:** Stream tool execution progress too — emit tool call/result events
as the agent works, giving frontends visibility into what the agent is doing.
### 3. Tool Transparency
Two modes:
- **Opaque (default):** Frontends see only the final response. Tool calls
happen server-side and are invisible. Best for general-purpose UIs.
- **Transparent (opt-in via header):** Tool calls are emitted as OpenAI-format
tool_call/tool_result messages in the stream. Useful for agent-aware frontends.
### 4. Authentication
- Bearer token via `Authorization: Bearer <key>` header
- Token configured via `API_SERVER_KEY` env var
- Optional: allow unauthenticated local-only access (127.0.0.1 bind)
- Follows the same pattern as other platform adapters
### 5. Model Mapping
Frontends send `"model": "hermes-agent"` (or whatever). The actual LLM model
used is configured server-side in config.yaml. The API server maps any
requested model name to the configured hermes-agent model.
Optionally, allow model passthrough: if the frontend sends
`"model": "anthropic/claude-sonnet-4"`, the agent uses that model. Controlled
by a config flag.
## Configuration
```yaml
# In config.yaml
api_server:
enabled: true
port: 8642
host: "127.0.0.1" # localhost only by default
key: "your-secret-key" # or via API_SERVER_KEY env var
allow_model_override: false # let clients choose the model
max_concurrent: 5 # max simultaneous requests
```
Environment variables:
```bash
API_SERVER_ENABLED=true
API_SERVER_PORT=8642
API_SERVER_HOST=127.0.0.1
API_SERVER_KEY=your-secret-key
```
## Implementation Plan
### Phase 1: MVP (non-streaming) — PR
1. `gateway/platforms/api_server.py` — new adapter
- aiohttp.web server with endpoints:
- `POST /v1/chat/completions` — Chat Completions API (universal compat)
- `POST /v1/responses` — Responses API (server-side state, tool preservation)
- `GET /v1/models` — list available models
- `GET /health` — health check
- Bearer token auth middleware
- Non-streaming responses (run agent, return full result)
- Chat Completions: stateless, messages array is the conversation
- Responses API: server-side conversation storage via previous_response_id
- Store full internal conversation (including tool calls) keyed by response ID
- On subsequent requests, reconstruct full context from stored chain
- Frontend system prompt layered on top of hermes-agent's core prompt
2. `gateway/config.py` — add `Platform.API_SERVER` enum + config
3. `gateway/run.py` — register adapter in `_create_adapter()`
4. Tests in `tests/gateway/test_api_server.py`
### Phase 2: SSE Streaming
1. Add response streaming to both endpoints
- Chat Completions: `choices[0].delta.content` SSE format
- Responses API: semantic events (response.output_text.delta, etc.)
- Run agent in thread, collect output via callback queue
- Handle client disconnect (cancel agent)
2. Add `stream_callback` parameter to `AIAgent.run_conversation()`
### Phase 3: Enhanced Features
1. Tool call transparency mode (opt-in)
2. Model passthrough/override
3. Concurrent request limiting
4. Usage tracking / rate limiting
5. CORS headers for browser-based frontends
6. GET /v1/responses/{id} — retrieve stored response
7. DELETE /v1/responses/{id} — delete stored response
## Files Changed
| File | Change |
|------|--------|
| `gateway/platforms/api_server.py` | NEW — main adapter (~300 lines) |
| `gateway/config.py` | Add Platform.API_SERVER + config (~20 lines) |
| `gateway/run.py` | Register adapter in _create_adapter() (~10 lines) |
| `tests/gateway/test_api_server.py` | NEW — tests (~200 lines) |
| `cli-config.yaml.example` | Add api_server section |
| `README.md` | Mention API server in platform list |
## Compatibility Matrix
Once implemented, hermes-agent works as a drop-in backend for:
| Frontend | Stars | How to Connect |
|----------|-------|---------------|
| Open WebUI | 126k | Settings → Connections → Add OpenAI API, URL: `http://localhost:8642/v1` |
| NextChat | 87k | BASE_URL env var |
| LobeChat | 73k | Custom provider endpoint |
| AnythingLLM | 56k | LLM Provider → Generic OpenAI |
| Oobabooga | 42k | Already a backend, not a frontend |
| ChatBox | 39k | API Host setting |
| LibreChat | 34k | librechat.yaml custom endpoint |
| Chatbot UI | 29k | Custom API endpoint |
| Jan | 26k | Remote model config |
| AionUI | 18k | Custom API endpoint |
| HF Chat-UI | 8k | OPENAI_BASE_URL env var |
| big-AGI | 7k | Custom endpoint |

View File

@@ -1,705 +0,0 @@
# Streaming LLM Response Support for Hermes Agent
## Overview
Add token-by-token streaming of LLM responses across all platforms. When enabled,
users see the response typing out live instead of waiting for the full generation.
Streaming is opt-in via config, defaults to off, and all existing non-streaming
code paths remain intact as the default.
## Design Principles
1. **Feature-flagged**: `streaming.enabled: true` in config.yaml. Off by default.
When off, all existing code paths are unchanged — zero risk to current behavior.
2. **Callback-based**: A simple `stream_callback(text_delta: str)` function injected
into AIAgent. The agent doesn't know or care what the consumer does with tokens.
3. **Graceful degradation**: If the provider doesn't support streaming, or streaming
fails for any reason, silently fall back to the non-streaming path.
4. **Platform-agnostic core**: The streaming mechanism in AIAgent works the same
regardless of whether the consumer is CLI, Telegram, Discord, or the API server.
---
## Architecture
```
stream_callback(delta)
┌─────────────┐ ┌─────────────▼──────────────┐
│ LLM API │ │ queue.Queue() │
│ (stream) │───►│ thread-safe bridge between │
│ │ │ agent thread & consumer │
└─────────────┘ └─────────────┬──────────────┘
┌──────────────┼──────────────┐
│ │ │
┌─────▼─────┐ ┌─────▼─────┐ ┌─────▼─────┐
│ CLI │ │ Gateway │ │ API Server│
│ print to │ │ edit msg │ │ SSE event │
│ terminal │ │ on Tg/Dc │ │ to client │
└───────────┘ └───────────┘ └───────────┘
```
The agent runs in a thread. The callback puts tokens into a thread-safe queue.
Each consumer reads the queue in its own context (async task, main thread, etc.).
---
## Configuration
### config.yaml
```yaml
streaming:
enabled: false # Master switch. Default off.
# Per-platform overrides (optional):
# cli: true # Override for CLI only
# telegram: true # Override for Telegram only
# discord: false # Keep Discord non-streaming
# api_server: true # Override for API server
```
### Environment variables
```
HERMES_STREAMING_ENABLED=true # Master switch via env
```
### How the flag is read
- **CLI**: `load_cli_config()` reads `streaming.enabled`, sets env var. AIAgent
checks at init time.
- **Gateway**: `_run_agent()` reads config, decides whether to pass
`stream_callback` to the AIAgent constructor.
- **API server**: For Chat Completions `stream=true` requests, always uses streaming
regardless of config (the client is explicitly requesting it). For non-stream
requests, uses config.
### Precedence
1. API server: client's `stream` field overrides everything
2. Per-platform config override (e.g., `streaming.telegram: true`)
3. Master `streaming.enabled` flag
4. Default: off
---
## Implementation Plan
### Phase 1: Core streaming infrastructure in AIAgent
**File: run_agent.py**
#### 1a. Add stream_callback parameter to __init__ (~5 lines)
```python
def __init__(self, ..., stream_callback: callable = None, ...):
self.stream_callback = stream_callback
```
No other init changes. The callback is optional — when None, everything
works exactly as before.
#### 1b. Add _run_streaming_chat_completion() method (~65 lines)
New method for Chat Completions API streaming:
```python
def _run_streaming_chat_completion(self, api_kwargs: dict):
"""Stream a chat completion, emitting text tokens via stream_callback.
Returns a fake response object compatible with the non-streaming code path.
Falls back to non-streaming on any error.
"""
stream_kwargs = dict(api_kwargs)
stream_kwargs["stream"] = True
stream_kwargs["stream_options"] = {"include_usage": True}
accumulated_content = []
accumulated_tool_calls = {} # index -> {id, name, arguments}
final_usage = None
try:
stream = self.client.chat.completions.create(**stream_kwargs)
for chunk in stream:
if not chunk.choices:
# Usage-only chunk (final)
if chunk.usage:
final_usage = chunk.usage
continue
delta = chunk.choices[0].delta
# Text content — emit via callback
if delta.content:
accumulated_content.append(delta.content)
if self.stream_callback:
try:
self.stream_callback(delta.content)
except Exception:
pass
# Tool call deltas — accumulate silently
if delta.tool_calls:
for tc_delta in delta.tool_calls:
idx = tc_delta.index
if idx not in accumulated_tool_calls:
accumulated_tool_calls[idx] = {
"id": tc_delta.id or "",
"name": "", "arguments": ""
}
if tc_delta.function:
if tc_delta.function.name:
accumulated_tool_calls[idx]["name"] = tc_delta.function.name
if tc_delta.function.arguments:
accumulated_tool_calls[idx]["arguments"] += tc_delta.function.arguments
# Build fake response compatible with existing code
tool_calls = []
for idx in sorted(accumulated_tool_calls):
tc = accumulated_tool_calls[idx]
if tc["name"]:
tool_calls.append(SimpleNamespace(
id=tc["id"], type="function",
function=SimpleNamespace(name=tc["name"], arguments=tc["arguments"]),
))
return SimpleNamespace(
choices=[SimpleNamespace(
message=SimpleNamespace(
content="".join(accumulated_content) or "",
tool_calls=tool_calls or None,
role="assistant",
),
finish_reason="tool_calls" if tool_calls else "stop",
)],
usage=final_usage,
model=self.model,
)
except Exception as e:
logger.debug("Streaming failed, falling back to non-streaming: %s", e)
return self.client.chat.completions.create(**api_kwargs)
```
#### 1c. Modify _run_codex_stream() for Responses API (~10 lines)
The method already iterates the stream. Add callback emission:
```python
def _run_codex_stream(self, api_kwargs: dict):
with self.client.responses.stream(**api_kwargs) as stream:
for event in stream:
# Emit text deltas if streaming callback is set
if self.stream_callback and hasattr(event, 'type'):
if event.type == 'response.output_text.delta':
try:
self.stream_callback(event.delta)
except Exception:
pass
return stream.get_final_response()
```
#### 1d. Modify _interruptible_api_call() (~5 lines)
Add the streaming branch:
```python
def _call():
try:
if self.api_mode == "codex_responses":
result["response"] = self._run_codex_stream(api_kwargs)
elif self.stream_callback is not None:
result["response"] = self._run_streaming_chat_completion(api_kwargs)
else:
result["response"] = self.client.chat.completions.create(**api_kwargs)
except Exception as e:
result["error"] = e
```
#### 1e. Signal end-of-stream to consumers (~5 lines)
After the API call returns, signal the callback that streaming is done
so consumers can finalize (remove cursor, close SSE, etc.):
```python
# In run_conversation(), after _interruptible_api_call returns:
if self.stream_callback:
try:
self.stream_callback(None) # None = end of stream signal
except Exception:
pass
```
Consumers check: `if delta is None: finalize()`
**Tests for Phase 1:** (~150 lines)
- Test _run_streaming_chat_completion with mocked stream
- Test fallback to non-streaming on error
- Test tool_call accumulation during streaming
- Test stream_callback receives correct deltas
- Test None signal at end of stream
- Test streaming disabled when callback is None
---
### Phase 2: Gateway consumers (Telegram, Discord, etc.)
**File: gateway/run.py**
#### 2a. Read streaming config (~15 lines)
In `_run_agent()`, before creating the AIAgent:
```python
# Read streaming config
_streaming_enabled = False
try:
# Check per-platform override first
platform_key = source.platform.value if source.platform else ""
_stream_cfg = {} # loaded from config.yaml streaming section
if _stream_cfg.get(platform_key) is not None:
_streaming_enabled = bool(_stream_cfg[platform_key])
else:
_streaming_enabled = bool(_stream_cfg.get("enabled", False))
except Exception:
pass
# Env var override
if os.getenv("HERMES_STREAMING_ENABLED", "").lower() in ("true", "1", "yes"):
_streaming_enabled = True
```
#### 2b. Set up queue + callback (~15 lines)
```python
_stream_q = None
_stream_done = None
_stream_msg_id = [None] # mutable ref for the async task
if _streaming_enabled:
import queue as _q
_stream_q = _q.Queue()
_stream_done = threading.Event()
def _on_token(delta):
if delta is None:
_stream_done.set()
else:
_stream_q.put(delta)
```
Pass `stream_callback=_on_token` to the AIAgent constructor.
#### 2c. Telegram/Discord stream preview task (~50 lines)
```python
async def stream_preview():
"""Progressively edit a message with streaming tokens."""
if not _stream_q:
return
adapter = self.adapters.get(source.platform)
if not adapter:
return
accumulated = []
token_count = 0
last_edit = 0.0
MIN_TOKENS = 20 # Don't show until enough context
EDIT_INTERVAL = 1.5 # Respect Telegram rate limits
try:
while not _stream_done.is_set():
try:
chunk = _stream_q.get(timeout=0.1)
accumulated.append(chunk)
token_count += 1
except queue.Empty:
continue
now = time.monotonic()
if token_count >= MIN_TOKENS and (now - last_edit) >= EDIT_INTERVAL:
preview = "".join(accumulated) + ""
if _stream_msg_id[0] is None:
r = await adapter.send(
chat_id=source.chat_id,
content=preview,
metadata=_thread_metadata,
)
if r.success and r.message_id:
_stream_msg_id[0] = r.message_id
else:
await adapter.edit_message(
chat_id=source.chat_id,
message_id=_stream_msg_id[0],
content=preview,
)
last_edit = now
# Drain remaining tokens
while not _stream_q.empty():
accumulated.append(_stream_q.get_nowait())
# Final edit — remove cursor, show complete text
if _stream_msg_id[0] and accumulated:
await adapter.edit_message(
chat_id=source.chat_id,
message_id=_stream_msg_id[0],
content="".join(accumulated),
)
except asyncio.CancelledError:
# Clean up on cancel
if _stream_msg_id[0] and accumulated:
try:
await adapter.edit_message(
chat_id=source.chat_id,
message_id=_stream_msg_id[0],
content="".join(accumulated),
)
except Exception:
pass
except Exception as e:
logger.debug("stream_preview error: %s", e)
```
#### 2d. Skip final send if already streamed (~10 lines)
In `_process_message_background()` (base.py), after getting the response,
if streaming was active and `_stream_msg_id[0]` is set, the final response
was already delivered via progressive edits. Skip the normal `self.send()`
call to avoid duplicating the message.
This is the most delicate integration point — we need to communicate from
the gateway's `_run_agent` back to the base adapter's response sender that
the response was already delivered. Options:
- **Option A**: Return a special marker in the result dict:
`result["_streamed_msg_id"] = _stream_msg_id[0]`
The base adapter checks this and skips `send()`.
- **Option B**: Edit the already-sent message with the final response
(which may differ slightly from accumulated tokens due to think-block
stripping, etc.) and don't send a new one.
- **Option C**: The stream preview task handles the FULL final response
(including any post-processing), and the handler returns None to skip
the normal send path.
Recommended: **Option A** — cleanest separation. The result dict already
carries metadata; adding one more field is low-risk.
**Platform-specific considerations:**
| Platform | Edit support | Rate limits | Streaming approach |
|----------|-------------|-------------|-------------------|
| Telegram | ✅ edit_message_text | ~20 edits/min | Edit every 1.5s |
| Discord | ✅ message.edit | 5 edits/5s per message | Edit every 1.2s |
| Slack | ✅ chat.update | Tier 3 (~50/min) | Edit every 1.5s |
| WhatsApp | ❌ no edit support | N/A | Skip streaming, use normal path |
| HomeAssistant | ❌ no edit | N/A | Skip streaming |
| API Server | ✅ SSE native | No limit | Real SSE events |
WhatsApp and HomeAssistant fall back to non-streaming automatically because
they don't support message editing.
**Tests for Phase 2:** (~100 lines)
- Test stream_preview sends/edits correctly
- Test skip-final-send when streaming delivered
- Test WhatsApp/HA graceful fallback
- Test streaming disabled per-platform config
- Test thread_id metadata forwarded in stream messages
---
### Phase 3: CLI streaming
**File: cli.py**
#### 3a. Set up callback in the CLI chat loop (~20 lines)
In `_chat_once()` or wherever the agent is invoked:
```python
if streaming_enabled:
_stream_q = queue.Queue()
_stream_done = threading.Event()
def _cli_stream_callback(delta):
if delta is None:
_stream_done.set()
else:
_stream_q.put(delta)
agent.stream_callback = _cli_stream_callback
```
#### 3b. Token display thread/task (~30 lines)
Start a thread that reads the queue and prints tokens:
```python
def _stream_display():
"""Print tokens to terminal as they arrive."""
first_token = True
while not _stream_done.is_set():
try:
delta = _stream_q.get(timeout=0.1)
except queue.Empty:
continue
if first_token:
# Print response box top border
_cprint(f"\n{top}")
first_token = False
sys.stdout.write(delta)
sys.stdout.flush()
# Drain remaining
while not _stream_q.empty():
sys.stdout.write(_stream_q.get_nowait())
sys.stdout.flush()
# Print bottom border
_cprint(f"\n\n{bot}")
```
**Integration challenge: prompt_toolkit**
The CLI uses prompt_toolkit which controls the terminal. Writing directly
to stdout while prompt_toolkit is active can cause display corruption.
The existing KawaiiSpinner already solves this by using prompt_toolkit's
`patch_stdout` context. The streaming display would need to do the same.
Alternative: use `_cprint()` for each token chunk (routes through
prompt_toolkit's renderer). But this might be slow for individual tokens.
Recommended approach: accumulate tokens in small batches (e.g., every 50ms)
and `_cprint()` the batch. This balances display responsiveness with
prompt_toolkit compatibility.
**Tests for Phase 3:** (~50 lines)
- Test CLI streaming callback setup
- Test response box borders with streaming
- Test fallback when streaming disabled
---
### Phase 4: API Server real streaming
**File: gateway/platforms/api_server.py**
Replace the pseudo-streaming `_write_sse_chat_completion()` with real
token-by-token SSE when the agent supports it.
#### 4a. Wire streaming callback for stream=true requests (~20 lines)
```python
if stream:
_stream_q = queue.Queue()
def _api_stream_callback(delta):
_stream_q.put(delta) # None = done
# Pass callback to _run_agent
result, usage = await self._run_agent(
..., stream_callback=_api_stream_callback,
)
```
#### 4b. Real SSE writer (~40 lines)
```python
async def _write_real_sse(self, request, completion_id, model, stream_q):
response = web.StreamResponse(
headers={"Content-Type": "text/event-stream", "Cache-Control": "no-cache"},
)
await response.prepare(request)
# Role chunk
await response.write(...)
# Stream content chunks as they arrive
while True:
try:
delta = await asyncio.get_event_loop().run_in_executor(
None, lambda: stream_q.get(timeout=0.1)
)
except queue.Empty:
continue
if delta is None: # End of stream
break
chunk = {"id": completion_id, "object": "chat.completion.chunk", ...
"choices": [{"delta": {"content": delta}, ...}]}
await response.write(f"data: {json.dumps(chunk)}\n\n".encode())
# Finish + [DONE]
await response.write(...)
await response.write(b"data: [DONE]\n\n")
return response
```
**Challenge: concurrent execution**
The agent runs in a thread executor. SSE writing happens in the async event
loop. The queue bridges them. But `_run_agent()` currently awaits the full
result before returning. For real streaming, we need to start the agent in
the background and stream tokens while it runs:
```python
# Start agent in background
agent_task = asyncio.create_task(self._run_agent_async(...))
# Stream tokens while agent runs
await self._write_real_sse(request, ..., stream_q)
# Agent is done by now (stream_q received None)
result, usage = await agent_task
```
This requires splitting `_run_agent` into an async version that doesn't
block waiting for the result, or running it in a separate task.
**Responses API SSE format:**
For `/v1/responses` with `stream=true`, the SSE events are different:
```
event: response.output_text.delta
data: {"type":"response.output_text.delta","delta":"Hello"}
event: response.completed
data: {"type":"response.completed","response":{...}}
```
This needs a separate SSE writer that emits Responses API format events.
**Tests for Phase 4:** (~80 lines)
- Test real SSE streaming with mocked agent
- Test SSE event format (Chat Completions vs Responses)
- Test client disconnect during streaming
- Test fallback to pseudo-streaming when callback not available
---
## Integration Issues & Edge Cases
### 1. Tool calls during streaming
When the model returns tool calls instead of text, no text tokens are emitted.
The stream_callback is simply never called with text. After tools execute, the
next API call may produce the final text response — streaming picks up again.
The stream preview task needs to handle this: if no tokens arrive during a
tool-call round, don't send/edit any message. The tool progress messages
continue working as before.
### 2. Duplicate messages
The biggest risk: the agent sends the final response normally (via the
existing send path) AND the stream preview already showed it. The user
sees the response twice.
Prevention: when streaming is active and tokens were delivered, the final
response send must be suppressed. The `result["_streamed_msg_id"]` marker
tells the base adapter to skip its normal send.
### 3. Response post-processing
The final response may differ from the accumulated streamed tokens:
- Think block stripping (`<think>...</think>` removed)
- Trailing whitespace cleanup
- Tool result media tag appending
The stream preview shows raw tokens. The final edit should use the
post-processed version. This means the final edit (removing the cursor)
should use the post-processed `final_response`, not just the accumulated
stream text.
### 4. Context compression during streaming
If the agent triggers context compression mid-conversation, the streaming
tokens from BEFORE compression are from a different context than those
after. This isn't a problem in practice — compression happens between
API calls, not during streaming.
### 5. Interrupt during streaming
User sends a new message while streaming → interrupt. The stream is killed
(HTTP connection closed), accumulated tokens are shown as-is (no cursor),
and the interrupt message is processed normally. This is already handled by
`_interruptible_api_call` closing the client.
### 6. Multi-model / fallback
If the primary model fails and the agent falls back to a different model,
streaming state resets. The fallback call may or may not support streaming.
The graceful fallback in `_run_streaming_chat_completion` handles this.
### 7. Rate limiting on edits
Telegram: ~20 edits/minute (~1 every 3 seconds to be safe)
Discord: 5 edits per 5 seconds per message
Slack: ~50 API calls/minute
The 1.5s edit interval is conservative enough for all platforms. If we get
429 rate limit errors on edits, just skip that edit cycle and try next time.
---
## Files Changed Summary
| File | Phase | Changes |
|------|-------|---------|
| `run_agent.py` | 1 | +stream_callback param, +_run_streaming_chat_completion(), modify _run_codex_stream(), modify _interruptible_api_call() |
| `gateway/run.py` | 2 | +streaming config reader, +queue/callback setup, +stream_preview task, +skip-final-send logic |
| `gateway/platforms/base.py` | 2 | +check for _streamed_msg_id in response handler |
| `cli.py` | 3 | +streaming setup, +token display, +response box integration |
| `gateway/platforms/api_server.py` | 4 | +real SSE writer, +streaming callback wiring |
| `hermes_cli/config.py` | 1 | +streaming config defaults |
| `cli-config.yaml.example` | 1 | +streaming section |
| `tests/test_streaming.py` | 1-4 | NEW — ~380 lines of tests |
**Total new code**: ~500 lines across all phases
**Total test code**: ~380 lines
---
## Rollout Plan
1. **Phase 1** (core): Merge to main. Streaming disabled by default.
Zero impact on existing behavior. Can be tested with env var.
2. **Phase 2** (gateway): Merge to main. Test on Telegram manually.
Enable per-platform: `streaming.telegram: true` in config.
3. **Phase 3** (CLI): Merge to main. Test in terminal.
Enable: `streaming.cli: true` or `streaming.enabled: true`.
4. **Phase 4** (API server): Merge to main. Test with Open WebUI.
Auto-enabled when client sends `stream: true`.
Each phase is independently mergeable and testable. Streaming stays
off by default throughout. Once all phases are stable, consider
changing the default to enabled.
---
## Config Reference (final state)
```yaml
# config.yaml
streaming:
enabled: false # Master switch (default: off)
cli: true # Per-platform override
telegram: true
discord: true
slack: true
api_server: true # API server always streams when client requests it
edit_interval: 1.5 # Seconds between message edits (default: 1.5)
min_tokens: 20 # Tokens before first display (default: 20)
```
```bash
# Environment variable override
HERMES_STREAMING_ENABLED=true
```

863
AGENTS.md
View File

@@ -1,68 +1,78 @@
# Hermes Agent - Development Guide
Instructions for AI coding assistants and developers working on the hermes-agent codebase.
Instructions for AI coding assistants (GitHub Copilot, Cursor, etc.) and human developers.
Hermes Agent is an AI agent harness with tool-calling capabilities, interactive CLI, messaging integrations, and scheduled tasks.
## Development Environment
**IMPORTANT**: Always use the virtual environment if it exists:
```bash
source venv/bin/activate # ALWAYS activate before running Python
source venv/bin/activate # Before running any Python commands
```
## Project Structure
```
hermes-agent/
├── run_agent.py # AIAgent class — core conversation loop
├── model_tools.py # Tool orchestration, _discover_tools(), handle_function_call()
├── toolsets.py # Toolset definitions, _HERMES_CORE_TOOLS list
├── cli.py # HermesCLI class — interactive CLI orchestrator
├── hermes_state.py # SessionDB — SQLite session store (FTS5 search)
├── agent/ # Agent internals
│ ├── prompt_builder.py # System prompt assembly
├── agent/ # Agent internals (extracted from run_agent.py)
├── model_metadata.py # Model context lengths, token estimation
│ ├── context_compressor.py # Auto context compression
│ ├── prompt_caching.py # Anthropic prompt caching
│ ├── auxiliary_client.py # Auxiliary LLM client (vision, summarization)
│ ├── model_metadata.py # Model context lengths, token estimation
│ ├── models_dev.py # models.dev registry integration (provider-aware context)
│ ├── prompt_builder.py # System prompt assembly (identity, skills index, context files)
│ ├── display.py # KawaiiSpinner, tool preview formatting
│ ├── skill_commands.py # Skill slash commands (shared CLI/gateway)
│ └── trajectory.py # Trajectory saving helpers
├── hermes_cli/ # CLI subcommands and setup
│ ├── main.py # Entry point — all `hermes` subcommands
│ ├── config.py # DEFAULT_CONFIG, OPTIONAL_ENV_VARS, migration
│ ├── commands.py # Slash command definitions + SlashCommandCompleter
│ ├── callbacks.py # Terminal callbacks (clarify, sudo, approval)
├── hermes_cli/ # CLI implementation
│ ├── main.py # Entry point, command dispatcher
│ ├── banner.py # Welcome banner, ASCII art, skills summary
│ ├── commands.py # Slash command definitions + autocomplete
│ ├── callbacks.py # Interactive prompt callbacks (clarify, sudo, approval)
│ ├── setup.py # Interactive setup wizard
│ ├── skin_engine.py # Skin/theme engine — CLI visual customization
│ ├── skills_config.py # `hermes skills` — enable/disable skills per platform
│ ├── tools_config.py # `hermes tools` — enable/disable tools per platform
│ ├── skills_hub.py # `/skills` slash command (search, browse, install)
│ ├── models.py # Model catalog, provider model lists
── auth.py # Provider credential resolution
├── tools/ # Tool implementations (one file per tool)
│ ├── registry.py # Central tool registry (schemas, handlers, dispatch)
│ ├── approval.py # Dangerous command detection
│ ├── terminal_tool.py # Terminal orchestration
│ ├── process_registry.py # Background process management
│ ├── file_tools.py # File read/write/search/patch
│ ├── web_tools.py # Web search/extract (Parallel + Firecrawl)
│ ├── browser_tool.py # Browserbase browser automation
│ ├── code_execution_tool.py # execute_code sandbox
│ ├── delegate_tool.py # Subagent delegation
│ ├── mcp_tool.py # MCP client (~1050 lines)
│ └── environments/ # Terminal backends (local, docker, ssh, modal, daytona, singularity)
├── gateway/ # Messaging platform gateway
│ ├── run.py # Main loop, slash commands, message dispatch
│ ├── session.py # SessionStore — conversation persistence
│ └── platforms/ # Adapters: telegram, discord, slack, whatsapp, homeassistant, signal
├── acp_adapter/ # ACP server (VS Code / Zed / JetBrains integration)
├── cron/ # Scheduler (jobs.py, scheduler.py)
├── environments/ # RL training environments (Atropos)
├── tests/ # Pytest suite (~3000 tests)
│ ├── config.py # Config management & migration
│ ├── status.py # Status display
│ ├── doctor.py # Diagnostics
│ ├── gateway.py # Gateway management
│ ├── uninstall.py # Uninstaller
── cron.py # Cron job management
│ └── skills_hub.py # Skills Hub CLI + /skills slash command
├── tools/ # Tool implementations
│ ├── registry.py # Central tool registry (schemas, handlers, dispatch)
│ ├── approval.py # Dangerous command detection + per-session approval
│ ├── environments/ # Terminal execution backends
│ ├── base.py # BaseEnvironment ABC
│ ├── local.py # Local execution with interrupt support
│ ├── docker.py # Docker container execution
│ ├── ssh.py # SSH remote execution
│ ├── singularity.py # Singularity/Apptainer + SIF management
│ ├── modal.py # Modal cloud execution
│ └── daytona.py # Daytona cloud sandboxes
│ ├── terminal_tool.py # Terminal orchestration (sudo, lifecycle, factory)
│ ├── todo_tool.py # Planning & task management
│ ├── process_registry.py # Background process management
│ └── ... # Other tool files
├── gateway/ # Messaging platform adapters
│ ├── platforms/ # Platform-specific adapters (telegram, discord, slack, whatsapp)
│ └── ...
├── cron/ # Scheduler implementation
├── environments/ # RL training environments (Atropos integration)
├── skills/ # Bundled skill sources
├── optional-skills/ # Official optional skills (not activated by default)
├── cli.py # Interactive CLI orchestrator (HermesCLI class)
├── run_agent.py # AIAgent class (core conversation loop)
├── model_tools.py # Tool orchestration (thin layer over tools/registry.py)
├── toolsets.py # Tool groupings
├── toolset_distributions.py # Probability-based tool selection
└── batch_runner.py # Parallel batch processing
```
**User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys)
**User Configuration** (stored in `~/.hermes/`):
- `~/.hermes/config.yaml` - Settings (model, terminal, toolsets, etc.)
- `~/.hermes/.env` - API keys and secrets
- `~/.hermes/pairing/` - DM pairing data
- `~/.hermes/hooks/` - Custom event hooks
- `~/.hermes/image_cache/` - Cached user images
- `~/.hermes/audio_cache/` - Cached user voice messages
- `~/.hermes/sticker_cache.json` - Telegram sticker descriptions
## File Dependency Chain
@@ -76,314 +86,603 @@ model_tools.py (imports tools/registry + triggers tool discovery)
run_agent.py, cli.py, batch_runner.py, environments/
```
Each tool file co-locates its schema, handler, and registration. `model_tools.py` is a thin orchestration layer.
---
## AIAgent Class (run_agent.py)
## AIAgent Class
The main agent is implemented in `run_agent.py`:
```python
class AIAgent:
def __init__(self,
model: str = "anthropic/claude-opus-4.6",
max_iterations: int = 90,
def __init__(
self,
model: str = "anthropic/claude-sonnet-4",
api_key: str = None,
base_url: str = "https://openrouter.ai/api/v1",
max_iterations: int = 60, # Max tool-calling loops
enabled_toolsets: list = None,
disabled_toolsets: list = None,
quiet_mode: bool = False,
save_trajectories: bool = False,
platform: str = None, # "cli", "telegram", etc.
session_id: str = None,
skip_context_files: bool = False,
skip_memory: bool = False,
# ... plus provider, api_mode, callbacks, routing params
): ...
def chat(self, message: str) -> str:
"""Simple interface — returns final response string."""
def run_conversation(self, user_message: str, system_message: str = None,
conversation_history: list = None, task_id: str = None) -> dict:
"""Full interface — returns dict with final_response + messages."""
verbose_logging: bool = False,
quiet_mode: bool = False, # Suppress progress output
tool_progress_callback: callable = None, # Called on each tool use
):
# Initialize OpenAI client, load tools based on toolsets
...
def chat(self, user_message: str, task_id: str = None) -> str:
# Main entry point - runs the agent loop
...
```
### Agent Loop
The core loop is inside `run_conversation()` — entirely synchronous:
The core loop in `_run_agent_loop()`:
```
1. Add user message to conversation
2. Call LLM with tools
3. If LLM returns tool calls:
- Execute each tool
- Add tool results to conversation
- Go to step 2
4. If LLM returns text response:
- Return response to user
```
```python
while api_call_count < self.max_iterations and self.iteration_budget.remaining > 0:
response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)
while turns < max_turns:
response = client.chat.completions.create(
model=model,
messages=messages,
tools=tool_schemas,
)
if response.tool_calls:
for tool_call in response.tool_calls:
result = handle_function_call(tool_call.name, tool_call.args, task_id)
result = await execute_tool(tool_call)
messages.append(tool_result_message(result))
api_call_count += 1
turns += 1
else:
return response.content
```
Messages follow OpenAI format: `{"role": "system/user/assistant/tool", ...}`. Reasoning content is stored in `assistant_msg["reasoning"]`.
### Conversation Management
Messages are stored as a list of dicts following OpenAI format:
```python
messages = [
{"role": "system", "content": "You are a helpful assistant..."},
{"role": "user", "content": "Search for Python tutorials"},
{"role": "assistant", "content": None, "tool_calls": [...]},
{"role": "tool", "tool_call_id": "...", "content": "..."},
{"role": "assistant", "content": "Here's what I found..."},
]
```
### Reasoning Model Support
For models that support chain-of-thought reasoning:
- Extract `reasoning_content` from API responses
- Store in `assistant_msg["reasoning"]` for trajectory export
- Pass back via `reasoning_content` field on subsequent turns
---
## CLI Architecture (cli.py)
- **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete
- **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results
- `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML
- **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text
- `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry
- Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching
The interactive CLI uses:
- **Rich** - For the welcome banner and styled panels
- **prompt_toolkit** - For fixed input area with history, `patch_stdout`, slash command autocomplete, and floating completion menus
- **KawaiiSpinner** (in run_agent.py) - Animated kawaii faces during API calls; clean `┊` activity feed for tool execution results
### Slash Command Registry (`hermes_cli/commands.py`)
Key components:
- `HermesCLI` class - Main CLI controller with commands and conversation loop
- `SlashCommandCompleter` - Autocomplete dropdown for `/commands` (type `/` to see all)
- `agent/skill_commands.py` - Scans skills and builds invocation messages (shared with gateway)
- `load_cli_config()` - Loads config, sets environment variables for terminal
- `build_welcome_banner()` - Displays ASCII art logo, tools, and skills summary
All slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:
CLI UX notes:
- Thinking spinner (during LLM API call) shows animated kawaii face + verb (`(⌐■_■) deliberating...`)
- When LLM returns tool calls, the spinner clears silently (no "got it!" noise)
- Tool execution results appear as a clean activity feed: `┊ {emoji} {verb} {detail} {duration}`
- "got it!" only appears when the LLM returns a final text response (`⚕ ready`)
- The prompt shows `⚕ ` when the agent is working, `` when idle
- Pasting 5+ lines auto-saves to `~/.hermes/pastes/` and collapses to a reference
- Multi-line input via Alt+Enter or Ctrl+J
- `/commands` - Process user commands like `/help`, `/clear`, `/personality`, etc.
- `/skill-name` - Invoke installed skills directly (e.g., `/axolotl`, `/gif-search`)
- **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name
- **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` for dispatch
- **Gateway help** — `gateway_help_lines()` generates `/help` output
- **Telegram** — `telegram_bot_commands()` generates the BotCommand menu
- **Slack** — `slack_subcommand_map()` generates `/hermes` subcommand routing
- **Autocomplete** — `COMMANDS` flat dict feeds `SlashCommandCompleter`
- **CLI help** — `COMMANDS_BY_CATEGORY` dict feeds `show_help()`
CLI uses `quiet_mode=True` when creating AIAgent to suppress verbose logging.
### Adding a Slash Command
### Skill Slash Commands
1. Add a `CommandDef` entry to `COMMAND_REGISTRY` in `hermes_cli/commands.py`:
```python
CommandDef("mycommand", "Description of what it does", "Session",
aliases=("mc",), args_hint="[arg]"),
Every installed skill in `~/.hermes/skills/` is automatically registered as a slash command.
The skill name (from frontmatter or folder name) becomes the command: `axolotl``/axolotl`.
Implementation (`agent/skill_commands.py`, shared between CLI and gateway):
1. `scan_skill_commands()` scans all SKILL.md files at startup, filtering out skills incompatible with the current OS platform (via the `platforms` frontmatter field)
2. `build_skill_invocation_message()` loads the SKILL.md content and builds a user-turn message
3. The message includes the full skill content, a list of supporting files (not loaded), and the user's instruction
4. Supporting files can be loaded on demand via the `skill_view` tool
5. Injected as a **user message** (not system prompt) to preserve prompt caching
### Adding CLI Commands
1. Add to `COMMANDS` dict with description
2. Add handler in `process_command()` method
3. For persistent settings, use `save_config_value()` to update config
---
## Hermes CLI Commands
The unified `hermes` command provides all functionality:
| Command | Description |
|---------|-------------|
| `hermes` | Interactive chat (default) |
| `hermes chat -q "..."` | Single query mode |
| `hermes setup` | Configure API keys and settings |
| `hermes config` | View current configuration |
| `hermes config edit` | Open config in editor |
| `hermes config set KEY VAL` | Set a specific value |
| `hermes config check` | Check for missing config |
| `hermes config migrate` | Prompt for missing config interactively |
| `hermes status` | Show configuration status |
| `hermes doctor` | Diagnose issues |
| `hermes update` | Update to latest (checks for new config) |
| `hermes uninstall` | Uninstall (can keep configs for reinstall) |
| `hermes gateway` | Start gateway (messaging + cron scheduler) |
| `hermes gateway setup` | Configure messaging platforms interactively |
| `hermes gateway install` | Install gateway as system service |
| `hermes cron list` | View scheduled jobs |
| `hermes cron status` | Check if cron scheduler is running |
| `hermes version` | Show version info |
| `hermes pairing list/approve/revoke` | Manage DM pairing codes |
---
## Messaging Gateway
The gateway connects Hermes to Telegram, Discord, Slack, and WhatsApp.
### Setup
The interactive setup wizard handles platform configuration:
```bash
hermes gateway setup # Arrow-key menu of all platforms, configure tokens/allowlists/home channels
```
2. Add handler in `HermesCLI.process_command()` in `cli.py`:
```python
elif canonical == "mycommand":
self._handle_mycommand(cmd_original)
```
3. If the command is available in the gateway, add a handler in `gateway/run.py`:
```python
if canonical == "mycommand":
return await self._handle_mycommand(event)
```
4. For persistent settings, use `save_config_value()` in `cli.py`
**CommandDef fields:**
- `name` — canonical name without slash (e.g. `"background"`)
- `description` — human-readable description
- `category` — one of `"Session"`, `"Configuration"`, `"Tools & Skills"`, `"Info"`, `"Exit"`
- `aliases` — tuple of alternative names (e.g. `("bg",)`)
- `args_hint` — argument placeholder shown in help (e.g. `"<prompt>"`, `"[name]"`)
- `cli_only` — only available in the interactive CLI
- `gateway_only` — only available in messaging platforms
This is the recommended way to configure messaging. It shows which platforms are already set up, walks through each one interactively, and offers to start/restart the gateway service at the end.
**Adding an alias** requires only adding it to the `aliases` tuple on the existing `CommandDef`. No other file changes needed — dispatch, help text, Telegram menu, Slack mapping, and autocomplete all update automatically.
Platforms can also be configured manually in `~/.hermes/.env`:
### Configuration (in `~/.hermes/.env`):
```bash
# Telegram
TELEGRAM_BOT_TOKEN=123456:ABC-DEF... # From @BotFather
TELEGRAM_ALLOWED_USERS=123456789,987654 # Comma-separated user IDs (from @userinfobot)
# Discord
DISCORD_BOT_TOKEN=MTIz... # From Developer Portal
DISCORD_ALLOWED_USERS=123456789012345678 # Comma-separated user IDs
# Agent Behavior
HERMES_MAX_ITERATIONS=60 # Max tool-calling iterations
MESSAGING_CWD=/home/myuser # Terminal working directory for messaging
# Tool progress is configured in config.yaml (display.tool_progress: off|new|all|verbose)
```
### Working Directory Behavior
- **CLI (`hermes` command)**: Uses current directory (`.``os.getcwd()`)
- **Messaging (Telegram/Discord)**: Uses `MESSAGING_CWD` (default: home directory)
This is intentional: CLI users are in a terminal and expect the agent to work in their current directory, while messaging users need a consistent starting location.
### Security (User Allowlists):
**IMPORTANT**: By default, the gateway denies all users who are not in an allowlist or paired via DM.
The gateway checks `{PLATFORM}_ALLOWED_USERS` environment variables:
- If set: Only listed user IDs can interact with the bot
- If unset: All users are denied unless `GATEWAY_ALLOW_ALL_USERS=true` is set
Users can find their IDs:
- **Telegram**: Message [@userinfobot](https://t.me/userinfobot)
- **Discord**: Enable Developer Mode, right-click name → Copy ID
### DM Pairing System
Instead of static allowlists, users can pair via one-time codes:
1. Unknown user DMs the bot → receives pairing code
2. Owner runs `hermes pairing approve <platform> <code>`
3. User is permanently authorized
Security: 8-char codes, 1-hour expiry, rate-limited (1/10min/user), max 3 pending per platform, lockout after 5 failed attempts, `chmod 0600` on data files.
Files: `gateway/pairing.py`, `hermes_cli/pairing.py`
### Event Hooks
Hooks fire at lifecycle points. Place hook directories in `~/.hermes/hooks/`:
```
~/.hermes/hooks/my-hook/
├── HOOK.yaml # name, description, events list
└── handler.py # async def handle(event_type, context): ...
```
Events: `gateway:startup`, `session:start`, `session:reset`, `agent:start`, `agent:step`, `agent:end`, `command:*`
The `agent:step` event fires each iteration of the tool-calling loop with tool names and results.
Files: `gateway/hooks.py`
### Tool Progress Notifications
When `tool_progress` is enabled in `config.yaml`, the bot sends status messages as it works:
- `💻 \`ls -la\`...` (terminal commands show the actual command)
- `🔍 web_search...`
- `📄 web_extract...`
- `🐍 execute_code...` (programmatic tool calling sandbox)
- `🔀 delegate_task...` (subagent delegation)
- `❓ clarify...` (user question, CLI-only)
Modes:
- `new`: Only when switching to a different tool (less spam)
- `all`: Every single tool call
### Typing Indicator
The gateway keeps the "typing..." indicator active throughout processing, refreshing every 4 seconds. This lets users know the bot is working even during long tool-calling sequences.
### Platform Toolsets:
Each platform has a dedicated toolset in `toolsets.py`:
- `hermes-telegram`: Full tools including terminal (with safety checks)
- `hermes-discord`: Full tools including terminal
- `hermes-whatsapp`: Full tools including terminal
---
## Configuration System
Configuration files are stored in `~/.hermes/` for easy user access:
- `~/.hermes/config.yaml` - All settings (model, terminal, compression, etc.)
- `~/.hermes/.env` - API keys and secrets
### Adding New Configuration Options
When adding new configuration variables, you MUST follow this process:
#### For config.yaml options:
1. Add to `DEFAULT_CONFIG` in `hermes_cli/config.py`
2. **CRITICAL**: Bump `_config_version` in `DEFAULT_CONFIG` when adding required fields
3. This triggers migration prompts for existing users on next `hermes update` or `hermes setup`
Example:
```python
DEFAULT_CONFIG = {
# ... existing config ...
"new_feature": {
"enabled": True,
"option": "default_value",
},
# BUMP THIS when adding required fields
"_config_version": 2, # Was 1, now 2
}
```
#### For .env variables (API keys/secrets):
1. Add to `REQUIRED_ENV_VARS` or `OPTIONAL_ENV_VARS` in `hermes_cli/config.py`
2. Include metadata for the migration system:
```python
OPTIONAL_ENV_VARS = {
# ... existing vars ...
"NEW_API_KEY": {
"description": "What this key is for",
"prompt": "Display name in prompts",
"url": "https://where-to-get-it.com/",
"tools": ["tools_it_enables"], # What tools need this
"password": True, # Mask input
},
}
```
#### Update related files:
- `hermes_cli/setup.py` - Add prompts in the setup wizard
- `cli-config.yaml.example` - Add example with comments
- Update README.md if user-facing
### Config Version Migration
The system uses `_config_version` to detect outdated configs:
1. `check_for_missing_config()` compares user config to `DEFAULT_CONFIG`
2. `migrate_config()` interactively prompts for missing values
3. Called automatically by `hermes update` and optionally by `hermes setup`
---
## Environment Variables
API keys are loaded from `~/.hermes/.env`:
- `OPENROUTER_API_KEY` - Main LLM API access (primary provider)
- `FIRECRAWL_API_KEY` - Web search/extract tools
- `FIRECRAWL_API_URL` - Self-hosted Firecrawl endpoint (optional)
- `BROWSERBASE_API_KEY` / `BROWSERBASE_PROJECT_ID` - Browser automation
- `FAL_KEY` - Image generation (FLUX model)
- `NOUS_API_KEY` - Vision and Mixture-of-Agents tools
Terminal tool configuration (in `~/.hermes/config.yaml`):
- `terminal.backend` - Backend: local, docker, singularity, modal, daytona, or ssh
- `terminal.cwd` - Working directory ("." = host CWD for local only; for remote backends set an absolute path inside the target, or omit to use the backend's default)
- `terminal.docker_image` - Image for Docker backend
- `terminal.singularity_image` - Image for Singularity backend
- `terminal.modal_image` - Image for Modal backend
- `terminal.daytona_image` - Image for Daytona backend
- `DAYTONA_API_KEY` - API key for Daytona backend (in .env)
- SSH: `TERMINAL_SSH_HOST`, `TERMINAL_SSH_USER`, `TERMINAL_SSH_KEY` in .env
Agent behavior (in `~/.hermes/.env`):
- `HERMES_MAX_ITERATIONS` - Max tool-calling iterations (default: 60)
- `MESSAGING_CWD` - Working directory for messaging platforms (default: ~)
- `display.tool_progress` in config.yaml - Tool progress: `off`, `new`, `all`, `verbose`
- `OPENAI_API_KEY` - Voice transcription (Whisper STT)
- `SLACK_BOT_TOKEN` / `SLACK_APP_TOKEN` - Slack integration (Socket Mode)
- `SLACK_ALLOWED_USERS` - Comma-separated Slack user IDs
- `HERMES_HUMAN_DELAY_MODE` - Response pacing: off/natural/custom
- `HERMES_HUMAN_DELAY_MIN_MS` / `HERMES_HUMAN_DELAY_MAX_MS` - Custom delay range
### Dangerous Command Approval
The terminal tool includes safety checks for potentially destructive commands (e.g., `rm -rf`, `DROP TABLE`, `chmod 777`, etc.):
**Behavior by Backend:**
- **Docker/Singularity/Modal**: Commands run unrestricted (isolated containers)
- **Local/SSH**: Dangerous commands trigger approval flow
**Approval Flow (CLI):**
```
⚠️ Potentially dangerous command detected: recursive delete
rm -rf /tmp/test
[o]nce | [s]ession | [a]lways | [d]eny
Choice [o/s/a/D]:
```
**Approval Flow (Messaging):**
- Command is blocked with explanation
- Agent explains the command was blocked for safety
- User must add the pattern to their allowlist via `hermes config edit` or run the command directly on their machine
**Configuration:**
- `command_allowlist` in `~/.hermes/config.yaml` stores permanently allowed patterns
- Add patterns via "always" approval or edit directly
**Sudo Handling (Messaging):**
- If sudo fails over messaging, output includes tip to add `SUDO_PASSWORD` to `~/.hermes/.env`
---
## Background Process Management
The `process` tool works alongside `terminal` for managing long-running background processes:
**Starting a background process:**
```python
terminal(command="pytest -v tests/", background=true)
# Returns: {"session_id": "proc_abc123", "pid": 12345, ...}
```
**Managing it with the process tool:**
- `process(action="list")` -- show all running/recent processes
- `process(action="poll", session_id="proc_abc123")` -- check status + new output
- `process(action="log", session_id="proc_abc123")` -- full output with pagination
- `process(action="wait", session_id="proc_abc123", timeout=600)` -- block until done
- `process(action="kill", session_id="proc_abc123")` -- terminate
- `process(action="write", session_id="proc_abc123", data="y")` -- send stdin
- `process(action="submit", session_id="proc_abc123", data="yes")` -- send + Enter
**Key behaviors:**
- Background processes execute through the configured terminal backend (local/Docker/Modal/Daytona/SSH/Singularity) -- never directly on the host unless `TERMINAL_ENV=local`
- The `wait` action blocks the tool call until the process finishes, times out, or is interrupted by a new user message
- PTY mode (`pty=true` on terminal) enables interactive CLI tools (Codex, Claude Code)
- In RL training, background processes are auto-killed when the episode ends (`tool_context.cleanup()`)
- In the gateway, sessions with active background processes are exempt from idle reset
- The process registry checkpoints to `~/.hermes/processes.json` for crash recovery
Files: `tools/process_registry.py` (registry + handler), `tools/terminal_tool.py` (spawn integration)
---
## Adding New Tools
Requires changes in **3 files**:
Adding a tool requires changes in **2 files** (the tool file and `toolsets.py`):
1. **Create `tools/your_tool.py`** with handler, schema, check function, and registry call:
**1. Create `tools/your_tool.py`:**
```python
import json, os
# tools/example_tool.py
import json
import os
from tools.registry import registry
def check_requirements() -> bool:
def check_example_requirements() -> bool:
"""Check if required API keys/dependencies are available."""
return bool(os.getenv("EXAMPLE_API_KEY"))
def example_tool(param: str, task_id: str = None) -> str:
return json.dumps({"success": True, "data": "..."})
"""Execute the tool and return JSON string result."""
try:
result = {"success": True, "data": "..."}
return json.dumps(result, ensure_ascii=False)
except Exception as e:
return json.dumps({"error": str(e)}, ensure_ascii=False)
EXAMPLE_SCHEMA = {
"name": "example_tool",
"description": "Does something useful.",
"parameters": {
"type": "object",
"properties": {
"param": {"type": "string", "description": "The parameter"}
},
"required": ["param"]
}
}
registry.register(
name="example_tool",
toolset="example",
schema={"name": "example_tool", "description": "...", "parameters": {...}},
handler=lambda args, **kw: example_tool(param=args.get("param", ""), task_id=kw.get("task_id")),
check_fn=check_requirements,
schema=EXAMPLE_SCHEMA,
handler=lambda args, **kw: example_tool(
param=args.get("param", ""), task_id=kw.get("task_id")),
check_fn=check_example_requirements,
requires_env=["EXAMPLE_API_KEY"],
)
```
**2. Add import** in `model_tools.py` `_discover_tools()` list.
2. **Add to `toolsets.py`**: Add `"example_tool"` to `_HERMES_CORE_TOOLS` if it should be in all platform toolsets, or create a new toolset entry.
**3. Add to `toolsets.py`** — either `_HERMES_CORE_TOOLS` (all platforms) or a new toolset.
3. **Add discovery import** in `model_tools.py`'s `_discover_tools()` list: `"tools.example_tool"`.
The registry handles schema collection, dispatch, availability checking, and error wrapping. All handlers MUST return a JSON string.
That's it. The registry handles schema collection, dispatch, availability checking, and error wrapping automatically. No edits to `TOOLSET_REQUIREMENTS`, `handle_function_call()`, `get_all_tool_names()`, or any other data structure.
**Agent-level tools** (todo, memory): intercepted by `run_agent.py` before `handle_function_call()`. See `todo_tool.py` for the pattern.
**Optional:** Add to `OPTIONAL_ENV_VARS` in `hermes_cli/config.py` for the setup wizard, and to `toolset_distributions.py` for batch processing.
**Special case: tools that need agent-level state** (like `todo`, `memory`):
These are intercepted by `run_agent.py`'s tool dispatch loop *before* `handle_function_call()`. The registry still holds their schemas, but dispatch returns a stub error as a safety fallback. See `todo_tool.py` for the pattern.
All tool handlers MUST return a JSON string. The registry's `dispatch()` wraps all exceptions in `{"error": "..."}` automatically.
### Dynamic Tool Availability
Tools declare their requirements at registration time via `check_fn` and `requires_env`. The registry checks `check_fn()` when building tool definitions -- tools whose check fails are silently excluded.
### Stateful Tools
Tools that maintain state (terminal, browser) require:
- `task_id` parameter for session isolation between concurrent tasks
- `cleanup_*()` function to release resources
- Cleanup is called automatically in run_agent.py after conversation completes
---
## Adding Configuration
## Trajectory Format
### config.yaml options:
1. Add to `DEFAULT_CONFIG` in `hermes_cli/config.py`
2. Bump `_config_version` (currently 5) to trigger migration for existing users
### .env variables:
1. Add to `OPTIONAL_ENV_VARS` in `hermes_cli/config.py` with metadata:
```python
"NEW_API_KEY": {
"description": "What it's for",
"prompt": "Display name",
"url": "https://...",
"password": True,
"category": "tool", # provider, tool, messaging, setting
},
Conversations are saved in ShareGPT format for training:
```json
{"from": "system", "value": "System prompt with <tools>...</tools>"}
{"from": "human", "value": "User message"}
{"from": "gpt", "value": "<think>reasoning</think>\n<tool_call>{...}</tool_call>"}
{"from": "tool", "value": "<tool_response>{...}</tool_response>"}
{"from": "gpt", "value": "Final response"}
```
### Config loaders (two separate systems):
Tool calls use `<tool_call>` XML tags, responses use `<tool_response>` tags, reasoning uses `<think>` tags.
| Loader | Used by | Location |
|--------|---------|----------|
| `load_cli_config()` | CLI mode | `cli.py` |
| `load_config()` | `hermes tools`, `hermes setup` | `hermes_cli/config.py` |
| Direct YAML load | Gateway | `gateway/run.py` |
---
## Skin/Theme System
The skin engine (`hermes_cli/skin_engine.py`) provides data-driven CLI visual customization. Skins are **pure data** — no code changes needed to add a new skin.
### Architecture
```
hermes_cli/skin_engine.py # SkinConfig dataclass, built-in skins, YAML loader
~/.hermes/skins/*.yaml # User-installed custom skins (drop-in)
```
- `init_skin_from_config()` — called at CLI startup, reads `display.skin` from config
- `get_active_skin()` — returns cached `SkinConfig` for the current skin
- `set_active_skin(name)` — switches skin at runtime (used by `/skin` command)
- `load_skin(name)` — loads from user skins first, then built-ins, then falls back to default
- Missing skin values inherit from the `default` skin automatically
### What skins customize
| Element | Skin Key | Used By |
|---------|----------|---------|
| Banner panel border | `colors.banner_border` | `banner.py` |
| Banner panel title | `colors.banner_title` | `banner.py` |
| Banner section headers | `colors.banner_accent` | `banner.py` |
| Banner dim text | `colors.banner_dim` | `banner.py` |
| Banner body text | `colors.banner_text` | `banner.py` |
| Response box border | `colors.response_border` | `cli.py` |
| Spinner faces (waiting) | `spinner.waiting_faces` | `display.py` |
| Spinner faces (thinking) | `spinner.thinking_faces` | `display.py` |
| Spinner verbs | `spinner.thinking_verbs` | `display.py` |
| Spinner wings (optional) | `spinner.wings` | `display.py` |
| Tool output prefix | `tool_prefix` | `display.py` |
| Per-tool emojis | `tool_emojis` | `display.py``get_tool_emoji()` |
| Agent name | `branding.agent_name` | `banner.py`, `cli.py` |
| Welcome message | `branding.welcome` | `cli.py` |
| Response box label | `branding.response_label` | `cli.py` |
| Prompt symbol | `branding.prompt_symbol` | `cli.py` |
### Built-in skins
- `default` — Classic Hermes gold/kawaii (the current look)
- `ares` — Crimson/bronze war-god theme with custom spinner wings
- `mono` — Clean grayscale monochrome
- `slate` — Cool blue developer-focused theme
### Adding a built-in skin
Add to `_BUILTIN_SKINS` dict in `hermes_cli/skin_engine.py`:
### Trajectory Export
```python
"mytheme": {
"name": "mytheme",
"description": "Short description",
"colors": { ... },
"spinner": { ... },
"branding": { ... },
"tool_prefix": "",
},
agent = AIAgent(save_trajectories=True)
agent.chat("Do something")
# Saves to trajectories/*.jsonl in ShareGPT format
```
### User skins (YAML)
Users create `~/.hermes/skins/<name>.yaml`:
```yaml
name: cyberpunk
description: Neon-soaked terminal theme
colors:
banner_border: "#FF00FF"
banner_title: "#00FFFF"
banner_accent: "#FF1493"
spinner:
thinking_verbs: ["jacking in", "decrypting", "uploading"]
wings:
- ["⟨⚡", "⚡⟩"]
branding:
agent_name: "Cyber Agent"
response_label: " ⚡ Cyber "
tool_prefix: "▏"
```
Activate with `/skin cyberpunk` or `display.skin: cyberpunk` in config.yaml.
---
## Important Policies
### Prompt Caching Must Not Break
## Batch Processing (batch_runner.py)
Hermes-Agent ensures caching remains valid throughout a conversation. **Do NOT implement changes that would:**
- Alter past context mid-conversation
- Change toolsets mid-conversation
- Reload memories or rebuild system prompts mid-conversation
Cache-breaking forces dramatically higher costs. The ONLY time we alter context is during context compression.
### Working Directory Behavior
- **CLI**: Uses current directory (`.``os.getcwd()`)
- **Messaging**: Uses `MESSAGING_CWD` env var (default: home directory)
### Background Process Notifications (Gateway)
When `terminal(background=true, check_interval=...)` is used, the gateway runs a watcher that
pushes status updates to the user's chat. Control verbosity with `display.background_process_notifications`
in config.yaml (or `HERMES_BACKGROUND_NOTIFICATIONS` env var):
- `all` — running-output updates + final message (default)
- `result` — only the final completion message
- `error` — only the final message when exit code != 0
- `off` — no watcher messages at all
---
## Known Pitfalls
### DO NOT use `simple_term_menu` for interactive menus
Rendering bugs in tmux/iTerm2 — ghosting on scroll. Use `curses` (stdlib) instead. See `hermes_cli/tools_config.py` for the pattern.
### DO NOT use `\033[K` (ANSI erase-to-EOL) in spinner/display code
Leaks as literal `?[K` text under `prompt_toolkit`'s `patch_stdout`. Use space-padding: `f"\r{line}{' ' * pad}"`.
### `_last_resolved_tool_names` is a process-global in `model_tools.py`
`_run_single_child()` in `delegate_tool.py` saves and restores this global around subagent execution. If you add new code that reads this global, be aware it may be temporarily stale during child agent runs.
### DO NOT hardcode cross-tool references in schema descriptions
Tool schema descriptions must not mention tools from other toolsets by name (e.g., `browser_navigate` saying "prefer web_search"). Those tools may be unavailable (missing API keys, disabled toolset), causing the model to hallucinate calls to non-existent tools. If a cross-reference is needed, add it dynamically in `get_tool_definitions()` in `model_tools.py` — see the `browser_navigate` / `execute_code` post-processing blocks for the pattern.
### Tests must not write to `~/.hermes/`
The `_isolate_hermes_home` autouse fixture in `tests/conftest.py` redirects `HERMES_HOME` to a temp dir. Never hardcode `~/.hermes/` paths in tests.
---
## Testing
For processing multiple prompts:
- Parallel execution with multiprocessing
- Content-based resume for fault tolerance (matches on prompt text, not indices)
- Toolset distributions control probabilistic tool availability per prompt
- Output: `data/<run_name>/trajectories.jsonl` (combined) + individual batch files
```bash
source venv/bin/activate
python -m pytest tests/ -q # Full suite (~3000 tests, ~3 min)
python -m pytest tests/test_model_tools.py -q # Toolset resolution
python -m pytest tests/test_cli_init.py -q # CLI config loading
python -m pytest tests/gateway/ -q # Gateway tests
python -m pytest tests/tools/ -q # Tool-level tests
python batch_runner.py \
--dataset_file=prompts.jsonl \
--batch_size=20 \
--num_workers=4 \
--run_name=my_run
```
Always run the full suite before pushing changes.
---
## Skills System
Skills are on-demand knowledge documents the agent can load. Compatible with the [agentskills.io](https://agentskills.io/specification) open standard.
```
skills/
├── mlops/ # Category folder
│ ├── axolotl/ # Skill folder
│ │ ├── SKILL.md # Main instructions (required)
│ │ ├── references/ # Additional docs, API specs
│ │ ├── templates/ # Output formats, configs
│ │ └── assets/ # Supplementary files (agentskills.io)
│ └── vllm/
│ └── SKILL.md
├── .hub/ # Skills Hub state (gitignored)
│ ├── lock.json # Installed skill provenance
│ ├── quarantine/ # Pending security review
│ ├── audit.log # Security scan history
│ ├── taps.json # Custom source repos
│ └── index-cache/ # Cached remote indexes
```
**Progressive disclosure** (token-efficient):
1. `skills_categories()` - List category names (~50 tokens)
2. `skills_list(category)` - Name + description per skill (~3k tokens)
3. `skill_view(name)` - Full content + tags + linked files
SKILL.md files use YAML frontmatter (agentskills.io format):
```yaml
---
name: skill-name
description: Brief description for listing
version: 1.0.0
platforms: [macos] # Optional — restrict to specific OS (macos/linux/windows)
metadata:
hermes:
tags: [tag1, tag2]
related_skills: [other-skill]
---
# Skill Content...
```
**Platform filtering** — Skills with a `platforms` field are automatically excluded from the system prompt index, `skills_list()`, and slash commands on incompatible platforms. Skills without the field load everywhere (backward compatible). See `skills/apple/` for macOS-only examples (iMessage, Reminders, Notes, FindMy).
**Skills Hub** — user-driven skill search/install from online registries and official optional skills. Sources: official optional skills (shipped with repo, labeled "official"), GitHub (openai/skills, anthropics/skills, custom taps), ClawHub, Claude marketplace, LobeHub. Not exposed as an agent tool — the model cannot search for or install skills. Users manage skills via `hermes skills browse/search/install` CLI commands or the `/skills` slash command in chat.
Key files:
- `tools/skills_tool.py` — Agent-facing skill list/view (progressive disclosure)
- `tools/skills_guard.py` — Security scanner (regex + LLM audit, trust-aware install policy)
- `tools/skills_hub.py` — Source adapters (OptionalSkillSource, GitHub, ClawHub, Claude marketplace, LobeHub), lock file, auth
- `hermes_cli/skills_hub.py` — CLI subcommands + `/skills` slash command handler
---
## Testing Changes
After making changes:
1. Run `hermes doctor` to check setup
2. Run `hermes config check` to verify config
3. Test with `hermes chat -q "test message"`
4. For new config options, test fresh install: `rm -rf ~/.hermes && hermes setup`

View File

@@ -118,7 +118,7 @@ hermes-agent/
├── cli.py # HermesCLI class — interactive TUI, prompt_toolkit integration
├── model_tools.py # Tool orchestration (thin layer over tools/registry.py)
├── toolsets.py # Tool groupings and presets (hermes-cli, hermes-telegram, etc.)
├── hermes_state.py # SQLite session database with FTS5 full-text search, session titles
├── hermes_state.py # SQLite session database with FTS5 full-text search
├── batch_runner.py # Parallel batch processing for trajectory generation
├── agent/ # Agent internals (extracted modules)
@@ -136,18 +136,17 @@ hermes-agent/
│ ├── auth.py # Provider resolution, OAuth, Nous Portal
│ ├── models.py # OpenRouter model selection lists
│ ├── banner.py # Welcome banner, ASCII art
│ ├── commands.py # Central slash command registry (CommandDef), autocomplete, gateway helpers
│ ├── commands.py # Slash command definitions + autocomplete
│ ├── callbacks.py # Interactive callbacks (clarify, sudo, approval)
│ ├── doctor.py # Diagnostics
── skills_hub.py # Skills Hub CLI + /skills slash command
│ └── skin_engine.py # Skin/theme engine — data-driven CLI visual customization
── skills_hub.py # Skills Hub CLI + /skills slash command
├── tools/ # Tool implementations (self-registering)
│ ├── registry.py # Central tool registry (schemas, handlers, dispatch)
│ ├── approval.py # Dangerous command detection + per-session approval
│ ├── terminal_tool.py # Terminal orchestration (sudo, env lifecycle, backends)
│ ├── file_operations.py # read_file, write_file, search, patch, etc.
│ ├── web_tools.py # web_search, web_extract (Parallel/Firecrawl + Gemini summarization)
│ ├── web_tools.py # web_search, web_extract (Firecrawl + Gemini summarization)
│ ├── vision_tools.py # Image analysis via multimodal models
│ ├── delegate_tool.py # Subagent spawning and parallel task execution
│ ├── code_execution_tool.py # Sandboxed Python with RPC tool access
@@ -219,7 +218,7 @@ User message → AIAgent._run_agent_loop()
- **Self-registering tools**: Each tool file calls `registry.register()` at import time. `model_tools.py` triggers discovery by importing all tool modules.
- **Toolset grouping**: Tools are grouped into toolsets (`web`, `terminal`, `file`, `browser`, etc.) that can be enabled/disabled per platform.
- **Session persistence**: All conversations are stored in SQLite (`hermes_state.py`) with full-text search and unique session titles. JSON logs go to `~/.hermes/sessions/`.
- **Session persistence**: All conversations are stored in SQLite (`hermes_state.py`) with full-text search. JSON logs go to `~/.hermes/sessions/`.
- **Ephemeral injection**: System prompts and prefill messages are injected at API call time, never persisted to the database or logs.
- **Provider abstraction**: The agent works with any OpenAI-compatible API. Provider resolution happens at init time (Nous Portal OAuth, OpenRouter API key, or custom endpoint).
- **Provider routing**: When using OpenRouter, `provider_routing` in config.yaml controls provider selection (sort by throughput/latency/price, allow/ignore specific providers, data retention policies). These are injected as `extra_body.provider` in API requests.
@@ -329,20 +328,10 @@ license: MIT
platforms: [macos, linux] # Optional — restrict to specific OS platforms
# Valid: macos, linux, windows
# Omit to load on all platforms (default)
required_environment_variables: # Optional — secure setup-on-load metadata
- name: MY_API_KEY
prompt: API key
help: Where to get it
required_for: full functionality
prerequisites: # Optional legacy runtime requirements
env_vars: [MY_API_KEY] # Backward-compatible alias for required env vars
commands: [curl, jq] # Advisory only; does not hide the skill
metadata:
hermes:
tags: [Category, Subcategory, Keywords]
related_skills: [other-skill-name]
fallback_for_toolsets: [web] # Optional — show only when toolset is unavailable
requires_toolsets: [terminal] # Optional — show only when toolset is available
---
# Skill Title
@@ -377,82 +366,6 @@ platforms: [windows] # Windows only
If the field is omitted or empty, the skill loads on all platforms (backward compatible). See `skills/apple/` for examples of macOS-only skills.
### Conditional skill activation
Skills can declare conditions that control when they appear in the system prompt, based on which tools and toolsets are available in the current session. This is primarily used for **fallback skills** — alternatives that should only be shown when a primary tool is unavailable.
Four fields are supported under `metadata.hermes`:
```yaml
metadata:
hermes:
fallback_for_toolsets: [web] # Show ONLY when these toolsets are unavailable
requires_toolsets: [terminal] # Show ONLY when these toolsets are available
fallback_for_tools: [web_search] # Show ONLY when these specific tools are unavailable
requires_tools: [terminal] # Show ONLY when these specific tools are available
```
**Semantics:**
- `fallback_for_*`: The skill is a backup. It is **hidden** when the listed tools/toolsets are available, and **shown** when they are unavailable. Use this for free alternatives to premium tools.
- `requires_*`: The skill needs certain tools to function. It is **hidden** when the listed tools/toolsets are unavailable. Use this for skills that depend on specific capabilities (e.g., a skill that only makes sense with terminal access).
- If both are specified, both conditions must be satisfied for the skill to appear.
- If neither is specified, the skill is always shown (backward compatible).
**Examples:**
```yaml
# DuckDuckGo search — shown when Firecrawl (web toolset) is unavailable
metadata:
hermes:
fallback_for_toolsets: [web]
# Smart home skill — only useful when terminal is available
metadata:
hermes:
requires_toolsets: [terminal]
# Local browser fallback — shown when Browserbase is unavailable
metadata:
hermes:
fallback_for_toolsets: [browser]
```
The filtering happens at prompt build time in `agent/prompt_builder.py`. The `build_skills_system_prompt()` function receives the set of available tools and toolsets from the agent and uses `_skill_should_show()` to evaluate each skill's conditions.
### Skill setup metadata
Skills can declare secure setup-on-load metadata via the `required_environment_variables` frontmatter field. Missing values do not hide the skill from discovery; they trigger a CLI-only secure prompt when the skill is actually loaded.
```yaml
required_environment_variables:
- name: TENOR_API_KEY
prompt: Tenor API key
help: Get a key from https://developers.google.com/tenor
required_for: full functionality
```
The user may skip setup and keep loading the skill. Hermes only exposes metadata (`stored_as`, `skipped`, `validated`) to the model — never the secret value.
Legacy `prerequisites.env_vars` remains supported and is normalized into the new representation.
```yaml
prerequisites:
env_vars: [TENOR_API_KEY] # Legacy alias for required_environment_variables
commands: [curl, jq] # Advisory CLI checks
```
Gateway and messaging sessions never collect secrets in-band; they instruct the user to run `hermes setup` or update `~/.hermes/.env` locally.
**When to declare required environment variables:**
- The skill uses an API key or token that should be collected securely at load time
- The skill can still be useful if the user skips setup, but may degrade gracefully
**When to declare command prerequisites:**
- The skill relies on a CLI tool that may not be installed (e.g., `himalaya`, `openhue`, `ddgs`)
- Treat command checks as guidance, not discovery-time hiding
See `skills/gifs/gif-search/` and `skills/email/himalaya/` for examples.
### Skill guidelines
- **No external dependencies unless absolutely necessary.** Prefer stdlib Python, curl, and existing Hermes tools (`web_extract`, `terminal`, `read_file`).
@@ -462,56 +375,6 @@ See `skills/gifs/gif-search/` and `skills/email/himalaya/` for examples.
---
## Adding a Skin / Theme
Hermes uses a data-driven skin system — no code changes needed to add a new skin.
**Option A: User skin (YAML file)**
Create `~/.hermes/skins/<name>.yaml`:
```yaml
name: mytheme
description: Short description of the theme
colors:
banner_border: "#HEX" # Panel border color
banner_title: "#HEX" # Panel title color
banner_accent: "#HEX" # Section header color
banner_dim: "#HEX" # Muted/dim text color
banner_text: "#HEX" # Body text color
response_border: "#HEX" # Response box border
spinner:
waiting_faces: ["(⚔)", "(⛨)"]
thinking_faces: ["(⚔)", "(⌁)"]
thinking_verbs: ["forging", "plotting"]
wings: # Optional left/right decorations
- ["⟪⚔", "⚔⟫"]
branding:
agent_name: "My Agent"
welcome: "Welcome message"
response_label: " ⚔ Agent "
prompt_symbol: "⚔ "
tool_prefix: "╎" # Tool output line prefix
```
All fields are optional — missing values inherit from the default skin.
**Option B: Built-in skin**
Add to `_BUILTIN_SKINS` dict in `hermes_cli/skin_engine.py`. Use the same schema as above but as a Python dict. Built-in skins ship with the package and are always available.
**Activating:**
- CLI: `/skin mytheme` or set `display.skin: mytheme` in config.yaml
- Config: `display: { skin: mytheme }`
See `hermes_cli/skin_engine.py` for the full schema and existing skins as examples.
---
## Cross-Platform Compatibility
Hermes runs on Linux, macOS, and Windows. When writing code that touches the OS:

21
LICENSE
View File

@@ -1,21 +0,0 @@
MIT License
Copyright (c) 2025 Nous Research
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

View File

@@ -2,7 +2,7 @@
<img src="assets/banner.png" alt="Hermes Agent" width="100%">
</p>
# Hermes Agent
# Hermes Agent
<p align="center">
<a href="https://hermes-agent.nousresearch.com/docs/"><img src="https://img.shields.io/badge/Docs-hermes--agent.nousresearch.com-FFD700?style=for-the-badge" alt="Documentation"></a>
@@ -17,7 +17,7 @@ Use any model you want — [Nous Portal](https://portal.nousresearch.com), [Open
<table>
<tr><td><b>A real terminal interface</b></td><td>Full TUI with multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, and streaming tool output.</td></tr>
<tr><td><b>Lives where you do</b></td><td>Telegram, Discord, Slack, WhatsApp, Signal, and CLI — all from a single gateway process. Voice memo transcription, cross-platform conversation continuity.</td></tr>
<tr><td><b>Lives where you do</b></td><td>Telegram, Discord, Slack, WhatsApp, and CLI — all from a single gateway process. Voice memo transcription, cross-platform conversation continuity.</td></tr>
<tr><td><b>A closed learning loop</b></td><td>Agent-curated memory with periodic nudges. Autonomous skill creation after complex tasks. Skills self-improve during use. FTS5 session search with LLM summarization for cross-session recall. <a href="https://github.com/plastic-labs/honcho">Honcho</a> dialectic user modeling. Compatible with the <a href="https://agentskills.io">agentskills.io</a> open standard.</td></tr>
<tr><td><b>Scheduled automations</b></td><td>Built-in cron scheduler with delivery to any platform. Daily reports, nightly backups, weekly audits — all in natural language, running unattended.</td></tr>
<tr><td><b>Delegates and parallelizes</b></td><td>Spawn isolated subagents for parallel workstreams. Write Python scripts that call tools via RPC, collapsing multi-step pipelines into zero-context-cost turns.</td></tr>
@@ -41,6 +41,7 @@ After installation:
```bash
source ~/.bashrc # reload shell (or: source ~/.zshrc)
hermes setup # configure your LLM provider
hermes # start chatting!
```
@@ -50,36 +51,15 @@ hermes # start chatting!
```bash
hermes # Interactive CLI — start a conversation
hermes model # Choose your LLM provider and model
hermes tools # Configure which tools are enabled
hermes config set # Set individual config values
hermes model # Switch provider or model
hermes setup # Re-run the setup wizard
hermes gateway # Start the messaging gateway (Telegram, Discord, etc.)
hermes setup # Run the full setup wizard (configures everything at once)
hermes claw migrate # Migrate from OpenClaw (if coming from OpenClaw)
hermes update # Update to the latest version
hermes doctor # Diagnose any issues
```
📖 **[Full documentation →](https://hermes-agent.nousresearch.com/docs/)**
## CLI vs Messaging Quick Reference
Hermes has two entry points: start the terminal UI with `hermes`, or run the gateway and talk to it from Telegram, Discord, Slack, WhatsApp, Signal, or Email. Once you're in a conversation, many slash commands are shared across both interfaces.
| Action | CLI | Messaging platforms |
|---------|-----|---------------------|
| Start chatting | `hermes` | Run `hermes gateway setup` + `hermes gateway start`, then send the bot a message |
| Start fresh conversation | `/new` or `/reset` | `/new` or `/reset` |
| Change model | `/model [provider:model]` | `/model [provider:model]` |
| Set a personality | `/personality [name]` | `/personality [name]` |
| Retry or undo the last turn | `/retry`, `/undo` | `/retry`, `/undo` |
| Compress context / check usage | `/compress`, `/usage`, `/insights [--days N]` | `/compress`, `/usage`, `/insights [days]` |
| Browse skills | `/skills` or `/<skill-name>` | `/skills` or `/<skill-name>` |
| Interrupt current work | `Ctrl+C` or send a new message | `/stop` or send a new message |
| Platform-specific status | `/platforms` | `/status`, `/sethome` |
For the full command lists, see the [CLI guide](https://hermes-agent.nousresearch.com/docs/user-guide/cli) and the [Messaging Gateway guide](https://hermes-agent.nousresearch.com/docs/user-guide/messaging).
---
## Documentation
@@ -91,7 +71,7 @@ All documentation lives at **[hermes-agent.nousresearch.com/docs](https://hermes
| [Quickstart](https://hermes-agent.nousresearch.com/docs/getting-started/quickstart) | Install → setup → first conversation in 2 minutes |
| [CLI Usage](https://hermes-agent.nousresearch.com/docs/user-guide/cli) | Commands, keybindings, personalities, sessions |
| [Configuration](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) | Config file, providers, models, all options |
| [Messaging Gateway](https://hermes-agent.nousresearch.com/docs/user-guide/messaging) | Telegram, Discord, Slack, WhatsApp, Signal, Home Assistant |
| [Messaging Gateway](https://hermes-agent.nousresearch.com/docs/user-guide/messaging) | Telegram, Discord, Slack, WhatsApp, Home Assistant |
| [Security](https://hermes-agent.nousresearch.com/docs/user-guide/security) | Command approval, DM pairing, container isolation |
| [Tools & Toolsets](https://hermes-agent.nousresearch.com/docs/user-guide/features/tools) | 40+ tools, toolset system, terminal backends |
| [Skills System](https://hermes-agent.nousresearch.com/docs/user-guide/features/skills) | Procedural memory, Skills Hub, creating skills |
@@ -106,35 +86,6 @@ All documentation lives at **[hermes-agent.nousresearch.com/docs](https://hermes
---
## Migrating from OpenClaw
If you're coming from OpenClaw, Hermes can automatically import your settings, memories, skills, and API keys.
**During first-time setup:** The setup wizard (`hermes setup`) automatically detects `~/.openclaw` and offers to migrate before configuration begins.
**Anytime after install:**
```bash
hermes claw migrate # Interactive migration (full preset)
hermes claw migrate --dry-run # Preview what would be migrated
hermes claw migrate --preset user-data # Migrate without secrets
hermes claw migrate --overwrite # Overwrite existing conflicts
```
What gets imported:
- **SOUL.md** — persona file
- **Memories** — MEMORY.md and USER.md entries
- **Skills** — user-created skills → `~/.hermes/skills/openclaw-imports/`
- **Command allowlist** — approval patterns
- **Messaging settings** — platform configs, allowed users, working directory
- **API keys** — allowlisted secrets (Telegram, OpenRouter, OpenAI, Anthropic, ElevenLabs)
- **TTS assets** — workspace audio files
- **Workspace instructions** — AGENTS.md (with `--workspace-target`)
See `hermes claw migrate --help` for all options, or use the `openclaw-migration` skill for an interactive agent-guided migration with dry-run previews.
---
## Contributing
We welcome contributions! See the [Contributing Guide](https://hermes-agent.nousresearch.com/docs/developer-guide/contributing) for development setup, code style, and PR process.
@@ -142,23 +93,16 @@ We welcome contributions! See the [Contributing Guide](https://hermes-agent.nous
Quick start for contributors:
```bash
git clone https://github.com/NousResearch/hermes-agent.git
git clone --recurse-submodules https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
git submodule update --init mini-swe-agent # required terminal backend
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv venv --python 3.11
source venv/bin/activate
uv venv .venv --python 3.11
source .venv/bin/activate
uv pip install -e ".[all,dev]"
uv pip install -e "./mini-swe-agent"
python -m pytest tests/ -q
```
> **RL Training (optional):** To work on the RL/Tinker-Atropos integration, also run:
> ```bash
> git submodule update --init tinker-atropos
> uv pip install -e "./tinker-atropos"
> ```
---
## Community

View File

@@ -1,383 +0,0 @@
# Hermes Agent v0.2.0 (v2026.3.12)
**Release Date:** March 12, 2026
> First tagged release since v0.1.0 (the initial pre-public foundation). In just over two weeks, Hermes Agent went from a small internal project to a full-featured AI agent platform — thanks to an explosion of community contributions. This release covers **216 merged pull requests** from **63 contributors**, resolving **119 issues**.
---
## ✨ Highlights
- **Multi-Platform Messaging Gateway** — Telegram, Discord, Slack, WhatsApp, Signal, Email (IMAP/SMTP), and Home Assistant platforms with unified session management, media attachments, and per-platform tool configuration.
- **MCP (Model Context Protocol) Client** — Native MCP support with stdio and HTTP transports, reconnection, resource/prompt discovery, and sampling (server-initiated LLM requests). ([#291](https://github.com/NousResearch/hermes-agent/pull/291) — @0xbyt4, [#301](https://github.com/NousResearch/hermes-agent/pull/301), [#753](https://github.com/NousResearch/hermes-agent/pull/753))
- **Skills Ecosystem** — 70+ bundled and optional skills across 15+ categories with a Skills Hub for community discovery, per-platform enable/disable, conditional activation based on tool availability, and prerequisite validation. ([#743](https://github.com/NousResearch/hermes-agent/pull/743) — @teyrebaz33, [#785](https://github.com/NousResearch/hermes-agent/pull/785) — @teyrebaz33)
- **Centralized Provider Router** — Unified `call_llm()`/`async_call_llm()` API replaces scattered provider logic across vision, summarization, compression, and trajectory saving. All auxiliary consumers route through a single code path with automatic credential resolution. ([#1003](https://github.com/NousResearch/hermes-agent/pull/1003))
- **ACP Server** — VS Code, Zed, and JetBrains editor integration via the Agent Communication Protocol standard. ([#949](https://github.com/NousResearch/hermes-agent/pull/949))
- **CLI Skin/Theme Engine** — Data-driven visual customization: banners, spinners, colors, branding. 7 built-in skins + custom YAML skins.
- **Git Worktree Isolation** — `hermes -w` launches isolated agent sessions in git worktrees for safe parallel work on the same repo. ([#654](https://github.com/NousResearch/hermes-agent/pull/654))
- **Filesystem Checkpoints & Rollback** — Automatic snapshots before destructive operations with `/rollback` to restore. ([#824](https://github.com/NousResearch/hermes-agent/pull/824))
- **3,289 Tests** — From near-zero test coverage to a comprehensive test suite covering agent, gateway, tools, cron, and CLI.
---
## 🏗️ Core Agent & Architecture
### Provider & Model Support
- Centralized provider router with `resolve_provider_client()` + `call_llm()` API ([#1003](https://github.com/NousResearch/hermes-agent/pull/1003))
- Nous Portal as first-class provider in setup ([#644](https://github.com/NousResearch/hermes-agent/issues/644))
- OpenAI Codex (Responses API) with ChatGPT subscription support ([#43](https://github.com/NousResearch/hermes-agent/pull/43)) — @grp06
- Codex OAuth vision support + multimodal content adapter
- Validate `/model` against live API instead of hardcoded lists
- Self-hosted Firecrawl support ([#460](https://github.com/NousResearch/hermes-agent/pull/460)) — @caentzminger
- Kimi Code API support ([#635](https://github.com/NousResearch/hermes-agent/pull/635)) — @christomitov
- MiniMax model ID update ([#473](https://github.com/NousResearch/hermes-agent/pull/473)) — @tars90percent
- OpenRouter provider routing configuration (provider_preferences)
- Nous credential refresh on 401 errors ([#571](https://github.com/NousResearch/hermes-agent/pull/571), [#269](https://github.com/NousResearch/hermes-agent/pull/269)) — @rewbs
- z.ai/GLM, Kimi/Moonshot, MiniMax, Azure OpenAI as first-class providers
- Unified `/model` and `/provider` into single view
### Agent Loop & Conversation
- Simple fallback model for provider resilience ([#740](https://github.com/NousResearch/hermes-agent/pull/740))
- Shared iteration budget across parent + subagent delegation
- Iteration budget pressure via tool result injection
- Configurable subagent provider/model with full credential resolution
- Handle 413 payload-too-large via compression instead of aborting ([#153](https://github.com/NousResearch/hermes-agent/pull/153)) — @tekelala
- Retry with rebuilt payload after compression ([#616](https://github.com/NousResearch/hermes-agent/pull/616)) — @tripledoublev
- Auto-compress pathologically large gateway sessions ([#628](https://github.com/NousResearch/hermes-agent/issues/628))
- Tool call repair middleware — auto-lowercase and invalid tool handler
- Reasoning effort configuration and `/reasoning` command ([#921](https://github.com/NousResearch/hermes-agent/pull/921))
- Detect and block file re-read/search loops after context compression ([#705](https://github.com/NousResearch/hermes-agent/pull/705)) — @0xbyt4
### Session & Memory
- Session naming with unique titles, auto-lineage, rich listing, and resume by name ([#720](https://github.com/NousResearch/hermes-agent/pull/720))
- Interactive session browser with search filtering ([#733](https://github.com/NousResearch/hermes-agent/pull/733))
- Display previous messages when resuming a session ([#734](https://github.com/NousResearch/hermes-agent/pull/734))
- Honcho AI-native cross-session user modeling ([#38](https://github.com/NousResearch/hermes-agent/pull/38)) — @erosika
- Proactive async memory flush on session expiry
- Smart context length probing with persistent caching + banner display
- `/resume` command for switching to named sessions in gateway
- Session reset policy for messaging platforms
---
## 📱 Messaging Platforms (Gateway)
### Telegram
- Native file attachments: send_document + send_video
- Document file processing for PDF, text, and Office files — @tekelala
- Forum topic session isolation ([#766](https://github.com/NousResearch/hermes-agent/pull/766)) — @spanishflu-est1918
- Browser screenshot sharing via MEDIA: protocol ([#657](https://github.com/NousResearch/hermes-agent/pull/657))
- Location support for find-nearby skill
- TTS voice message accumulation fix ([#176](https://github.com/NousResearch/hermes-agent/pull/176)) — @Bartok9
- Improved error handling and logging ([#763](https://github.com/NousResearch/hermes-agent/pull/763)) — @aydnOktay
- Italic regex newline fix + 43 format tests ([#204](https://github.com/NousResearch/hermes-agent/pull/204)) — @0xbyt4
### Discord
- Channel topic included in session context ([#248](https://github.com/NousResearch/hermes-agent/pull/248)) — @Bartok9
- DISCORD_ALLOW_BOTS config for bot message filtering ([#758](https://github.com/NousResearch/hermes-agent/pull/758))
- Document and video support ([#784](https://github.com/NousResearch/hermes-agent/pull/784))
- Improved error handling and logging ([#761](https://github.com/NousResearch/hermes-agent/pull/761)) — @aydnOktay
### Slack
- App_mention 404 fix + document/video support ([#784](https://github.com/NousResearch/hermes-agent/pull/784))
- Structured logging replacing print statements — @aydnOktay
### WhatsApp
- Native media sending — images, videos, documents ([#292](https://github.com/NousResearch/hermes-agent/pull/292)) — @satelerd
- Multi-user session isolation ([#75](https://github.com/NousResearch/hermes-agent/pull/75)) — @satelerd
- Cross-platform port cleanup replacing Linux-only fuser ([#433](https://github.com/NousResearch/hermes-agent/pull/433)) — @Farukest
- DM interrupt key mismatch fix ([#350](https://github.com/NousResearch/hermes-agent/pull/350)) — @Farukest
### Signal
- Full Signal messenger gateway via signal-cli-rest-api ([#405](https://github.com/NousResearch/hermes-agent/issues/405))
- Media URL support in message events ([#871](https://github.com/NousResearch/hermes-agent/pull/871))
### Email (IMAP/SMTP)
- New email gateway platform — @0xbyt4
### Home Assistant
- REST tools + WebSocket gateway integration ([#184](https://github.com/NousResearch/hermes-agent/pull/184)) — @0xbyt4
- Service discovery and enhanced setup
- Toolset mapping fix ([#538](https://github.com/NousResearch/hermes-agent/pull/538)) — @Himess
### Gateway Core
- Expose subagent tool calls and thinking to users ([#186](https://github.com/NousResearch/hermes-agent/pull/186)) — @cutepawss
- Configurable background process watcher notifications ([#840](https://github.com/NousResearch/hermes-agent/pull/840))
- `edit_message()` for Telegram/Discord/Slack with fallback
- `/compress`, `/usage`, `/update` slash commands
- Eliminated 3x SQLite message duplication in gateway sessions ([#873](https://github.com/NousResearch/hermes-agent/pull/873))
- Stabilize system prompt across gateway turns for cache hits ([#754](https://github.com/NousResearch/hermes-agent/pull/754))
- MCP server shutdown on gateway exit ([#796](https://github.com/NousResearch/hermes-agent/pull/796)) — @0xbyt4
- Pass session_db to AIAgent, fixing session_search error ([#108](https://github.com/NousResearch/hermes-agent/pull/108)) — @Bartok9
- Persist transcript changes in /retry, /undo; fix /reset attribute ([#217](https://github.com/NousResearch/hermes-agent/pull/217)) — @Farukest
- UTF-8 encoding fix preventing Windows crashes ([#369](https://github.com/NousResearch/hermes-agent/pull/369)) — @ch3ronsa
---
## 🖥️ CLI & User Experience
### Interactive CLI
- Data-driven skin/theme engine — 7 built-in skins (default, ares, mono, slate, poseidon, sisyphus, charizard) + custom YAML skins
- `/personality` command with custom personality + disable support ([#773](https://github.com/NousResearch/hermes-agent/pull/773)) — @teyrebaz33
- User-defined quick commands that bypass the agent loop ([#746](https://github.com/NousResearch/hermes-agent/pull/746)) — @teyrebaz33
- `/reasoning` command for effort level and display toggle ([#921](https://github.com/NousResearch/hermes-agent/pull/921))
- `/verbose` slash command to toggle debug at runtime ([#94](https://github.com/NousResearch/hermes-agent/pull/94)) — @cesareth
- `/insights` command — usage analytics, cost estimation & activity patterns ([#552](https://github.com/NousResearch/hermes-agent/pull/552))
- `/background` command for managing background processes
- `/help` formatting with command categories
- Bell-on-complete — terminal bell when agent finishes ([#738](https://github.com/NousResearch/hermes-agent/pull/738))
- Up/down arrow history navigation
- Clipboard image paste (Alt+V / Ctrl+V)
- Loading indicators for slow slash commands ([#882](https://github.com/NousResearch/hermes-agent/pull/882))
- Spinner flickering fix under patch_stdout ([#91](https://github.com/NousResearch/hermes-agent/pull/91)) — @0xbyt4
- `--quiet/-Q` flag for programmatic single-query mode
- `--fuck-it-ship-it` flag to bypass all approval prompts ([#724](https://github.com/NousResearch/hermes-agent/pull/724)) — @dmahan93
- Tools summary flag ([#767](https://github.com/NousResearch/hermes-agent/pull/767)) — @luisv-1
- Terminal blinking fix on SSH ([#284](https://github.com/NousResearch/hermes-agent/pull/284)) — @ygd58
- Multi-line paste detection fix ([#84](https://github.com/NousResearch/hermes-agent/pull/84)) — @0xbyt4
### Setup & Configuration
- Modular setup wizard with section subcommands and tool-first UX
- Container resource configuration prompts
- Backend validation for required binaries
- Config migration system (currently v7)
- API keys properly routed to .env instead of config.yaml ([#469](https://github.com/NousResearch/hermes-agent/pull/469)) — @ygd58
- Atomic write for .env to prevent API key loss on crash ([#954](https://github.com/NousResearch/hermes-agent/pull/954))
- `hermes tools` — per-platform tool enable/disable with curses UI
- `hermes doctor` for health checks across all configured providers
- `hermes update` with auto-restart for gateway service
- Show update-available notice in CLI banner
- Multiple named custom providers
- Shell config detection improvement for PATH setup ([#317](https://github.com/NousResearch/hermes-agent/pull/317)) — @mehmetkr-31
- Consistent HERMES_HOME and .env path resolution ([#51](https://github.com/NousResearch/hermes-agent/pull/51), [#48](https://github.com/NousResearch/hermes-agent/pull/48)) — @deankerr
- Docker backend fix on macOS + subagent auth for Nous Portal ([#46](https://github.com/NousResearch/hermes-agent/pull/46)) — @rsavitt
---
## 🔧 Tool System
### MCP (Model Context Protocol)
- Native MCP client with stdio + HTTP transports ([#291](https://github.com/NousResearch/hermes-agent/pull/291) — @0xbyt4, [#301](https://github.com/NousResearch/hermes-agent/pull/301))
- Sampling support — server-initiated LLM requests ([#753](https://github.com/NousResearch/hermes-agent/pull/753))
- Resource and prompt discovery
- Automatic reconnection and security hardening
- Banner integration, `/reload-mcp` command
- `hermes tools` UI integration
### Browser
- Local browser backend — zero-cost headless Chromium (no Browserbase needed)
- Console/errors tool, annotated screenshots, auto-recording, dogfood QA skill ([#745](https://github.com/NousResearch/hermes-agent/pull/745))
- Screenshot sharing via MEDIA: on all messaging platforms ([#657](https://github.com/NousResearch/hermes-agent/pull/657))
### Terminal & Execution
- `execute_code` sandbox with json_parse, shell_quote, retry helpers
- Docker: custom volume mounts ([#158](https://github.com/NousResearch/hermes-agent/pull/158)) — @Indelwin
- Daytona cloud sandbox backend ([#451](https://github.com/NousResearch/hermes-agent/pull/451)) — @rovle
- SSH backend fix ([#59](https://github.com/NousResearch/hermes-agent/pull/59)) — @deankerr
- Shell noise filtering and login shell execution for environment consistency
- Head+tail truncation for execute_code stdout overflow
- Configurable background process notification modes
### File Operations
- Filesystem checkpoints and `/rollback` command ([#824](https://github.com/NousResearch/hermes-agent/pull/824))
- Structured tool result hints (next-action guidance) for patch and search_files ([#722](https://github.com/NousResearch/hermes-agent/issues/722))
- Docker volumes passed to sandbox container config ([#687](https://github.com/NousResearch/hermes-agent/pull/687)) — @manuelschipper
---
## 🧩 Skills Ecosystem
### Skills System
- Per-platform skill enable/disable ([#743](https://github.com/NousResearch/hermes-agent/pull/743)) — @teyrebaz33
- Conditional skill activation based on tool availability ([#785](https://github.com/NousResearch/hermes-agent/pull/785)) — @teyrebaz33
- Skill prerequisites — hide skills with unmet dependencies ([#659](https://github.com/NousResearch/hermes-agent/pull/659)) — @kshitijk4poor
- Optional skills — shipped but not activated by default
- `hermes skills browse` — paginated hub browsing
- Skills sub-category organization
- Platform-conditional skill loading
- Atomic skill file writes ([#551](https://github.com/NousResearch/hermes-agent/pull/551)) — @aydnOktay
- Skills sync data loss prevention ([#563](https://github.com/NousResearch/hermes-agent/pull/563)) — @0xbyt4
- Dynamic skill slash commands for CLI and gateway
### New Skills (selected)
- **ASCII Art** — pyfiglet (571 fonts), cowsay, image-to-ascii ([#209](https://github.com/NousResearch/hermes-agent/pull/209)) — @0xbyt4
- **ASCII Video** — Full production pipeline ([#854](https://github.com/NousResearch/hermes-agent/pull/854)) — @SHL0MS
- **DuckDuckGo Search** — Firecrawl fallback ([#267](https://github.com/NousResearch/hermes-agent/pull/267)) — @gamedevCloudy; DDGS API expansion ([#598](https://github.com/NousResearch/hermes-agent/pull/598)) — @areu01or00
- **Solana Blockchain** — Wallet balances, USD pricing, token names ([#212](https://github.com/NousResearch/hermes-agent/pull/212)) — @gizdusum
- **AgentMail** — Agent-owned email inboxes ([#330](https://github.com/NousResearch/hermes-agent/pull/330)) — @teyrebaz33
- **Polymarket** — Prediction market data (read-only) ([#629](https://github.com/NousResearch/hermes-agent/pull/629))
- **OpenClaw Migration** — Official migration tool ([#570](https://github.com/NousResearch/hermes-agent/pull/570)) — @unmodeled-tyler
- **Domain Intelligence** — Passive recon: subdomains, SSL, WHOIS, DNS ([#136](https://github.com/NousResearch/hermes-agent/pull/136)) — @FurkanL0
- **Superpowers** — Software development skills ([#137](https://github.com/NousResearch/hermes-agent/pull/137)) — @kaos35
- **Hermes-Atropos** — RL environment development skill ([#815](https://github.com/NousResearch/hermes-agent/pull/815))
- Plus: arXiv search, OCR/documents, Excalidraw diagrams, YouTube transcripts, GIF search, Pokémon player, Minecraft modpack server, OpenHue (Philips Hue), Google Workspace, Notion, PowerPoint, Obsidian, find-nearby, and 40+ MLOps skills
---
## 🔒 Security & Reliability
### Security Hardening
- Path traversal fix in skill_view — prevented reading arbitrary files ([#220](https://github.com/NousResearch/hermes-agent/issues/220)) — @Farukest
- Shell injection prevention in sudo password piping ([#65](https://github.com/NousResearch/hermes-agent/pull/65)) — @leonsgithub
- Dangerous command detection: multiline bypass fix ([#233](https://github.com/NousResearch/hermes-agent/pull/233)) — @Farukest; tee/process substitution patterns ([#280](https://github.com/NousResearch/hermes-agent/pull/280)) — @dogiladeveloper
- Symlink boundary check fix in skills_guard ([#386](https://github.com/NousResearch/hermes-agent/pull/386)) — @Farukest
- Symlink bypass fix in write deny list on macOS ([#61](https://github.com/NousResearch/hermes-agent/pull/61)) — @0xbyt4
- Multi-word prompt injection bypass prevention ([#192](https://github.com/NousResearch/hermes-agent/pull/192)) — @0xbyt4
- Cron prompt injection scanner bypass fix ([#63](https://github.com/NousResearch/hermes-agent/pull/63)) — @0xbyt4
- Enforce 0600/0700 file permissions on sensitive files ([#757](https://github.com/NousResearch/hermes-agent/pull/757))
- .env file permissions restricted to owner-only ([#529](https://github.com/NousResearch/hermes-agent/pull/529)) — @Himess
- `--force` flag properly blocked from overriding dangerous verdicts ([#388](https://github.com/NousResearch/hermes-agent/pull/388)) — @Farukest
- FTS5 query sanitization + DB connection leak fix ([#565](https://github.com/NousResearch/hermes-agent/pull/565)) — @0xbyt4
- Expand secret redaction patterns + config toggle to disable
- In-memory permanent allowlist to prevent data leak ([#600](https://github.com/NousResearch/hermes-agent/pull/600)) — @alireza78a
### Atomic Writes (data loss prevention)
- sessions.json ([#611](https://github.com/NousResearch/hermes-agent/pull/611)) — @alireza78a
- Cron jobs ([#146](https://github.com/NousResearch/hermes-agent/pull/146)) — @alireza78a
- .env config ([#954](https://github.com/NousResearch/hermes-agent/pull/954))
- Process checkpoints ([#298](https://github.com/NousResearch/hermes-agent/pull/298)) — @aydnOktay
- Batch runner ([#297](https://github.com/NousResearch/hermes-agent/pull/297)) — @aydnOktay
- Skill files ([#551](https://github.com/NousResearch/hermes-agent/pull/551)) — @aydnOktay
### Reliability
- Guard all print() against OSError for systemd/headless environments ([#963](https://github.com/NousResearch/hermes-agent/pull/963))
- Reset all retry counters at start of run_conversation ([#607](https://github.com/NousResearch/hermes-agent/pull/607)) — @0xbyt4
- Return deny on approval callback timeout instead of None ([#603](https://github.com/NousResearch/hermes-agent/pull/603)) — @0xbyt4
- Fix None message content crashes across codebase ([#277](https://github.com/NousResearch/hermes-agent/pull/277))
- Fix context overrun crash with local LLM backends ([#403](https://github.com/NousResearch/hermes-agent/pull/403)) — @ch3ronsa
- Prevent `_flush_sentinel` from leaking to external APIs ([#227](https://github.com/NousResearch/hermes-agent/pull/227)) — @Farukest
- Prevent conversation_history mutation in callers ([#229](https://github.com/NousResearch/hermes-agent/pull/229)) — @Farukest
- Fix systemd restart loop ([#614](https://github.com/NousResearch/hermes-agent/pull/614)) — @voidborne-d
- Close file handles and sockets to prevent fd leaks ([#568](https://github.com/NousResearch/hermes-agent/pull/568) — @alireza78a, [#296](https://github.com/NousResearch/hermes-agent/pull/296) — @alireza78a, [#709](https://github.com/NousResearch/hermes-agent/pull/709) — @memosr)
- Prevent data loss in clipboard PNG conversion ([#602](https://github.com/NousResearch/hermes-agent/pull/602)) — @0xbyt4
- Eliminate shell noise from terminal output ([#293](https://github.com/NousResearch/hermes-agent/pull/293)) — @0xbyt4
- Timezone-aware now() for prompt, cron, and execute_code ([#309](https://github.com/NousResearch/hermes-agent/pull/309)) — @areu01or00
### Windows Compatibility
- Guard POSIX-only process functions ([#219](https://github.com/NousResearch/hermes-agent/pull/219)) — @Farukest
- Windows native support via Git Bash + ZIP-based update fallback
- pywinpty for PTY support ([#457](https://github.com/NousResearch/hermes-agent/pull/457)) — @shitcoinsherpa
- Explicit UTF-8 encoding on all config/data file I/O ([#458](https://github.com/NousResearch/hermes-agent/pull/458)) — @shitcoinsherpa
- Windows-compatible path handling ([#354](https://github.com/NousResearch/hermes-agent/pull/354), [#390](https://github.com/NousResearch/hermes-agent/pull/390)) — @Farukest
- Regex-based search output parsing for drive-letter paths ([#533](https://github.com/NousResearch/hermes-agent/pull/533)) — @Himess
- Auth store file lock for Windows ([#455](https://github.com/NousResearch/hermes-agent/pull/455)) — @shitcoinsherpa
---
## 🐛 Notable Bug Fixes
- Fix DeepSeek V3 tool call parser silently dropping multi-line JSON arguments ([#444](https://github.com/NousResearch/hermes-agent/pull/444)) — @PercyDikec
- Fix gateway transcript losing 1 message per turn due to offset mismatch ([#395](https://github.com/NousResearch/hermes-agent/pull/395)) — @PercyDikec
- Fix /retry command silently discarding the agent's final response ([#441](https://github.com/NousResearch/hermes-agent/pull/441)) — @PercyDikec
- Fix max-iterations retry returning empty string after think-block stripping ([#438](https://github.com/NousResearch/hermes-agent/pull/438)) — @PercyDikec
- Fix max-iterations retry using hardcoded max_tokens ([#436](https://github.com/NousResearch/hermes-agent/pull/436)) — @Farukest
- Fix Codex status dict key mismatch ([#448](https://github.com/NousResearch/hermes-agent/pull/448)) and visibility filter ([#446](https://github.com/NousResearch/hermes-agent/pull/446)) — @PercyDikec
- Strip \<think\> blocks from final user-facing responses ([#174](https://github.com/NousResearch/hermes-agent/pull/174)) — @Bartok9
- Fix \<think\> block regex stripping visible content when model discusses tags literally ([#786](https://github.com/NousResearch/hermes-agent/issues/786))
- Fix Mistral 422 errors from leftover finish_reason in assistant messages ([#253](https://github.com/NousResearch/hermes-agent/pull/253)) — @Sertug17
- Fix OPENROUTER_API_KEY resolution order across all code paths ([#295](https://github.com/NousResearch/hermes-agent/pull/295)) — @0xbyt4
- Fix OPENAI_BASE_URL API key priority ([#420](https://github.com/NousResearch/hermes-agent/pull/420)) — @manuelschipper
- Fix Anthropic "prompt is too long" 400 error not detected as context length error ([#813](https://github.com/NousResearch/hermes-agent/issues/813))
- Fix SQLite session transcript accumulating duplicate messages — 3-4x token inflation ([#860](https://github.com/NousResearch/hermes-agent/issues/860))
- Fix setup wizard skipping API key prompts on first install ([#748](https://github.com/NousResearch/hermes-agent/pull/748))
- Fix setup wizard showing OpenRouter model list for Nous Portal ([#575](https://github.com/NousResearch/hermes-agent/pull/575)) — @PercyDikec
- Fix provider selection not persisting when switching via hermes model ([#881](https://github.com/NousResearch/hermes-agent/pull/881))
- Fix Docker backend failing when docker not in PATH on macOS ([#889](https://github.com/NousResearch/hermes-agent/pull/889))
- Fix ClawHub Skills Hub adapter for API endpoint changes ([#286](https://github.com/NousResearch/hermes-agent/pull/286)) — @BP602
- Fix Honcho auto-enable when API key is present ([#243](https://github.com/NousResearch/hermes-agent/pull/243)) — @Bartok9
- Fix duplicate 'skills' subparser crash on Python 3.11+ ([#898](https://github.com/NousResearch/hermes-agent/issues/898))
- Fix memory tool entry parsing when content contains section sign ([#162](https://github.com/NousResearch/hermes-agent/pull/162)) — @aydnOktay
- Fix piped install silently aborting when interactive prompts fail ([#72](https://github.com/NousResearch/hermes-agent/pull/72)) — @cutepawss
- Fix false positives in recursive delete detection ([#68](https://github.com/NousResearch/hermes-agent/pull/68)) — @cutepawss
- Fix Ruff lint warnings across codebase ([#608](https://github.com/NousResearch/hermes-agent/pull/608)) — @JackTheGit
- Fix Anthropic native base URL fail-fast ([#173](https://github.com/NousResearch/hermes-agent/pull/173)) — @adavyas
- Fix install.sh creating ~/.hermes before moving Node.js directory ([#53](https://github.com/NousResearch/hermes-agent/pull/53)) — @JoshuaMart
- Fix SystemExit traceback during atexit cleanup on Ctrl+C ([#55](https://github.com/NousResearch/hermes-agent/pull/55)) — @bierlingm
- Restore missing MIT license file ([#620](https://github.com/NousResearch/hermes-agent/pull/620)) — @stablegenius49
---
## 🧪 Testing
- **3,289 tests** across agent, gateway, tools, cron, and CLI
- Parallelized test suite with pytest-xdist ([#802](https://github.com/NousResearch/hermes-agent/pull/802)) — @OutThisLife
- Unit tests batch 1: 8 core modules ([#60](https://github.com/NousResearch/hermes-agent/pull/60)) — @0xbyt4
- Unit tests batch 2: 8 more modules ([#62](https://github.com/NousResearch/hermes-agent/pull/62)) — @0xbyt4
- Unit tests batch 3: 8 untested modules ([#191](https://github.com/NousResearch/hermes-agent/pull/191)) — @0xbyt4
- Unit tests batch 4: 5 security/logic-critical modules ([#193](https://github.com/NousResearch/hermes-agent/pull/193)) — @0xbyt4
- AIAgent (run_agent.py) unit tests ([#67](https://github.com/NousResearch/hermes-agent/pull/67)) — @0xbyt4
- Trajectory compressor tests ([#203](https://github.com/NousResearch/hermes-agent/pull/203)) — @0xbyt4
- Clarify tool tests ([#121](https://github.com/NousResearch/hermes-agent/pull/121)) — @Bartok9
- Telegram format tests — 43 tests for italic/bold/code rendering ([#204](https://github.com/NousResearch/hermes-agent/pull/204)) — @0xbyt4
- Vision tools type hints + 42 tests ([#792](https://github.com/NousResearch/hermes-agent/pull/792))
- Compressor tool-call boundary regression tests ([#648](https://github.com/NousResearch/hermes-agent/pull/648)) — @intertwine
- Test structure reorganization ([#34](https://github.com/NousResearch/hermes-agent/pull/34)) — @0xbyt4
- Shell noise elimination + fix 36 test failures ([#293](https://github.com/NousResearch/hermes-agent/pull/293)) — @0xbyt4
---
## 🔬 RL & Evaluation Environments
- WebResearchEnv — Multi-step web research RL environment ([#434](https://github.com/NousResearch/hermes-agent/pull/434)) — @jackx707
- Modal sandbox concurrency limits to avoid deadlocks ([#621](https://github.com/NousResearch/hermes-agent/pull/621)) — @voteblake
- Hermes-atropos-environments bundled skill ([#815](https://github.com/NousResearch/hermes-agent/pull/815))
- Local vLLM instance support for evaluation — @dmahan93
- YC-Bench long-horizon agent benchmark environment
- OpenThoughts-TBLite evaluation environment and scripts
---
## 📚 Documentation
- Full documentation website (Docusaurus) with 37+ pages
- Comprehensive platform setup guides for Telegram, Discord, Slack, WhatsApp, Signal, Email
- AGENTS.md — development guide for AI coding assistants
- CONTRIBUTING.md ([#117](https://github.com/NousResearch/hermes-agent/pull/117)) — @Bartok9
- Slash commands reference ([#142](https://github.com/NousResearch/hermes-agent/pull/142)) — @Bartok9
- Comprehensive AGENTS.md accuracy audit ([#732](https://github.com/NousResearch/hermes-agent/pull/732))
- Skin/theme system documentation
- MCP documentation and examples
- Docs accuracy audit — 35+ corrections
- Documentation typo fixes ([#825](https://github.com/NousResearch/hermes-agent/pull/825), [#439](https://github.com/NousResearch/hermes-agent/pull/439)) — @JackTheGit
- CLI config precedence and terminology standardization ([#166](https://github.com/NousResearch/hermes-agent/pull/166), [#167](https://github.com/NousResearch/hermes-agent/pull/167), [#168](https://github.com/NousResearch/hermes-agent/pull/168)) — @Jr-kenny
- Telegram token regex documentation ([#713](https://github.com/NousResearch/hermes-agent/pull/713)) — @VolodymyrBg
---
## 👥 Contributors
Thank you to the 63 contributors who made this release possible! In just over two weeks, the Hermes Agent community came together to ship an extraordinary amount of work.
### Core
- **@teknium1** — 43 PRs: Project lead, core architecture, provider router, sessions, skills, CLI, documentation
### Top Community Contributors
- **@0xbyt4** — 40 PRs: MCP client, Home Assistant, security fixes (symlink, prompt injection, cron), extensive test coverage (6 batches), ascii-art skill, shell noise elimination, skills sync, Telegram formatting, and dozens more
- **@Farukest** — 16 PRs: Security hardening (path traversal, dangerous command detection, symlink boundary), Windows compatibility (POSIX guards, path handling), WhatsApp fixes, max-iterations retry, gateway fixes
- **@aydnOktay** — 11 PRs: Atomic writes (process checkpoints, batch runner, skill files), error handling improvements across Telegram, Discord, code execution, transcription, TTS, and skills
- **@Bartok9** — 9 PRs: CONTRIBUTING.md, slash commands reference, Discord channel topics, think-block stripping, TTS fix, Honcho fix, session count fix, clarify tests
- **@PercyDikec** — 7 PRs: DeepSeek V3 parser fix, /retry response discard, gateway transcript offset, Codex status/visibility, max-iterations retry, setup wizard fix
- **@teyrebaz33** — 5 PRs: Skills enable/disable system, quick commands, personality customization, conditional skill activation
- **@alireza78a** — 5 PRs: Atomic writes (cron, sessions), fd leak prevention, security allowlist, code execution socket cleanup
- **@shitcoinsherpa** — 3 PRs: Windows support (pywinpty, UTF-8 encoding, auth store lock)
- **@Himess** — 3 PRs: Cron/HomeAssistant/Daytona fix, Windows drive-letter parsing, .env permissions
- **@satelerd** — 2 PRs: WhatsApp native media, multi-user session isolation
- **@rovle** — 1 PR: Daytona cloud sandbox backend (4 commits)
- **@erosika** — 1 PR: Honcho AI-native memory integration
- **@dmahan93** — 1 PR: --fuck-it-ship-it flag + RL environment work
- **@SHL0MS** — 1 PR: ASCII video skill
### All Contributors
@0xbyt4, @BP602, @Bartok9, @Farukest, @FurkanL0, @Himess, @Indelwin, @JackTheGit, @JoshuaMart, @Jr-kenny, @OutThisLife, @PercyDikec, @SHL0MS, @Sertug17, @VencentSoliman, @VolodymyrBg, @adavyas, @alireza78a, @areu01or00, @aydnOktay, @batuhankocyigit, @bierlingm, @caentzminger, @cesareth, @ch3ronsa, @christomitov, @cutepawss, @deankerr, @dmahan93, @dogiladeveloper, @dragonkhoi, @erosika, @gamedevCloudy, @gizdusum, @grp06, @intertwine, @jackx707, @jdblackstar, @johnh4098, @kaos35, @kshitijk4poor, @leonsgithub, @luisv-1, @manuelschipper, @mehmetkr-31, @memosr, @PeterFile, @rewbs, @rovle, @rsavitt, @satelerd, @spanishflu-est1918, @stablegenius49, @tars90percent, @tekelala, @teknium1, @teyrebaz33, @tripledoublev, @unmodeled-tyler, @voidborne-d, @voteblake, @ygd58
---
**Full Changelog**: [v0.1.0...v2026.3.12](https://github.com/NousResearch/hermes-agent/compare/v0.1.0...v2026.3.12)

View File

@@ -1,377 +0,0 @@
# Hermes Agent v0.3.0 (v2026.3.17)
**Release Date:** March 17, 2026
> The streaming, plugins, and provider release — unified real-time token delivery, first-class plugin architecture, rebuilt provider system with Vercel AI Gateway, native Anthropic provider, smart approvals, live Chrome CDP browser connect, ACP IDE integration, Honcho memory, voice mode, persistent shell, and 50+ bug fixes across every platform.
---
## ✨ Highlights
- **Unified Streaming Infrastructure** — Real-time token-by-token delivery in CLI and all gateway platforms. Responses stream as they're generated instead of arriving as a block. ([#1538](https://github.com/NousResearch/hermes-agent/pull/1538))
- **First-Class Plugin Architecture** — Drop Python files into `~/.hermes/plugins/` to extend Hermes with custom tools, commands, and hooks. No forking required. ([#1544](https://github.com/NousResearch/hermes-agent/pull/1544), [#1555](https://github.com/NousResearch/hermes-agent/pull/1555))
- **Native Anthropic Provider** — Direct Anthropic API calls with Claude Code credential auto-discovery, OAuth PKCE flows, and native prompt caching. No OpenRouter middleman needed. ([#1097](https://github.com/NousResearch/hermes-agent/pull/1097))
- **Smart Approvals + /stop Command** — Codex-inspired approval system that learns which commands are safe and remembers your preferences. `/stop` kills the current agent run immediately. ([#1543](https://github.com/NousResearch/hermes-agent/pull/1543))
- **Honcho Memory Integration** — Async memory writes, configurable recall modes, session title integration, and multi-user isolation in gateway mode. By @erosika. ([#736](https://github.com/NousResearch/hermes-agent/pull/736))
- **Voice Mode** — Push-to-talk in CLI, voice notes in Telegram/Discord, Discord voice channel support, and local Whisper transcription via faster-whisper. ([#1299](https://github.com/NousResearch/hermes-agent/pull/1299), [#1185](https://github.com/NousResearch/hermes-agent/pull/1185), [#1429](https://github.com/NousResearch/hermes-agent/pull/1429))
- **Concurrent Tool Execution** — Multiple independent tool calls now run in parallel via ThreadPoolExecutor, significantly reducing latency for multi-tool turns. ([#1152](https://github.com/NousResearch/hermes-agent/pull/1152))
- **PII Redaction** — When `privacy.redact_pii` is enabled, personally identifiable information is automatically scrubbed before sending context to LLM providers. ([#1542](https://github.com/NousResearch/hermes-agent/pull/1542))
- **`/browser connect` via CDP** — Attach browser tools to a live Chrome instance through Chrome DevTools Protocol. Debug, inspect, and interact with pages you already have open. ([#1549](https://github.com/NousResearch/hermes-agent/pull/1549))
- **Vercel AI Gateway Provider** — Route Hermes through Vercel's AI Gateway for access to their model catalog and infrastructure. ([#1628](https://github.com/NousResearch/hermes-agent/pull/1628))
- **Centralized Provider Router** — Rebuilt provider system with `call_llm` API, unified `/model` command, auto-detect provider on model switch, and direct endpoint overrides for auxiliary/delegation clients. ([#1003](https://github.com/NousResearch/hermes-agent/pull/1003), [#1506](https://github.com/NousResearch/hermes-agent/pull/1506), [#1375](https://github.com/NousResearch/hermes-agent/pull/1375))
- **ACP Server (IDE Integration)** — VS Code, Zed, and JetBrains can now connect to Hermes as an agent backend, with full slash command support. ([#1254](https://github.com/NousResearch/hermes-agent/pull/1254), [#1532](https://github.com/NousResearch/hermes-agent/pull/1532))
- **Persistent Shell Mode** — Local and SSH terminal backends can maintain shell state across tool calls — cd, env vars, and aliases persist. By @alt-glitch. ([#1067](https://github.com/NousResearch/hermes-agent/pull/1067), [#1483](https://github.com/NousResearch/hermes-agent/pull/1483))
- **Agentic On-Policy Distillation (OPD)** — New RL training environment for distilling agent policies, expanding the Atropos training ecosystem. ([#1149](https://github.com/NousResearch/hermes-agent/pull/1149))
---
## 🏗️ Core Agent & Architecture
### Provider & Model Support
- **Centralized provider router** with `call_llm` API and unified `/model` command — switch models and providers seamlessly ([#1003](https://github.com/NousResearch/hermes-agent/pull/1003))
- **Vercel AI Gateway** provider support ([#1628](https://github.com/NousResearch/hermes-agent/pull/1628))
- **Auto-detect provider** when switching models via `/model` ([#1506](https://github.com/NousResearch/hermes-agent/pull/1506))
- **Direct endpoint overrides** for auxiliary and delegation clients — point vision/subagent calls at specific endpoints ([#1375](https://github.com/NousResearch/hermes-agent/pull/1375))
- **Native Anthropic auxiliary vision** — use Claude's native vision API instead of routing through OpenAI-compatible endpoints ([#1377](https://github.com/NousResearch/hermes-agent/pull/1377))
- Anthropic OAuth flow improvements — auto-run `claude setup-token`, reauthentication, PKCE state persistence, identity fingerprinting ([#1132](https://github.com/NousResearch/hermes-agent/pull/1132), [#1360](https://github.com/NousResearch/hermes-agent/pull/1360), [#1396](https://github.com/NousResearch/hermes-agent/pull/1396), [#1597](https://github.com/NousResearch/hermes-agent/pull/1597))
- Fix adaptive thinking without `budget_tokens` for Claude 4.6 models — by @ASRagab ([#1128](https://github.com/NousResearch/hermes-agent/pull/1128))
- Fix Anthropic cache markers through adapter — by @brandtcormorant ([#1216](https://github.com/NousResearch/hermes-agent/pull/1216))
- Retry Anthropic 429/529 errors and surface details to users — by @0xbyt4 ([#1585](https://github.com/NousResearch/hermes-agent/pull/1585))
- Fix Anthropic adapter max_tokens, fallback crash, proxy base_url — by @0xbyt4 ([#1121](https://github.com/NousResearch/hermes-agent/pull/1121))
- Fix DeepSeek V3 parser dropping multiple parallel tool calls — by @mr-emmett-one ([#1365](https://github.com/NousResearch/hermes-agent/pull/1365), [#1300](https://github.com/NousResearch/hermes-agent/pull/1300))
- Accept unlisted models with warning instead of rejecting ([#1047](https://github.com/NousResearch/hermes-agent/pull/1047), [#1102](https://github.com/NousResearch/hermes-agent/pull/1102))
- Skip reasoning params for unsupported OpenRouter models ([#1485](https://github.com/NousResearch/hermes-agent/pull/1485))
- MiniMax Anthropic API compatibility fix ([#1623](https://github.com/NousResearch/hermes-agent/pull/1623))
- Custom endpoint `/models` verification and `/v1` base URL suggestion ([#1480](https://github.com/NousResearch/hermes-agent/pull/1480))
- Resolve delegation providers from `custom_providers` config ([#1328](https://github.com/NousResearch/hermes-agent/pull/1328))
- Kimi model additions and User-Agent fix ([#1039](https://github.com/NousResearch/hermes-agent/pull/1039))
- Strip `call_id`/`response_item_id` for Mistral compatibility ([#1058](https://github.com/NousResearch/hermes-agent/pull/1058))
### Agent Loop & Conversation
- **Anthropic Context Editing API** support ([#1147](https://github.com/NousResearch/hermes-agent/pull/1147))
- Improved context compaction handoff summaries — compressor now preserves more actionable state ([#1273](https://github.com/NousResearch/hermes-agent/pull/1273))
- Sync session_id after mid-run context compression ([#1160](https://github.com/NousResearch/hermes-agent/pull/1160))
- Session hygiene threshold tuned to 50% for more proactive compression ([#1096](https://github.com/NousResearch/hermes-agent/pull/1096), [#1161](https://github.com/NousResearch/hermes-agent/pull/1161))
- Include session ID in system prompt via `--pass-session-id` flag ([#1040](https://github.com/NousResearch/hermes-agent/pull/1040))
- Prevent closed OpenAI client reuse across retries ([#1391](https://github.com/NousResearch/hermes-agent/pull/1391))
- Sanitize chat payloads and provider precedence ([#1253](https://github.com/NousResearch/hermes-agent/pull/1253))
- Handle dict tool call arguments from Codex and local backends ([#1393](https://github.com/NousResearch/hermes-agent/pull/1393), [#1440](https://github.com/NousResearch/hermes-agent/pull/1440))
### Memory & Sessions
- **Improve memory prioritization** — user preferences and corrections weighted above procedural knowledge ([#1548](https://github.com/NousResearch/hermes-agent/pull/1548))
- Tighter memory and session recall guidance in system prompts ([#1329](https://github.com/NousResearch/hermes-agent/pull/1329))
- Persist CLI token counts to session DB for `/insights` ([#1498](https://github.com/NousResearch/hermes-agent/pull/1498))
- Keep Honcho recall out of the cached system prefix ([#1201](https://github.com/NousResearch/hermes-agent/pull/1201))
- Correct `seed_ai_identity` to use `session.add_messages()` ([#1475](https://github.com/NousResearch/hermes-agent/pull/1475))
- Isolate Honcho session routing for multi-user gateway ([#1500](https://github.com/NousResearch/hermes-agent/pull/1500))
---
## 📱 Messaging Platforms (Gateway)
### Gateway Core
- **System gateway service mode** — run as a system-level systemd service, not just user-level ([#1371](https://github.com/NousResearch/hermes-agent/pull/1371))
- **Gateway install scope prompts** — choose user vs system scope during setup ([#1374](https://github.com/NousResearch/hermes-agent/pull/1374))
- **Reasoning hot reload** — change reasoning settings without restarting the gateway ([#1275](https://github.com/NousResearch/hermes-agent/pull/1275))
- Default group sessions to per-user isolation — no more shared state across users in group chats ([#1495](https://github.com/NousResearch/hermes-agent/pull/1495), [#1417](https://github.com/NousResearch/hermes-agent/pull/1417))
- Harden gateway restart recovery ([#1310](https://github.com/NousResearch/hermes-agent/pull/1310))
- Cancel active runs during shutdown ([#1427](https://github.com/NousResearch/hermes-agent/pull/1427))
- SSL certificate auto-detection for NixOS and non-standard systems ([#1494](https://github.com/NousResearch/hermes-agent/pull/1494))
- Auto-detect D-Bus session bus for `systemctl --user` on headless servers ([#1601](https://github.com/NousResearch/hermes-agent/pull/1601))
- Auto-enable systemd linger during gateway install on headless servers ([#1334](https://github.com/NousResearch/hermes-agent/pull/1334))
- Fall back to module entrypoint when `hermes` is not on PATH ([#1355](https://github.com/NousResearch/hermes-agent/pull/1355))
- Fix dual gateways on macOS launchd after `hermes update` ([#1567](https://github.com/NousResearch/hermes-agent/pull/1567))
- Remove recursive ExecStop from systemd units ([#1530](https://github.com/NousResearch/hermes-agent/pull/1530))
- Prevent logging handler accumulation in gateway mode ([#1251](https://github.com/NousResearch/hermes-agent/pull/1251))
- Restart on retryable startup failures — by @jplew ([#1517](https://github.com/NousResearch/hermes-agent/pull/1517))
- Backfill model on gateway sessions after agent runs ([#1306](https://github.com/NousResearch/hermes-agent/pull/1306))
- PID-based gateway kill and deferred config write ([#1499](https://github.com/NousResearch/hermes-agent/pull/1499))
### Telegram
- Buffer media groups to prevent self-interruption from photo bursts ([#1341](https://github.com/NousResearch/hermes-agent/pull/1341), [#1422](https://github.com/NousResearch/hermes-agent/pull/1422))
- Retry on transient TLS failures during connect and send ([#1535](https://github.com/NousResearch/hermes-agent/pull/1535))
- Harden polling conflict handling ([#1339](https://github.com/NousResearch/hermes-agent/pull/1339))
- Escape chunk indicators and inline code in MarkdownV2 ([#1478](https://github.com/NousResearch/hermes-agent/pull/1478), [#1626](https://github.com/NousResearch/hermes-agent/pull/1626))
- Check updater/app state before disconnect ([#1389](https://github.com/NousResearch/hermes-agent/pull/1389))
### Discord
- `/thread` command with `auto_thread` config and media metadata fixes ([#1178](https://github.com/NousResearch/hermes-agent/pull/1178))
- Auto-thread on @mention, skip mention text in bot threads ([#1438](https://github.com/NousResearch/hermes-agent/pull/1438))
- Retry without reply reference for system messages ([#1385](https://github.com/NousResearch/hermes-agent/pull/1385))
- Preserve native document and video attachment support ([#1392](https://github.com/NousResearch/hermes-agent/pull/1392))
- Defer discord adapter annotations to avoid optional import crashes ([#1314](https://github.com/NousResearch/hermes-agent/pull/1314))
### Slack
- Thread handling overhaul — progress messages, responses, and session isolation all respect threads ([#1103](https://github.com/NousResearch/hermes-agent/pull/1103))
- Formatting, reactions, user resolution, and command improvements ([#1106](https://github.com/NousResearch/hermes-agent/pull/1106))
- Fix MAX_MESSAGE_LENGTH 3900 → 39000 ([#1117](https://github.com/NousResearch/hermes-agent/pull/1117))
- File upload fallback preserves thread context — by @0xbyt4 ([#1122](https://github.com/NousResearch/hermes-agent/pull/1122))
- Improve setup guidance ([#1387](https://github.com/NousResearch/hermes-agent/pull/1387))
### Email
- Fix IMAP UID tracking and SMTP TLS verification ([#1305](https://github.com/NousResearch/hermes-agent/pull/1305))
- Add `skip_attachments` option via config.yaml ([#1536](https://github.com/NousResearch/hermes-agent/pull/1536))
### Home Assistant
- Event filtering closed by default ([#1169](https://github.com/NousResearch/hermes-agent/pull/1169))
---
## 🖥️ CLI & User Experience
### Interactive CLI
- **Persistent CLI status bar** — always-visible model, provider, and token counts ([#1522](https://github.com/NousResearch/hermes-agent/pull/1522))
- **File path autocomplete** in the input prompt ([#1545](https://github.com/NousResearch/hermes-agent/pull/1545))
- **`/plan` command** — generate implementation plans from specs ([#1372](https://github.com/NousResearch/hermes-agent/pull/1372), [#1381](https://github.com/NousResearch/hermes-agent/pull/1381))
- **Major `/rollback` improvements** — richer checkpoint history, clearer UX ([#1505](https://github.com/NousResearch/hermes-agent/pull/1505))
- **Preload CLI skills on launch** — skills are ready before the first prompt ([#1359](https://github.com/NousResearch/hermes-agent/pull/1359))
- **Centralized slash command registry** — all commands defined once, consumed everywhere ([#1603](https://github.com/NousResearch/hermes-agent/pull/1603))
- `/bg` alias for `/background` ([#1590](https://github.com/NousResearch/hermes-agent/pull/1590))
- Prefix matching for slash commands — `/mod` resolves to `/model` ([#1320](https://github.com/NousResearch/hermes-agent/pull/1320))
- `/new`, `/reset`, `/clear` now start genuinely fresh sessions ([#1237](https://github.com/NousResearch/hermes-agent/pull/1237))
- Accept session ID prefixes for session actions ([#1425](https://github.com/NousResearch/hermes-agent/pull/1425))
- TUI prompt and accent output now respect active skin ([#1282](https://github.com/NousResearch/hermes-agent/pull/1282))
- Centralize tool emoji metadata in registry + skin integration ([#1484](https://github.com/NousResearch/hermes-agent/pull/1484))
- "View full command" option added to dangerous command approval — by @teknium1 based on design by community ([#887](https://github.com/NousResearch/hermes-agent/pull/887))
- Non-blocking startup update check and banner deduplication ([#1386](https://github.com/NousResearch/hermes-agent/pull/1386))
- `/reasoning` command output ordering and inline think extraction fixes ([#1031](https://github.com/NousResearch/hermes-agent/pull/1031))
- Verbose mode shows full untruncated output ([#1472](https://github.com/NousResearch/hermes-agent/pull/1472))
- Fix `/status` to report live state and tokens ([#1476](https://github.com/NousResearch/hermes-agent/pull/1476))
- Seed a default global SOUL.md ([#1311](https://github.com/NousResearch/hermes-agent/pull/1311))
### Setup & Configuration
- **OpenClaw migration** during first-time setup — by @kshitijk4poor ([#981](https://github.com/NousResearch/hermes-agent/pull/981))
- `hermes claw migrate` command + migration docs ([#1059](https://github.com/NousResearch/hermes-agent/pull/1059))
- Smart vision setup that respects the user's chosen provider ([#1323](https://github.com/NousResearch/hermes-agent/pull/1323))
- Handle headless setup flows end-to-end ([#1274](https://github.com/NousResearch/hermes-agent/pull/1274))
- Prefer curses over `simple_term_menu` in setup.py ([#1487](https://github.com/NousResearch/hermes-agent/pull/1487))
- Show effective model and provider in `/status` ([#1284](https://github.com/NousResearch/hermes-agent/pull/1284))
- Config set examples use placeholder syntax ([#1322](https://github.com/NousResearch/hermes-agent/pull/1322))
- Reload .env over stale shell overrides ([#1434](https://github.com/NousResearch/hermes-agent/pull/1434))
- Fix is_coding_plan NameError crash — by @0xbyt4 ([#1123](https://github.com/NousResearch/hermes-agent/pull/1123))
- Add missing packages to setuptools config — by @alt-glitch ([#912](https://github.com/NousResearch/hermes-agent/pull/912))
- Installer: clarify why sudo is needed at every prompt ([#1602](https://github.com/NousResearch/hermes-agent/pull/1602))
---
## 🔧 Tool System
### Terminal & Execution
- **Persistent shell mode** for local and SSH backends — maintain shell state across tool calls — by @alt-glitch ([#1067](https://github.com/NousResearch/hermes-agent/pull/1067), [#1483](https://github.com/NousResearch/hermes-agent/pull/1483))
- **Tirith pre-exec command scanning** — security layer that analyzes commands before execution ([#1256](https://github.com/NousResearch/hermes-agent/pull/1256))
- Strip Hermes provider env vars from all subprocess environments ([#1157](https://github.com/NousResearch/hermes-agent/pull/1157), [#1172](https://github.com/NousResearch/hermes-agent/pull/1172), [#1399](https://github.com/NousResearch/hermes-agent/pull/1399), [#1419](https://github.com/NousResearch/hermes-agent/pull/1419)) — initial fix by @eren-karakus0
- SSH preflight check ([#1486](https://github.com/NousResearch/hermes-agent/pull/1486))
- Docker backend: make cwd workspace mount explicit opt-in ([#1534](https://github.com/NousResearch/hermes-agent/pull/1534))
- Add project root to PYTHONPATH in execute_code sandbox ([#1383](https://github.com/NousResearch/hermes-agent/pull/1383))
- Eliminate execute_code progress spam on gateway platforms ([#1098](https://github.com/NousResearch/hermes-agent/pull/1098))
- Clearer docker backend preflight errors ([#1276](https://github.com/NousResearch/hermes-agent/pull/1276))
### Browser
- **`/browser connect`** — attach browser tools to a live Chrome instance via CDP ([#1549](https://github.com/NousResearch/hermes-agent/pull/1549))
- Improve browser cleanup, local browser PATH setup, and screenshot recovery ([#1333](https://github.com/NousResearch/hermes-agent/pull/1333))
### MCP
- **Selective tool loading** with utility policies — filter which MCP tools are available ([#1302](https://github.com/NousResearch/hermes-agent/pull/1302))
- Auto-reload MCP tools when `mcp_servers` config changes without restart ([#1474](https://github.com/NousResearch/hermes-agent/pull/1474))
- Resolve npx stdio connection failures ([#1291](https://github.com/NousResearch/hermes-agent/pull/1291))
- Preserve MCP toolsets when saving platform tool config ([#1421](https://github.com/NousResearch/hermes-agent/pull/1421))
### Vision
- Unify vision backend gating ([#1367](https://github.com/NousResearch/hermes-agent/pull/1367))
- Surface actual error reason instead of generic message ([#1338](https://github.com/NousResearch/hermes-agent/pull/1338))
- Make Claude image handling work end-to-end ([#1408](https://github.com/NousResearch/hermes-agent/pull/1408))
### Cron
- **Compress cron management into one tool** — single `cronjob` tool replaces multiple commands ([#1343](https://github.com/NousResearch/hermes-agent/pull/1343))
- Suppress duplicate cron sends to auto-delivery targets ([#1357](https://github.com/NousResearch/hermes-agent/pull/1357))
- Persist cron sessions to SQLite ([#1255](https://github.com/NousResearch/hermes-agent/pull/1255))
- Per-job runtime overrides (provider, model, base_url) ([#1398](https://github.com/NousResearch/hermes-agent/pull/1398))
- Atomic write in `save_job_output` to prevent data loss on crash ([#1173](https://github.com/NousResearch/hermes-agent/pull/1173))
- Preserve thread context for `deliver=origin` ([#1437](https://github.com/NousResearch/hermes-agent/pull/1437))
### Patch Tool
- Avoid corrupting pipe chars in V4A patch apply ([#1286](https://github.com/NousResearch/hermes-agent/pull/1286))
- Permissive `block_anchor` thresholds and unicode normalization ([#1539](https://github.com/NousResearch/hermes-agent/pull/1539))
### Delegation
- Add observability metadata to subagent results (model, tokens, duration, tool trace) ([#1175](https://github.com/NousResearch/hermes-agent/pull/1175))
---
## 🧩 Skills Ecosystem
### Skills System
- **Integrate skills.sh** as a hub source alongside ClawHub ([#1303](https://github.com/NousResearch/hermes-agent/pull/1303))
- Secure skill env setup on load ([#1153](https://github.com/NousResearch/hermes-agent/pull/1153))
- Honor policy table for dangerous verdicts ([#1330](https://github.com/NousResearch/hermes-agent/pull/1330))
- Harden ClawHub skill search exact matches ([#1400](https://github.com/NousResearch/hermes-agent/pull/1400))
- Fix ClawHub skill install — use `/download` ZIP endpoint ([#1060](https://github.com/NousResearch/hermes-agent/pull/1060))
- Avoid mislabeling local skills as builtin — by @arceus77-7 ([#862](https://github.com/NousResearch/hermes-agent/pull/862))
### New Skills
- **Linear** project management ([#1230](https://github.com/NousResearch/hermes-agent/pull/1230))
- **X/Twitter** via x-cli ([#1285](https://github.com/NousResearch/hermes-agent/pull/1285))
- **Telephony** — Twilio, SMS, and AI calls ([#1289](https://github.com/NousResearch/hermes-agent/pull/1289))
- **1Password** — by @arceus77-7 ([#883](https://github.com/NousResearch/hermes-agent/pull/883), [#1179](https://github.com/NousResearch/hermes-agent/pull/1179))
- **NeuroSkill BCI** integration ([#1135](https://github.com/NousResearch/hermes-agent/pull/1135))
- **Blender MCP** for 3D modeling ([#1531](https://github.com/NousResearch/hermes-agent/pull/1531))
- **OSS Security Forensics** ([#1482](https://github.com/NousResearch/hermes-agent/pull/1482))
- **Parallel CLI** research skill ([#1301](https://github.com/NousResearch/hermes-agent/pull/1301))
- **OpenCode** CLI skill ([#1174](https://github.com/NousResearch/hermes-agent/pull/1174))
- **ASCII Video** skill refactored — by @SHL0MS ([#1213](https://github.com/NousResearch/hermes-agent/pull/1213), [#1598](https://github.com/NousResearch/hermes-agent/pull/1598))
---
## 🎙️ Voice Mode
- Voice mode foundation — push-to-talk CLI, Telegram/Discord voice notes ([#1299](https://github.com/NousResearch/hermes-agent/pull/1299))
- Free local Whisper transcription via faster-whisper ([#1185](https://github.com/NousResearch/hermes-agent/pull/1185))
- Discord voice channel reliability fixes ([#1429](https://github.com/NousResearch/hermes-agent/pull/1429))
- Restore local STT fallback for gateway voice notes ([#1490](https://github.com/NousResearch/hermes-agent/pull/1490))
- Honor `stt.enabled: false` across gateway transcription ([#1394](https://github.com/NousResearch/hermes-agent/pull/1394))
- Fix bogus incapability message on Telegram voice notes (Issue [#1033](https://github.com/NousResearch/hermes-agent/issues/1033))
---
## 🔌 ACP (IDE Integration)
- Restore ACP server implementation ([#1254](https://github.com/NousResearch/hermes-agent/pull/1254))
- Support slash commands in ACP adapter ([#1532](https://github.com/NousResearch/hermes-agent/pull/1532))
---
## 🧪 RL Training
- **Agentic On-Policy Distillation (OPD)** environment — new RL training environment for agent policy distillation ([#1149](https://github.com/NousResearch/hermes-agent/pull/1149))
- Make tinker-atropos RL training fully optional ([#1062](https://github.com/NousResearch/hermes-agent/pull/1062))
---
## 🔒 Security & Reliability
### Security Hardening
- **Tirith pre-exec command scanning** — static analysis of terminal commands before execution ([#1256](https://github.com/NousResearch/hermes-agent/pull/1256))
- **PII redaction** when `privacy.redact_pii` is enabled ([#1542](https://github.com/NousResearch/hermes-agent/pull/1542))
- Strip Hermes provider/gateway/tool env vars from all subprocess environments ([#1157](https://github.com/NousResearch/hermes-agent/pull/1157), [#1172](https://github.com/NousResearch/hermes-agent/pull/1172), [#1399](https://github.com/NousResearch/hermes-agent/pull/1399), [#1419](https://github.com/NousResearch/hermes-agent/pull/1419))
- Docker cwd workspace mount now explicit opt-in — never auto-mount host directories ([#1534](https://github.com/NousResearch/hermes-agent/pull/1534))
- Escape parens and braces in fork bomb regex pattern ([#1397](https://github.com/NousResearch/hermes-agent/pull/1397))
- Harden `.worktreeinclude` path containment ([#1388](https://github.com/NousResearch/hermes-agent/pull/1388))
- Use description as `pattern_key` to prevent approval collisions ([#1395](https://github.com/NousResearch/hermes-agent/pull/1395))
### Reliability
- Guard init-time stdio writes ([#1271](https://github.com/NousResearch/hermes-agent/pull/1271))
- Session log writes reuse shared atomic JSON helper ([#1280](https://github.com/NousResearch/hermes-agent/pull/1280))
- Atomic temp cleanup protected on interrupts ([#1401](https://github.com/NousResearch/hermes-agent/pull/1401))
---
## 🐛 Notable Bug Fixes
- **`/status` always showing 0 tokens** — now reports live state (Issue [#1465](https://github.com/NousResearch/hermes-agent/issues/1465), [#1476](https://github.com/NousResearch/hermes-agent/pull/1476))
- **Custom model endpoints not working** — restored config-saved endpoint resolution (Issue [#1460](https://github.com/NousResearch/hermes-agent/issues/1460), [#1373](https://github.com/NousResearch/hermes-agent/pull/1373))
- **MCP tools not visible until restart** — auto-reload on config change (Issue [#1036](https://github.com/NousResearch/hermes-agent/issues/1036), [#1474](https://github.com/NousResearch/hermes-agent/pull/1474))
- **`hermes tools` removing MCP tools** — preserve MCP toolsets when saving (Issue [#1247](https://github.com/NousResearch/hermes-agent/issues/1247), [#1421](https://github.com/NousResearch/hermes-agent/pull/1421))
- **Terminal subprocesses inheriting `OPENAI_BASE_URL`** breaking external tools (Issue [#1002](https://github.com/NousResearch/hermes-agent/issues/1002), [#1399](https://github.com/NousResearch/hermes-agent/pull/1399))
- **Background process lost on gateway restart** — improved recovery (Issue [#1144](https://github.com/NousResearch/hermes-agent/issues/1144))
- **Cron jobs not persisting state** — now stored in SQLite (Issue [#1416](https://github.com/NousResearch/hermes-agent/issues/1416), [#1255](https://github.com/NousResearch/hermes-agent/pull/1255))
- **Cronjob `deliver: origin` not preserving thread context** (Issue [#1219](https://github.com/NousResearch/hermes-agent/issues/1219), [#1437](https://github.com/NousResearch/hermes-agent/pull/1437))
- **Gateway systemd service failing to auto-restart** when browser processes orphaned (Issue [#1617](https://github.com/NousResearch/hermes-agent/issues/1617))
- **`/background` completion report cut off in Telegram** (Issue [#1443](https://github.com/NousResearch/hermes-agent/issues/1443))
- **Model switching not taking effect** (Issue [#1244](https://github.com/NousResearch/hermes-agent/issues/1244), [#1183](https://github.com/NousResearch/hermes-agent/pull/1183))
- **`hermes doctor` reporting cronjob as unavailable** (Issue [#878](https://github.com/NousResearch/hermes-agent/issues/878), [#1180](https://github.com/NousResearch/hermes-agent/pull/1180))
- **WhatsApp bridge messages not received** from mobile (Issue [#1142](https://github.com/NousResearch/hermes-agent/issues/1142))
- **Setup wizard hanging on headless SSH** (Issue [#905](https://github.com/NousResearch/hermes-agent/issues/905), [#1274](https://github.com/NousResearch/hermes-agent/pull/1274))
- **Log handler accumulation** degrading gateway performance (Issue [#990](https://github.com/NousResearch/hermes-agent/issues/990), [#1251](https://github.com/NousResearch/hermes-agent/pull/1251))
- **Gateway NULL model in DB** (Issue [#987](https://github.com/NousResearch/hermes-agent/issues/987), [#1306](https://github.com/NousResearch/hermes-agent/pull/1306))
- **Strict endpoints rejecting replayed tool_calls** (Issue [#893](https://github.com/NousResearch/hermes-agent/issues/893))
- **Remaining hardcoded `~/.hermes` paths** — all now respect `HERMES_HOME` (Issue [#892](https://github.com/NousResearch/hermes-agent/issues/892), [#1233](https://github.com/NousResearch/hermes-agent/pull/1233))
- **Delegate tool not working with custom inference providers** (Issue [#1011](https://github.com/NousResearch/hermes-agent/issues/1011), [#1328](https://github.com/NousResearch/hermes-agent/pull/1328))
- **Skills Guard blocking official skills** (Issue [#1006](https://github.com/NousResearch/hermes-agent/issues/1006), [#1330](https://github.com/NousResearch/hermes-agent/pull/1330))
- **Setup writing provider before model selection** (Issue [#1182](https://github.com/NousResearch/hermes-agent/issues/1182))
- **`GatewayConfig.get()` AttributeError** crashing all message handling (Issue [#1158](https://github.com/NousResearch/hermes-agent/issues/1158), [#1287](https://github.com/NousResearch/hermes-agent/pull/1287))
- **`/update` hard-failing with "command not found"** (Issue [#1049](https://github.com/NousResearch/hermes-agent/issues/1049))
- **Image analysis failing silently** (Issue [#1034](https://github.com/NousResearch/hermes-agent/issues/1034), [#1338](https://github.com/NousResearch/hermes-agent/pull/1338))
- **API `BadRequestError` from `'dict'` object has no attribute `'strip'`** (Issue [#1071](https://github.com/NousResearch/hermes-agent/issues/1071))
- **Slash commands requiring exact full name** — now uses prefix matching (Issue [#928](https://github.com/NousResearch/hermes-agent/issues/928), [#1320](https://github.com/NousResearch/hermes-agent/pull/1320))
- **Gateway stops responding when terminal is closed on headless** (Issue [#1005](https://github.com/NousResearch/hermes-agent/issues/1005))
---
## 🧪 Testing
- Cover empty cached Anthropic tool-call turns ([#1222](https://github.com/NousResearch/hermes-agent/pull/1222))
- Fix stale CI assumptions in parser and quick-command coverage ([#1236](https://github.com/NousResearch/hermes-agent/pull/1236))
- Fix gateway async tests without implicit event loop ([#1278](https://github.com/NousResearch/hermes-agent/pull/1278))
- Make gateway async tests xdist-safe ([#1281](https://github.com/NousResearch/hermes-agent/pull/1281))
- Cross-timezone naive timestamp regression for cron ([#1319](https://github.com/NousResearch/hermes-agent/pull/1319))
- Isolate codex provider tests from local env ([#1335](https://github.com/NousResearch/hermes-agent/pull/1335))
- Lock retry replacement semantics ([#1379](https://github.com/NousResearch/hermes-agent/pull/1379))
- Improve error logging in session search tool — by @aydnOktay ([#1533](https://github.com/NousResearch/hermes-agent/pull/1533))
---
## 📚 Documentation
- Comprehensive SOUL.md guide ([#1315](https://github.com/NousResearch/hermes-agent/pull/1315))
- Voice mode documentation ([#1316](https://github.com/NousResearch/hermes-agent/pull/1316), [#1362](https://github.com/NousResearch/hermes-agent/pull/1362))
- Provider contribution guide ([#1361](https://github.com/NousResearch/hermes-agent/pull/1361))
- ACP and internal systems implementation guides ([#1259](https://github.com/NousResearch/hermes-agent/pull/1259))
- Expand Docusaurus coverage across CLI, tools, skills, and skins ([#1232](https://github.com/NousResearch/hermes-agent/pull/1232))
- Terminal backend and Windows troubleshooting ([#1297](https://github.com/NousResearch/hermes-agent/pull/1297))
- Skills hub reference section ([#1317](https://github.com/NousResearch/hermes-agent/pull/1317))
- Checkpoint, /rollback, and git worktrees guide ([#1493](https://github.com/NousResearch/hermes-agent/pull/1493), [#1524](https://github.com/NousResearch/hermes-agent/pull/1524))
- CLI status bar and /usage reference ([#1523](https://github.com/NousResearch/hermes-agent/pull/1523))
- Fallback providers + /background command docs ([#1430](https://github.com/NousResearch/hermes-agent/pull/1430))
- Gateway service scopes docs ([#1378](https://github.com/NousResearch/hermes-agent/pull/1378))
- Slack thread reply behavior docs ([#1407](https://github.com/NousResearch/hermes-agent/pull/1407))
- Redesigned landing page with Nous blue palette — by @austinpickett ([#974](https://github.com/NousResearch/hermes-agent/pull/974))
- Fix several documentation typos — by @JackTheGit ([#953](https://github.com/NousResearch/hermes-agent/pull/953))
- Stabilize website diagrams ([#1405](https://github.com/NousResearch/hermes-agent/pull/1405))
- CLI vs messaging quick reference in README ([#1491](https://github.com/NousResearch/hermes-agent/pull/1491))
- Add search to Docusaurus ([#1053](https://github.com/NousResearch/hermes-agent/pull/1053))
- Home Assistant integration docs ([#1170](https://github.com/NousResearch/hermes-agent/pull/1170))
---
## 👥 Contributors
### Core
- **@teknium1** — 220+ PRs spanning every area of the codebase
### Top Community Contributors
- **@0xbyt4** (4 PRs) — Anthropic adapter fixes (max_tokens, fallback crash, 429/529 retry), Slack file upload thread context, setup NameError fix
- **@erosika** (1 PR) — Honcho memory integration: async writes, memory modes, session title integration
- **@SHL0MS** (2 PRs) — ASCII video skill design patterns and refactoring
- **@alt-glitch** (2 PRs) — Persistent shell mode for local/SSH backends, setuptools packaging fix
- **@arceus77-7** (2 PRs) — 1Password skill, fix skills list mislabeling
- **@kshitijk4poor** (1 PR) — OpenClaw migration during setup wizard
- **@ASRagab** (1 PR) — Fix adaptive thinking for Claude 4.6 models
- **@eren-karakus0** (1 PR) — Strip Hermes provider env vars from subprocess environment
- **@mr-emmett-one** (1 PR) — Fix DeepSeek V3 parser multi-tool call support
- **@jplew** (1 PR) — Gateway restart on retryable startup failures
- **@brandtcormorant** (1 PR) — Fix Anthropic cache control for empty text blocks
- **@aydnOktay** (1 PR) — Improve error logging in session search tool
- **@austinpickett** (1 PR) — Landing page redesign with Nous blue palette
- **@JackTheGit** (1 PR) — Documentation typo fixes
### All Contributors
@0xbyt4, @alt-glitch, @arceus77-7, @ASRagab, @austinpickett, @aydnOktay, @brandtcormorant, @eren-karakus0, @erosika, @JackTheGit, @jplew, @kshitijk4poor, @mr-emmett-one, @SHL0MS, @teknium1
---
**Full Changelog**: [v2026.3.12...v2026.3.17](https://github.com/NousResearch/hermes-agent/compare/v2026.3.12...v2026.3.17)

View File

@@ -1 +0,0 @@
"""ACP (Agent Communication Protocol) adapter for hermes-agent."""

View File

@@ -1,5 +0,0 @@
"""Allow running the ACP adapter as ``python -m acp_adapter``."""
from .entry import main
main()

View File

@@ -1,24 +0,0 @@
"""ACP auth helpers — detect the currently configured Hermes provider."""
from __future__ import annotations
from typing import Optional
def detect_provider() -> Optional[str]:
"""Resolve the active Hermes runtime provider, or None if unavailable."""
try:
from hermes_cli.runtime_provider import resolve_runtime_provider
runtime = resolve_runtime_provider()
api_key = runtime.get("api_key")
provider = runtime.get("provider")
if isinstance(api_key, str) and api_key.strip() and isinstance(provider, str) and provider.strip():
return provider.strip().lower()
except Exception:
return None
return None
def has_provider() -> bool:
"""Return True if Hermes can resolve any runtime provider credentials."""
return detect_provider() is not None

View File

@@ -1,85 +0,0 @@
"""CLI entry point for the hermes-agent ACP adapter.
Loads environment variables from ``~/.hermes/.env``, configures logging
to write to stderr (so stdout is reserved for ACP JSON-RPC transport),
and starts the ACP agent server.
Usage::
python -m acp_adapter.entry
# or
hermes acp
# or
hermes-acp
"""
import asyncio
import logging
import os
import sys
from pathlib import Path
def _setup_logging() -> None:
"""Route all logging to stderr so stdout stays clean for ACP stdio."""
handler = logging.StreamHandler(sys.stderr)
handler.setFormatter(
logging.Formatter(
"%(asctime)s [%(levelname)s] %(name)s: %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
)
root = logging.getLogger()
root.handlers.clear()
root.addHandler(handler)
root.setLevel(logging.INFO)
# Quiet down noisy libraries
logging.getLogger("httpx").setLevel(logging.WARNING)
logging.getLogger("httpcore").setLevel(logging.WARNING)
logging.getLogger("openai").setLevel(logging.WARNING)
def _load_env() -> None:
"""Load .env from HERMES_HOME (default ``~/.hermes``)."""
from hermes_cli.env_loader import load_hermes_dotenv
hermes_home = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
loaded = load_hermes_dotenv(hermes_home=hermes_home)
if loaded:
for env_file in loaded:
logging.getLogger(__name__).info("Loaded env from %s", env_file)
else:
logging.getLogger(__name__).info(
"No .env found at %s, using system env", hermes_home / ".env"
)
def main() -> None:
"""Entry point: load env, configure logging, run the ACP agent."""
_setup_logging()
_load_env()
logger = logging.getLogger(__name__)
logger.info("Starting hermes-agent ACP adapter")
# Ensure the project root is on sys.path so ``from run_agent import AIAgent`` works
project_root = str(Path(__file__).resolve().parent.parent)
if project_root not in sys.path:
sys.path.insert(0, project_root)
import acp
from .server import HermesACPAgent
agent = HermesACPAgent()
try:
asyncio.run(acp.run_agent(agent))
except KeyboardInterrupt:
logger.info("Shutting down (KeyboardInterrupt)")
except Exception:
logger.exception("ACP agent crashed")
sys.exit(1)
if __name__ == "__main__":
main()

View File

@@ -1,171 +0,0 @@
"""Callback factories for bridging AIAgent events to ACP notifications.
Each factory returns a callable with the signature that AIAgent expects
for its callbacks. Internally, the callbacks push ACP session updates
to the client via ``conn.session_update()`` using
``asyncio.run_coroutine_threadsafe()`` (since AIAgent runs in a worker
thread while the event loop lives on the main thread).
"""
import asyncio
import json
import logging
from collections import defaultdict, deque
from typing import Any, Callable, Deque, Dict
import acp
from .tools import (
build_tool_complete,
build_tool_start,
make_tool_call_id,
)
logger = logging.getLogger(__name__)
def _send_update(
conn: acp.Client,
session_id: str,
loop: asyncio.AbstractEventLoop,
update: Any,
) -> None:
"""Fire-and-forget an ACP session update from a worker thread."""
try:
future = asyncio.run_coroutine_threadsafe(
conn.session_update(session_id, update), loop
)
future.result(timeout=5)
except Exception:
logger.debug("Failed to send ACP update", exc_info=True)
# ------------------------------------------------------------------
# Tool progress callback
# ------------------------------------------------------------------
def make_tool_progress_cb(
conn: acp.Client,
session_id: str,
loop: asyncio.AbstractEventLoop,
tool_call_ids: Dict[str, Deque[str]],
) -> Callable:
"""Create a ``tool_progress_callback`` for AIAgent.
Signature expected by AIAgent::
tool_progress_callback(name: str, preview: str, args: dict)
Emits ``ToolCallStart`` for each tool invocation and tracks IDs in a FIFO
queue per tool name so duplicate/parallel same-name calls still complete
against the correct ACP tool call.
"""
def _tool_progress(name: str, preview: str, args: Any = None) -> None:
if isinstance(args, str):
try:
args = json.loads(args)
except (json.JSONDecodeError, TypeError):
args = {"raw": args}
if not isinstance(args, dict):
args = {}
tc_id = make_tool_call_id()
queue = tool_call_ids.get(name)
if queue is None:
queue = deque()
tool_call_ids[name] = queue
elif isinstance(queue, str):
queue = deque([queue])
tool_call_ids[name] = queue
queue.append(tc_id)
update = build_tool_start(tc_id, name, args)
_send_update(conn, session_id, loop, update)
return _tool_progress
# ------------------------------------------------------------------
# Thinking callback
# ------------------------------------------------------------------
def make_thinking_cb(
conn: acp.Client,
session_id: str,
loop: asyncio.AbstractEventLoop,
) -> Callable:
"""Create a ``thinking_callback`` for AIAgent."""
def _thinking(text: str) -> None:
if not text:
return
update = acp.update_agent_thought_text(text)
_send_update(conn, session_id, loop, update)
return _thinking
# ------------------------------------------------------------------
# Step callback
# ------------------------------------------------------------------
def make_step_cb(
conn: acp.Client,
session_id: str,
loop: asyncio.AbstractEventLoop,
tool_call_ids: Dict[str, Deque[str]],
) -> Callable:
"""Create a ``step_callback`` for AIAgent.
Signature expected by AIAgent::
step_callback(api_call_count: int, prev_tools: list)
"""
def _step(api_call_count: int, prev_tools: Any = None) -> None:
if prev_tools and isinstance(prev_tools, list):
for tool_info in prev_tools:
tool_name = None
result = None
if isinstance(tool_info, dict):
tool_name = tool_info.get("name") or tool_info.get("function_name")
result = tool_info.get("result") or tool_info.get("output")
elif isinstance(tool_info, str):
tool_name = tool_info
queue = tool_call_ids.get(tool_name or "")
if isinstance(queue, str):
queue = deque([queue])
tool_call_ids[tool_name] = queue
if tool_name and queue:
tc_id = queue.popleft()
update = build_tool_complete(
tc_id, tool_name, result=str(result) if result is not None else None
)
_send_update(conn, session_id, loop, update)
if not queue:
tool_call_ids.pop(tool_name, None)
return _step
# ------------------------------------------------------------------
# Agent message callback
# ------------------------------------------------------------------
def make_message_cb(
conn: acp.Client,
session_id: str,
loop: asyncio.AbstractEventLoop,
) -> Callable:
"""Create a callback that streams agent response text to the editor."""
def _message(text: str) -> None:
if not text:
return
update = acp.update_agent_message_text(text)
_send_update(conn, session_id, loop, update)
return _message

View File

@@ -1,80 +0,0 @@
"""ACP permission bridging — maps ACP approval requests to hermes approval callbacks."""
from __future__ import annotations
import asyncio
import logging
from concurrent.futures import TimeoutError as FutureTimeout
from typing import Any, Callable, Optional
from acp.schema import (
AllowedOutcome,
DeniedOutcome,
PermissionOption,
RequestPermissionRequest,
SelectedPermissionOutcome,
)
logger = logging.getLogger(__name__)
# Maps ACP PermissionOptionKind -> hermes approval result strings
_KIND_TO_HERMES = {
"allow_once": "once",
"allow_always": "always",
"reject_once": "deny",
"reject_always": "deny",
}
def make_approval_callback(
request_permission_fn: Callable,
loop: asyncio.AbstractEventLoop,
session_id: str,
timeout: float = 60.0,
) -> Callable[[str, str], str]:
"""
Return a hermes-compatible ``approval_callback(command, description) -> str``
that bridges to the ACP client's ``request_permission`` call.
Args:
request_permission_fn: The ACP connection's ``request_permission`` coroutine.
loop: The event loop on which the ACP connection lives.
session_id: Current ACP session id.
timeout: Seconds to wait for a response before auto-denying.
"""
def _callback(command: str, description: str) -> str:
options = [
PermissionOption(option_id="allow_once", kind="allow_once", name="Allow once"),
PermissionOption(option_id="allow_always", kind="allow_always", name="Allow always"),
PermissionOption(option_id="deny", kind="reject_once", name="Deny"),
]
import acp as _acp
tool_call = _acp.start_tool_call("perm-check", command, kind="execute")
coro = request_permission_fn(
session_id=session_id,
tool_call=tool_call,
options=options,
)
try:
future = asyncio.run_coroutine_threadsafe(coro, loop)
response = future.result(timeout=timeout)
except (FutureTimeout, Exception) as exc:
logger.warning("Permission request timed out or failed: %s", exc)
return "deny"
outcome = response.outcome
if isinstance(outcome, AllowedOutcome):
option_id = outcome.option_id
# Look up the kind from our options list
for opt in options:
if opt.option_id == option_id:
return _KIND_TO_HERMES.get(opt.kind, "deny")
return "once" # fallback for unknown option_id
else:
return "deny"
return _callback

View File

@@ -1,492 +0,0 @@
"""ACP agent server — exposes Hermes Agent via the Agent Client Protocol."""
from __future__ import annotations
import asyncio
import logging
from collections import defaultdict, deque
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Deque, Optional
import acp
from acp.schema import (
AgentCapabilities,
AuthenticateResponse,
AuthMethod,
ClientCapabilities,
EmbeddedResourceContentBlock,
ForkSessionResponse,
ImageContentBlock,
AudioContentBlock,
Implementation,
InitializeResponse,
ListSessionsResponse,
LoadSessionResponse,
NewSessionResponse,
PromptResponse,
ResumeSessionResponse,
ResourceContentBlock,
SessionCapabilities,
SessionForkCapabilities,
SessionListCapabilities,
SessionInfo,
TextContentBlock,
Usage,
)
from acp_adapter.auth import detect_provider, has_provider
from acp_adapter.events import (
make_message_cb,
make_step_cb,
make_thinking_cb,
make_tool_progress_cb,
)
from acp_adapter.permissions import make_approval_callback
from acp_adapter.session import SessionManager, SessionState
logger = logging.getLogger(__name__)
try:
from hermes_cli import __version__ as HERMES_VERSION
except Exception:
HERMES_VERSION = "0.0.0"
# Thread pool for running AIAgent (synchronous) in parallel.
_executor = ThreadPoolExecutor(max_workers=4, thread_name_prefix="acp-agent")
def _extract_text(
prompt: list[
TextContentBlock
| ImageContentBlock
| AudioContentBlock
| ResourceContentBlock
| EmbeddedResourceContentBlock
],
) -> str:
"""Extract plain text from ACP content blocks."""
parts: list[str] = []
for block in prompt:
if isinstance(block, TextContentBlock):
parts.append(block.text)
elif hasattr(block, "text"):
parts.append(str(block.text))
# Non-text blocks are ignored for now.
return "\n".join(parts)
class HermesACPAgent(acp.Agent):
"""ACP Agent implementation wrapping Hermes AIAgent."""
def __init__(self, session_manager: SessionManager | None = None):
super().__init__()
self.session_manager = session_manager or SessionManager()
self._conn: Optional[acp.Client] = None
# ---- Connection lifecycle -----------------------------------------------
def on_connect(self, conn: acp.Client) -> None:
"""Store the client connection for sending session updates."""
self._conn = conn
logger.info("ACP client connected")
# ---- ACP lifecycle ------------------------------------------------------
async def initialize(
self,
protocol_version: int,
client_capabilities: ClientCapabilities | None = None,
client_info: Implementation | None = None,
**kwargs: Any,
) -> InitializeResponse:
provider = detect_provider()
auth_methods = None
if provider:
auth_methods = [
AuthMethod(
id=provider,
name=f"{provider} runtime credentials",
description=f"Authenticate Hermes using the currently configured {provider} runtime credentials.",
)
]
client_name = client_info.name if client_info else "unknown"
logger.info("Initialize from %s (protocol v%s)", client_name, protocol_version)
return InitializeResponse(
protocol_version=acp.PROTOCOL_VERSION,
agent_info=Implementation(name="hermes-agent", version=HERMES_VERSION),
agent_capabilities=AgentCapabilities(
session_capabilities=SessionCapabilities(
fork=SessionForkCapabilities(),
list=SessionListCapabilities(),
),
),
auth_methods=auth_methods,
)
async def authenticate(self, method_id: str, **kwargs: Any) -> AuthenticateResponse | None:
if has_provider():
return AuthenticateResponse()
return None
# ---- Session management -------------------------------------------------
async def new_session(
self,
cwd: str,
mcp_servers: list | None = None,
**kwargs: Any,
) -> NewSessionResponse:
state = self.session_manager.create_session(cwd=cwd)
logger.info("New session %s (cwd=%s)", state.session_id, cwd)
return NewSessionResponse(session_id=state.session_id)
async def load_session(
self,
cwd: str,
session_id: str,
mcp_servers: list | None = None,
**kwargs: Any,
) -> LoadSessionResponse | None:
state = self.session_manager.update_cwd(session_id, cwd)
if state is None:
logger.warning("load_session: session %s not found", session_id)
return None
logger.info("Loaded session %s", session_id)
return LoadSessionResponse()
async def resume_session(
self,
cwd: str,
session_id: str,
mcp_servers: list | None = None,
**kwargs: Any,
) -> ResumeSessionResponse:
state = self.session_manager.update_cwd(session_id, cwd)
if state is None:
logger.warning("resume_session: session %s not found, creating new", session_id)
state = self.session_manager.create_session(cwd=cwd)
logger.info("Resumed session %s", state.session_id)
return ResumeSessionResponse()
async def cancel(self, session_id: str, **kwargs: Any) -> None:
state = self.session_manager.get_session(session_id)
if state and state.cancel_event:
state.cancel_event.set()
try:
if getattr(state, "agent", None) and hasattr(state.agent, "interrupt"):
state.agent.interrupt()
except Exception:
logger.debug("Failed to interrupt ACP session %s", session_id, exc_info=True)
logger.info("Cancelled session %s", session_id)
async def fork_session(
self,
cwd: str,
session_id: str,
mcp_servers: list | None = None,
**kwargs: Any,
) -> ForkSessionResponse:
state = self.session_manager.fork_session(session_id, cwd=cwd)
new_id = state.session_id if state else ""
logger.info("Forked session %s -> %s", session_id, new_id)
return ForkSessionResponse(session_id=new_id)
async def list_sessions(
self,
cursor: str | None = None,
cwd: str | None = None,
**kwargs: Any,
) -> ListSessionsResponse:
infos = self.session_manager.list_sessions()
sessions = [
SessionInfo(session_id=s["session_id"], cwd=s["cwd"])
for s in infos
]
return ListSessionsResponse(sessions=sessions)
# ---- Prompt (core) ------------------------------------------------------
async def prompt(
self,
prompt: list[
TextContentBlock
| ImageContentBlock
| AudioContentBlock
| ResourceContentBlock
| EmbeddedResourceContentBlock
],
session_id: str,
**kwargs: Any,
) -> PromptResponse:
"""Run Hermes on the user's prompt and stream events back to the editor."""
state = self.session_manager.get_session(session_id)
if state is None:
logger.error("prompt: session %s not found", session_id)
return PromptResponse(stop_reason="refusal")
user_text = _extract_text(prompt).strip()
if not user_text:
return PromptResponse(stop_reason="end_turn")
# Intercept slash commands — handle locally without calling the LLM
if user_text.startswith("/"):
response_text = self._handle_slash_command(user_text, state)
if response_text is not None:
if self._conn:
update = acp.update_agent_message_text(response_text)
await self._conn.session_update(session_id, update)
return PromptResponse(stop_reason="end_turn")
logger.info("Prompt on session %s: %s", session_id, user_text[:100])
conn = self._conn
loop = asyncio.get_running_loop()
if state.cancel_event:
state.cancel_event.clear()
tool_call_ids: dict[str, Deque[str]] = defaultdict(deque)
previous_approval_cb = None
if conn:
tool_progress_cb = make_tool_progress_cb(conn, session_id, loop, tool_call_ids)
thinking_cb = make_thinking_cb(conn, session_id, loop)
step_cb = make_step_cb(conn, session_id, loop, tool_call_ids)
message_cb = make_message_cb(conn, session_id, loop)
approval_cb = make_approval_callback(conn.request_permission, loop, session_id)
else:
tool_progress_cb = None
thinking_cb = None
step_cb = None
message_cb = None
approval_cb = None
agent = state.agent
agent.tool_progress_callback = tool_progress_cb
agent.thinking_callback = thinking_cb
agent.step_callback = step_cb
agent.message_callback = message_cb
if approval_cb:
try:
from tools import terminal_tool as _terminal_tool
previous_approval_cb = getattr(_terminal_tool, "_approval_callback", None)
_terminal_tool.set_approval_callback(approval_cb)
except Exception:
logger.debug("Could not set ACP approval callback", exc_info=True)
def _run_agent() -> dict:
try:
result = agent.run_conversation(
user_message=user_text,
conversation_history=state.history,
task_id=session_id,
)
return result
except Exception as e:
logger.exception("Agent error in session %s", session_id)
return {"final_response": f"Error: {e}", "messages": state.history}
finally:
if approval_cb:
try:
from tools import terminal_tool as _terminal_tool
_terminal_tool.set_approval_callback(previous_approval_cb)
except Exception:
logger.debug("Could not restore approval callback", exc_info=True)
try:
result = await loop.run_in_executor(_executor, _run_agent)
except Exception:
logger.exception("Executor error for session %s", session_id)
return PromptResponse(stop_reason="end_turn")
if result.get("messages"):
state.history = result["messages"]
# Persist updated history so sessions survive process restarts.
self.session_manager.save_session(session_id)
final_response = result.get("final_response", "")
if final_response and conn:
update = acp.update_agent_message_text(final_response)
await conn.session_update(session_id, update)
usage = None
usage_data = result.get("usage")
if usage_data and isinstance(usage_data, dict):
usage = Usage(
input_tokens=usage_data.get("prompt_tokens", 0),
output_tokens=usage_data.get("completion_tokens", 0),
total_tokens=usage_data.get("total_tokens", 0),
thought_tokens=usage_data.get("reasoning_tokens"),
cached_read_tokens=usage_data.get("cached_tokens"),
)
stop_reason = "cancelled" if state.cancel_event and state.cancel_event.is_set() else "end_turn"
return PromptResponse(stop_reason=stop_reason, usage=usage)
# ---- Slash commands (headless) -------------------------------------------
_SLASH_COMMANDS = {
"help": "Show available commands",
"model": "Show or change current model",
"tools": "List available tools",
"context": "Show conversation context info",
"reset": "Clear conversation history",
"compact": "Compress conversation context",
"version": "Show Hermes version",
}
def _handle_slash_command(self, text: str, state: SessionState) -> str | None:
"""Dispatch a slash command and return the response text.
Returns ``None`` for unrecognized commands so they fall through
to the LLM (the user may have typed ``/something`` as prose).
"""
parts = text.split(maxsplit=1)
cmd = parts[0].lstrip("/").lower()
args = parts[1].strip() if len(parts) > 1 else ""
handler = {
"help": self._cmd_help,
"model": self._cmd_model,
"tools": self._cmd_tools,
"context": self._cmd_context,
"reset": self._cmd_reset,
"compact": self._cmd_compact,
"version": self._cmd_version,
}.get(cmd)
if handler is None:
return None # not a known command — let the LLM handle it
try:
return handler(args, state)
except Exception as e:
logger.error("Slash command /%s error: %s", cmd, e, exc_info=True)
return f"Error executing /{cmd}: {e}"
def _cmd_help(self, args: str, state: SessionState) -> str:
lines = ["Available commands:", ""]
for cmd, desc in self._SLASH_COMMANDS.items():
lines.append(f" /{cmd:10s} {desc}")
lines.append("")
lines.append("Unrecognized /commands are sent to the model as normal messages.")
return "\n".join(lines)
def _cmd_model(self, args: str, state: SessionState) -> str:
if not args:
model = state.model or getattr(state.agent, "model", "unknown")
provider = getattr(state.agent, "provider", None) or "auto"
return f"Current model: {model}\nProvider: {provider}"
new_model = args.strip()
target_provider = None
current_provider = getattr(state.agent, "provider", None) or "openrouter"
# Auto-detect provider for the requested model
try:
from hermes_cli.models import parse_model_input, detect_provider_for_model
target_provider, new_model = parse_model_input(new_model, current_provider)
if target_provider == current_provider:
detected = detect_provider_for_model(new_model, current_provider)
if detected:
target_provider, new_model = detected
except Exception:
logger.debug("Provider detection failed, using model as-is", exc_info=True)
state.model = new_model
state.agent = self.session_manager._make_agent(
session_id=state.session_id,
cwd=state.cwd,
model=new_model,
requested_provider=target_provider or current_provider,
)
self.session_manager.save_session(state.session_id)
provider_label = getattr(state.agent, "provider", None) or target_provider or current_provider
logger.info("Session %s: model switched to %s", state.session_id, new_model)
return f"Model switched to: {new_model}\nProvider: {provider_label}"
def _cmd_tools(self, args: str, state: SessionState) -> str:
try:
from model_tools import get_tool_definitions
toolsets = getattr(state.agent, "enabled_toolsets", None) or ["hermes-acp"]
tools = get_tool_definitions(enabled_toolsets=toolsets, quiet_mode=True)
if not tools:
return "No tools available."
lines = [f"Available tools ({len(tools)}):"]
for t in tools:
name = t.get("function", {}).get("name", "?")
desc = t.get("function", {}).get("description", "")
# Truncate long descriptions
if len(desc) > 80:
desc = desc[:77] + "..."
lines.append(f" {name}: {desc}")
return "\n".join(lines)
except Exception as e:
return f"Could not list tools: {e}"
def _cmd_context(self, args: str, state: SessionState) -> str:
n_messages = len(state.history)
if n_messages == 0:
return "Conversation is empty (no messages yet)."
# Count by role
roles: dict[str, int] = {}
for msg in state.history:
role = msg.get("role", "unknown")
roles[role] = roles.get(role, 0) + 1
lines = [
f"Conversation: {n_messages} messages",
f" user: {roles.get('user', 0)}, assistant: {roles.get('assistant', 0)}, "
f"tool: {roles.get('tool', 0)}, system: {roles.get('system', 0)}",
]
model = state.model or getattr(state.agent, "model", "")
if model:
lines.append(f"Model: {model}")
return "\n".join(lines)
def _cmd_reset(self, args: str, state: SessionState) -> str:
state.history.clear()
self.session_manager.save_session(state.session_id)
return "Conversation history cleared."
def _cmd_compact(self, args: str, state: SessionState) -> str:
if not state.history:
return "Nothing to compress — conversation is empty."
try:
agent = state.agent
if hasattr(agent, "compress_context"):
agent.compress_context(state.history)
self.session_manager.save_session(state.session_id)
return f"Context compressed. Messages: {len(state.history)}"
return "Context compression not available for this agent."
except Exception as e:
return f"Compression failed: {e}"
def _cmd_version(self, args: str, state: SessionState) -> str:
return f"Hermes Agent v{HERMES_VERSION}"
# ---- Model switching (ACP protocol method) -------------------------------
async def set_session_model(
self, model_id: str, session_id: str, **kwargs: Any
):
"""Switch the model for a session (called by ACP protocol)."""
state = self.session_manager.get_session(session_id)
if state:
state.model = model_id
current_provider = getattr(state.agent, "provider", None)
current_base_url = getattr(state.agent, "base_url", None)
current_api_mode = getattr(state.agent, "api_mode", None)
state.agent = self.session_manager._make_agent(
session_id=session_id,
cwd=state.cwd,
model=model_id,
requested_provider=current_provider,
base_url=current_base_url,
api_mode=current_api_mode,
)
self.session_manager.save_session(session_id)
logger.info("Session %s: model switched to %s", session_id, model_id)
return None

View File

@@ -1,459 +0,0 @@
"""ACP session manager — maps ACP sessions to Hermes AIAgent instances.
Sessions are persisted to the shared SessionDB (``~/.hermes/state.db``) so they
survive process restarts and appear in ``session_search``. When the editor
reconnects after idle/restart, the ``load_session`` / ``resume_session`` calls
find the persisted session in the database and restore the full conversation
history.
"""
from __future__ import annotations
import copy
import json
import logging
import uuid
from dataclasses import dataclass, field
from threading import Lock
from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
def _register_task_cwd(task_id: str, cwd: str) -> None:
"""Bind a task/session id to the editor's working directory for tools."""
if not task_id:
return
try:
from tools.terminal_tool import register_task_env_overrides
register_task_env_overrides(task_id, {"cwd": cwd})
except Exception:
logger.debug("Failed to register ACP task cwd override", exc_info=True)
def _clear_task_cwd(task_id: str) -> None:
"""Remove task-specific cwd overrides for an ACP session."""
if not task_id:
return
try:
from tools.terminal_tool import clear_task_env_overrides
clear_task_env_overrides(task_id)
except Exception:
logger.debug("Failed to clear ACP task cwd override", exc_info=True)
@dataclass
class SessionState:
"""Tracks per-session state for an ACP-managed Hermes agent."""
session_id: str
agent: Any # AIAgent instance
cwd: str = "."
model: str = ""
history: List[Dict[str, Any]] = field(default_factory=list)
cancel_event: Any = None # threading.Event
class SessionManager:
"""Thread-safe manager for ACP sessions backed by Hermes AIAgent instances.
Sessions are held in-memory for fast access **and** persisted to the
shared SessionDB so they survive process restarts and are searchable
via ``session_search``.
"""
def __init__(self, agent_factory=None, db=None):
"""
Args:
agent_factory: Optional callable that creates an AIAgent-like object.
Used by tests. When omitted, a real AIAgent is created
using the current Hermes runtime provider configuration.
db: Optional SessionDB instance. When omitted, the default
SessionDB (``~/.hermes/state.db``) is lazily created.
"""
self._sessions: Dict[str, SessionState] = {}
self._lock = Lock()
self._agent_factory = agent_factory
self._db_instance = db # None → lazy-init on first use
# ---- public API ---------------------------------------------------------
def create_session(self, cwd: str = ".") -> SessionState:
"""Create a new session with a unique ID and a fresh AIAgent."""
import threading
session_id = str(uuid.uuid4())
agent = self._make_agent(session_id=session_id, cwd=cwd)
state = SessionState(
session_id=session_id,
agent=agent,
cwd=cwd,
model=getattr(agent, "model", "") or "",
cancel_event=threading.Event(),
)
with self._lock:
self._sessions[session_id] = state
_register_task_cwd(session_id, cwd)
self._persist(state)
logger.info("Created ACP session %s (cwd=%s)", session_id, cwd)
return state
def get_session(self, session_id: str) -> Optional[SessionState]:
"""Return the session for *session_id*, or ``None``.
If the session is not in memory but exists in the database (e.g. after
a process restart), it is transparently restored.
"""
with self._lock:
state = self._sessions.get(session_id)
if state is not None:
return state
# Attempt to restore from database.
return self._restore(session_id)
def remove_session(self, session_id: str) -> bool:
"""Remove a session from memory and database. Returns True if it existed."""
with self._lock:
existed = self._sessions.pop(session_id, None) is not None
db_existed = self._delete_persisted(session_id)
if existed or db_existed:
_clear_task_cwd(session_id)
return existed or db_existed
def fork_session(self, session_id: str, cwd: str = ".") -> Optional[SessionState]:
"""Deep-copy a session's history into a new session."""
import threading
original = self.get_session(session_id) # checks DB too
if original is None:
return None
new_id = str(uuid.uuid4())
agent = self._make_agent(
session_id=new_id,
cwd=cwd,
model=original.model or None,
)
state = SessionState(
session_id=new_id,
agent=agent,
cwd=cwd,
model=getattr(agent, "model", original.model) or original.model,
history=copy.deepcopy(original.history),
cancel_event=threading.Event(),
)
with self._lock:
self._sessions[new_id] = state
_register_task_cwd(new_id, cwd)
self._persist(state)
logger.info("Forked ACP session %s -> %s", session_id, new_id)
return state
def list_sessions(self) -> List[Dict[str, Any]]:
"""Return lightweight info dicts for all sessions (memory + database)."""
# Collect in-memory sessions first.
with self._lock:
seen_ids = set(self._sessions.keys())
results = [
{
"session_id": s.session_id,
"cwd": s.cwd,
"model": s.model,
"history_len": len(s.history),
}
for s in self._sessions.values()
]
# Merge any persisted sessions not currently in memory.
db = self._get_db()
if db is not None:
try:
rows = db.search_sessions(source="acp", limit=1000)
for row in rows:
sid = row["id"]
if sid in seen_ids:
continue
# Extract cwd from model_config JSON.
cwd = "."
mc = row.get("model_config")
if mc:
try:
cwd = json.loads(mc).get("cwd", ".")
except (json.JSONDecodeError, TypeError):
pass
results.append({
"session_id": sid,
"cwd": cwd,
"model": row.get("model") or "",
"history_len": row.get("message_count") or 0,
})
except Exception:
logger.debug("Failed to list ACP sessions from DB", exc_info=True)
return results
def update_cwd(self, session_id: str, cwd: str) -> Optional[SessionState]:
"""Update the working directory for a session and its tool overrides."""
state = self.get_session(session_id) # checks DB too
if state is None:
return None
state.cwd = cwd
_register_task_cwd(session_id, cwd)
self._persist(state)
return state
def cleanup(self) -> None:
"""Remove all sessions (memory and database) and clear task-specific cwd overrides."""
with self._lock:
session_ids = list(self._sessions.keys())
self._sessions.clear()
for session_id in session_ids:
_clear_task_cwd(session_id)
self._delete_persisted(session_id)
# Also remove any DB-only ACP sessions not currently in memory.
db = self._get_db()
if db is not None:
try:
rows = db.search_sessions(source="acp", limit=10000)
for row in rows:
sid = row["id"]
_clear_task_cwd(sid)
db.delete_session(sid)
except Exception:
logger.debug("Failed to cleanup ACP sessions from DB", exc_info=True)
def save_session(self, session_id: str) -> None:
"""Persist the current state of a session to the database.
Called by the server after prompt completion, slash commands that
mutate history, and model switches.
"""
with self._lock:
state = self._sessions.get(session_id)
if state is not None:
self._persist(state)
# ---- persistence via SessionDB ------------------------------------------
def _get_db(self):
"""Lazily initialise and return the SessionDB instance.
Returns ``None`` if the DB is unavailable (e.g. import error in a
minimal test environment).
Note: we resolve ``HERMES_HOME`` dynamically rather than relying on
the module-level ``DEFAULT_DB_PATH`` constant, because that constant
is evaluated at import time and won't reflect env-var changes made
later (e.g. by the test fixture ``_isolate_hermes_home``).
"""
if self._db_instance is not None:
return self._db_instance
try:
import os
from pathlib import Path
from hermes_state import SessionDB
hermes_home = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
self._db_instance = SessionDB(db_path=hermes_home / "state.db")
return self._db_instance
except Exception:
logger.debug("SessionDB unavailable for ACP persistence", exc_info=True)
return None
def _persist(self, state: SessionState) -> None:
"""Write session state to the database.
Creates the session record if it doesn't exist, then replaces all
stored messages with the current in-memory history.
"""
db = self._get_db()
if db is None:
return
# Ensure model is a plain string (not a MagicMock or other proxy).
model_str = str(state.model) if state.model else None
session_meta = {"cwd": state.cwd}
provider = getattr(state.agent, "provider", None)
base_url = getattr(state.agent, "base_url", None)
api_mode = getattr(state.agent, "api_mode", None)
if isinstance(provider, str) and provider.strip():
session_meta["provider"] = provider.strip()
if isinstance(base_url, str) and base_url.strip():
session_meta["base_url"] = base_url.strip()
if isinstance(api_mode, str) and api_mode.strip():
session_meta["api_mode"] = api_mode.strip()
cwd_json = json.dumps(session_meta)
try:
# Ensure the session record exists.
existing = db.get_session(state.session_id)
if existing is None:
db.create_session(
session_id=state.session_id,
source="acp",
model=model_str,
model_config={"cwd": state.cwd},
)
else:
# Update model_config (contains cwd) if changed.
try:
with db._lock:
db._conn.execute(
"UPDATE sessions SET model_config = ?, model = COALESCE(?, model) WHERE id = ?",
(cwd_json, model_str, state.session_id),
)
db._conn.commit()
except Exception:
logger.debug("Failed to update ACP session metadata", exc_info=True)
# Replace stored messages with current history.
db.clear_messages(state.session_id)
for msg in state.history:
db.append_message(
session_id=state.session_id,
role=msg.get("role", "user"),
content=msg.get("content"),
tool_name=msg.get("tool_name") or msg.get("name"),
tool_calls=msg.get("tool_calls"),
tool_call_id=msg.get("tool_call_id"),
)
except Exception:
logger.warning("Failed to persist ACP session %s", state.session_id, exc_info=True)
def _restore(self, session_id: str) -> Optional[SessionState]:
"""Load a session from the database into memory, recreating the AIAgent."""
import threading
db = self._get_db()
if db is None:
return None
try:
row = db.get_session(session_id)
except Exception:
logger.debug("Failed to query DB for ACP session %s", session_id, exc_info=True)
return None
if row is None:
return None
# Only restore ACP sessions.
if row.get("source") != "acp":
return None
# Extract cwd from model_config.
cwd = "."
requested_provider = row.get("billing_provider")
restored_base_url = row.get("billing_base_url")
restored_api_mode = None
mc = row.get("model_config")
if mc:
try:
meta = json.loads(mc)
if isinstance(meta, dict):
cwd = meta.get("cwd", ".")
requested_provider = meta.get("provider") or requested_provider
restored_base_url = meta.get("base_url") or restored_base_url
restored_api_mode = meta.get("api_mode") or restored_api_mode
except (json.JSONDecodeError, TypeError):
pass
model = row.get("model") or None
# Load conversation history.
try:
history = db.get_messages_as_conversation(session_id)
except Exception:
logger.warning("Failed to load messages for ACP session %s", session_id, exc_info=True)
history = []
try:
agent = self._make_agent(
session_id=session_id,
cwd=cwd,
model=model,
requested_provider=requested_provider,
base_url=restored_base_url,
api_mode=restored_api_mode,
)
except Exception:
logger.warning("Failed to recreate agent for ACP session %s", session_id, exc_info=True)
return None
state = SessionState(
session_id=session_id,
agent=agent,
cwd=cwd,
model=model or getattr(agent, "model", "") or "",
history=history,
cancel_event=threading.Event(),
)
with self._lock:
self._sessions[session_id] = state
_register_task_cwd(session_id, cwd)
logger.info("Restored ACP session %s from DB (%d messages)", session_id, len(history))
return state
def _delete_persisted(self, session_id: str) -> bool:
"""Delete a session from the database. Returns True if it existed."""
db = self._get_db()
if db is None:
return False
try:
return db.delete_session(session_id)
except Exception:
logger.debug("Failed to delete ACP session %s from DB", session_id, exc_info=True)
return False
# ---- internal -----------------------------------------------------------
def _make_agent(
self,
*,
session_id: str,
cwd: str,
model: str | None = None,
requested_provider: str | None = None,
base_url: str | None = None,
api_mode: str | None = None,
):
if self._agent_factory is not None:
return self._agent_factory()
from run_agent import AIAgent
from hermes_cli.config import load_config
from hermes_cli.runtime_provider import resolve_runtime_provider
config = load_config()
model_cfg = config.get("model")
default_model = "anthropic/claude-opus-4.6"
config_provider = None
if isinstance(model_cfg, dict):
default_model = str(model_cfg.get("default") or default_model)
config_provider = model_cfg.get("provider")
elif isinstance(model_cfg, str) and model_cfg.strip():
default_model = model_cfg.strip()
kwargs = {
"platform": "acp",
"enabled_toolsets": ["hermes-acp"],
"quiet_mode": True,
"session_id": session_id,
"model": model or default_model,
}
try:
runtime = resolve_runtime_provider(requested=requested_provider or config_provider)
kwargs.update(
{
"provider": runtime.get("provider"),
"api_mode": api_mode or runtime.get("api_mode"),
"base_url": base_url or runtime.get("base_url"),
"api_key": runtime.get("api_key"),
"command": runtime.get("command"),
"args": list(runtime.get("args") or []),
}
)
except Exception:
logger.debug("ACP session falling back to default provider resolution", exc_info=True)
_register_task_cwd(session_id, cwd)
return AIAgent(**kwargs)

View File

@@ -1,215 +0,0 @@
"""ACP tool-call helpers for mapping hermes tools to ACP ToolKind and building content."""
from __future__ import annotations
import uuid
from typing import Any, Dict, List, Optional
import acp
from acp.schema import (
ToolCallLocation,
ToolCallStart,
ToolCallProgress,
ToolKind,
)
# ---------------------------------------------------------------------------
# Map hermes tool names -> ACP ToolKind
# ---------------------------------------------------------------------------
TOOL_KIND_MAP: Dict[str, ToolKind] = {
# File operations
"read_file": "read",
"write_file": "edit",
"patch": "edit",
"search_files": "search",
# Terminal / execution
"terminal": "execute",
"process": "execute",
"execute_code": "execute",
# Web / fetch
"web_search": "fetch",
"web_extract": "fetch",
# Browser
"browser_navigate": "fetch",
"browser_click": "execute",
"browser_type": "execute",
"browser_snapshot": "read",
"browser_vision": "read",
"browser_scroll": "execute",
"browser_press": "execute",
"browser_back": "execute",
"browser_close": "execute",
"browser_get_images": "read",
# Agent internals
"delegate_task": "execute",
"vision_analyze": "read",
"image_generate": "execute",
"text_to_speech": "execute",
# Thinking / meta
"_thinking": "think",
}
def get_tool_kind(tool_name: str) -> ToolKind:
"""Return the ACP ToolKind for a hermes tool, defaulting to 'other'."""
return TOOL_KIND_MAP.get(tool_name, "other")
def make_tool_call_id() -> str:
"""Generate a unique tool call ID."""
return f"tc-{uuid.uuid4().hex[:12]}"
def build_tool_title(tool_name: str, args: Dict[str, Any]) -> str:
"""Build a human-readable title for a tool call."""
if tool_name == "terminal":
cmd = args.get("command", "")
if len(cmd) > 80:
cmd = cmd[:77] + "..."
return f"terminal: {cmd}"
if tool_name == "read_file":
return f"read: {args.get('path', '?')}"
if tool_name == "write_file":
return f"write: {args.get('path', '?')}"
if tool_name == "patch":
mode = args.get("mode", "replace")
path = args.get("path", "?")
return f"patch ({mode}): {path}"
if tool_name == "search_files":
return f"search: {args.get('pattern', '?')}"
if tool_name == "web_search":
return f"web search: {args.get('query', '?')}"
if tool_name == "web_extract":
urls = args.get("urls", [])
if urls:
return f"extract: {urls[0]}" + (f" (+{len(urls)-1})" if len(urls) > 1 else "")
return "web extract"
if tool_name == "delegate_task":
goal = args.get("goal", "")
if goal and len(goal) > 60:
goal = goal[:57] + "..."
return f"delegate: {goal}" if goal else "delegate task"
if tool_name == "execute_code":
return "execute code"
if tool_name == "vision_analyze":
return f"analyze image: {args.get('question', '?')[:50]}"
return tool_name
# ---------------------------------------------------------------------------
# Build ACP content objects for tool-call events
# ---------------------------------------------------------------------------
def build_tool_start(
tool_call_id: str,
tool_name: str,
arguments: Dict[str, Any],
) -> ToolCallStart:
"""Create a ToolCallStart event for the given hermes tool invocation."""
kind = get_tool_kind(tool_name)
title = build_tool_title(tool_name, arguments)
locations = extract_locations(arguments)
if tool_name == "patch":
mode = arguments.get("mode", "replace")
if mode == "replace":
path = arguments.get("path", "")
old = arguments.get("old_string", "")
new = arguments.get("new_string", "")
content = [acp.tool_diff_content(path=path, new_text=new, old_text=old)]
else:
# Patch mode — show the patch content as text
patch_text = arguments.get("patch", "")
content = [acp.tool_content(acp.text_block(patch_text))]
return acp.start_tool_call(
tool_call_id, title, kind=kind, content=content, locations=locations,
raw_input=arguments,
)
if tool_name == "write_file":
path = arguments.get("path", "")
file_content = arguments.get("content", "")
content = [acp.tool_diff_content(path=path, new_text=file_content)]
return acp.start_tool_call(
tool_call_id, title, kind=kind, content=content, locations=locations,
raw_input=arguments,
)
if tool_name == "terminal":
command = arguments.get("command", "")
content = [acp.tool_content(acp.text_block(f"$ {command}"))]
return acp.start_tool_call(
tool_call_id, title, kind=kind, content=content, locations=locations,
raw_input=arguments,
)
if tool_name == "read_file":
path = arguments.get("path", "")
content = [acp.tool_content(acp.text_block(f"Reading {path}"))]
return acp.start_tool_call(
tool_call_id, title, kind=kind, content=content, locations=locations,
raw_input=arguments,
)
if tool_name == "search_files":
pattern = arguments.get("pattern", "")
target = arguments.get("target", "content")
content = [acp.tool_content(acp.text_block(f"Searching for '{pattern}' ({target})"))]
return acp.start_tool_call(
tool_call_id, title, kind=kind, content=content, locations=locations,
raw_input=arguments,
)
# Generic fallback
import json
try:
args_text = json.dumps(arguments, indent=2, default=str)
except (TypeError, ValueError):
args_text = str(arguments)
content = [acp.tool_content(acp.text_block(args_text))]
return acp.start_tool_call(
tool_call_id, title, kind=kind, content=content, locations=locations,
raw_input=arguments,
)
def build_tool_complete(
tool_call_id: str,
tool_name: str,
result: Optional[str] = None,
) -> ToolCallProgress:
"""Create a ToolCallUpdate (progress) event for a completed tool call."""
kind = get_tool_kind(tool_name)
# Truncate very large results for the UI
display_result = result or ""
if len(display_result) > 5000:
display_result = display_result[:4900] + f"\n... ({len(result)} chars total, truncated)"
content = [acp.tool_content(acp.text_block(display_result))]
return acp.update_tool_call(
tool_call_id,
kind=kind,
status="completed",
content=content,
raw_output=result,
)
# ---------------------------------------------------------------------------
# Location extraction
# ---------------------------------------------------------------------------
def extract_locations(
arguments: Dict[str, Any],
) -> List[ToolCallLocation]:
"""Extract file-system locations from tool arguments."""
locations: List[ToolCallLocation] = []
path = arguments.get("path")
if path:
line = arguments.get("offset") or arguments.get("line")
locations.append(ToolCallLocation(path=path, line=line))
return locations

View File

@@ -1,12 +0,0 @@
{
"schema_version": 1,
"name": "hermes-agent",
"display_name": "Hermes Agent",
"description": "AI agent by Nous Research with 90+ tools, persistent memory, and multi-platform support",
"icon": "icon.svg",
"distribution": {
"type": "command",
"command": "hermes",
"args": ["acp"]
}
}

View File

@@ -1,25 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" width="64" height="64">
<defs>
<linearGradient id="gold" x1="0%" y1="0%" x2="0%" y2="100%">
<stop offset="0%" style="stop-color:#F5C542;stop-opacity:1" />
<stop offset="100%" style="stop-color:#D4961C;stop-opacity:1" />
</linearGradient>
</defs>
<!-- Staff -->
<rect x="30" y="10" width="4" height="46" rx="2" fill="url(#gold)" />
<!-- Wings (left) -->
<path d="M30 18 C24 14, 14 14, 10 18 C14 16, 22 16, 28 20" fill="#F5C542" opacity="0.9" />
<path d="M30 22 C26 19, 18 19, 14 22 C18 20, 24 20, 28 24" fill="#D4961C" opacity="0.8" />
<!-- Wings (right) -->
<path d="M34 18 C40 14, 50 14, 54 18 C50 16, 42 16, 36 20" fill="#F5C542" opacity="0.9" />
<path d="M34 22 C38 19, 46 19, 50 22 C46 20, 40 20, 36 24" fill="#D4961C" opacity="0.8" />
<!-- Left serpent -->
<path d="M32 48 C22 44, 20 38, 26 34 C20 36, 18 42, 24 46 C18 40, 22 30, 30 28 C24 32, 22 38, 28 42"
fill="none" stroke="#F5C542" stroke-width="2.5" stroke-linecap="round" />
<!-- Right serpent -->
<path d="M32 48 C42 44, 44 38, 38 34 C44 36, 46 42, 40 46 C46 40, 42 30, 34 28 C40 32, 42 38, 36 42"
fill="none" stroke="#D4961C" stroke-width="2.5" stroke-linecap="round" />
<!-- Orb at top -->
<circle cx="32" cy="10" r="4" fill="#F5C542" />
<circle cx="32" cy="10" r="2" fill="#FFF8E1" opacity="0.7" />
</svg>

Before

Width:  |  Height:  |  Size: 1.4 KiB

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

View File

@@ -1,23 +1,15 @@
"""Automatic context window compression for long conversations.
Self-contained class with its own OpenAI client for summarization.
Uses auxiliary model (cheap/fast) to summarize middle turns while
Uses Gemini Flash (cheap/fast) to summarize middle turns while
protecting head and tail context.
Improvements over v1:
- Structured summary template (Goal, Progress, Decisions, Files, Next Steps)
- Iterative summary updates (preserves info across multiple compactions)
- Token-budget tail protection instead of fixed message count
- Tool output pruning before LLM summarization (cheap pre-pass)
- Scaled summary budget (proportional to compressed content)
- Richer tool call/result detail in summarizer input
"""
import logging
import os
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List
from agent.auxiliary_client import call_llm
from agent.auxiliary_client import get_text_auxiliary_client
from agent.model_metadata import (
get_model_context_length,
estimate_messages_tokens_rough,
@@ -25,92 +17,44 @@ from agent.model_metadata import (
logger = logging.getLogger(__name__)
SUMMARY_PREFIX = (
"[CONTEXT COMPACTION] Earlier turns in this conversation were compacted "
"to save context space. The summary below describes work that was "
"already completed, and the current session state may still reflect "
"that work (for example, files may already be changed). Use the summary "
"and the current state to continue from where things left off, and "
"avoid repeating work:"
)
LEGACY_SUMMARY_PREFIX = "[CONTEXT SUMMARY]:"
# Minimum / maximum tokens for the summary output
_MIN_SUMMARY_TOKENS = 2000
_MAX_SUMMARY_TOKENS = 8000
# Proportion of compressed content to allocate for summary
_SUMMARY_RATIO = 0.20
# Token budget for tail protection (keep most-recent context)
_DEFAULT_TAIL_TOKEN_BUDGET = 20_000
# Placeholder used when pruning old tool results
_PRUNED_TOOL_PLACEHOLDER = "[Old tool output cleared to save context space]"
# Chars per token rough estimate
_CHARS_PER_TOKEN = 4
class ContextCompressor:
"""Compresses conversation context when approaching the model's context limit.
Algorithm:
1. Prune old tool results (cheap, no LLM call)
2. Protect head messages (system prompt + first exchange)
3. Protect tail messages by token budget (most recent ~20K tokens)
4. Summarize middle turns with structured LLM prompt
5. On subsequent compactions, iteratively update the previous summary
Algorithm: protect first N + last N turns, summarize everything in between.
Token tracking uses actual counts from API responses for accuracy.
"""
def __init__(
self,
model: str,
threshold_percent: float = 0.50,
threshold_percent: float = 0.85,
protect_first_n: int = 3,
protect_last_n: int = 4,
summary_target_tokens: int = 2500,
quiet_mode: bool = False,
summary_model_override: str = None,
base_url: str = "",
api_key: str = "",
config_context_length: int | None = None,
provider: str = "",
):
self.model = model
self.base_url = base_url
self.api_key = api_key
self.provider = provider
self.threshold_percent = threshold_percent
self.protect_first_n = protect_first_n
self.protect_last_n = protect_last_n
self.summary_target_tokens = summary_target_tokens
self.quiet_mode = quiet_mode
self.context_length = get_model_context_length(
model, base_url=base_url, api_key=api_key,
config_context_length=config_context_length,
provider=provider,
)
self.context_length = get_model_context_length(model, base_url=base_url)
self.threshold_tokens = int(self.context_length * threshold_percent)
self.compression_count = 0
if not quiet_mode:
logger.info(
"Context compressor initialized: model=%s context_length=%d "
"threshold=%d (%.0f%%) provider=%s base_url=%s",
model, self.context_length, self.threshold_tokens,
threshold_percent * 100, provider or "none", base_url or "none",
)
self._context_probed = False # True after a step-down from context error
self.last_prompt_tokens = 0
self.last_completion_tokens = 0
self.last_total_tokens = 0
self.summary_model = summary_model_override or ""
# Stores the previous compaction summary for iterative updates
self._previous_summary: Optional[str] = None
self.client, default_model = get_text_auxiliary_client("compression")
self.summary_model = summary_model_override or default_model
def update_from_response(self, usage: Dict[str, Any]):
"""Update tracked token usage from API response."""
@@ -138,233 +82,119 @@ class ContextCompressor:
"compression_count": self.compression_count,
}
# ------------------------------------------------------------------
# Tool output pruning (cheap pre-pass, no LLM call)
# ------------------------------------------------------------------
def _generate_summary(self, turns_to_summarize: List[Dict[str, Any]]) -> str:
"""Generate a concise summary of conversation turns using a fast model."""
if not self.client:
return "[CONTEXT SUMMARY]: Previous conversation turns have been compressed to save space. The assistant performed various actions and received responses."
def _prune_old_tool_results(
self, messages: List[Dict[str, Any]], protect_tail_count: int,
) -> tuple[List[Dict[str, Any]], int]:
"""Replace old tool result contents with a short placeholder.
Walks backward from the end, protecting the most recent
``protect_tail_count`` messages. Older tool results get their
content replaced with a placeholder string.
Returns (pruned_messages, pruned_count).
"""
if not messages:
return messages, 0
result = [m.copy() for m in messages]
pruned = 0
prune_boundary = len(result) - protect_tail_count
for i in range(prune_boundary):
msg = result[i]
if msg.get("role") != "tool":
continue
content = msg.get("content", "")
if not content or content == _PRUNED_TOOL_PLACEHOLDER:
continue
# Only prune if the content is substantial (>200 chars)
if len(content) > 200:
result[i] = {**msg, "content": _PRUNED_TOOL_PLACEHOLDER}
pruned += 1
return result, pruned
# ------------------------------------------------------------------
# Summarization
# ------------------------------------------------------------------
def _compute_summary_budget(self, turns_to_summarize: List[Dict[str, Any]]) -> int:
"""Scale summary token budget with the amount of content being compressed."""
content_tokens = estimate_messages_tokens_rough(turns_to_summarize)
budget = int(content_tokens * _SUMMARY_RATIO)
return max(_MIN_SUMMARY_TOKENS, min(budget, _MAX_SUMMARY_TOKENS))
def _serialize_for_summary(self, turns: List[Dict[str, Any]]) -> str:
"""Serialize conversation turns into labeled text for the summarizer.
Includes tool call arguments and result content (up to 3000 chars
per message) so the summarizer can preserve specific details like
file paths, commands, and outputs.
"""
parts = []
for msg in turns:
for msg in turns_to_summarize:
role = msg.get("role", "unknown")
content = msg.get("content") or ""
# Tool results: keep more content than before (3000 chars)
if role == "tool":
tool_id = msg.get("tool_call_id", "")
if len(content) > 3000:
content = content[:2000] + "\n...[truncated]...\n" + content[-800:]
parts.append(f"[TOOL RESULT {tool_id}]: {content}")
continue
# Assistant messages: include tool call names AND arguments
if role == "assistant":
if len(content) > 3000:
content = content[:2000] + "\n...[truncated]...\n" + content[-800:]
tool_calls = msg.get("tool_calls", [])
if tool_calls:
tc_parts = []
for tc in tool_calls:
if isinstance(tc, dict):
fn = tc.get("function", {})
name = fn.get("name", "?")
args = fn.get("arguments", "")
# Truncate long arguments but keep enough for context
if len(args) > 500:
args = args[:400] + "..."
tc_parts.append(f" {name}({args})")
else:
fn = getattr(tc, "function", None)
name = getattr(fn, "name", "?") if fn else "?"
tc_parts.append(f" {name}(...)")
content += "\n[Tool calls:\n" + "\n".join(tc_parts) + "\n]"
parts.append(f"[ASSISTANT]: {content}")
continue
# User and other roles
if len(content) > 3000:
content = content[:2000] + "\n...[truncated]...\n" + content[-800:]
if len(content) > 2000:
content = content[:1000] + "\n...[truncated]...\n" + content[-500:]
tool_calls = msg.get("tool_calls", [])
if tool_calls:
tool_names = [tc.get("function", {}).get("name", "?") for tc in tool_calls if isinstance(tc, dict)]
content += f"\n[Tool calls: {', '.join(tool_names)}]"
parts.append(f"[{role.upper()}]: {content}")
return "\n\n".join(parts)
content_to_summarize = "\n\n".join(parts)
prompt = f"""Summarize these conversation turns concisely. This summary will replace these turns in the conversation history.
def _generate_summary(self, turns_to_summarize: List[Dict[str, Any]]) -> Optional[str]:
"""Generate a structured summary of conversation turns.
Write from a neutral perspective describing:
1. What actions were taken (tool calls, searches, file operations)
2. Key information or results obtained
3. Important decisions or findings
4. Relevant data, file names, or outputs
Uses a structured template (Goal, Progress, Decisions, Files, Next Steps)
inspired by Pi-mono and OpenCode. When a previous summary exists,
generates an iterative update instead of summarizing from scratch.
Returns None if all attempts fail — the caller should drop
the middle turns without a summary rather than inject a useless
placeholder.
"""
summary_budget = self._compute_summary_budget(turns_to_summarize)
content_to_summarize = self._serialize_for_summary(turns_to_summarize)
if self._previous_summary:
# Iterative update: preserve existing info, add new progress
prompt = f"""You are updating a context compaction summary. A previous compaction produced the summary below. New conversation turns have occurred since then and need to be incorporated.
PREVIOUS SUMMARY:
{self._previous_summary}
NEW TURNS TO INCORPORATE:
{content_to_summarize}
Update the summary using this exact structure. PRESERVE all existing information that is still relevant. ADD new progress. Move items from "In Progress" to "Done" when completed. Remove information only if it is clearly obsolete.
## Goal
[What the user is trying to accomplish — preserve from previous summary, update if goal evolved]
## Constraints & Preferences
[User preferences, coding style, constraints, important decisions — accumulate across compactions]
## Progress
### Done
[Completed work — include specific file paths, commands run, results obtained]
### In Progress
[Work currently underway]
### Blocked
[Any blockers or issues encountered]
## Key Decisions
[Important technical decisions and why they were made]
## Relevant Files
[Files read, modified, or created — with brief note on each. Accumulate across compactions.]
## Next Steps
[What needs to happen next to continue the work]
## Critical Context
[Any specific values, error messages, configuration details, or data that would be lost without explicit preservation]
Target ~{summary_budget} tokens. Be specific — include file paths, command outputs, error messages, and concrete values rather than vague descriptions.
Write only the summary body. Do not include any preamble or prefix."""
else:
# First compaction: summarize from scratch
prompt = f"""Create a structured handoff summary for a later assistant that will continue this conversation after earlier turns are compacted.
Keep factual and informative. Target ~{self.summary_target_tokens} tokens.
---
TURNS TO SUMMARIZE:
{content_to_summarize}
---
Use this exact structure:
## Goal
[What the user is trying to accomplish]
## Constraints & Preferences
[User preferences, coding style, constraints, important decisions]
## Progress
### Done
[Completed work — include specific file paths, commands run, results obtained]
### In Progress
[Work currently underway]
### Blocked
[Any blockers or issues encountered]
## Key Decisions
[Important technical decisions and why they were made]
## Relevant Files
[Files read, modified, or created — with brief note on each]
## Next Steps
[What needs to happen next to continue the work]
## Critical Context
[Any specific values, error messages, configuration details, or data that would be lost without explicit preservation]
Target ~{summary_budget} tokens. Be specific — include file paths, command outputs, error messages, and concrete values rather than vague descriptions. The goal is to prevent the next assistant from repeating work or losing important details.
Write only the summary body. Do not include any preamble or prefix."""
Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix."""
try:
call_kwargs = {
"task": "compression",
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.3,
"max_tokens": summary_budget * 2,
"timeout": 45.0,
}
if self.summary_model:
call_kwargs["model"] = self.summary_model
response = call_llm(**call_kwargs)
content = response.choices[0].message.content
# Handle cases where content is not a string (e.g., dict from llama.cpp)
if not isinstance(content, str):
content = str(content) if content else ""
summary = content.strip()
# Store for iterative updates on next compaction
self._previous_summary = summary
return self._with_summary_prefix(summary)
except RuntimeError:
logging.warning("Context compression: no provider available for "
"summary. Middle turns will be dropped without summary.")
return None
return self._call_summary_model(self.client, self.summary_model, prompt)
except Exception as e:
logging.warning("Failed to generate context summary: %s", e)
return None
logging.warning(f"Failed to generate context summary with auxiliary model: {e}")
@staticmethod
def _with_summary_prefix(summary: str) -> str:
"""Normalize summary text to the current compaction handoff format."""
text = (summary or "").strip()
for prefix in (LEGACY_SUMMARY_PREFIX, SUMMARY_PREFIX):
if text.startswith(prefix):
text = text[len(prefix):].lstrip()
break
return f"{SUMMARY_PREFIX}\n{text}" if text else SUMMARY_PREFIX
# Fallback: try the main model's endpoint. This handles the common
# case where the user switched providers (e.g. OpenRouter → local LLM)
# but a stale API key causes the auxiliary client to pick the old
# provider which then fails (402, auth error, etc.).
fallback_client, fallback_model = self._get_fallback_client()
if fallback_client is not None:
try:
logger.info("Retrying context summary with fallback client (%s)", fallback_model)
summary = self._call_summary_model(fallback_client, fallback_model, prompt)
# Success — swap in the working client for future compressions
self.client = fallback_client
self.summary_model = fallback_model
return summary
except Exception as fallback_err:
logging.warning(f"Fallback summary model also failed: {fallback_err}")
return "[CONTEXT SUMMARY]: Previous conversation turns have been compressed. The assistant performed tool calls and received responses."
def _call_summary_model(self, client, model: str, prompt: str) -> str:
"""Make the actual LLM call to generate a summary. Raises on failure."""
kwargs = {
"model": model,
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.3,
"timeout": 30.0,
}
# Most providers (OpenRouter, local models) use max_tokens.
# Direct OpenAI with newer models (gpt-4o, o-series, gpt-5+)
# requires max_completion_tokens instead.
try:
kwargs["max_tokens"] = self.summary_target_tokens * 2
response = client.chat.completions.create(**kwargs)
except Exception as first_err:
if "max_tokens" in str(first_err) or "unsupported_parameter" in str(first_err):
kwargs.pop("max_tokens", None)
kwargs["max_completion_tokens"] = self.summary_target_tokens * 2
response = client.chat.completions.create(**kwargs)
else:
raise
summary = response.choices[0].message.content.strip()
if not summary.startswith("[CONTEXT SUMMARY]:"):
summary = "[CONTEXT SUMMARY]: " + summary
return summary
def _get_fallback_client(self):
"""Try to build a fallback client from the main model's endpoint config.
When the primary auxiliary client fails (e.g. stale OpenRouter key), this
creates a client using the user's active custom endpoint (OPENAI_BASE_URL)
so compression can still produce a real summary instead of a static string.
Returns (client, model) or (None, None).
"""
custom_base = os.getenv("OPENAI_BASE_URL")
custom_key = os.getenv("OPENAI_API_KEY")
if not custom_base or not custom_key:
return None, None
# Don't fallback to the same provider that just failed
from hermes_constants import OPENROUTER_BASE_URL
if custom_base.rstrip("/") == OPENROUTER_BASE_URL.rstrip("/"):
return None, None
model = os.getenv("LLM_MODEL") or os.getenv("OPENAI_MODEL") or self.model
try:
from openai import OpenAI as _OpenAI
client = _OpenAI(api_key=custom_key, base_url=custom_base)
logger.debug("Built fallback auxiliary client: %s via %s", model, custom_base)
return client, model
except Exception as exc:
logger.debug("Could not build fallback auxiliary client: %s", exc)
return None, None
# ------------------------------------------------------------------
# Tool-call / tool-result pair integrity helpers
@@ -451,194 +281,86 @@ Write only the summary body. Do not include any preamble or prefix."""
"""Pull a compress-end boundary backward to avoid splitting a
tool_call / result group.
If the boundary falls in the middle of a tool-result group (i.e.
there are consecutive tool messages before ``idx``), walk backward
past all of them to find the parent assistant message. If found,
move the boundary before the assistant so the entire
assistant + tool_results group is included in the summarised region
rather than being split (which causes silent data loss when
``_sanitize_tool_pairs`` removes the orphaned tail results).
If the message just before ``idx`` is an assistant message with
tool_calls, those tool results will start at ``idx`` and would be
separated from their parent. Move backwards to include the whole
group in the summarised region.
"""
if idx <= 0 or idx >= len(messages):
return idx
# Walk backward past consecutive tool results
check = idx - 1
while check >= 0 and messages[check].get("role") == "tool":
check -= 1
# If we landed on the parent assistant with tool_calls, pull the
# boundary before it so the whole group gets summarised together.
if check >= 0 and messages[check].get("role") == "assistant" and messages[check].get("tool_calls"):
idx = check
prev = messages[idx - 1]
if prev.get("role") == "assistant" and prev.get("tool_calls"):
# The results for this assistant turn sit at idx..idx+k.
# Include the assistant message in the summarised region too.
idx -= 1
return idx
# ------------------------------------------------------------------
# Tail protection by token budget
# ------------------------------------------------------------------
def _find_tail_cut_by_tokens(
self, messages: List[Dict[str, Any]], head_end: int,
token_budget: int = _DEFAULT_TAIL_TOKEN_BUDGET,
) -> int:
"""Walk backward from the end of messages, accumulating tokens until
the budget is reached. Returns the index where the tail starts.
Never cuts inside a tool_call/result group. Falls back to the old
``protect_last_n`` if the budget would protect fewer messages.
"""
n = len(messages)
min_tail = self.protect_last_n
accumulated = 0
cut_idx = n # start from beyond the end
for i in range(n - 1, head_end - 1, -1):
msg = messages[i]
content = msg.get("content") or ""
msg_tokens = len(content) // _CHARS_PER_TOKEN + 10 # +10 for role/metadata
# Include tool call arguments in estimate
for tc in msg.get("tool_calls") or []:
if isinstance(tc, dict):
args = tc.get("function", {}).get("arguments", "")
msg_tokens += len(args) // _CHARS_PER_TOKEN
if accumulated + msg_tokens > token_budget and (n - i) >= min_tail:
break
accumulated += msg_tokens
cut_idx = i
# Ensure we protect at least protect_last_n messages
fallback_cut = n - min_tail
if cut_idx > fallback_cut:
cut_idx = fallback_cut
# If the token budget would protect everything (small conversations),
# fall back to the fixed protect_last_n approach so compression can
# still remove middle turns.
if cut_idx <= head_end:
cut_idx = fallback_cut
# Align to avoid splitting tool groups
cut_idx = self._align_boundary_backward(messages, cut_idx)
return max(cut_idx, head_end + 1)
# ------------------------------------------------------------------
# Main compression entry point
# ------------------------------------------------------------------
def compress(self, messages: List[Dict[str, Any]], current_tokens: int = None) -> List[Dict[str, Any]]:
"""Compress conversation messages by summarizing middle turns.
Algorithm:
1. Prune old tool results (cheap pre-pass, no LLM call)
2. Protect head messages (system prompt + first exchange)
3. Find tail boundary by token budget (~20K tokens of recent context)
4. Summarize middle turns with structured LLM prompt
5. On re-compression, iteratively update the previous summary
Keeps first N + last N turns, summarizes everything in between.
After compression, orphaned tool_call / tool_result pairs are cleaned
up so the API never receives mismatched IDs.
"""
n_messages = len(messages)
if n_messages <= self.protect_first_n + self.protect_last_n + 1:
if not self.quiet_mode:
logger.warning(
"Cannot compress: only %d messages (need > %d)",
n_messages,
self.protect_first_n + self.protect_last_n + 1,
)
print(f"⚠️ Cannot compress: only {n_messages} messages (need > {self.protect_first_n + self.protect_last_n + 1})")
return messages
display_tokens = current_tokens if current_tokens else self.last_prompt_tokens or estimate_messages_tokens_rough(messages)
# Phase 1: Prune old tool results (cheap, no LLM call)
messages, pruned_count = self._prune_old_tool_results(
messages, protect_tail_count=self.protect_last_n * 3,
)
if pruned_count and not self.quiet_mode:
logger.info("Pre-compression: pruned %d old tool result(s)", pruned_count)
# Phase 2: Determine boundaries
compress_start = self.protect_first_n
compress_end = n_messages - self.protect_last_n
if compress_start >= compress_end:
return messages
# Adjust boundaries to avoid splitting tool_call/result groups.
compress_start = self._align_boundary_forward(messages, compress_start)
# Use token-budget tail protection instead of fixed message count
compress_end = self._find_tail_cut_by_tokens(messages, compress_start)
compress_end = self._align_boundary_backward(messages, compress_end)
if compress_start >= compress_end:
return messages
turns_to_summarize = messages[compress_start:compress_end]
display_tokens = current_tokens if current_tokens else self.last_prompt_tokens or estimate_messages_tokens_rough(messages)
if not self.quiet_mode:
logger.info(
"Context compression triggered (%d tokens >= %d threshold)",
display_tokens,
self.threshold_tokens,
)
logger.info(
"Model context limit: %d tokens (%.0f%% = %d)",
self.context_length,
self.threshold_percent * 100,
self.threshold_tokens,
)
tail_msgs = n_messages - compress_end
logger.info(
"Summarizing turns %d-%d (%d turns), protecting %d head + %d tail messages",
compress_start + 1,
compress_end,
len(turns_to_summarize),
compress_start,
tail_msgs,
)
print(f"\n📦 Context compression triggered ({display_tokens:,} tokens ≥ {self.threshold_tokens:,} threshold)")
print(f" 📊 Model context limit: {self.context_length:,} tokens ({self.threshold_percent*100:.0f}% = {self.threshold_tokens:,})")
# Truncation fallback when no auxiliary model is available
if self.client is None:
print("⚠️ Context compression: no auxiliary model available. Falling back to message truncation.")
# Keep system message(s) at the front and the protected tail;
# simply drop the oldest non-system messages until under threshold.
kept = []
for msg in messages:
if msg.get("role") == "system":
kept.append(msg.copy())
else:
break
tail = messages[-self.protect_last_n:]
kept.extend(m.copy() for m in tail)
self.compression_count += 1
kept = self._sanitize_tool_pairs(kept)
if not self.quiet_mode:
print(f" ✂️ Truncated: {len(messages)}{len(kept)} messages (dropped middle turns)")
return kept
if not self.quiet_mode:
print(f" 🗜️ Summarizing turns {compress_start+1}-{compress_end} ({len(turns_to_summarize)} turns)")
# Phase 3: Generate structured summary
summary = self._generate_summary(turns_to_summarize)
# Phase 4: Assemble compressed message list
compressed = []
for i in range(compress_start):
msg = messages[i].copy()
if i == 0 and msg.get("role") == "system" and self.compression_count == 0:
msg["content"] = (
(msg.get("content") or "")
+ "\n\n[Note: Some earlier conversation turns have been compacted into a handoff summary to preserve context space. The current session state may still reflect earlier work, so build on that summary and state rather than re-doing work.]"
)
msg["content"] = (msg.get("content") or "") + "\n\n[Note: Some earlier conversation turns may be summarized to preserve context space.]"
compressed.append(msg)
_merge_summary_into_tail = False
if summary:
last_head_role = messages[compress_start - 1].get("role", "user") if compress_start > 0 else "user"
first_tail_role = messages[compress_end].get("role", "user") if compress_end < n_messages else "user"
# Pick a role that avoids consecutive same-role with both neighbors.
# Priority: avoid colliding with head (already committed), then tail.
if last_head_role in ("assistant", "tool"):
summary_role = "user"
else:
summary_role = "assistant"
# If the chosen role collides with the tail AND flipping wouldn't
# collide with the head, flip it.
if summary_role == first_tail_role:
flipped = "assistant" if summary_role == "user" else "user"
if flipped != last_head_role:
summary_role = flipped
else:
# Both roles would create consecutive same-role messages
# (e.g. head=assistant, tail=user — neither role works).
# Merge the summary into the first tail message instead
# of inserting a standalone message that breaks alternation.
_merge_summary_into_tail = True
if not _merge_summary_into_tail:
compressed.append({"role": summary_role, "content": summary})
else:
if not self.quiet_mode:
logger.warning("No summary model available — middle turns dropped without summary")
compressed.append({"role": "user", "content": summary})
for i in range(compress_end, n_messages):
msg = messages[i].copy()
if _merge_summary_into_tail and i == compress_end:
original = msg.get("content") or ""
msg["content"] = summary + "\n\n" + original
_merge_summary_into_tail = False
compressed.append(msg)
compressed.append(messages[i].copy())
self.compression_count += 1
@@ -647,12 +369,7 @@ Write only the summary body. Do not include any preamble or prefix."""
if not self.quiet_mode:
new_estimate = estimate_messages_tokens_rough(compressed)
saved_estimate = display_tokens - new_estimate
logger.info(
"Compressed: %d -> %d messages (~%d tokens saved)",
n_messages,
len(compressed),
saved_estimate,
)
logger.info("Compression #%d complete", self.compression_count)
print(f" ✅ Compressed: {n_messages}{len(compressed)} messages (~{saved_estimate:,} tokens saved)")
print(f" 💡 Compression #{self.compression_count} complete")
return compressed

View File

@@ -1,485 +0,0 @@
from __future__ import annotations
import asyncio
import inspect
import json
import mimetypes
import os
import re
import subprocess
from dataclasses import dataclass, field
from pathlib import Path
from typing import Awaitable, Callable
from agent.model_metadata import estimate_tokens_rough
REFERENCE_PATTERN = re.compile(
r"(?<![\w/])@(?:(?P<simple>diff|staged)\b|(?P<kind>file|folder|git|url):(?P<value>\S+))"
)
TRAILING_PUNCTUATION = ",.;!?"
_SENSITIVE_HOME_DIRS = (".ssh", ".aws", ".gnupg", ".kube")
_SENSITIVE_HERMES_DIRS = (Path("skills") / ".hub",)
_SENSITIVE_HOME_FILES = (
Path(".ssh") / "authorized_keys",
Path(".ssh") / "id_rsa",
Path(".ssh") / "id_ed25519",
Path(".ssh") / "config",
Path(".bashrc"),
Path(".zshrc"),
Path(".profile"),
Path(".bash_profile"),
Path(".zprofile"),
Path(".netrc"),
Path(".pgpass"),
Path(".npmrc"),
Path(".pypirc"),
)
@dataclass(frozen=True)
class ContextReference:
raw: str
kind: str
target: str
start: int
end: int
line_start: int | None = None
line_end: int | None = None
@dataclass
class ContextReferenceResult:
message: str
original_message: str
references: list[ContextReference] = field(default_factory=list)
warnings: list[str] = field(default_factory=list)
injected_tokens: int = 0
expanded: bool = False
blocked: bool = False
def parse_context_references(message: str) -> list[ContextReference]:
refs: list[ContextReference] = []
if not message:
return refs
for match in REFERENCE_PATTERN.finditer(message):
simple = match.group("simple")
if simple:
refs.append(
ContextReference(
raw=match.group(0),
kind=simple,
target="",
start=match.start(),
end=match.end(),
)
)
continue
kind = match.group("kind")
value = _strip_trailing_punctuation(match.group("value") or "")
line_start = None
line_end = None
target = value
if kind == "file":
range_match = re.match(r"^(?P<path>.+?):(?P<start>\d+)(?:-(?P<end>\d+))?$", value)
if range_match:
target = range_match.group("path")
line_start = int(range_match.group("start"))
line_end = int(range_match.group("end") or range_match.group("start"))
refs.append(
ContextReference(
raw=match.group(0),
kind=kind,
target=target,
start=match.start(),
end=match.end(),
line_start=line_start,
line_end=line_end,
)
)
return refs
def preprocess_context_references(
message: str,
*,
cwd: str | Path,
context_length: int,
url_fetcher: Callable[[str], str | Awaitable[str]] | None = None,
allowed_root: str | Path | None = None,
) -> ContextReferenceResult:
coro = preprocess_context_references_async(
message,
cwd=cwd,
context_length=context_length,
url_fetcher=url_fetcher,
allowed_root=allowed_root,
)
# Safe for both CLI (no loop) and gateway (loop already running).
try:
loop = asyncio.get_running_loop()
except RuntimeError:
loop = None
if loop and loop.is_running():
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
return pool.submit(asyncio.run, coro).result()
return asyncio.run(coro)
async def preprocess_context_references_async(
message: str,
*,
cwd: str | Path,
context_length: int,
url_fetcher: Callable[[str], str | Awaitable[str]] | None = None,
allowed_root: str | Path | None = None,
) -> ContextReferenceResult:
refs = parse_context_references(message)
if not refs:
return ContextReferenceResult(message=message, original_message=message)
cwd_path = Path(cwd).expanduser().resolve()
# Default to the current working directory so @ references cannot escape
# the active workspace unless a caller explicitly widens the root.
allowed_root_path = (
Path(allowed_root).expanduser().resolve() if allowed_root is not None else cwd_path
)
warnings: list[str] = []
blocks: list[str] = []
injected_tokens = 0
for ref in refs:
warning, block = await _expand_reference(
ref,
cwd_path,
url_fetcher=url_fetcher,
allowed_root=allowed_root_path,
)
if warning:
warnings.append(warning)
if block:
blocks.append(block)
injected_tokens += estimate_tokens_rough(block)
hard_limit = max(1, int(context_length * 0.50))
soft_limit = max(1, int(context_length * 0.25))
if injected_tokens > hard_limit:
warnings.append(
f"@ context injection refused: {injected_tokens} tokens exceeds the 50% hard limit ({hard_limit})."
)
return ContextReferenceResult(
message=message,
original_message=message,
references=refs,
warnings=warnings,
injected_tokens=injected_tokens,
expanded=False,
blocked=True,
)
if injected_tokens > soft_limit:
warnings.append(
f"@ context injection warning: {injected_tokens} tokens exceeds the 25% soft limit ({soft_limit})."
)
stripped = _remove_reference_tokens(message, refs)
final = stripped
if warnings:
final = f"{final}\n\n--- Context Warnings ---\n" + "\n".join(f"- {warning}" for warning in warnings)
if blocks:
final = f"{final}\n\n--- Attached Context ---\n\n" + "\n\n".join(blocks)
return ContextReferenceResult(
message=final.strip(),
original_message=message,
references=refs,
warnings=warnings,
injected_tokens=injected_tokens,
expanded=bool(blocks or warnings),
blocked=False,
)
async def _expand_reference(
ref: ContextReference,
cwd: Path,
*,
url_fetcher: Callable[[str], str | Awaitable[str]] | None = None,
allowed_root: Path | None = None,
) -> tuple[str | None, str | None]:
try:
if ref.kind == "file":
return _expand_file_reference(ref, cwd, allowed_root=allowed_root)
if ref.kind == "folder":
return _expand_folder_reference(ref, cwd, allowed_root=allowed_root)
if ref.kind == "diff":
return _expand_git_reference(ref, cwd, ["diff"], "git diff")
if ref.kind == "staged":
return _expand_git_reference(ref, cwd, ["diff", "--staged"], "git diff --staged")
if ref.kind == "git":
count = max(1, min(int(ref.target or "1"), 10))
return _expand_git_reference(ref, cwd, ["log", f"-{count}", "-p"], f"git log -{count} -p")
if ref.kind == "url":
content = await _fetch_url_content(ref.target, url_fetcher=url_fetcher)
if not content:
return f"{ref.raw}: no content extracted", None
return None, f"🌐 {ref.raw} ({estimate_tokens_rough(content)} tokens)\n{content}"
except Exception as exc:
return f"{ref.raw}: {exc}", None
return f"{ref.raw}: unsupported reference type", None
def _expand_file_reference(
ref: ContextReference,
cwd: Path,
*,
allowed_root: Path | None = None,
) -> tuple[str | None, str | None]:
path = _resolve_path(cwd, ref.target, allowed_root=allowed_root)
_ensure_reference_path_allowed(path)
if not path.exists():
return f"{ref.raw}: file not found", None
if not path.is_file():
return f"{ref.raw}: path is not a file", None
if _is_binary_file(path):
return f"{ref.raw}: binary files are not supported", None
text = path.read_text(encoding="utf-8")
if ref.line_start is not None:
lines = text.splitlines()
start_idx = max(ref.line_start - 1, 0)
end_idx = min(ref.line_end or ref.line_start, len(lines))
text = "\n".join(lines[start_idx:end_idx])
lang = _code_fence_language(path)
label = ref.raw
return None, f"📄 {label} ({estimate_tokens_rough(text)} tokens)\n```{lang}\n{text}\n```"
def _expand_folder_reference(
ref: ContextReference,
cwd: Path,
*,
allowed_root: Path | None = None,
) -> tuple[str | None, str | None]:
path = _resolve_path(cwd, ref.target, allowed_root=allowed_root)
_ensure_reference_path_allowed(path)
if not path.exists():
return f"{ref.raw}: folder not found", None
if not path.is_dir():
return f"{ref.raw}: path is not a folder", None
listing = _build_folder_listing(path, cwd)
return None, f"📁 {ref.raw} ({estimate_tokens_rough(listing)} tokens)\n{listing}"
def _expand_git_reference(
ref: ContextReference,
cwd: Path,
args: list[str],
label: str,
) -> tuple[str | None, str | None]:
result = subprocess.run(
["git", *args],
cwd=cwd,
capture_output=True,
text=True,
)
if result.returncode != 0:
stderr = (result.stderr or "").strip() or "git command failed"
return f"{ref.raw}: {stderr}", None
content = result.stdout.strip()
if not content:
content = "(no output)"
return None, f"🧾 {label} ({estimate_tokens_rough(content)} tokens)\n```diff\n{content}\n```"
async def _fetch_url_content(
url: str,
*,
url_fetcher: Callable[[str], str | Awaitable[str]] | None = None,
) -> str:
fetcher = url_fetcher or _default_url_fetcher
content = fetcher(url)
if inspect.isawaitable(content):
content = await content
return str(content or "").strip()
async def _default_url_fetcher(url: str) -> str:
from tools.web_tools import web_extract_tool
raw = await web_extract_tool([url], format="markdown", use_llm_processing=True)
payload = json.loads(raw)
docs = payload.get("data", {}).get("documents", [])
if not docs:
return ""
doc = docs[0]
return str(doc.get("content") or doc.get("raw_content") or "").strip()
def _resolve_path(cwd: Path, target: str, *, allowed_root: Path | None = None) -> Path:
path = Path(os.path.expanduser(target))
if not path.is_absolute():
path = cwd / path
resolved = path.resolve()
if allowed_root is not None:
try:
resolved.relative_to(allowed_root)
except ValueError as exc:
raise ValueError("path is outside the allowed workspace") from exc
return resolved
def _ensure_reference_path_allowed(path: Path) -> None:
home = Path(os.path.expanduser("~")).resolve()
hermes_home = Path(
os.getenv("HERMES_HOME", str(home / ".hermes"))
).expanduser().resolve()
blocked_exact = {home / rel for rel in _SENSITIVE_HOME_FILES}
blocked_exact.add(hermes_home / ".env")
blocked_dirs = [home / rel for rel in _SENSITIVE_HOME_DIRS]
blocked_dirs.extend(hermes_home / rel for rel in _SENSITIVE_HERMES_DIRS)
if path in blocked_exact:
raise ValueError("path is a sensitive credential file and cannot be attached")
for blocked_dir in blocked_dirs:
try:
path.relative_to(blocked_dir)
except ValueError:
continue
raise ValueError("path is a sensitive credential or internal Hermes path and cannot be attached")
def _strip_trailing_punctuation(value: str) -> str:
stripped = value.rstrip(TRAILING_PUNCTUATION)
while stripped.endswith((")", "]", "}")):
closer = stripped[-1]
opener = {")": "(", "]": "[", "}": "{"}[closer]
if stripped.count(closer) > stripped.count(opener):
stripped = stripped[:-1]
continue
break
return stripped
def _remove_reference_tokens(message: str, refs: list[ContextReference]) -> str:
pieces: list[str] = []
cursor = 0
for ref in refs:
pieces.append(message[cursor:ref.start])
cursor = ref.end
pieces.append(message[cursor:])
text = "".join(pieces)
text = re.sub(r"\s{2,}", " ", text)
text = re.sub(r"\s+([,.;:!?])", r"\1", text)
return text.strip()
def _is_binary_file(path: Path) -> bool:
mime, _ = mimetypes.guess_type(path.name)
if mime and not mime.startswith("text/") and not any(
path.name.endswith(ext) for ext in (".py", ".md", ".txt", ".json", ".yaml", ".yml", ".toml", ".js", ".ts")
):
return True
chunk = path.read_bytes()[:4096]
return b"\x00" in chunk
def _build_folder_listing(path: Path, cwd: Path, limit: int = 200) -> str:
lines = [f"{path.relative_to(cwd)}/"]
entries = _iter_visible_entries(path, cwd, limit=limit)
for entry in entries:
rel = entry.relative_to(cwd)
indent = " " * max(len(rel.parts) - len(path.relative_to(cwd).parts) - 1, 0)
if entry.is_dir():
lines.append(f"{indent}- {entry.name}/")
else:
meta = _file_metadata(entry)
lines.append(f"{indent}- {entry.name} ({meta})")
if len(entries) >= limit:
lines.append("- ...")
return "\n".join(lines)
def _iter_visible_entries(path: Path, cwd: Path, limit: int) -> list[Path]:
rg_entries = _rg_files(path, cwd, limit=limit)
if rg_entries is not None:
output: list[Path] = []
seen_dirs: set[Path] = set()
for rel in rg_entries:
full = cwd / rel
for parent in full.parents:
if parent == cwd or parent in seen_dirs or path not in {parent, *parent.parents}:
continue
seen_dirs.add(parent)
output.append(parent)
output.append(full)
return sorted({p for p in output if p.exists()}, key=lambda p: (not p.is_dir(), str(p)))
output = []
for root, dirs, files in os.walk(path):
dirs[:] = sorted(d for d in dirs if not d.startswith(".") and d != "__pycache__")
files = sorted(f for f in files if not f.startswith("."))
root_path = Path(root)
for d in dirs:
output.append(root_path / d)
if len(output) >= limit:
return output
for f in files:
output.append(root_path / f)
if len(output) >= limit:
return output
return output
def _rg_files(path: Path, cwd: Path, limit: int) -> list[Path] | None:
try:
result = subprocess.run(
["rg", "--files", str(path.relative_to(cwd))],
cwd=cwd,
capture_output=True,
text=True,
)
except FileNotFoundError:
return None
if result.returncode != 0:
return None
files = [Path(line.strip()) for line in result.stdout.splitlines() if line.strip()]
return files[:limit]
def _file_metadata(path: Path) -> str:
if _is_binary_file(path):
return f"{path.stat().st_size} bytes"
try:
line_count = path.read_text(encoding="utf-8").count("\n") + 1
except Exception:
return f"{path.stat().st_size} bytes"
return f"{line_count} lines"
def _code_fence_language(path: Path) -> str:
mapping = {
".py": "python",
".js": "javascript",
".ts": "typescript",
".tsx": "tsx",
".jsx": "jsx",
".json": "json",
".md": "markdown",
".sh": "bash",
".yml": "yaml",
".yaml": "yaml",
".toml": "toml",
}
return mapping.get(path.suffix.lower(), "")

View File

@@ -1,447 +0,0 @@
"""OpenAI-compatible shim that forwards Hermes requests to `copilot --acp`.
This adapter lets Hermes treat the GitHub Copilot ACP server as a chat-style
backend. Each request starts a short-lived ACP session, sends the formatted
conversation as a single prompt, collects text chunks, and converts the result
back into the minimal shape Hermes expects from an OpenAI client.
"""
from __future__ import annotations
import json
import os
import queue
import shlex
import subprocess
import threading
import time
from collections import deque
from pathlib import Path
from types import SimpleNamespace
from typing import Any
ACP_MARKER_BASE_URL = "acp://copilot"
_DEFAULT_TIMEOUT_SECONDS = 900.0
def _resolve_command() -> str:
return (
os.getenv("HERMES_COPILOT_ACP_COMMAND", "").strip()
or os.getenv("COPILOT_CLI_PATH", "").strip()
or "copilot"
)
def _resolve_args() -> list[str]:
raw = os.getenv("HERMES_COPILOT_ACP_ARGS", "").strip()
if not raw:
return ["--acp", "--stdio"]
return shlex.split(raw)
def _jsonrpc_error(message_id: Any, code: int, message: str) -> dict[str, Any]:
return {
"jsonrpc": "2.0",
"id": message_id,
"error": {
"code": code,
"message": message,
},
}
def _format_messages_as_prompt(messages: list[dict[str, Any]], model: str | None = None) -> str:
sections: list[str] = [
"You are being used as the active ACP agent backend for Hermes.",
"Use your own ACP capabilities and respond directly in natural language.",
"Do not emit OpenAI tool-call JSON.",
]
if model:
sections.append(f"Hermes requested model hint: {model}")
transcript: list[str] = []
for message in messages:
if not isinstance(message, dict):
continue
role = str(message.get("role") or "unknown").strip().lower()
if role == "tool":
role = "tool"
elif role not in {"system", "user", "assistant"}:
role = "context"
content = message.get("content")
rendered = _render_message_content(content)
if not rendered:
continue
label = {
"system": "System",
"user": "User",
"assistant": "Assistant",
"tool": "Tool",
"context": "Context",
}.get(role, role.title())
transcript.append(f"{label}:\n{rendered}")
if transcript:
sections.append("Conversation transcript:\n\n" + "\n\n".join(transcript))
sections.append("Continue the conversation from the latest user request.")
return "\n\n".join(section.strip() for section in sections if section and section.strip())
def _render_message_content(content: Any) -> str:
if content is None:
return ""
if isinstance(content, str):
return content.strip()
if isinstance(content, dict):
if "text" in content:
return str(content.get("text") or "").strip()
if "content" in content and isinstance(content.get("content"), str):
return str(content.get("content") or "").strip()
return json.dumps(content, ensure_ascii=True)
if isinstance(content, list):
parts: list[str] = []
for item in content:
if isinstance(item, str):
parts.append(item)
elif isinstance(item, dict):
text = item.get("text")
if isinstance(text, str) and text.strip():
parts.append(text.strip())
return "\n".join(parts).strip()
return str(content).strip()
def _ensure_path_within_cwd(path_text: str, cwd: str) -> Path:
candidate = Path(path_text)
if not candidate.is_absolute():
raise PermissionError("ACP file-system paths must be absolute.")
resolved = candidate.resolve()
root = Path(cwd).resolve()
try:
resolved.relative_to(root)
except ValueError as exc:
raise PermissionError(f"Path '{resolved}' is outside the session cwd '{root}'.") from exc
return resolved
class _ACPChatCompletions:
def __init__(self, client: "CopilotACPClient"):
self._client = client
def create(self, **kwargs: Any) -> Any:
return self._client._create_chat_completion(**kwargs)
class _ACPChatNamespace:
def __init__(self, client: "CopilotACPClient"):
self.completions = _ACPChatCompletions(client)
class CopilotACPClient:
"""Minimal OpenAI-client-compatible facade for Copilot ACP."""
def __init__(
self,
*,
api_key: str | None = None,
base_url: str | None = None,
default_headers: dict[str, str] | None = None,
acp_command: str | None = None,
acp_args: list[str] | None = None,
acp_cwd: str | None = None,
command: str | None = None,
args: list[str] | None = None,
**_: Any,
):
self.api_key = api_key or "copilot-acp"
self.base_url = base_url or ACP_MARKER_BASE_URL
self._default_headers = dict(default_headers or {})
self._acp_command = acp_command or command or _resolve_command()
self._acp_args = list(acp_args or args or _resolve_args())
self._acp_cwd = str(Path(acp_cwd or os.getcwd()).resolve())
self.chat = _ACPChatNamespace(self)
self.is_closed = False
self._active_process: subprocess.Popen[str] | None = None
self._active_process_lock = threading.Lock()
def close(self) -> None:
proc: subprocess.Popen[str] | None
with self._active_process_lock:
proc = self._active_process
self._active_process = None
self.is_closed = True
if proc is None:
return
try:
proc.terminate()
proc.wait(timeout=2)
except Exception:
try:
proc.kill()
except Exception:
pass
def _create_chat_completion(
self,
*,
model: str | None = None,
messages: list[dict[str, Any]] | None = None,
timeout: float | None = None,
**_: Any,
) -> Any:
prompt_text = _format_messages_as_prompt(messages or [], model=model)
response_text, reasoning_text = self._run_prompt(
prompt_text,
timeout_seconds=float(timeout or _DEFAULT_TIMEOUT_SECONDS),
)
usage = SimpleNamespace(
prompt_tokens=0,
completion_tokens=0,
total_tokens=0,
prompt_tokens_details=SimpleNamespace(cached_tokens=0),
)
assistant_message = SimpleNamespace(
content=response_text,
tool_calls=[],
reasoning=reasoning_text or None,
reasoning_content=reasoning_text or None,
reasoning_details=None,
)
choice = SimpleNamespace(message=assistant_message, finish_reason="stop")
return SimpleNamespace(
choices=[choice],
usage=usage,
model=model or "copilot-acp",
)
def _run_prompt(self, prompt_text: str, *, timeout_seconds: float) -> tuple[str, str]:
try:
proc = subprocess.Popen(
[self._acp_command] + self._acp_args,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
bufsize=1,
cwd=self._acp_cwd,
)
except FileNotFoundError as exc:
raise RuntimeError(
f"Could not start Copilot ACP command '{self._acp_command}'. "
"Install GitHub Copilot CLI or set HERMES_COPILOT_ACP_COMMAND/COPILOT_CLI_PATH."
) from exc
if proc.stdin is None or proc.stdout is None:
proc.kill()
raise RuntimeError("Copilot ACP process did not expose stdin/stdout pipes.")
self.is_closed = False
with self._active_process_lock:
self._active_process = proc
inbox: queue.Queue[dict[str, Any]] = queue.Queue()
stderr_tail: deque[str] = deque(maxlen=40)
def _stdout_reader() -> None:
for line in proc.stdout:
try:
inbox.put(json.loads(line))
except Exception:
inbox.put({"raw": line.rstrip("\n")})
def _stderr_reader() -> None:
if proc.stderr is None:
return
for line in proc.stderr:
stderr_tail.append(line.rstrip("\n"))
out_thread = threading.Thread(target=_stdout_reader, daemon=True)
err_thread = threading.Thread(target=_stderr_reader, daemon=True)
out_thread.start()
err_thread.start()
next_id = 0
def _request(method: str, params: dict[str, Any], *, text_parts: list[str] | None = None, reasoning_parts: list[str] | None = None) -> Any:
nonlocal next_id
next_id += 1
request_id = next_id
payload = {
"jsonrpc": "2.0",
"id": request_id,
"method": method,
"params": params,
}
proc.stdin.write(json.dumps(payload) + "\n")
proc.stdin.flush()
deadline = time.time() + timeout_seconds
while time.time() < deadline:
if proc.poll() is not None:
break
try:
msg = inbox.get(timeout=0.1)
except queue.Empty:
continue
if self._handle_server_message(
msg,
process=proc,
cwd=self._acp_cwd,
text_parts=text_parts,
reasoning_parts=reasoning_parts,
):
continue
if msg.get("id") != request_id:
continue
if "error" in msg:
err = msg.get("error") or {}
raise RuntimeError(
f"Copilot ACP {method} failed: {err.get('message') or err}"
)
return msg.get("result")
stderr_text = "\n".join(stderr_tail).strip()
if proc.poll() is not None and stderr_text:
raise RuntimeError(f"Copilot ACP process exited early: {stderr_text}")
raise TimeoutError(f"Timed out waiting for Copilot ACP response to {method}.")
try:
_request(
"initialize",
{
"protocolVersion": 1,
"clientCapabilities": {
"fs": {
"readTextFile": True,
"writeTextFile": True,
}
},
"clientInfo": {
"name": "hermes-agent",
"title": "Hermes Agent",
"version": "0.0.0",
},
},
)
session = _request(
"session/new",
{
"cwd": self._acp_cwd,
"mcpServers": [],
},
) or {}
session_id = str(session.get("sessionId") or "").strip()
if not session_id:
raise RuntimeError("Copilot ACP did not return a sessionId.")
text_parts: list[str] = []
reasoning_parts: list[str] = []
_request(
"session/prompt",
{
"sessionId": session_id,
"prompt": [
{
"type": "text",
"text": prompt_text,
}
],
},
text_parts=text_parts,
reasoning_parts=reasoning_parts,
)
return "".join(text_parts), "".join(reasoning_parts)
finally:
self.close()
def _handle_server_message(
self,
msg: dict[str, Any],
*,
process: subprocess.Popen[str],
cwd: str,
text_parts: list[str] | None,
reasoning_parts: list[str] | None,
) -> bool:
method = msg.get("method")
if not isinstance(method, str):
return False
if method == "session/update":
params = msg.get("params") or {}
update = params.get("update") or {}
kind = str(update.get("sessionUpdate") or "").strip()
content = update.get("content") or {}
chunk_text = ""
if isinstance(content, dict):
chunk_text = str(content.get("text") or "")
if kind == "agent_message_chunk" and chunk_text and text_parts is not None:
text_parts.append(chunk_text)
elif kind == "agent_thought_chunk" and chunk_text and reasoning_parts is not None:
reasoning_parts.append(chunk_text)
return True
if process.stdin is None:
return True
message_id = msg.get("id")
params = msg.get("params") or {}
if method == "session/request_permission":
response = {
"jsonrpc": "2.0",
"id": message_id,
"result": {
"outcome": {
"outcome": "allow_once",
}
},
}
elif method == "fs/read_text_file":
try:
path = _ensure_path_within_cwd(str(params.get("path") or ""), cwd)
content = path.read_text() if path.exists() else ""
line = params.get("line")
limit = params.get("limit")
if isinstance(line, int) and line > 1:
lines = content.splitlines(keepends=True)
start = line - 1
end = start + limit if isinstance(limit, int) and limit > 0 else None
content = "".join(lines[start:end])
response = {
"jsonrpc": "2.0",
"id": message_id,
"result": {
"content": content,
},
}
except Exception as exc:
response = _jsonrpc_error(message_id, -32602, str(exc))
elif method == "fs/write_text_file":
try:
path = _ensure_path_within_cwd(str(params.get("path") or ""), cwd)
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(str(params.get("content") or ""))
response = {
"jsonrpc": "2.0",
"id": message_id,
"result": None,
}
except Exception as exc:
response = _jsonrpc_error(message_id, -32602, str(exc))
else:
response = _jsonrpc_error(
message_id,
-32601,
f"ACP client method '{method}' is not supported by Hermes yet.",
)
process.stdin.write(json.dumps(response) + "\n")
process.stdin.flush()
return True

View File

@@ -5,8 +5,8 @@ Used by AIAgent._execute_tool_calls for CLI feedback.
"""
import json
import logging
import os
import random
import sys
import threading
import time
@@ -15,89 +15,13 @@ import time
_RED = "\033[31m"
_RESET = "\033[0m"
logger = logging.getLogger(__name__)
# =========================================================================
# Skin-aware helpers (lazy import to avoid circular deps)
# =========================================================================
def _get_skin():
"""Get the active skin config, or None if not available."""
try:
from hermes_cli.skin_engine import get_active_skin
return get_active_skin()
except Exception:
return None
def get_skin_faces(key: str, default: list) -> list:
"""Get spinner face list from active skin, falling back to default."""
skin = _get_skin()
if skin:
faces = skin.get_spinner_list(key)
if faces:
return faces
return default
def get_skin_verbs() -> list:
"""Get thinking verbs from active skin."""
skin = _get_skin()
if skin:
verbs = skin.get_spinner_list("thinking_verbs")
if verbs:
return verbs
return KawaiiSpinner.THINKING_VERBS
def get_skin_tool_prefix() -> str:
"""Get tool output prefix character from active skin."""
skin = _get_skin()
if skin:
return skin.tool_prefix
return ""
def get_tool_emoji(tool_name: str, default: str = "") -> str:
"""Get the display emoji for a tool.
Resolution order:
1. Active skin's ``tool_emojis`` overrides (if a skin is loaded)
2. Tool registry's per-tool ``emoji`` field
3. *default* fallback
"""
# 1. Skin override
skin = _get_skin()
if skin and skin.tool_emojis:
override = skin.tool_emojis.get(tool_name)
if override:
return override
# 2. Registry default
try:
from tools.registry import registry
emoji = registry.get_emoji(tool_name, default="")
if emoji:
return emoji
except Exception:
pass
# 3. Hardcoded fallback
return default
# =========================================================================
# Tool preview (one-line summary of a tool call's primary argument)
# =========================================================================
def _oneline(text: str) -> str:
"""Collapse whitespace (including newlines) to single spaces."""
return " ".join(text.split())
def build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str | None:
def build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str:
"""Build a short preview of a tool call's primary argument for display."""
if not args:
return None
primary_args = {
"terminal": "command", "web_search": "query", "web_extract": "urls",
"read_file": "path", "write_file": "path", "patch": "path",
@@ -106,7 +30,7 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str | N
"image_generate": "prompt", "text_to_speech": "text",
"vision_analyze": "question", "mixture_of_agents": "user_prompt",
"skill_view": "name", "skills_list": "category",
"cronjob": "action",
"schedule_cronjob": "name",
"execute_code": "code", "delegate_task": "goal",
"clarify": "question", "skill_manage": "name",
}
@@ -120,7 +44,7 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str | N
if sid:
parts.append(sid[:16])
if data:
parts.append(f'"{_oneline(data[:20])}"')
parts.append(f'"{data[:20]}"')
if timeout_val and action == "wait":
parts.append(f"{timeout_val}s")
return " ".join(parts) if parts else None
@@ -136,24 +60,24 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str | N
return f"planning {len(todos_arg)} task(s)"
if tool_name == "session_search":
query = _oneline(args.get("query", ""))
query = args.get("query", "")
return f"recall: \"{query[:25]}{'...' if len(query) > 25 else ''}\""
if tool_name == "memory":
action = args.get("action", "")
target = args.get("target", "")
if action == "add":
content = _oneline(args.get("content", ""))
content = args.get("content", "")
return f"+{target}: \"{content[:25]}{'...' if len(content) > 25 else ''}\""
elif action == "replace":
return f"~{target}: \"{_oneline(args.get('old_text', '')[:20])}\""
return f"~{target}: \"{args.get('old_text', '')[:20]}\""
elif action == "remove":
return f"-{target}: \"{_oneline(args.get('old_text', '')[:20])}\""
return f"-{target}: \"{args.get('old_text', '')[:20]}\""
return action
if tool_name == "send_message":
target = args.get("target", "?")
msg = _oneline(args.get("message", ""))
msg = args.get("message", "")
if len(msg) > 20:
msg = msg[:17] + "..."
return f"to {target}: \"{msg}\""
@@ -187,7 +111,7 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str | N
if isinstance(value, list):
value = value[0] if value else ""
preview = _oneline(str(value))
preview = str(value).strip()
if not preview:
return None
if len(preview) > max_len:
@@ -239,7 +163,6 @@ class KawaiiSpinner:
self.frame_idx = 0
self.start_time = None
self.last_line_len = 0
self._last_flush_time = 0.0 # Rate-limit flushes for patch_stdout compat
# Capture stdout NOW, before any redirect_stdout(devnull) from
# child agents can replace sys.stdout with a black hole.
self._out = sys.stdout
@@ -254,43 +177,15 @@ class KawaiiSpinner:
pass
def _animate(self):
# When stdout is not a real terminal (e.g. Docker, systemd, pipe),
# skip the animation entirely — it creates massive log bloat.
# Just log the start once and let stop() log the completion.
if not hasattr(self._out, 'isatty') or not self._out.isatty():
self._write(f" [tool] {self.message}", flush=True)
while self.running:
time.sleep(0.5)
return
# Cache skin wings at start (avoid per-frame imports)
skin = _get_skin()
wings = skin.get_spinner_wings() if skin else []
while self.running:
if os.getenv("HERMES_SPINNER_PAUSE"):
time.sleep(0.1)
continue
frame = self.spinner_frames[self.frame_idx % len(self.spinner_frames)]
elapsed = time.time() - self.start_time
if wings:
left, right = wings[self.frame_idx % len(wings)]
line = f" {left} {frame} {self.message} {right} ({elapsed:.1f}s)"
else:
line = f" {frame} {self.message} ({elapsed:.1f}s)"
line = f" {frame} {self.message} ({elapsed:.1f}s)"
pad = max(self.last_line_len - len(line), 0)
# Rate-limit flush() calls to avoid spinner spam under
# prompt_toolkit's patch_stdout. Each flush() pushes a queue
# item that may trigger a separate run_in_terminal() call; if
# items are processed one-at-a-time the \r overwrite is lost
# and every frame appears on its own line. By flushing at
# most every 0.4s we guarantee multiple \r-frames are batched
# into a single write, so the terminal collapses them correctly.
now = time.time()
should_flush = (now - self._last_flush_time) >= 0.4
self._write(f"\r{line}{' ' * pad}", end='', flush=should_flush)
if should_flush:
self._last_flush_time = now
self._write(f"\r{line}{' ' * pad}", end='', flush=True)
self.last_line_len = len(line)
self.frame_idx += 1
time.sleep(0.12)
@@ -328,19 +223,12 @@ class KawaiiSpinner:
self.running = False
if self.thread:
self.thread.join(timeout=0.5)
is_tty = hasattr(self._out, 'isatty') and self._out.isatty()
if is_tty:
# Clear the spinner line with spaces instead of \033[K to avoid
# garbled escape codes when prompt_toolkit's patch_stdout is active.
blanks = ' ' * max(self.last_line_len + 5, 40)
self._write(f"\r{blanks}\r", end='', flush=True)
# Clear the spinner line with spaces instead of \033[K to avoid
# garbled escape codes when prompt_toolkit's patch_stdout is active.
blanks = ' ' * max(self.last_line_len + 5, 40)
self._write(f"\r{blanks}\r", end='', flush=True)
if final_message:
elapsed = f" ({time.time() - self.start_time:.1f}s)" if self.start_time else ""
if is_tty:
self._write(f" {final_message}", flush=True)
else:
self._write(f" [done] {final_message}{elapsed}", flush=True)
self._write(f" {final_message}", flush=True)
def __enter__(self):
self.start()
@@ -412,7 +300,7 @@ def _detect_tool_failure(tool_name: str, result: str | None) -> tuple[bool, str]
if exit_code is not None and exit_code != 0:
return True, f" [exit {exit_code}]"
except (json.JSONDecodeError, TypeError, AttributeError):
logger.debug("Could not parse terminal result as JSON for exit code check")
pass
return False, ""
# Memory-specific: distinguish "full" from real errors
@@ -422,7 +310,7 @@ def _detect_tool_failure(tool_name: str, result: str | None) -> tuple[bool, str]
if data.get("success") is False and "exceed the limit" in data.get("error", ""):
return True, " [full]"
except (json.JSONDecodeError, TypeError, AttributeError):
logger.debug("Could not parse memory result as JSON for capacity check")
pass
# Generic heuristic for non-terminal tools
lower = result[:500].lower()
@@ -444,7 +332,6 @@ def get_cute_tool_message(
"""
dur = f"{duration:.1f}s"
is_failure, failure_suffix = _detect_tool_failure(tool_name, result)
skin_prefix = get_skin_tool_prefix()
def _trunc(s, n=40):
s = str(s)
@@ -455,9 +342,7 @@ def get_cute_tool_message(
return ("..." + p[-(n-3):]) if len(p) > n else p
def _wrap(line: str) -> str:
"""Apply skin tool prefix and failure suffix."""
if skin_prefix != "":
line = line.replace("", skin_prefix, 1)
"""Append failure suffix when the tool failed."""
if not is_failure:
return line
return f"{line}{failure_suffix}"
@@ -555,15 +440,12 @@ def get_cute_tool_message(
return _wrap(f"┊ 🧠 reason {_trunc(args.get('user_prompt', ''), 30)} {dur}")
if tool_name == "send_message":
return _wrap(f"┊ 📨 send {args.get('target', '?')}: \"{_trunc(args.get('message', ''), 25)}\" {dur}")
if tool_name == "cronjob":
action = args.get("action", "?")
if action == "create":
skills = args.get("skills") or ([] if not args.get("skill") else [args.get("skill")])
label = args.get("name") or (skills[0] if skills else None) or args.get("prompt", "task")
return _wrap(f"┊ ⏰ cron create {_trunc(label, 24)} {dur}")
if action == "list":
return _wrap(f"┊ ⏰ cron listing {dur}")
return _wrap(f"┊ ⏰ cron {action} {args.get('job_id', '')} {dur}")
if tool_name == "schedule_cronjob":
return _wrap(f"┊ ⏰ schedule {_trunc(args.get('name', args.get('prompt', 'task')), 30)} {dur}")
if tool_name == "list_cronjobs":
return _wrap(f"┊ ⏰ jobs listing {dur}")
if tool_name == "remove_cronjob":
return _wrap(f"┊ ⏰ remove job {args.get('job_id', '?')} {dur}")
if tool_name.startswith("rl_"):
rl = {
"rl_list_environments": "list envs", "rl_select_environment": f"select {args.get('name', '')}",
@@ -585,138 +467,3 @@ def get_cute_tool_message(
preview = build_tool_preview(tool_name, args) or ""
return _wrap(f"┊ ⚡ {tool_name[:9]:9} {_trunc(preview, 35)} {dur}")
# =========================================================================
# Honcho session line (one-liner with clickable OSC 8 hyperlink)
# =========================================================================
_DIM = "\033[2m"
_SKY_BLUE = "\033[38;5;117m"
_ANSI_RESET = "\033[0m"
def honcho_session_url(workspace: str, session_name: str) -> str:
"""Build a Honcho app URL for a session."""
from urllib.parse import quote
return (
f"https://app.honcho.dev/explore"
f"?workspace={quote(workspace, safe='')}"
f"&view=sessions"
f"&session={quote(session_name, safe='')}"
)
def _osc8_link(url: str, text: str) -> str:
"""OSC 8 terminal hyperlink (clickable in iTerm2, Ghostty, WezTerm, etc.)."""
return f"\033]8;;{url}\033\\{text}\033]8;;\033\\"
def honcho_session_line(workspace: str, session_name: str) -> str:
"""One-line session indicator: `Honcho session: <clickable name>`."""
url = honcho_session_url(workspace, session_name)
linked_name = _osc8_link(url, f"{_SKY_BLUE}{session_name}{_ANSI_RESET}")
return f"{_DIM}Honcho session:{_ANSI_RESET} {linked_name}"
def write_tty(text: str) -> None:
"""Write directly to /dev/tty, bypassing stdout capture."""
try:
fd = os.open("/dev/tty", os.O_WRONLY)
os.write(fd, text.encode("utf-8"))
os.close(fd)
except OSError:
sys.stdout.write(text)
sys.stdout.flush()
# =========================================================================
# Context pressure display (CLI user-facing warnings)
# =========================================================================
# ANSI color codes for context pressure tiers
_CYAN = "\033[36m"
_YELLOW = "\033[33m"
_BOLD = "\033[1m"
_DIM_ANSI = "\033[2m"
# Bar characters
_BAR_FILLED = ""
_BAR_EMPTY = ""
_BAR_WIDTH = 20
def format_context_pressure(
compaction_progress: float,
threshold_tokens: int,
threshold_percent: float,
compression_enabled: bool = True,
) -> str:
"""Build a formatted context pressure line for CLI display.
The bar and percentage show progress toward the compaction threshold,
NOT the raw context window. 100% = compaction fires.
Uses ANSI colors:
- cyan at ~60% to compaction = informational
- bold yellow at ~85% to compaction = warning
Args:
compaction_progress: How close to compaction (0.01.0, 1.0 = fires).
threshold_tokens: Compaction threshold in tokens.
threshold_percent: Compaction threshold as a fraction of context window.
compression_enabled: Whether auto-compression is active.
"""
pct_int = int(compaction_progress * 100)
filled = min(int(compaction_progress * _BAR_WIDTH), _BAR_WIDTH)
bar = _BAR_FILLED * filled + _BAR_EMPTY * (_BAR_WIDTH - filled)
threshold_k = f"{threshold_tokens // 1000}k" if threshold_tokens >= 1000 else str(threshold_tokens)
threshold_pct_int = int(threshold_percent * 100)
# Tier styling
if compaction_progress >= 0.85:
color = f"{_BOLD}{_YELLOW}"
icon = ""
if compression_enabled:
hint = "compaction imminent"
else:
hint = "no auto-compaction"
else:
color = _CYAN
icon = ""
hint = "approaching compaction"
return (
f" {color}{icon} context {bar} {pct_int}% to compaction{_ANSI_RESET}"
f" {_DIM_ANSI}{threshold_k} threshold ({threshold_pct_int}%) · {hint}{_ANSI_RESET}"
)
def format_context_pressure_gateway(
compaction_progress: float,
threshold_percent: float,
compression_enabled: bool = True,
) -> str:
"""Build a plain-text context pressure notification for messaging platforms.
No ANSI — just Unicode and plain text suitable for Telegram/Discord/etc.
The percentage shows progress toward the compaction threshold.
"""
pct_int = int(compaction_progress * 100)
filled = min(int(compaction_progress * _BAR_WIDTH), _BAR_WIDTH)
bar = _BAR_FILLED * filled + _BAR_EMPTY * (_BAR_WIDTH - filled)
threshold_pct_int = int(threshold_percent * 100)
if compaction_progress >= 0.85:
icon = "⚠️"
if compression_enabled:
hint = f"Context compaction is imminent (threshold: {threshold_pct_int}% of window)."
else:
hint = "Auto-compaction is disabled — context may be truncated."
else:
icon = ""
hint = f"Compaction threshold is at {threshold_pct_int}% of context window."
return f"{icon} Context: {bar} {pct_int}% to compaction\n{hint}"

View File

@@ -20,23 +20,65 @@ import json
import time
from collections import Counter, defaultdict
from datetime import datetime
from typing import Any, Dict, List
from typing import Any, Dict, List, Optional
from agent.usage_pricing import (
CanonicalUsage,
DEFAULT_PRICING,
estimate_usage_cost,
format_duration_compact,
get_pricing,
has_known_pricing,
)
# =========================================================================
# Model pricing (USD per million tokens) — approximate as of early 2026
# =========================================================================
MODEL_PRICING = {
# OpenAI
"gpt-4o": {"input": 2.50, "output": 10.00},
"gpt-4o-mini": {"input": 0.15, "output": 0.60},
"gpt-4.1": {"input": 2.00, "output": 8.00},
"gpt-4.1-mini": {"input": 0.40, "output": 1.60},
"gpt-4.1-nano": {"input": 0.10, "output": 0.40},
"gpt-4.5-preview": {"input": 75.00, "output": 150.00},
"gpt-5": {"input": 10.00, "output": 30.00},
"gpt-5.4": {"input": 10.00, "output": 30.00},
"o3": {"input": 10.00, "output": 40.00},
"o3-mini": {"input": 1.10, "output": 4.40},
"o4-mini": {"input": 1.10, "output": 4.40},
# Anthropic
"claude-opus-4-20250514": {"input": 15.00, "output": 75.00},
"claude-sonnet-4-20250514": {"input": 3.00, "output": 15.00},
"claude-3-5-sonnet-20241022": {"input": 3.00, "output": 15.00},
"claude-3-5-haiku-20241022": {"input": 0.80, "output": 4.00},
"claude-3-opus-20240229": {"input": 15.00, "output": 75.00},
"claude-3-haiku-20240307": {"input": 0.25, "output": 1.25},
# DeepSeek
"deepseek-chat": {"input": 0.14, "output": 0.28},
"deepseek-reasoner": {"input": 0.55, "output": 2.19},
# Google
"gemini-2.5-pro": {"input": 1.25, "output": 10.00},
"gemini-2.5-flash": {"input": 0.15, "output": 0.60},
"gemini-2.0-flash": {"input": 0.10, "output": 0.40},
# Meta (via providers)
"llama-4-maverick": {"input": 0.50, "output": 0.70},
"llama-4-scout": {"input": 0.20, "output": 0.30},
# Z.AI / GLM (direct provider — pricing not published externally, treat as local)
"glm-5": {"input": 0.0, "output": 0.0},
"glm-4.7": {"input": 0.0, "output": 0.0},
"glm-4.5": {"input": 0.0, "output": 0.0},
"glm-4.5-flash": {"input": 0.0, "output": 0.0},
# Kimi / Moonshot (direct provider — pricing not published externally, treat as local)
"kimi-k2.5": {"input": 0.0, "output": 0.0},
"kimi-k2-thinking": {"input": 0.0, "output": 0.0},
"kimi-k2-turbo-preview": {"input": 0.0, "output": 0.0},
"kimi-k2-0905-preview": {"input": 0.0, "output": 0.0},
# MiniMax (direct provider — pricing not published externally, treat as local)
"MiniMax-M2.5": {"input": 0.0, "output": 0.0},
"MiniMax-M2.5-highspeed": {"input": 0.0, "output": 0.0},
"MiniMax-M2.1": {"input": 0.0, "output": 0.0},
}
_DEFAULT_PRICING = DEFAULT_PRICING
# Fallback: unknown/custom models get zero cost (we can't assume pricing
# for self-hosted models, custom OAI endpoints, local inference, etc.)
_DEFAULT_PRICING = {"input": 0.0, "output": 0.0}
def _has_known_pricing(model_name: str, provider: str = None, base_url: str = None) -> bool:
def _has_known_pricing(model_name: str) -> bool:
"""Check if a model has known pricing (vs unknown/custom endpoint)."""
return has_known_pricing(model_name, provider=provider, base_url=base_url)
return _get_pricing(model_name) is not _DEFAULT_PRICING
def _get_pricing(model_name: str) -> Dict[str, float]:
@@ -45,51 +87,67 @@ def _get_pricing(model_name: str) -> Dict[str, float]:
Returns _DEFAULT_PRICING (zero cost) for unknown/custom models —
we can't assume costs for self-hosted endpoints, local inference, etc.
"""
return get_pricing(model_name)
if not model_name:
return _DEFAULT_PRICING
# Strip provider prefix (e.g., "anthropic/claude-..." -> "claude-...")
bare = model_name.split("/")[-1].lower()
# Exact match first
if bare in MODEL_PRICING:
return MODEL_PRICING[bare]
# Fuzzy prefix match — prefer the LONGEST matching key to avoid
# e.g. "gpt-4o" matching before "gpt-4o-mini" for "gpt-4o-mini-2024-07-18"
best_match = None
best_len = 0
for key, price in MODEL_PRICING.items():
if bare.startswith(key) and len(key) > best_len:
best_match = price
best_len = len(key)
if best_match:
return best_match
# Keyword heuristics (checked in most-specific-first order)
if "opus" in bare:
return {"input": 15.00, "output": 75.00}
if "sonnet" in bare:
return {"input": 3.00, "output": 15.00}
if "haiku" in bare:
return {"input": 0.80, "output": 4.00}
if "gpt-4o-mini" in bare:
return {"input": 0.15, "output": 0.60}
if "gpt-4o" in bare:
return {"input": 2.50, "output": 10.00}
if "gpt-5" in bare:
return {"input": 10.00, "output": 30.00}
if "deepseek" in bare:
return {"input": 0.14, "output": 0.28}
if "gemini" in bare:
return {"input": 0.15, "output": 0.60}
return _DEFAULT_PRICING
def _estimate_cost(
session_or_model: Dict[str, Any] | str,
input_tokens: int = 0,
output_tokens: int = 0,
*,
cache_read_tokens: int = 0,
cache_write_tokens: int = 0,
provider: str = None,
base_url: str = None,
) -> tuple[float, str]:
"""Estimate the USD cost for a session row or a model/token tuple."""
if isinstance(session_or_model, dict):
session = session_or_model
model = session.get("model") or ""
usage = CanonicalUsage(
input_tokens=session.get("input_tokens") or 0,
output_tokens=session.get("output_tokens") or 0,
cache_read_tokens=session.get("cache_read_tokens") or 0,
cache_write_tokens=session.get("cache_write_tokens") or 0,
)
provider = session.get("billing_provider")
base_url = session.get("billing_base_url")
else:
model = session_or_model or ""
usage = CanonicalUsage(
input_tokens=input_tokens,
output_tokens=output_tokens,
cache_read_tokens=cache_read_tokens,
cache_write_tokens=cache_write_tokens,
)
result = estimate_usage_cost(
model,
usage,
provider=provider,
base_url=base_url,
)
return float(result.amount_usd or 0.0), result.status
def _estimate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
"""Estimate the USD cost for a given model and token counts."""
pricing = _get_pricing(model)
return (input_tokens * pricing["input"] + output_tokens * pricing["output"]) / 1_000_000
def _format_duration(seconds: float) -> str:
"""Format seconds into a human-readable duration string."""
return format_duration_compact(seconds)
if seconds < 60:
return f"{seconds:.0f}s"
minutes = seconds / 60
if minutes < 60:
return f"{minutes:.0f}m"
hours = minutes / 60
if hours < 24:
remaining_min = int(minutes % 60)
return f"{int(hours)}h {remaining_min}m" if remaining_min else f"{int(hours)}h"
days = hours / 24
return f"{days:.1f}d"
def _bar_chart(values: List[int], max_width: int = 20) -> List[str]:
@@ -176,30 +234,24 @@ class InsightsEngine:
# Columns we actually need (skip system_prompt, model_config blobs)
_SESSION_COLS = ("id, source, model, started_at, ended_at, "
"message_count, tool_call_count, input_tokens, output_tokens, "
"cache_read_tokens, cache_write_tokens, billing_provider, "
"billing_base_url, billing_mode, estimated_cost_usd, "
"actual_cost_usd, cost_status, cost_source")
# Pre-computed query strings — f-string evaluated once at class definition,
# not at runtime, so no user-controlled value can alter the query structure.
_GET_SESSIONS_WITH_SOURCE = (
f"SELECT {_SESSION_COLS} FROM sessions"
" WHERE started_at >= ? AND source = ?"
" ORDER BY started_at DESC"
)
_GET_SESSIONS_ALL = (
f"SELECT {_SESSION_COLS} FROM sessions"
" WHERE started_at >= ?"
" ORDER BY started_at DESC"
)
"message_count, tool_call_count, input_tokens, output_tokens")
def _get_sessions(self, cutoff: float, source: str = None) -> List[Dict]:
"""Fetch sessions within the time window."""
if source:
cursor = self._conn.execute(self._GET_SESSIONS_WITH_SOURCE, (cutoff, source))
cursor = self._conn.execute(
f"""SELECT {self._SESSION_COLS} FROM sessions
WHERE started_at >= ? AND source = ?
ORDER BY started_at DESC""",
(cutoff, source),
)
else:
cursor = self._conn.execute(self._GET_SESSIONS_ALL, (cutoff,))
cursor = self._conn.execute(
f"""SELECT {self._SESSION_COLS} FROM sessions
WHERE started_at >= ?
ORDER BY started_at DESC""",
(cutoff,),
)
return [dict(row) for row in cursor.fetchall()]
def _get_tool_usage(self, cutoff: float, source: str = None) -> List[Dict]:
@@ -334,30 +386,21 @@ class InsightsEngine:
"""Compute high-level overview statistics."""
total_input = sum(s.get("input_tokens") or 0 for s in sessions)
total_output = sum(s.get("output_tokens") or 0 for s in sessions)
total_cache_read = sum(s.get("cache_read_tokens") or 0 for s in sessions)
total_cache_write = sum(s.get("cache_write_tokens") or 0 for s in sessions)
total_tokens = total_input + total_output + total_cache_read + total_cache_write
total_tokens = total_input + total_output
total_tool_calls = sum(s.get("tool_call_count") or 0 for s in sessions)
total_messages = sum(s.get("message_count") or 0 for s in sessions)
# Cost estimation (weighted by model)
total_cost = 0.0
actual_cost = 0.0
models_with_pricing = set()
models_without_pricing = set()
unknown_cost_sessions = 0
included_cost_sessions = 0
for s in sessions:
model = s.get("model") or ""
estimated, status = _estimate_cost(s)
total_cost += estimated
actual_cost += s.get("actual_cost_usd") or 0.0
inp = s.get("input_tokens") or 0
out = s.get("output_tokens") or 0
total_cost += _estimate_cost(model, inp, out)
display = model.split("/")[-1] if "/" in model else (model or "unknown")
if status == "included":
included_cost_sessions += 1
elif status == "unknown":
unknown_cost_sessions += 1
if _has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url")):
if _has_known_pricing(model):
models_with_pricing.add(display)
else:
models_without_pricing.add(display)
@@ -384,11 +427,8 @@ class InsightsEngine:
"total_tool_calls": total_tool_calls,
"total_input_tokens": total_input,
"total_output_tokens": total_output,
"total_cache_read_tokens": total_cache_read,
"total_cache_write_tokens": total_cache_write,
"total_tokens": total_tokens,
"estimated_cost": total_cost,
"actual_cost": actual_cost,
"total_hours": total_hours,
"avg_session_duration": avg_duration,
"avg_messages_per_session": total_messages / len(sessions) if sessions else 0,
@@ -400,15 +440,12 @@ class InsightsEngine:
"date_range_end": date_range_end,
"models_with_pricing": sorted(models_with_pricing),
"models_without_pricing": sorted(models_without_pricing),
"unknown_cost_sessions": unknown_cost_sessions,
"included_cost_sessions": included_cost_sessions,
}
def _compute_model_breakdown(self, sessions: List[Dict]) -> List[Dict]:
"""Break down usage by model."""
model_data = defaultdict(lambda: {
"sessions": 0, "input_tokens": 0, "output_tokens": 0,
"cache_read_tokens": 0, "cache_write_tokens": 0,
"total_tokens": 0, "tool_calls": 0, "cost": 0.0,
})
@@ -420,18 +457,12 @@ class InsightsEngine:
d["sessions"] += 1
inp = s.get("input_tokens") or 0
out = s.get("output_tokens") or 0
cache_read = s.get("cache_read_tokens") or 0
cache_write = s.get("cache_write_tokens") or 0
d["input_tokens"] += inp
d["output_tokens"] += out
d["cache_read_tokens"] += cache_read
d["cache_write_tokens"] += cache_write
d["total_tokens"] += inp + out + cache_read + cache_write
d["total_tokens"] += inp + out
d["tool_calls"] += s.get("tool_call_count") or 0
estimate, status = _estimate_cost(s)
d["cost"] += estimate
d["has_pricing"] = _has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url"))
d["cost_status"] = status
d["cost"] += _estimate_cost(model, inp, out)
d["has_pricing"] = _has_known_pricing(model)
result = [
{"model": model, **data}
@@ -445,8 +476,7 @@ class InsightsEngine:
"""Break down usage by platform/source."""
platform_data = defaultdict(lambda: {
"sessions": 0, "messages": 0, "input_tokens": 0,
"output_tokens": 0, "cache_read_tokens": 0,
"cache_write_tokens": 0, "total_tokens": 0, "tool_calls": 0,
"output_tokens": 0, "total_tokens": 0, "tool_calls": 0,
})
for s in sessions:
@@ -456,13 +486,9 @@ class InsightsEngine:
d["messages"] += s.get("message_count") or 0
inp = s.get("input_tokens") or 0
out = s.get("output_tokens") or 0
cache_read = s.get("cache_read_tokens") or 0
cache_write = s.get("cache_write_tokens") or 0
d["input_tokens"] += inp
d["output_tokens"] += out
d["cache_read_tokens"] += cache_read
d["cache_write_tokens"] += cache_write
d["total_tokens"] += inp + out + cache_read + cache_write
d["total_tokens"] += inp + out
d["tool_calls"] += s.get("tool_call_count") or 0
result = [

View File

@@ -10,7 +10,6 @@ import re
import time
from pathlib import Path
from typing import Any, Dict, List, Optional
from urllib.parse import urlparse
import requests
import yaml
@@ -19,346 +18,50 @@ from hermes_constants import OPENROUTER_MODELS_URL
logger = logging.getLogger(__name__)
# Provider names that can appear as a "provider:" prefix before a model ID.
# Only these are stripped — Ollama-style "model:tag" colons (e.g. "qwen3.5:27b")
# are preserved so the full model name reaches cache lookups and server queries.
_PROVIDER_PREFIXES: frozenset[str] = frozenset({
"openrouter", "nous", "openai-codex", "copilot", "copilot-acp",
"zai", "kimi-coding", "minimax", "minimax-cn", "anthropic", "deepseek",
"opencode-zen", "opencode-go", "ai-gateway", "kilocode", "alibaba",
"custom", "local",
# Common aliases
"glm", "z-ai", "z.ai", "zhipu", "github", "github-copilot",
"github-models", "kimi", "moonshot", "claude", "deep-seek",
"opencode", "zen", "go", "vercel", "kilo", "dashscope", "aliyun", "qwen",
})
_OLLAMA_TAG_PATTERN = re.compile(
r"^(\d+\.?\d*b|latest|stable|q\d|fp?\d|instruct|chat|coder|vision|text)",
re.IGNORECASE,
)
def _strip_provider_prefix(model: str) -> str:
"""Strip a recognised provider prefix from a model string.
``"local:my-model"`` → ``"my-model"``
``"qwen3.5:27b"`` → ``"qwen3.5:27b"`` (unchanged — not a provider prefix)
``"qwen:0.5b"`` → ``"qwen:0.5b"`` (unchanged — Ollama model:tag)
``"deepseek:latest"``→ ``"deepseek:latest"``(unchanged — Ollama model:tag)
"""
if ":" not in model or model.startswith("http"):
return model
prefix, suffix = model.split(":", 1)
prefix_lower = prefix.strip().lower()
if prefix_lower in _PROVIDER_PREFIXES:
# Don't strip if suffix looks like an Ollama tag (e.g. "7b", "latest", "q4_0")
if _OLLAMA_TAG_PATTERN.match(suffix.strip()):
return model
return suffix
return model
_model_metadata_cache: Dict[str, Dict[str, Any]] = {}
_model_metadata_cache_time: float = 0
_MODEL_CACHE_TTL = 3600
_endpoint_model_metadata_cache: Dict[str, Dict[str, Dict[str, Any]]] = {}
_endpoint_model_metadata_cache_time: Dict[str, float] = {}
_ENDPOINT_MODEL_CACHE_TTL = 300
# Descending tiers for context length probing when the model is unknown.
# We start at 128K (a safe default for most modern models) and step down
# on context-length errors until one works.
# We start high and step down on context-length errors until one works.
CONTEXT_PROBE_TIERS = [
2_000_000,
1_000_000,
512_000,
200_000,
128_000,
64_000,
32_000,
16_000,
8_000,
]
# Default context length when no detection method succeeds.
DEFAULT_FALLBACK_CONTEXT = CONTEXT_PROBE_TIERS[0]
# Thin fallback defaults — only broad model family patterns.
# These fire only when provider is unknown AND models.dev/OpenRouter/Anthropic
# all miss. Replaced the previous 80+ entry dict.
# For provider-specific context lengths, models.dev is the primary source.
DEFAULT_CONTEXT_LENGTHS = {
# Anthropic Claude 4.6 (1M context) — bare IDs only to avoid
# fuzzy-match collisions (e.g. "anthropic/claude-sonnet-4" is a
# substring of "anthropic/claude-sonnet-4.6").
# OpenRouter-prefixed models resolve via OpenRouter live API or models.dev.
"claude-opus-4-6": 1000000,
"claude-sonnet-4-6": 1000000,
"claude-opus-4.6": 1000000,
"claude-sonnet-4.6": 1000000,
# Catch-all for older Claude models (must sort after specific entries)
"claude": 200000,
# OpenAI
"gpt-4.1": 1047576,
"gpt-5": 128000,
"gpt-4": 128000,
# Google
"gemini": 1048576,
# DeepSeek
"deepseek": 128000,
# Meta
"llama": 131072,
# Qwen
"qwen": 131072,
# MiniMax
"minimax": 204800,
# GLM
"glm": 202752,
# Kimi
"kimi": 262144,
"anthropic/claude-opus-4": 200000,
"anthropic/claude-opus-4.5": 200000,
"anthropic/claude-opus-4.6": 200000,
"anthropic/claude-sonnet-4": 200000,
"anthropic/claude-sonnet-4-20250514": 200000,
"anthropic/claude-haiku-4.5": 200000,
"openai/gpt-4o": 128000,
"openai/gpt-4-turbo": 128000,
"openai/gpt-4o-mini": 128000,
"google/gemini-2.0-flash": 1048576,
"google/gemini-2.5-pro": 1048576,
"meta-llama/llama-3.3-70b-instruct": 131072,
"deepseek/deepseek-chat-v3": 65536,
"qwen/qwen-2.5-72b-instruct": 32768,
"glm-4.7": 202752,
"glm-5": 202752,
"glm-4.5": 131072,
"glm-4.5-flash": 131072,
"kimi-k2.5": 262144,
"kimi-k2-thinking": 262144,
"kimi-k2-turbo-preview": 262144,
"kimi-k2-0905-preview": 131072,
"MiniMax-M2.5": 204800,
"MiniMax-M2.5-highspeed": 204800,
"MiniMax-M2.1": 204800,
}
_CONTEXT_LENGTH_KEYS = (
"context_length",
"context_window",
"max_context_length",
"max_position_embeddings",
"max_model_len",
"max_input_tokens",
"max_sequence_length",
"max_seq_len",
"n_ctx_train",
"n_ctx",
)
_MAX_COMPLETION_KEYS = (
"max_completion_tokens",
"max_output_tokens",
"max_tokens",
)
# Local server hostnames / address patterns
_LOCAL_HOSTS = ("localhost", "127.0.0.1", "::1", "0.0.0.0")
def _normalize_base_url(base_url: str) -> str:
return (base_url or "").strip().rstrip("/")
def _is_openrouter_base_url(base_url: str) -> bool:
return "openrouter.ai" in _normalize_base_url(base_url).lower()
def _is_custom_endpoint(base_url: str) -> bool:
normalized = _normalize_base_url(base_url)
return bool(normalized) and not _is_openrouter_base_url(normalized)
_URL_TO_PROVIDER: Dict[str, str] = {
"api.openai.com": "openai",
"chatgpt.com": "openai",
"api.anthropic.com": "anthropic",
"api.z.ai": "zai",
"api.moonshot.ai": "kimi-coding",
"api.kimi.com": "kimi-coding",
"api.minimax": "minimax",
"dashscope.aliyuncs.com": "alibaba",
"dashscope-intl.aliyuncs.com": "alibaba",
"openrouter.ai": "openrouter",
"inference-api.nousresearch.com": "nous",
"api.deepseek.com": "deepseek",
"api.githubcopilot.com": "copilot",
"models.github.ai": "copilot",
}
def _infer_provider_from_url(base_url: str) -> Optional[str]:
"""Infer the models.dev provider name from a base URL.
This allows context length resolution via models.dev for custom endpoints
like DashScope (Alibaba), Z.AI, Kimi, etc. without requiring the user to
explicitly set the provider name in config.
"""
normalized = _normalize_base_url(base_url)
if not normalized:
return None
parsed = urlparse(normalized if "://" in normalized else f"https://{normalized}")
host = parsed.netloc.lower() or parsed.path.lower()
for url_part, provider in _URL_TO_PROVIDER.items():
if url_part in host:
return provider
return None
def _is_known_provider_base_url(base_url: str) -> bool:
return _infer_provider_from_url(base_url) is not None
def is_local_endpoint(base_url: str) -> bool:
"""Return True if base_url points to a local machine (localhost / RFC-1918 / WSL)."""
normalized = _normalize_base_url(base_url)
if not normalized:
return False
url = normalized if "://" in normalized else f"http://{normalized}"
try:
parsed = urlparse(url)
host = parsed.hostname or ""
except Exception:
return False
if host in _LOCAL_HOSTS:
return True
# RFC-1918 private ranges and link-local
import ipaddress
try:
addr = ipaddress.ip_address(host)
return addr.is_private or addr.is_loopback or addr.is_link_local
except ValueError:
pass
# Bare IP that looks like a private range (e.g. 172.26.x.x for WSL)
parts = host.split(".")
if len(parts) == 4:
try:
first, second = int(parts[0]), int(parts[1])
if first == 10:
return True
if first == 172 and 16 <= second <= 31:
return True
if first == 192 and second == 168:
return True
except ValueError:
pass
return False
def detect_local_server_type(base_url: str) -> Optional[str]:
"""Detect which local server is running at base_url by probing known endpoints.
Returns one of: "ollama", "lm-studio", "vllm", "llamacpp", or None.
"""
import httpx
normalized = _normalize_base_url(base_url)
server_url = normalized
if server_url.endswith("/v1"):
server_url = server_url[:-3]
try:
with httpx.Client(timeout=2.0) as client:
# LM Studio exposes /api/v1/models — check first (most specific)
try:
r = client.get(f"{server_url}/api/v1/models")
if r.status_code == 200:
return "lm-studio"
except Exception:
pass
# Ollama exposes /api/tags and responds with {"models": [...]}
# LM Studio returns {"error": "Unexpected endpoint"} with status 200
# on this path, so we must verify the response contains "models".
try:
r = client.get(f"{server_url}/api/tags")
if r.status_code == 200:
try:
data = r.json()
if "models" in data:
return "ollama"
except Exception:
pass
except Exception:
pass
# llama.cpp exposes /v1/props (older builds used /props without the /v1 prefix)
try:
r = client.get(f"{server_url}/v1/props")
if r.status_code != 200:
r = client.get(f"{server_url}/props") # fallback for older builds
if r.status_code == 200 and "default_generation_settings" in r.text:
return "llamacpp"
except Exception:
pass
# vLLM: /version
try:
r = client.get(f"{server_url}/version")
if r.status_code == 200:
data = r.json()
if "version" in data:
return "vllm"
except Exception:
pass
except Exception:
pass
return None
def _iter_nested_dicts(value: Any):
if isinstance(value, dict):
yield value
for nested in value.values():
yield from _iter_nested_dicts(nested)
elif isinstance(value, list):
for item in value:
yield from _iter_nested_dicts(item)
def _coerce_reasonable_int(value: Any, minimum: int = 1024, maximum: int = 10_000_000) -> Optional[int]:
try:
if isinstance(value, bool):
return None
if isinstance(value, str):
value = value.strip().replace(",", "")
result = int(value)
except (TypeError, ValueError):
return None
if minimum <= result <= maximum:
return result
return None
def _extract_first_int(payload: Dict[str, Any], keys: tuple[str, ...]) -> Optional[int]:
keyset = {key.lower() for key in keys}
for mapping in _iter_nested_dicts(payload):
for key, value in mapping.items():
if str(key).lower() not in keyset:
continue
coerced = _coerce_reasonable_int(value)
if coerced is not None:
return coerced
return None
def _extract_context_length(payload: Dict[str, Any]) -> Optional[int]:
return _extract_first_int(payload, _CONTEXT_LENGTH_KEYS)
def _extract_max_completion_tokens(payload: Dict[str, Any]) -> Optional[int]:
return _extract_first_int(payload, _MAX_COMPLETION_KEYS)
def _extract_pricing(payload: Dict[str, Any]) -> Dict[str, Any]:
alias_map = {
"prompt": ("prompt", "input", "input_cost_per_token", "prompt_token_cost"),
"completion": ("completion", "output", "output_cost_per_token", "completion_token_cost"),
"request": ("request", "request_cost"),
"cache_read": ("cache_read", "cached_prompt", "input_cache_read", "cache_read_cost_per_token"),
"cache_write": ("cache_write", "cache_creation", "input_cache_write", "cache_write_cost_per_token"),
}
for mapping in _iter_nested_dicts(payload):
normalized = {str(key).lower(): value for key, value in mapping.items()}
if not any(any(alias in normalized for alias in aliases) for aliases in alias_map.values()):
continue
pricing: Dict[str, Any] = {}
for target, aliases in alias_map.items():
for alias in aliases:
if alias in normalized and normalized[alias] not in (None, ""):
pricing[target] = normalized[alias]
break
if pricing:
return pricing
return {}
def _add_model_aliases(cache: Dict[str, Dict[str, Any]], model_id: str, entry: Dict[str, Any]) -> None:
cache[model_id] = entry
if "/" in model_id:
bare_model = model_id.split("/", 1)[1]
cache.setdefault(bare_model, entry)
def fetch_model_metadata(force_refresh: bool = False) -> Dict[str, Dict[str, Any]]:
"""Fetch model metadata from OpenRouter (cached for 1 hour)."""
@@ -375,16 +78,15 @@ def fetch_model_metadata(force_refresh: bool = False) -> Dict[str, Dict[str, Any
cache = {}
for model in data.get("data", []):
model_id = model.get("id", "")
entry = {
cache[model_id] = {
"context_length": model.get("context_length", 128000),
"max_completion_tokens": model.get("top_provider", {}).get("max_completion_tokens", 4096),
"name": model.get("name", model_id),
"pricing": model.get("pricing", {}),
}
_add_model_aliases(cache, model_id, entry)
canonical = model.get("canonical_slug", "")
if canonical and canonical != model_id:
_add_model_aliases(cache, canonical, entry)
cache[canonical] = cache[model_id]
_model_metadata_cache = cache
_model_metadata_cache_time = time.time()
@@ -396,97 +98,6 @@ def fetch_model_metadata(force_refresh: bool = False) -> Dict[str, Dict[str, Any
return _model_metadata_cache or {}
def fetch_endpoint_model_metadata(
base_url: str,
api_key: str = "",
force_refresh: bool = False,
) -> Dict[str, Dict[str, Any]]:
"""Fetch model metadata from an OpenAI-compatible ``/models`` endpoint.
This is used for explicit custom endpoints where hardcoded global model-name
defaults are unreliable. Results are cached in memory per base URL.
"""
normalized = _normalize_base_url(base_url)
if not normalized or _is_openrouter_base_url(normalized):
return {}
if not force_refresh:
cached = _endpoint_model_metadata_cache.get(normalized)
cached_at = _endpoint_model_metadata_cache_time.get(normalized, 0)
if cached is not None and (time.time() - cached_at) < _ENDPOINT_MODEL_CACHE_TTL:
return cached
candidates = [normalized]
if normalized.endswith("/v1"):
alternate = normalized[:-3].rstrip("/")
else:
alternate = normalized + "/v1"
if alternate and alternate not in candidates:
candidates.append(alternate)
headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
last_error: Optional[Exception] = None
for candidate in candidates:
url = candidate.rstrip("/") + "/models"
try:
response = requests.get(url, headers=headers, timeout=10)
response.raise_for_status()
payload = response.json()
cache: Dict[str, Dict[str, Any]] = {}
for model in payload.get("data", []):
if not isinstance(model, dict):
continue
model_id = model.get("id")
if not model_id:
continue
entry: Dict[str, Any] = {"name": model.get("name", model_id)}
context_length = _extract_context_length(model)
if context_length is not None:
entry["context_length"] = context_length
max_completion_tokens = _extract_max_completion_tokens(model)
if max_completion_tokens is not None:
entry["max_completion_tokens"] = max_completion_tokens
pricing = _extract_pricing(model)
if pricing:
entry["pricing"] = pricing
_add_model_aliases(cache, model_id, entry)
# If this is a llama.cpp server, query /props for actual allocated context
is_llamacpp = any(
m.get("owned_by") == "llamacpp"
for m in payload.get("data", []) if isinstance(m, dict)
)
if is_llamacpp:
try:
# Try /v1/props first (current llama.cpp); fall back to /props for older builds
base = candidate.rstrip("/").replace("/v1", "")
props_resp = requests.get(base + "/v1/props", headers=headers, timeout=5)
if not props_resp.ok:
props_resp = requests.get(base + "/props", headers=headers, timeout=5)
if props_resp.ok:
props = props_resp.json()
gen_settings = props.get("default_generation_settings", {})
n_ctx = gen_settings.get("n_ctx")
model_alias = props.get("model_alias", "")
if n_ctx and model_alias and model_alias in cache:
cache[model_alias]["context_length"] = n_ctx
except Exception:
pass
_endpoint_model_metadata_cache[normalized] = cache
_endpoint_model_metadata_cache_time[normalized] = time.time()
return cache
except Exception as exc:
last_error = exc
if last_error:
logger.debug("Failed to fetch model metadata from %s/models: %s", normalized, last_error)
_endpoint_model_metadata_cache[normalized] = {}
_endpoint_model_metadata_cache_time[normalized] = time.time()
return {}
def _get_context_cache_path() -> Path:
"""Return path to the persistent context length cache file."""
hermes_home = Path(os.environ.get("HERMES_HOME", Path.home() / ".hermes"))
@@ -494,7 +105,7 @@ def _get_context_cache_path() -> Path:
def _load_context_cache() -> Dict[str, int]:
"""Load the model+provider -> context_length cache from disk."""
"""Load the model+provider context_length cache from disk."""
path = _get_context_cache_path()
if not path.exists():
return {}
@@ -523,7 +134,7 @@ def save_context_length(model: str, base_url: str, length: int) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w") as f:
yaml.dump({"context_lengths": cache}, f, default_flow_style=False)
logger.info("Cached context length %s -> %s tokens", key, f"{length:,}")
logger.info("Cached context length %s %s tokens", key, f"{length:,}")
except Exception as e:
logger.debug("Failed to save context length cache: %s", e)
@@ -571,317 +182,33 @@ def parse_context_limit_from_error(error_msg: str) -> Optional[int]:
return None
def _model_id_matches(candidate_id: str, lookup_model: str) -> bool:
"""Return True if *candidate_id* (from server) matches *lookup_model* (configured).
Supports two forms:
- Exact match: "nvidia-nemotron-super-49b-v1" == "nvidia-nemotron-super-49b-v1"
- Slug match: "nvidia/nvidia-nemotron-super-49b-v1" matches "nvidia-nemotron-super-49b-v1"
(the part after the last "/" equals lookup_model)
This covers LM Studio's native API which stores models as "publisher/slug"
while users typically configure only the slug after the "local:" prefix.
"""
if candidate_id == lookup_model:
return True
# Slug match: basename of candidate equals the lookup name
if "/" in candidate_id and candidate_id.rsplit("/", 1)[1] == lookup_model:
return True
return False
def _query_local_context_length(model: str, base_url: str) -> Optional[int]:
"""Query a local server for the model's context length."""
import httpx
# Strip recognised provider prefix (e.g., "local:model-name" → "model-name").
# Ollama "model:tag" colons (e.g. "qwen3.5:27b") are intentionally preserved.
model = _strip_provider_prefix(model)
# Strip /v1 suffix to get the server root
server_url = base_url.rstrip("/")
if server_url.endswith("/v1"):
server_url = server_url[:-3]
try:
server_type = detect_local_server_type(base_url)
except Exception:
server_type = None
try:
with httpx.Client(timeout=3.0) as client:
# Ollama: /api/show returns model details with context info
if server_type == "ollama":
resp = client.post(f"{server_url}/api/show", json={"name": model})
if resp.status_code == 200:
data = resp.json()
# Check model_info for context length
model_info = data.get("model_info", {})
for key, value in model_info.items():
if "context_length" in key and isinstance(value, (int, float)):
return int(value)
# Check parameters string for num_ctx
params = data.get("parameters", "")
if "num_ctx" in params:
for line in params.split("\n"):
if "num_ctx" in line:
parts = line.strip().split()
if len(parts) >= 2:
try:
return int(parts[-1])
except ValueError:
pass
# LM Studio native API: /api/v1/models returns max_context_length.
# This is more reliable than the OpenAI-compat /v1/models which
# doesn't include context window information for LM Studio servers.
# Use _model_id_matches for fuzzy matching: LM Studio stores models as
# "publisher/slug" but users configure only "slug" after "local:" prefix.
if server_type == "lm-studio":
resp = client.get(f"{server_url}/api/v1/models")
if resp.status_code == 200:
data = resp.json()
for m in data.get("models", []):
if _model_id_matches(m.get("key", ""), model) or _model_id_matches(m.get("id", ""), model):
# Prefer loaded instance context (actual runtime value)
for inst in m.get("loaded_instances", []):
cfg = inst.get("config", {})
ctx = cfg.get("context_length")
if ctx and isinstance(ctx, (int, float)):
return int(ctx)
# Fall back to max_context_length (theoretical model max)
ctx = m.get("max_context_length") or m.get("context_length")
if ctx and isinstance(ctx, (int, float)):
return int(ctx)
# LM Studio / vLLM / llama.cpp: try /v1/models/{model}
resp = client.get(f"{server_url}/v1/models/{model}")
if resp.status_code == 200:
data = resp.json()
# vLLM returns max_model_len
ctx = data.get("max_model_len") or data.get("context_length") or data.get("max_tokens")
if ctx and isinstance(ctx, (int, float)):
return int(ctx)
# Try /v1/models and find the model in the list.
# Use _model_id_matches to handle "publisher/slug" vs bare "slug".
resp = client.get(f"{server_url}/v1/models")
if resp.status_code == 200:
data = resp.json()
models_list = data.get("data", [])
for m in models_list:
if _model_id_matches(m.get("id", ""), model):
ctx = m.get("max_model_len") or m.get("context_length") or m.get("max_tokens")
if ctx and isinstance(ctx, (int, float)):
return int(ctx)
except Exception:
pass
return None
def _normalize_model_version(model: str) -> str:
"""Normalize version separators for matching.
Nous uses dashes: claude-opus-4-6, claude-sonnet-4-5
OpenRouter uses dots: claude-opus-4.6, claude-sonnet-4.5
Normalize both to dashes for comparison.
"""
return model.replace(".", "-")
def _query_anthropic_context_length(model: str, base_url: str, api_key: str) -> Optional[int]:
"""Query Anthropic's /v1/models endpoint for context length.
Only works with regular ANTHROPIC_API_KEY (sk-ant-api*).
OAuth tokens (sk-ant-oat*) from Claude Code return 401.
"""
if not api_key or api_key.startswith("sk-ant-oat"):
return None # OAuth tokens can't access /v1/models
try:
base = base_url.rstrip("/")
if base.endswith("/v1"):
base = base[:-3]
url = f"{base}/v1/models?limit=1000"
headers = {
"x-api-key": api_key,
"anthropic-version": "2023-06-01",
}
resp = requests.get(url, headers=headers, timeout=10)
if resp.status_code != 200:
return None
data = resp.json()
for m in data.get("data", []):
if m.get("id") == model:
ctx = m.get("max_input_tokens")
if isinstance(ctx, int) and ctx > 0:
return ctx
except Exception as e:
logger.debug("Anthropic /v1/models query failed: %s", e)
return None
def _resolve_nous_context_length(model: str) -> Optional[int]:
"""Resolve Nous Portal model context length via OpenRouter metadata.
Nous model IDs are bare (e.g. 'claude-opus-4-6') while OpenRouter uses
prefixed IDs (e.g. 'anthropic/claude-opus-4.6'). Try suffix matching
with version normalization (dot↔dash).
"""
metadata = fetch_model_metadata() # OpenRouter cache
# Exact match first
if model in metadata:
return metadata[model].get("context_length")
normalized = _normalize_model_version(model).lower()
for or_id, entry in metadata.items():
bare = or_id.split("/", 1)[1] if "/" in or_id else or_id
if bare.lower() == model.lower() or _normalize_model_version(bare).lower() == normalized:
return entry.get("context_length")
# Partial prefix match for cases like gemini-3-flash → gemini-3-flash-preview
# Require match to be at a word boundary (followed by -, :, or end of string)
model_lower = model.lower()
for or_id, entry in metadata.items():
bare = or_id.split("/", 1)[1] if "/" in or_id else or_id
for candidate, query in [(bare.lower(), model_lower), (_normalize_model_version(bare).lower(), normalized)]:
if candidate.startswith(query) and (
len(candidate) == len(query) or candidate[len(query)] in "-:."
):
return entry.get("context_length")
return None
def get_model_context_length(
model: str,
base_url: str = "",
api_key: str = "",
config_context_length: int | None = None,
provider: str = "",
) -> int:
def get_model_context_length(model: str, base_url: str = "") -> int:
"""Get the context length for a model.
Resolution order:
0. Explicit config override (model.context_length or custom_providers per-model)
1. Persistent cache (previously discovered via probing)
2. Active endpoint metadata (/models for explicit custom endpoints)
3. Local server query (for local endpoints)
4. Anthropic /v1/models API (API-key users only, not OAuth)
5. OpenRouter live API metadata
6. Nous suffix-match via OpenRouter cache
7. models.dev registry lookup (provider-aware)
8. Thin hardcoded defaults (broad family patterns)
9. Default fallback (128K)
2. OpenRouter API metadata
3. Hardcoded DEFAULT_CONTEXT_LENGTHS (fuzzy match)
4. First probe tier (2M) — will be narrowed on first context error
"""
# 0. Explicit config override — user knows best
if config_context_length is not None and isinstance(config_context_length, int) and config_context_length > 0:
return config_context_length
# Normalise provider-prefixed model names (e.g. "local:model-name" →
# "model-name") so cache lookups and server queries use the bare ID that
# local servers actually know about. Ollama "model:tag" colons are preserved.
model = _strip_provider_prefix(model)
# 1. Check persistent cache (model+provider)
if base_url:
cached = get_cached_context_length(model, base_url)
if cached is not None:
return cached
# 2. Active endpoint metadata for truly custom/unknown endpoints.
# Known providers (Copilot, OpenAI, Anthropic, etc.) skip this — their
# /models endpoint may report a provider-imposed limit (e.g. Copilot
# returns 128k) instead of the model's full context (400k). models.dev
# has the correct per-provider values and is checked at step 5+.
if _is_custom_endpoint(base_url) and not _is_known_provider_base_url(base_url):
endpoint_metadata = fetch_endpoint_model_metadata(base_url, api_key=api_key)
matched = endpoint_metadata.get(model)
if not matched:
# Single-model servers: if only one model is loaded, use it
if len(endpoint_metadata) == 1:
matched = next(iter(endpoint_metadata.values()))
else:
# Fuzzy match: substring in either direction
for key, entry in endpoint_metadata.items():
if model in key or key in model:
matched = entry
break
if matched:
context_length = matched.get("context_length")
if isinstance(context_length, int):
return context_length
if not _is_known_provider_base_url(base_url):
# 3. Try querying local server directly
if is_local_endpoint(base_url):
local_ctx = _query_local_context_length(model, base_url)
if local_ctx and local_ctx > 0:
save_context_length(model, base_url, local_ctx)
return local_ctx
logger.info(
"Could not detect context length for model %r at %s"
"defaulting to %s tokens (probe-down). Set model.context_length "
"in config.yaml to override.",
model, base_url, f"{DEFAULT_FALLBACK_CONTEXT:,}",
)
return DEFAULT_FALLBACK_CONTEXT
# 4. Anthropic /v1/models API (only for regular API keys, not OAuth)
if provider == "anthropic" or (
base_url and "api.anthropic.com" in base_url
):
ctx = _query_anthropic_context_length(model, base_url or "https://api.anthropic.com", api_key)
if ctx:
return ctx
# 5. Provider-aware lookups (before generic OpenRouter cache)
# These are provider-specific and take priority over the generic OR cache,
# since the same model can have different context limits per provider
# (e.g. claude-opus-4.6 is 1M on Anthropic but 128K on GitHub Copilot).
# If provider is generic (openrouter/custom/empty), try to infer from URL.
effective_provider = provider
if not effective_provider or effective_provider in ("openrouter", "custom"):
if base_url:
inferred = _infer_provider_from_url(base_url)
if inferred:
effective_provider = inferred
if effective_provider == "nous":
ctx = _resolve_nous_context_length(model)
if ctx:
return ctx
if effective_provider:
from agent.models_dev import lookup_models_dev_context
ctx = lookup_models_dev_context(effective_provider, model)
if ctx:
return ctx
# 6. OpenRouter live API metadata (provider-unaware fallback)
# 2. OpenRouter API metadata
metadata = fetch_model_metadata()
if model in metadata:
return metadata[model].get("context_length", 128000)
# 8. Hardcoded defaults (fuzzy match — longest key first for specificity)
# Only check `default_model in model` (is the key a substring of the input).
# The reverse (`model in default_model`) causes shorter names like
# "claude-sonnet-4" to incorrectly match "claude-sonnet-4-6" and return 1M.
model_lower = model.lower()
for default_model, length in sorted(
DEFAULT_CONTEXT_LENGTHS.items(), key=lambda x: len(x[0]), reverse=True
):
if default_model in model_lower:
# 3. Hardcoded defaults (fuzzy match)
for default_model, length in DEFAULT_CONTEXT_LENGTHS.items():
if default_model in model or model in default_model:
return length
# 9. Query local server as last resort
if base_url and is_local_endpoint(base_url):
local_ctx = _query_local_context_length(model, base_url)
if local_ctx and local_ctx > 0:
save_context_length(model, base_url, local_ctx)
return local_ctx
# 10. Default fallback — 128K
return DEFAULT_FALLBACK_CONTEXT
# 4. Unknown model — start at highest probe tier
return CONTEXT_PROBE_TIERS[0]
def estimate_tokens_rough(text: str) -> int:

View File

@@ -1,171 +0,0 @@
"""Models.dev registry integration for provider-aware context length detection.
Fetches model metadata from https://models.dev/api.json — a community-maintained
database of 3800+ models across 100+ providers, including per-provider context
windows, pricing, and capabilities.
Data is cached in memory (1hr TTL) and on disk (~/.hermes/models_dev_cache.json)
to avoid cold-start network latency.
"""
import json
import logging
import os
import time
from pathlib import Path
from typing import Any, Dict, Optional
import requests
logger = logging.getLogger(__name__)
MODELS_DEV_URL = "https://models.dev/api.json"
_MODELS_DEV_CACHE_TTL = 3600 # 1 hour in-memory
# In-memory cache
_models_dev_cache: Dict[str, Any] = {}
_models_dev_cache_time: float = 0
# Provider ID mapping: Hermes provider names → models.dev provider IDs
PROVIDER_TO_MODELS_DEV: Dict[str, str] = {
"openrouter": "openrouter",
"anthropic": "anthropic",
"zai": "zai",
"kimi-coding": "kimi-for-coding",
"minimax": "minimax",
"minimax-cn": "minimax-cn",
"deepseek": "deepseek",
"alibaba": "alibaba",
"copilot": "github-copilot",
"ai-gateway": "vercel",
"opencode-zen": "opencode",
"opencode-go": "opencode-go",
"kilocode": "kilo",
}
def _get_cache_path() -> Path:
"""Return path to disk cache file."""
env_val = os.environ.get("HERMES_HOME", "")
hermes_home = Path(env_val) if env_val else Path.home() / ".hermes"
return hermes_home / "models_dev_cache.json"
def _load_disk_cache() -> Dict[str, Any]:
"""Load models.dev data from disk cache."""
try:
cache_path = _get_cache_path()
if cache_path.exists():
with open(cache_path, encoding="utf-8") as f:
return json.load(f)
except Exception as e:
logger.debug("Failed to load models.dev disk cache: %s", e)
return {}
def _save_disk_cache(data: Dict[str, Any]) -> None:
"""Save models.dev data to disk cache."""
try:
cache_path = _get_cache_path()
cache_path.parent.mkdir(parents=True, exist_ok=True)
with open(cache_path, "w", encoding="utf-8") as f:
json.dump(data, f, separators=(",", ":"))
except Exception as e:
logger.debug("Failed to save models.dev disk cache: %s", e)
def fetch_models_dev(force_refresh: bool = False) -> Dict[str, Any]:
"""Fetch models.dev registry. In-memory cache (1hr) + disk fallback.
Returns the full registry dict keyed by provider ID, or empty dict on failure.
"""
global _models_dev_cache, _models_dev_cache_time
# Check in-memory cache
if (
not force_refresh
and _models_dev_cache
and (time.time() - _models_dev_cache_time) < _MODELS_DEV_CACHE_TTL
):
return _models_dev_cache
# Try network fetch
try:
response = requests.get(MODELS_DEV_URL, timeout=15)
response.raise_for_status()
data = response.json()
if isinstance(data, dict) and len(data) > 0:
_models_dev_cache = data
_models_dev_cache_time = time.time()
_save_disk_cache(data)
logger.debug(
"Fetched models.dev registry: %d providers, %d total models",
len(data),
sum(len(p.get("models", {})) for p in data.values() if isinstance(p, dict)),
)
return data
except Exception as e:
logger.debug("Failed to fetch models.dev: %s", e)
# Fall back to disk cache — use a short TTL (5 min) so we retry
# the network fetch soon instead of serving stale data for a full hour.
if not _models_dev_cache:
_models_dev_cache = _load_disk_cache()
if _models_dev_cache:
_models_dev_cache_time = time.time() - _MODELS_DEV_CACHE_TTL + 300
logger.debug("Loaded models.dev from disk cache (%d providers)", len(_models_dev_cache))
return _models_dev_cache
def lookup_models_dev_context(provider: str, model: str) -> Optional[int]:
"""Look up context_length for a provider+model combo in models.dev.
Returns the context window in tokens, or None if not found.
Handles case-insensitive matching and filters out context=0 entries.
"""
mdev_provider_id = PROVIDER_TO_MODELS_DEV.get(provider)
if not mdev_provider_id:
return None
data = fetch_models_dev()
provider_data = data.get(mdev_provider_id)
if not isinstance(provider_data, dict):
return None
models = provider_data.get("models", {})
if not isinstance(models, dict):
return None
# Exact match
entry = models.get(model)
if entry:
ctx = _extract_context(entry)
if ctx:
return ctx
# Case-insensitive match
model_lower = model.lower()
for mid, mdata in models.items():
if mid.lower() == model_lower:
ctx = _extract_context(mdata)
if ctx:
return ctx
return None
def _extract_context(entry: Dict[str, Any]) -> Optional[int]:
"""Extract context_length from a models.dev model entry.
Returns None for invalid/zero values (some audio/image models have context=0).
"""
if not isinstance(entry, dict):
return None
limit = entry.get("limit")
if not isinstance(limit, dict):
return None
ctx = limit.get("context")
if isinstance(ctx, (int, float)) and ctx > 0:
return int(ctx)
return None

View File

@@ -56,61 +56,6 @@ def _scan_context_content(content: str, filename: str) -> str:
return content
def _find_git_root(start: Path) -> Optional[Path]:
"""Walk *start* and its parents looking for a ``.git`` directory.
Returns the directory containing ``.git``, or ``None`` if we hit the
filesystem root without finding one.
"""
current = start.resolve()
for parent in [current, *current.parents]:
if (parent / ".git").exists():
return parent
return None
_HERMES_MD_NAMES = (".hermes.md", "HERMES.md")
def _find_hermes_md(cwd: Path) -> Optional[Path]:
"""Discover the nearest ``.hermes.md`` or ``HERMES.md``.
Search order: *cwd* first, then each parent directory up to (and
including) the git repository root. Returns the first match, or
``None`` if nothing is found.
"""
stop_at = _find_git_root(cwd)
current = cwd.resolve()
for directory in [current, *current.parents]:
for name in _HERMES_MD_NAMES:
candidate = directory / name
if candidate.is_file():
return candidate
# Stop walking at the git root (or filesystem root).
if stop_at and directory == stop_at:
break
return None
def _strip_yaml_frontmatter(content: str) -> str:
"""Remove optional YAML frontmatter (``---`` delimited) from *content*.
The frontmatter may contain structured config (model overrides, tool
settings) that will be handled separately in a future PR. For now we
strip it so only the human-readable markdown body is injected into the
system prompt.
"""
if content.startswith("---"):
end = content.find("\n---", 3)
if end != -1:
# Skip past the closing --- and any trailing newline
body = content[end + 4:].lstrip("\n")
return body if body else content
return content
# =========================================================================
# Constants
# =========================================================================
@@ -121,37 +66,25 @@ DEFAULT_AGENT_IDENTITY = (
"range of tasks including answering questions, writing and editing code, "
"analyzing information, creative work, and executing actions via your tools. "
"You communicate clearly, admit uncertainty when appropriate, and prioritize "
"being genuinely useful over being verbose unless otherwise directed below. "
"Be targeted and efficient in your exploration and investigations."
"being genuinely useful over being verbose unless otherwise directed below."
)
MEMORY_GUIDANCE = (
"You have persistent memory across sessions. Save durable facts using the memory "
"tool: user preferences, environment details, tool quirks, and stable conventions. "
"Memory is injected into every turn, so keep it compact and focused on facts that "
"will still matter later.\n"
"Prioritize what reduces future user steering — the most valuable memory is one "
"that prevents the user from having to correct or remind you again. "
"User preferences and recurring corrections matter more than procedural task details.\n"
"Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO "
"state to memory; use session_search to recall those from past transcripts. "
"If you've discovered a new way to do something, solved a problem that could be "
"necessary later, save it as a skill with the skill tool."
"You have persistent memory across sessions. Proactively save important things "
"you learn (user preferences, environment details, useful approaches) and do "
"(like a diary!) using the memory tool -- don't wait to be asked."
)
SESSION_SEARCH_GUIDANCE = (
"When the user references something from a past conversation or you suspect "
"relevant cross-session context exists, use session_search to recall it before "
"asking them to repeat themselves."
"relevant prior context exists, use session_search to recall it before asking "
"them to repeat themselves."
)
SKILLS_GUIDANCE = (
"After completing a complex task (5+ tool calls), fixing a tricky error, "
"or discovering a non-trivial workflow, save the approach as a "
"skill with skill_manage so you can reuse it next time.\n"
"When using a skill and finding it outdated, incomplete, or wrong, "
"patch it immediately with skill_manage(action='patch') — don't wait to be asked. "
"Skills that aren't maintained become liabilities."
"or discovering a non-trivial workflow, consider saving the approach as a "
"skill with skill_manage so you can reuse it next time."
)
PLATFORM_HINTS = {
@@ -169,58 +102,17 @@ PLATFORM_HINTS = {
"You are on a text messaging communication platform, Telegram. "
"Please do not use markdown as it does not render. "
"You can send media files natively: to deliver a file to the user, "
"include MEDIA:/absolute/path/to/file in your response. Images "
"(.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice "
"bubbles, and videos (.mp4) play inline. You can also include image "
"URLs in markdown format ![alt](url) and they will be sent as native photos."
"include MEDIA:/absolute/path/to/file in your response. Audio "
"(.ogg) sends as voice bubbles. You can also include image URLs "
"in markdown format ![alt](url) and they will be sent as native photos."
),
"discord": (
"You are in a Discord server or group chat communicating with your user. "
"You can send media files natively: include MEDIA:/absolute/path/to/file "
"in your response. Images (.png, .jpg, .webp) are sent as photo "
"attachments, audio as file attachments. You can also include image URLs "
"in markdown format ![alt](url) and they will be sent as attachments."
),
"slack": (
"You are in a Slack workspace communicating with your user. "
"You can send media files natively: include MEDIA:/absolute/path/to/file "
"in your response. Images (.png, .jpg, .webp) are uploaded as photo "
"attachments, audio as file attachments. You can also include image URLs "
"in markdown format ![alt](url) and they will be uploaded as attachments."
),
"signal": (
"You are on a text messaging communication platform, Signal. "
"Please do not use markdown as it does not render. "
"You can send media files natively: to deliver a file to the user, "
"include MEDIA:/absolute/path/to/file in your response. Images "
"(.png, .jpg, .webp) appear as photos, audio as attachments, and other "
"files arrive as downloadable documents. You can also include image "
"URLs in markdown format ![alt](url) and they will be sent as photos."
),
"email": (
"You are communicating via email. Write clear, well-structured responses "
"suitable for email. Use plain text formatting (no markdown). "
"Keep responses concise but complete. You can send file attachments — "
"include MEDIA:/absolute/path/to/file in your response. The subject line "
"is preserved for threading. Do not include greetings or sign-offs unless "
"contextually appropriate."
),
"cron": (
"You are running as a scheduled cron job. There is no user present — you "
"cannot ask questions, request clarification, or wait for follow-up. Execute "
"the task fully and autonomously, making reasonable decisions where needed. "
"Your final response is automatically delivered to the job's configured "
"destination — put the primary content directly in your response."
"You are in a Discord server or group chat communicating with your user."
),
"cli": (
"You are a CLI AI Agent. Try not to use markdown but simple text "
"renderable inside a terminal."
),
"sms": (
"You are communicating via SMS. Keep responses concise and use plain text "
"only — no markdown, no formatting. SMS messages are limited to ~1600 "
"characters, so be brief and direct."
),
}
CONTEXT_FILE_MAX_CHARS = 20_000
@@ -232,87 +124,40 @@ CONTEXT_TRUNCATE_TAIL_RATIO = 0.2
# Skills index
# =========================================================================
def _parse_skill_file(skill_file: Path) -> tuple[bool, dict, str]:
"""Read a SKILL.md once and return platform compatibility, frontmatter, and description.
def _read_skill_description(skill_file: Path, max_chars: int = 60) -> str:
"""Read the description from a SKILL.md frontmatter, capped at max_chars."""
try:
raw = skill_file.read_text(encoding="utf-8")[:2000]
match = re.search(
r"^---\s*\n.*?description:\s*(.+?)\s*\n.*?^---",
raw, re.MULTILINE | re.DOTALL,
)
if match:
desc = match.group(1).strip().strip("'\"")
if len(desc) > max_chars:
desc = desc[:max_chars - 3] + "..."
return desc
except Exception:
pass
return ""
Returns (is_compatible, frontmatter, description). On any error, returns
(True, {}, "") to err on the side of showing the skill.
def _skill_is_platform_compatible(skill_file: Path) -> bool:
"""Quick check if a SKILL.md is compatible with the current OS platform.
Reads just enough to parse the ``platforms`` frontmatter field.
Skills without the field (the vast majority) are always compatible.
"""
try:
from tools.skills_tool import _parse_frontmatter, skill_matches_platform
raw = skill_file.read_text(encoding="utf-8")[:2000]
frontmatter, _ = _parse_frontmatter(raw)
if not skill_matches_platform(frontmatter):
return False, {}, ""
desc = ""
raw_desc = frontmatter.get("description", "")
if raw_desc:
desc = str(raw_desc).strip().strip("'\"")
if len(desc) > 60:
desc = desc[:57] + "..."
return True, frontmatter, desc
except Exception as e:
logger.debug("Failed to parse skill file %s: %s", skill_file, e)
return True, {}, ""
return skill_matches_platform(frontmatter)
except Exception:
return True # Err on the side of showing the skill
def _read_skill_conditions(skill_file: Path) -> dict:
"""Extract conditional activation fields from SKILL.md frontmatter."""
try:
from tools.skills_tool import _parse_frontmatter
raw = skill_file.read_text(encoding="utf-8")[:2000]
frontmatter, _ = _parse_frontmatter(raw)
hermes = frontmatter.get("metadata", {}).get("hermes", {})
return {
"fallback_for_toolsets": hermes.get("fallback_for_toolsets", []),
"requires_toolsets": hermes.get("requires_toolsets", []),
"fallback_for_tools": hermes.get("fallback_for_tools", []),
"requires_tools": hermes.get("requires_tools", []),
}
except Exception as e:
logger.debug("Failed to read skill conditions from %s: %s", skill_file, e)
return {}
def _skill_should_show(
conditions: dict,
available_tools: "set[str] | None",
available_toolsets: "set[str] | None",
) -> bool:
"""Return False if the skill's conditional activation rules exclude it."""
if available_tools is None and available_toolsets is None:
return True # No filtering info — show everything (backward compat)
at = available_tools or set()
ats = available_toolsets or set()
# fallback_for: hide when the primary tool/toolset IS available
for ts in conditions.get("fallback_for_toolsets", []):
if ts in ats:
return False
for t in conditions.get("fallback_for_tools", []):
if t in at:
return False
# requires: hide when a required tool/toolset is NOT available
for ts in conditions.get("requires_toolsets", []):
if ts not in ats:
return False
for t in conditions.get("requires_tools", []):
if t not in at:
return False
return True
def build_skills_system_prompt(
available_tools: "set[str] | None" = None,
available_toolsets: "set[str] | None" = None,
) -> str:
def build_skills_system_prompt() -> str:
"""Build a compact skill index for the system prompt.
Scans ~/.hermes/skills/ for SKILL.md files grouped by category.
@@ -326,49 +171,31 @@ def build_skills_system_prompt(
if not skills_dir.exists():
return ""
# Collect skills with descriptions, grouped by category.
# Collect skills with descriptions, grouped by category
# Each entry: (skill_name, description)
# Supports sub-categories: skills/mlops/training/axolotl/SKILL.md
# -> category "mlops/training", skill "axolotl"
# Load disabled skill names once for the entire scan
try:
from tools.skills_tool import _get_disabled_skill_names
disabled = _get_disabled_skill_names()
except Exception:
disabled = set()
skills_by_category: dict[str, list[tuple[str, str]]] = {}
for skill_file in skills_dir.rglob("SKILL.md"):
is_compatible, frontmatter, desc = _parse_skill_file(skill_file)
if not is_compatible:
# Skip skills incompatible with the current OS platform
if not _skill_is_platform_compatible(skill_file):
continue
rel_path = skill_file.relative_to(skills_dir)
parts = rel_path.parts
if len(parts) >= 2:
category = parts[0]
skill_name = parts[-2]
category = "/".join(parts[:-2]) if len(parts) > 2 else parts[0]
else:
category = "general"
skill_name = skill_file.parent.name
# Respect user's disabled skills config
fm_name = frontmatter.get("name", skill_name)
if fm_name in disabled or skill_name in disabled:
continue
# Skip skills whose conditional activation rules exclude them
conditions = _read_skill_conditions(skill_file)
if not _skill_should_show(conditions, available_tools, available_toolsets):
continue
desc = _read_skill_description(skill_file)
skills_by_category.setdefault(category, []).append((skill_name, desc))
if not skills_by_category:
return ""
# Read category-level descriptions from DESCRIPTION.md
# Checks both the exact category path and parent directories
category_descriptions = {}
for category in skills_by_category:
cat_path = Path(category)
desc_file = skills_dir / cat_path / "DESCRIPTION.md"
desc_file = skills_dir / category / "DESCRIPTION.md"
if desc_file.exists():
try:
content = desc_file.read_text(encoding="utf-8")
@@ -401,9 +228,6 @@ def build_skills_system_prompt(
"Before replying, scan the skills below. If one clearly matches your task, "
"load it with skill_view(name) and follow its instructions. "
"If a skill has issues, fix it with skill_manage(action='patch').\n"
"After difficult/iterative tasks, offer to save as a skill. "
"If a skill you loaded was missing steps, had wrong commands, or needed "
"pitfalls you discovered, update it before finishing.\n"
"\n"
"<available_skills>\n"
+ "\n".join(index_lines) + "\n"
@@ -429,59 +253,19 @@ def _truncate_content(content: str, filename: str, max_chars: int = CONTEXT_FILE
return head + marker + tail
def load_soul_md() -> Optional[str]:
"""Load SOUL.md from HERMES_HOME and return its content, or None.
def build_context_files_prompt(cwd: Optional[str] = None) -> str:
"""Discover and load context files for the system prompt.
Used as the agent identity (slot #1 in the system prompt). When this
returns content, ``build_context_files_prompt`` should be called with
``skip_soul=True`` so SOUL.md isn't injected twice.
Discovery: AGENTS.md (recursive), .cursorrules / .cursor/rules/*.mdc,
SOUL.md (cwd then ~/.hermes/ fallback). Each capped at 20,000 chars.
"""
try:
from hermes_cli.config import ensure_hermes_home
ensure_hermes_home()
except Exception as e:
logger.debug("Could not ensure HERMES_HOME before loading SOUL.md: %s", e)
if cwd is None:
cwd = os.getcwd()
soul_path = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes")) / "SOUL.md"
if not soul_path.exists():
return None
try:
content = soul_path.read_text(encoding="utf-8").strip()
if not content:
return None
content = _scan_context_content(content, "SOUL.md")
content = _truncate_content(content, "SOUL.md")
return content
except Exception as e:
logger.debug("Could not read SOUL.md from %s: %s", soul_path, e)
return None
cwd_path = Path(cwd).resolve()
sections = []
def _load_hermes_md(cwd_path: Path) -> str:
""".hermes.md / HERMES.md — walk to git root."""
hermes_md_path = _find_hermes_md(cwd_path)
if not hermes_md_path:
return ""
try:
content = hermes_md_path.read_text(encoding="utf-8").strip()
if not content:
return ""
content = _strip_yaml_frontmatter(content)
rel = hermes_md_path.name
try:
rel = str(hermes_md_path.relative_to(cwd_path))
except ValueError:
pass
content = _scan_context_content(content, rel)
result = f"## {rel}\n\n{content}"
return _truncate_content(result, ".hermes.md")
except Exception as e:
logger.debug("Could not read %s: %s", hermes_md_path, e)
return ""
def _load_agents_md(cwd_path: Path) -> str:
"""AGENTS.md — hierarchical, recursive directory walk."""
# AGENTS.md (hierarchical, recursive)
top_level_agents = None
for name in ["AGENTS.md", "agents.md"]:
candidate = cwd_path / name
@@ -489,51 +273,31 @@ def _load_agents_md(cwd_path: Path) -> str:
top_level_agents = candidate
break
if not top_level_agents:
return ""
if top_level_agents:
agents_files = []
for root, dirs, files in os.walk(cwd_path):
dirs[:] = [d for d in dirs if not d.startswith('.') and d not in ('node_modules', '__pycache__', 'venv', '.venv')]
for f in files:
if f.lower() == "agents.md":
agents_files.append(Path(root) / f)
agents_files.sort(key=lambda p: len(p.parts))
agents_files = []
for root, dirs, files in os.walk(cwd_path):
dirs[:] = [d for d in dirs if not d.startswith('.') and d not in ('node_modules', '__pycache__', 'venv', '.venv')]
for f in files:
if f.lower() == "agents.md":
agents_files.append(Path(root) / f)
agents_files.sort(key=lambda p: len(p.parts))
total_content = ""
for agents_path in agents_files:
try:
content = agents_path.read_text(encoding="utf-8").strip()
if content:
rel_path = agents_path.relative_to(cwd_path)
content = _scan_context_content(content, str(rel_path))
total_content += f"## {rel_path}\n\n{content}\n\n"
except Exception as e:
logger.debug("Could not read %s: %s", agents_path, e)
if not total_content:
return ""
return _truncate_content(total_content, "AGENTS.md")
def _load_claude_md(cwd_path: Path) -> str:
"""CLAUDE.md / claude.md — cwd only."""
for name in ["CLAUDE.md", "claude.md"]:
candidate = cwd_path / name
if candidate.exists():
total_agents_content = ""
for agents_path in agents_files:
try:
content = candidate.read_text(encoding="utf-8").strip()
content = agents_path.read_text(encoding="utf-8").strip()
if content:
content = _scan_context_content(content, name)
result = f"## {name}\n\n{content}"
return _truncate_content(result, "CLAUDE.md")
rel_path = agents_path.relative_to(cwd_path)
content = _scan_context_content(content, str(rel_path))
total_agents_content += f"## {rel_path}\n\n{content}\n\n"
except Exception as e:
logger.debug("Could not read %s: %s", candidate, e)
return ""
logger.debug("Could not read %s: %s", agents_path, e)
if total_agents_content:
total_agents_content = _truncate_content(total_agents_content, "AGENTS.md")
sections.append(total_agents_content)
def _load_cursorrules(cwd_path: Path) -> str:
""".cursorrules + .cursor/rules/*.mdc — cwd only."""
# .cursorrules
cursorrules_content = ""
cursorrules_file = cwd_path / ".cursorrules"
if cursorrules_file.exists():
@@ -557,47 +321,35 @@ def _load_cursorrules(cwd_path: Path) -> str:
except Exception as e:
logger.debug("Could not read %s: %s", mdc_file, e)
if not cursorrules_content:
return ""
return _truncate_content(cursorrules_content, ".cursorrules")
if cursorrules_content:
cursorrules_content = _truncate_content(cursorrules_content, ".cursorrules")
sections.append(cursorrules_content)
# SOUL.md (cwd first, then ~/.hermes/ fallback)
soul_path = None
for name in ["SOUL.md", "soul.md"]:
candidate = cwd_path / name
if candidate.exists():
soul_path = candidate
break
if not soul_path:
global_soul = Path.home() / ".hermes" / "SOUL.md"
if global_soul.exists():
soul_path = global_soul
def build_context_files_prompt(cwd: Optional[str] = None, skip_soul: bool = False) -> str:
"""Discover and load context files for the system prompt.
Priority (first found wins — only ONE project context type is loaded):
1. .hermes.md / HERMES.md (walk to git root)
2. AGENTS.md / agents.md (recursive directory walk)
3. CLAUDE.md / claude.md (cwd only)
4. .cursorrules / .cursor/rules/*.mdc (cwd only)
SOUL.md from HERMES_HOME is independent and always included when present.
Each context source is capped at 20,000 chars.
When *skip_soul* is True, SOUL.md is not included here (it was already
loaded via ``load_soul_md()`` for the identity slot).
"""
if cwd is None:
cwd = os.getcwd()
cwd_path = Path(cwd).resolve()
sections = []
# Priority-based project context: first match wins
project_context = (
_load_hermes_md(cwd_path)
or _load_agents_md(cwd_path)
or _load_claude_md(cwd_path)
or _load_cursorrules(cwd_path)
)
if project_context:
sections.append(project_context)
# SOUL.md from HERMES_HOME only — skip when already loaded as identity
if not skip_soul:
soul_content = load_soul_md()
if soul_content:
sections.append(soul_content)
if soul_path:
try:
content = soul_path.read_text(encoding="utf-8").strip()
if content:
content = _scan_context_content(content, "SOUL.md")
content = _truncate_content(content, "SOUL.md")
sections.append(
f"## SOUL.md\n\nIf SOUL.md is present, embody its persona and tone. "
f"Avoid stiff, generic replies; follow its guidance unless higher-priority "
f"instructions override it.\n\n{content}"
)
except Exception as e:
logger.debug("Could not read SOUL.md from %s: %s", soul_path, e)
if not sections:
return ""

View File

@@ -12,24 +12,21 @@ import copy
from typing import Any, Dict, List
def _apply_cache_marker(msg: dict, cache_marker: dict, native_anthropic: bool = False) -> None:
def _apply_cache_marker(msg: dict, cache_marker: dict) -> None:
"""Add cache_control to a single message, handling all format variations."""
role = msg.get("role", "")
content = msg.get("content")
if role == "tool":
if native_anthropic:
msg["cache_control"] = cache_marker
msg["cache_control"] = cache_marker
return
if content is None or content == "":
if content is None:
msg["cache_control"] = cache_marker
return
if isinstance(content, str):
msg["content"] = [
{"type": "text", "text": content, "cache_control": cache_marker}
]
msg["content"] = [{"type": "text", "text": content, "cache_control": cache_marker}]
return
if isinstance(content, list) and content:
@@ -41,7 +38,6 @@ def _apply_cache_marker(msg: dict, cache_marker: dict, native_anthropic: bool =
def apply_anthropic_cache_control(
api_messages: List[Dict[str, Any]],
cache_ttl: str = "5m",
native_anthropic: bool = False,
) -> List[Dict[str, Any]]:
"""Apply system_and_3 caching strategy to messages for Anthropic models.
@@ -61,12 +57,12 @@ def apply_anthropic_cache_control(
breakpoints_used = 0
if messages[0].get("role") == "system":
_apply_cache_marker(messages[0], marker, native_anthropic=native_anthropic)
_apply_cache_marker(messages[0], marker)
breakpoints_used += 1
remaining = 4 - breakpoints_used
non_sys = [i for i in range(len(messages)) if messages[i].get("role") != "system"]
for idx in non_sys[-remaining:]:
_apply_cache_marker(messages[idx], marker, native_anthropic=native_anthropic)
_apply_cache_marker(messages[idx], marker)
return messages

View File

@@ -8,14 +8,14 @@ the first 6 and last 4 characters for debuggability.
"""
import logging
import os
import re
from typing import Optional
logger = logging.getLogger(__name__)
# Known API key prefixes -- match the prefix + contiguous token chars
_PREFIX_PATTERNS = [
r"sk-[A-Za-z0-9_-]{10,}", # OpenAI / OpenRouter / Anthropic (sk-ant-*)
r"sk-[A-Za-z0-9_-]{10,}", # OpenAI / OpenRouter
r"ghp_[A-Za-z0-9]{10,}", # GitHub PAT (classic)
r"github_pat_[A-Za-z0-9_]{10,}", # GitHub PAT (fine-grained)
r"xox[baprs]-[A-Za-z0-9-]{10,}", # Slack tokens
@@ -25,18 +25,6 @@ _PREFIX_PATTERNS = [
r"fc-[A-Za-z0-9]{10,}", # Firecrawl
r"bb_live_[A-Za-z0-9_-]{10,}", # BrowserBase
r"gAAAA[A-Za-z0-9_=-]{20,}", # Codex encrypted tokens
r"AKIA[A-Z0-9]{16}", # AWS Access Key ID
r"sk_live_[A-Za-z0-9]{10,}", # Stripe secret key (live)
r"sk_test_[A-Za-z0-9]{10,}", # Stripe secret key (test)
r"rk_live_[A-Za-z0-9]{10,}", # Stripe restricted key
r"SG\.[A-Za-z0-9_-]{10,}", # SendGrid API key
r"hf_[A-Za-z0-9]{10,}", # HuggingFace token
r"r8_[A-Za-z0-9]{10,}", # Replicate API token
r"npm_[A-Za-z0-9]{10,}", # npm access token
r"pypi-[A-Za-z0-9_-]{10,}", # PyPI API token
r"dop_v1_[A-Za-z0-9]{10,}", # DigitalOcean PAT
r"doo_v1_[A-Za-z0-9]{10,}", # DigitalOcean OAuth
r"am_[A-Za-z0-9_-]{10,}", # AgentMail API key
]
# ENV assignment patterns: KEY=value where KEY contains a secret-like name
@@ -47,7 +35,7 @@ _ENV_ASSIGN_RE = re.compile(
)
# JSON field patterns: "apiKey": "value", "token": "value", etc.
_JSON_KEY_NAMES = r"(?:api_?[Kk]ey|token|secret|password|access_token|refresh_token|auth_token|bearer|secret_value|raw_secret|secret_input|key_material)"
_JSON_KEY_NAMES = r"(?:api_?[Kk]ey|token|secret|password|access_token|refresh_token|auth_token|bearer)"
_JSON_FIELD_RE = re.compile(
rf'("{_JSON_KEY_NAMES}")\s*:\s*"([^"]+)"',
re.IGNORECASE,
@@ -59,28 +47,11 @@ _AUTH_HEADER_RE = re.compile(
re.IGNORECASE,
)
# Telegram bot tokens: bot<digits>:<token> or <digits>:<token>,
# where token part is restricted to [-A-Za-z0-9_] and length >= 30
# Telegram bot tokens: bot<digits>:<token> or <digits>:<alphanum>
_TELEGRAM_RE = re.compile(
r"(bot)?(\d{8,}):([-A-Za-z0-9_]{30,})",
)
# Private key blocks: -----BEGIN RSA PRIVATE KEY----- ... -----END RSA PRIVATE KEY-----
_PRIVATE_KEY_RE = re.compile(
r"-----BEGIN[A-Z ]*PRIVATE KEY-----[\s\S]*?-----END[A-Z ]*PRIVATE KEY-----"
)
# Database connection strings: protocol://user:PASSWORD@host
# Catches postgres, mysql, mongodb, redis, amqp URLs and redacts the password
_DB_CONNSTR_RE = re.compile(
r"((?:postgres(?:ql)?|mysql|mongodb(?:\+srv)?|redis|amqp)://[^:]+:)([^@]+)(@)",
re.IGNORECASE,
)
# E.164 phone numbers: +<country><number>, 7-15 digits
# Negative lookahead prevents matching hex strings or identifiers
_SIGNAL_PHONE_RE = re.compile(r"(\+[1-9]\d{6,14})(?![A-Za-z0-9])")
# Compile known prefix patterns into one alternation
_PREFIX_RE = re.compile(
r"(?<![A-Za-z0-9_-])(" + "|".join(_PREFIX_PATTERNS) + r")(?![A-Za-z0-9_-])"
@@ -98,16 +69,9 @@ def redact_sensitive_text(text: str) -> str:
"""Apply all redaction patterns to a block of text.
Safe to call on any string -- non-matching text passes through unchanged.
Disabled when security.redact_secrets is false in config.yaml.
"""
if text is None:
return None
if not isinstance(text, str):
text = str(text)
if not text:
return text
if os.getenv("HERMES_REDACT_SECRETS", "").lower() in ("0", "false", "no", "off"):
return text
# Known prefixes (sk-, ghp_, etc.)
text = _PREFIX_RE.sub(lambda m: _mask_token(m.group(1)), text)
@@ -137,20 +101,6 @@ def redact_sensitive_text(text: str) -> str:
return f"{prefix}{digits}:***"
text = _TELEGRAM_RE.sub(_redact_telegram, text)
# Private key blocks
text = _PRIVATE_KEY_RE.sub("[REDACTED PRIVATE KEY]", text)
# Database connection string passwords
text = _DB_CONNSTR_RE.sub(lambda m: f"{m.group(1)}***{m.group(3)}", text)
# E.164 phone numbers (Signal, WhatsApp)
def _redact_phone(m):
phone = m.group(1)
if len(phone) <= 8:
return phone[:2] + "****" + phone[-2:]
return phone[:4] + "****" + phone[-4:]
text = _SIGNAL_PHONE_RE.sub(_redact_phone, text)
return text

View File

@@ -1,151 +1,16 @@
"""Shared slash command helpers for skills and built-in prompt-style modes.
"""Skill slash commands — scan installed skills and build invocation messages.
Shared between CLI (cli.py) and gateway (gateway/run.py) so both surfaces
can invoke skills via /skill-name commands and prompt-only built-ins like
/plan.
can invoke skills via /skill-name commands.
"""
import json
import logging
import re
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Optional
logger = logging.getLogger(__name__)
_skill_commands: Dict[str, Dict[str, Any]] = {}
_PLAN_SLUG_RE = re.compile(r"[^a-z0-9]+")
def build_plan_path(
user_instruction: str = "",
*,
now: datetime | None = None,
) -> Path:
"""Return the default workspace-relative markdown path for a /plan invocation.
Relative paths are intentional: file tools are task/backend-aware and resolve
them against the active working directory for local, docker, ssh, modal,
daytona, and similar terminal backends. That keeps the plan with the active
workspace instead of the Hermes host's global home directory.
"""
slug_source = (user_instruction or "").strip().splitlines()[0] if user_instruction else ""
slug = _PLAN_SLUG_RE.sub("-", slug_source.lower()).strip("-")
if slug:
slug = "-".join(part for part in slug.split("-")[:8] if part)[:48].strip("-")
slug = slug or "conversation-plan"
timestamp = (now or datetime.now()).strftime("%Y-%m-%d_%H%M%S")
return Path(".hermes") / "plans" / f"{timestamp}-{slug}.md"
def _load_skill_payload(skill_identifier: str, task_id: str | None = None) -> tuple[dict[str, Any], Path | None, str] | None:
"""Load a skill by name/path and return (loaded_payload, skill_dir, display_name)."""
raw_identifier = (skill_identifier or "").strip()
if not raw_identifier:
return None
try:
from tools.skills_tool import SKILLS_DIR, skill_view
identifier_path = Path(raw_identifier).expanduser()
if identifier_path.is_absolute():
try:
normalized = str(identifier_path.resolve().relative_to(SKILLS_DIR.resolve()))
except Exception:
normalized = raw_identifier
else:
normalized = raw_identifier.lstrip("/")
loaded_skill = json.loads(skill_view(normalized, task_id=task_id))
except Exception:
return None
if not loaded_skill.get("success"):
return None
skill_name = str(loaded_skill.get("name") or normalized)
skill_path = str(loaded_skill.get("path") or "")
skill_dir = None
if skill_path:
try:
skill_dir = SKILLS_DIR / Path(skill_path).parent
except Exception:
skill_dir = None
return loaded_skill, skill_dir, skill_name
def _build_skill_message(
loaded_skill: dict[str, Any],
skill_dir: Path | None,
activation_note: str,
user_instruction: str = "",
runtime_note: str = "",
) -> str:
"""Format a loaded skill into a user/system message payload."""
from tools.skills_tool import SKILLS_DIR
content = str(loaded_skill.get("content") or "")
parts = [activation_note, "", content.strip()]
if loaded_skill.get("setup_skipped"):
parts.extend(
[
"",
"[Skill setup note: Required environment setup was skipped. Continue loading the skill and explain any reduced functionality if it matters.]",
]
)
elif loaded_skill.get("gateway_setup_hint"):
parts.extend(
[
"",
f"[Skill setup note: {loaded_skill['gateway_setup_hint']}]",
]
)
elif loaded_skill.get("setup_needed") and loaded_skill.get("setup_note"):
parts.extend(
[
"",
f"[Skill setup note: {loaded_skill['setup_note']}]",
]
)
supporting = []
linked_files = loaded_skill.get("linked_files") or {}
for entries in linked_files.values():
if isinstance(entries, list):
supporting.extend(entries)
if not supporting and skill_dir:
for subdir in ("references", "templates", "scripts", "assets"):
subdir_path = skill_dir / subdir
if subdir_path.exists():
for f in sorted(subdir_path.rglob("*")):
if f.is_file():
rel = str(f.relative_to(skill_dir))
supporting.append(rel)
if supporting and skill_dir:
skill_view_target = str(skill_dir.relative_to(SKILLS_DIR))
parts.append("")
parts.append("[This skill has supporting files you can load with the skill_view tool:]")
for sf in supporting:
parts.append(f"- {sf}")
parts.append(
f'\nTo view any of these, use: skill_view(name="{skill_view_target}", file_path="<path>")'
)
if user_instruction:
parts.append("")
parts.append(f"The user has provided the following instruction alongside the skill invocation: {user_instruction}")
if runtime_note:
parts.append("")
parts.append(f"[Runtime note: {runtime_note}]")
return "\n".join(parts)
def scan_skill_commands() -> Dict[str, Dict[str, Any]]:
@@ -157,10 +22,9 @@ def scan_skill_commands() -> Dict[str, Dict[str, Any]]:
global _skill_commands
_skill_commands = {}
try:
from tools.skills_tool import SKILLS_DIR, _parse_frontmatter, skill_matches_platform, _get_disabled_skill_names
from tools.skills_tool import SKILLS_DIR, _parse_frontmatter, skill_matches_platform
if not SKILLS_DIR.exists():
return _skill_commands
disabled = _get_disabled_skill_names()
for skill_md in SKILLS_DIR.rglob("SKILL.md"):
if any(part in ('.git', '.github', '.hub') for part in skill_md.parts):
continue
@@ -171,9 +35,6 @@ def scan_skill_commands() -> Dict[str, Dict[str, Any]]:
if not skill_matches_platform(frontmatter):
continue
name = frontmatter.get('name', skill_md.parent.name)
# Respect user's disabled skills config
if name in disabled:
continue
description = frontmatter.get('description', '')
if not description:
for line in body.strip().split('\n'):
@@ -202,12 +63,7 @@ def get_skill_commands() -> Dict[str, Dict[str, Any]]:
return _skill_commands
def build_skill_invocation_message(
cmd_key: str,
user_instruction: str = "",
task_id: str | None = None,
runtime_note: str = "",
) -> Optional[str]:
def build_skill_invocation_message(cmd_key: str, user_instruction: str = "") -> Optional[str]:
"""Build the user message content for a skill slash command invocation.
Args:
@@ -222,61 +78,39 @@ def build_skill_invocation_message(
if not skill_info:
return None
loaded = _load_skill_payload(skill_info["skill_dir"], task_id=task_id)
if not loaded:
return f"[Failed to load skill: {skill_info['name']}]"
skill_md_path = Path(skill_info["skill_md_path"])
skill_dir = Path(skill_info["skill_dir"])
skill_name = skill_info["name"]
loaded_skill, skill_dir, skill_name = loaded
activation_note = (
f'[SYSTEM: The user has invoked the "{skill_name}" skill, indicating they want '
"you to follow its instructions. The full skill content is loaded below.]"
)
return _build_skill_message(
loaded_skill,
skill_dir,
activation_note,
user_instruction=user_instruction,
runtime_note=runtime_note,
)
try:
content = skill_md_path.read_text(encoding='utf-8')
except Exception:
return f"[Failed to load skill: {skill_name}]"
parts = [
f'[SYSTEM: The user has invoked the "{skill_name}" skill, indicating they want you to follow its instructions. The full skill content is loaded below.]',
"",
content.strip(),
]
def build_preloaded_skills_prompt(
skill_identifiers: list[str],
task_id: str | None = None,
) -> tuple[str, list[str], list[str]]:
"""Load one or more skills for session-wide CLI preloading.
supporting = []
for subdir in ("references", "templates", "scripts", "assets"):
subdir_path = skill_dir / subdir
if subdir_path.exists():
for f in sorted(subdir_path.rglob("*")):
if f.is_file():
rel = str(f.relative_to(skill_dir))
supporting.append(rel)
Returns (prompt_text, loaded_skill_names, missing_identifiers).
"""
prompt_parts: list[str] = []
loaded_names: list[str] = []
missing: list[str] = []
if supporting:
parts.append("")
parts.append("[This skill has supporting files you can load with the skill_view tool:]")
for sf in supporting:
parts.append(f"- {sf}")
parts.append(f'\nTo view any of these, use: skill_view(name="{skill_name}", file="<path>")')
seen: set[str] = set()
for raw_identifier in skill_identifiers:
identifier = (raw_identifier or "").strip()
if not identifier or identifier in seen:
continue
seen.add(identifier)
if user_instruction:
parts.append("")
parts.append(f"The user has provided the following instruction alongside the skill invocation: {user_instruction}")
loaded = _load_skill_payload(identifier, task_id=task_id)
if not loaded:
missing.append(identifier)
continue
loaded_skill, skill_dir, skill_name = loaded
activation_note = (
f'[SYSTEM: The user launched this CLI session with the "{skill_name}" skill '
"preloaded. Treat its instructions as active guidance for the duration of this "
"session unless the user overrides them.]"
)
prompt_parts.append(
_build_skill_message(
loaded_skill,
skill_dir,
activation_note,
)
)
loaded_names.append(skill_name)
return "\n\n".join(prompt_parts), loaded_names, missing
return "\n".join(parts)

View File

@@ -1,196 +0,0 @@
"""Helpers for optional cheap-vs-strong model routing."""
from __future__ import annotations
import os
import re
from typing import Any, Dict, Optional
_COMPLEX_KEYWORDS = {
"debug",
"debugging",
"implement",
"implementation",
"refactor",
"patch",
"traceback",
"stacktrace",
"exception",
"error",
"analyze",
"analysis",
"investigate",
"architecture",
"design",
"compare",
"benchmark",
"optimize",
"optimise",
"review",
"terminal",
"shell",
"tool",
"tools",
"pytest",
"test",
"tests",
"plan",
"planning",
"delegate",
"subagent",
"cron",
"docker",
"kubernetes",
}
_URL_RE = re.compile(r"https?://|www\.", re.IGNORECASE)
def _coerce_bool(value: Any, default: bool = False) -> bool:
if value is None:
return default
if isinstance(value, bool):
return value
if isinstance(value, str):
return value.strip().lower() in {"1", "true", "yes", "on"}
return bool(value)
def _coerce_int(value: Any, default: int) -> int:
try:
return int(value)
except (TypeError, ValueError):
return default
def choose_cheap_model_route(user_message: str, routing_config: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
"""Return the configured cheap-model route when a message looks simple.
Conservative by design: if the message has signs of code/tool/debugging/
long-form work, keep the primary model.
"""
cfg = routing_config or {}
if not _coerce_bool(cfg.get("enabled"), False):
return None
cheap_model = cfg.get("cheap_model") or {}
if not isinstance(cheap_model, dict):
return None
provider = str(cheap_model.get("provider") or "").strip().lower()
model = str(cheap_model.get("model") or "").strip()
if not provider or not model:
return None
text = (user_message or "").strip()
if not text:
return None
max_chars = _coerce_int(cfg.get("max_simple_chars"), 160)
max_words = _coerce_int(cfg.get("max_simple_words"), 28)
if len(text) > max_chars:
return None
if len(text.split()) > max_words:
return None
if text.count("\n") > 1:
return None
if "```" in text or "`" in text:
return None
if _URL_RE.search(text):
return None
lowered = text.lower()
words = {token.strip(".,:;!?()[]{}\"'`") for token in lowered.split()}
if words & _COMPLEX_KEYWORDS:
return None
route = dict(cheap_model)
route["provider"] = provider
route["model"] = model
route["routing_reason"] = "simple_turn"
return route
def resolve_turn_route(user_message: str, routing_config: Optional[Dict[str, Any]], primary: Dict[str, Any]) -> Dict[str, Any]:
"""Resolve the effective model/runtime for one turn.
Returns a dict with model/runtime/signature/label fields.
"""
route = choose_cheap_model_route(user_message, routing_config)
if not route:
return {
"model": primary.get("model"),
"runtime": {
"api_key": primary.get("api_key"),
"base_url": primary.get("base_url"),
"provider": primary.get("provider"),
"api_mode": primary.get("api_mode"),
"command": primary.get("command"),
"args": list(primary.get("args") or []),
},
"label": None,
"signature": (
primary.get("model"),
primary.get("provider"),
primary.get("base_url"),
primary.get("api_mode"),
primary.get("command"),
tuple(primary.get("args") or ()),
),
}
from hermes_cli.runtime_provider import resolve_runtime_provider
explicit_api_key = None
api_key_env = str(route.get("api_key_env") or "").strip()
if api_key_env:
explicit_api_key = os.getenv(api_key_env) or None
try:
runtime = resolve_runtime_provider(
requested=route.get("provider"),
explicit_api_key=explicit_api_key,
explicit_base_url=route.get("base_url"),
)
except Exception:
return {
"model": primary.get("model"),
"runtime": {
"api_key": primary.get("api_key"),
"base_url": primary.get("base_url"),
"provider": primary.get("provider"),
"api_mode": primary.get("api_mode"),
"command": primary.get("command"),
"args": list(primary.get("args") or []),
},
"label": None,
"signature": (
primary.get("model"),
primary.get("provider"),
primary.get("base_url"),
primary.get("api_mode"),
primary.get("command"),
tuple(primary.get("args") or ()),
),
}
return {
"model": route.get("model"),
"runtime": {
"api_key": runtime.get("api_key"),
"base_url": runtime.get("base_url"),
"provider": runtime.get("provider"),
"api_mode": runtime.get("api_mode"),
"command": runtime.get("command"),
"args": list(runtime.get("args") or []),
},
"label": f"smart route → {route.get('model')} ({runtime.get('provider')})",
"signature": (
route.get("model"),
runtime.get("provider"),
runtime.get("base_url"),
runtime.get("api_mode"),
runtime.get("command"),
tuple(runtime.get("args") or ()),
),
}

View File

@@ -1,125 +0,0 @@
"""Auto-generate short session titles from the first user/assistant exchange.
Runs asynchronously after the first response is delivered so it never
adds latency to the user-facing reply.
"""
import logging
import threading
from typing import Optional
from agent.auxiliary_client import call_llm
logger = logging.getLogger(__name__)
_TITLE_PROMPT = (
"Generate a short, descriptive title (3-7 words) for a conversation that starts with the "
"following exchange. The title should capture the main topic or intent. "
"Return ONLY the title text, nothing else. No quotes, no punctuation at the end, no prefixes."
)
def generate_title(user_message: str, assistant_response: str, timeout: float = 15.0) -> Optional[str]:
"""Generate a session title from the first exchange.
Uses the auxiliary LLM client (cheapest/fastest available model).
Returns the title string or None on failure.
"""
# Truncate long messages to keep the request small
user_snippet = user_message[:500] if user_message else ""
assistant_snippet = assistant_response[:500] if assistant_response else ""
messages = [
{"role": "system", "content": _TITLE_PROMPT},
{"role": "user", "content": f"User: {user_snippet}\n\nAssistant: {assistant_snippet}"},
]
try:
response = call_llm(
task="compression", # reuse compression task config (cheap/fast model)
messages=messages,
max_tokens=30,
temperature=0.3,
timeout=timeout,
)
title = (response.choices[0].message.content or "").strip()
# Clean up: remove quotes, trailing punctuation, prefixes like "Title: "
title = title.strip('"\'')
if title.lower().startswith("title:"):
title = title[6:].strip()
# Enforce reasonable length
if len(title) > 80:
title = title[:77] + "..."
return title if title else None
except Exception as e:
logger.debug("Title generation failed: %s", e)
return None
def auto_title_session(
session_db,
session_id: str,
user_message: str,
assistant_response: str,
) -> None:
"""Generate and set a session title if one doesn't already exist.
Called in a background thread after the first exchange completes.
Silently skips if:
- session_db is None
- session already has a title (user-set or previously auto-generated)
- title generation fails
"""
if not session_db or not session_id:
return
# Check if title already exists (user may have set one via /title before first response)
try:
existing = session_db.get_session_title(session_id)
if existing:
return
except Exception:
return
title = generate_title(user_message, assistant_response)
if not title:
return
try:
session_db.set_session_title(session_id, title)
logger.debug("Auto-generated session title: %s", title)
except Exception as e:
logger.debug("Failed to set auto-generated title: %s", e)
def maybe_auto_title(
session_db,
session_id: str,
user_message: str,
assistant_response: str,
conversation_history: list,
) -> None:
"""Fire-and-forget title generation after the first exchange.
Only generates a title when:
- This appears to be the first user→assistant exchange
- No title is already set
"""
if not session_db or not session_id or not user_message or not assistant_response:
return
# Count user messages in history to detect first exchange.
# conversation_history includes the exchange that just happened,
# so for a first exchange we expect exactly 1 user message
# (or 2 counting system). Be generous: generate on first 2 exchanges.
user_msg_count = sum(1 for m in (conversation_history or []) if m.get("role") == "user")
if user_msg_count > 2:
return
thread = threading.Thread(
target=auto_title_session,
args=(session_db, session_id, user_message, assistant_response),
daemon=True,
name="auto-title",
)
thread.start()

View File

@@ -1,655 +0,0 @@
from __future__ import annotations
from dataclasses import dataclass
from datetime import datetime, timezone
from decimal import Decimal
from typing import Any, Dict, Literal, Optional
from agent.model_metadata import fetch_endpoint_model_metadata, fetch_model_metadata
DEFAULT_PRICING = {"input": 0.0, "output": 0.0}
_ZERO = Decimal("0")
_ONE_MILLION = Decimal("1000000")
CostStatus = Literal["actual", "estimated", "included", "unknown"]
CostSource = Literal[
"provider_cost_api",
"provider_generation_api",
"provider_models_api",
"official_docs_snapshot",
"user_override",
"custom_contract",
"none",
]
@dataclass(frozen=True)
class CanonicalUsage:
input_tokens: int = 0
output_tokens: int = 0
cache_read_tokens: int = 0
cache_write_tokens: int = 0
reasoning_tokens: int = 0
request_count: int = 1
raw_usage: Optional[dict[str, Any]] = None
@property
def prompt_tokens(self) -> int:
return self.input_tokens + self.cache_read_tokens + self.cache_write_tokens
@property
def total_tokens(self) -> int:
return self.prompt_tokens + self.output_tokens
@dataclass(frozen=True)
class BillingRoute:
provider: str
model: str
base_url: str = ""
billing_mode: str = "unknown"
@dataclass(frozen=True)
class PricingEntry:
input_cost_per_million: Optional[Decimal] = None
output_cost_per_million: Optional[Decimal] = None
cache_read_cost_per_million: Optional[Decimal] = None
cache_write_cost_per_million: Optional[Decimal] = None
request_cost: Optional[Decimal] = None
source: CostSource = "none"
source_url: Optional[str] = None
pricing_version: Optional[str] = None
fetched_at: Optional[datetime] = None
@dataclass(frozen=True)
class CostResult:
amount_usd: Optional[Decimal]
status: CostStatus
source: CostSource
label: str
fetched_at: Optional[datetime] = None
pricing_version: Optional[str] = None
notes: tuple[str, ...] = ()
_UTC_NOW = lambda: datetime.now(timezone.utc)
# Official docs snapshot entries. Models whose published pricing and cache
# semantics are stable enough to encode exactly.
_OFFICIAL_DOCS_PRICING: Dict[tuple[str, str], PricingEntry] = {
(
"anthropic",
"claude-opus-4-20250514",
): PricingEntry(
input_cost_per_million=Decimal("15.00"),
output_cost_per_million=Decimal("75.00"),
cache_read_cost_per_million=Decimal("1.50"),
cache_write_cost_per_million=Decimal("18.75"),
source="official_docs_snapshot",
source_url="https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching",
pricing_version="anthropic-prompt-caching-2026-03-16",
),
(
"anthropic",
"claude-sonnet-4-20250514",
): PricingEntry(
input_cost_per_million=Decimal("3.00"),
output_cost_per_million=Decimal("15.00"),
cache_read_cost_per_million=Decimal("0.30"),
cache_write_cost_per_million=Decimal("3.75"),
source="official_docs_snapshot",
source_url="https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching",
pricing_version="anthropic-prompt-caching-2026-03-16",
),
# OpenAI
(
"openai",
"gpt-4o",
): PricingEntry(
input_cost_per_million=Decimal("2.50"),
output_cost_per_million=Decimal("10.00"),
cache_read_cost_per_million=Decimal("1.25"),
source="official_docs_snapshot",
source_url="https://openai.com/api/pricing/",
pricing_version="openai-pricing-2026-03-16",
),
(
"openai",
"gpt-4o-mini",
): PricingEntry(
input_cost_per_million=Decimal("0.15"),
output_cost_per_million=Decimal("0.60"),
cache_read_cost_per_million=Decimal("0.075"),
source="official_docs_snapshot",
source_url="https://openai.com/api/pricing/",
pricing_version="openai-pricing-2026-03-16",
),
(
"openai",
"gpt-4.1",
): PricingEntry(
input_cost_per_million=Decimal("2.00"),
output_cost_per_million=Decimal("8.00"),
cache_read_cost_per_million=Decimal("0.50"),
source="official_docs_snapshot",
source_url="https://openai.com/api/pricing/",
pricing_version="openai-pricing-2026-03-16",
),
(
"openai",
"gpt-4.1-mini",
): PricingEntry(
input_cost_per_million=Decimal("0.40"),
output_cost_per_million=Decimal("1.60"),
cache_read_cost_per_million=Decimal("0.10"),
source="official_docs_snapshot",
source_url="https://openai.com/api/pricing/",
pricing_version="openai-pricing-2026-03-16",
),
(
"openai",
"gpt-4.1-nano",
): PricingEntry(
input_cost_per_million=Decimal("0.10"),
output_cost_per_million=Decimal("0.40"),
cache_read_cost_per_million=Decimal("0.025"),
source="official_docs_snapshot",
source_url="https://openai.com/api/pricing/",
pricing_version="openai-pricing-2026-03-16",
),
(
"openai",
"o3",
): PricingEntry(
input_cost_per_million=Decimal("10.00"),
output_cost_per_million=Decimal("40.00"),
cache_read_cost_per_million=Decimal("2.50"),
source="official_docs_snapshot",
source_url="https://openai.com/api/pricing/",
pricing_version="openai-pricing-2026-03-16",
),
(
"openai",
"o3-mini",
): PricingEntry(
input_cost_per_million=Decimal("1.10"),
output_cost_per_million=Decimal("4.40"),
cache_read_cost_per_million=Decimal("0.55"),
source="official_docs_snapshot",
source_url="https://openai.com/api/pricing/",
pricing_version="openai-pricing-2026-03-16",
),
# Anthropic older models (pre-4.6 generation)
(
"anthropic",
"claude-3-5-sonnet-20241022",
): PricingEntry(
input_cost_per_million=Decimal("3.00"),
output_cost_per_million=Decimal("15.00"),
cache_read_cost_per_million=Decimal("0.30"),
cache_write_cost_per_million=Decimal("3.75"),
source="official_docs_snapshot",
source_url="https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching",
pricing_version="anthropic-pricing-2026-03-16",
),
(
"anthropic",
"claude-3-5-haiku-20241022",
): PricingEntry(
input_cost_per_million=Decimal("0.80"),
output_cost_per_million=Decimal("4.00"),
cache_read_cost_per_million=Decimal("0.08"),
cache_write_cost_per_million=Decimal("1.00"),
source="official_docs_snapshot",
source_url="https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching",
pricing_version="anthropic-pricing-2026-03-16",
),
(
"anthropic",
"claude-3-opus-20240229",
): PricingEntry(
input_cost_per_million=Decimal("15.00"),
output_cost_per_million=Decimal("75.00"),
cache_read_cost_per_million=Decimal("1.50"),
cache_write_cost_per_million=Decimal("18.75"),
source="official_docs_snapshot",
source_url="https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching",
pricing_version="anthropic-pricing-2026-03-16",
),
(
"anthropic",
"claude-3-haiku-20240307",
): PricingEntry(
input_cost_per_million=Decimal("0.25"),
output_cost_per_million=Decimal("1.25"),
cache_read_cost_per_million=Decimal("0.03"),
cache_write_cost_per_million=Decimal("0.30"),
source="official_docs_snapshot",
source_url="https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching",
pricing_version="anthropic-pricing-2026-03-16",
),
# DeepSeek
(
"deepseek",
"deepseek-chat",
): PricingEntry(
input_cost_per_million=Decimal("0.14"),
output_cost_per_million=Decimal("0.28"),
source="official_docs_snapshot",
source_url="https://api-docs.deepseek.com/quick_start/pricing",
pricing_version="deepseek-pricing-2026-03-16",
),
(
"deepseek",
"deepseek-reasoner",
): PricingEntry(
input_cost_per_million=Decimal("0.55"),
output_cost_per_million=Decimal("2.19"),
source="official_docs_snapshot",
source_url="https://api-docs.deepseek.com/quick_start/pricing",
pricing_version="deepseek-pricing-2026-03-16",
),
# Google Gemini
(
"google",
"gemini-2.5-pro",
): PricingEntry(
input_cost_per_million=Decimal("1.25"),
output_cost_per_million=Decimal("10.00"),
source="official_docs_snapshot",
source_url="https://ai.google.dev/pricing",
pricing_version="google-pricing-2026-03-16",
),
(
"google",
"gemini-2.5-flash",
): PricingEntry(
input_cost_per_million=Decimal("0.15"),
output_cost_per_million=Decimal("0.60"),
source="official_docs_snapshot",
source_url="https://ai.google.dev/pricing",
pricing_version="google-pricing-2026-03-16",
),
(
"google",
"gemini-2.0-flash",
): PricingEntry(
input_cost_per_million=Decimal("0.10"),
output_cost_per_million=Decimal("0.40"),
source="official_docs_snapshot",
source_url="https://ai.google.dev/pricing",
pricing_version="google-pricing-2026-03-16",
),
}
def _to_decimal(value: Any) -> Optional[Decimal]:
if value is None:
return None
try:
return Decimal(str(value))
except Exception:
return None
def _to_int(value: Any) -> int:
try:
return int(value or 0)
except Exception:
return 0
def resolve_billing_route(
model_name: str,
provider: Optional[str] = None,
base_url: Optional[str] = None,
) -> BillingRoute:
provider_name = (provider or "").strip().lower()
base = (base_url or "").strip().lower()
model = (model_name or "").strip()
if not provider_name and "/" in model:
inferred_provider, bare_model = model.split("/", 1)
if inferred_provider in {"anthropic", "openai", "google"}:
provider_name = inferred_provider
model = bare_model
if provider_name == "openai-codex":
return BillingRoute(provider="openai-codex", model=model, base_url=base_url or "", billing_mode="subscription_included")
if provider_name == "openrouter" or "openrouter.ai" in base:
return BillingRoute(provider="openrouter", model=model, base_url=base_url or "", billing_mode="official_models_api")
if provider_name == "anthropic":
return BillingRoute(provider="anthropic", model=model.split("/")[-1], base_url=base_url or "", billing_mode="official_docs_snapshot")
if provider_name == "openai":
return BillingRoute(provider="openai", model=model.split("/")[-1], base_url=base_url or "", billing_mode="official_docs_snapshot")
if provider_name in {"custom", "local"} or (base and "localhost" in base):
return BillingRoute(provider=provider_name or "custom", model=model, base_url=base_url or "", billing_mode="unknown")
return BillingRoute(provider=provider_name or "unknown", model=model.split("/")[-1] if model else "", base_url=base_url or "", billing_mode="unknown")
def _lookup_official_docs_pricing(route: BillingRoute) -> Optional[PricingEntry]:
return _OFFICIAL_DOCS_PRICING.get((route.provider, route.model.lower()))
def _openrouter_pricing_entry(route: BillingRoute) -> Optional[PricingEntry]:
return _pricing_entry_from_metadata(
fetch_model_metadata(),
route.model,
source_url="https://openrouter.ai/docs/api/api-reference/models/get-models",
pricing_version="openrouter-models-api",
)
def _pricing_entry_from_metadata(
metadata: Dict[str, Dict[str, Any]],
model_id: str,
*,
source_url: str,
pricing_version: str,
) -> Optional[PricingEntry]:
if model_id not in metadata:
return None
pricing = metadata[model_id].get("pricing") or {}
prompt = _to_decimal(pricing.get("prompt"))
completion = _to_decimal(pricing.get("completion"))
request = _to_decimal(pricing.get("request"))
cache_read = _to_decimal(
pricing.get("cache_read")
or pricing.get("cached_prompt")
or pricing.get("input_cache_read")
)
cache_write = _to_decimal(
pricing.get("cache_write")
or pricing.get("cache_creation")
or pricing.get("input_cache_write")
)
if prompt is None and completion is None and request is None:
return None
def _per_token_to_per_million(value: Optional[Decimal]) -> Optional[Decimal]:
if value is None:
return None
return value * _ONE_MILLION
return PricingEntry(
input_cost_per_million=_per_token_to_per_million(prompt),
output_cost_per_million=_per_token_to_per_million(completion),
cache_read_cost_per_million=_per_token_to_per_million(cache_read),
cache_write_cost_per_million=_per_token_to_per_million(cache_write),
request_cost=request,
source="provider_models_api",
source_url=source_url,
pricing_version=pricing_version,
fetched_at=_UTC_NOW(),
)
def get_pricing_entry(
model_name: str,
provider: Optional[str] = None,
base_url: Optional[str] = None,
api_key: Optional[str] = None,
) -> Optional[PricingEntry]:
route = resolve_billing_route(model_name, provider=provider, base_url=base_url)
if route.billing_mode == "subscription_included":
return PricingEntry(
input_cost_per_million=_ZERO,
output_cost_per_million=_ZERO,
cache_read_cost_per_million=_ZERO,
cache_write_cost_per_million=_ZERO,
source="none",
pricing_version="included-route",
)
if route.provider == "openrouter":
return _openrouter_pricing_entry(route)
if route.base_url:
entry = _pricing_entry_from_metadata(
fetch_endpoint_model_metadata(route.base_url, api_key=api_key or ""),
route.model,
source_url=f"{route.base_url.rstrip('/')}/models",
pricing_version="openai-compatible-models-api",
)
if entry:
return entry
return _lookup_official_docs_pricing(route)
def normalize_usage(
response_usage: Any,
*,
provider: Optional[str] = None,
api_mode: Optional[str] = None,
) -> CanonicalUsage:
"""Normalize raw API response usage into canonical token buckets.
Handles three API shapes:
- Anthropic: input_tokens/output_tokens/cache_read_input_tokens/cache_creation_input_tokens
- Codex Responses: input_tokens includes cache tokens; input_tokens_details.cached_tokens separates them
- OpenAI Chat Completions: prompt_tokens includes cache tokens; prompt_tokens_details.cached_tokens separates them
In both Codex and OpenAI modes, input_tokens is derived by subtracting cache
tokens from the total — the API contract is that input/prompt totals include
cached tokens and the details object breaks them out.
"""
if not response_usage:
return CanonicalUsage()
provider_name = (provider or "").strip().lower()
mode = (api_mode or "").strip().lower()
if mode == "anthropic_messages" or provider_name == "anthropic":
input_tokens = _to_int(getattr(response_usage, "input_tokens", 0))
output_tokens = _to_int(getattr(response_usage, "output_tokens", 0))
cache_read_tokens = _to_int(getattr(response_usage, "cache_read_input_tokens", 0))
cache_write_tokens = _to_int(getattr(response_usage, "cache_creation_input_tokens", 0))
elif mode == "codex_responses":
input_total = _to_int(getattr(response_usage, "input_tokens", 0))
output_tokens = _to_int(getattr(response_usage, "output_tokens", 0))
details = getattr(response_usage, "input_tokens_details", None)
cache_read_tokens = _to_int(getattr(details, "cached_tokens", 0) if details else 0)
cache_write_tokens = _to_int(
getattr(details, "cache_creation_tokens", 0) if details else 0
)
input_tokens = max(0, input_total - cache_read_tokens - cache_write_tokens)
else:
prompt_total = _to_int(getattr(response_usage, "prompt_tokens", 0))
output_tokens = _to_int(getattr(response_usage, "completion_tokens", 0))
details = getattr(response_usage, "prompt_tokens_details", None)
cache_read_tokens = _to_int(getattr(details, "cached_tokens", 0) if details else 0)
cache_write_tokens = _to_int(
getattr(details, "cache_write_tokens", 0) if details else 0
)
input_tokens = max(0, prompt_total - cache_read_tokens - cache_write_tokens)
reasoning_tokens = 0
output_details = getattr(response_usage, "output_tokens_details", None)
if output_details:
reasoning_tokens = _to_int(getattr(output_details, "reasoning_tokens", 0))
return CanonicalUsage(
input_tokens=input_tokens,
output_tokens=output_tokens,
cache_read_tokens=cache_read_tokens,
cache_write_tokens=cache_write_tokens,
reasoning_tokens=reasoning_tokens,
)
def estimate_usage_cost(
model_name: str,
usage: CanonicalUsage,
*,
provider: Optional[str] = None,
base_url: Optional[str] = None,
api_key: Optional[str] = None,
) -> CostResult:
route = resolve_billing_route(model_name, provider=provider, base_url=base_url)
if route.billing_mode == "subscription_included":
return CostResult(
amount_usd=_ZERO,
status="included",
source="none",
label="included",
pricing_version="included-route",
)
entry = get_pricing_entry(model_name, provider=provider, base_url=base_url, api_key=api_key)
if not entry:
return CostResult(amount_usd=None, status="unknown", source="none", label="n/a")
notes: list[str] = []
amount = _ZERO
if usage.input_tokens and entry.input_cost_per_million is None:
return CostResult(amount_usd=None, status="unknown", source=entry.source, label="n/a")
if usage.output_tokens and entry.output_cost_per_million is None:
return CostResult(amount_usd=None, status="unknown", source=entry.source, label="n/a")
if usage.cache_read_tokens:
if entry.cache_read_cost_per_million is None:
return CostResult(
amount_usd=None,
status="unknown",
source=entry.source,
label="n/a",
notes=("cache-read pricing unavailable for route",),
)
if usage.cache_write_tokens:
if entry.cache_write_cost_per_million is None:
return CostResult(
amount_usd=None,
status="unknown",
source=entry.source,
label="n/a",
notes=("cache-write pricing unavailable for route",),
)
if entry.input_cost_per_million is not None:
amount += Decimal(usage.input_tokens) * entry.input_cost_per_million / _ONE_MILLION
if entry.output_cost_per_million is not None:
amount += Decimal(usage.output_tokens) * entry.output_cost_per_million / _ONE_MILLION
if entry.cache_read_cost_per_million is not None:
amount += Decimal(usage.cache_read_tokens) * entry.cache_read_cost_per_million / _ONE_MILLION
if entry.cache_write_cost_per_million is not None:
amount += Decimal(usage.cache_write_tokens) * entry.cache_write_cost_per_million / _ONE_MILLION
if entry.request_cost is not None and usage.request_count:
amount += Decimal(usage.request_count) * entry.request_cost
status: CostStatus = "estimated"
label = f"~${amount:.2f}"
if entry.source == "none" and amount == _ZERO:
status = "included"
label = "included"
if route.provider == "openrouter":
notes.append("OpenRouter cost is estimated from the models API until reconciled.")
return CostResult(
amount_usd=amount,
status=status,
source=entry.source,
label=label,
fetched_at=entry.fetched_at,
pricing_version=entry.pricing_version,
notes=tuple(notes),
)
def has_known_pricing(
model_name: str,
provider: Optional[str] = None,
base_url: Optional[str] = None,
api_key: Optional[str] = None,
) -> bool:
"""Check whether we have pricing data for this model+route.
Uses direct lookup instead of routing through the full estimation
pipeline — avoids creating dummy usage objects just to check status.
"""
route = resolve_billing_route(model_name, provider=provider, base_url=base_url)
if route.billing_mode == "subscription_included":
return True
entry = get_pricing_entry(model_name, provider=provider, base_url=base_url, api_key=api_key)
return entry is not None
def get_pricing(
model_name: str,
provider: Optional[str] = None,
base_url: Optional[str] = None,
api_key: Optional[str] = None,
) -> Dict[str, float]:
"""Backward-compatible thin wrapper for legacy callers.
Returns only non-cache input/output fields when a pricing entry exists.
Unknown routes return zeroes.
"""
entry = get_pricing_entry(model_name, provider=provider, base_url=base_url, api_key=api_key)
if not entry:
return {"input": 0.0, "output": 0.0}
return {
"input": float(entry.input_cost_per_million or _ZERO),
"output": float(entry.output_cost_per_million or _ZERO),
}
def estimate_cost_usd(
model: str,
input_tokens: int,
output_tokens: int,
*,
provider: Optional[str] = None,
base_url: Optional[str] = None,
api_key: Optional[str] = None,
) -> float:
"""Backward-compatible helper for legacy callers.
This uses non-cached input/output only. New code should call
`estimate_usage_cost()` with canonical usage buckets.
"""
result = estimate_usage_cost(
model,
CanonicalUsage(input_tokens=input_tokens, output_tokens=output_tokens),
provider=provider,
base_url=base_url,
api_key=api_key,
)
return float(result.amount_usd or _ZERO)
def format_duration_compact(seconds: float) -> str:
if seconds < 60:
return f"{seconds:.0f}s"
minutes = seconds / 60
if minutes < 60:
return f"{minutes:.0f}m"
hours = minutes / 60
if hours < 24:
remaining_min = int(minutes % 60)
return f"{int(hours)}h {remaining_min}m" if remaining_min else f"{int(hours)}h"
days = hours / 24
return f"{days:.1f}d"
def format_token_count_compact(value: int) -> str:
abs_value = abs(int(value))
if abs_value < 1_000:
return str(int(value))
sign = "-" if value < 0 else ""
units = ((1_000_000_000, "B"), (1_000_000, "M"), (1_000, "K"))
for threshold, suffix in units:
if abs_value >= threshold:
scaled = abs_value / threshold
if scaled < 10:
text = f"{scaled:.2f}"
elif scaled < 100:
text = f"{scaled:.1f}"
else:
text = f"{scaled:.0f}"
text = text.rstrip("0").rstrip(".")
return f"{sign}{text}{suffix}"
return f"{value:,}"

View File

@@ -128,7 +128,6 @@ def _extract_tool_stats(messages: List[Dict[str, Any]]) -> Dict[str, Dict[str, i
# Track tool calls from assistant messages
if msg["role"] == "assistant" and "tool_calls" in msg and msg["tool_calls"]:
for tool_call in msg["tool_calls"]:
if not tool_call or not isinstance(tool_call, dict): continue
tool_name = tool_call["function"]["name"]
tool_call_id = tool_call["id"]
@@ -607,7 +606,7 @@ class BatchRunner:
# Create batches
self.batches = self._create_batches()
print("📊 Batch Runner Initialized")
print(f"📊 Batch Runner Initialized")
print(f" Dataset: {self.dataset_file} ({len(self.dataset)} prompts)")
print(f" Batch size: {self.batch_size}")
print(f" Total batches: {len(self.batches)}")
@@ -827,7 +826,7 @@ class BatchRunner:
print("=" * 70)
print(f" Original dataset size: {len(self.dataset):,} prompts")
print(f" Already completed: {len(skipped_indices):,} prompts")
print(" ─────────────────────────────────────────")
print(f" ─────────────────────────────────────────")
print(f" 🎯 RESUMING WITH: {len(filtered_entries):,} prompts")
print(f" New batches created: {len(batches_to_process)}")
print("=" * 70 + "\n")
@@ -889,7 +888,7 @@ class BatchRunner:
]
print(f"✅ Created {len(tasks)} batch tasks")
print("🚀 Starting parallel batch processing...\n")
print(f"🚀 Starting parallel batch processing...\n")
# Use rich Progress for better visual tracking with persistent bottom bar
# redirect_stdout/stderr lets rich manage all output so progress bar stays clean
@@ -1058,7 +1057,7 @@ class BatchRunner:
print(f"✅ Total trajectories in merged file: {total_entries - filtered_entries}")
print(f"✅ Total batch files merged: {batch_files_found}")
print(f"⏱️ Total duration: {round(time.time() - start_time, 2)}s")
print("\n📈 Tool Usage Statistics:")
print(f"\n📈 Tool Usage Statistics:")
print("-" * 70)
if total_tool_stats:
@@ -1085,7 +1084,7 @@ class BatchRunner:
# Print reasoning coverage stats
total_discarded = sum(r.get("discarded_no_reasoning", 0) for r in results)
print("\n🧠 Reasoning Coverage:")
print(f"\n🧠 Reasoning Coverage:")
print("-" * 70)
total_turns = total_reasoning_stats["total_assistant_turns"]
with_reasoning = total_reasoning_stats["turns_with_reasoning"]
@@ -1102,8 +1101,8 @@ class BatchRunner:
print(f" 🚫 Samples discarded (zero reasoning): {total_discarded:,}")
print(f"\n💾 Results saved to: {self.output_dir}")
print(" - Trajectories: trajectories.jsonl (combined)")
print(" - Individual batches: batch_*.jsonl (for debugging)")
print(f" - Trajectories: trajectories.jsonl (combined)")
print(f" - Individual batches: batch_*.jsonl (for debugging)")
print(f" - Statistics: {self.stats_file.name}")
print(f" - Checkpoint: {self.checkpoint_file.name}")
@@ -1113,7 +1112,7 @@ def main(
batch_size: int = None,
run_name: str = None,
distribution: str = "default",
model: str = "anthropic/claude-sonnet-4.6",
model: str = "anthropic/claude-sonnet-4-20250514",
api_key: str = None,
base_url: str = "https://openrouter.ai/api/v1",
max_turns: int = 10,
@@ -1156,7 +1155,7 @@ def main(
providers_order (str): Comma-separated list of OpenRouter providers to try in order (e.g. "anthropic,openai,google")
provider_sort (str): Sort providers by "price", "throughput", or "latency" (OpenRouter only)
max_tokens (int): Maximum tokens for model responses (optional, uses model default if not set)
reasoning_effort (str): OpenRouter reasoning effort level: "xhigh", "high", "medium", "low", "minimal", "none" (default: "medium")
reasoning_effort (str): OpenRouter reasoning effort level: "xhigh", "high", "medium", "low", "minimal", "none" (default: "xhigh")
reasoning_disabled (bool): Completely disable reasoning/thinking tokens (default: False)
prefill_messages_file (str): Path to JSON file containing prefill messages (list of {role, content} dicts)
max_samples (int): Only process the first N samples from the dataset (optional, processes all if not set)
@@ -1217,7 +1216,7 @@ def main(
providers_order_list = [p.strip() for p in providers_order.split(",")] if providers_order else None
# Build reasoning_config from CLI flags
# --reasoning_disabled takes priority, then --reasoning_effort, then default (medium)
# --reasoning_disabled takes priority, then --reasoning_effort, then default (xhigh)
reasoning_config = None
if reasoning_disabled:
# Completely disable reasoning/thinking tokens
@@ -1239,7 +1238,7 @@ def main(
with open(prefill_messages_file, 'r', encoding='utf-8') as f:
prefill_messages = json.load(f)
if not isinstance(prefill_messages, list):
print("❌ Error: prefill_messages_file must contain a JSON array of messages")
print(f"❌ Error: prefill_messages_file must contain a JSON array of messages")
return
print(f"💬 Loaded {len(prefill_messages)} prefill messages from {prefill_messages_file}")
except Exception as e:

View File

@@ -11,7 +11,6 @@ model:
# Inference provider selection:
# "auto" - Use Nous Portal if logged in, otherwise OpenRouter/env vars (default)
# "nous-api" - Use Nous Portal via API key (requires: NOUS_API_KEY)
# "openrouter" - Always use OpenRouter API key from OPENROUTER_API_KEY
# "nous" - Always use Nous Portal (requires: hermes login)
# "zai" - Use z.ai / ZhipuAI GLM models (requires: GLM_API_KEY)
@@ -51,30 +50,6 @@ model:
# # Data policy: "allow" (default) or "deny" to exclude providers that may store data
# # data_collection: "deny"
# =============================================================================
# Smart Model Routing (optional)
# =============================================================================
# Use a cheaper model for short/simple turns while keeping your main model for
# more complex requests. Disabled by default.
#
# smart_model_routing:
# enabled: true
# max_simple_chars: 160
# max_simple_words: 28
# cheap_model:
# provider: openrouter
# model: google/gemini-2.5-flash
# =============================================================================
# Git Worktree Isolation
# =============================================================================
# When enabled, each CLI session creates an isolated git worktree so multiple
# agents can work on the same repo concurrently without file collisions.
# Equivalent to always passing --worktree / -w on the command line.
#
# worktree: true # Always create a worktree when in a git repo
# worktree: false # Default — only create when -w flag is passed
# =============================================================================
# Terminal Tool Configuration
# =============================================================================
@@ -90,9 +65,8 @@ model:
# - Messaging (Telegram/Discord): Uses MESSAGING_CWD from .env (default: home)
terminal:
backend: "local"
cwd: "." # For local backend: "." = current directory. Ignored for remote backends unless a backend documents otherwise.
cwd: "." # For local backend: "." = current directory. Ignored for remote backends.
timeout: 180
docker_mount_cwd_to_workspace: false # SECURITY: off by default. Opt in to mount the launch cwd into Docker /workspace.
lifetime_seconds: 300
# sudo_password: "" # Enable sudo commands (pipes via sudo -S) - SECURITY WARNING: plaintext!
@@ -122,13 +96,6 @@ terminal:
# timeout: 180
# lifetime_seconds: 300
# docker_image: "nikolaik/python-nodejs:python3.11-nodejs20"
# docker_mount_cwd_to_workspace: true # Explicit opt-in: mount your launch cwd into /workspace
# # Optional: explicitly forward selected env vars into Docker.
# # These values come from your current shell first, then ~/.hermes/.env.
# # Warning: anything forwarded here is visible to commands run in the container.
# docker_forward_env:
# - "GITHUB_TOKEN"
# - "NPM_TOKEN"
# -----------------------------------------------------------------------------
# OPTION 4: Singularity/Apptainer container
@@ -200,20 +167,6 @@ terminal:
# Example (add to your terminal section):
# sudo_password: "your-password-here"
# =============================================================================
# Security Scanning (tirith)
# =============================================================================
# Optional pre-exec command security scanning via tirith.
# Detects homograph URLs, pipe-to-shell, terminal injection, env manipulation.
# Install: brew install sheeki03/tap/tirith
# Docs: https://github.com/sheeki03/tirith
#
# security:
# tirith_enabled: true # Enable/disable tirith scanning
# tirith_path: "tirith" # Path to tirith binary (supports ~ expansion)
# tirith_timeout: 5 # Scan timeout in seconds
# tirith_fail_open: true # Allow commands if tirith unavailable
# =============================================================================
# Browser Tool Configuration
# =============================================================================
@@ -278,11 +231,11 @@ compression:
# "auto" - Best available: OpenRouter → Nous Portal → main endpoint (default)
# "openrouter" - Force OpenRouter (requires OPENROUTER_API_KEY)
# "nous" - Force Nous Portal (requires: hermes login)
# "codex" - Force Codex OAuth (requires: hermes model → Codex).
# Uses gpt-5.3-codex which supports vision.
# "main" - Use your custom endpoint (OPENAI_BASE_URL + OPENAI_API_KEY).
# Works with OpenAI API, local models, or any OpenAI-compatible
# endpoint. Also falls back to Codex OAuth and API-key providers.
# "main" - Use the same provider & credentials as your main chat model.
# Skips OpenRouter/Nous and uses your custom endpoint
# (OPENAI_BASE_URL), Codex OAuth, or API-key provider directly.
# Useful if you run a local model and want auxiliary tasks to
# use it too.
#
# Model: leave empty to use the provider's default. When empty, OpenRouter
# uses "google/gemini-3-flash-preview" and Nous uses "gemini-3-flash".
@@ -355,25 +308,6 @@ session_reset:
idle_minutes: 1440 # Inactivity timeout in minutes (default: 1440 = 24 hours)
at_hour: 4 # Daily reset hour, 0-23 local time (default: 4 AM)
# When true, group/channel chats use one session per participant when the platform
# provides a user ID. This is the secure default and prevents users in the same
# room from sharing context, interrupts, and token costs. Set false only if you
# explicitly want one shared "room brain" per group/channel.
group_sessions_per_user: true
# ─────────────────────────────────────────────────────────────────────────────
# Gateway Streaming
# ─────────────────────────────────────────────────────────────────────────────
# Stream tokens to messaging platforms in real-time. The bot sends a message
# on first token, then progressively edits it as more tokens arrive.
# Disabled by default — enable to try the streaming UX on Telegram/Discord/Slack.
streaming:
enabled: false
# transport: edit # "edit" = progressive editMessageText
# edit_interval: 0.3 # seconds between message edits
# buffer_threshold: 40 # chars before forcing an edit flush
# cursor: " ▉" # cursor shown during streaming
# =============================================================================
# Skills Configuration
# =============================================================================
@@ -401,7 +335,7 @@ agent:
# Reasoning effort level (OpenRouter and Nous Portal)
# Controls how much "thinking" the model does before responding.
# Options: "xhigh" (max), "high", "medium", "low", "minimal", "none" (disable)
reasoning_effort: "medium"
reasoning_effort: "xhigh"
# Predefined personalities (use with /personality command)
personalities:
@@ -424,7 +358,7 @@ agent:
# Toolsets
# =============================================================================
# Control which tools the agent has access to.
# Use `hermes tools` to interactively enable/disable tools per platform.
# Use "all" to enable everything, or specify individual toolsets.
# =============================================================================
# Platform Toolsets (per-platform tool configuration)
@@ -458,13 +392,11 @@ agent:
# discord: [web, vision, skills, todo]
#
# If not set, defaults are:
# cli: hermes-cli (everything + cronjob management)
# telegram: hermes-telegram (terminal, file, web, vision, image, tts, browser, skills, todo, cronjob, messaging)
# discord: hermes-discord (same as telegram)
# whatsapp: hermes-whatsapp (same as telegram)
# slack: hermes-slack (same as telegram)
# signal: hermes-signal (same as telegram)
# homeassistant: hermes-homeassistant (same as telegram)
# cli: hermes-cli (everything + cronjob management)
# telegram: hermes-telegram (terminal, file, web, vision, image, tts, browser, skills, todo, cronjob, messaging)
# discord: hermes-discord (same as telegram)
# whatsapp: hermes-whatsapp (same as telegram)
# slack: hermes-slack (same as telegram)
#
platform_toolsets:
cli: [hermes-cli]
@@ -472,8 +404,6 @@ platform_toolsets:
discord: [hermes-discord]
whatsapp: [hermes-whatsapp]
slack: [hermes-slack]
signal: [hermes-signal]
homeassistant: [hermes-homeassistant]
# ─────────────────────────────────────────────────────────────────────────────
# Available toolsets (use these names in platform_toolsets or the toolsets list)
@@ -497,7 +427,7 @@ platform_toolsets:
# moa - mixture_of_agents (requires OPENROUTER_API_KEY)
# todo - todo (in-memory task planning, no deps)
# tts - text_to_speech (Edge TTS free, or ELEVENLABS/OPENAI key)
# cronjob - cronjob (create/list/update/pause/resume/run/remove scheduled tasks)
# cronjob - schedule_cronjob, list_cronjobs, remove_cronjob
# rl - rl_list_environments, rl_start_training, etc. (requires TINKER_API_KEY)
#
# PRESETS (curated bundles):
@@ -533,11 +463,53 @@ platform_toolsets:
# debugging - terminal + web + file (for troubleshooting)
# safe - web + vision + moa (no terminal access)
# NOTE: The top-level "toolsets" key is deprecated and ignored.
# Tool configuration is managed per-platform via platform_toolsets above.
# Use `hermes tools` to configure interactively, or edit platform_toolsets directly.
#
# CLI override: hermes chat --toolsets terminal,web,file
# -----------------------------------------------------------------------------
# OPTION 1: Enable all tools (default)
# -----------------------------------------------------------------------------
toolsets:
- all
# -----------------------------------------------------------------------------
# OPTION 2: Minimal - just web search and terminal
# Great for: Simple coding tasks, quick lookups
# -----------------------------------------------------------------------------
# toolsets:
# - web
# - terminal
# -----------------------------------------------------------------------------
# OPTION 3: Research mode - no execution capabilities
# Great for: Safe information gathering, research tasks
# -----------------------------------------------------------------------------
# toolsets:
# - web
# - vision
# - skills
# -----------------------------------------------------------------------------
# OPTION 4: Full automation - browser + terminal
# Great for: Web scraping, automation tasks, testing
# -----------------------------------------------------------------------------
# toolsets:
# - terminal
# - browser
# - web
# -----------------------------------------------------------------------------
# OPTION 5: Creative mode - vision + image generation
# Great for: Design work, image analysis, creative tasks
# -----------------------------------------------------------------------------
# toolsets:
# - vision
# - image_gen
# - web
# -----------------------------------------------------------------------------
# OPTION 6: Safe mode - no terminal or browser
# Great for: Restricted environments, untrusted queries
# -----------------------------------------------------------------------------
# toolsets:
# - safe
# =============================================================================
# MCP (Model Context Protocol) Servers
@@ -573,21 +545,6 @@ platform_toolsets:
# args: ["-y", "@modelcontextprotocol/server-github"]
# env:
# GITHUB_PERSONAL_ACCESS_TOKEN: "ghp_..."
#
# Sampling (server-initiated LLM requests) — enabled by default.
# Per-server config under the 'sampling' key:
# analysis:
# command: npx
# args: ["-y", "analysis-server"]
# sampling:
# enabled: true # default: true
# model: "gemini-3-flash" # override model (optional)
# max_tokens_cap: 4096 # max tokens per request
# timeout: 30 # LLM call timeout (seconds)
# max_rpm: 10 # max requests per minute
# allowed_models: [] # model whitelist (empty = all)
# max_tool_rounds: 5 # tool loop limit (0 = disable)
# log_level: "info" # audit verbosity
# =============================================================================
# Voice Transcription (Speech-to-Text)
@@ -639,10 +596,6 @@ code_execution:
delegation:
max_iterations: 50 # Max tool-calling turns per child (default: 50)
default_toolsets: ["terminal", "file", "web"] # Default toolsets for subagents
# model: "google/gemini-3-flash-preview" # Override model for subagents (empty = inherit parent)
# provider: "openrouter" # Override provider for subagents (empty = inherit parent)
# # Resolves full credentials (base_url, api_key) automatically.
# # Supported: openrouter, nous, zai, kimi-coding, minimax
# =============================================================================
# Honcho Integration (Cross-Session User Modeling)
@@ -672,81 +625,3 @@ display:
# verbose: Full args, results, and debug logs (same as /verbose)
# Toggle at runtime with /verbose in the CLI
tool_progress: all
# Background process notifications (gateway/messaging only).
# Controls how chatty the process watcher is when you use
# terminal(background=true, check_interval=...) from Telegram/Discord/etc.
# off: No watcher messages at all
# result: Only the final completion message
# error: Only the final message when exit code != 0
# all: Running output updates + final message (default)
background_process_notifications: all
# Play terminal bell when agent finishes a response.
# Useful for long-running tasks — your terminal will ding when the agent is done.
# Works over SSH. Most terminals can be configured to flash the taskbar or play a sound.
bell_on_complete: false
# Show model reasoning/thinking before each response.
# When enabled, a dim box shows the model's thought process above the response.
# Toggle at runtime with /reasoning show or /reasoning hide.
show_reasoning: false
# Stream tokens to the terminal as they arrive instead of waiting for the
# full response. The response box opens on first token and text appears
# line-by-line. Tool calls are still captured silently.
# Stream tokens to the terminal in real-time. Disable to wait for full responses.
streaming: true
# ───────────────────────────────────────────────────────────────────────────
# Skin / Theme
# ───────────────────────────────────────────────────────────────────────────
# Customize CLI visual appearance — banner colors, spinner faces, tool prefix,
# response box label, and branding text. Change at runtime with /skin <name>.
#
# Built-in skins:
# default — Classic Hermes gold/kawaii
# ares — Crimson/bronze war-god theme with spinner wings
# mono — Clean grayscale monochrome
# slate — Cool blue developer-focused
#
# Custom skins: drop a YAML file in ~/.hermes/skins/<name>.yaml
# Schema (all fields optional, missing values inherit from default):
#
# name: my-theme
# description: Short description
# colors:
# banner_border: "#HEX" # Panel border
# banner_title: "#HEX" # Panel title
# banner_accent: "#HEX" # Section headers (Available Tools, etc.)
# banner_dim: "#HEX" # Dim/muted text
# banner_text: "#HEX" # Body text (tool names, skill names)
# ui_accent: "#HEX" # UI accent color
# response_border: "#HEX" # Response box border color
# spinner:
# waiting_faces: ["(⚔)", "(⛨)"] # Faces shown while waiting
# thinking_faces: ["(⚔)", "(⌁)"] # Faces shown while thinking
# thinking_verbs: ["forging", "plotting"] # Verbs for spinner messages
# wings: # Optional left/right spinner decorations
# - ["⟪⚔", "⚔⟫"]
# - ["⟪▲", "▲⟫"]
# branding:
# agent_name: "My Agent" # Banner title and branding
# welcome: "Welcome message" # Shown at CLI startup
# response_label: " ⚔ Agent " # Response box header label
# prompt_symbol: "⚔ " # Prompt symbol
# tool_prefix: "╎" # Tool output line prefix (default: ┊)
#
skin: default
# =============================================================================
# Privacy
# =============================================================================
# privacy:
# # Redact PII from the LLM context prompt.
# # When true, phone numbers are stripped and user/chat IDs are replaced
# # with deterministic hashes before being sent to the model.
# # Names and usernames are NOT affected (user-chosen, publicly visible).
# # Routing/delivery still uses the original values internally.
# redact_pii: false

5515
cli.py Normal file → Executable file

File diff suppressed because it is too large Load Diff

View File

@@ -7,8 +7,7 @@ This module provides scheduled task execution, allowing the agent to:
- Execute tasks in isolated sessions (no prior context)
Cron jobs are executed automatically by the gateway daemon:
hermes gateway install # Install as a user service
sudo hermes gateway install --system # Linux servers: boot-time system service
hermes gateway install # Install as system service (recommended)
hermes gateway # Or run in foreground
The gateway ticks the scheduler every 60 seconds. A file lock prevents
@@ -21,9 +20,6 @@ from cron.jobs import (
list_jobs,
remove_job,
update_job,
pause_job,
resume_job,
trigger_job,
JOBS_FILE,
)
from cron.scheduler import tick
@@ -34,9 +30,6 @@ __all__ = [
"list_jobs",
"remove_job",
"update_job",
"pause_job",
"resume_job",
"trigger_job",
"tick",
"JOBS_FILE",
]

View File

@@ -5,9 +5,7 @@ Jobs are stored in ~/.hermes/cron/jobs.json
Output is saved to ~/.hermes/cron/output/{job_id}/{timestamp}.md
"""
import copy
import json
import logging
import tempfile
import os
import re
@@ -16,8 +14,6 @@ from datetime import datetime, timedelta
from pathlib import Path
from typing import Optional, Dict, List, Any
logger = logging.getLogger(__name__)
from hermes_time import now as _hermes_now
try:
@@ -30,62 +26,16 @@ except ImportError:
# Configuration
# =============================================================================
HERMES_DIR = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
HERMES_DIR = Path.home() / ".hermes"
CRON_DIR = HERMES_DIR / "cron"
JOBS_FILE = CRON_DIR / "jobs.json"
OUTPUT_DIR = CRON_DIR / "output"
ONESHOT_GRACE_SECONDS = 120
def _normalize_skill_list(skill: Optional[str] = None, skills: Optional[Any] = None) -> List[str]:
"""Normalize legacy/single-skill and multi-skill inputs into a unique ordered list."""
if skills is None:
raw_items = [skill] if skill else []
elif isinstance(skills, str):
raw_items = [skills]
else:
raw_items = list(skills)
normalized: List[str] = []
for item in raw_items:
text = str(item or "").strip()
if text and text not in normalized:
normalized.append(text)
return normalized
def _apply_skill_fields(job: Dict[str, Any]) -> Dict[str, Any]:
"""Return a job dict with canonical `skills` and legacy `skill` fields aligned."""
normalized = dict(job)
skills = _normalize_skill_list(normalized.get("skill"), normalized.get("skills"))
normalized["skills"] = skills
normalized["skill"] = skills[0] if skills else None
return normalized
def _secure_dir(path: Path):
"""Set directory to owner-only access (0700). No-op on Windows."""
try:
os.chmod(path, 0o700)
except (OSError, NotImplementedError):
pass # Windows or other platforms where chmod is not supported
def _secure_file(path: Path):
"""Set file to owner-only read/write (0600). No-op on Windows."""
try:
if path.exists():
os.chmod(path, 0o600)
except (OSError, NotImplementedError):
pass
def ensure_dirs():
"""Ensure cron directories exist with secure permissions."""
"""Ensure cron directories exist."""
CRON_DIR.mkdir(parents=True, exist_ok=True)
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
_secure_dir(CRON_DIR)
_secure_dir(OUTPUT_DIR)
# =============================================================================
@@ -169,10 +119,6 @@ def parse_schedule(schedule: str) -> Dict[str, Any]:
try:
# Parse and validate
dt = datetime.fromisoformat(schedule.replace('Z', '+00:00'))
# Make naive timestamps timezone-aware at parse time so the stored
# value doesn't depend on the system timezone matching at check time.
if dt.tzinfo is None:
dt = dt.astimezone() # Interpret as local timezone
return {
"kind": "once",
"run_at": dt.isoformat(),
@@ -203,81 +149,16 @@ def parse_schedule(schedule: str) -> Dict[str, Any]:
def _ensure_aware(dt: datetime) -> datetime:
"""Return a timezone-aware datetime in Hermes configured timezone.
"""Make a naive datetime tz-aware using the configured timezone.
Backward compatibility:
- Older stored timestamps may be naive.
- Naive values are interpreted as *system-local wall time* (the timezone
`datetime.now()` used when they were created), then converted to the
configured Hermes timezone.
This preserves relative ordering for legacy naive timestamps across
timezone changes and avoids false not-due results.
Handles backward compatibility: timestamps stored before timezone support
are naive (server-local). We assume they were in the same timezone as
the current configuration so comparisons work without crashing.
"""
target_tz = _hermes_now().tzinfo
if dt.tzinfo is None:
local_tz = datetime.now().astimezone().tzinfo
return dt.replace(tzinfo=local_tz).astimezone(target_tz)
return dt.astimezone(target_tz)
def _recoverable_oneshot_run_at(
schedule: Dict[str, Any],
now: datetime,
*,
last_run_at: Optional[str] = None,
) -> Optional[str]:
"""Return a one-shot run time if it is still eligible to fire.
One-shot jobs get a small grace window so jobs created a few seconds after
their requested minute still run on the next tick. Once a one-shot has
already run, it is never eligible again.
"""
if schedule.get("kind") != "once":
return None
if last_run_at:
return None
run_at = schedule.get("run_at")
if not run_at:
return None
run_at_dt = _ensure_aware(datetime.fromisoformat(run_at))
if run_at_dt >= now - timedelta(seconds=ONESHOT_GRACE_SECONDS):
return run_at
return None
def _compute_grace_seconds(schedule: dict) -> int:
"""Compute how late a job can be and still catch up instead of fast-forwarding.
Uses half the schedule period, clamped between 120 seconds and 2 hours.
This ensures daily jobs can catch up if missed by up to 2 hours,
while frequent jobs (every 5-10 min) still fast-forward quickly.
"""
MIN_GRACE = 120
MAX_GRACE = 7200 # 2 hours
kind = schedule.get("kind")
if kind == "interval":
period_seconds = schedule.get("minutes", 1) * 60
grace = period_seconds // 2
return max(MIN_GRACE, min(grace, MAX_GRACE))
if kind == "cron" and HAS_CRONITER:
try:
now = _hermes_now()
cron = croniter(schedule["expr"], now)
first = cron.get_next(datetime)
second = cron.get_next(datetime)
period_seconds = int((second - first).total_seconds())
grace = period_seconds // 2
return max(MIN_GRACE, min(grace, MAX_GRACE))
except Exception:
pass
return MIN_GRACE
tz = _hermes_now().tzinfo
return dt.replace(tzinfo=tz)
return dt
def compute_next_run(schedule: Dict[str, Any], last_run_at: Optional[str] = None) -> Optional[str]:
@@ -289,7 +170,9 @@ def compute_next_run(schedule: Dict[str, Any], last_run_at: Optional[str] = None
now = _hermes_now()
if schedule["kind"] == "once":
return _recoverable_oneshot_run_at(schedule, now, last_run_at=last_run_at)
run_at = _ensure_aware(datetime.fromisoformat(schedule["run_at"]))
# If in the future, return it; if in the past, no more runs
return schedule["run_at"] if run_at > now else None
elif schedule["kind"] == "interval":
minutes = schedule["minutes"]
@@ -340,7 +223,6 @@ def save_jobs(jobs: List[Dict[str, Any]]):
f.flush()
os.fsync(f.fileno())
os.replace(tmp_path, JOBS_FILE)
_secure_file(JOBS_FILE)
except BaseException:
try:
os.unlink(tmp_path)
@@ -355,67 +237,39 @@ def create_job(
name: Optional[str] = None,
repeat: Optional[int] = None,
deliver: Optional[str] = None,
origin: Optional[Dict[str, Any]] = None,
skill: Optional[str] = None,
skills: Optional[List[str]] = None,
model: Optional[str] = None,
provider: Optional[str] = None,
base_url: Optional[str] = None,
origin: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Create a new cron job.
Args:
prompt: The prompt to run (must be self-contained, or a task instruction when skill is set)
prompt: The prompt to run (must be self-contained)
schedule: Schedule string (see parse_schedule)
name: Optional friendly name
repeat: How many times to run (None = forever, 1 = once)
deliver: Where to deliver output ("origin", "local", "telegram", etc.)
origin: Source info where job was created (for "origin" delivery)
skill: Optional legacy single skill name to load before running the prompt
skills: Optional ordered list of skills to load before running the prompt
model: Optional per-job model override
provider: Optional per-job provider override
base_url: Optional per-job base URL override
Returns:
The created job dict
"""
parsed_schedule = parse_schedule(schedule)
# Normalize repeat: treat 0 or negative values as None (infinite)
if repeat is not None and repeat <= 0:
repeat = None
# Auto-set repeat=1 for one-shot schedules if not specified
if parsed_schedule["kind"] == "once" and repeat is None:
repeat = 1
# Default delivery to origin if available, otherwise local
if deliver is None:
deliver = "origin" if origin else "local"
job_id = uuid.uuid4().hex[:12]
now = _hermes_now().isoformat()
normalized_skills = _normalize_skill_list(skill, skills)
normalized_model = str(model).strip() if isinstance(model, str) else None
normalized_provider = str(provider).strip() if isinstance(provider, str) else None
normalized_base_url = str(base_url).strip().rstrip("/") if isinstance(base_url, str) else None
normalized_model = normalized_model or None
normalized_provider = normalized_provider or None
normalized_base_url = normalized_base_url or None
label_source = (prompt or (normalized_skills[0] if normalized_skills else None)) or "cron job"
job = {
"id": job_id,
"name": name or label_source[:50].strip(),
"name": name or prompt[:50].strip(),
"prompt": prompt,
"skills": normalized_skills,
"skill": normalized_skills[0] if normalized_skills else None,
"model": normalized_model,
"provider": normalized_provider,
"base_url": normalized_base_url,
"schedule": parsed_schedule,
"schedule_display": parsed_schedule.get("display", schedule),
"repeat": {
@@ -423,9 +277,6 @@ def create_job(
"completed": 0
},
"enabled": True,
"state": "scheduled",
"paused_at": None,
"paused_reason": None,
"created_at": now,
"next_run_at": compute_next_run(parsed_schedule),
"last_run_at": None,
@@ -435,11 +286,11 @@ def create_job(
"deliver": deliver,
"origin": origin, # Tracks where job was created for "origin" delivery
}
jobs = load_jobs()
jobs.append(job)
save_jobs(jobs)
return job
@@ -448,100 +299,29 @@ def get_job(job_id: str) -> Optional[Dict[str, Any]]:
jobs = load_jobs()
for job in jobs:
if job["id"] == job_id:
return _apply_skill_fields(job)
return job
return None
def list_jobs(include_disabled: bool = False) -> List[Dict[str, Any]]:
"""List all jobs, optionally including disabled ones."""
jobs = [_apply_skill_fields(j) for j in load_jobs()]
jobs = load_jobs()
if not include_disabled:
jobs = [j for j in jobs if j.get("enabled", True)]
return jobs
def update_job(job_id: str, updates: Dict[str, Any]) -> Optional[Dict[str, Any]]:
"""Update a job by ID, refreshing derived schedule fields when needed."""
"""Update a job by ID."""
jobs = load_jobs()
for i, job in enumerate(jobs):
if job["id"] != job_id:
continue
updated = _apply_skill_fields({**job, **updates})
schedule_changed = "schedule" in updates
if "skills" in updates or "skill" in updates:
normalized_skills = _normalize_skill_list(updated.get("skill"), updated.get("skills"))
updated["skills"] = normalized_skills
updated["skill"] = normalized_skills[0] if normalized_skills else None
if schedule_changed:
updated_schedule = updated["schedule"]
updated["schedule_display"] = updates.get(
"schedule_display",
updated_schedule.get("display", updated.get("schedule_display")),
)
if updated.get("state") != "paused":
updated["next_run_at"] = compute_next_run(updated_schedule)
if updated.get("enabled", True) and updated.get("state") != "paused" and not updated.get("next_run_at"):
updated["next_run_at"] = compute_next_run(updated["schedule"])
jobs[i] = updated
save_jobs(jobs)
return _apply_skill_fields(jobs[i])
if job["id"] == job_id:
jobs[i] = {**job, **updates}
save_jobs(jobs)
return jobs[i]
return None
def pause_job(job_id: str, reason: Optional[str] = None) -> Optional[Dict[str, Any]]:
"""Pause a job without deleting it."""
return update_job(
job_id,
{
"enabled": False,
"state": "paused",
"paused_at": _hermes_now().isoformat(),
"paused_reason": reason,
},
)
def resume_job(job_id: str) -> Optional[Dict[str, Any]]:
"""Resume a paused job and compute the next future run from now."""
job = get_job(job_id)
if not job:
return None
next_run_at = compute_next_run(job["schedule"])
return update_job(
job_id,
{
"enabled": True,
"state": "scheduled",
"paused_at": None,
"paused_reason": None,
"next_run_at": next_run_at,
},
)
def trigger_job(job_id: str) -> Optional[Dict[str, Any]]:
"""Schedule a job to run on the next scheduler tick."""
job = get_job(job_id)
if not job:
return None
return update_job(
job_id,
{
"enabled": True,
"state": "scheduled",
"paused_at": None,
"paused_reason": None,
"next_run_at": _hermes_now().isoformat(),
},
)
def remove_job(job_id: str) -> bool:
"""Remove a job by ID."""
jobs = load_jobs()
@@ -575,7 +355,7 @@ def mark_job_run(job_id: str, success: bool, error: Optional[str] = None):
# Check if we've hit the repeat limit
times = job["repeat"].get("times")
completed = job["repeat"]["completed"]
if times is not None and times > 0 and completed >= times:
if times is not None and completed >= times:
# Remove the job (limit reached)
jobs.pop(i)
save_jobs(jobs)
@@ -583,14 +363,11 @@ def mark_job_run(job_id: str, success: bool, error: Optional[str] = None):
# Compute next run
job["next_run_at"] = compute_next_run(job["schedule"], now)
# If no next run (one-shot completed), disable
if job["next_run_at"] is None:
job["enabled"] = False
job["state"] = "completed"
elif job.get("state") != "paused":
job["state"] = "scheduled"
save_jobs(jobs)
return
@@ -598,81 +375,23 @@ def mark_job_run(job_id: str, success: bool, error: Optional[str] = None):
def get_due_jobs() -> List[Dict[str, Any]]:
"""Get all jobs that are due to run now.
For recurring jobs (cron/interval), if the scheduled time is stale
(more than one period in the past, e.g. because the gateway was down),
the job is fast-forwarded to the next future run instead of firing
immediately. This prevents a burst of missed jobs on gateway restart.
"""
"""Get all jobs that are due to run now."""
now = _hermes_now()
raw_jobs = load_jobs()
jobs = [_apply_skill_fields(j) for j in copy.deepcopy(raw_jobs)]
jobs = load_jobs()
due = []
needs_save = False
for job in jobs:
if not job.get("enabled", True):
continue
next_run = job.get("next_run_at")
if not next_run:
recovered_next = _recoverable_oneshot_run_at(
job.get("schedule", {}),
now,
last_run_at=job.get("last_run_at"),
)
if not recovered_next:
continue
job["next_run_at"] = recovered_next
next_run = recovered_next
logger.info(
"Job '%s' had no next_run_at; recovering one-shot run at %s",
job.get("name", job["id"]),
recovered_next,
)
for rj in raw_jobs:
if rj["id"] == job["id"]:
rj["next_run_at"] = recovered_next
needs_save = True
break
continue
next_run_dt = _ensure_aware(datetime.fromisoformat(next_run))
if next_run_dt <= now:
schedule = job.get("schedule", {})
kind = schedule.get("kind")
# For recurring jobs, check if the scheduled time is stale
# (gateway was down and missed the window). Fast-forward to
# the next future occurrence instead of firing a stale run.
grace = _compute_grace_seconds(schedule)
if kind in ("cron", "interval") and (now - next_run_dt).total_seconds() > grace:
# Job is past its catch-up grace window — this is a stale missed run.
# Grace scales with schedule period: daily=2h, hourly=30m, 10min=5m.
new_next = compute_next_run(schedule, now.isoformat())
if new_next:
logger.info(
"Job '%s' missed its scheduled time (%s, grace=%ds). "
"Fast-forwarding to next run: %s",
job.get("name", job["id"]),
next_run,
grace,
new_next,
)
# Update the job in storage
for rj in raw_jobs:
if rj["id"] == job["id"]:
rj["next_run_at"] = new_next
needs_save = True
break
continue # Skip this run
due.append(job)
if needs_save:
save_jobs(raw_jobs)
return due
@@ -681,24 +400,11 @@ def save_job_output(job_id: str, output: str):
ensure_dirs()
job_output_dir = OUTPUT_DIR / job_id
job_output_dir.mkdir(parents=True, exist_ok=True)
_secure_dir(job_output_dir)
timestamp = _hermes_now().strftime("%Y-%m-%d_%H-%M-%S")
output_file = job_output_dir / f"{timestamp}.md"
fd, tmp_path = tempfile.mkstemp(dir=str(job_output_dir), suffix='.tmp', prefix='.output_')
try:
with os.fdopen(fd, 'w', encoding='utf-8') as f:
f.write(output)
f.flush()
os.fsync(f.fileno())
os.replace(tmp_path, output_file)
_secure_file(output_file)
except BaseException:
try:
os.unlink(tmp_path)
except OSError:
pass
raise
with open(output_file, 'w', encoding='utf-8') as f:
f.write(output)
return output_file

View File

@@ -9,7 +9,6 @@ runs at a time if multiple processes overlap.
"""
import asyncio
import json
import logging
import os
import sys
@@ -37,11 +36,6 @@ sys.path.insert(0, str(Path(__file__).parent.parent))
from cron.jobs import get_due_jobs, mark_job_run, save_job_output
# Sentinel: when a cron agent has nothing new to report, it can start its
# response with this marker to suppress delivery. Output is still saved
# locally for audit.
SILENT_MARKER = "[SILENT]"
# Resolve Hermes home directory (respects HERMES_HOME override)
_hermes_home = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
@@ -51,7 +45,7 @@ _LOCK_FILE = _LOCK_DIR / ".tick.lock"
def _resolve_origin(job: dict) -> Optional[dict]:
"""Extract origin info from a job, preserving any extra routing metadata."""
"""Extract origin info from a job, returning {platform, chat_id, chat_name} or None."""
origin = job.get("origin")
if not origin:
return None
@@ -62,55 +56,6 @@ def _resolve_origin(job: dict) -> Optional[dict]:
return None
def _resolve_delivery_target(job: dict) -> Optional[dict]:
"""Resolve the concrete auto-delivery target for a cron job, if any."""
deliver = job.get("deliver", "local")
origin = _resolve_origin(job)
if deliver == "local":
return None
if deliver == "origin":
if not origin:
return None
return {
"platform": origin["platform"],
"chat_id": str(origin["chat_id"]),
"thread_id": origin.get("thread_id"),
}
if ":" in deliver:
platform_name, rest = deliver.split(":", 1)
# Check for thread_id suffix (e.g. "telegram:-1003724596514:17")
if ":" in rest:
chat_id, thread_id = rest.split(":", 1)
else:
chat_id, thread_id = rest, None
return {
"platform": platform_name,
"chat_id": chat_id,
"thread_id": thread_id,
}
platform_name = deliver
if origin and origin.get("platform") == platform_name:
return {
"platform": platform_name,
"chat_id": str(origin["chat_id"]),
"thread_id": origin.get("thread_id"),
}
chat_id = os.getenv(f"{platform_name.upper()}_HOME_CHANNEL", "")
if not chat_id:
return None
return {
"platform": platform_name,
"chat_id": chat_id,
"thread_id": None,
}
def _deliver_result(job: dict, content: str) -> None:
"""
Deliver job output to the configured target (origin chat, specific platform, etc.).
@@ -118,19 +63,32 @@ def _deliver_result(job: dict, content: str) -> None:
Uses the standalone platform send functions from send_message_tool so delivery
works whether or not the gateway is running.
"""
target = _resolve_delivery_target(job)
if not target:
if job.get("deliver", "local") != "local":
logger.warning(
"Job '%s' deliver=%s but no concrete delivery target could be resolved",
job["id"],
job.get("deliver", "local"),
)
deliver = job.get("deliver", "local")
origin = _resolve_origin(job)
if deliver == "local":
return
platform_name = target["platform"]
chat_id = target["chat_id"]
thread_id = target.get("thread_id")
# Resolve target platform + chat_id
if deliver == "origin":
if not origin:
logger.warning("Job '%s' deliver=origin but no origin stored, skipping delivery", job["id"])
return
platform_name = origin["platform"]
chat_id = origin["chat_id"]
elif ":" in deliver:
platform_name, chat_id = deliver.split(":", 1)
else:
# Bare platform name like "telegram" — need to resolve to origin or home channel
platform_name = deliver
if origin and origin.get("platform") == platform_name:
chat_id = origin["chat_id"]
else:
# Fall back to home channel
chat_id = os.getenv(f"{platform_name.upper()}_HOME_CHANNEL", "")
if not chat_id:
logger.warning("Job '%s' deliver=%s but no chat_id or home channel. Set via: hermes config set %s_HOME_CHANNEL <channel_id>", job["id"], deliver, platform_name.upper())
return
from tools.send_message_tool import _send_to_platform
from gateway.config import load_gateway_config, Platform
@@ -140,13 +98,6 @@ def _deliver_result(job: dict, content: str) -> None:
"discord": Platform.DISCORD,
"slack": Platform.SLACK,
"whatsapp": Platform.WHATSAPP,
"signal": Platform.SIGNAL,
"matrix": Platform.MATRIX,
"mattermost": Platform.MATTERMOST,
"homeassistant": Platform.HOMEASSISTANT,
"dingtalk": Platform.DINGTALK,
"email": Platform.EMAIL,
"sms": Platform.SMS,
}
platform = platform_map.get(platform_name.lower())
if not platform:
@@ -164,29 +115,15 @@ def _deliver_result(job: dict, content: str) -> None:
logger.warning("Job '%s': platform '%s' not configured/enabled", job["id"], platform_name)
return
# Wrap the content so the user knows this is a cron delivery and that
# the interactive agent has no visibility into it.
task_name = job.get("name", job["id"])
wrapped = (
f"Cronjob Response: {task_name}\n"
f"-------------\n\n"
f"{content}\n\n"
f"Note: The agent cannot see this message, and therefore cannot respond to it."
)
# Run the async send in a fresh event loop (safe from any thread)
coro = _send_to_platform(platform, pconfig, chat_id, wrapped, thread_id=thread_id)
try:
result = asyncio.run(coro)
result = asyncio.run(_send_to_platform(platform, pconfig, chat_id, content))
except RuntimeError:
# asyncio.run() checks for a running loop before awaiting the coroutine;
# when it raises, the original coro was never started — close it to
# prevent "coroutine was never awaited" RuntimeWarning, then retry in a
# fresh thread that has no running loop.
coro.close()
# asyncio.run() fails if there's already a running loop in this thread;
# spin up a new thread to avoid that.
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
future = pool.submit(asyncio.run, _send_to_platform(platform, pconfig, chat_id, wrapped, thread_id=thread_id))
future = pool.submit(asyncio.run, _send_to_platform(platform, pconfig, chat_id, content))
result = future.result(timeout=30)
except Exception as e:
logger.error("Job '%s': delivery to %s:%s failed: %s", job["id"], platform_name, chat_id, e)
@@ -196,66 +133,12 @@ def _deliver_result(job: dict, content: str) -> None:
logger.error("Job '%s': delivery error: %s", job["id"], result["error"])
else:
logger.info("Job '%s': delivered to %s:%s", job["id"], platform_name, chat_id)
def _build_job_prompt(job: dict) -> str:
"""Build the effective prompt for a cron job, optionally loading one or more skills first."""
prompt = job.get("prompt", "")
skills = job.get("skills")
# Always prepend [SILENT] guidance so the cron agent can suppress
# delivery when it has nothing new or noteworthy to report.
silent_hint = (
"[SYSTEM: If you have nothing new or noteworthy to report, respond "
"with exactly \"[SILENT]\" (optionally followed by a brief internal "
"note). This suppresses delivery to the user while still saving "
"output locally. Only use [SILENT] when there are genuinely no "
"changes worth reporting.]\n\n"
)
prompt = silent_hint + prompt
if skills is None:
legacy = job.get("skill")
skills = [legacy] if legacy else []
skill_names = [str(name).strip() for name in skills if str(name).strip()]
if not skill_names:
return prompt
from tools.skills_tool import skill_view
parts = []
skipped: list[str] = []
for skill_name in skill_names:
loaded = json.loads(skill_view(skill_name))
if not loaded.get("success"):
error = loaded.get("error") or f"Failed to load skill '{skill_name}'"
logger.warning("Cron job '%s': skill not found, skipping — %s", job.get("name", job.get("id")), error)
skipped.append(skill_name)
continue
content = str(loaded.get("content") or "").strip()
if parts:
parts.append("")
parts.extend(
[
f'[SYSTEM: The user has invoked the "{skill_name}" skill, indicating they want you to follow its instructions. The full skill content is loaded below.]',
"",
content,
]
)
if skipped:
notice = (
f"[SYSTEM: The following skill(s) were listed for this job but could not be found "
f"and were skipped: {', '.join(skipped)}. "
f"Start your response with a brief notice so the user is aware, e.g.: "
f"'⚠️ Skill(s) not found and skipped: {', '.join(skipped)}']"
)
parts.insert(0, notice)
if prompt:
parts.extend(["", f"The user has provided the following instruction alongside the skill invocation: {prompt}"])
return "\n".join(parts)
# Mirror the delivered content into the target's gateway session
try:
from gateway.mirror import mirror_to_session
mirror_to_session(platform_name, chat_id, content, source_label="cron")
except Exception:
pass
def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
@@ -267,20 +150,11 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
"""
from run_agent import AIAgent
# Initialize SQLite session store so cron job messages are persisted
# and discoverable via session_search (same pattern as gateway/run.py).
_session_db = None
try:
from hermes_state import SessionDB
_session_db = SessionDB()
except Exception as e:
logger.debug("Job '%s': SQLite session store not available: %s", job.get("id", "?"), e)
job_id = job["id"]
job_name = job["name"]
prompt = _build_job_prompt(job)
prompt = job["prompt"]
origin = _resolve_origin(job)
logger.info("Running job '%s' (ID: %s)", job_name, job_id)
logger.info("Prompt: %s", prompt[:100])
@@ -300,17 +174,8 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
except UnicodeDecodeError:
load_dotenv(str(_hermes_home / ".env"), override=True, encoding="latin-1")
delivery_target = _resolve_delivery_target(job)
if delivery_target:
os.environ["HERMES_CRON_AUTO_DELIVER_PLATFORM"] = delivery_target["platform"]
os.environ["HERMES_CRON_AUTO_DELIVER_CHAT_ID"] = str(delivery_target["chat_id"])
if delivery_target.get("thread_id") is not None:
os.environ["HERMES_CRON_AUTO_DELIVER_THREAD_ID"] = str(delivery_target["thread_id"])
model = os.getenv("HERMES_MODEL") or os.getenv("LLM_MODEL") or "anthropic/claude-opus-4.6"
model = job.get("model") or os.getenv("HERMES_MODEL") or "anthropic/claude-opus-4.6"
# Load config.yaml for model, reasoning, prefill, toolsets, provider routing
_cfg = {}
try:
import yaml
_cfg_path = str(_hermes_home / "config.yaml")
@@ -318,109 +183,40 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
with open(_cfg_path) as _f:
_cfg = yaml.safe_load(_f) or {}
_model_cfg = _cfg.get("model", {})
if not job.get("model"):
if isinstance(_model_cfg, str):
model = _model_cfg
elif isinstance(_model_cfg, dict):
model = _model_cfg.get("default", model)
except Exception as e:
logger.warning("Job '%s': failed to load config.yaml, using defaults: %s", job_id, e)
# Reasoning config from env or config.yaml
reasoning_config = None
effort = os.getenv("HERMES_REASONING_EFFORT", "")
if not effort:
effort = str(_cfg.get("agent", {}).get("reasoning_effort", "")).strip()
if effort and effort.lower() != "none":
valid = ("xhigh", "high", "medium", "low", "minimal")
if effort.lower() in valid:
reasoning_config = {"enabled": True, "effort": effort.lower()}
elif effort.lower() == "none":
reasoning_config = {"enabled": False}
# Prefill messages from env or config.yaml
prefill_messages = None
prefill_file = os.getenv("HERMES_PREFILL_MESSAGES_FILE", "") or _cfg.get("prefill_messages_file", "")
if prefill_file:
import json as _json
pfpath = Path(prefill_file).expanduser()
if not pfpath.is_absolute():
pfpath = _hermes_home / pfpath
if pfpath.exists():
try:
with open(pfpath, "r", encoding="utf-8") as _pf:
prefill_messages = _json.load(_pf)
if not isinstance(prefill_messages, list):
prefill_messages = None
except Exception as e:
logger.warning("Job '%s': failed to parse prefill messages file '%s': %s", job_id, pfpath, e)
prefill_messages = None
# Max iterations
max_iterations = _cfg.get("agent", {}).get("max_turns") or _cfg.get("max_turns") or 90
# Provider routing
pr = _cfg.get("provider_routing", {})
smart_routing = _cfg.get("smart_model_routing", {}) or {}
if isinstance(_model_cfg, str):
model = _model_cfg
elif isinstance(_model_cfg, dict):
model = _model_cfg.get("default", model)
except Exception:
pass
from hermes_cli.runtime_provider import (
resolve_runtime_provider,
format_runtime_provider_error,
)
try:
runtime_kwargs = {
"requested": job.get("provider") or os.getenv("HERMES_INFERENCE_PROVIDER"),
}
if job.get("base_url"):
runtime_kwargs["explicit_base_url"] = job.get("base_url")
runtime = resolve_runtime_provider(**runtime_kwargs)
runtime = resolve_runtime_provider(
requested=os.getenv("HERMES_INFERENCE_PROVIDER"),
)
except Exception as exc:
message = format_runtime_provider_error(exc)
raise RuntimeError(message) from exc
from agent.smart_model_routing import resolve_turn_route
turn_route = resolve_turn_route(
prompt,
smart_routing,
{
"model": model,
"api_key": runtime.get("api_key"),
"base_url": runtime.get("base_url"),
"provider": runtime.get("provider"),
"api_mode": runtime.get("api_mode"),
"command": runtime.get("command"),
"args": list(runtime.get("args") or []),
},
)
agent = AIAgent(
model=turn_route["model"],
api_key=turn_route["runtime"].get("api_key"),
base_url=turn_route["runtime"].get("base_url"),
provider=turn_route["runtime"].get("provider"),
api_mode=turn_route["runtime"].get("api_mode"),
acp_command=turn_route["runtime"].get("command"),
acp_args=turn_route["runtime"].get("args"),
max_iterations=max_iterations,
reasoning_config=reasoning_config,
prefill_messages=prefill_messages,
providers_allowed=pr.get("only"),
providers_ignored=pr.get("ignore"),
providers_order=pr.get("order"),
provider_sort=pr.get("sort"),
disabled_toolsets=["cronjob", "messaging", "clarify"],
model=model,
api_key=runtime.get("api_key"),
base_url=runtime.get("base_url"),
provider=runtime.get("provider"),
api_mode=runtime.get("api_mode"),
quiet_mode=True,
platform="cron",
session_id=f"cron_{job_id}_{_hermes_now().strftime('%Y%m%d_%H%M%S')}",
session_db=_session_db,
session_id=f"cron_{job_id}_{_hermes_now().strftime('%Y%m%d_%H%M%S')}"
)
result = agent.run_conversation(prompt)
final_response = result.get("final_response", "") or ""
# Use a separate variable for log display; keep final_response clean
# for delivery logic (empty response = no delivery).
logged_response = final_response if final_response else "(No response generated)"
final_response = result.get("final_response", "")
if not final_response:
final_response = "(No response generated)"
output = f"""# Cron Job: {job_name}
@@ -434,7 +230,7 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
## Response
{logged_response}
{final_response}
"""
logger.info("Job '%s' completed successfully", job_name)
@@ -466,20 +262,8 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
finally:
# Clean up injected env vars so they don't leak to other jobs
for key in (
"HERMES_SESSION_PLATFORM",
"HERMES_SESSION_CHAT_ID",
"HERMES_SESSION_CHAT_NAME",
"HERMES_CRON_AUTO_DELIVER_PLATFORM",
"HERMES_CRON_AUTO_DELIVER_CHAT_ID",
"HERMES_CRON_AUTO_DELIVER_THREAD_ID",
):
for key in ("HERMES_SESSION_PLATFORM", "HERMES_SESSION_CHAT_ID", "HERMES_SESSION_CHAT_NAME"):
os.environ.pop(key, None)
if _session_db:
try:
_session_db.close()
except Exception as e:
logger.debug("Job '%s': failed to close SQLite session store: %s", job_id, e)
def tick(verbose: bool = True) -> int:
@@ -530,16 +314,9 @@ def tick(verbose: bool = True) -> int:
if verbose:
logger.info("Output saved to: %s", output_file)
# Deliver the final response to the origin/target chat.
# If the agent responded with [SILENT], skip delivery (but
# output is already saved above). Failed jobs always deliver.
# Deliver the final response to the origin/target chat
deliver_content = final_response if success else f"⚠️ Cron job '{job.get('name', job['id'])}' failed:\n{error}"
should_deliver = bool(deliver_content)
if should_deliver and success and deliver_content.strip().upper().startswith(SILENT_MARKER):
logger.info("Job '%s': agent returned %s — skipping delivery", job["id"], SILENT_MARKER)
should_deliver = False
if should_deliver:
if deliver_content:
try:
_deliver_result(job, deliver_content)
except Exception as de:

View File

@@ -1,46 +0,0 @@
# datagen-config-examples/web_research.yaml
#
# Batch data generation config for WebResearchEnv.
# Generates tool-calling trajectories for multi-step web research tasks.
#
# Usage:
# python batch_runner.py \
# --config datagen-config-examples/web_research.yaml \
# --run_name web_research_v1
environment: web-research
# Toolsets available to the agent during data generation
toolsets:
- web
- file
# How many parallel workers to use
num_workers: 4
# Questions per batch
batch_size: 20
# Total trajectories to generate (comment out to run full dataset)
max_items: 500
# Model to use for generation (override with --model flag)
model: openrouter/nousresearch/hermes-3-llama-3.1-405b
# System prompt additions (ephemeral — not saved to trajectories)
ephemeral_system_prompt: |
You are a highly capable research agent. When asked a factual question,
always use web_search to find current, accurate information before answering.
Cite at least 2 sources. Be concise and accurate.
# Output directory
output_dir: data/web_research_v1
# Trajectory compression settings (for fitting into training token budgets)
compression:
enabled: true
target_max_tokens: 16000
# Eval settings
eval_every: 100 # Run eval every N trajectories
eval_size: 25 # Number of held-out questions per eval run

7
docs/README.md Normal file
View File

@@ -0,0 +1,7 @@
# Documentation
All documentation has moved to the website:
**📖 [hermes-agent.nousresearch.com/docs](https://hermes-agent.nousresearch.com/docs/)**
The documentation source files live in [`website/docs/`](../website/docs/).

View File

@@ -1,229 +0,0 @@
# Hermes Agent — ACP (Agent Client Protocol) Setup Guide
Hermes Agent supports the **Agent Client Protocol (ACP)**, allowing it to run as
a coding agent inside your editor. ACP lets your IDE send tasks to Hermes, and
Hermes responds with file edits, terminal commands, and explanations — all shown
natively in the editor UI.
---
## Prerequisites
- Hermes Agent installed and configured (`hermes setup` completed)
- An API key / provider set up in `~/.hermes/.env` or via `hermes login`
- Python 3.11+
Install the ACP extra:
```bash
pip install -e ".[acp]"
```
---
## VS Code Setup
### 1. Install the ACP Client extension
Open VS Code and install **ACP Client** from the marketplace:
- Press `Ctrl+Shift+X` (or `Cmd+Shift+X` on macOS)
- Search for **"ACP Client"**
- Click **Install**
Or install from the command line:
```bash
code --install-extension anysphere.acp-client
```
### 2. Configure settings.json
Open your VS Code settings (`Ctrl+,` → click the `{}` icon for JSON) and add:
```json
{
"acpClient.agents": [
{
"name": "hermes-agent",
"registryDir": "/path/to/hermes-agent/acp_registry"
}
]
}
```
Replace `/path/to/hermes-agent` with the actual path to your Hermes Agent
installation (e.g. `~/.hermes/hermes-agent`).
Alternatively, if `hermes` is on your PATH, the ACP Client can discover it
automatically via the registry directory.
### 3. Restart VS Code
After configuring, restart VS Code. You should see **Hermes Agent** appear in
the ACP agent picker in the chat/agent panel.
---
## Zed Setup
Zed has built-in ACP support.
### 1. Configure Zed settings
Open Zed settings (`Cmd+,` on macOS or `Ctrl+,` on Linux) and add to your
`settings.json`:
```json
{
"acp": {
"agents": [
{
"name": "hermes-agent",
"registry_dir": "/path/to/hermes-agent/acp_registry"
}
]
}
}
```
### 2. Restart Zed
Hermes Agent will appear in the agent panel. Select it and start a conversation.
---
## JetBrains Setup (IntelliJ, PyCharm, WebStorm, etc.)
### 1. Install the ACP plugin
- Open **Settings****Plugins****Marketplace**
- Search for **"ACP"** or **"Agent Client Protocol"**
- Install and restart the IDE
### 2. Configure the agent
- Open **Settings****Tools****ACP Agents**
- Click **+** to add a new agent
- Set the registry directory to your `acp_registry/` folder:
`/path/to/hermes-agent/acp_registry`
- Click **OK**
### 3. Use the agent
Open the ACP panel (usually in the right sidebar) and select **Hermes Agent**.
---
## What You Will See
Once connected, your editor provides a native interface to Hermes Agent:
### Chat Panel
A conversational interface where you can describe tasks, ask questions, and
give instructions. Hermes responds with explanations and actions.
### File Diffs
When Hermes edits files, you see standard diffs in the editor. You can:
- **Accept** individual changes
- **Reject** changes you don't want
- **Review** the full diff before applying
### Terminal Commands
When Hermes needs to run shell commands (builds, tests, installs), the editor
shows them in an integrated terminal. Depending on your settings:
- Commands may run automatically
- Or you may be prompted to **approve** each command
### Approval Flow
For potentially destructive operations, the editor will prompt you for
approval before Hermes proceeds. This includes:
- File deletions
- Shell commands
- Git operations
---
## Configuration
Hermes Agent under ACP uses the **same configuration** as the CLI:
- **API keys / providers**: `~/.hermes/.env`
- **Agent config**: `~/.hermes/config.yaml`
- **Skills**: `~/.hermes/skills/`
- **Sessions**: `~/.hermes/state.db`
You can run `hermes setup` to configure providers, or edit `~/.hermes/.env`
directly.
### Changing the model
Edit `~/.hermes/config.yaml`:
```yaml
model: openrouter/nous/hermes-3-llama-3.1-70b
```
Or set the `HERMES_MODEL` environment variable.
### Toolsets
ACP sessions use the curated `hermes-acp` toolset by default. It is designed for editor workflows and intentionally excludes things like messaging delivery, cronjob management, and audio-first UX features.
---
## Troubleshooting
### Agent doesn't appear in the editor
1. **Check the registry path** — make sure the `acp_registry/` directory path
in your editor settings is correct and contains `agent.json`.
2. **Check `hermes` is on PATH** — run `which hermes` in a terminal. If not
found, you may need to activate your virtualenv or add it to PATH.
3. **Restart the editor** after changing settings.
### Agent starts but errors immediately
1. Run `hermes doctor` to check your configuration.
2. Check that you have a valid API key: `hermes status`
3. Try running `hermes acp` directly in a terminal to see error output.
### "Module not found" errors
Make sure you installed the ACP extra:
```bash
pip install -e ".[acp]"
```
### Slow responses
- ACP streams responses, so you should see incremental output. If the agent
appears stuck, check your network connection and API provider status.
- Some providers have rate limits. Try switching to a different model/provider.
### Permission denied for terminal commands
If the editor blocks terminal commands, check your ACP Client extension
settings for auto-approval or manual-approval preferences.
### Logs
Hermes logs are written to stderr when running in ACP mode. Check:
- VS Code: **Output** panel → select **ACP Client** or **Hermes Agent**
- Zed: **View****Toggle Terminal** and check the process output
- JetBrains: **Event Log** or the ACP tool window
You can also enable verbose logging:
```bash
HERMES_LOG_LEVEL=DEBUG hermes acp
```
---
## Further Reading
- [ACP Specification](https://github.com/anysphere/acp)
- [Hermes Agent Documentation](https://github.com/NousResearch/hermes-agent)
- Run `hermes --help` for all CLI options

View File

@@ -1,698 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>honcho-integration-spec</title>
<style>
:root {
--bg: #0b0e14;
--bg-surface: #11151c;
--bg-elevated: #181d27;
--bg-code: #0d1018;
--fg: #c9d1d9;
--fg-bright: #e6edf3;
--fg-muted: #6e7681;
--fg-subtle: #484f58;
--accent: #7eb8f6;
--accent-dim: #3d6ea5;
--accent-glow: rgba(126, 184, 246, 0.08);
--green: #7ee6a8;
--green-dim: #2ea04f;
--orange: #e6a855;
--red: #f47067;
--purple: #bc8cff;
--cyan: #56d4dd;
--border: #21262d;
--border-subtle: #161b22;
--radius: 6px;
--font-sans: 'New York', ui-serif, 'Iowan Old Style', 'Apple Garamond', Baskerville, 'Times New Roman', 'Noto Emoji', serif;
--font-mono: 'Departure Mono', 'Noto Emoji', monospace;
}
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
html { scroll-behavior: smooth; scroll-padding-top: 2rem; }
body {
font-family: var(--font-sans);
background: var(--bg);
color: var(--fg);
line-height: 1.7;
font-size: 15px;
-webkit-font-smoothing: antialiased;
}
.container { max-width: 860px; margin: 0 auto; padding: 3rem 2rem 6rem; }
.hero {
text-align: center;
padding: 4rem 0 3rem;
border-bottom: 1px solid var(--border);
margin-bottom: 3rem;
}
.hero h1 { font-family: var(--font-mono); font-size: 2.2rem; font-weight: 700; color: var(--fg-bright); letter-spacing: -0.03em; margin-bottom: 0.5rem; }
.hero h1 span { color: var(--accent); }
.hero .subtitle { font-family: var(--font-sans); color: var(--fg-muted); font-size: 0.92rem; max-width: 560px; margin: 0 auto; line-height: 1.6; }
.hero .meta { margin-top: 1.5rem; display: flex; justify-content: center; gap: 1.5rem; flex-wrap: wrap; }
.hero .meta span { font-size: 0.8rem; color: var(--fg-subtle); font-family: var(--font-mono); }
.toc { background: var(--bg-surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 1.5rem 2rem; margin-bottom: 3rem; }
.toc h2 { font-size: 0.75rem; text-transform: uppercase; letter-spacing: 0.1em; color: var(--fg-muted); margin-bottom: 1rem; }
.toc ol { list-style: none; counter-reset: toc; columns: 2; column-gap: 2rem; }
.toc li { counter-increment: toc; break-inside: avoid; margin-bottom: 0.35rem; }
.toc li::before { content: counter(toc, decimal-leading-zero) " "; color: var(--fg-subtle); font-family: var(--font-mono); font-size: 0.75rem; margin-right: 0.25rem; }
.toc a { font-family: var(--font-mono); color: var(--fg); text-decoration: none; font-size: 0.82rem; transition: color 0.15s; }
.toc a:hover { color: var(--accent); }
section { margin-bottom: 4rem; }
section + section { padding-top: 1rem; }
h2 { font-family: var(--font-mono); font-size: 1.3rem; font-weight: 700; color: var(--fg-bright); letter-spacing: -0.01em; margin-bottom: 1.25rem; padding-bottom: 0.5rem; border-bottom: 1px solid var(--border); }
h3 { font-family: var(--font-mono); font-size: 1rem; font-weight: 600; color: var(--fg-bright); margin-top: 2rem; margin-bottom: 0.75rem; }
h4 { font-family: var(--font-mono); font-size: 0.9rem; font-weight: 600; color: var(--accent); margin-top: 1.5rem; margin-bottom: 0.5rem; }
p { margin-bottom: 1rem; font-size: 0.95rem; line-height: 1.75; }
strong { color: var(--fg-bright); font-weight: 600; }
a { color: var(--accent); text-decoration: none; }
a:hover { text-decoration: underline; }
ul, ol { margin-bottom: 1rem; padding-left: 1.5rem; font-size: 0.93rem; line-height: 1.7; }
li { margin-bottom: 0.35rem; }
li::marker { color: var(--fg-subtle); }
.table-wrap { overflow-x: auto; margin-bottom: 1.5rem; }
table { width: 100%; border-collapse: collapse; font-size: 0.88rem; }
th, td { text-align: left; padding: 0.6rem 1rem; border-bottom: 1px solid var(--border-subtle); }
th { font-family: var(--font-mono); font-size: 0.72rem; text-transform: uppercase; letter-spacing: 0.06em; color: var(--fg-muted); background: var(--bg-surface); border-bottom-color: var(--border); white-space: nowrap; }
td { font-family: var(--font-sans); font-size: 0.88rem; color: var(--fg); }
tr:hover td { background: var(--accent-glow); }
td code { background: var(--bg-elevated); padding: 0.15em 0.4em; border-radius: 3px; font-family: var(--font-mono); font-size: 0.82em; color: var(--cyan); }
pre { background: var(--bg-code); border: 1px solid var(--border); border-radius: var(--radius); padding: 1.25rem 1.5rem; overflow-x: auto; margin-bottom: 1.5rem; font-family: var(--font-mono); font-size: 0.82rem; line-height: 1.65; color: var(--fg); }
pre code { background: none; padding: 0; color: inherit; font-size: inherit; }
code { font-family: var(--font-mono); font-size: 0.85em; }
p code, li code { background: var(--bg-elevated); padding: 0.15em 0.4em; border-radius: 3px; color: var(--cyan); font-size: 0.85em; }
.kw { color: var(--purple); }
.str { color: var(--green); }
.cm { color: var(--fg-subtle); font-style: italic; }
.num { color: var(--orange); }
.key { color: var(--accent); }
.mermaid { margin: 1.5rem 0 2rem; text-align: center; }
.mermaid svg { max-width: 100%; height: auto; }
.callout { font-family: var(--font-sans); background: var(--bg-surface); border-left: 3px solid var(--accent-dim); border-radius: 0 var(--radius) var(--radius) 0; padding: 1rem 1.25rem; margin-bottom: 1.5rem; font-size: 0.88rem; color: var(--fg-muted); line-height: 1.6; }
.callout strong { font-family: var(--font-mono); color: var(--fg-bright); }
.callout.success { border-left-color: var(--green-dim); }
.callout.warn { border-left-color: var(--orange); }
.badge { display: inline-block; font-family: var(--font-mono); font-size: 0.65rem; font-weight: 600; text-transform: uppercase; letter-spacing: 0.05em; padding: 0.2em 0.6em; border-radius: 3px; vertical-align: middle; margin-left: 0.4rem; }
.badge-done { background: var(--green-dim); color: #fff; }
.badge-wip { background: var(--orange); color: #0b0e14; }
.badge-todo { background: var(--fg-subtle); color: var(--fg); }
.checklist { list-style: none; padding-left: 0; }
.checklist li { padding-left: 1.5rem; position: relative; margin-bottom: 0.5rem; }
.checklist li::before { position: absolute; left: 0; font-family: var(--font-mono); font-size: 0.85rem; }
.checklist li.done { color: var(--fg-muted); }
.checklist li.done::before { content: "\2713"; color: var(--green); }
.checklist li.todo::before { content: "\25CB"; color: var(--fg-subtle); }
.checklist li.wip::before { content: "\25D4"; color: var(--orange); }
.compare { display: grid; grid-template-columns: 1fr 1fr; gap: 1rem; margin-bottom: 2rem; }
.compare-card { background: var(--bg-surface); border: 1px solid var(--border); border-radius: var(--radius); padding: 1.25rem; }
.compare-card h4 { margin-top: 0; font-size: 0.82rem; }
.compare-card.after { border-color: var(--accent-dim); }
.compare-card ul { font-family: var(--font-mono); padding-left: 1.25rem; font-size: 0.8rem; }
hr { border: none; border-top: 1px solid var(--border); margin: 3rem 0; }
.progress-bar { position: fixed; top: 0; left: 0; height: 2px; background: var(--accent); z-index: 999; transition: width 0.1s linear; }
@media (max-width: 640px) {
.container { padding: 2rem 1rem 4rem; }
.hero h1 { font-size: 1.6rem; }
.toc ol { columns: 1; }
.compare { grid-template-columns: 1fr; }
table { font-size: 0.8rem; }
th, td { padding: 0.4rem 0.6rem; }
}
</style>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=Noto+Emoji&display=swap" rel="stylesheet">
<style>
@font-face {
font-family: 'Departure Mono';
src: url('https://cdn.jsdelivr.net/gh/rektdeckard/departure-mono@latest/fonts/DepartureMono-Regular.woff2') format('woff2');
font-weight: normal;
font-style: normal;
font-display: swap;
}
</style>
</head>
<body>
<div class="progress-bar" id="progress"></div>
<div class="container">
<header class="hero">
<h1>honcho<span>-integration-spec</span></h1>
<p class="subtitle">Comparison of Hermes Agent vs. openclaw-honcho — and a porting spec for bringing Hermes patterns into other Honcho integrations.</p>
<div class="meta">
<span>hermes-agent / openclaw-honcho</span>
<span>Python + TypeScript</span>
<span>2026-03-09</span>
</div>
</header>
<nav class="toc">
<h2>Contents</h2>
<ol>
<li><a href="#overview">Overview</a></li>
<li><a href="#architecture">Architecture comparison</a></li>
<li><a href="#diff-table">Diff table</a></li>
<li><a href="#patterns">Hermes patterns to port</a></li>
<li><a href="#spec-async">Spec: async prefetch</a></li>
<li><a href="#spec-reasoning">Spec: dynamic reasoning level</a></li>
<li><a href="#spec-modes">Spec: per-peer memory modes</a></li>
<li><a href="#spec-identity">Spec: AI peer identity formation</a></li>
<li><a href="#spec-sessions">Spec: session naming strategies</a></li>
<li><a href="#spec-cli">Spec: CLI surface injection</a></li>
<li><a href="#openclaw-checklist">openclaw-honcho checklist</a></li>
<li><a href="#nanobot-checklist">nanobot-honcho checklist</a></li>
</ol>
</nav>
<!-- OVERVIEW -->
<section id="overview">
<h2>Overview</h2>
<p>Two independent Honcho integrations have been built for two different agent runtimes: <strong>Hermes Agent</strong> (Python, baked into the runner) and <strong>openclaw-honcho</strong> (TypeScript plugin via hook/tool API). Both use the same Honcho peer paradigm — dual peer model, <code>session.context()</code>, <code>peer.chat()</code> — but they made different tradeoffs at every layer.</p>
<p>This document maps those tradeoffs and defines a porting spec: a set of Hermes-originated patterns, each stated as an integration-agnostic interface, that any Honcho integration can adopt regardless of runtime or language.</p>
<div class="callout">
<strong>Scope</strong> Both integrations work correctly today. This spec is about the delta — patterns in Hermes that are worth propagating and patterns in openclaw-honcho that Hermes should eventually adopt. The spec is additive, not prescriptive.
</div>
</section>
<!-- ARCHITECTURE -->
<section id="architecture">
<h2>Architecture comparison</h2>
<h3>Hermes: baked-in runner</h3>
<p>Honcho is initialised directly inside <code>AIAgent.__init__</code>. There is no plugin boundary. Session management, context injection, async prefetch, and CLI surface are all first-class concerns of the runner. Context is injected once per session (baked into <code>_cached_system_prompt</code>) and never re-fetched mid-session — this maximises prefix cache hits at the LLM provider.</p>
<div class="mermaid">
%%{init: {'theme': 'dark', 'themeVariables': { 'primaryColor': '#1f3150', 'primaryTextColor': '#c9d1d9', 'primaryBorderColor': '#3d6ea5', 'lineColor': '#3d6ea5', 'secondaryColor': '#162030', 'tertiaryColor': '#11151c' }}}%%
flowchart TD
U["user message"] --> P["_honcho_prefetch()<br/>(reads cache — no HTTP)"]
P --> SP["_build_system_prompt()<br/>(first turn only, cached)"]
SP --> LLM["LLM call"]
LLM --> R["response"]
R --> FP["_honcho_fire_prefetch()<br/>(daemon threads, turn end)"]
FP --> C1["prefetch_context() thread"]
FP --> C2["prefetch_dialectic() thread"]
C1 --> CACHE["_context_cache / _dialectic_cache"]
C2 --> CACHE
style U fill:#162030,stroke:#3d6ea5,color:#c9d1d9
style P fill:#1f3150,stroke:#3d6ea5,color:#c9d1d9
style SP fill:#1f3150,stroke:#3d6ea5,color:#c9d1d9
style LLM fill:#162030,stroke:#3d6ea5,color:#c9d1d9
style R fill:#162030,stroke:#3d6ea5,color:#c9d1d9
style FP fill:#2a1a40,stroke:#bc8cff,color:#c9d1d9
style C1 fill:#2a1a40,stroke:#bc8cff,color:#c9d1d9
style C2 fill:#2a1a40,stroke:#bc8cff,color:#c9d1d9
style CACHE fill:#11151c,stroke:#484f58,color:#6e7681
</div>
<h3>openclaw-honcho: hook-based plugin</h3>
<p>The plugin registers hooks against OpenClaw's event bus. Context is fetched synchronously inside <code>before_prompt_build</code> on every turn. Message capture happens in <code>agent_end</code>. The multi-agent hierarchy is tracked via <code>subagent_spawned</code>. This model is correct but every turn pays a blocking Honcho round-trip before the LLM call can begin.</p>
<div class="mermaid">
%%{init: {'theme': 'dark', 'themeVariables': { 'primaryColor': '#1f3150', 'primaryTextColor': '#c9d1d9', 'primaryBorderColor': '#3d6ea5', 'lineColor': '#3d6ea5', 'secondaryColor': '#162030', 'tertiaryColor': '#11151c' }}}%%
flowchart TD
U2["user message"] --> BPB["before_prompt_build<br/>(BLOCKING HTTP — every turn)"]
BPB --> CTX["session.context()"]
CTX --> SP2["system prompt assembled"]
SP2 --> LLM2["LLM call"]
LLM2 --> R2["response"]
R2 --> AE["agent_end hook"]
AE --> SAVE["session.addMessages()<br/>session.setMetadata()"]
style U2 fill:#162030,stroke:#3d6ea5,color:#c9d1d9
style BPB fill:#3a1515,stroke:#f47067,color:#c9d1d9
style CTX fill:#3a1515,stroke:#f47067,color:#c9d1d9
style SP2 fill:#1f3150,stroke:#3d6ea5,color:#c9d1d9
style LLM2 fill:#162030,stroke:#3d6ea5,color:#c9d1d9
style R2 fill:#162030,stroke:#3d6ea5,color:#c9d1d9
style AE fill:#162030,stroke:#3d6ea5,color:#c9d1d9
style SAVE fill:#11151c,stroke:#484f58,color:#6e7681
</div>
</section>
<!-- DIFF TABLE -->
<section id="diff-table">
<h2>Diff table</h2>
<div class="table-wrap">
<table>
<thead>
<tr>
<th>Dimension</th>
<th>Hermes Agent</th>
<th>openclaw-honcho</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Context injection timing</strong></td>
<td>Once per session (cached). Zero HTTP on response path after turn 1.</td>
<td>Every turn, blocking. Fresh context per turn but adds latency.</td>
</tr>
<tr>
<td><strong>Prefetch strategy</strong></td>
<td>Daemon threads fire at turn end; consumed next turn from cache.</td>
<td>None. Blocking call at prompt-build time.</td>
</tr>
<tr>
<td><strong>Dialectic (peer.chat)</strong></td>
<td>Prefetched async; result injected into system prompt next turn.</td>
<td>On-demand via <code>honcho_recall</code> / <code>honcho_analyze</code> tools.</td>
</tr>
<tr>
<td><strong>Reasoning level</strong></td>
<td>Dynamic: scales with message length. Floor = config default. Cap = "high".</td>
<td>Fixed per tool: recall=minimal, analyze=medium.</td>
</tr>
<tr>
<td><strong>Memory modes</strong></td>
<td><code>user_memory_mode</code> / <code>agent_memory_mode</code>: hybrid / honcho / local.</td>
<td>None. Always writes to Honcho.</td>
</tr>
<tr>
<td><strong>Write frequency</strong></td>
<td>async (background queue), turn, session, N turns.</td>
<td>After every agent_end (no control).</td>
</tr>
<tr>
<td><strong>AI peer identity</strong></td>
<td><code>observe_me=True</code>, <code>seed_ai_identity()</code>, <code>get_ai_representation()</code>, SOUL.md → AI peer.</td>
<td>Agent files uploaded to agent peer at setup. No ongoing self-observation seeding.</td>
</tr>
<tr>
<td><strong>Context scope</strong></td>
<td>User peer + AI peer representation, both injected.</td>
<td>User peer (owner) representation + conversation summary. <code>peerPerspective</code> on context call.</td>
</tr>
<tr>
<td><strong>Session naming</strong></td>
<td>per-directory / global / manual map / title-based.</td>
<td>Derived from platform session key.</td>
</tr>
<tr>
<td><strong>Multi-agent</strong></td>
<td>Single-agent only.</td>
<td>Parent observer hierarchy via <code>subagent_spawned</code>.</td>
</tr>
<tr>
<td><strong>Tool surface</strong></td>
<td>Single <code>query_user_context</code> tool (on-demand dialectic).</td>
<td>6 tools: session, profile, search, context (fast) + recall, analyze (LLM).</td>
</tr>
<tr>
<td><strong>Platform metadata</strong></td>
<td>Not stripped.</td>
<td>Explicitly stripped before Honcho storage.</td>
</tr>
<tr>
<td><strong>Message dedup</strong></td>
<td>None (sends on every save cycle).</td>
<td><code>lastSavedIndex</code> in session metadata prevents re-sending.</td>
</tr>
<tr>
<td><strong>CLI surface in prompt</strong></td>
<td>Management commands injected into system prompt. Agent knows its own CLI.</td>
<td>Not injected.</td>
</tr>
<tr>
<td><strong>AI peer name in identity</strong></td>
<td>Replaces "Hermes Agent" in DEFAULT_AGENT_IDENTITY when configured.</td>
<td>Not implemented.</td>
</tr>
<tr>
<td><strong>QMD / local file search</strong></td>
<td>Not implemented.</td>
<td>Passthrough tools when QMD backend configured.</td>
</tr>
<tr>
<td><strong>Workspace metadata</strong></td>
<td>Not implemented.</td>
<td><code>agentPeerMap</code> in workspace metadata tracks agent&#8594;peer ID.</td>
</tr>
</tbody>
</table>
</div>
</section>
<!-- PATTERNS -->
<section id="patterns">
<h2>Hermes patterns to port</h2>
<p>Six patterns from Hermes are worth adopting in any Honcho integration. They are described below as integration-agnostic interfaces — the implementation will differ per runtime, but the contract is the same.</p>
<div class="compare">
<div class="compare-card">
<h4>Patterns Hermes contributes</h4>
<ul>
<li>Async prefetch (zero-latency)</li>
<li>Dynamic reasoning level</li>
<li>Per-peer memory modes</li>
<li>AI peer identity formation</li>
<li>Session naming strategies</li>
<li>CLI surface injection</li>
</ul>
</div>
<div class="compare-card after">
<h4>Patterns openclaw contributes back</h4>
<ul>
<li>lastSavedIndex dedup</li>
<li>Platform metadata stripping</li>
<li>Multi-agent observer hierarchy</li>
<li>peerPerspective on context()</li>
<li>Tiered tool surface (fast/LLM)</li>
<li>Workspace agentPeerMap</li>
</ul>
</div>
</div>
</section>
<!-- SPEC: ASYNC PREFETCH -->
<section id="spec-async">
<h2>Spec: async prefetch</h2>
<h3>Problem</h3>
<p>Calling <code>session.context()</code> and <code>peer.chat()</code> synchronously before each LLM call adds 200800ms of Honcho round-trip latency to every turn. Users experience this as the agent "thinking slowly."</p>
<h3>Pattern</h3>
<p>Fire both calls as non-blocking background work at the <strong>end</strong> of each turn. Store results in a per-session cache keyed by session ID. At the <strong>start</strong> of the next turn, pop from cache — the HTTP is already done. First turn is cold (empty cache); all subsequent turns are zero-latency on the response path.</p>
<h3>Interface contract</h3>
<pre><code><span class="cm">// TypeScript (openclaw / nanobot plugin shape)</span>
<span class="kw">interface</span> <span class="key">AsyncPrefetch</span> {
<span class="cm">// Fire context + dialectic fetches at turn end. Non-blocking.</span>
firePrefetch(sessionId: <span class="str">string</span>, userMessage: <span class="str">string</span>): <span class="kw">void</span>;
<span class="cm">// Pop cached results at turn start. Returns empty if cache is cold.</span>
popContextResult(sessionId: <span class="str">string</span>): ContextResult | <span class="kw">null</span>;
popDialecticResult(sessionId: <span class="str">string</span>): <span class="str">string</span> | <span class="kw">null</span>;
}
<span class="kw">type</span> <span class="key">ContextResult</span> = {
representation: <span class="str">string</span>;
card: <span class="str">string</span>[];
aiRepresentation?: <span class="str">string</span>; <span class="cm">// AI peer context if enabled</span>
summary?: <span class="str">string</span>; <span class="cm">// conversation summary if fetched</span>
};</code></pre>
<h3>Implementation notes</h3>
<ul>
<li>Python: <code>threading.Thread(daemon=True)</code>. Write to <code>dict[session_id, result]</code> — GIL makes this safe for simple writes.</li>
<li>TypeScript: <code>Promise</code> stored in <code>Map&lt;string, Promise&lt;ContextResult&gt;&gt;</code>. Await at pop time. If not resolved yet, skip (return null) — do not block.</li>
<li>The pop is destructive: clears the cache entry after reading so stale data never accumulates.</li>
<li>Prefetch should also fire on first turn (even though it won't be consumed until turn 2) — this ensures turn 2 is never cold.</li>
</ul>
<h3>openclaw-honcho adoption</h3>
<p>Move <code>session.context()</code> from <code>before_prompt_build</code> to a post-<code>agent_end</code> background task. Store result in <code>state.contextCache</code>. In <code>before_prompt_build</code>, read from cache instead of calling Honcho. If cache is empty (turn 1), inject nothing — the prompt is still valid without Honcho context on the first turn.</p>
</section>
<!-- SPEC: DYNAMIC REASONING LEVEL -->
<section id="spec-reasoning">
<h2>Spec: dynamic reasoning level</h2>
<h3>Problem</h3>
<p>Honcho's dialectic endpoint supports reasoning levels from <code>minimal</code> to <code>max</code>. A fixed level per tool wastes budget on simple queries and under-serves complex ones.</p>
<h3>Pattern</h3>
<p>Select the reasoning level dynamically based on the user's message. Use the configured default as a floor. Bump by message length. Cap auto-selection at <code>high</code> — never select <code>max</code> automatically.</p>
<h3>Interface contract</h3>
<pre><code><span class="cm">// Shared helper — identical logic in any language</span>
<span class="kw">const</span> LEVELS = [<span class="str">"minimal"</span>, <span class="str">"low"</span>, <span class="str">"medium"</span>, <span class="str">"high"</span>, <span class="str">"max"</span>];
<span class="kw">function</span> <span class="key">dynamicReasoningLevel</span>(
query: <span class="str">string</span>,
configDefault: <span class="str">string</span> = <span class="str">"low"</span>
): <span class="str">string</span> {
<span class="kw">const</span> baseIdx = Math.max(<span class="num">0</span>, LEVELS.indexOf(configDefault));
<span class="kw">const</span> n = query.length;
<span class="kw">const</span> bump = n &lt; <span class="num">120</span> ? <span class="num">0</span> : n &lt; <span class="num">400</span> ? <span class="num">1</span> : <span class="num">2</span>;
<span class="kw">return</span> LEVELS[Math.min(baseIdx + bump, <span class="num">3</span>)]; <span class="cm">// cap at "high" (idx 3)</span>
}</code></pre>
<h3>Config key</h3>
<p>Add a <code>dialecticReasoningLevel</code> config field (string, default <code>"low"</code>). This sets the floor. Users can raise or lower it. The dynamic bump always applies on top.</p>
<h3>openclaw-honcho adoption</h3>
<p>Apply in <code>honcho_recall</code> and <code>honcho_analyze</code>: replace the fixed <code>reasoningLevel</code> with the dynamic selector. <code>honcho_recall</code> should use floor <code>"minimal"</code> and <code>honcho_analyze</code> floor <code>"medium"</code> — both still bump with message length.</p>
</section>
<!-- SPEC: PER-PEER MEMORY MODES -->
<section id="spec-modes">
<h2>Spec: per-peer memory modes</h2>
<h3>Problem</h3>
<p>Users want independent control over whether user context and agent context are written locally, to Honcho, or both. A single <code>memoryMode</code> shorthand is not granular enough.</p>
<h3>Pattern</h3>
<p>Three modes per peer: <code>hybrid</code> (write both local + Honcho), <code>honcho</code> (Honcho only, disable local files), <code>local</code> (local files only, skip Honcho sync for this peer). Two orthogonal axes: user peer and agent peer.</p>
<h3>Config schema</h3>
<pre><code><span class="cm">// ~/.openclaw/openclaw.json (or ~/.nanobot/config.json)</span>
{
<span class="str">"plugins"</span>: {
<span class="str">"openclaw-honcho"</span>: {
<span class="str">"config"</span>: {
<span class="str">"apiKey"</span>: <span class="str">"..."</span>,
<span class="str">"memoryMode"</span>: <span class="str">"hybrid"</span>, <span class="cm">// shorthand: both peers</span>
<span class="str">"userMemoryMode"</span>: <span class="str">"honcho"</span>, <span class="cm">// override for user peer</span>
<span class="str">"agentMemoryMode"</span>: <span class="str">"hybrid"</span> <span class="cm">// override for agent peer</span>
}
}
}
}</code></pre>
<h3>Resolution order</h3>
<ol>
<li>Per-peer field (<code>userMemoryMode</code> / <code>agentMemoryMode</code>) — wins if present.</li>
<li>Shorthand <code>memoryMode</code> — applies to both peers as default.</li>
<li>Hardcoded default: <code>"hybrid"</code>.</li>
</ol>
<h3>Effect on Honcho sync</h3>
<ul>
<li><code>userMemoryMode=local</code>: skip adding user peer messages to Honcho.</li>
<li><code>agentMemoryMode=local</code>: skip adding assistant peer messages to Honcho.</li>
<li>Both local: skip <code>session.addMessages()</code> entirely.</li>
<li><code>userMemoryMode=honcho</code>: disable local USER.md writes.</li>
<li><code>agentMemoryMode=honcho</code>: disable local MEMORY.md / SOUL.md writes.</li>
</ul>
</section>
<!-- SPEC: AI PEER IDENTITY -->
<section id="spec-identity">
<h2>Spec: AI peer identity formation</h2>
<h3>Problem</h3>
<p>Honcho builds the user's representation organically by observing what the user says. The same mechanism exists for the AI peer — but only if <code>observe_me=True</code> is set for the agent peer. Without it, the agent peer accumulates nothing and Honcho's AI-side model never forms.</p>
<p>Additionally, existing persona files (SOUL.md, IDENTITY.md) should seed the AI peer's Honcho representation at first activation, rather than waiting for it to emerge from scratch.</p>
<h3>Part A: observe_me=True for agent peer</h3>
<pre><code><span class="cm">// TypeScript — in session.addPeers() call</span>
<span class="kw">await</span> session.addPeers([
[ownerPeer.id, { observeMe: <span class="kw">true</span>, observeOthers: <span class="kw">false</span> }],
[agentPeer.id, { observeMe: <span class="kw">true</span>, observeOthers: <span class="kw">true</span> }], <span class="cm">// was false</span>
]);</code></pre>
<p>This is a one-line change but foundational. Without it, Honcho's AI peer representation stays empty regardless of what the agent says.</p>
<h3>Part B: seedAiIdentity()</h3>
<pre><code><span class="kw">async function</span> <span class="key">seedAiIdentity</span>(
session: HonchoSession,
agentPeer: Peer,
content: <span class="str">string</span>,
source: <span class="str">string</span>
): Promise&lt;<span class="kw">boolean</span>&gt; {
<span class="kw">const</span> wrapped = [
<span class="str">`&lt;ai_identity_seed&gt;`</span>,
<span class="str">`&lt;source&gt;${source}&lt;/source&gt;`</span>,
<span class="str">``</span>,
content.trim(),
<span class="str">`&lt;/ai_identity_seed&gt;`</span>,
].join(<span class="str">"\n"</span>);
<span class="kw">await</span> agentPeer.addMessage(<span class="str">"assistant"</span>, wrapped);
<span class="kw">return true</span>;
}</code></pre>
<h3>Part C: migrate agent files at setup</h3>
<p>During <code>openclaw honcho setup</code>, upload agent-self files (SOUL.md, IDENTITY.md, AGENTS.md, BOOTSTRAP.md) to the agent peer using <code>seedAiIdentity()</code> instead of <code>session.uploadFile()</code>. This routes the content through Honcho's observation pipeline rather than the file store.</p>
<h3>Part D: AI peer name in identity</h3>
<p>When the agent has a configured name (non-default), inject it into the agent's self-identity prefix. In OpenClaw this means adding to the injected system prompt section:</p>
<pre><code><span class="cm">// In context hook return value</span>
<span class="kw">return</span> {
systemPrompt: [
agentName ? <span class="str">`You are ${agentName}.`</span> : <span class="str">""</span>,
<span class="str">"## User Memory Context"</span>,
...sections,
].filter(Boolean).join(<span class="str">"\n\n"</span>)
};</code></pre>
<h3>CLI surface: honcho identity subcommand</h3>
<pre><code>openclaw honcho identity &lt;file&gt; <span class="cm"># seed from file</span>
openclaw honcho identity --show <span class="cm"># show current AI peer representation</span></code></pre>
</section>
<!-- SPEC: SESSION NAMING -->
<section id="spec-sessions">
<h2>Spec: session naming strategies</h2>
<h3>Problem</h3>
<p>When Honcho is used across multiple projects or directories, a single global session means every project shares the same context. Per-directory sessions provide isolation without requiring users to name sessions manually.</p>
<h3>Strategies</h3>
<div class="table-wrap">
<table>
<thead><tr><th>Strategy</th><th>Session key</th><th>When to use</th></tr></thead>
<tbody>
<tr><td><code>per-directory</code></td><td>basename of CWD</td><td>Default. Each project gets its own session.</td></tr>
<tr><td><code>global</code></td><td>fixed string <code>"global"</code></td><td>Single cross-project session.</td></tr>
<tr><td>manual map</td><td>user-configured per path</td><td><code>sessions</code> config map overrides directory basename.</td></tr>
<tr><td>title-based</td><td>sanitized session title</td><td>When agent supports named sessions; title set mid-conversation.</td></tr>
</tbody>
</table>
</div>
<h3>Config schema</h3>
<pre><code>{
<span class="str">"sessionStrategy"</span>: <span class="str">"per-directory"</span>, <span class="cm">// "per-directory" | "global"</span>
<span class="str">"sessionPeerPrefix"</span>: <span class="kw">false</span>, <span class="cm">// prepend peer name to session key</span>
<span class="str">"sessions"</span>: { <span class="cm">// manual overrides</span>
<span class="str">"/home/user/projects/foo"</span>: <span class="str">"foo-project"</span>
}
}</code></pre>
<h3>CLI surface</h3>
<pre><code>openclaw honcho sessions <span class="cm"># list all mappings</span>
openclaw honcho map &lt;name&gt; <span class="cm"># map cwd to session name</span>
openclaw honcho map <span class="cm"># no-arg = list mappings</span></code></pre>
<p>Resolution order: manual map wins &rarr; session title &rarr; directory basename &rarr; platform key.</p>
</section>
<!-- SPEC: CLI SURFACE INJECTION -->
<section id="spec-cli">
<h2>Spec: CLI surface injection</h2>
<h3>Problem</h3>
<p>When a user asks "how do I change my memory settings?" or "what Honcho commands are available?" the agent either hallucinates or says it doesn't know. The agent should know its own management interface.</p>
<h3>Pattern</h3>
<p>When Honcho is active, append a compact command reference to the system prompt. The agent can cite these commands directly instead of guessing.</p>
<pre><code><span class="cm">// In context hook, append to systemPrompt</span>
<span class="kw">const</span> honchoSection = [
<span class="str">"# Honcho memory integration"</span>,
<span class="str">`Active. Session: ${sessionKey}. Mode: ${mode}.`</span>,
<span class="str">"Management commands:"</span>,
<span class="str">" openclaw honcho status — show config + connection"</span>,
<span class="str">" openclaw honcho mode [hybrid|honcho|local] — show or set memory mode"</span>,
<span class="str">" openclaw honcho sessions — list session mappings"</span>,
<span class="str">" openclaw honcho map &lt;name&gt; — map directory to session"</span>,
<span class="str">" openclaw honcho identity [file] [--show] — seed or show AI identity"</span>,
<span class="str">" openclaw honcho setup — full interactive wizard"</span>,
].join(<span class="str">"\n"</span>);</code></pre>
<div class="callout warn">
<strong>Keep it compact.</strong> This section is injected every turn. Keep it under 300 chars of context. List commands, not explanations — the agent can explain them on request.
</div>
</section>
<!-- OPENCLAW CHECKLIST -->
<section id="openclaw-checklist">
<h2>openclaw-honcho checklist</h2>
<p>Ordered by impact. Each item maps to a spec section above.</p>
<ul class="checklist">
<li class="todo"><strong>Async prefetch</strong> — move <code>session.context()</code> out of <code>before_prompt_build</code> into post-<code>agent_end</code> background Promise. Pop from cache at prompt build. (<a href="#spec-async">spec</a>)</li>
<li class="todo"><strong>observe_me=True for agent peer</strong> — one-line change in <code>session.addPeers()</code> config for agent peer. (<a href="#spec-identity">spec</a>)</li>
<li class="todo"><strong>Dynamic reasoning level</strong> — add <code>dynamicReasoningLevel()</code> helper; apply in <code>honcho_recall</code> and <code>honcho_analyze</code>. Add <code>dialecticReasoningLevel</code> to config schema. (<a href="#spec-reasoning">spec</a>)</li>
<li class="todo"><strong>Per-peer memory modes</strong> — add <code>userMemoryMode</code> / <code>agentMemoryMode</code> to config; gate Honcho sync and local writes accordingly. (<a href="#spec-modes">spec</a>)</li>
<li class="todo"><strong>seedAiIdentity()</strong> — add helper; apply during setup migration for SOUL.md / IDENTITY.md instead of <code>session.uploadFile()</code>. (<a href="#spec-identity">spec</a>)</li>
<li class="todo"><strong>Session naming strategies</strong> — add <code>sessionStrategy</code>, <code>sessions</code> map, <code>sessionPeerPrefix</code> to config; implement resolution function. (<a href="#spec-sessions">spec</a>)</li>
<li class="todo"><strong>CLI surface injection</strong> — append command reference to <code>before_prompt_build</code> return value when Honcho is active. (<a href="#spec-cli">spec</a>)</li>
<li class="todo"><strong>honcho identity subcommand</strong> — add <code>openclaw honcho identity</code> CLI command. (<a href="#spec-identity">spec</a>)</li>
<li class="todo"><strong>AI peer name injection</strong> — if <code>aiPeer</code> name configured, prepend to injected system prompt. (<a href="#spec-identity">spec</a>)</li>
<li class="todo"><strong>honcho mode / honcho sessions / honcho map</strong> — CLI parity with Hermes. (<a href="#spec-sessions">spec</a>)</li>
</ul>
<div class="callout success">
<strong>Already done in openclaw-honcho (do not re-implement):</strong> lastSavedIndex dedup, platform metadata stripping, multi-agent parent observer hierarchy, peerPerspective on context(), tiered tool surface (fast/LLM), workspace agentPeerMap, QMD passthrough, self-hosted Honcho support.
</div>
</section>
<!-- NANOBOT CHECKLIST -->
<section id="nanobot-checklist">
<h2>nanobot-honcho checklist</h2>
<p>nanobot-honcho is a greenfield integration. Start from openclaw-honcho's architecture (hook-based, dual peer) and apply all Hermes patterns from day one rather than retrofitting. Priority order:</p>
<h3>Phase 1 — core correctness</h3>
<ul class="checklist">
<li class="todo">Dual peer model (owner + agent peer), both with <code>observe_me=True</code></li>
<li class="todo">Message capture at turn end with <code>lastSavedIndex</code> dedup</li>
<li class="todo">Platform metadata stripping before Honcho storage</li>
<li class="todo">Async prefetch from day one — do not implement blocking context injection</li>
<li class="todo">Legacy file migration at first activation (USER.md → owner peer, SOUL.md → <code>seedAiIdentity()</code>)</li>
</ul>
<h3>Phase 2 — configuration</h3>
<ul class="checklist">
<li class="todo">Config schema: <code>apiKey</code>, <code>workspaceId</code>, <code>baseUrl</code>, <code>memoryMode</code>, <code>userMemoryMode</code>, <code>agentMemoryMode</code>, <code>dialecticReasoningLevel</code>, <code>sessionStrategy</code>, <code>sessions</code></li>
<li class="todo">Per-peer memory mode gating</li>
<li class="todo">Dynamic reasoning level</li>
<li class="todo">Session naming strategies</li>
</ul>
<h3>Phase 3 — tools and CLI</h3>
<ul class="checklist">
<li class="todo">Tool surface: <code>honcho_profile</code>, <code>honcho_recall</code>, <code>honcho_analyze</code>, <code>honcho_search</code>, <code>honcho_context</code></li>
<li class="todo">CLI: <code>setup</code>, <code>status</code>, <code>sessions</code>, <code>map</code>, <code>mode</code>, <code>identity</code></li>
<li class="todo">CLI surface injection into system prompt</li>
<li class="todo">AI peer name wired into agent identity</li>
</ul>
</section>
</div>
<script type="module">
import mermaid from 'https://cdn.jsdelivr.net/npm/mermaid@11/dist/mermaid.esm.min.mjs';
mermaid.initialize({ startOnLoad: true, securityLevel: 'loose', fontFamily: 'Departure Mono, Noto Emoji, monospace' });
</script>
<script>
window.addEventListener('scroll', () => {
const bar = document.getElementById('progress');
const max = document.documentElement.scrollHeight - window.innerHeight;
bar.style.width = (max > 0 ? (window.scrollY / max) * 100 : 0) + '%';
});
</script>
</body>
</html>

View File

@@ -1,377 +0,0 @@
# honcho-integration-spec
Comparison of Hermes Agent vs. openclaw-honcho — and a porting spec for bringing Hermes patterns into other Honcho integrations.
---
## Overview
Two independent Honcho integrations have been built for two different agent runtimes: **Hermes Agent** (Python, baked into the runner) and **openclaw-honcho** (TypeScript plugin via hook/tool API). Both use the same Honcho peer paradigm — dual peer model, `session.context()`, `peer.chat()` — but they made different tradeoffs at every layer.
This document maps those tradeoffs and defines a porting spec: a set of Hermes-originated patterns, each stated as an integration-agnostic interface, that any Honcho integration can adopt regardless of runtime or language.
> **Scope** Both integrations work correctly today. This spec is about the delta — patterns in Hermes that are worth propagating and patterns in openclaw-honcho that Hermes should eventually adopt. The spec is additive, not prescriptive.
---
## Architecture comparison
### Hermes: baked-in runner
Honcho is initialised directly inside `AIAgent.__init__`. There is no plugin boundary. Session management, context injection, async prefetch, and CLI surface are all first-class concerns of the runner. Context is injected once per session (baked into `_cached_system_prompt`) and never re-fetched mid-session — this maximises prefix cache hits at the LLM provider.
Turn flow:
```
user message
→ _honcho_prefetch() (reads cache — no HTTP)
→ _build_system_prompt() (first turn only, cached)
→ LLM call
→ response
→ _honcho_fire_prefetch() (daemon threads, turn end)
→ prefetch_context() thread ──┐
→ prefetch_dialectic() thread ─┴→ _context_cache / _dialectic_cache
```
### openclaw-honcho: hook-based plugin
The plugin registers hooks against OpenClaw's event bus. Context is fetched synchronously inside `before_prompt_build` on every turn. Message capture happens in `agent_end`. The multi-agent hierarchy is tracked via `subagent_spawned`. This model is correct but every turn pays a blocking Honcho round-trip before the LLM call can begin.
Turn flow:
```
user message
→ before_prompt_build (BLOCKING HTTP — every turn)
→ session.context()
→ system prompt assembled
→ LLM call
→ response
→ agent_end hook
→ session.addMessages()
→ session.setMetadata()
```
---
## Diff table
| Dimension | Hermes Agent | openclaw-honcho |
|---|---|---|
| **Context injection timing** | Once per session (cached). Zero HTTP on response path after turn 1. | Every turn, blocking. Fresh context per turn but adds latency. |
| **Prefetch strategy** | Daemon threads fire at turn end; consumed next turn from cache. | None. Blocking call at prompt-build time. |
| **Dialectic (peer.chat)** | Prefetched async; result injected into system prompt next turn. | On-demand via `honcho_recall` / `honcho_analyze` tools. |
| **Reasoning level** | Dynamic: scales with message length. Floor = config default. Cap = "high". | Fixed per tool: recall=minimal, analyze=medium. |
| **Memory modes** | `user_memory_mode` / `agent_memory_mode`: hybrid / honcho / local. | None. Always writes to Honcho. |
| **Write frequency** | async (background queue), turn, session, N turns. | After every agent_end (no control). |
| **AI peer identity** | `observe_me=True`, `seed_ai_identity()`, `get_ai_representation()`, SOUL.md → AI peer. | Agent files uploaded to agent peer at setup. No ongoing self-observation. |
| **Context scope** | User peer + AI peer representation, both injected. | User peer (owner) representation + conversation summary. `peerPerspective` on context call. |
| **Session naming** | per-directory / global / manual map / title-based. | Derived from platform session key. |
| **Multi-agent** | Single-agent only. | Parent observer hierarchy via `subagent_spawned`. |
| **Tool surface** | Single `query_user_context` tool (on-demand dialectic). | 6 tools: session, profile, search, context (fast) + recall, analyze (LLM). |
| **Platform metadata** | Not stripped. | Explicitly stripped before Honcho storage. |
| **Message dedup** | None. | `lastSavedIndex` in session metadata prevents re-sending. |
| **CLI surface in prompt** | Management commands injected into system prompt. Agent knows its own CLI. | Not injected. |
| **AI peer name in identity** | Replaces "Hermes Agent" in DEFAULT_AGENT_IDENTITY when configured. | Not implemented. |
| **QMD / local file search** | Not implemented. | Passthrough tools when QMD backend configured. |
| **Workspace metadata** | Not implemented. | `agentPeerMap` in workspace metadata tracks agent→peer ID. |
---
## Patterns
Six patterns from Hermes are worth adopting in any Honcho integration. Each is described as an integration-agnostic interface.
**Hermes contributes:**
- Async prefetch (zero-latency)
- Dynamic reasoning level
- Per-peer memory modes
- AI peer identity formation
- Session naming strategies
- CLI surface injection
**openclaw-honcho contributes back (Hermes should adopt):**
- `lastSavedIndex` dedup
- Platform metadata stripping
- Multi-agent observer hierarchy
- `peerPerspective` on `context()`
- Tiered tool surface (fast/LLM)
- Workspace `agentPeerMap`
---
## Spec: async prefetch
### Problem
Calling `session.context()` and `peer.chat()` synchronously before each LLM call adds 200800ms of Honcho round-trip latency to every turn.
### Pattern
Fire both calls as non-blocking background work at the **end** of each turn. Store results in a per-session cache keyed by session ID. At the **start** of the next turn, pop from cache — the HTTP is already done. First turn is cold (empty cache); all subsequent turns are zero-latency on the response path.
### Interface contract
```typescript
interface AsyncPrefetch {
// Fire context + dialectic fetches at turn end. Non-blocking.
firePrefetch(sessionId: string, userMessage: string): void;
// Pop cached results at turn start. Returns empty if cache is cold.
popContextResult(sessionId: string): ContextResult | null;
popDialecticResult(sessionId: string): string | null;
}
type ContextResult = {
representation: string;
card: string[];
aiRepresentation?: string; // AI peer context if enabled
summary?: string; // conversation summary if fetched
};
```
### Implementation notes
- **Python:** `threading.Thread(daemon=True)`. Write to `dict[session_id, result]` — GIL makes this safe for simple writes.
- **TypeScript:** `Promise` stored in `Map<string, Promise<ContextResult>>`. Await at pop time. If not resolved yet, return null — do not block.
- The pop is destructive: clears the cache entry after reading so stale data never accumulates.
- Prefetch should also fire on first turn (even though it won't be consumed until turn 2).
### openclaw-honcho adoption
Move `session.context()` from `before_prompt_build` to a post-`agent_end` background task. Store result in `state.contextCache`. In `before_prompt_build`, read from cache instead of calling Honcho. If cache is empty (turn 1), inject nothing — the prompt is still valid without Honcho context on the first turn.
---
## Spec: dynamic reasoning level
### Problem
Honcho's dialectic endpoint supports reasoning levels from `minimal` to `max`. A fixed level per tool wastes budget on simple queries and under-serves complex ones.
### Pattern
Select the reasoning level dynamically based on the user's message. Use the configured default as a floor. Bump by message length. Cap auto-selection at `high` — never select `max` automatically.
### Logic
```
< 120 chars → default (typically "low")
120400 chars → one level above default (cap at "high")
> 400 chars → two levels above default (cap at "high")
```
### Config key
Add `dialecticReasoningLevel` (string, default `"low"`). This sets the floor. The dynamic bump always applies on top.
### openclaw-honcho adoption
Apply in `honcho_recall` and `honcho_analyze`: replace fixed `reasoningLevel` with the dynamic selector. `honcho_recall` uses floor `"minimal"`, `honcho_analyze` uses floor `"medium"` — both still bump with message length.
---
## Spec: per-peer memory modes
### Problem
Users want independent control over whether user context and agent context are written locally, to Honcho, or both.
### Modes
| Mode | Effect |
|---|---|
| `hybrid` | Write to both local files and Honcho (default) |
| `honcho` | Honcho only — disable corresponding local file writes |
| `local` | Local files only — skip Honcho sync for this peer |
### Config schema
```json
{
"memoryMode": "hybrid",
"userMemoryMode": "honcho",
"agentMemoryMode": "hybrid"
}
```
Resolution order: per-peer field wins → shorthand `memoryMode` → default `"hybrid"`.
### Effect on Honcho sync
- `userMemoryMode=local`: skip adding user peer messages to Honcho
- `agentMemoryMode=local`: skip adding assistant peer messages to Honcho
- Both local: skip `session.addMessages()` entirely
- `userMemoryMode=honcho`: disable local USER.md writes
- `agentMemoryMode=honcho`: disable local MEMORY.md / SOUL.md writes
---
## Spec: AI peer identity formation
### Problem
Honcho builds the user's representation organically by observing what the user says. The same mechanism exists for the AI peer — but only if `observe_me=True` is set for the agent peer. Without it, the agent peer accumulates nothing.
Additionally, existing persona files (SOUL.md, IDENTITY.md) should seed the AI peer's Honcho representation at first activation.
### Part A: observe_me=True for agent peer
```typescript
await session.addPeers([
[ownerPeer.id, { observeMe: true, observeOthers: false }],
[agentPeer.id, { observeMe: true, observeOthers: true }], // was false
]);
```
One-line change. Foundational. Without it, the AI peer representation stays empty regardless of what the agent says.
### Part B: seedAiIdentity()
```typescript
async function seedAiIdentity(
agentPeer: Peer,
content: string,
source: string
): Promise<boolean> {
const wrapped = [
`<ai_identity_seed>`,
`<source>${source}</source>`,
``,
content.trim(),
`</ai_identity_seed>`,
].join("\n");
await agentPeer.addMessage("assistant", wrapped);
return true;
}
```
### Part C: migrate agent files at setup
During `honcho setup`, upload agent-self files (SOUL.md, IDENTITY.md, AGENTS.md) to the agent peer via `seedAiIdentity()` instead of `session.uploadFile()`. This routes content through Honcho's observation pipeline.
### Part D: AI peer name in identity
When the agent has a configured name, prepend it to the injected system prompt:
```typescript
const namePrefix = agentName ? `You are ${agentName}.\n\n` : "";
return { systemPrompt: namePrefix + "## User Memory Context\n\n" + sections };
```
### CLI surface
```
honcho identity <file> # seed from file
honcho identity --show # show current AI peer representation
```
---
## Spec: session naming strategies
### Problem
A single global session means every project shares the same Honcho context. Per-directory sessions provide isolation without requiring users to name sessions manually.
### Strategies
| Strategy | Session key | When to use |
|---|---|---|
| `per-directory` | basename of CWD | Default. Each project gets its own session. |
| `global` | fixed string `"global"` | Single cross-project session. |
| manual map | user-configured per path | `sessions` config map overrides directory basename. |
| title-based | sanitized session title | When agent supports named sessions set mid-conversation. |
### Config schema
```json
{
"sessionStrategy": "per-directory",
"sessionPeerPrefix": false,
"sessions": {
"/home/user/projects/foo": "foo-project"
}
}
```
### CLI surface
```
honcho sessions # list all mappings
honcho map <name> # map cwd to session name
honcho map # no-arg = list mappings
```
Resolution order: manual map → session title → directory basename → platform key.
---
## Spec: CLI surface injection
### Problem
When a user asks "how do I change my memory settings?" the agent either hallucinates or says it doesn't know. The agent should know its own management interface.
### Pattern
When Honcho is active, append a compact command reference to the system prompt. Keep it under 300 chars.
```
# Honcho memory integration
Active. Session: {sessionKey}. Mode: {mode}.
Management commands:
honcho status — show config + connection
honcho mode [hybrid|honcho|local] — show or set memory mode
honcho sessions — list session mappings
honcho map <name> — map directory to session
honcho identity [file] [--show] — seed or show AI identity
honcho setup — full interactive wizard
```
---
## openclaw-honcho checklist
Ordered by impact:
- [ ] **Async prefetch** — move `session.context()` out of `before_prompt_build` into post-`agent_end` background Promise
- [ ] **observe_me=True for agent peer** — one-line change in `session.addPeers()`
- [ ] **Dynamic reasoning level** — add helper; apply in `honcho_recall` and `honcho_analyze`; add `dialecticReasoningLevel` to config
- [ ] **Per-peer memory modes** — add `userMemoryMode` / `agentMemoryMode` to config; gate Honcho sync and local writes
- [ ] **seedAiIdentity()** — add helper; use during setup migration for SOUL.md / IDENTITY.md
- [ ] **Session naming strategies** — add `sessionStrategy`, `sessions` map, `sessionPeerPrefix`
- [ ] **CLI surface injection** — append command reference to `before_prompt_build` return value
- [ ] **honcho identity subcommand** — seed from file or `--show` current representation
- [ ] **AI peer name injection** — if `aiPeer` name configured, prepend to injected system prompt
- [ ] **honcho mode / sessions / map** — CLI parity with Hermes
Already done in openclaw-honcho (do not re-implement): `lastSavedIndex` dedup, platform metadata stripping, multi-agent parent observer, `peerPerspective` on `context()`, tiered tool surface, workspace `agentPeerMap`, QMD passthrough, self-hosted Honcho.
---
## nanobot-honcho checklist
Greenfield integration. Start from openclaw-honcho's architecture and apply all Hermes patterns from day one.
### Phase 1 — core correctness
- [ ] Dual peer model (owner + agent peer), both with `observe_me=True`
- [ ] Message capture at turn end with `lastSavedIndex` dedup
- [ ] Platform metadata stripping before Honcho storage
- [ ] Async prefetch from day one — do not implement blocking context injection
- [ ] Legacy file migration at first activation (USER.md → owner peer, SOUL.md → `seedAiIdentity()`)
### Phase 2 — configuration
- [ ] Config schema: `apiKey`, `workspaceId`, `baseUrl`, `memoryMode`, `userMemoryMode`, `agentMemoryMode`, `dialecticReasoningLevel`, `sessionStrategy`, `sessions`
- [ ] Per-peer memory mode gating
- [ ] Dynamic reasoning level
- [ ] Session naming strategies
### Phase 3 — tools and CLI
- [ ] Tool surface: `honcho_profile`, `honcho_recall`, `honcho_analyze`, `honcho_search`, `honcho_context`
- [ ] CLI: `setup`, `status`, `sessions`, `map`, `mode`, `identity`
- [ ] CLI surface injection into system prompt
- [ ] AI peer name wired into agent identity

View File

@@ -1,110 +0,0 @@
# Migrating from OpenClaw to Hermes Agent
This guide covers how to import your OpenClaw settings, memories, skills, and API keys into Hermes Agent.
## Three Ways to Migrate
### 1. Automatic (during first-time setup)
When you run `hermes setup` for the first time and Hermes detects `~/.openclaw`, it automatically offers to import your OpenClaw data before configuration begins. Just accept the prompt and everything is handled for you.
### 2. CLI Command (quick, scriptable)
```bash
hermes claw migrate # Full migration with confirmation prompt
hermes claw migrate --dry-run # Preview what would happen
hermes claw migrate --preset user-data # Migrate without API keys/secrets
hermes claw migrate --yes # Skip confirmation prompt
```
**All options:**
| Flag | Description |
|------|-------------|
| `--source PATH` | Path to OpenClaw directory (default: `~/.openclaw`) |
| `--dry-run` | Preview only — no files are modified |
| `--preset {user-data,full}` | Migration preset (default: `full`). `user-data` excludes secrets |
| `--overwrite` | Overwrite existing files (default: skip conflicts) |
| `--migrate-secrets` | Include allowlisted secrets (auto-enabled with `full` preset) |
| `--workspace-target PATH` | Copy workspace instructions (AGENTS.md) to this absolute path |
| `--skill-conflict {skip,overwrite,rename}` | How to handle skill name conflicts (default: `skip`) |
| `--yes`, `-y` | Skip confirmation prompts |
### 3. Agent-Guided (interactive, with previews)
Ask the agent to run the migration for you:
```
> Migrate my OpenClaw setup to Hermes
```
The agent will use the `openclaw-migration` skill to:
1. Run a dry-run first to preview changes
2. Ask about conflict resolution (SOUL.md, skills, etc.)
3. Let you choose between `user-data` and `full` presets
4. Execute the migration with your choices
5. Print a detailed summary of what was migrated
## What Gets Migrated
### `user-data` preset
| Item | Source | Destination |
|------|--------|-------------|
| SOUL.md | `~/.openclaw/workspace/SOUL.md` | `~/.hermes/SOUL.md` |
| Memory entries | `~/.openclaw/workspace/MEMORY.md` | `~/.hermes/memories/MEMORY.md` |
| User profile | `~/.openclaw/workspace/USER.md` | `~/.hermes/memories/USER.md` |
| Skills | `~/.openclaw/workspace/skills/` | `~/.hermes/skills/openclaw-imports/` |
| Command allowlist | `~/.openclaw/workspace/exec_approval_patterns.yaml` | Merged into `~/.hermes/config.yaml` |
| Messaging settings | `~/.openclaw/config.yaml` (TELEGRAM_ALLOWED_USERS, MESSAGING_CWD) | `~/.hermes/.env` |
| TTS assets | `~/.openclaw/workspace/tts/` | `~/.hermes/tts/` |
### `full` preset (adds to `user-data`)
| Item | Source | Destination |
|------|--------|-------------|
| Telegram bot token | `~/.openclaw/config.yaml` | `~/.hermes/.env` |
| OpenRouter API key | `~/.openclaw/.env` or config | `~/.hermes/.env` |
| OpenAI API key | `~/.openclaw/.env` or config | `~/.hermes/.env` |
| Anthropic API key | `~/.openclaw/.env` or config | `~/.hermes/.env` |
| ElevenLabs API key | `~/.openclaw/.env` or config | `~/.hermes/.env` |
Only these 6 allowlisted secrets are ever imported. Other credentials are skipped and reported.
## Conflict Handling
By default, the migration **will not overwrite** existing Hermes data:
- **SOUL.md** — skipped if one already exists in `~/.hermes/`
- **Memory entries** — skipped if memories already exist (to avoid duplicates)
- **Skills** — skipped if a skill with the same name already exists
- **API keys** — skipped if the key is already set in `~/.hermes/.env`
To overwrite conflicts, use `--overwrite`. The migration creates backups before overwriting.
For skills, you can also use `--skill-conflict rename` to import conflicting skills under a new name (e.g., `skill-name-imported`).
## Migration Report
Every migration (including dry runs) produces a report showing:
- **Migrated items** — what was successfully imported
- **Conflicts** — items skipped because they already exist
- **Skipped items** — items not found in the source
- **Errors** — items that failed to import
For execute runs, the full report is saved to `~/.hermes/migration/openclaw/<timestamp>/`.
## Troubleshooting
### "OpenClaw directory not found"
The migration looks for `~/.openclaw` by default. If your OpenClaw is installed elsewhere, use `--source`:
```bash
hermes claw migrate --source /path/to/.openclaw
```
### "Migration script not found"
The migration script ships with Hermes Agent. If you installed via pip (not git clone), the `optional-skills/` directory may not be present. Install the skill from the Skills Hub:
```bash
hermes skills install openclaw-migration
```
### Memory overflow
If your OpenClaw MEMORY.md or USER.md exceeds Hermes' character limits, excess entries are exported to an overflow file in the migration report directory. You can manually review and add the most important ones.

View File

@@ -1,608 +0,0 @@
# Pricing Accuracy Architecture
Date: 2026-03-16
## Goal
Hermes should only show dollar costs when they are backed by an official source for the user's actual billing path.
This design replaces the current static, heuristic pricing flow in:
- `run_agent.py`
- `agent/usage_pricing.py`
- `agent/insights.py`
- `cli.py`
with a provider-aware pricing system that:
- handles cache billing correctly
- distinguishes `actual` vs `estimated` vs `included` vs `unknown`
- reconciles post-hoc costs when providers expose authoritative billing data
- supports direct providers, OpenRouter, subscriptions, enterprise pricing, and custom endpoints
## Problems In The Current Design
Current Hermes behavior has four structural issues:
1. It stores only `prompt_tokens` and `completion_tokens`, which is insufficient for providers that bill cache reads and cache writes separately.
2. It uses a static model price table and fuzzy heuristics, which can drift from current official pricing.
3. It assumes public API list pricing matches the user's real billing path.
4. It has no distinction between live estimates and reconciled billed cost.
## Design Principles
1. Normalize usage before pricing.
2. Never fold cached tokens into plain input cost.
3. Track certainty explicitly.
4. Treat the billing path as part of the model identity.
5. Prefer official machine-readable sources over scraped docs.
6. Use post-hoc provider cost APIs when available.
7. Show `n/a` rather than inventing precision.
## High-Level Architecture
The new system has four layers:
1. `usage_normalization`
Converts raw provider usage into a canonical usage record.
2. `pricing_source_resolution`
Determines the billing path, source of truth, and applicable pricing source.
3. `cost_estimation_and_reconciliation`
Produces an immediate estimate when possible, then replaces or annotates it with actual billed cost later.
4. `presentation`
`/usage`, `/insights`, and the status bar display cost with certainty metadata.
## Canonical Usage Record
Add a canonical usage model that every provider path maps into before any pricing math happens.
Suggested structure:
```python
@dataclass
class CanonicalUsage:
provider: str
billing_provider: str
model: str
billing_route: str
input_tokens: int = 0
output_tokens: int = 0
cache_read_tokens: int = 0
cache_write_tokens: int = 0
reasoning_tokens: int = 0
request_count: int = 1
raw_usage: dict[str, Any] | None = None
raw_usage_fields: dict[str, str] | None = None
computed_fields: set[str] | None = None
provider_request_id: str | None = None
provider_generation_id: str | None = None
provider_response_id: str | None = None
```
Rules:
- `input_tokens` means non-cached input only.
- `cache_read_tokens` and `cache_write_tokens` are never merged into `input_tokens`.
- `output_tokens` excludes cache metrics.
- `reasoning_tokens` is telemetry unless a provider officially bills it separately.
This is the same normalization pattern used by `opencode`, extended with provenance and reconciliation ids.
## Provider Normalization Rules
### OpenAI Direct
Source usage fields:
- `prompt_tokens`
- `completion_tokens`
- `prompt_tokens_details.cached_tokens`
Normalization:
- `cache_read_tokens = cached_tokens`
- `input_tokens = prompt_tokens - cached_tokens`
- `cache_write_tokens = 0` unless OpenAI exposes it in the relevant route
- `output_tokens = completion_tokens`
### Anthropic Direct
Source usage fields:
- `input_tokens`
- `output_tokens`
- `cache_read_input_tokens`
- `cache_creation_input_tokens`
Normalization:
- `input_tokens = input_tokens`
- `output_tokens = output_tokens`
- `cache_read_tokens = cache_read_input_tokens`
- `cache_write_tokens = cache_creation_input_tokens`
### OpenRouter
Estimate-time usage normalization should use the response usage payload with the same rules as the underlying provider when possible.
Reconciliation-time records should also store:
- OpenRouter generation id
- native token fields when available
- `total_cost`
- `cache_discount`
- `upstream_inference_cost`
- `is_byok`
### Gemini / Vertex
Use official Gemini or Vertex usage fields where available.
If cached content tokens are exposed:
- map them to `cache_read_tokens`
If a route exposes no cache creation metric:
- store `cache_write_tokens = 0`
- preserve the raw usage payload for later extension
### DeepSeek And Other Direct Providers
Normalize only the fields that are officially exposed.
If a provider does not expose cache buckets:
- do not infer them unless the provider explicitly documents how to derive them
### Subscription / Included-Cost Routes
These still use the canonical usage model.
Tokens are tracked normally. Cost depends on billing mode, not on whether usage exists.
## Billing Route Model
Hermes must stop keying pricing solely by `model`.
Introduce a billing route descriptor:
```python
@dataclass
class BillingRoute:
provider: str
base_url: str | None
model: str
billing_mode: str
organization_hint: str | None = None
```
`billing_mode` values:
- `official_cost_api`
- `official_generation_api`
- `official_models_api`
- `official_docs_snapshot`
- `subscription_included`
- `user_override`
- `custom_contract`
- `unknown`
Examples:
- OpenAI direct API with Costs API access: `official_cost_api`
- Anthropic direct API with Usage & Cost API access: `official_cost_api`
- OpenRouter request before reconciliation: `official_models_api`
- OpenRouter request after generation lookup: `official_generation_api`
- GitHub Copilot style subscription route: `subscription_included`
- local OpenAI-compatible server: `unknown`
- enterprise contract with configured rates: `custom_contract`
## Cost Status Model
Every displayed cost should have:
```python
@dataclass
class CostResult:
amount_usd: Decimal | None
status: Literal["actual", "estimated", "included", "unknown"]
source: Literal[
"provider_cost_api",
"provider_generation_api",
"provider_models_api",
"official_docs_snapshot",
"user_override",
"custom_contract",
"none",
]
label: str
fetched_at: datetime | None
pricing_version: str | None
notes: list[str]
```
Presentation rules:
- `actual`: show dollar amount as final
- `estimated`: show dollar amount with estimate labeling
- `included`: show `included` or `$0.00 (included)` depending on UX choice
- `unknown`: show `n/a`
## Official Source Hierarchy
Resolve cost using this order:
1. Request-level or account-level official billed cost
2. Official machine-readable model pricing
3. Official docs snapshot
4. User override or custom contract
5. Unknown
The system must never skip to a lower level if a higher-confidence source exists for the current billing route.
## Provider-Specific Truth Rules
### OpenAI Direct
Preferred truth:
1. Costs API for reconciled spend
2. Official pricing page for live estimate
### Anthropic Direct
Preferred truth:
1. Usage & Cost API for reconciled spend
2. Official pricing docs for live estimate
### OpenRouter
Preferred truth:
1. `GET /api/v1/generation` for reconciled `total_cost`
2. `GET /api/v1/models` pricing for live estimate
Do not use underlying provider public pricing as the source of truth for OpenRouter billing.
### Gemini / Vertex
Preferred truth:
1. official billing export or billing API for reconciled spend when available for the route
2. official pricing docs for estimate
### DeepSeek
Preferred truth:
1. official machine-readable cost source if available in the future
2. official pricing docs snapshot today
### Subscription-Included Routes
Preferred truth:
1. explicit route config marking the model as included in subscription
These should display `included`, not an API list-price estimate.
### Custom Endpoint / Local Model
Preferred truth:
1. user override
2. custom contract config
3. unknown
These should default to `unknown`.
## Pricing Catalog
Replace the current `MODEL_PRICING` dict with a richer pricing catalog.
Suggested record:
```python
@dataclass
class PricingEntry:
provider: str
route_pattern: str
model_pattern: str
input_cost_per_million: Decimal | None = None
output_cost_per_million: Decimal | None = None
cache_read_cost_per_million: Decimal | None = None
cache_write_cost_per_million: Decimal | None = None
request_cost: Decimal | None = None
image_cost: Decimal | None = None
source: str = "official_docs_snapshot"
source_url: str | None = None
fetched_at: datetime | None = None
pricing_version: str | None = None
```
The catalog should be route-aware:
- `openai:gpt-5`
- `anthropic:claude-opus-4-6`
- `openrouter:anthropic/claude-opus-4.6`
- `copilot:gpt-4o`
This avoids conflating direct-provider billing with aggregator billing.
## Pricing Sync Architecture
Introduce a pricing sync subsystem instead of manually maintaining a single hardcoded table.
Suggested modules:
- `agent/pricing/catalog.py`
- `agent/pricing/sources.py`
- `agent/pricing/sync.py`
- `agent/pricing/reconcile.py`
- `agent/pricing/types.py`
### Sync Sources
- OpenRouter models API
- official provider docs snapshots where no API exists
- user overrides from config
### Sync Output
Cache pricing entries locally with:
- source URL
- fetch timestamp
- version/hash
- confidence/source type
### Sync Frequency
- startup warm cache
- background refresh every 6 to 24 hours depending on source
- manual `hermes pricing sync`
## Reconciliation Architecture
Live requests may produce only an estimate initially. Hermes should reconcile them later when a provider exposes actual billed cost.
Suggested flow:
1. Agent call completes.
2. Hermes stores canonical usage plus reconciliation ids.
3. Hermes computes an immediate estimate if a pricing source exists.
4. A reconciliation worker fetches actual cost when supported.
5. Session and message records are updated with `actual` cost.
This can run:
- inline for cheap lookups
- asynchronously for delayed provider accounting
## Persistence Changes
Session storage should stop storing only aggregate prompt/completion totals.
Add fields for both usage and cost certainty:
- `input_tokens`
- `output_tokens`
- `cache_read_tokens`
- `cache_write_tokens`
- `reasoning_tokens`
- `estimated_cost_usd`
- `actual_cost_usd`
- `cost_status`
- `cost_source`
- `pricing_version`
- `billing_provider`
- `billing_mode`
If schema expansion is too large for one PR, add a new pricing events table:
```text
session_cost_events
id
session_id
request_id
provider
model
billing_mode
input_tokens
output_tokens
cache_read_tokens
cache_write_tokens
estimated_cost_usd
actual_cost_usd
cost_status
cost_source
pricing_version
created_at
updated_at
```
## Hermes Touchpoints
### `run_agent.py`
Current responsibility:
- parse raw provider usage
- update session token counters
New responsibility:
- build `CanonicalUsage`
- update canonical counters
- store reconciliation ids
- emit usage event to pricing subsystem
### `agent/usage_pricing.py`
Current responsibility:
- static lookup table
- direct cost arithmetic
New responsibility:
- move or replace with pricing catalog facade
- no fuzzy model-family heuristics
- no direct pricing without billing-route context
### `cli.py`
Current responsibility:
- compute session cost directly from prompt/completion totals
New responsibility:
- display `CostResult`
- show status badges:
- `actual`
- `estimated`
- `included`
- `n/a`
### `agent/insights.py`
Current responsibility:
- recompute historical estimates from static pricing
New responsibility:
- aggregate stored pricing events
- prefer actual cost over estimate
- surface estimates only when reconciliation is unavailable
## UX Rules
### Status Bar
Show one of:
- `$1.42`
- `~$1.42`
- `included`
- `cost n/a`
Where:
- `$1.42` means `actual`
- `~$1.42` means `estimated`
- `included` means subscription-backed or explicitly zero-cost route
- `cost n/a` means unknown
### `/usage`
Show:
- token buckets
- estimated cost
- actual cost if available
- cost status
- pricing source
### `/insights`
Aggregate:
- actual cost totals
- estimated-only totals
- unknown-cost sessions count
- included-cost sessions count
## Config And Overrides
Add user-configurable pricing overrides in config:
```yaml
pricing:
mode: hybrid
sync_on_startup: true
sync_interval_hours: 12
overrides:
- provider: openrouter
model: anthropic/claude-opus-4.6
billing_mode: custom_contract
input_cost_per_million: 4.25
output_cost_per_million: 22.0
cache_read_cost_per_million: 0.5
cache_write_cost_per_million: 6.0
included_routes:
- provider: copilot
model: "*"
- provider: codex-subscription
model: "*"
```
Overrides must win over catalog defaults for the matching billing route.
## Rollout Plan
### Phase 1
- add canonical usage model
- split cache token buckets in `run_agent.py`
- stop pricing cache-inflated prompt totals
- preserve current UI with improved backend math
### Phase 2
- add route-aware pricing catalog
- integrate OpenRouter models API sync
- add `estimated` vs `included` vs `unknown`
### Phase 3
- add reconciliation for OpenRouter generation cost
- add actual cost persistence
- update `/insights` to prefer actual cost
### Phase 4
- add direct OpenAI and Anthropic reconciliation paths
- add user overrides and contract pricing
- add pricing sync CLI command
## Testing Strategy
Add tests for:
- OpenAI cached token subtraction
- Anthropic cache read/write separation
- OpenRouter estimated vs actual reconciliation
- subscription-backed models showing `included`
- custom endpoints showing `n/a`
- override precedence
- stale catalog fallback behavior
Current tests that assume heuristic pricing should be replaced with route-aware expectations.
## Non-Goals
- exact enterprise billing reconstruction without an official source or user override
- backfilling perfect historical cost for old sessions that lack cache bucket data
- scraping arbitrary provider web pages at request time
## Recommendation
Do not expand the existing `MODEL_PRICING` dict.
That path cannot satisfy the product requirement. Hermes should instead migrate to:
- canonical usage normalization
- route-aware pricing sources
- estimate-then-reconcile cost lifecycle
- explicit certainty states in the UI
This is the minimum architecture that makes the statement "Hermes pricing is backed by official sources where possible, and otherwise clearly labeled" defensible.

View File

@@ -0,0 +1,344 @@
# send_file Integration Map — Hermes Agent Codebase Deep Dive
## 1. environments/tool_context.py — Base64 File Transfer Implementation
### upload_file() (lines 153-205)
- Reads local file as raw bytes, base64-encodes to ASCII string
- Creates parent dirs in sandbox via `self.terminal(f"mkdir -p {parent}")`
- **Chunk size:** 60,000 chars (~60KB per shell command)
- **Small files (<=60KB b64):** Single `printf '%s' '{b64}' | base64 -d > {remote_path}`
- **Large files:** Writes chunks to `/tmp/_hermes_upload.b64` via `printf >> append`, then `base64 -d` to target
- **Error handling:** Checks local file exists; returns `{exit_code, output}`
- **Size limits:** No explicit limit, but shell arg limit ~2MB means chunking is necessary for files >~45KB raw
- **No theoretical max** — but very large files would be slow (many terminal round trips)
### download_file() (lines 234-278)
- Runs `base64 {remote_path}` inside sandbox, captures stdout
- Strips output, base64-decodes to raw bytes
- Writes to host filesystem with parent dir creation
- **Error handling:** Checks exit code, empty output, decode errors
- Returns `{success: bool, bytes: int}` or `{success: false, error: str}`
- **Size limit:** Bounded by terminal output buffer (practical limit ~few MB via base64 terminal output)
### Promotion potential:
- These methods work via `self.terminal()` — they're environment-agnostic
- Could be directly lifted into a new tool that operates on the agent's current sandbox
- For send_file, this `download_file()` pattern is the key: it extracts files from sandbox → host
## 2. tools/environments/base.py — BaseEnvironment Interface
### Current methods:
- `execute(command, cwd, timeout, stdin_data)``{output, returncode}`
- `cleanup()` — release resources
- `stop()` — alias for cleanup
- `_prepare_command()` — sudo transformation
- `_build_run_kwargs()` — subprocess kwargs
- `_timeout_result()` — standard timeout dict
### What would need to be added for file transfer:
- **Nothing required at this level.** File transfer can be implemented via `execute()` (base64 over terminal, like ToolContext does) or via environment-specific methods.
- Optional: `upload_file(local_path, remote_path)` and `download_file(remote_path, local_path)` methods could be added to BaseEnvironment for optimized per-backend transfers, but the base64-over-terminal approach already works universally.
## 3. tools/environments/docker.py — Docker Container Details
### Container ID tracking:
- `self._container_id` stored at init from `self._inner.container_id`
- Inner is `minisweagent.environments.docker.DockerEnvironment`
- Container ID is a standard Docker container hash
### docker cp feasibility:
- **YES**, `docker cp` could be used for optimized file transfer:
- `docker cp {container_id}:{remote_path} {local_path}` (download)
- `docker cp {local_path} {container_id}:{remote_path}` (upload)
- Much faster than base64-over-terminal for large files
- Container ID is directly accessible via `env._container_id` or `env._inner.container_id`
### Volumes mounted:
- **Persistent mode:** Bind mounts at `~/.hermes/sandboxes/docker/{task_id}/workspace``/workspace` and `.../home``/root`
- **Ephemeral mode:** tmpfs at `/workspace` (10GB), `/home` (1GB), `/root` (1GB)
- **User volumes:** From `config.yaml docker_volumes` (arbitrary `-v` mounts)
- **Security tmpfs:** `/tmp` (512MB), `/var/tmp` (256MB), `/run` (64MB)
### Direct host access for persistent mode:
- If persistent, files at `/workspace/foo.txt` are just `~/.hermes/sandboxes/docker/{task_id}/workspace/foo.txt` on host — no transfer needed!
## 4. tools/environments/ssh.py — SSH Connection Management
### Connection management:
- Uses SSH ControlMaster for persistent connection
- Control socket at `/tmp/hermes-ssh/{user}@{host}:{port}.sock`
- ControlPersist=300 (5 min keepalive)
- BatchMode=yes (non-interactive)
- Stores: `self.host`, `self.user`, `self.port`, `self.key_path`
### SCP/SFTP feasibility:
- **YES**, SCP can piggyback on the ControlMaster socket:
- `scp -o ControlPath={socket} {user}@{host}:{remote} {local}` (download)
- `scp -o ControlPath={socket} {local} {user}@{host}:{remote}` (upload)
- Same SSH key and connection reuse — zero additional auth
- Would be much faster than base64-over-terminal for large files
## 5. tools/environments/modal.py — Modal Sandbox Filesystem
### Filesystem API exposure:
- **Not directly.** The inner `SwerexModalEnvironment` wraps Modal's sandbox
- The sandbox object is accessible at: `env._inner.deployment._sandbox`
- Modal's Python SDK exposes `sandbox.open()` for file I/O — but only via async API
- Currently only used for `snapshot_filesystem()` during cleanup
- **Could use:** `sandbox.open(path, "rb")` to read files or `sandbox.open(path, "wb")` to write
- **Alternative:** Base64-over-terminal already works via `execute()` — simpler, no SDK dependency
## 6. gateway/platforms/base.py — MEDIA: Tag Flow (Complete)
### extract_media() (lines 587-620):
- **Pattern:** `MEDIA:\S+` — extracts file paths after MEDIA: prefix
- **Voice flag:** `[[audio_as_voice]]` global directive sets `is_voice=True` for all media in message
- Returns `List[Tuple[str, bool]]` (path, is_voice) and cleaned content
### _process_message_background() media routing (lines 752-786):
- After extracting MEDIA tags, routes by file extension:
- `.ogg .opus .mp3 .wav .m4a``send_voice()`
- `.mp4 .mov .avi .mkv .3gp``send_video()`
- `.jpg .jpeg .png .webp .gif``send_image_file()`
- **Everything else** → `send_document()`
- This routing already supports arbitrary files!
### send_* method inventory (base class):
- `send(chat_id, content, reply_to, metadata)` — ABSTRACT, text
- `send_image(chat_id, image_url, caption, reply_to)` — URL-based images
- `send_animation(chat_id, animation_url, caption, reply_to)` — GIF animations
- `send_voice(chat_id, audio_path, caption, reply_to)` — voice messages
- `send_video(chat_id, video_path, caption, reply_to)` — video files
- `send_document(chat_id, file_path, caption, file_name, reply_to)` — generic files
- `send_image_file(chat_id, image_path, caption, reply_to)` — local image files
- `send_typing(chat_id)` — typing indicator
- `edit_message(chat_id, message_id, content)` — edit sent messages
### What's missing:
- **Telegram:** No override for `send_document` or `send_image_file` — falls back to text!
- **Discord:** No override for `send_document` — falls back to text!
- **WhatsApp:** Has `send_document` and `send_image_file` via bridge — COMPLETE.
- The base class defaults just send "📎 File: /path" as text — useless for actual file delivery.
## 7. gateway/platforms/telegram.py — Send Method Analysis
### Implemented send methods:
- `send()` — MarkdownV2 text with fallback to plain
- `send_voice()``.ogg`/`.opus` as `send_voice()`, others as `send_audio()`
- `send_image()` — URL-based via `send_photo()`
- `send_animation()` — GIF via `send_animation()`
- `send_typing()` — "typing" chat action
- `edit_message()` — edit text messages
### MISSING:
- **`send_document()` NOT overridden** — Need to add `self._bot.send_document(chat_id, document=open(file_path, 'rb'), ...)`
- **`send_image_file()` NOT overridden** — Need to add `self._bot.send_photo(chat_id, photo=open(path, 'rb'), ...)`
- **`send_video()` NOT overridden** — Need to add `self._bot.send_video(...)`
## 8. gateway/platforms/discord.py — Send Method Analysis
### Implemented send methods:
- `send()` — text messages with chunking
- `send_voice()` — discord.File attachment
- `send_image()` — downloads URL, creates discord.File attachment
- `send_typing()` — channel.typing()
- `edit_message()` — edit text messages
### MISSING:
- **`send_document()` NOT overridden** — Need to add discord.File attachment
- **`send_image_file()` NOT overridden** — Need to add discord.File from local path
- **`send_video()` NOT overridden** — Need to add discord.File attachment
## 9. gateway/run.py — User File Attachment Handling
### Current attachment flow:
1. **Telegram photos** (line 509-529): Download via `photo.get_file()``cache_image_from_bytes()` → vision auto-analysis
2. **Telegram voice** (line 532-541): Download → `cache_audio_from_bytes()` → STT transcription
3. **Telegram audio** (line 542-551): Same pattern
4. **Telegram documents** (line 553-617): Extension validation against `SUPPORTED_DOCUMENT_TYPES`, 20MB limit, content injection for text files
5. **Discord attachments** (line 717-751): Content-type detection, image/audio caching, URL fallback for other types
6. **Gateway run.py** (lines 818-883): Auto-analyzes images with vision, transcribes audio, enriches document messages with context notes
### Key insight: Files are always cached to host filesystem first, then processed. The agent sees local file paths.
## 10. tools/terminal_tool.py — Terminal Tool & Environment Interaction
### How it manages environments:
- Global dict `_active_environments: Dict[str, Any]` keyed by task_id
- Per-task creation locks prevent duplicate sandbox creation
- Auto-cleanup thread kills idle environments after `TERMINAL_LIFETIME_SECONDS`
- `_get_env_config()` reads all TERMINAL_* env vars for backend selection
- `_create_environment()` factory creates the right backend type
### Could send_file piggyback?
- **YES.** send_file needs access to the same environment to extract files from sandboxes.
- It can reuse `_active_environments[task_id]` to get the environment, then:
- Docker: Use `docker cp` via `env._container_id`
- SSH: Use `scp` via `env.control_socket`
- Local: Just read the file directly
- Modal: Use base64-over-terminal via `env.execute()`
- The file_tools.py module already does this with `ShellFileOperations` — read_file/write_file/search/patch all share the same env instance.
## 11. tools/tts_tool.py — Working Example of File Delivery
### Flow:
1. Generate audio file to `~/.hermes/audio_cache/tts_TIMESTAMP.{ogg,mp3}`
2. Return JSON with `media_tag: "MEDIA:/path/to/file"`
3. For Telegram voice: prepend `[[audio_as_voice]]` directive
4. The LLM includes the MEDIA tag in its response text
5. `BasePlatformAdapter._process_message_background()` calls `extract_media()` to find the tag
6. Routes by extension → `send_voice()` for audio files
7. Platform adapter sends the file natively
### Key pattern: Tool saves file to host → returns MEDIA: path → LLM echoes it → gateway extracts → platform delivers
## 12. tools/image_generation_tool.py — Working Example of Image Delivery
### Flow:
1. Call FAL.ai API → get image URL
2. Return JSON with `image: "https://fal.media/..."` URL
3. The LLM includes the URL in markdown: `![description](URL)`
4. `BasePlatformAdapter.extract_images()` finds `![alt](url)` patterns
5. Routes through `send_image()` (URL) or `send_animation()` (GIF)
6. Platform downloads and sends natively
### Key difference from TTS: Images are URL-based, not local files. The gateway downloads at send time.
---
# INTEGRATION MAP: Where send_file Hooks In
## Architecture Decision: MEDIA: Tag Protocol vs. New Tool
The MEDIA: tag protocol is already the established pattern for file delivery. Two options:
### Option A: Pure MEDIA: Tag (Minimal Change)
- No new tool needed
- Agent downloads file from sandbox to host using terminal (base64)
- Saves to known location (e.g., `~/.hermes/file_cache/`)
- Includes `MEDIA:/path` in response text
- Existing routing in `_process_message_background()` handles delivery
- **Problem:** Agent has to manually do base64 dance + know about MEDIA: convention
### Option B: Dedicated send_file Tool (Recommended)
- New tool that the agent calls with `(file_path, caption?)`
- Tool handles the sandbox → host extraction automatically
- Returns MEDIA: tag that gets routed through existing pipeline
- Much cleaner agent experience
## Implementation Plan for Option B
### Files to CREATE:
1. **`tools/send_file_tool.py`** — The new tool
- Accepts: `file_path` (path in sandbox), `caption` (optional)
- Detects environment backend from `_active_environments`
- Extracts file from sandbox:
- **local:** `shutil.copy()` or direct path
- **docker:** `docker cp {container_id}:{path} {local_cache}/`
- **ssh:** `scp -o ControlPath=... {user}@{host}:{path} {local_cache}/`
- **modal:** base64-over-terminal via `env.execute("base64 {path}")`
- Saves to `~/.hermes/file_cache/{uuid}_{filename}`
- Returns: `MEDIA:/cached/path` in response for gateway to pick up
- Register with `registry.register(name="send_file", toolset="file", ...)`
### Files to MODIFY:
2. **`gateway/platforms/telegram.py`** — Add missing send methods:
```python
async def send_document(self, chat_id, file_path, caption=None, file_name=None, reply_to=None):
with open(file_path, "rb") as f:
msg = await self._bot.send_document(
chat_id=int(chat_id), document=f,
caption=caption, filename=file_name or os.path.basename(file_path))
return SendResult(success=True, message_id=str(msg.message_id))
async def send_image_file(self, chat_id, image_path, caption=None, reply_to=None):
with open(image_path, "rb") as f:
msg = await self._bot.send_photo(chat_id=int(chat_id), photo=f, caption=caption)
return SendResult(success=True, message_id=str(msg.message_id))
async def send_video(self, chat_id, video_path, caption=None, reply_to=None):
with open(video_path, "rb") as f:
msg = await self._bot.send_video(chat_id=int(chat_id), video=f, caption=caption)
return SendResult(success=True, message_id=str(msg.message_id))
```
3. **`gateway/platforms/discord.py`** — Add missing send methods:
```python
async def send_document(self, chat_id, file_path, caption=None, file_name=None, reply_to=None):
channel = self._client.get_channel(int(chat_id)) or await self._client.fetch_channel(int(chat_id))
with open(file_path, "rb") as f:
file = discord.File(io.BytesIO(f.read()), filename=file_name or os.path.basename(file_path))
msg = await channel.send(content=caption, file=file)
return SendResult(success=True, message_id=str(msg.id))
async def send_image_file(self, chat_id, image_path, caption=None, reply_to=None):
# Same pattern as send_document with image filename
async def send_video(self, chat_id, video_path, caption=None, reply_to=None):
# Same pattern, discord renders video attachments inline
```
4. **`toolsets.py`** — Add `"send_file"` to `_HERMES_CORE_TOOLS` list
5. **`agent/prompt_builder.py`** — Update platform hints to mention send_file tool
### Code that can be REUSED (zero rewrite):
- `BasePlatformAdapter.extract_media()` — Already extracts MEDIA: tags
- `BasePlatformAdapter._process_message_background()` — Already routes by extension
- `ToolContext.download_file()` — Base64-over-terminal extraction pattern
- `tools/terminal_tool.py` _active_environments dict — Environment access
- `tools/registry.py` — Tool registration infrastructure
- `gateway/platforms/base.py` send_document/send_image_file/send_video signatures — Already defined
### Code that needs to be WRITTEN from scratch:
1. `tools/send_file_tool.py` (~150 lines):
- File extraction from each environment backend type
- Local file cache management
- Registry registration
2. Telegram `send_document` + `send_image_file` + `send_video` overrides (~40 lines)
3. Discord `send_document` + `send_image_file` + `send_video` overrides (~50 lines)
### Total effort: ~240 lines of new code, ~5 lines of config changes
## Key Environment-Specific Extract Strategies
| Backend | Extract Method | Speed | Complexity |
|------------|-------------------------------|----------|------------|
| local | shutil.copy / direct path | Instant | None |
| docker | `docker cp container:path .` | Fast | Low |
| docker+vol | Direct host path access | Instant | None |
| ssh | `scp -o ControlPath=...` | Fast | Low |
| modal | base64-over-terminal | Moderate | Medium |
| singularity| Direct path (overlay mount) | Fast | Low |
## Data Flow Summary
```
Agent calls send_file(file_path="/workspace/output.pdf", caption="Here's the report")
send_file_tool.py:
1. Get environment from _active_environments[task_id]
2. Detect backend type (docker/ssh/modal/local)
3. Extract file to ~/.hermes/file_cache/{uuid}_{filename}
4. Return: '{"success": true, "media_tag": "MEDIA:/home/user/.hermes/file_cache/abc123_output.pdf"}'
LLM includes MEDIA: tag in its response text
BasePlatformAdapter._process_message_background():
1. extract_media(response) → finds MEDIA:/path
2. Checks extension: .pdf → send_document()
3. Calls platform-specific send_document(chat_id, file_path, caption)
TelegramAdapter.send_document() / DiscordAdapter.send_document():
Opens file, sends via platform API as native document attachment
User receives downloadable file in chat
```

View File

@@ -1,89 +0,0 @@
# ============================================================================
# Hermes Agent — Example Skin Template
# ============================================================================
#
# Copy this file to ~/.hermes/skins/<name>.yaml to create a custom skin.
# All fields are optional — missing values inherit from the default skin.
# Activate with: /skin <name> or display.skin: <name> in config.yaml
#
# See hermes_cli/skin_engine.py for the full schema reference.
# ============================================================================
# Required: unique skin name (used in /skin command and config)
name: example
description: An example custom skin — copy and modify this template
# ── Colors ──────────────────────────────────────────────────────────────────
# Hex color values for Rich markup. These control the CLI's visual palette.
colors:
# Banner panel (the startup welcome box)
banner_border: "#CD7F32" # Panel border
banner_title: "#FFD700" # Panel title text
banner_accent: "#FFBF00" # Section headers (Available Tools, Skills, etc.)
banner_dim: "#B8860B" # Dim/muted text (separators, model info)
banner_text: "#FFF8DC" # Body text (tool names, skill names)
# UI elements
ui_accent: "#FFBF00" # General accent color
ui_label: "#4dd0e1" # Labels
ui_ok: "#4caf50" # Success indicators
ui_error: "#ef5350" # Error indicators
ui_warn: "#ffa726" # Warning indicators
# Input area
prompt: "#FFF8DC" # Prompt text color
input_rule: "#CD7F32" # Horizontal rule around input
# Response box
response_border: "#FFD700" # Response box border (ANSI color)
# Session display
session_label: "#DAA520" # Session label
session_border: "#8B8682" # Session ID dim color
# ── Spinner ─────────────────────────────────────────────────────────────────
# Customize the animated spinner shown during API calls and tool execution.
spinner:
# Faces shown while waiting for the API response
waiting_faces:
- "(。◕‿◕。)"
- "(◕‿◕✿)"
- "٩(◕‿◕。)۶"
# Faces shown during extended thinking/reasoning
thinking_faces:
- "(。•́︿•̀。)"
- "(◔_◔)"
- "(¬‿¬)"
# Verbs used in spinner messages (e.g., "pondering your request...")
thinking_verbs:
- "pondering"
- "contemplating"
- "musing"
- "ruminating"
# Optional: left/right decorations around the spinner
# Each entry is a [left, right] pair. Omit entirely for no wings.
# wings:
# - ["⟪⚔", "⚔⟫"]
# - ["⟪▲", "▲⟫"]
# ── Branding ────────────────────────────────────────────────────────────────
# Text strings used throughout the CLI interface.
branding:
agent_name: "Hermes Agent" # Banner title, about display
welcome: "Welcome! Type your message or /help for commands."
goodbye: "Goodbye! ⚕" # Exit message
response_label: " ⚕ Hermes " # Response box header label
prompt_symbol: " " # Input prompt symbol
help_header: "(^_^)? Available Commands" # /help header text
# ── Tool Output ─────────────────────────────────────────────────────────────
# Character used as the prefix for tool output lines.
# Default is "┊" (thin dotted vertical line). Some alternatives:
# "╎" (light triple dash vertical)
# "▏" (left one-eighth block)
# "│" (box drawing light vertical)
# "┃" (box drawing heavy vertical)
tool_prefix: "┊"

View File

@@ -18,14 +18,9 @@ Benchmarks (eval-only):
- benchmarks/terminalbench_2/: Terminal-Bench 2.0 evaluation
"""
try:
from environments.agent_loop import AgentResult, HermesAgentLoop
from environments.tool_context import ToolContext
from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
except ImportError:
# atroposlib not installed — environments are unavailable but
# submodules like tool_call_parsers can still be imported directly.
pass
from environments.agent_loop import AgentResult, HermesAgentLoop
from environments.tool_context import ToolContext
from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
__all__ = [
"AgentResult",

View File

@@ -39,9 +39,7 @@ def resize_tool_pool(max_workers: int):
Safe to call before any tasks are submitted.
"""
global _tool_executor
old_executor = _tool_executor
_tool_executor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)
old_executor.shutdown(wait=False)
logger.info("Tool thread pool resized to %d workers", max_workers)
logger = logging.getLogger(__name__)
@@ -251,62 +249,23 @@ class HermesAgentLoop:
reasoning = _extract_reasoning_from_message(assistant_msg)
reasoning_per_turn.append(reasoning)
# Check for tool calls -- standard OpenAI spec.
# Fallback: if response has no structured tool_calls but content
# contains raw tool call tags (e.g. <tool_call>), parse them using
# hermes-agent's standalone parsers. This handles the case where
# ManagedServer's ToolCallTranslator couldn't parse because vLLM
# isn't installed.
if (
not assistant_msg.tool_calls
and assistant_msg.content
and self.tool_schemas
and "<tool_call>" in (assistant_msg.content or "")
):
try:
from environments.tool_call_parsers import get_parser
fallback_parser = get_parser("hermes")
parsed_content, parsed_calls = fallback_parser.parse(
assistant_msg.content
)
if parsed_calls:
assistant_msg.tool_calls = parsed_calls
if parsed_content is not None:
assistant_msg.content = parsed_content
logger.debug(
"Fallback parser extracted %d tool calls from raw content",
len(parsed_calls),
)
except Exception:
pass # Fall through to no tool calls
# Check for tool calls -- standard OpenAI spec
if assistant_msg.tool_calls:
# Normalize tool calls to dicts — they may come as objects
# (OpenAI API) or dicts (vLLM ToolCallTranslator).
def _tc_to_dict(tc):
if isinstance(tc, dict):
return {
"id": tc.get("id", f"call_{uuid.uuid4().hex[:8]}"),
"type": "function",
"function": {
"name": tc.get("function", {}).get("name", tc.get("name", "")),
"arguments": tc.get("function", {}).get("arguments", tc.get("arguments", "{}")),
},
}
return {
"id": tc.id,
"type": "function",
"function": {
"name": tc.function.name,
"arguments": tc.function.arguments,
},
}
# Build the assistant message dict for conversation history
msg_dict: Dict[str, Any] = {
"role": "assistant",
"content": assistant_msg.content or "",
"tool_calls": [_tc_to_dict(tc) for tc in assistant_msg.tool_calls],
"tool_calls": [
{
"id": tc.id,
"type": "function",
"function": {
"name": tc.function.name,
"arguments": tc.function.arguments,
},
}
for tc in assistant_msg.tool_calls
],
}
# Preserve reasoning_content for multi-turn chat template handling
@@ -319,13 +278,8 @@ class HermesAgentLoop:
# Execute each tool call via hermes-agent's dispatch
for tc in assistant_msg.tool_calls:
# Handle both object (OpenAI) and dict (vLLM) formats
if isinstance(tc, dict):
tool_name = tc.get("function", {}).get("name", tc.get("name", ""))
tool_args_raw = tc.get("function", {}).get("arguments", tc.get("arguments", "{}"))
else:
tool_name = tc.function.name
tool_args_raw = tc.function.arguments
tool_name = tc.function.name
tool_args_raw = tc.function.arguments
# Validate tool name
if tool_name not in self.valid_tool_names:
@@ -346,89 +300,78 @@ class HermesAgentLoop:
tool_name, turn + 1,
)
else:
# Parse arguments
# Parse arguments and dispatch
try:
args = json.loads(tool_args_raw)
except json.JSONDecodeError as e:
args = None
tool_result = json.dumps(
{"error": f"Invalid JSON in tool arguments: {e}. Please retry with valid JSON."}
)
tool_errors.append(ToolError(
turn=turn + 1, tool_name=tool_name,
arguments=tool_args_raw[:200],
error=f"Invalid JSON: {e}",
tool_result=tool_result,
))
except json.JSONDecodeError:
args = {}
logger.warning(
"Invalid JSON in tool call arguments for '%s': %s",
tool_name, tool_args_raw[:200],
)
# Dispatch tool only if arguments parsed successfully
if args is not None:
try:
if tool_name == "terminal":
backend = os.getenv("TERMINAL_ENV", "local")
cmd_preview = args.get("command", "")[:80]
logger.info(
"[%s] $ %s", self.task_id[:8], cmd_preview,
)
tool_submit_time = _time.monotonic()
# Todo tool -- handle locally (needs per-loop TodoStore)
if tool_name == "todo":
tool_result = _todo_tool(
todos=args.get("todos"),
merge=args.get("merge", False),
store=_todo_store,
)
tool_elapsed = _time.monotonic() - tool_submit_time
elif tool_name == "memory":
tool_result = json.dumps({"error": "Memory is not available in RL environments."})
tool_elapsed = _time.monotonic() - tool_submit_time
elif tool_name == "session_search":
tool_result = json.dumps({"error": "Session search is not available in RL environments."})
tool_elapsed = _time.monotonic() - tool_submit_time
else:
# Run tool calls in a thread pool so backends that
# use asyncio.run() internally (modal, docker, daytona) get
# a clean event loop instead of deadlocking.
loop = asyncio.get_event_loop()
# Capture current tool_name/args for the lambda
_tn, _ta, _tid = tool_name, args, self.task_id
tool_result = await loop.run_in_executor(
_tool_executor,
lambda: handle_function_call(
_tn, _ta, task_id=_tid,
user_task=_user_task,
),
)
tool_elapsed = _time.monotonic() - tool_submit_time
# Log slow tools and thread pool stats for debugging
pool_active = _tool_executor._work_queue.qsize()
if tool_elapsed > 30:
logger.warning(
"[%s] turn %d: %s took %.1fs (pool queue=%d)",
self.task_id[:8], turn + 1, tool_name,
tool_elapsed, pool_active,
)
except Exception as e:
tool_result = json.dumps(
{"error": f"Tool execution failed: {type(e).__name__}: {str(e)}"}
try:
if tool_name == "terminal":
backend = os.getenv("TERMINAL_ENV", "local")
cmd_preview = args.get("command", "")[:80]
logger.info(
"[%s] $ %s", self.task_id[:8], cmd_preview,
)
tool_errors.append(ToolError(
turn=turn + 1, tool_name=tool_name,
arguments=tool_args_raw[:200],
error=f"{type(e).__name__}: {str(e)}",
tool_result=tool_result,
))
logger.error(
"Tool '%s' execution failed on turn %d: %s",
tool_name, turn + 1, e,
tool_submit_time = _time.monotonic()
# Todo tool -- handle locally (needs per-loop TodoStore)
if tool_name == "todo":
tool_result = _todo_tool(
todos=args.get("todos"),
merge=args.get("merge", False),
store=_todo_store,
)
tool_elapsed = _time.monotonic() - tool_submit_time
elif tool_name == "memory":
tool_result = json.dumps({"error": "Memory is not available in RL environments."})
tool_elapsed = _time.monotonic() - tool_submit_time
elif tool_name == "session_search":
tool_result = json.dumps({"error": "Session search is not available in RL environments."})
tool_elapsed = _time.monotonic() - tool_submit_time
else:
# Run tool calls in a thread pool so backends that
# use asyncio.run() internally (modal, docker, daytona) get
# a clean event loop instead of deadlocking.
loop = asyncio.get_event_loop()
# Capture current tool_name/args for the lambda
_tn, _ta, _tid = tool_name, args, self.task_id
tool_result = await loop.run_in_executor(
_tool_executor,
lambda: handle_function_call(
_tn, _ta, task_id=_tid,
user_task=_user_task,
),
)
tool_elapsed = _time.monotonic() - tool_submit_time
# Log slow tools and thread pool stats for debugging
pool_active = _tool_executor._work_queue.qsize()
if tool_elapsed > 30:
logger.warning(
"[%s] turn %d: %s took %.1fs (pool queue=%d)",
self.task_id[:8], turn + 1, tool_name,
tool_elapsed, pool_active,
)
except Exception as e:
tool_result = json.dumps(
{"error": f"Tool execution failed: {type(e).__name__}: {str(e)}"}
)
tool_errors.append(ToolError(
turn=turn + 1, tool_name=tool_name,
arguments=tool_args_raw[:200],
error=f"{type(e).__name__}: {str(e)}",
tool_result=tool_result,
))
logger.error(
"Tool '%s' execution failed on turn %d: %s",
tool_name, turn + 1, e,
)
# Also check if the tool returned an error in its JSON result
try:
@@ -447,11 +390,10 @@ class HermesAgentLoop:
pass
# Add tool response to conversation
tc_id = tc.get("id", "") if isinstance(tc, dict) else tc.id
messages.append(
{
"role": "tool",
"tool_call_id": tc_id,
"tool_call_id": tc.id,
"content": tool_result,
}
)

File diff suppressed because it is too large Load Diff

View File

@@ -1,38 +0,0 @@
# OpenThoughts-TBLite Evaluation -- Docker Backend (Local Compute)
#
# Runs tasks in Docker containers on the local machine.
# Sandboxed like Modal but no cloud costs. Good for dev/testing.
#
# Usage:
# python environments/benchmarks/tblite/tblite_env.py evaluate \
# --config environments/benchmarks/tblite/local.yaml
#
# # Override concurrency:
# python environments/benchmarks/tblite/tblite_env.py evaluate \
# --config environments/benchmarks/tblite/local.yaml \
# --env.eval_concurrency 4
env:
enabled_toolsets: ["terminal", "file"]
max_agent_turns: 60
max_token_length: 32000
agent_temperature: 0.8
terminal_backend: "docker"
terminal_timeout: 300
tool_pool_size: 16
dataset_name: "NousResearch/openthoughts-tblite"
test_timeout: 600
task_timeout: 1200
eval_concurrency: 8 # max 8 tasks at once
tokenizer_name: "NousResearch/Hermes-3-Llama-3.1-8B"
use_wandb: false
wandb_name: "openthoughts-tblite-local"
ensure_scores_are_not_same: false
data_dir_to_save_evals: "environments/benchmarks/evals/openthoughts-tblite-local"
openai:
base_url: "https://openrouter.ai/api/v1"
model_name: "anthropic/claude-sonnet-4"
server_type: "openai"
health_check: false
# api_key loaded from OPENROUTER_API_KEY in .env

View File

@@ -1,40 +0,0 @@
# OpenThoughts-TBLite Evaluation -- Local vLLM Backend
#
# Runs against a local vLLM server with Docker sandboxes.
#
# Start the vLLM server from the atropos directory:
# python -m example_trainer.vllm_api_server \
# --model Qwen/Qwen3-4B-Instruct-2507 \
# --port 9001 \
# --gpu-memory-utilization 0.8 \
# --max-model-len=32000
#
# Then run:
# python environments/benchmarks/tblite/tblite_env.py evaluate \
# --config environments/benchmarks/tblite/local_vllm.yaml
env:
enabled_toolsets: ["terminal", "file"]
max_agent_turns: 60
max_token_length: 16000
agent_temperature: 0.6
terminal_backend: "docker"
terminal_timeout: 300
tool_pool_size: 16
dataset_name: "NousResearch/openthoughts-tblite"
test_timeout: 600
task_timeout: 1200
eval_concurrency: 8
tool_call_parser: "hermes"
system_prompt: "You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands."
tokenizer_name: "Qwen/Qwen3-4B-Instruct-2507"
use_wandb: false
wandb_name: "tblite-qwen3-4b-instruct"
ensure_scores_are_not_same: false
data_dir_to_save_evals: "environments/benchmarks/evals/tblite-qwen3-4b-local"
openai:
base_url: "http://localhost:9001"
model_name: "Qwen/Qwen3-4B-Instruct-2507"
server_type: "vllm"
health_check: false

View File

@@ -29,10 +29,6 @@ env:
wandb_name: "terminal-bench-2"
ensure_scores_are_not_same: false
data_dir_to_save_evals: "environments/benchmarks/evals/terminal-bench-2"
# CRITICAL: Limit concurrent Modal sandbox creations to avoid deadlocks.
# Modal's blocking calls (App.lookup, etc.) deadlock when too many sandboxes
# are created simultaneously inside thread pool workers via asyncio.run().
max_concurrent_tasks: 8
openai:
base_url: "https://openrouter.ai/api/v1"

View File

@@ -118,23 +118,6 @@ class TerminalBench2EvalConfig(HermesAgentEnvConfig):
"Tasks exceeding this are scored as FAIL. Default 30 minutes.",
)
# --- Concurrency control ---
max_concurrent_tasks: int = Field(
default=8,
description="Maximum number of tasks to run concurrently. "
"Limits concurrent Modal sandbox creations to avoid async/threading deadlocks. "
"Modal has internal limits and creating too many sandboxes simultaneously "
"causes blocking calls to deadlock inside the thread pool.",
)
# --- Eval concurrency ---
eval_concurrency: int = Field(
default=0,
description="Maximum number of tasks to evaluate in parallel. "
"0 means unlimited (all tasks run concurrently). "
"Set to 8 for local backends to avoid overwhelming the machine.",
)
# Tasks that cannot run properly on Modal and are excluded from scoring.
MODAL_INCOMPATIBLE_TASKS = {
@@ -209,7 +192,7 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
# Agent settings -- TB2 tasks are complex, need many turns
max_agent_turns=60,
max_token_length=***
max_token_length=16000,
agent_temperature=0.6,
system_prompt=None,
@@ -233,7 +216,7 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
steps_per_eval=1,
total_steps=1,
tokenizer_name="NousRe...1-8B",
tokenizer_name="NousResearch/Hermes-3-Llama-3.1-8B",
use_wandb=True,
wandb_name="terminal-bench-2",
ensure_scores_are_not_same=False, # Binary rewards may all be 0 or 1
@@ -245,7 +228,7 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
base_url="https://openrouter.ai/api/v1",
model_name="anthropic/claude-sonnet-4",
server_type="openai",
api_key=os.get...EY", ""),
api_key=os.getenv("OPENROUTER_API_KEY", ""),
health_check=False,
)
]
@@ -446,14 +429,8 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
"error": "no_image",
}
# --- 2. Register per-task image override ---
# Set both modal_image and docker_image so the task image is used
# regardless of which backend is configured.
register_task_env_overrides(task_id, {
"modal_image": modal_image,
"docker_image": modal_image,
"cwd": "/app",
})
# --- 2. Register per-task Modal image override ---
register_task_env_overrides(task_id, {"modal_image": modal_image})
logger.info(
"Task %s: registered image override for task_id %s",
task_name, task_id[:8],
@@ -468,37 +445,17 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
messages.append({"role": "user", "content": self.format_prompt(eval_item)})
# --- 4. Run agent loop ---
# Use ManagedServer (Phase 2) for vLLM/SGLang backends to get
# token-level tracking via /generate. Falls back to direct
# ServerManager (Phase 1) for OpenAI endpoints.
if self._use_managed_server():
async with self.server.managed_server(
tokenizer=self.tokenizer,
preserve_think_blocks=bool(self.config.thinking_mode),
) as managed:
agent = HermesAgentLoop(
server=managed,
tool_schemas=tools,
valid_tool_names=valid_names,
max_turns=self.config.max_agent_turns,
task_id=task_id,
temperature=self.config.agent_temperature,
max_tokens=self.config.max_token_length,
extra_body=self.config.extra_body,
)
result = await agent.run(messages)
else:
agent = HermesAgentLoop(
server=self.server,
tool_schemas=tools,
valid_tool_names=valid_names,
max_turns=self.config.max_agent_turns,
task_id=task_id,
temperature=self.config.agent_temperature,
max_tokens=self.config.max_token_length,
extra_body=self.config.extra_body,
)
result = await agent.run(messages)
agent = HermesAgentLoop(
server=self.server,
tool_schemas=tools,
valid_tool_names=valid_names,
max_turns=self.config.max_agent_turns,
task_id=task_id,
temperature=self.config.agent_temperature,
max_tokens=self.config.max_token_length,
extra_body=self.config.extra_body,
)
result = await agent.run(messages)
# --- 5. Verify -- run test suite in the agent's sandbox ---
# Skip verification if the agent produced no meaningful output
@@ -513,3 +470,435 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
reward = 0.0
else:
# Run tests in a thread so the blocking ctx.terminal() calls
# don't freeze the entire event loop (which would stall all
# other tasks, tqdm updates, and timeout timers).
ctx = ToolContext(task_id)
try:
loop = asyncio.get_event_loop()
reward = await loop.run_in_executor(
None, # default thread pool
self._run_tests, eval_item, ctx, task_name,
)
except Exception as e:
logger.error("Task %s: test verification failed: %s", task_name, e)
reward = 0.0
finally:
ctx.cleanup()
passed = reward == 1.0
status = "PASS" if passed else "FAIL"
elapsed = time.time() - task_start
tqdm.write(f" [{status}] {task_name} (turns={result.turns_used}, {elapsed:.0f}s)")
logger.info(
"Task %s: reward=%.1f, turns=%d, finished=%s",
task_name, reward, result.turns_used, result.finished_naturally,
)
out = {
"passed": passed,
"reward": reward,
"task_name": task_name,
"category": category,
"turns_used": result.turns_used,
"finished_naturally": result.finished_naturally,
"messages": result.messages,
}
self._save_result(out)
return out
except Exception as e:
elapsed = time.time() - task_start
logger.error("Task %s: rollout failed: %s", task_name, e, exc_info=True)
tqdm.write(f" [ERROR] {task_name}: {e} ({elapsed:.0f}s)")
out = {
"passed": False, "reward": 0.0,
"task_name": task_name, "category": category,
"error": str(e),
}
self._save_result(out)
return out
finally:
# --- Cleanup: clear overrides, sandbox, and temp files ---
clear_task_env_overrides(task_id)
try:
cleanup_vm(task_id)
except Exception as e:
logger.debug("VM cleanup for %s: %s", task_id[:8], e)
if task_dir and task_dir.exists():
shutil.rmtree(task_dir, ignore_errors=True)
def _run_tests(
self, item: Dict[str, Any], ctx: ToolContext, task_name: str
) -> float:
"""
Upload and execute the test suite in the agent's sandbox, then
download the verifier output locally to read the reward.
Follows Harbor's verification pattern:
1. Upload tests/ directory into the sandbox
2. Execute test.sh inside the sandbox
3. Download /logs/verifier/ directory to a local temp dir
4. Read reward.txt locally with native Python I/O
Downloading locally avoids issues with the file_read tool on
the Modal VM and matches how Harbor handles verification.
TB2 test scripts (test.sh) typically:
1. Install pytest via uv/pip
2. Run pytest against the test files in /tests/
3. Write results to /logs/verifier/reward.txt
Args:
item: The TB2 task dict (contains tests_tar, test_sh)
ctx: ToolContext scoped to this task's sandbox
task_name: For logging
Returns:
1.0 if tests pass, 0.0 otherwise
"""
tests_tar = item.get("tests_tar", "")
test_sh = item.get("test_sh", "")
if not test_sh:
logger.warning("Task %s: no test_sh content, reward=0", task_name)
return 0.0
# Create required directories in the sandbox
ctx.terminal("mkdir -p /tests /logs/verifier")
# Upload test files into the sandbox (binary-safe via base64)
if tests_tar:
tests_temp = Path(tempfile.mkdtemp(prefix=f"tb2-tests-{task_name}-"))
try:
_extract_base64_tar(tests_tar, tests_temp)
ctx.upload_dir(str(tests_temp), "/tests")
except Exception as e:
logger.warning("Task %s: failed to upload test files: %s", task_name, e)
finally:
shutil.rmtree(tests_temp, ignore_errors=True)
# Write the test runner script (test.sh)
ctx.write_file("/tests/test.sh", test_sh)
ctx.terminal("chmod +x /tests/test.sh")
# Execute the test suite
logger.info(
"Task %s: running test suite (timeout=%ds)",
task_name, self.config.test_timeout,
)
test_result = ctx.terminal(
"bash /tests/test.sh",
timeout=self.config.test_timeout,
)
exit_code = test_result.get("exit_code", -1)
output = test_result.get("output", "")
# Download the verifier output directory locally, then read reward.txt
# with native Python I/O. This avoids issues with file_read on the
# Modal VM and matches Harbor's verification pattern.
reward = 0.0
local_verifier_dir = Path(tempfile.mkdtemp(prefix=f"tb2-verifier-{task_name}-"))
try:
ctx.download_dir("/logs/verifier", str(local_verifier_dir))
reward_file = local_verifier_dir / "reward.txt"
if reward_file.exists() and reward_file.stat().st_size > 0:
content = reward_file.read_text().strip()
if content == "1":
reward = 1.0
elif content == "0":
reward = 0.0
else:
# Unexpected content -- try parsing as float
try:
reward = float(content)
except (ValueError, TypeError):
logger.warning(
"Task %s: reward.txt content unexpected (%r), "
"falling back to exit_code=%d",
task_name, content, exit_code,
)
reward = 1.0 if exit_code == 0 else 0.0
else:
# reward.txt not written -- fall back to exit code
logger.warning(
"Task %s: reward.txt not found after download, "
"falling back to exit_code=%d",
task_name, exit_code,
)
reward = 1.0 if exit_code == 0 else 0.0
except Exception as e:
logger.warning(
"Task %s: failed to download verifier dir: %s, "
"falling back to exit_code=%d",
task_name, e, exit_code,
)
reward = 1.0 if exit_code == 0 else 0.0
finally:
shutil.rmtree(local_verifier_dir, ignore_errors=True)
# Log test output for debugging failures
if reward == 0.0:
output_preview = output[-500:] if output else "(no output)"
logger.info(
"Task %s: FAIL (exit_code=%d)\n%s",
task_name, exit_code, output_preview,
)
return reward
# =========================================================================
# Evaluate -- main entry point for the eval subcommand
# =========================================================================
async def _eval_with_timeout(self, item: Dict[str, Any]) -> Dict:
"""
Wrap rollout_and_score_eval with a per-task wall-clock timeout.
If the task exceeds task_timeout seconds, it's automatically scored
as FAIL. This prevents any single task from hanging indefinitely.
"""
task_name = item.get("task_name", "unknown")
category = item.get("category", "unknown")
try:
return await asyncio.wait_for(
self.rollout_and_score_eval(item),
timeout=self.config.task_timeout,
)
except asyncio.TimeoutError:
from tqdm import tqdm
elapsed = self.config.task_timeout
tqdm.write(f" [TIMEOUT] {task_name} (exceeded {elapsed}s wall-clock limit)")
logger.error("Task %s: wall-clock timeout after %ds", task_name, elapsed)
out = {
"passed": False, "reward": 0.0,
"task_name": task_name, "category": category,
"error": f"timeout ({elapsed}s)",
}
self._save_result(out)
return out
async def evaluate(self, *args, **kwargs) -> None:
"""
Run Terminal-Bench 2.0 evaluation over all tasks.
This is the main entry point when invoked via:
python environments/terminalbench2_env.py evaluate
Runs all tasks through rollout_and_score_eval() via asyncio.gather()
(same pattern as GPQA and other Atropos eval envs). Each task is
wrapped with a wall-clock timeout so hung tasks auto-fail.
Suppresses noisy Modal/terminal output (HERMES_QUIET) so the tqdm
bar stays visible.
"""
start_time = time.time()
# Route all logging through tqdm.write() so the progress bar stays
# pinned at the bottom while log lines scroll above it.
from tqdm import tqdm
class _TqdmHandler(logging.Handler):
def emit(self, record):
try:
tqdm.write(self.format(record))
except Exception:
self.handleError(record)
handler = _TqdmHandler()
handler.setFormatter(logging.Formatter(
"%(asctime)s [%(name)s] %(levelname)s: %(message)s",
datefmt="%H:%M:%S",
))
root = logging.getLogger()
root.handlers = [handler] # Replace any existing handlers
root.setLevel(logging.INFO)
# Silence noisy third-party loggers that flood the output
logging.getLogger("httpx").setLevel(logging.WARNING) # Every HTTP request
logging.getLogger("openai").setLevel(logging.WARNING) # OpenAI client retries
logging.getLogger("rex-deploy").setLevel(logging.WARNING) # Swerex deployment
logging.getLogger("rex_image_builder").setLevel(logging.WARNING) # Image builds
print(f"\n{'='*60}")
print("Starting Terminal-Bench 2.0 Evaluation")
print(f"{'='*60}")
print(f" Dataset: {self.config.dataset_name}")
print(f" Total tasks: {len(self.all_eval_items)}")
print(f" Max agent turns: {self.config.max_agent_turns}")
print(f" Task timeout: {self.config.task_timeout}s")
print(f" Terminal backend: {self.config.terminal_backend}")
print(f" Tool thread pool: {self.config.tool_pool_size}")
print(f" Terminal timeout: {self.config.terminal_timeout}s/cmd")
print(f" Terminal lifetime: {self.config.terminal_lifetime}s (auto: task_timeout + 120)")
print(f"{'='*60}\n")
# Fire all tasks with wall-clock timeout, track live accuracy on the bar
total_tasks = len(self.all_eval_items)
eval_tasks = [
asyncio.ensure_future(self._eval_with_timeout(item))
for item in self.all_eval_items
]
results = []
passed_count = 0
pbar = tqdm(total=total_tasks, desc="Evaluating TB2", dynamic_ncols=True)
try:
for coro in asyncio.as_completed(eval_tasks):
result = await coro
results.append(result)
if result and result.get("passed"):
passed_count += 1
done = len(results)
pct = (passed_count / done * 100) if done else 0
pbar.set_postfix_str(f"pass={passed_count}/{done} ({pct:.1f}%)")
pbar.update(1)
except (KeyboardInterrupt, asyncio.CancelledError):
pbar.close()
print(f"\n\nInterrupted! Cleaning up {len(eval_tasks)} tasks...")
# Cancel all pending tasks
for task in eval_tasks:
task.cancel()
# Let cancellations propagate (finally blocks run cleanup_vm)
await asyncio.gather(*eval_tasks, return_exceptions=True)
# Belt-and-suspenders: clean up any remaining sandboxes
from tools.terminal_tool import cleanup_all_environments
cleanup_all_environments()
print("All sandboxes cleaned up.")
return
finally:
pbar.close()
end_time = time.time()
# Filter out None results (shouldn't happen, but be safe)
valid_results = [r for r in results if r is not None]
if not valid_results:
print("Warning: No valid evaluation results obtained")
return
# ---- Compute metrics ----
total = len(valid_results)
passed = sum(1 for r in valid_results if r.get("passed"))
overall_pass_rate = passed / total if total > 0 else 0.0
# Per-category breakdown
cat_results: Dict[str, List[Dict]] = defaultdict(list)
for r in valid_results:
cat_results[r.get("category", "unknown")].append(r)
# Build metrics dict
eval_metrics = {
"eval/pass_rate": overall_pass_rate,
"eval/total_tasks": total,
"eval/passed_tasks": passed,
"eval/evaluation_time_seconds": end_time - start_time,
}
# Per-category metrics
for category, cat_items in sorted(cat_results.items()):
cat_passed = sum(1 for r in cat_items if r.get("passed"))
cat_total = len(cat_items)
cat_pass_rate = cat_passed / cat_total if cat_total > 0 else 0.0
cat_key = category.replace(" ", "_").replace("-", "_").lower()
eval_metrics[f"eval/pass_rate_{cat_key}"] = cat_pass_rate
# Store metrics for wandb_log
self.eval_metrics = [(k, v) for k, v in eval_metrics.items()]
# ---- Print summary ----
print(f"\n{'='*60}")
print("Terminal-Bench 2.0 Evaluation Results")
print(f"{'='*60}")
print(f"Overall Pass Rate: {overall_pass_rate:.4f} ({passed}/{total})")
print(f"Evaluation Time: {end_time - start_time:.1f} seconds")
print("\nCategory Breakdown:")
for category, cat_items in sorted(cat_results.items()):
cat_passed = sum(1 for r in cat_items if r.get("passed"))
cat_total = len(cat_items)
cat_rate = cat_passed / cat_total if cat_total > 0 else 0.0
print(f" {category}: {cat_rate:.1%} ({cat_passed}/{cat_total})")
# Print individual task results
print("\nTask Results:")
for r in sorted(valid_results, key=lambda x: x.get("task_name", "")):
status = "PASS" if r.get("passed") else "FAIL"
turns = r.get("turns_used", "?")
error = r.get("error", "")
extra = f" (error: {error})" if error else ""
print(f" [{status}] {r['task_name']} (turns={turns}){extra}")
print(f"{'='*60}\n")
# Build sample records for evaluate_log (includes full conversations)
samples = [
{
"task_name": r.get("task_name"),
"category": r.get("category"),
"passed": r.get("passed"),
"reward": r.get("reward"),
"turns_used": r.get("turns_used"),
"error": r.get("error"),
"messages": r.get("messages"),
}
for r in valid_results
]
# Log evaluation results
try:
await self.evaluate_log(
metrics=eval_metrics,
samples=samples,
start_time=start_time,
end_time=end_time,
generation_parameters={
"temperature": self.config.agent_temperature,
"max_tokens": self.config.max_token_length,
"max_agent_turns": self.config.max_agent_turns,
"terminal_backend": self.config.terminal_backend,
},
)
except Exception as e:
print(f"Error logging evaluation results: {e}")
# Close streaming file
if hasattr(self, "_streaming_file") and not self._streaming_file.closed:
self._streaming_file.close()
print(f" Live results saved to: {self._streaming_path}")
# Kill all remaining sandboxes. Timed-out tasks leave orphaned thread
# pool workers still executing commands -- cleanup_all stops them.
from tools.terminal_tool import cleanup_all_environments
print("\nCleaning up all sandboxes...")
cleanup_all_environments()
# Shut down the tool thread pool so orphaned workers from timed-out
# tasks are killed immediately instead of retrying against dead
# sandboxes and spamming the console with TimeoutError warnings.
from environments.agent_loop import _tool_executor
_tool_executor.shutdown(wait=False, cancel_futures=True)
print("Done.")
# =========================================================================
# Wandb logging
# =========================================================================
async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
"""Log TB2-specific metrics to wandb."""
if wandb_metrics is None:
wandb_metrics = {}
# Add stored eval metrics
for metric_name, metric_value in self.eval_metrics:
wandb_metrics[metric_name] = metric_value
self.eval_metrics = []
await super().wandb_log(wandb_metrics)
if __name__ == "__main__":
TerminalBench2EvalEnv.cli()

View File

@@ -229,12 +229,6 @@ class HermesAgentBaseEnv(BaseEnv):
from environments.agent_loop import resize_tool_pool
resize_tool_pool(config.tool_pool_size)
# Set tool_parser on the ServerManager so ManagedServer uses it
# for bidirectional tool call translation (raw text ↔ OpenAI tool_calls).
if hasattr(self.server, 'tool_parser'):
self.server.tool_parser = config.tool_call_parser
print(f"🔧 Tool parser: {config.tool_call_parser}")
# Current group's resolved tools (set in collect_trajectories)
self._current_group_tools: Optional[Tuple[List[Dict], Set[str]]] = None
@@ -472,14 +466,22 @@ class HermesAgentBaseEnv(BaseEnv):
# Run the agent loop
result: AgentResult
if self._use_managed_server():
# Phase 2: ManagedServer with ToolCallTranslator -- exact tokens + logprobs
# tool_parser is set on ServerManager in __init__ and passed through
# to ManagedServer, which uses ToolCallTranslator for bidirectional
# translation between raw text and OpenAI tool_calls.
# Phase 2: ManagedServer with parser -- exact tokens + logprobs
# Load the tool call parser from registry based on config
from environments.tool_call_parsers import get_parser
try:
tc_parser = get_parser(self.config.tool_call_parser)
except KeyError:
logger.warning(
"Tool call parser '%s' not found, falling back to 'hermes'",
self.config.tool_call_parser,
)
tc_parser = get_parser("hermes")
try:
async with self.server.managed_server(
tokenizer=self.tokenizer,
preserve_think_blocks=bool(self.config.thinking_mode),
tool_call_parser=tc_parser,
) as managed:
agent = HermesAgentLoop(
server=managed,

View File

@@ -114,27 +114,11 @@ def _patch_swerex_modal():
self._worker = _AsyncWorker()
self._worker.start()
# Pre-build a modal.Image with pip fix for Modal's legacy image builder.
# Modal requires `python -m pip` to work during image build, but some
# task images (e.g., TBLite's broken-python) have intentionally broken pip.
# Fix: remove stale pip dist-info and reinstall via ensurepip before Modal
# tries to use it. This is a no-op for images where pip already works.
import modal as _modal
image_spec = self.config.image
if isinstance(image_spec, str):
image_spec = _modal.Image.from_registry(
image_spec,
setup_dockerfile_commands=[
"RUN rm -rf /usr/local/lib/python*/site-packages/pip* 2>/dev/null; "
"python -m ensurepip --upgrade --default-pip 2>/dev/null || true",
],
)
# Create AND start the deployment entirely on the worker's loop/thread
# so all gRPC channels and async state are bound to that loop
async def _create_and_start():
deployment = ModalDeployment(
image=image_spec,
image=self.config.image,
startup_timeout=self.config.startup_timeout,
runtime_timeout=self.config.runtime_timeout,
deployment_timeout=self.config.deployment_timeout,

View File

@@ -10,13 +10,12 @@ Format uses special unicode tokens:
<tool▁call▁end>
<tool▁calls▁end>
Fixes Issue #989: Support for multiple simultaneous tool calls.
Based on VLLM's DeepSeekV3ToolParser.extract_tool_calls()
"""
import re
import uuid
import logging
from typing import List, Optional, Tuple
from typing import List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
@@ -25,7 +24,6 @@ from openai.types.chat.chat_completion_message_tool_call import (
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
logger = logging.getLogger(__name__)
@register_parser("deepseek_v3")
class DeepSeekV3ToolCallParser(ToolCallParser):
@@ -34,56 +32,45 @@ class DeepSeekV3ToolCallParser(ToolCallParser):
Uses special unicode tokens with fullwidth angle brackets and block elements.
Extracts type, function name, and JSON arguments from the structured format.
Ensures all tool calls are captured when the model executes multiple actions.
"""
START_TOKEN = "<tool▁calls▁begin>"
# Updated PATTERN: Using \s* instead of literal \n for increased robustness
# against variations in model formatting (Issue #989).
# Regex captures: type, function_name, function_arguments
PATTERN = re.compile(
r"<tool▁call▁begin>(?P<type>.*?)<tool▁sep>(?P<function_name>.*?)\s*```json\s*(?P<function_arguments>.*?)\s*```\s*<tool▁call▁end>",
r"<tool▁call▁begin>(?P<type>.*)<tool▁sep>(?P<function_name>.*)\n```json\n(?P<function_arguments>.*)\n```<tool▁call▁end>",
re.DOTALL,
)
def parse(self, text: str) -> ParseResult:
"""
Parses the input text and extracts all available tool calls.
"""
if self.START_TOKEN not in text:
return text, None
try:
# Using finditer to capture ALL tool calls in the sequence
matches = list(self.PATTERN.finditer(text))
matches = self.PATTERN.findall(text)
if not matches:
return text, None
tool_calls: List[ChatCompletionMessageToolCall] = []
for match in matches:
func_name = match.group("function_name").strip()
func_args = match.group("function_arguments").strip()
tc_type, func_name, func_args = match
tool_calls.append(
ChatCompletionMessageToolCall(
id=f"call_{uuid.uuid4().hex[:8]}",
type="function",
function=Function(
name=func_name,
arguments=func_args,
name=func_name.strip(),
arguments=func_args.strip(),
),
)
)
if tool_calls:
# Content is text before the first tool call block
content_index = text.find(self.START_TOKEN)
content = text[:content_index].strip()
return content if content else None, tool_calls
if not tool_calls:
return text, None
return text, None
# Content is everything before the tool calls section
content = text[: text.find(self.START_TOKEN)].strip()
return content if content else None, tool_calls
except Exception as e:
logger.error(f"Error parsing DeepSeek V3 tool calls: {e}")
except Exception:
return text, None

View File

@@ -10,6 +10,7 @@ The [TOOL_CALLS] token is the bot_token used by Mistral models.
"""
import json
import re
import uuid
from typing import List, Optional
@@ -41,6 +42,9 @@ class MistralToolCallParser(ToolCallParser):
# The [TOOL_CALLS] token -- may appear as different strings depending on tokenizer
BOT_TOKEN = "[TOOL_CALLS]"
# Fallback regex for pre-v11 format when JSON parsing fails
TOOL_CALL_REGEX = re.compile(r"\[?\s*(\{.*?\})\s*\]?", re.DOTALL)
def parse(self, text: str) -> ParseResult:
if self.BOT_TOKEN not in text:
return text, None
@@ -67,13 +71,6 @@ class MistralToolCallParser(ToolCallParser):
tool_name = raw[:brace_idx].strip()
args_str = raw[brace_idx:]
# Validate and clean the JSON arguments
try:
parsed_args = json.loads(args_str)
args_str = json.dumps(parsed_args, ensure_ascii=False)
except json.JSONDecodeError:
pass # Keep raw if parsing fails
tool_calls.append(
ChatCompletionMessageToolCall(
id=_generate_mistral_id(),
@@ -103,14 +100,13 @@ class MistralToolCallParser(ToolCallParser):
)
)
except json.JSONDecodeError:
# Fallback: extract JSON objects using raw_decode
decoder = json.JSONDecoder()
idx = 0
while idx < len(first_raw):
try:
obj, end_idx = decoder.raw_decode(first_raw, idx)
if isinstance(obj, dict) and "name" in obj:
args = obj.get("arguments", {})
# Fallback regex extraction
match = self.TOOL_CALL_REGEX.findall(first_raw)
if match:
for raw_json in match:
try:
tc = json.loads(raw_json)
args = tc.get("arguments", {})
if isinstance(args, dict):
args = json.dumps(args, ensure_ascii=False)
tool_calls.append(
@@ -118,13 +114,12 @@ class MistralToolCallParser(ToolCallParser):
id=_generate_mistral_id(),
type="function",
function=Function(
name=obj["name"], arguments=args
name=tc["name"], arguments=args
),
)
)
idx = end_idx
except json.JSONDecodeError:
idx += 1
except (json.JSONDecodeError, KeyError):
continue
if not tool_calls:
return text, None

View File

@@ -1,718 +0,0 @@
"""
WebResearchEnv — RL Environment for Multi-Step Web Research
============================================================
Trains models to do accurate, efficient, multi-source web research.
Reward signals:
- Answer correctness (LLM judge, 0.01.0)
- Source diversity (used ≥2 distinct domains)
- Efficiency (penalizes excessive tool calls)
- Tool usage (bonus for actually using web tools)
Dataset: FRAMES benchmark (Google, 2024) — multi-hop factual questions
HuggingFace: google/frames-benchmark
Fallback: built-in sample questions (no HF token needed)
Usage:
# Phase 1 (OpenAI-compatible server)
python environments/web_research_env.py serve \\
--openai.base_url http://localhost:8000/v1 \\
--openai.model_name YourModel \\
--openai.server_type openai
# Process mode (offline data generation)
python environments/web_research_env.py process \\
--env.data_path_to_save_groups data/web_research.jsonl
# Standalone eval
python environments/web_research_env.py evaluate \\
--openai.base_url http://localhost:8000/v1 \\
--openai.model_name YourModel
Built by: github.com/jackx707
Inspired by: GroceryMind — production Hermes agent doing live web research
across German grocery stores (firecrawl + hermes-agent)
"""
from __future__ import annotations
import asyncio
import json
import logging
import os
import random
import re
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from urllib.parse import urlparse
from pydantic import Field
# Ensure hermes-agent root is on path
_repo_root = Path(__file__).resolve().parent.parent
if str(_repo_root) not in sys.path:
sys.path.insert(0, str(_repo_root))
# ---------------------------------------------------------------------------
# Optional HuggingFace datasets import
# ---------------------------------------------------------------------------
try:
from datasets import load_dataset
HF_AVAILABLE = True
except ImportError:
HF_AVAILABLE = False
from atroposlib.envs.base import ScoredDataGroup
from atroposlib.envs.server_handling.server_manager import APIServerConfig
from atroposlib.type_definitions import Item
from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
from environments.agent_loop import AgentResult
from environments.tool_context import ToolContext
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Fallback sample dataset (used when HuggingFace is unavailable)
# Multi-hop questions requiring real web search to answer.
# ---------------------------------------------------------------------------
SAMPLE_QUESTIONS = [
{
"question": "What is the current population of the capital city of the country that won the 2022 FIFA World Cup?",
"answer": "Buenos Aires has approximately 3 million people in the city proper, or around 15 million in the greater metro area.",
"difficulty": "medium",
"hops": 2,
},
{
"question": "Who is the CEO of the company that makes the most widely used open-source container orchestration platform?",
"answer": "The Linux Foundation oversees Kubernetes. CNCF (Cloud Native Computing Foundation) is the specific body — it does not have a traditional CEO but has an executive director.",
"difficulty": "medium",
"hops": 2,
},
{
"question": "What programming language was used to write the original version of the web framework used by Instagram?",
"answer": "Django, which Instagram was built on, is written in Python.",
"difficulty": "easy",
"hops": 2,
},
{
"question": "In what year was the university founded where the inventor of the World Wide Web currently holds a professorship?",
"answer": "Tim Berners-Lee holds a professorship at MIT (founded 1861) and the University of Southampton (founded 1952).",
"difficulty": "hard",
"hops": 3,
},
{
"question": "What is the latest stable version of the programming language that ranks #1 on the TIOBE index as of this year?",
"answer": "Python is currently #1 on TIOBE. The latest stable version should be verified via the official python.org site.",
"difficulty": "medium",
"hops": 2,
},
{
"question": "How many employees does the parent company of Instagram have?",
"answer": "Meta Platforms (parent of Instagram) employs approximately 70,000+ people as of recent reports.",
"difficulty": "medium",
"hops": 2,
},
{
"question": "What is the current interest rate set by the central bank of the country where the Eiffel Tower is located?",
"answer": "The European Central Bank sets rates for France/eurozone. The current rate should be verified — it has changed frequently in 2023-2025.",
"difficulty": "hard",
"hops": 2,
},
{
"question": "Which company acquired the startup founded by the creator of Oculus VR?",
"answer": "Palmer Luckey founded Oculus VR, which was acquired by Facebook (now Meta). He later founded Anduril Industries.",
"difficulty": "medium",
"hops": 2,
},
{
"question": "What is the market cap of the company that owns the most popular search engine in Russia?",
"answer": "Yandex (now split into separate entities after 2024 restructuring). Current market cap should be verified via financial sources.",
"difficulty": "hard",
"hops": 2,
},
{
"question": "What was the GDP growth rate of the country that hosted the most recent Summer Olympics?",
"answer": "Paris, France hosted the 2024 Summer Olympics. France's recent GDP growth should be verified via World Bank or IMF data.",
"difficulty": "hard",
"hops": 2,
},
]
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
class WebResearchEnvConfig(HermesAgentEnvConfig):
"""Configuration for the web research RL environment."""
# Reward weights
correctness_weight: float = Field(
default=0.6,
description="Weight for answer correctness in reward (LLM judge score).",
)
tool_usage_weight: float = Field(
default=0.2,
description="Weight for tool usage signal (did the model actually use web tools?).",
)
efficiency_weight: float = Field(
default=0.2,
description="Weight for efficiency signal (penalizes excessive tool calls).",
)
diversity_bonus: float = Field(
default=0.1,
description="Bonus reward for citing ≥2 distinct domains.",
)
# Efficiency thresholds
efficient_max_calls: int = Field(
default=5,
description="Maximum tool calls before efficiency penalty begins.",
)
heavy_penalty_calls: int = Field(
default=10,
description="Tool call count where efficiency penalty steepens.",
)
# Eval
eval_size: int = Field(
default=20,
description="Number of held-out items for evaluation.",
)
eval_split_ratio: float = Field(
default=0.1,
description="Fraction of dataset to hold out for evaluation (0.01.0).",
)
# Dataset
dataset_name: str = Field(
default="google/frames-benchmark",
description="HuggingFace dataset name for research questions.",
)
# ---------------------------------------------------------------------------
# Environment
# ---------------------------------------------------------------------------
class WebResearchEnv(HermesAgentBaseEnv):
"""
RL environment for training multi-step web research skills.
The model is given a factual question requiring 2-3 hops of web research
and must use web_search / web_extract tools to find and synthesize the answer.
Reward is multi-signal:
60% — answer correctness (LLM judge)
20% — tool usage (did the model actually search the web?)
20% — efficiency (penalizes >5 tool calls)
Bonus +0.1 for source diversity (≥2 distinct domains cited).
"""
name = "web-research"
env_config_cls = WebResearchEnvConfig
# Default toolsets for this environment — web + file for saving notes
default_toolsets = ["web", "file"]
@classmethod
def config_init(cls) -> Tuple[WebResearchEnvConfig, List[APIServerConfig]]:
"""Default configuration for the web research environment."""
env_config = WebResearchEnvConfig(
enabled_toolsets=["web", "file"],
max_agent_turns=15,
agent_temperature=1.0,
system_prompt=(
"You are a highly capable research agent. When asked a factual question, "
"always use web_search to find current, accurate information before answering. "
"Cite at least 2 sources. Be concise and accurate."
),
group_size=4,
total_steps=1000,
steps_per_eval=100,
use_wandb=True,
wandb_name="web-research",
)
server_configs = [
APIServerConfig(
base_url="https://openrouter.ai/api/v1",
model_name="anthropic/claude-sonnet-4.5",
server_type="openai",
api_key=os.getenv("OPENROUTER_API_KEY", ""),
health_check=False,
)
]
return env_config, server_configs
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._items: list[dict] = []
self._eval_items: list[dict] = []
self._index: int = 0
# Metrics tracking for wandb
self._reward_buffer: list[float] = []
self._correctness_buffer: list[float] = []
self._tool_usage_buffer: list[float] = []
self._efficiency_buffer: list[float] = []
self._diversity_buffer: list[float] = []
# ------------------------------------------------------------------
# 1. Setup — load dataset
# ------------------------------------------------------------------
async def setup(self) -> None:
"""Load the FRAMES benchmark or fall back to built-in samples."""
if HF_AVAILABLE:
try:
logger.info("Loading FRAMES benchmark from HuggingFace...")
ds = load_dataset(self.config.dataset_name, split="test")
self._items = [
{
"question": row["Prompt"],
"answer": row["Answer"],
"difficulty": row.get("reasoning_types", "unknown"),
"hops": 2,
}
for row in ds
]
# Hold out for eval
eval_size = max(
self.config.eval_size,
int(len(self._items) * self.config.eval_split_ratio),
)
random.shuffle(self._items)
self._eval_items = self._items[:eval_size]
self._items = self._items[eval_size:]
logger.info(
f"Loaded {len(self._items)} train / {len(self._eval_items)} eval items "
f"from FRAMES benchmark."
)
return
except Exception as e:
logger.warning(f"Could not load FRAMES from HuggingFace: {e}. Using built-in samples.")
# Fallback
random.shuffle(SAMPLE_QUESTIONS)
split = max(1, len(SAMPLE_QUESTIONS) * 8 // 10)
self._items = SAMPLE_QUESTIONS[:split]
self._eval_items = SAMPLE_QUESTIONS[split:]
logger.info(
f"Using built-in sample dataset: {len(self._items)} train / "
f"{len(self._eval_items)} eval items."
)
# ------------------------------------------------------------------
# 2. get_next_item — return the next question
# ------------------------------------------------------------------
async def get_next_item(self) -> dict:
"""Return the next item, cycling through the dataset."""
if not self._items:
raise RuntimeError("Dataset is empty. Did you call setup()?")
item = self._items[self._index % len(self._items)]
self._index += 1
return item
# ------------------------------------------------------------------
# 3. format_prompt — build the user-facing prompt
# ------------------------------------------------------------------
def format_prompt(self, item: dict) -> str:
"""Format the research question as a task prompt."""
return (
f"Research the following question thoroughly using web search. "
f"You MUST search the web to find current, accurate information — "
f"do not rely solely on your training data.\n\n"
f"Question: {item['question']}\n\n"
f"Requirements:\n"
f"- Use web_search and/or web_extract tools to find information\n"
f"- Search at least 2 different sources\n"
f"- Provide a concise, accurate answer (2-4 sentences)\n"
f"- Cite the sources you used"
)
# ------------------------------------------------------------------
# 4. compute_reward — multi-signal scoring
# ------------------------------------------------------------------
async def compute_reward(
self,
item: dict,
result: AgentResult,
ctx: ToolContext,
) -> float:
"""
Multi-signal reward function:
correctness_weight * correctness — LLM judge comparing answer to ground truth
tool_usage_weight * tool_used — binary: did the model use web tools?
efficiency_weight * efficiency — penalizes wasteful tool usage
+ diversity_bonus — source diversity (≥2 distinct domains)
"""
# Extract final response from messages (last assistant message with content)
final_response = ""
tools_used: list[str] = []
for msg in reversed(result.messages):
if msg.get("role") == "assistant" and msg.get("content") and not final_response:
final_response = msg["content"]
# Collect tool names from tool call messages
if msg.get("role") == "assistant" and msg.get("tool_calls"):
for tc in msg["tool_calls"]:
fn = tc.get("function", {}) if isinstance(tc, dict) else {}
name = fn.get("name", "")
if name:
tools_used.append(name)
tool_call_count: int = result.turns_used or len(tools_used)
cfg = self.config
# ---- Signal 1: Answer correctness (LLM judge) ----------------
correctness = await self._llm_judge(
question=item["question"],
expected=item["answer"],
model_answer=final_response,
)
# ---- Signal 2: Web tool usage --------------------------------
web_tools = {"web_search", "web_extract", "search", "firecrawl"}
tool_used = 1.0 if any(t in web_tools for t in tools_used) else 0.0
# ---- Signal 3: Efficiency ------------------------------------
if tool_call_count <= cfg.efficient_max_calls:
efficiency = 1.0
elif tool_call_count <= cfg.heavy_penalty_calls:
efficiency = 1.0 - (tool_call_count - cfg.efficient_max_calls) * 0.08
else:
efficiency = max(0.0, 1.0 - (tool_call_count - cfg.efficient_max_calls) * 0.12)
# ---- Bonus: Source diversity ---------------------------------
domains = self._extract_domains(final_response)
diversity = cfg.diversity_bonus if len(domains) >= 2 else 0.0
# ---- Combine ------------------------------------------------
reward = (
cfg.correctness_weight * correctness
+ cfg.tool_usage_weight * tool_used
+ cfg.efficiency_weight * efficiency
+ diversity
)
reward = min(1.0, max(0.0, reward)) # clamp to [0, 1]
# Track for wandb
self._reward_buffer.append(reward)
self._correctness_buffer.append(correctness)
self._tool_usage_buffer.append(tool_used)
self._efficiency_buffer.append(efficiency)
self._diversity_buffer.append(diversity)
logger.debug(
f"Reward breakdown — correctness={correctness:.2f}, "
f"tool_used={tool_used:.1f}, efficiency={efficiency:.2f}, "
f"diversity={diversity:.1f} → total={reward:.3f}"
)
return reward
# ------------------------------------------------------------------
# 5. evaluate — run on held-out eval split
# ------------------------------------------------------------------
async def evaluate(self, *args, **kwargs) -> None:
"""Run evaluation on the held-out split using the full agent loop with tools.
Each eval item runs through the same agent loop as training —
the model can use web_search, web_extract, etc. to research answers.
This measures actual agentic research capability, not just knowledge.
"""
import time
import uuid
from environments.agent_loop import HermesAgentLoop
from environments.tool_context import ToolContext
items = self._eval_items
if not items:
logger.warning("No eval items available.")
return
eval_size = min(self.config.eval_size, len(items))
eval_items = items[:eval_size]
logger.info(f"Running eval on {len(eval_items)} questions (with agent loop + tools)...")
start_time = time.time()
samples = []
# Resolve tools once for all eval items
tools, valid_names = self._resolve_tools_for_group()
for i, item in enumerate(eval_items):
task_id = str(uuid.uuid4())
logger.info(f"Eval [{i+1}/{len(eval_items)}]: {item['question'][:80]}...")
try:
# Build messages
messages: List[Dict[str, Any]] = []
if self.config.system_prompt:
messages.append({"role": "system", "content": self.config.system_prompt})
messages.append({"role": "user", "content": self.format_prompt(item)})
# Run the full agent loop with tools
agent = HermesAgentLoop(
server=self.server,
tool_schemas=tools,
valid_tool_names=valid_names,
max_turns=self.config.max_agent_turns,
task_id=task_id,
temperature=0.0, # Deterministic for eval
max_tokens=self.config.max_token_length,
extra_body=self.config.extra_body,
)
result = await agent.run(messages)
# Extract final response and tool usage from messages
final_response = ""
tool_call_count = 0
for msg in reversed(result.messages):
if msg.get("role") == "assistant" and msg.get("content") and not final_response:
final_response = msg["content"]
if msg.get("role") == "assistant" and msg.get("tool_calls"):
tool_call_count += len(msg["tool_calls"])
# Compute reward (includes LLM judge for correctness)
# Temporarily save buffer lengths so we can extract the
# correctness score without calling judge twice, and avoid
# polluting training metric buffers with eval data.
buf_len = len(self._correctness_buffer)
ctx = ToolContext(task_id)
try:
reward = await self.compute_reward(item, result, ctx)
finally:
ctx.cleanup()
# Extract correctness from the buffer (compute_reward appended it)
# then remove eval entries from training buffers
correctness = (
self._correctness_buffer[buf_len]
if len(self._correctness_buffer) > buf_len
else 0.0
)
# Roll back buffers to avoid polluting training metrics
for buf in (
self._reward_buffer, self._correctness_buffer,
self._tool_usage_buffer, self._efficiency_buffer,
self._diversity_buffer,
):
if len(buf) > buf_len:
buf.pop()
samples.append({
"prompt": item["question"],
"response": final_response[:500],
"expected": item["answer"],
"correctness": correctness,
"reward": reward,
"tool_calls": tool_call_count,
"turns": result.turns_used,
})
logger.info(
f" → correctness={correctness:.2f}, reward={reward:.3f}, "
f"tools={tool_call_count}, turns={result.turns_used}"
)
except Exception as e:
logger.error(f"Eval error on item: {e}")
samples.append({
"prompt": item["question"],
"response": f"ERROR: {e}",
"expected": item["answer"],
"correctness": 0.0,
"reward": 0.0,
"tool_calls": 0,
"turns": 0,
})
end_time = time.time()
# Compute aggregate metrics
correctness_scores = [s["correctness"] for s in samples]
rewards = [s["reward"] for s in samples]
tool_counts = [s["tool_calls"] for s in samples]
n = len(samples)
eval_metrics = {
"eval/mean_correctness": sum(correctness_scores) / n if n else 0.0,
"eval/mean_reward": sum(rewards) / n if n else 0.0,
"eval/mean_tool_calls": sum(tool_counts) / n if n else 0.0,
"eval/tool_usage_rate": sum(1 for t in tool_counts if t > 0) / n if n else 0.0,
"eval/n_items": n,
}
logger.info(
f"Eval complete — correctness={eval_metrics['eval/mean_correctness']:.3f}, "
f"reward={eval_metrics['eval/mean_reward']:.3f}, "
f"tool_usage={eval_metrics['eval/tool_usage_rate']:.0%}"
)
await self.evaluate_log(
metrics=eval_metrics,
samples=samples,
start_time=start_time,
end_time=end_time,
)
# ------------------------------------------------------------------
# 6. wandb_log — custom metrics
# ------------------------------------------------------------------
async def wandb_log(self, wandb_metrics: Optional[Dict] = None) -> None:
"""Log reward breakdown metrics to wandb."""
if wandb_metrics is None:
wandb_metrics = {}
if self._reward_buffer:
n = len(self._reward_buffer)
wandb_metrics["train/mean_reward"] = sum(self._reward_buffer) / n
wandb_metrics["train/mean_correctness"] = sum(self._correctness_buffer) / n
wandb_metrics["train/mean_tool_usage"] = sum(self._tool_usage_buffer) / n
wandb_metrics["train/mean_efficiency"] = sum(self._efficiency_buffer) / n
wandb_metrics["train/mean_diversity"] = sum(self._diversity_buffer) / n
wandb_metrics["train/total_rollouts"] = n
# Accuracy buckets
wandb_metrics["train/correct_rate"] = (
sum(1 for c in self._correctness_buffer if c >= 0.7) / n
)
wandb_metrics["train/tool_usage_rate"] = (
sum(1 for t in self._tool_usage_buffer if t > 0) / n
)
# Clear buffers
self._reward_buffer.clear()
self._correctness_buffer.clear()
self._tool_usage_buffer.clear()
self._efficiency_buffer.clear()
self._diversity_buffer.clear()
await super().wandb_log(wandb_metrics)
# ------------------------------------------------------------------
# Private helpers
# ------------------------------------------------------------------
async def _llm_judge(
self,
question: str,
expected: str,
model_answer: str,
) -> float:
"""
Use the server's LLM to judge answer correctness.
Falls back to keyword heuristic if LLM call fails.
"""
if not model_answer or not model_answer.strip():
return 0.0
judge_prompt = (
"You are an impartial judge evaluating the quality of an AI research answer.\n\n"
f"Question: {question}\n\n"
f"Reference answer: {expected}\n\n"
f"Model answer: {model_answer}\n\n"
"Score the model answer on a scale from 0.0 to 1.0 where:\n"
" 1.0 = fully correct and complete\n"
" 0.7 = mostly correct with minor gaps\n"
" 0.4 = partially correct\n"
" 0.1 = mentions relevant topic but wrong or very incomplete\n"
" 0.0 = completely wrong or no answer\n\n"
"Consider: factual accuracy, completeness, and relevance.\n"
'Respond with ONLY a JSON object: {"score": <float>, "reason": "<one sentence>"}'
)
try:
response = await self.server.chat_completion(
messages=[{"role": "user", "content": judge_prompt}],
n=1,
max_tokens=150,
temperature=0.0,
split="eval",
)
text = response.choices[0].message.content if response.choices else ""
parsed = self._parse_judge_json(text)
if parsed is not None:
return float(parsed)
except Exception as e:
logger.debug(f"LLM judge failed: {e}. Using heuristic.")
return self._heuristic_score(expected, model_answer)
@staticmethod
def _parse_judge_json(text: str) -> Optional[float]:
"""Extract the score float from LLM judge JSON response."""
try:
clean = re.sub(r"```(?:json)?|```", "", text).strip()
data = json.loads(clean)
score = float(data.get("score", -1))
if 0.0 <= score <= 1.0:
return score
except Exception:
match = re.search(r'"score"\s*:\s*([0-9.]+)', text)
if match:
score = float(match.group(1))
if 0.0 <= score <= 1.0:
return score
return None
@staticmethod
def _heuristic_score(expected: str, model_answer: str) -> float:
"""Lightweight keyword overlap score as fallback."""
stopwords = {
"the", "a", "an", "is", "are", "was", "were", "of", "in", "on",
"at", "to", "for", "with", "and", "or", "but", "it", "its",
"this", "that", "as", "by", "from", "be", "has", "have", "had",
}
def tokenize(text: str) -> set:
tokens = re.findall(r'\b\w+\b', text.lower())
return {t for t in tokens if t not in stopwords and len(t) > 2}
expected_tokens = tokenize(expected)
answer_tokens = tokenize(model_answer)
if not expected_tokens:
return 0.5
overlap = len(expected_tokens & answer_tokens)
union = len(expected_tokens | answer_tokens)
jaccard = overlap / union if union > 0 else 0.0
recall = overlap / len(expected_tokens)
return min(1.0, 0.4 * jaccard + 0.6 * recall)
@staticmethod
def _extract_domains(text: str) -> set:
"""Extract unique domains from URLs cited in the response."""
urls = re.findall(r'https?://[^\s\)>\]"\']+', text)
domains = set()
for url in urls:
try:
parsed = urlparse(url)
domain = parsed.netloc.lower().lstrip("www.")
if domain:
domains.add(domain)
except Exception:
pass
return domains
# ---------------------------------------------------------------------------
# Entry point
# ---------------------------------------------------------------------------
if __name__ == "__main__":
WebResearchEnv.cli()

View File

@@ -12,31 +12,9 @@ from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
DIRECTORY_PATH = get_hermes_home() / "channel_directory.json"
def _session_entry_id(origin: Dict[str, Any]) -> Optional[str]:
chat_id = origin.get("chat_id")
if not chat_id:
return None
thread_id = origin.get("thread_id")
if thread_id:
return f"{chat_id}:{thread_id}"
return str(chat_id)
def _session_entry_name(origin: Dict[str, Any]) -> str:
base_name = origin.get("chat_name") or origin.get("user_name") or str(origin.get("chat_id"))
thread_id = origin.get("thread_id")
if not thread_id:
return base_name
topic_label = origin.get("chat_topic") or f"topic {thread_id}"
return f"{base_name} / {topic_label}"
DIRECTORY_PATH = Path.home() / ".hermes" / "channel_directory.json"
# ---------------------------------------------------------------------------
@@ -62,8 +40,8 @@ def build_channel_directory(adapters: Dict[Any, Any]) -> Dict[str, Any]:
except Exception as e:
logger.warning("Channel directory: failed to build %s: %s", platform.value, e)
# Telegram, WhatsApp & Signal can't enumerate chats -- pull from session history
for plat_name in ("telegram", "whatsapp", "signal", "email", "sms"):
# Telegram & WhatsApp can't enumerate chats -- pull from session history
for plat_name in ("telegram", "whatsapp"):
if plat_name not in platforms:
platforms[plat_name] = _build_from_sessions(plat_name)
@@ -74,7 +52,7 @@ def build_channel_directory(adapters: Dict[Any, Any]) -> Dict[str, Any]:
try:
DIRECTORY_PATH.parent.mkdir(parents=True, exist_ok=True)
with open(DIRECTORY_PATH, "w", encoding="utf-8") as f:
with open(DIRECTORY_PATH, "w") as f:
json.dump(directory, f, indent=2, ensure_ascii=False)
except Exception as e:
logger.warning("Channel directory: failed to write: %s", e)
@@ -131,13 +109,13 @@ def _build_slack(adapter) -> List[Dict[str, str]]:
def _build_from_sessions(platform_name: str) -> List[Dict[str, str]]:
"""Pull known channels/contacts from sessions.json origin data."""
sessions_path = get_hermes_home() / "sessions" / "sessions.json"
sessions_path = Path.home() / ".hermes" / "sessions" / "sessions.json"
if not sessions_path.exists():
return []
entries = []
try:
with open(sessions_path, encoding="utf-8") as f:
with open(sessions_path) as f:
data = json.load(f)
seen_ids = set()
@@ -145,15 +123,14 @@ def _build_from_sessions(platform_name: str) -> List[Dict[str, str]]:
origin = session.get("origin") or {}
if origin.get("platform") != platform_name:
continue
entry_id = _session_entry_id(origin)
if not entry_id or entry_id in seen_ids:
chat_id = origin.get("chat_id")
if not chat_id or chat_id in seen_ids:
continue
seen_ids.add(entry_id)
seen_ids.add(chat_id)
entries.append({
"id": entry_id,
"name": _session_entry_name(origin),
"id": str(chat_id),
"name": origin.get("chat_name") or origin.get("user_name") or str(chat_id),
"type": session.get("chat_type", "dm"),
"thread_id": origin.get("thread_id"),
})
except Exception as e:
logger.debug("Channel directory: failed to read sessions for %s: %s", platform_name, e)
@@ -170,7 +147,7 @@ def load_directory() -> Dict[str, Any]:
if not DIRECTORY_PATH.exists():
return {"updated_at": None, "platforms": {}}
try:
with open(DIRECTORY_PATH, encoding="utf-8") as f:
with open(DIRECTORY_PATH) as f:
return json.load(f)
except Exception:
return {"updated_at": None, "platforms": {}}

View File

@@ -16,31 +16,9 @@ from dataclasses import dataclass, field
from typing import Dict, List, Optional, Any
from enum import Enum
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
def _coerce_bool(value: Any, default: bool = True) -> bool:
"""Coerce bool-ish config values, preserving a caller-provided default."""
if value is None:
return default
if isinstance(value, bool):
return value
if isinstance(value, str):
return value.strip().lower() in ("true", "1", "yes", "on")
return bool(value)
def _normalize_unauthorized_dm_behavior(value: Any, default: str = "pair") -> str:
"""Normalize unauthorized DM behavior to a supported value."""
if isinstance(value, str):
normalized = value.strip().lower()
if normalized in {"pair", "ignore"}:
return normalized
return default
class Platform(Enum):
"""Supported messaging platforms."""
LOCAL = "local"
@@ -48,15 +26,7 @@ class Platform(Enum):
DISCORD = "discord"
WHATSAPP = "whatsapp"
SLACK = "slack"
SIGNAL = "signal"
MATTERMOST = "mattermost"
MATRIX = "matrix"
HOMEASSISTANT = "homeassistant"
EMAIL = "email"
SMS = "sms"
DINGTALK = "dingtalk"
API_SERVER = "api_server"
WEBHOOK = "webhook"
@dataclass
@@ -101,32 +71,20 @@ class SessionResetPolicy:
mode: str = "both" # "daily", "idle", "both", or "none"
at_hour: int = 4 # Hour for daily reset (0-23, local time)
idle_minutes: int = 1440 # Minutes of inactivity before reset (24 hours)
notify: bool = True # Send a notification to the user when auto-reset occurs
notify_exclude_platforms: tuple = ("api_server", "webhook") # Platforms that don't get reset notifications
def to_dict(self) -> Dict[str, Any]:
return {
"mode": self.mode,
"at_hour": self.at_hour,
"idle_minutes": self.idle_minutes,
"notify": self.notify,
"notify_exclude_platforms": list(self.notify_exclude_platforms),
}
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "SessionResetPolicy":
# Handle both missing keys and explicit null values (YAML null → None)
mode = data.get("mode")
at_hour = data.get("at_hour")
idle_minutes = data.get("idle_minutes")
notify = data.get("notify")
exclude = data.get("notify_exclude_platforms")
return cls(
mode=mode if mode is not None else "both",
at_hour=at_hour if at_hour is not None else 4,
idle_minutes=idle_minutes if idle_minutes is not None else 1440,
notify=notify if notify is not None else True,
notify_exclude_platforms=tuple(exclude) if exclude is not None else ("api_server", "webhook"),
mode=data.get("mode", "both"),
at_hour=data.get("at_hour", 4),
idle_minutes=data.get("idle_minutes", 1440),
)
@@ -169,37 +127,6 @@ class PlatformConfig:
)
@dataclass
class StreamingConfig:
"""Configuration for real-time token streaming to messaging platforms."""
enabled: bool = False
transport: str = "edit" # "edit" (progressive editMessageText) or "off"
edit_interval: float = 0.3 # Seconds between message edits
buffer_threshold: int = 40 # Chars before forcing an edit
cursor: str = "" # Cursor shown during streaming
def to_dict(self) -> Dict[str, Any]:
return {
"enabled": self.enabled,
"transport": self.transport,
"edit_interval": self.edit_interval,
"buffer_threshold": self.buffer_threshold,
"cursor": self.cursor,
}
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "StreamingConfig":
if not data:
return cls()
return cls(
enabled=data.get("enabled", False),
transport=data.get("transport", "edit"),
edit_interval=float(data.get("edit_interval", 0.3)),
buffer_threshold=int(data.get("buffer_threshold", 40)),
cursor=data.get("cursor", ""),
)
@dataclass
class GatewayConfig:
"""
@@ -217,54 +144,18 @@ class GatewayConfig:
# Reset trigger commands
reset_triggers: List[str] = field(default_factory=lambda: ["/new", "/reset"])
# User-defined quick commands (slash commands that bypass the agent loop)
quick_commands: Dict[str, Any] = field(default_factory=dict)
# Storage paths
sessions_dir: Path = field(default_factory=lambda: get_hermes_home() / "sessions")
sessions_dir: Path = field(default_factory=lambda: Path.home() / ".hermes" / "sessions")
# Delivery settings
always_log_local: bool = True # Always save cron outputs to local files
# STT settings
stt_enabled: bool = True # Whether to auto-transcribe inbound voice messages
# Session isolation in shared chats
group_sessions_per_user: bool = True # Isolate group/channel sessions per participant when user IDs are available
# Unauthorized DM policy
unauthorized_dm_behavior: str = "pair" # "pair" or "ignore"
# Streaming configuration
streaming: StreamingConfig = field(default_factory=StreamingConfig)
def get_connected_platforms(self) -> List[Platform]:
"""Return list of platforms that are enabled and configured."""
connected = []
for platform, config in self.platforms.items():
if not config.enabled:
continue
# Platforms that use token/api_key auth
if config.token or config.api_key:
connected.append(platform)
# WhatsApp uses enabled flag only (bridge handles auth)
elif platform == Platform.WHATSAPP:
connected.append(platform)
# Signal uses extra dict for config (http_url + account)
elif platform == Platform.SIGNAL and config.extra.get("http_url"):
connected.append(platform)
# Email uses extra dict for config (address + imap_host + smtp_host)
elif platform == Platform.EMAIL and config.extra.get("address"):
connected.append(platform)
# SMS uses api_key (Twilio auth token) — SID checked via env
elif platform == Platform.SMS and os.getenv("TWILIO_ACCOUNT_SID"):
connected.append(platform)
# API Server uses enabled flag only (no token needed)
elif platform == Platform.API_SERVER:
connected.append(platform)
# Webhook uses enabled flag only (secrets are per-route)
elif platform == Platform.WEBHOOK:
if config.enabled and (config.token or config.api_key):
connected.append(platform)
return connected
@@ -308,13 +199,8 @@ class GatewayConfig:
p.value: v.to_dict() for p, v in self.reset_by_platform.items()
},
"reset_triggers": self.reset_triggers,
"quick_commands": self.quick_commands,
"sessions_dir": str(self.sessions_dir),
"always_log_local": self.always_log_local,
"stt_enabled": self.stt_enabled,
"group_sessions_per_user": self.group_sessions_per_user,
"unauthorized_dm_behavior": self.unauthorized_dm_behavior,
"streaming": self.streaming.to_dict(),
}
@classmethod
@@ -343,195 +229,57 @@ class GatewayConfig:
if "default_reset_policy" in data:
default_policy = SessionResetPolicy.from_dict(data["default_reset_policy"])
sessions_dir = get_hermes_home() / "sessions"
sessions_dir = Path.home() / ".hermes" / "sessions"
if "sessions_dir" in data:
sessions_dir = Path(data["sessions_dir"])
quick_commands = data.get("quick_commands", {})
if not isinstance(quick_commands, dict):
quick_commands = {}
stt_enabled = data.get("stt_enabled")
if stt_enabled is None:
stt_enabled = data.get("stt", {}).get("enabled") if isinstance(data.get("stt"), dict) else None
group_sessions_per_user = data.get("group_sessions_per_user")
unauthorized_dm_behavior = _normalize_unauthorized_dm_behavior(
data.get("unauthorized_dm_behavior"),
"pair",
)
return cls(
platforms=platforms,
default_reset_policy=default_policy,
reset_by_type=reset_by_type,
reset_by_platform=reset_by_platform,
reset_triggers=data.get("reset_triggers", ["/new", "/reset"]),
quick_commands=quick_commands,
sessions_dir=sessions_dir,
always_log_local=data.get("always_log_local", True),
stt_enabled=_coerce_bool(stt_enabled, True),
group_sessions_per_user=_coerce_bool(group_sessions_per_user, True),
unauthorized_dm_behavior=unauthorized_dm_behavior,
streaming=StreamingConfig.from_dict(data.get("streaming", {})),
)
def get_unauthorized_dm_behavior(self, platform: Optional[Platform] = None) -> str:
"""Return the effective unauthorized-DM behavior for a platform."""
if platform:
platform_cfg = self.platforms.get(platform)
if platform_cfg and "unauthorized_dm_behavior" in platform_cfg.extra:
return _normalize_unauthorized_dm_behavior(
platform_cfg.extra.get("unauthorized_dm_behavior"),
self.unauthorized_dm_behavior,
)
return self.unauthorized_dm_behavior
def load_gateway_config() -> GatewayConfig:
"""
Load gateway configuration from multiple sources.
Priority (highest to lowest):
1. Environment variables
2. ~/.hermes/config.yaml (primary user-facing config)
3. ~/.hermes/gateway.json (legacy — provides defaults under config.yaml)
4. Built-in defaults
2. ~/.hermes/gateway.json
3. cli-config.yaml gateway section
4. Defaults
"""
_home = get_hermes_home()
gw_data: dict = {}
# Legacy fallback: gateway.json provides the base layer.
# config.yaml keys always win when both specify the same setting.
gateway_json_path = _home / "gateway.json"
if gateway_json_path.exists():
config = GatewayConfig()
# Try loading from ~/.hermes/gateway.json
gateway_config_path = Path.home() / ".hermes" / "gateway.json"
if gateway_config_path.exists():
try:
with open(gateway_json_path, "r", encoding="utf-8") as f:
gw_data = json.load(f) or {}
logger.info(
"Loaded legacy %s — consider moving settings to config.yaml",
gateway_json_path,
)
with open(gateway_config_path, "r") as f:
data = json.load(f)
config = GatewayConfig.from_dict(data)
except Exception as e:
logger.warning("Failed to load %s: %s", gateway_json_path, e)
# Primary source: config.yaml
print(f"[gateway] Warning: Failed to load {gateway_config_path}: {e}")
# Bridge session_reset from config.yaml (the user-facing config file)
# into the gateway config. config.yaml takes precedence over gateway.json
# for session reset policy since that's where hermes setup writes it.
try:
import yaml
config_yaml_path = _home / "config.yaml"
config_yaml_path = Path.home() / ".hermes" / "config.yaml"
if config_yaml_path.exists():
with open(config_yaml_path, encoding="utf-8") as f:
with open(config_yaml_path) as f:
yaml_cfg = yaml.safe_load(f) or {}
# Map config.yaml keys → GatewayConfig.from_dict() schema.
# Each key overwrites whatever gateway.json may have set.
sr = yaml_cfg.get("session_reset")
if sr and isinstance(sr, dict):
gw_data["default_reset_policy"] = sr
qc = yaml_cfg.get("quick_commands")
if qc is not None:
if isinstance(qc, dict):
gw_data["quick_commands"] = qc
else:
logger.warning(
"Ignoring invalid quick_commands in config.yaml "
"(expected mapping, got %s)",
type(qc).__name__,
)
stt_cfg = yaml_cfg.get("stt")
if isinstance(stt_cfg, dict):
gw_data["stt"] = stt_cfg
if "group_sessions_per_user" in yaml_cfg:
gw_data["group_sessions_per_user"] = yaml_cfg["group_sessions_per_user"]
streaming_cfg = yaml_cfg.get("streaming")
if isinstance(streaming_cfg, dict):
gw_data["streaming"] = streaming_cfg
if "reset_triggers" in yaml_cfg:
gw_data["reset_triggers"] = yaml_cfg["reset_triggers"]
if "always_log_local" in yaml_cfg:
gw_data["always_log_local"] = yaml_cfg["always_log_local"]
if "unauthorized_dm_behavior" in yaml_cfg:
gw_data["unauthorized_dm_behavior"] = _normalize_unauthorized_dm_behavior(
yaml_cfg.get("unauthorized_dm_behavior"),
"pair",
)
# Merge platforms section from config.yaml into gw_data so that
# nested keys like platforms.webhook.extra.routes are loaded.
yaml_platforms = yaml_cfg.get("platforms")
platforms_data = gw_data.setdefault("platforms", {})
if not isinstance(platforms_data, dict):
platforms_data = {}
gw_data["platforms"] = platforms_data
if isinstance(yaml_platforms, dict):
for plat_name, plat_block in yaml_platforms.items():
if not isinstance(plat_block, dict):
continue
existing = platforms_data.get(plat_name, {})
if not isinstance(existing, dict):
existing = {}
# Deep-merge extra dicts so gateway.json defaults survive
merged_extra = {**existing.get("extra", {}), **plat_block.get("extra", {})}
merged = {**existing, **plat_block}
if merged_extra:
merged["extra"] = merged_extra
platforms_data[plat_name] = merged
gw_data["platforms"] = platforms_data
for plat in Platform:
if plat == Platform.LOCAL:
continue
platform_cfg = yaml_cfg.get(plat.value)
if not isinstance(platform_cfg, dict):
continue
# Collect bridgeable keys from this platform section
bridged = {}
if "unauthorized_dm_behavior" in platform_cfg:
bridged["unauthorized_dm_behavior"] = _normalize_unauthorized_dm_behavior(
platform_cfg.get("unauthorized_dm_behavior"),
gw_data.get("unauthorized_dm_behavior", "pair"),
)
if "reply_prefix" in platform_cfg:
bridged["reply_prefix"] = platform_cfg["reply_prefix"]
if not bridged:
continue
plat_data = platforms_data.setdefault(plat.value, {})
if not isinstance(plat_data, dict):
plat_data = {}
platforms_data[plat.value] = plat_data
extra = plat_data.setdefault("extra", {})
if not isinstance(extra, dict):
extra = {}
plat_data["extra"] = extra
extra.update(bridged)
# Discord settings → env vars (env vars take precedence)
discord_cfg = yaml_cfg.get("discord", {})
if isinstance(discord_cfg, dict):
if "require_mention" in discord_cfg and not os.getenv("DISCORD_REQUIRE_MENTION"):
os.environ["DISCORD_REQUIRE_MENTION"] = str(discord_cfg["require_mention"]).lower()
frc = discord_cfg.get("free_response_channels")
if frc is not None and not os.getenv("DISCORD_FREE_RESPONSE_CHANNELS"):
if isinstance(frc, list):
frc = ",".join(str(v) for v in frc)
os.environ["DISCORD_FREE_RESPONSE_CHANNELS"] = str(frc)
if "auto_thread" in discord_cfg and not os.getenv("DISCORD_AUTO_THREAD"):
os.environ["DISCORD_AUTO_THREAD"] = str(discord_cfg["auto_thread"]).lower()
except Exception as e:
logger.warning(
"Failed to process config.yaml — falling back to .env / gateway.json values. "
"Check %s for syntax errors. Error: %s",
_home / "config.yaml",
e,
)
config = GatewayConfig.from_dict(gw_data)
config.default_reset_policy = SessionResetPolicy.from_dict(sr)
except Exception:
pass
# Override with environment variables
_apply_env_overrides(config)
@@ -558,8 +306,6 @@ def load_gateway_config() -> GatewayConfig:
Platform.TELEGRAM: "TELEGRAM_BOT_TOKEN",
Platform.DISCORD: "DISCORD_BOT_TOKEN",
Platform.SLACK: "SLACK_BOT_TOKEN",
Platform.MATTERMOST: "MATTERMOST_TOKEN",
Platform.MATRIX: "MATRIX_ACCESS_TOKEN",
}
for platform, pconfig in config.platforms.items():
if not pconfig.enabled:
@@ -633,73 +379,6 @@ def _apply_env_overrides(config: GatewayConfig) -> None:
name=os.getenv("SLACK_HOME_CHANNEL_NAME", ""),
)
# Signal
signal_url = os.getenv("SIGNAL_HTTP_URL")
signal_account = os.getenv("SIGNAL_ACCOUNT")
if signal_url and signal_account:
if Platform.SIGNAL not in config.platforms:
config.platforms[Platform.SIGNAL] = PlatformConfig()
config.platforms[Platform.SIGNAL].enabled = True
config.platforms[Platform.SIGNAL].extra.update({
"http_url": signal_url,
"account": signal_account,
"ignore_stories": os.getenv("SIGNAL_IGNORE_STORIES", "true").lower() in ("true", "1", "yes"),
})
signal_home = os.getenv("SIGNAL_HOME_CHANNEL")
if signal_home:
config.platforms[Platform.SIGNAL].home_channel = HomeChannel(
platform=Platform.SIGNAL,
chat_id=signal_home,
name=os.getenv("SIGNAL_HOME_CHANNEL_NAME", "Home"),
)
# Mattermost
mattermost_token = os.getenv("MATTERMOST_TOKEN")
if mattermost_token:
mattermost_url = os.getenv("MATTERMOST_URL", "")
if not mattermost_url:
logger.warning("MATTERMOST_TOKEN set but MATTERMOST_URL is missing")
if Platform.MATTERMOST not in config.platforms:
config.platforms[Platform.MATTERMOST] = PlatformConfig()
config.platforms[Platform.MATTERMOST].enabled = True
config.platforms[Platform.MATTERMOST].token = mattermost_token
config.platforms[Platform.MATTERMOST].extra["url"] = mattermost_url
mattermost_home = os.getenv("MATTERMOST_HOME_CHANNEL")
if mattermost_home:
config.platforms[Platform.MATTERMOST].home_channel = HomeChannel(
platform=Platform.MATTERMOST,
chat_id=mattermost_home,
name=os.getenv("MATTERMOST_HOME_CHANNEL_NAME", "Home"),
)
# Matrix
matrix_token = os.getenv("MATRIX_ACCESS_TOKEN")
matrix_homeserver = os.getenv("MATRIX_HOMESERVER", "")
if matrix_token or os.getenv("MATRIX_PASSWORD"):
if not matrix_homeserver:
logger.warning("MATRIX_ACCESS_TOKEN/MATRIX_PASSWORD set but MATRIX_HOMESERVER is missing")
if Platform.MATRIX not in config.platforms:
config.platforms[Platform.MATRIX] = PlatformConfig()
config.platforms[Platform.MATRIX].enabled = True
if matrix_token:
config.platforms[Platform.MATRIX].token = matrix_token
config.platforms[Platform.MATRIX].extra["homeserver"] = matrix_homeserver
matrix_user = os.getenv("MATRIX_USER_ID", "")
if matrix_user:
config.platforms[Platform.MATRIX].extra["user_id"] = matrix_user
matrix_password = os.getenv("MATRIX_PASSWORD", "")
if matrix_password:
config.platforms[Platform.MATRIX].extra["password"] = matrix_password
matrix_e2ee = os.getenv("MATRIX_ENCRYPTION", "").lower() in ("true", "1", "yes")
config.platforms[Platform.MATRIX].extra["encryption"] = matrix_e2ee
matrix_home = os.getenv("MATRIX_HOME_ROOM")
if matrix_home:
config.platforms[Platform.MATRIX].home_channel = HomeChannel(
platform=Platform.MATRIX,
chat_id=matrix_home,
name=os.getenv("MATRIX_HOME_ROOM_NAME", "Home"),
)
# Home Assistant
hass_token = os.getenv("HASS_TOKEN")
if hass_token:
@@ -711,83 +390,6 @@ def _apply_env_overrides(config: GatewayConfig) -> None:
if hass_url:
config.platforms[Platform.HOMEASSISTANT].extra["url"] = hass_url
# Email
email_addr = os.getenv("EMAIL_ADDRESS")
email_pwd = os.getenv("EMAIL_PASSWORD")
email_imap = os.getenv("EMAIL_IMAP_HOST")
email_smtp = os.getenv("EMAIL_SMTP_HOST")
if all([email_addr, email_pwd, email_imap, email_smtp]):
if Platform.EMAIL not in config.platforms:
config.platforms[Platform.EMAIL] = PlatformConfig()
config.platforms[Platform.EMAIL].enabled = True
config.platforms[Platform.EMAIL].extra.update({
"address": email_addr,
"imap_host": email_imap,
"smtp_host": email_smtp,
})
email_home = os.getenv("EMAIL_HOME_ADDRESS")
if email_home:
config.platforms[Platform.EMAIL].home_channel = HomeChannel(
platform=Platform.EMAIL,
chat_id=email_home,
name=os.getenv("EMAIL_HOME_ADDRESS_NAME", "Home"),
)
# SMS (Twilio)
twilio_sid = os.getenv("TWILIO_ACCOUNT_SID")
if twilio_sid:
if Platform.SMS not in config.platforms:
config.platforms[Platform.SMS] = PlatformConfig()
config.platforms[Platform.SMS].enabled = True
config.platforms[Platform.SMS].api_key = os.getenv("TWILIO_AUTH_TOKEN", "")
sms_home = os.getenv("SMS_HOME_CHANNEL")
if sms_home:
config.platforms[Platform.SMS].home_channel = HomeChannel(
platform=Platform.SMS,
chat_id=sms_home,
name=os.getenv("SMS_HOME_CHANNEL_NAME", "Home"),
)
# API Server
api_server_enabled = os.getenv("API_SERVER_ENABLED", "").lower() in ("true", "1", "yes")
api_server_key = os.getenv("API_SERVER_KEY", "")
api_server_cors_origins = os.getenv("API_SERVER_CORS_ORIGINS", "")
api_server_port = os.getenv("API_SERVER_PORT")
api_server_host = os.getenv("API_SERVER_HOST")
if api_server_enabled or api_server_key:
if Platform.API_SERVER not in config.platforms:
config.platforms[Platform.API_SERVER] = PlatformConfig()
config.platforms[Platform.API_SERVER].enabled = True
if api_server_key:
config.platforms[Platform.API_SERVER].extra["key"] = api_server_key
if api_server_cors_origins:
origins = [origin.strip() for origin in api_server_cors_origins.split(",") if origin.strip()]
if origins:
config.platforms[Platform.API_SERVER].extra["cors_origins"] = origins
if api_server_port:
try:
config.platforms[Platform.API_SERVER].extra["port"] = int(api_server_port)
except ValueError:
pass
if api_server_host:
config.platforms[Platform.API_SERVER].extra["host"] = api_server_host
# Webhook platform
webhook_enabled = os.getenv("WEBHOOK_ENABLED", "").lower() in ("true", "1", "yes")
webhook_port = os.getenv("WEBHOOK_PORT")
webhook_secret = os.getenv("WEBHOOK_SECRET", "")
if webhook_enabled:
if Platform.WEBHOOK not in config.platforms:
config.platforms[Platform.WEBHOOK] = PlatformConfig()
config.platforms[Platform.WEBHOOK].enabled = True
if webhook_port:
try:
config.platforms[Platform.WEBHOOK].extra["port"] = int(webhook_port)
except ValueError:
pass
if webhook_secret:
config.platforms[Platform.WEBHOOK].extra["secret"] = webhook_secret
# Session settings
idle_minutes = os.getenv("SESSION_IDLE_MINUTES")
if idle_minutes:
@@ -804,3 +406,10 @@ def _apply_env_overrides(config: GatewayConfig) -> None:
pass
def save_gateway_config(config: GatewayConfig) -> None:
"""Save gateway configuration to ~/.hermes/gateway.json."""
gateway_config_path = Path.home() / ".hermes" / "gateway.json"
gateway_config_path.parent.mkdir(parents=True, exist_ok=True)
with open(gateway_config_path, "w") as f:
json.dump(config.to_dict(), f, indent=2)

View File

@@ -15,8 +15,6 @@ from dataclasses import dataclass
from typing import Dict, List, Optional, Any, Union
from enum import Enum
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
MAX_PLATFORM_OUTPUT = 4000
@@ -39,7 +37,6 @@ class DeliveryTarget:
"""
platform: Platform
chat_id: Optional[str] = None # None means use home channel
thread_id: Optional[str] = None
is_origin: bool = False
is_explicit: bool = False # True if chat_id was explicitly specified
@@ -61,7 +58,6 @@ class DeliveryTarget:
return cls(
platform=origin.platform,
chat_id=origin.chat_id,
thread_id=origin.thread_id,
is_origin=True,
)
else:
@@ -118,7 +114,7 @@ class DeliveryRouter:
"""
self.config = config
self.adapters = adapters or {}
self.output_dir = get_hermes_home() / "cron" / "output"
self.output_dir = Path.home() / ".hermes" / "cron" / "output"
def resolve_targets(
self,
@@ -154,14 +150,14 @@ class DeliveryRouter:
continue
# Deduplicate
key = (target.platform, target.chat_id, target.thread_id)
key = (target.platform, target.chat_id)
if key not in seen_platforms:
seen_platforms.add(key)
targets.append(target)
# Always include local if configured
if self.config.always_log_local:
local_key = (Platform.LOCAL, None, None)
local_key = (Platform.LOCAL, None)
if local_key not in seen_platforms:
targets.append(DeliveryTarget(platform=Platform.LOCAL))
@@ -258,7 +254,7 @@ class DeliveryRouter:
def _save_full_output(self, content: str, job_id: str) -> Path:
"""Save full cron output to disk and return the file path."""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
out_dir = get_hermes_home() / "cron" / "output"
out_dir = Path.home() / ".hermes" / "cron" / "output"
out_dir.mkdir(parents=True, exist_ok=True)
path = out_dir / f"{job_id}_{timestamp}.txt"
path.write_text(content)
@@ -289,10 +285,7 @@ class DeliveryRouter:
+ f"\n\n... [truncated, full output saved to {saved_path}]"
)
send_metadata = dict(metadata or {})
if target.thread_id and "thread_id" not in send_metadata:
send_metadata["thread_id"] = target.thread_id
return await adapter.send(target.chat_id, content, metadata=send_metadata or None)
return await adapter.send(target.chat_id, content, metadata=metadata)
def parse_deliver_spec(
@@ -315,7 +308,7 @@ def build_delivery_context_for_tool(
origin: Optional[SessionSource] = None
) -> Dict[str, Any]:
"""
Build context for the unified cronjob tool to understand delivery options.
Build context for the schedule_cronjob tool to understand delivery options.
This is passed to the tool so it can validate and explain delivery targets.
"""

View File

@@ -8,9 +8,8 @@ Hooks are discovered from ~/.hermes/hooks/ directories, each containing:
Events:
- gateway:startup -- Gateway process starts
- session:start -- New session created (first message of a new session)
- session:end -- Session ends (user ran /new or /reset)
- session:reset -- Session reset completed (new session entry created)
- session:start -- New session created
- session:reset -- User ran /new or /reset
- agent:start -- Agent begins processing a message
- agent:step -- Each turn in the tool-calling loop
- agent:end -- Agent finishes processing
@@ -27,10 +26,8 @@ from typing import Any, Callable, Dict, List, Optional
import yaml
from hermes_cli.config import get_hermes_home
HOOKS_DIR = get_hermes_home() / "hooks"
HOOKS_DIR = Path(os.path.expanduser("~/.hermes/hooks"))
class HookRegistry:

View File

@@ -15,11 +15,9 @@ from datetime import datetime
from pathlib import Path
from typing import Optional
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
_SESSIONS_DIR = get_hermes_home() / "sessions"
_SESSIONS_DIR = Path.home() / ".hermes" / "sessions"
_SESSIONS_INDEX = _SESSIONS_DIR / "sessions.json"
@@ -28,7 +26,6 @@ def mirror_to_session(
chat_id: str,
message_text: str,
source_label: str = "cli",
thread_id: Optional[str] = None,
) -> bool:
"""
Append a delivery-mirror message to the target session's transcript.
@@ -40,9 +37,9 @@ def mirror_to_session(
All errors are caught -- this is never fatal.
"""
try:
session_id = _find_session_id(platform, str(chat_id), thread_id=thread_id)
session_id = _find_session_id(platform, str(chat_id))
if not session_id:
logger.debug("Mirror: no session found for %s:%s:%s", platform, chat_id, thread_id)
logger.debug("Mirror: no session found for %s:%s", platform, chat_id)
return False
mirror_msg = {
@@ -60,11 +57,11 @@ def mirror_to_session(
return True
except Exception as e:
logger.debug("Mirror failed for %s:%s:%s: %s", platform, chat_id, thread_id, e)
logger.debug("Mirror failed for %s:%s: %s", platform, chat_id, e)
return False
def _find_session_id(platform: str, chat_id: str, thread_id: Optional[str] = None) -> Optional[str]:
def _find_session_id(platform: str, chat_id: str) -> Optional[str]:
"""
Find the active session_id for a platform + chat_id pair.
@@ -76,7 +73,7 @@ def _find_session_id(platform: str, chat_id: str, thread_id: Optional[str] = Non
return None
try:
with open(_SESSIONS_INDEX, encoding="utf-8") as f:
with open(_SESSIONS_INDEX) as f:
data = json.load(f)
except Exception:
return None
@@ -94,9 +91,6 @@ def _find_session_id(platform: str, chat_id: str, thread_id: Optional[str] = Non
origin_chat_id = str(origin.get("chat_id", ""))
if origin_chat_id == str(chat_id):
origin_thread_id = origin.get("thread_id")
if thread_id is not None and str(origin_thread_id or "") != str(thread_id):
continue
updated = entry.get("updated_at", "")
if updated > best_updated:
best_updated = updated
@@ -109,7 +103,7 @@ def _append_to_jsonl(session_id: str, message: dict) -> None:
"""Append a message to the JSONL transcript file."""
transcript_path = _SESSIONS_DIR / f"{session_id}.jsonl"
try:
with open(transcript_path, "a", encoding="utf-8") as f:
with open(transcript_path, "a") as f:
f.write(json.dumps(message, ensure_ascii=False) + "\n")
except Exception as e:
logger.debug("Mirror JSONL write failed: %s", e)
@@ -117,7 +111,6 @@ def _append_to_jsonl(session_id: str, message: dict) -> None:
def _append_to_sqlite(session_id: str, message: dict) -> None:
"""Append a message to the SQLite session database."""
db = None
try:
from hermes_state import SessionDB
db = SessionDB()
@@ -128,6 +121,3 @@ def _append_to_sqlite(session_id: str, message: dict) -> None:
)
except Exception as e:
logger.debug("Mirror SQLite write failed: %s", e)
finally:
if db is not None:
db.close()

View File

@@ -25,8 +25,6 @@ import time
from pathlib import Path
from typing import Optional
from hermes_cli.config import get_hermes_home
# Unambiguous alphabet -- excludes 0/O, 1/I to prevent confusion
ALPHABET = "ABCDEFGHJKLMNPQRSTUVWXYZ23456789"
@@ -41,7 +39,7 @@ LOCKOUT_SECONDS = 3600 # Lockout duration after too many failures
MAX_PENDING_PER_PLATFORM = 3 # Max pending codes per platform
MAX_FAILED_ATTEMPTS = 5 # Failed approvals before lockout
PAIRING_DIR = get_hermes_home() / "pairing"
PAIRING_DIR = Path(os.path.expanduser("~/.hermes/pairing"))
def _secure_write(path: Path, data: str) -> None:

View File

@@ -1,313 +0,0 @@
# Adding a New Messaging Platform
Checklist for integrating a new messaging platform into the Hermes gateway.
Use this as a reference when building a new adapter — every item here is a
real integration point that exists in the codebase. Missing any of them will
cause broken functionality, missing features, or inconsistent behavior.
---
## 1. Core Adapter (`gateway/platforms/<platform>.py`)
The adapter is a subclass of `BasePlatformAdapter` from `gateway/platforms/base.py`.
### Required methods
| Method | Purpose |
|--------|---------|
| `__init__(self, config)` | Parse config, init state. Call `super().__init__(config, Platform.YOUR_PLATFORM)` |
| `connect() -> bool` | Connect to the platform, start listeners. Return True on success |
| `disconnect()` | Stop listeners, close connections, cancel tasks |
| `send(chat_id, text, ...) -> SendResult` | Send a text message |
| `send_typing(chat_id)` | Send typing indicator |
| `send_image(chat_id, image_url, caption) -> SendResult` | Send an image |
| `get_chat_info(chat_id) -> dict` | Return `{name, type, chat_id}` for a chat |
### Optional methods (have default stubs in base)
| Method | Purpose |
|--------|---------|
| `send_document(chat_id, path, caption)` | Send a file attachment |
| `send_voice(chat_id, path)` | Send a voice message |
| `send_video(chat_id, path, caption)` | Send a video |
| `send_animation(chat_id, path, caption)` | Send a GIF/animation |
| `send_image_file(chat_id, path, caption)` | Send image from local file |
### Required function
```python
def check_<platform>_requirements() -> bool:
"""Check if this platform's dependencies are available."""
```
### Key patterns to follow
- Use `self.build_source(...)` to construct `SessionSource` objects
- Call `self.handle_message(event)` to dispatch inbound messages to the gateway
- Use `MessageEvent`, `MessageType`, `SendResult` from base
- Use `cache_image_from_bytes`, `cache_audio_from_bytes`, `cache_document_from_bytes` for attachments
- Filter self-messages (prevent reply loops)
- Filter sync/echo messages if the platform has them
- Redact sensitive identifiers (phone numbers, tokens) in all log output
- Implement reconnection with exponential backoff + jitter for streaming connections
- Set `MAX_MESSAGE_LENGTH` if the platform has message size limits
---
## 2. Platform Enum (`gateway/config.py`)
Add the platform to the `Platform` enum:
```python
class Platform(Enum):
...
YOUR_PLATFORM = "your_platform"
```
Add env var loading in `_apply_env_overrides()`:
```python
# Your Platform
your_token = os.getenv("YOUR_PLATFORM_TOKEN")
if your_token:
if Platform.YOUR_PLATFORM not in config.platforms:
config.platforms[Platform.YOUR_PLATFORM] = PlatformConfig()
config.platforms[Platform.YOUR_PLATFORM].enabled = True
config.platforms[Platform.YOUR_PLATFORM].token = your_token
```
Update `get_connected_platforms()` if your platform doesn't use token/api_key
(e.g., WhatsApp uses `enabled` flag, Signal uses `extra` dict).
---
## 3. Adapter Factory (`gateway/run.py`)
Add to `_create_adapter()`:
```python
elif platform == Platform.YOUR_PLATFORM:
from gateway.platforms.your_platform import YourAdapter, check_your_requirements
if not check_your_requirements():
logger.warning("Your Platform: dependencies not met")
return None
return YourAdapter(config)
```
---
## 4. Authorization Maps (`gateway/run.py`)
Add to BOTH dicts in `_is_user_authorized()`:
```python
platform_env_map = {
...
Platform.YOUR_PLATFORM: "YOUR_PLATFORM_ALLOWED_USERS",
}
platform_allow_all_map = {
...
Platform.YOUR_PLATFORM: "YOUR_PLATFORM_ALLOW_ALL_USERS",
}
```
---
## 5. Session Source (`gateway/session.py`)
If your platform needs extra identity fields (e.g., Signal's UUID alongside
phone number), add them to the `SessionSource` dataclass with `Optional` defaults,
and update `to_dict()`, `from_dict()`, and `build_source()` in base.py.
---
## 6. System Prompt Hints (`agent/prompt_builder.py`)
Add a `PLATFORM_HINTS` entry so the agent knows what platform it's on:
```python
PLATFORM_HINTS = {
...
"your_platform": (
"You are on Your Platform. "
"Describe formatting capabilities, media support, etc."
),
}
```
Without this, the agent won't know it's on your platform and may use
inappropriate formatting (e.g., markdown on platforms that don't render it).
---
## 7. Toolset (`toolsets.py`)
Add a named toolset for your platform:
```python
"hermes-your-platform": {
"description": "Your Platform bot toolset",
"tools": _HERMES_CORE_TOOLS,
"includes": []
},
```
And add it to the `hermes-gateway` composite:
```python
"hermes-gateway": {
"includes": [..., "hermes-your-platform"]
}
```
---
## 8. Cron Delivery (`cron/scheduler.py`)
Add to `platform_map` in `_deliver_result()`:
```python
platform_map = {
...
"your_platform": Platform.YOUR_PLATFORM,
}
```
Without this, `cronjob(action="create", deliver="your_platform", ...)` silently fails.
---
## 9. Send Message Tool (`tools/send_message_tool.py`)
Add to `platform_map` in `send_message_tool()`:
```python
platform_map = {
...
"your_platform": Platform.YOUR_PLATFORM,
}
```
Add routing in `_send_to_platform()`:
```python
elif platform == Platform.YOUR_PLATFORM:
return await _send_your_platform(pconfig, chat_id, message)
```
Implement `_send_your_platform()` — a standalone async function that sends
a single message without requiring the full adapter (for use by cron jobs
and the send_message tool outside the gateway process).
Update the tool schema `target` description to include your platform example.
---
## 10. Cronjob Tool Schema (`tools/cronjob_tools.py`)
Update the `deliver` parameter description and docstring to mention your
platform as a delivery option.
---
## 11. Channel Directory (`gateway/channel_directory.py`)
If your platform can't enumerate chats (most can't), add it to the
session-based discovery list:
```python
for plat_name in ("telegram", "whatsapp", "signal", "your_platform"):
```
---
## 12. Status Display (`hermes_cli/status.py`)
Add to the `platforms` dict in the Messaging Platforms section:
```python
platforms = {
...
"Your Platform": ("YOUR_PLATFORM_TOKEN", "YOUR_PLATFORM_HOME_CHANNEL"),
}
```
---
## 13. Gateway Setup Wizard (`hermes_cli/gateway.py`)
Add to the `_PLATFORMS` list:
```python
{
"key": "your_platform",
"label": "Your Platform",
"emoji": "📱",
"token_var": "YOUR_PLATFORM_TOKEN",
"setup_instructions": [...],
"vars": [...],
}
```
If your platform needs custom setup logic (connectivity testing, QR codes,
policy choices), add a `_setup_your_platform()` function and route to it
in the platform selection switch.
Update `_platform_status()` if your platform's "configured" check differs
from the standard `bool(get_env_value(token_var))`.
---
## 14. Phone/ID Redaction (`agent/redact.py`)
If your platform uses sensitive identifiers (phone numbers, etc.), add a
regex pattern and redaction function to `agent/redact.py`. This ensures
identifiers are masked in ALL log output, not just your adapter's logs.
---
## 15. Documentation
| File | What to update |
|------|---------------|
| `README.md` | Platform list in feature table + documentation table |
| `AGENTS.md` | Gateway description + env var config section |
| `website/docs/user-guide/messaging/<platform>.md` | **NEW** — Full setup guide (see existing platform docs for template) |
| `website/docs/user-guide/messaging/index.md` | Architecture diagram, toolset table, security examples, Next Steps links |
| `website/docs/reference/environment-variables.md` | All env vars for the platform |
---
## 16. Tests (`tests/gateway/test_<platform>.py`)
Recommended test coverage:
- Platform enum exists with correct value
- Config loading from env vars via `_apply_env_overrides`
- Adapter init (config parsing, allowlist handling, default values)
- Helper functions (redaction, parsing, file type detection)
- Session source round-trip (to_dict → from_dict)
- Authorization integration (platform in allowlist maps)
- Send message tool routing (platform in platform_map)
Optional but valuable:
- Async tests for message handling flow (mock the platform API)
- SSE/WebSocket reconnection logic
- Attachment processing
- Group message filtering
---
## Quick Verification
After implementing everything, verify with:
```bash
# All tests pass
python -m pytest tests/ -q
# Grep for your platform name to find any missed integration points
grep -r "telegram\|discord\|whatsapp\|slack" gateway/ tools/ agent/ cron/ hermes_cli/ toolsets.py \
--include="*.py" -l | sort -u
# Check each file in the output — if it mentions other platforms but not yours, you missed it
```

File diff suppressed because it is too large Load Diff

View File

@@ -24,14 +24,7 @@ from pathlib import Path as _Path
sys.path.insert(0, str(_Path(__file__).resolve().parents[2]))
from gateway.config import Platform, PlatformConfig
from gateway.session import SessionSource, build_session_key
from hermes_cli.config import get_hermes_home
GATEWAY_SECRET_CAPTURE_UNSUPPORTED_MESSAGE = (
"Secure secret entry is not supported over messaging. "
"Load this skill in the local CLI to be prompted, or add the key to ~/.hermes/.env manually."
)
from gateway.session import SessionSource
# ---------------------------------------------------------------------------
@@ -43,8 +36,8 @@ GATEWAY_SECRET_CAPTURE_UNSUPPORTED_MESSAGE = (
# (e.g. Telegram file URLs expire after ~1 hour).
# ---------------------------------------------------------------------------
# Default location: {HERMES_HOME}/image_cache/
IMAGE_CACHE_DIR = get_hermes_home() / "image_cache"
# Default location: ~/.hermes/image_cache/
IMAGE_CACHE_DIR = Path(os.path.expanduser("~/.hermes/image_cache"))
def get_image_cache_dir() -> Path:
@@ -126,7 +119,7 @@ def cleanup_image_cache(max_age_hours: int = 24) -> int:
# here so the STT tool (OpenAI Whisper) can transcribe them from local files.
# ---------------------------------------------------------------------------
AUDIO_CACHE_DIR = get_hermes_home() / "audio_cache"
AUDIO_CACHE_DIR = Path(os.path.expanduser("~/.hermes/audio_cache"))
def get_audio_cache_dir() -> Path:
@@ -185,7 +178,7 @@ async def cache_audio_from_url(url: str, ext: str = ".ogg") -> str:
# here so the agent can reference them by local file path.
# ---------------------------------------------------------------------------
DOCUMENT_CACHE_DIR = get_hermes_home() / "document_cache"
DOCUMENT_CACHE_DIR = Path(os.path.expanduser("~/.hermes/document_cache"))
SUPPORTED_DOCUMENT_TYPES = {
".pdf": "application/pdf",
@@ -259,7 +252,6 @@ def cleanup_document_cache(max_age_hours: int = 24) -> int:
class MessageType(Enum):
"""Types of incoming messages."""
TEXT = "text"
LOCATION = "location"
PHOTO = "photo"
VIDEO = "video"
AUDIO = "audio"
@@ -288,13 +280,11 @@ class MessageEvent:
message_id: Optional[str] = None
# Media attachments
# media_urls: local file paths (for vision tool access)
media_urls: List[str] = field(default_factory=list)
media_types: List[str] = field(default_factory=list)
# Reply context
reply_to_message_id: Optional[str] = None
reply_to_text: Optional[str] = None # Text of the replied-to message (for context injection)
# Timestamps
timestamp: datetime = field(default_factory=datetime.now)
@@ -348,85 +338,11 @@ class BasePlatformAdapter(ABC):
self.platform = platform
self._message_handler: Optional[MessageHandler] = None
self._running = False
self._fatal_error_code: Optional[str] = None
self._fatal_error_message: Optional[str] = None
self._fatal_error_retryable = True
self._fatal_error_handler: Optional[Callable[["BasePlatformAdapter"], Awaitable[None] | None]] = None
# Track active message handlers per session for interrupt support
# Key: session_key (e.g., chat_id), Value: (event, asyncio.Event for interrupt)
self._active_sessions: Dict[str, asyncio.Event] = {}
self._pending_messages: Dict[str, MessageEvent] = {}
# Background message-processing tasks spawned by handle_message().
# Gateway shutdown cancels these so an old gateway instance doesn't keep
# working on a task after --replace or manual restarts.
self._background_tasks: set[asyncio.Task] = set()
# Chats where auto-TTS on voice input is disabled (set by /voice off)
self._auto_tts_disabled_chats: set = set()
@property
def has_fatal_error(self) -> bool:
return self._fatal_error_message is not None
@property
def fatal_error_message(self) -> Optional[str]:
return self._fatal_error_message
@property
def fatal_error_code(self) -> Optional[str]:
return self._fatal_error_code
@property
def fatal_error_retryable(self) -> bool:
return self._fatal_error_retryable
def set_fatal_error_handler(self, handler: Callable[["BasePlatformAdapter"], Awaitable[None] | None]) -> None:
self._fatal_error_handler = handler
def _mark_connected(self) -> None:
self._running = True
self._fatal_error_code = None
self._fatal_error_message = None
self._fatal_error_retryable = True
try:
from gateway.status import write_runtime_status
write_runtime_status(platform=self.platform.value, platform_state="connected", error_code=None, error_message=None)
except Exception:
pass
def _mark_disconnected(self) -> None:
self._running = False
if self.has_fatal_error:
return
try:
from gateway.status import write_runtime_status
write_runtime_status(platform=self.platform.value, platform_state="disconnected", error_code=None, error_message=None)
except Exception:
pass
def _set_fatal_error(self, code: str, message: str, *, retryable: bool) -> None:
self._running = False
self._fatal_error_code = code
self._fatal_error_message = message
self._fatal_error_retryable = retryable
try:
from gateway.status import write_runtime_status
write_runtime_status(
platform=self.platform.value,
platform_state="fatal",
error_code=code,
error_message=message,
)
except Exception:
pass
async def _notify_fatal_error(self) -> None:
handler = self._fatal_error_handler
if not handler:
return
result = handler(self)
if asyncio.iscoroutine(result):
await result
@property
def name(self) -> str:
@@ -496,20 +412,11 @@ class BasePlatformAdapter(ABC):
"""
return SendResult(success=False, error="Not supported")
async def send_typing(self, chat_id: str, metadata=None) -> None:
async def send_typing(self, chat_id: str) -> None:
"""
Send a typing indicator.
Override in subclasses if the platform supports it.
metadata: optional dict with platform-specific context (e.g. thread_id for Slack).
"""
pass
async def stop_typing(self, chat_id: str) -> None:
"""Stop a persistent typing indicator (if the platform uses one).
Override in subclasses that start background typing loops.
Default is a no-op for platforms with one-shot typing indicators.
"""
pass
@@ -519,7 +426,6 @@ class BasePlatformAdapter(ABC):
image_url: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""
Send an image natively via the platform API.
@@ -538,7 +444,6 @@ class BasePlatformAdapter(ABC):
animation_url: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""
Send an animated GIF natively via the platform API.
@@ -547,7 +452,7 @@ class BasePlatformAdapter(ABC):
(e.g., Telegram send_animation) so they auto-play inline.
Default falls back to send_image.
"""
return await self.send_image(chat_id=chat_id, image_url=animation_url, caption=caption, reply_to=reply_to, metadata=metadata)
return await self.send_image(chat_id=chat_id, image_url=animation_url, caption=caption, reply_to=reply_to)
@staticmethod
def _is_animation_url(url: str) -> bool:
@@ -609,7 +514,6 @@ class BasePlatformAdapter(ABC):
audio_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
**kwargs,
) -> SendResult:
"""
Send an audio file as a native voice message via the platform API.
@@ -623,27 +527,12 @@ class BasePlatformAdapter(ABC):
text = f"{caption}\n{text}"
return await self.send(chat_id=chat_id, content=text, reply_to=reply_to)
async def play_tts(
self,
chat_id: str,
audio_path: str,
**kwargs,
) -> SendResult:
"""
Play auto-TTS audio for voice replies.
Override in subclasses for invisible playback (e.g. Web UI).
Default falls back to send_voice (shows audio player).
"""
return await self.send_voice(chat_id=chat_id, audio_path=audio_path, **kwargs)
async def send_video(
self,
chat_id: str,
video_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
**kwargs,
) -> SendResult:
"""
Send a video natively via the platform API.
@@ -663,7 +552,6 @@ class BasePlatformAdapter(ABC):
caption: Optional[str] = None,
file_name: Optional[str] = None,
reply_to: Optional[str] = None,
**kwargs,
) -> SendResult:
"""
Send a document/file natively via the platform API.
@@ -682,7 +570,6 @@ class BasePlatformAdapter(ABC):
image_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
**kwargs,
) -> SendResult:
"""
Send a local image file natively via the platform API.
@@ -718,95 +605,21 @@ class BasePlatformAdapter(ABC):
has_voice_tag = "[[audio_as_voice]]" in content
cleaned = cleaned.replace("[[audio_as_voice]]", "")
# Extract MEDIA:<path> tags, allowing optional whitespace after the colon
# and quoted/backticked paths for LLM-formatted outputs.
media_pattern = re.compile(
r'''[`"']?MEDIA:\s*(?P<path>`[^`\n]+`|"[^"\n]+"|'[^'\n]+'|(?:~/|/)\S+(?:[^\S\n]+\S+)*?\.(?:png|jpe?g|gif|webp|mp4|mov|avi|mkv|webm|ogg|opus|mp3|wav|m4a)(?=[\s`"',;:)\]}]|$)|\S+)[`"']?'''
)
for match in media_pattern.finditer(content):
path = match.group("path").strip()
if len(path) >= 2 and path[0] == path[-1] and path[0] in "`\"'":
path = path[1:-1].strip()
path = path.lstrip("`\"'").rstrip("`\"',.;:)}]")
# Extract MEDIA:<path> tags (path may contain spaces)
media_pattern = r'MEDIA:(\S+)'
for match in re.finditer(media_pattern, content):
path = match.group(1).strip()
if path:
media.append((path, has_voice_tag))
# Remove MEDIA tags from content (including surrounding quote/backtick wrappers)
# Remove MEDIA tags from content
if media:
cleaned = media_pattern.sub('', cleaned)
cleaned = re.sub(media_pattern, '', cleaned)
cleaned = re.sub(r'\n{3,}', '\n\n', cleaned).strip()
return media, cleaned
@staticmethod
def extract_local_files(content: str) -> Tuple[List[str], str]:
"""
Detect bare local file paths in response text for native media delivery.
Matches absolute paths (/...) and tilde paths (~/) ending in common
image or video extensions. Validates each candidate with
``os.path.isfile()`` to avoid false positives from URLs or
non-existent paths.
Paths inside fenced code blocks (``` ... ```) and inline code
(`...`) are ignored so that code samples are never mutilated.
Returns:
Tuple of (list of expanded file paths, cleaned text with the
raw path strings removed).
"""
_LOCAL_MEDIA_EXTS = (
'.png', '.jpg', '.jpeg', '.gif', '.webp',
'.mp4', '.mov', '.avi', '.mkv', '.webm',
)
ext_part = '|'.join(e.lstrip('.') for e in _LOCAL_MEDIA_EXTS)
# (?<![/:\w.]) prevents matching inside URLs (e.g. https://…/img.png)
# and relative paths (./foo.png)
# (?:~/|/) anchors to absolute or home-relative paths
path_re = re.compile(
r'(?<![/:\w.])(?:~/|/)(?:[\w.\-]+/)*[\w.\-]+\.(?:' + ext_part + r')\b',
re.IGNORECASE,
)
# Build spans covered by fenced code blocks and inline code
code_spans: list = []
for m in re.finditer(r'```[^\n]*\n.*?```', content, re.DOTALL):
code_spans.append((m.start(), m.end()))
for m in re.finditer(r'`[^`\n]+`', content):
code_spans.append((m.start(), m.end()))
def _in_code(pos: int) -> bool:
return any(s <= pos < e for s, e in code_spans)
found: list = [] # (raw_match_text, expanded_path)
for match in path_re.finditer(content):
if _in_code(match.start()):
continue
raw = match.group(0)
expanded = os.path.expanduser(raw)
if os.path.isfile(expanded):
found.append((raw, expanded))
# Deduplicate by expanded path, preserving discovery order
seen: set = set()
unique: list = []
for raw, expanded in found:
if expanded not in seen:
seen.add(expanded)
unique.append((raw, expanded))
paths = [expanded for _, expanded in unique]
cleaned = content
if unique:
for raw, _exp in unique:
cleaned = cleaned.replace(raw, '')
cleaned = re.sub(r'\n{3,}', '\n\n', cleaned).strip()
return paths, cleaned
async def _keep_typing(self, chat_id: str, interval: float = 2.0, metadata=None) -> None:
async def _keep_typing(self, chat_id: str, interval: float = 2.0) -> None:
"""
Continuously send typing indicator until cancelled.
@@ -815,7 +628,7 @@ class BasePlatformAdapter(ABC):
"""
try:
while True:
await self.send_typing(chat_id, metadata=metadata)
await self.send_typing(chat_id)
await asyncio.sleep(interval)
except asyncio.CancelledError:
pass # Normal cancellation when handler completes
@@ -831,32 +644,11 @@ class BasePlatformAdapter(ABC):
if not self._message_handler:
return
session_key = build_session_key(
event.source,
group_sessions_per_user=self.config.extra.get("group_sessions_per_user", True),
)
session_key = event.source.chat_id
# Check if there's already an active handler for this session
if session_key in self._active_sessions:
# Special case: photo bursts/albums frequently arrive as multiple near-
# simultaneous messages. Queue them without interrupting the active run,
# then process them immediately after the current task finishes.
if event.message_type == MessageType.PHOTO:
print(f"[{self.name}] 🖼️ Queuing photo follow-up for session {session_key} without interrupt")
existing = self._pending_messages.get(session_key)
if existing and existing.message_type == MessageType.PHOTO:
existing.media_urls.extend(event.media_urls)
existing.media_types.extend(event.media_types)
if event.text:
if not existing.text:
existing.text = event.text
elif event.text not in existing.text:
existing.text = f"{existing.text}\n\n{event.text}".strip()
else:
self._pending_messages[session_key] = event
return # Don't interrupt now - will run after current task completes
# Default behavior for non-photo follow-ups: interrupt the running agent
# Store this as a pending message - it will interrupt the running agent
print(f"[{self.name}] ⚡ New message while session {session_key} is active - triggering interrupt")
self._pending_messages[session_key] = event
# Signal the interrupt (the processing task checks this)
@@ -864,15 +656,7 @@ class BasePlatformAdapter(ABC):
return # Don't process now - will be handled after current task finishes
# Spawn background task to process this message
task = asyncio.create_task(self._process_message_background(event, session_key))
try:
self._background_tasks.add(task)
except TypeError:
# Some tests stub create_task() with lightweight sentinels that are not
# hashable and do not support lifecycle callbacks.
return
if hasattr(task, "add_done_callback"):
task.add_done_callback(self._background_tasks.discard)
asyncio.create_task(self._process_message_background(event, session_key))
@staticmethod
def _get_human_delay() -> float:
@@ -902,8 +686,7 @@ class BasePlatformAdapter(ABC):
self._active_sessions[session_key] = interrupt_event
# Start continuous typing indicator (refreshes every 2 seconds)
_thread_metadata = {"thread_id": event.source.thread_id} if event.source.thread_id else None
typing_task = asyncio.create_task(self._keep_typing(event.source.chat_id, metadata=_thread_metadata))
typing_task = asyncio.create_task(self._keep_typing(event.source.chat_id))
try:
# Call the handler (this can take a while with tool calls)
@@ -918,64 +701,16 @@ class BasePlatformAdapter(ABC):
# Extract image URLs and send them as native platform attachments
images, text_content = self.extract_images(response)
# Strip any remaining internal directives from message body (fixes #1561)
text_content = text_content.replace("[[audio_as_voice]]", "").strip()
text_content = re.sub(r"MEDIA:\s*\S+", "", text_content).strip()
if images:
logger.info("[%s] extract_images found %d image(s) in response (%d chars)", self.name, len(images), len(response))
# Auto-detect bare local file paths for native media delivery
# (helps small models that don't use MEDIA: syntax)
local_files, text_content = self.extract_local_files(text_content)
if local_files:
logger.info("[%s] extract_local_files found %d file(s) in response", self.name, len(local_files))
# Auto-TTS: if voice message, generate audio FIRST (before sending text)
# Skipped when the chat has voice mode disabled (/voice off)
_tts_path = None
if (event.message_type == MessageType.VOICE
and text_content
and not media_files
and event.source.chat_id not in self._auto_tts_disabled_chats):
try:
from tools.tts_tool import text_to_speech_tool, check_tts_requirements
if check_tts_requirements():
import json as _json
speech_text = re.sub(r'[*_`#\[\]()]', '', text_content)[:4000].strip()
if not speech_text:
raise ValueError("Empty text after markdown cleanup")
tts_result_str = await asyncio.to_thread(
text_to_speech_tool, text=speech_text
)
tts_data = _json.loads(tts_result_str)
_tts_path = tts_data.get("file_path")
except Exception as tts_err:
logger.warning("[%s] Auto-TTS failed: %s", self.name, tts_err)
# Play TTS audio before text (voice-first experience)
if _tts_path and Path(_tts_path).exists():
try:
await self.play_tts(
chat_id=event.source.chat_id,
audio_path=_tts_path,
metadata=_thread_metadata,
)
finally:
try:
os.remove(_tts_path)
except OSError:
pass
# Send the text portion
# Send the text portion first (if any remains after extractions)
if text_content:
logger.info("[%s] Sending response (%d chars) to %s", self.name, len(text_content), event.source.chat_id)
result = await self.send(
chat_id=event.source.chat_id,
content=text_content,
reply_to=event.message_id,
metadata=_thread_metadata,
reply_to=event.message_id
)
# Log send failures (don't raise - user already saw tool progress)
if not result.success:
print(f"[{self.name}] Failed to send response: {result.error}")
@@ -983,46 +718,40 @@ class BasePlatformAdapter(ABC):
fallback_result = await self.send(
chat_id=event.source.chat_id,
content=f"(Response formatting failed, plain text:)\n\n{text_content[:3500]}",
reply_to=event.message_id,
metadata=_thread_metadata,
reply_to=event.message_id
)
if not fallback_result.success:
print(f"[{self.name}] Fallback send also failed: {fallback_result.error}")
# Human-like pacing delay between text and media
human_delay = self._get_human_delay()
# Send extracted images as native attachments
if images:
logger.info("[%s] Extracted %d image(s) to send as attachments", self.name, len(images))
for image_url, alt_text in images:
if human_delay > 0:
await asyncio.sleep(human_delay)
try:
logger.info("[%s] Sending image: %s (alt=%s)", self.name, image_url[:80], alt_text[:30] if alt_text else "")
# Route animated GIFs through send_animation for proper playback
if self._is_animation_url(image_url):
img_result = await self.send_animation(
chat_id=event.source.chat_id,
animation_url=image_url,
caption=alt_text if alt_text else None,
metadata=_thread_metadata,
)
else:
img_result = await self.send_image(
chat_id=event.source.chat_id,
image_url=image_url,
caption=alt_text if alt_text else None,
metadata=_thread_metadata,
)
if not img_result.success:
logger.error("[%s] Failed to send image: %s", self.name, img_result.error)
print(f"[{self.name}] Failed to send image: {img_result.error}")
except Exception as img_err:
logger.error("[%s] Error sending image: %s", self.name, img_err, exc_info=True)
print(f"[{self.name}] Error sending image: {img_err}")
# Send extracted media files — route by file type
_AUDIO_EXTS = {'.ogg', '.opus', '.mp3', '.wav', '.m4a'}
_VIDEO_EXTS = {'.mp4', '.mov', '.avi', '.mkv', '.webm', '.3gp'}
_VIDEO_EXTS = {'.mp4', '.mov', '.avi', '.mkv', '.3gp'}
_IMAGE_EXTS = {'.jpg', '.jpeg', '.png', '.webp', '.gif'}
for media_path, is_voice in media_files:
@@ -1034,59 +763,28 @@ class BasePlatformAdapter(ABC):
media_result = await self.send_voice(
chat_id=event.source.chat_id,
audio_path=media_path,
metadata=_thread_metadata,
)
elif ext in _VIDEO_EXTS:
media_result = await self.send_video(
chat_id=event.source.chat_id,
video_path=media_path,
metadata=_thread_metadata,
)
elif ext in _IMAGE_EXTS:
media_result = await self.send_image_file(
chat_id=event.source.chat_id,
image_path=media_path,
metadata=_thread_metadata,
)
else:
media_result = await self.send_document(
chat_id=event.source.chat_id,
file_path=media_path,
metadata=_thread_metadata,
)
if not media_result.success:
print(f"[{self.name}] Failed to send media ({ext}): {media_result.error}")
except Exception as media_err:
print(f"[{self.name}] Error sending media: {media_err}")
# Send auto-detected local files as native attachments
for file_path in local_files:
if human_delay > 0:
await asyncio.sleep(human_delay)
try:
ext = Path(file_path).suffix.lower()
if ext in _IMAGE_EXTS:
await self.send_image_file(
chat_id=event.source.chat_id,
image_path=file_path,
metadata=_thread_metadata,
)
elif ext in _VIDEO_EXTS:
await self.send_video(
chat_id=event.source.chat_id,
video_path=file_path,
metadata=_thread_metadata,
)
else:
await self.send_document(
chat_id=event.source.chat_id,
file_path=file_path,
metadata=_thread_metadata,
)
except Exception as file_err:
logger.error("[%s] Error sending local file %s: %s", self.name, file_path, file_err)
# Check if there's a pending message that was queued during our processing
if session_key in self._pending_messages:
pending_event = self._pending_messages.pop(session_key)
@@ -1107,22 +805,6 @@ class BasePlatformAdapter(ABC):
print(f"[{self.name}] Error handling message: {e}")
import traceback
traceback.print_exc()
# Send the error to the user so they aren't left with radio silence
try:
error_type = type(e).__name__
error_detail = str(e)[:300] if str(e) else "no details available"
_thread_metadata = {"thread_id": event.source.thread_id} if event.source.thread_id else None
await self.send(
chat_id=event.source.chat_id,
content=(
f"Sorry, I encountered an error ({error_type}).\n"
f"{error_detail}\n"
"Try again or use /reset to start a fresh session."
),
metadata=_thread_metadata,
)
except Exception:
pass # Last resort — don't let error reporting crash the handler
finally:
# Stop typing indicator
typing_task.cancel()
@@ -1134,21 +816,6 @@ class BasePlatformAdapter(ABC):
if session_key in self._active_sessions:
del self._active_sessions[session_key]
async def cancel_background_tasks(self) -> None:
"""Cancel any in-flight background message-processing tasks.
Used during gateway shutdown/replacement so active sessions from the old
process do not keep running after adapters are being torn down.
"""
tasks = [task for task in self._background_tasks if not task.done()]
for task in tasks:
task.cancel()
if tasks:
await asyncio.gather(*tasks, return_exceptions=True)
self._background_tasks.clear()
self._pending_messages.clear()
self._active_sessions.clear()
def has_pending_interrupt(self, session_key: str) -> bool:
"""Check if there's a pending interrupt for a session."""
return session_key in self._active_sessions and self._active_sessions[session_key].is_set()
@@ -1166,8 +833,6 @@ class BasePlatformAdapter(ABC):
user_name: Optional[str] = None,
thread_id: Optional[str] = None,
chat_topic: Optional[str] = None,
user_id_alt: Optional[str] = None,
chat_id_alt: Optional[str] = None,
) -> SessionSource:
"""Helper to build a SessionSource for this platform."""
# Normalize empty topic to None
@@ -1182,8 +847,6 @@ class BasePlatformAdapter(ABC):
user_name=user_name,
thread_id=str(thread_id) if thread_id else None,
chat_topic=chat_topic.strip() if chat_topic else None,
user_id_alt=user_id_alt,
chat_id_alt=chat_id_alt,
)
@abstractmethod
@@ -1208,8 +871,7 @@ class BasePlatformAdapter(ABC):
"""
return content
@staticmethod
def truncate_message(content: str, max_length: int = 4096) -> List[str]:
def truncate_message(self, content: str, max_length: int = 4096) -> List[str]:
"""
Split a long message into chunks, preserving code block boundaries.
@@ -1261,27 +923,6 @@ class BasePlatformAdapter(ABC):
if split_at < 1:
split_at = headroom
# Avoid splitting inside an inline code span (`...`).
# If the text before split_at has an odd number of unescaped
# backticks, the split falls inside inline code — the resulting
# chunk would have an unpaired backtick and any special characters
# (like parentheses) inside the broken span would be unescaped,
# causing MarkdownV2 parse errors on Telegram.
candidate = remaining[:split_at]
backtick_count = candidate.count("`") - candidate.count("\\`")
if backtick_count % 2 == 1:
# Find the last unescaped backtick and split before it
last_bt = candidate.rfind("`")
while last_bt > 0 and candidate[last_bt - 1] == "\\":
last_bt = candidate.rfind("`", 0, last_bt)
if last_bt > 0:
# Try to find a space or newline just before the backtick
safe_split = candidate.rfind(" ", 0, last_bt)
nl_split = candidate.rfind("\n", 0, last_bt)
safe_split = max(safe_split, nl_split)
if safe_split > headroom // 4:
split_at = safe_split
chunk_body = remaining[:split_at]
remaining = remaining[split_at:].lstrip()

View File

@@ -1,340 +0,0 @@
"""
DingTalk platform adapter using Stream Mode.
Uses dingtalk-stream SDK for real-time message reception without webhooks.
Responses are sent via DingTalk's session webhook (markdown format).
Requires:
pip install dingtalk-stream httpx
DINGTALK_CLIENT_ID and DINGTALK_CLIENT_SECRET env vars
Configuration in config.yaml:
platforms:
dingtalk:
enabled: true
extra:
client_id: "your-app-key" # or DINGTALK_CLIENT_ID env var
client_secret: "your-secret" # or DINGTALK_CLIENT_SECRET env var
"""
import asyncio
import logging
import os
import time
import uuid
from datetime import datetime, timezone
from typing import Any, Dict, Optional
try:
import dingtalk_stream
from dingtalk_stream import ChatbotHandler, ChatbotMessage
DINGTALK_STREAM_AVAILABLE = True
except ImportError:
DINGTALK_STREAM_AVAILABLE = False
dingtalk_stream = None # type: ignore[assignment]
try:
import httpx
HTTPX_AVAILABLE = True
except ImportError:
HTTPX_AVAILABLE = False
httpx = None # type: ignore[assignment]
from gateway.config import Platform, PlatformConfig
from gateway.platforms.base import (
BasePlatformAdapter,
MessageEvent,
MessageType,
SendResult,
)
logger = logging.getLogger(__name__)
MAX_MESSAGE_LENGTH = 20000
DEDUP_WINDOW_SECONDS = 300
DEDUP_MAX_SIZE = 1000
RECONNECT_BACKOFF = [2, 5, 10, 30, 60]
def check_dingtalk_requirements() -> bool:
"""Check if DingTalk dependencies are available and configured."""
if not DINGTALK_STREAM_AVAILABLE or not HTTPX_AVAILABLE:
return False
if not os.getenv("DINGTALK_CLIENT_ID") or not os.getenv("DINGTALK_CLIENT_SECRET"):
return False
return True
class DingTalkAdapter(BasePlatformAdapter):
"""DingTalk chatbot adapter using Stream Mode.
The dingtalk-stream SDK maintains a long-lived WebSocket connection.
Incoming messages arrive via a ChatbotHandler callback. Replies are
sent via the incoming message's session_webhook URL using httpx.
"""
MAX_MESSAGE_LENGTH = MAX_MESSAGE_LENGTH
def __init__(self, config: PlatformConfig):
super().__init__(config, Platform.DINGTALK)
extra = config.extra or {}
self._client_id: str = extra.get("client_id") or os.getenv("DINGTALK_CLIENT_ID", "")
self._client_secret: str = extra.get("client_secret") or os.getenv("DINGTALK_CLIENT_SECRET", "")
self._stream_client: Any = None
self._stream_task: Optional[asyncio.Task] = None
self._http_client: Optional["httpx.AsyncClient"] = None
# Message deduplication: msg_id -> timestamp
self._seen_messages: Dict[str, float] = {}
# Map chat_id -> session_webhook for reply routing
self._session_webhooks: Dict[str, str] = {}
# -- Connection lifecycle -----------------------------------------------
async def connect(self) -> bool:
"""Connect to DingTalk via Stream Mode."""
if not DINGTALK_STREAM_AVAILABLE:
logger.warning("[%s] dingtalk-stream not installed. Run: pip install dingtalk-stream", self.name)
return False
if not HTTPX_AVAILABLE:
logger.warning("[%s] httpx not installed. Run: pip install httpx", self.name)
return False
if not self._client_id or not self._client_secret:
logger.warning("[%s] DINGTALK_CLIENT_ID and DINGTALK_CLIENT_SECRET required", self.name)
return False
try:
self._http_client = httpx.AsyncClient(timeout=30.0)
credential = dingtalk_stream.Credential(self._client_id, self._client_secret)
self._stream_client = dingtalk_stream.DingTalkStreamClient(credential)
# Capture the current event loop for cross-thread dispatch
loop = asyncio.get_running_loop()
handler = _IncomingHandler(self, loop)
self._stream_client.register_callback_handler(
dingtalk_stream.ChatbotMessage.TOPIC, handler
)
self._stream_task = asyncio.create_task(self._run_stream())
self._mark_connected()
logger.info("[%s] Connected via Stream Mode", self.name)
return True
except Exception as e:
logger.error("[%s] Failed to connect: %s", self.name, e)
return False
async def _run_stream(self) -> None:
"""Run the blocking stream client with auto-reconnection."""
backoff_idx = 0
while self._running:
try:
logger.debug("[%s] Starting stream client...", self.name)
await asyncio.to_thread(self._stream_client.start)
except asyncio.CancelledError:
return
except Exception as e:
if not self._running:
return
logger.warning("[%s] Stream client error: %s", self.name, e)
if not self._running:
return
delay = RECONNECT_BACKOFF[min(backoff_idx, len(RECONNECT_BACKOFF) - 1)]
logger.info("[%s] Reconnecting in %ds...", self.name, delay)
await asyncio.sleep(delay)
backoff_idx += 1
async def disconnect(self) -> None:
"""Disconnect from DingTalk."""
self._running = False
self._mark_disconnected()
if self._stream_task:
self._stream_task.cancel()
try:
await self._stream_task
except asyncio.CancelledError:
pass
self._stream_task = None
if self._http_client:
await self._http_client.aclose()
self._http_client = None
self._stream_client = None
self._session_webhooks.clear()
self._seen_messages.clear()
logger.info("[%s] Disconnected", self.name)
# -- Inbound message processing -----------------------------------------
async def _on_message(self, message: "ChatbotMessage") -> None:
"""Process an incoming DingTalk chatbot message."""
msg_id = getattr(message, "message_id", None) or uuid.uuid4().hex
if self._is_duplicate(msg_id):
logger.debug("[%s] Duplicate message %s, skipping", self.name, msg_id)
return
text = self._extract_text(message)
if not text:
logger.debug("[%s] Empty message, skipping", self.name)
return
# Chat context
conversation_id = getattr(message, "conversation_id", "") or ""
conversation_type = getattr(message, "conversation_type", "1")
is_group = str(conversation_type) == "2"
sender_id = getattr(message, "sender_id", "") or ""
sender_nick = getattr(message, "sender_nick", "") or sender_id
sender_staff_id = getattr(message, "sender_staff_id", "") or ""
chat_id = conversation_id or sender_id
chat_type = "group" if is_group else "dm"
# Store session webhook for reply routing
session_webhook = getattr(message, "session_webhook", None) or ""
if session_webhook and chat_id:
self._session_webhooks[chat_id] = session_webhook
source = self.build_source(
chat_id=chat_id,
chat_name=getattr(message, "conversation_title", None),
chat_type=chat_type,
user_id=sender_id,
user_name=sender_nick,
user_id_alt=sender_staff_id if sender_staff_id else None,
)
# Parse timestamp
create_at = getattr(message, "create_at", None)
try:
timestamp = datetime.fromtimestamp(int(create_at) / 1000, tz=timezone.utc) if create_at else datetime.now(tz=timezone.utc)
except (ValueError, OSError, TypeError):
timestamp = datetime.now(tz=timezone.utc)
event = MessageEvent(
text=text,
message_type=MessageType.TEXT,
source=source,
message_id=msg_id,
raw_message=message,
timestamp=timestamp,
)
logger.debug("[%s] Message from %s in %s: %s",
self.name, sender_nick, chat_id[:20] if chat_id else "?", text[:50])
await self.handle_message(event)
@staticmethod
def _extract_text(message: "ChatbotMessage") -> str:
"""Extract plain text from a DingTalk chatbot message."""
text = getattr(message, "text", None) or ""
if isinstance(text, dict):
content = text.get("content", "").strip()
else:
content = str(text).strip()
# Fall back to rich text if present
if not content:
rich_text = getattr(message, "rich_text", None)
if rich_text and isinstance(rich_text, list):
parts = [item["text"] for item in rich_text
if isinstance(item, dict) and item.get("text")]
content = " ".join(parts).strip()
return content
# -- Deduplication ------------------------------------------------------
def _is_duplicate(self, msg_id: str) -> bool:
"""Check and record a message ID. Returns True if already seen."""
now = time.time()
if len(self._seen_messages) > DEDUP_MAX_SIZE:
cutoff = now - DEDUP_WINDOW_SECONDS
self._seen_messages = {k: v for k, v in self._seen_messages.items() if v > cutoff}
if msg_id in self._seen_messages:
return True
self._seen_messages[msg_id] = now
return False
# -- Outbound messaging -------------------------------------------------
async def send(
self,
chat_id: str,
content: str,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send a markdown reply via DingTalk session webhook."""
metadata = metadata or {}
session_webhook = metadata.get("session_webhook") or self._session_webhooks.get(chat_id)
if not session_webhook:
return SendResult(success=False,
error="No session_webhook available. Reply must follow an incoming message.")
if not self._http_client:
return SendResult(success=False, error="HTTP client not initialized")
payload = {
"msgtype": "markdown",
"markdown": {"title": "Hermes", "text": content[:self.MAX_MESSAGE_LENGTH]},
}
try:
resp = await self._http_client.post(session_webhook, json=payload, timeout=15.0)
if resp.status_code < 300:
return SendResult(success=True, message_id=uuid.uuid4().hex[:12])
body = resp.text
logger.warning("[%s] Send failed HTTP %d: %s", self.name, resp.status_code, body[:200])
return SendResult(success=False, error=f"HTTP {resp.status_code}: {body[:200]}")
except httpx.TimeoutException:
return SendResult(success=False, error="Timeout sending message to DingTalk")
except Exception as e:
logger.error("[%s] Send error: %s", self.name, e)
return SendResult(success=False, error=str(e))
async def send_typing(self, chat_id: str, metadata=None) -> None:
"""DingTalk does not support typing indicators."""
pass
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
"""Return basic info about a DingTalk conversation."""
return {"name": chat_id, "type": "group" if "group" in chat_id.lower() else "dm"}
# ---------------------------------------------------------------------------
# Internal stream handler
# ---------------------------------------------------------------------------
class _IncomingHandler(ChatbotHandler if DINGTALK_STREAM_AVAILABLE else object):
"""dingtalk-stream ChatbotHandler that forwards messages to the adapter."""
def __init__(self, adapter: DingTalkAdapter, loop: asyncio.AbstractEventLoop):
if DINGTALK_STREAM_AVAILABLE:
super().__init__()
self._adapter = adapter
self._loop = loop
def process(self, message: "ChatbotMessage"):
"""Called by dingtalk-stream in its thread when a message arrives.
Schedules the async handler on the main event loop.
"""
loop = self._loop
if loop is None or loop.is_closed():
logger.error("[DingTalk] Event loop unavailable, cannot dispatch message")
return dingtalk_stream.AckMessage.STATUS_OK, "OK"
future = asyncio.run_coroutine_threadsafe(self._adapter._on_message(message), loop)
try:
future.result(timeout=60)
except Exception:
logger.exception("[DingTalk] Error processing incoming message")
return dingtalk_stream.AckMessage.STATUS_OK, "OK"

File diff suppressed because it is too large Load Diff

View File

@@ -1,550 +0,0 @@
"""
Email platform adapter for the Hermes gateway.
Allows users to interact with Hermes by sending emails.
Uses IMAP to receive and SMTP to send messages.
Environment variables:
EMAIL_IMAP_HOST — IMAP server host (e.g., imap.gmail.com)
EMAIL_IMAP_PORT — IMAP server port (default: 993)
EMAIL_SMTP_HOST — SMTP server host (e.g., smtp.gmail.com)
EMAIL_SMTP_PORT — SMTP server port (default: 587)
EMAIL_ADDRESS — Email address for the agent
EMAIL_PASSWORD — Email password or app-specific password
EMAIL_POLL_INTERVAL — Seconds between mailbox checks (default: 15)
EMAIL_ALLOWED_USERS — Comma-separated list of allowed sender addresses
"""
import asyncio
import email as email_lib
import imaplib
import logging
import os
import re
import smtplib
import ssl
import uuid
from datetime import datetime
from email.header import decode_header
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email import encoders
from pathlib import Path
from typing import Any, Dict, List, Optional
from gateway.platforms.base import (
BasePlatformAdapter,
MessageEvent,
MessageType,
SendResult,
cache_document_from_bytes,
cache_image_from_bytes,
)
from gateway.config import Platform, PlatformConfig
logger = logging.getLogger(__name__)
# Gmail-safe max length per email body
MAX_MESSAGE_LENGTH = 50_000
# Supported image extensions for inline detection
_IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".gif", ".webp"}
def check_email_requirements() -> bool:
"""Check if email platform dependencies are available."""
addr = os.getenv("EMAIL_ADDRESS")
pwd = os.getenv("EMAIL_PASSWORD")
imap = os.getenv("EMAIL_IMAP_HOST")
smtp = os.getenv("EMAIL_SMTP_HOST")
if not all([addr, pwd, imap, smtp]):
return False
return True
def _decode_header_value(raw: str) -> str:
"""Decode an RFC 2047 encoded email header into a plain string."""
parts = decode_header(raw)
decoded = []
for part, charset in parts:
if isinstance(part, bytes):
decoded.append(part.decode(charset or "utf-8", errors="replace"))
else:
decoded.append(part)
return " ".join(decoded)
def _extract_text_body(msg: email_lib.message.Message) -> str:
"""Extract the plain-text body from a potentially multipart email."""
if msg.is_multipart():
for part in msg.walk():
content_type = part.get_content_type()
disposition = str(part.get("Content-Disposition", ""))
# Skip attachments
if "attachment" in disposition:
continue
if content_type == "text/plain":
payload = part.get_payload(decode=True)
if payload:
charset = part.get_content_charset() or "utf-8"
return payload.decode(charset, errors="replace")
# Fallback: try text/html and strip tags
for part in msg.walk():
content_type = part.get_content_type()
disposition = str(part.get("Content-Disposition", ""))
if "attachment" in disposition:
continue
if content_type == "text/html":
payload = part.get_payload(decode=True)
if payload:
charset = part.get_content_charset() or "utf-8"
html = payload.decode(charset, errors="replace")
return _strip_html(html)
return ""
else:
payload = msg.get_payload(decode=True)
if payload:
charset = msg.get_content_charset() or "utf-8"
text = payload.decode(charset, errors="replace")
if msg.get_content_type() == "text/html":
return _strip_html(text)
return text
return ""
def _strip_html(html: str) -> str:
"""Naive HTML tag stripper for fallback text extraction."""
text = re.sub(r"<br\s*/?>", "\n", html, flags=re.IGNORECASE)
text = re.sub(r"<p[^>]*>", "\n", text, flags=re.IGNORECASE)
text = re.sub(r"</p>", "\n", text, flags=re.IGNORECASE)
text = re.sub(r"<[^>]+>", "", text)
text = re.sub(r"&nbsp;", " ", text)
text = re.sub(r"&amp;", "&", text)
text = re.sub(r"&lt;", "<", text)
text = re.sub(r"&gt;", ">", text)
text = re.sub(r"\n{3,}", "\n\n", text)
return text.strip()
def _extract_email_address(raw: str) -> str:
"""Extract bare email address from 'Name <addr>' format."""
match = re.search(r"<([^>]+)>", raw)
if match:
return match.group(1).strip().lower()
return raw.strip().lower()
def _extract_attachments(
msg: email_lib.message.Message,
skip_attachments: bool = False,
) -> List[Dict[str, Any]]:
"""Extract attachment metadata and cache files locally.
When *skip_attachments* is True, all attachment/inline parts are ignored
(useful for malware protection or bandwidth savings).
"""
attachments = []
if not msg.is_multipart():
return attachments
for part in msg.walk():
disposition = str(part.get("Content-Disposition", ""))
if skip_attachments and ("attachment" in disposition or "inline" in disposition):
continue
if "attachment" not in disposition and "inline" not in disposition:
continue
# Skip text/plain and text/html body parts
content_type = part.get_content_type()
if content_type in ("text/plain", "text/html") and "attachment" not in disposition:
continue
filename = part.get_filename()
if filename:
filename = _decode_header_value(filename)
else:
ext = part.get_content_subtype() or "bin"
filename = f"attachment.{ext}"
payload = part.get_payload(decode=True)
if not payload:
continue
ext = Path(filename).suffix.lower()
if ext in _IMAGE_EXTS:
cached_path = cache_image_from_bytes(payload, ext)
attachments.append({
"path": cached_path,
"filename": filename,
"type": "image",
"media_type": content_type,
})
else:
cached_path = cache_document_from_bytes(payload, filename)
attachments.append({
"path": cached_path,
"filename": filename,
"type": "document",
"media_type": content_type,
})
return attachments
class EmailAdapter(BasePlatformAdapter):
"""Email gateway adapter using IMAP (receive) and SMTP (send)."""
def __init__(self, config: PlatformConfig):
super().__init__(config, Platform.EMAIL)
self._address = os.getenv("EMAIL_ADDRESS", "")
self._password = os.getenv("EMAIL_PASSWORD", "")
self._imap_host = os.getenv("EMAIL_IMAP_HOST", "")
self._imap_port = int(os.getenv("EMAIL_IMAP_PORT", "993"))
self._smtp_host = os.getenv("EMAIL_SMTP_HOST", "")
self._smtp_port = int(os.getenv("EMAIL_SMTP_PORT", "587"))
self._poll_interval = int(os.getenv("EMAIL_POLL_INTERVAL", "15"))
# Skip attachments — configured via config.yaml:
# platforms:
# email:
# skip_attachments: true
extra = config.extra or {}
self._skip_attachments = extra.get("skip_attachments", False)
# Track message IDs we've already processed to avoid duplicates
self._seen_uids: set = set()
self._poll_task: Optional[asyncio.Task] = None
# Map chat_id (sender email) -> last subject + message-id for threading
self._thread_context: Dict[str, Dict[str, str]] = {}
logger.info("[Email] Adapter initialized for %s", self._address)
async def connect(self) -> bool:
"""Connect to the IMAP server and start polling for new messages."""
try:
# Test IMAP connection
imap = imaplib.IMAP4_SSL(self._imap_host, self._imap_port)
imap.login(self._address, self._password)
# Mark all existing messages as seen so we only process new ones
imap.select("INBOX")
status, data = imap.uid("search", None, "ALL")
if status == "OK" and data and data[0]:
for uid in data[0].split():
self._seen_uids.add(uid)
imap.logout()
logger.info("[Email] IMAP connection test passed. %d existing messages skipped.", len(self._seen_uids))
except Exception as e:
logger.error("[Email] IMAP connection failed: %s", e)
return False
try:
# Test SMTP connection
smtp = smtplib.SMTP(self._smtp_host, self._smtp_port)
smtp.starttls(context=ssl.create_default_context())
smtp.login(self._address, self._password)
smtp.quit()
logger.info("[Email] SMTP connection test passed.")
except Exception as e:
logger.error("[Email] SMTP connection failed: %s", e)
return False
self._running = True
self._poll_task = asyncio.create_task(self._poll_loop())
print(f"[Email] Connected as {self._address}")
return True
async def disconnect(self) -> None:
"""Stop polling and disconnect."""
self._running = False
if self._poll_task:
self._poll_task.cancel()
try:
await self._poll_task
except asyncio.CancelledError:
pass
self._poll_task = None
logger.info("[Email] Disconnected.")
async def _poll_loop(self) -> None:
"""Poll IMAP for new messages at regular intervals."""
while self._running:
try:
await self._check_inbox()
except asyncio.CancelledError:
break
except Exception as e:
logger.error("[Email] Poll error: %s", e)
await asyncio.sleep(self._poll_interval)
async def _check_inbox(self) -> None:
"""Check INBOX for unseen messages and dispatch them."""
# Run IMAP operations in a thread to avoid blocking the event loop
loop = asyncio.get_running_loop()
messages = await loop.run_in_executor(None, self._fetch_new_messages)
for msg_data in messages:
await self._dispatch_message(msg_data)
def _fetch_new_messages(self) -> List[Dict[str, Any]]:
"""Fetch new (unseen) messages from IMAP. Runs in executor thread."""
results = []
try:
imap = imaplib.IMAP4_SSL(self._imap_host, self._imap_port)
imap.login(self._address, self._password)
imap.select("INBOX")
status, data = imap.uid("search", None, "UNSEEN")
if status != "OK" or not data or not data[0]:
imap.logout()
return results
for uid in data[0].split():
if uid in self._seen_uids:
continue
self._seen_uids.add(uid)
status, msg_data = imap.uid("fetch", uid, "(RFC822)")
if status != "OK":
continue
raw_email = msg_data[0][1]
msg = email_lib.message_from_bytes(raw_email)
sender_raw = msg.get("From", "")
sender_addr = _extract_email_address(sender_raw)
sender_name = _decode_header_value(sender_raw)
# Remove email from name if present
if "<" in sender_name:
sender_name = sender_name.split("<")[0].strip().strip('"')
subject = _decode_header_value(msg.get("Subject", "(no subject)"))
message_id = msg.get("Message-ID", "")
in_reply_to = msg.get("In-Reply-To", "")
body = _extract_text_body(msg)
attachments = _extract_attachments(msg, skip_attachments=self._skip_attachments)
results.append({
"uid": uid,
"sender_addr": sender_addr,
"sender_name": sender_name,
"subject": subject,
"message_id": message_id,
"in_reply_to": in_reply_to,
"body": body,
"attachments": attachments,
"date": msg.get("Date", ""),
})
imap.logout()
except Exception as e:
logger.error("[Email] IMAP fetch error: %s", e)
return results
async def _dispatch_message(self, msg_data: Dict[str, Any]) -> None:
"""Convert a fetched email into a MessageEvent and dispatch it."""
sender_addr = msg_data["sender_addr"]
# Skip self-messages
if sender_addr == self._address.lower():
return
subject = msg_data["subject"]
body = msg_data["body"].strip()
attachments = msg_data["attachments"]
# Build message text: include subject as context
text = body
if subject and not subject.startswith("Re:"):
text = f"[Subject: {subject}]\n\n{body}"
# Determine message type and media
media_urls = []
media_types = []
msg_type = MessageType.TEXT
for att in attachments:
media_urls.append(att["path"])
media_types.append(att["media_type"])
if att["type"] == "image":
msg_type = MessageType.PHOTO
# Store thread context for reply threading
self._thread_context[sender_addr] = {
"subject": subject,
"message_id": msg_data["message_id"],
}
source = self.build_source(
chat_id=sender_addr,
chat_name=msg_data["sender_name"] or sender_addr,
chat_type="dm",
user_id=sender_addr,
user_name=msg_data["sender_name"] or sender_addr,
)
event = MessageEvent(
text=text or "(empty email)",
message_type=msg_type,
source=source,
message_id=msg_data["message_id"],
media_urls=media_urls,
media_types=media_types,
reply_to_message_id=msg_data["in_reply_to"] or None,
)
logger.info("[Email] New message from %s: %s", sender_addr, subject)
await self.handle_message(event)
async def send(
self,
chat_id: str,
content: str,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send an email reply to the given address."""
try:
loop = asyncio.get_running_loop()
message_id = await loop.run_in_executor(
None, self._send_email, chat_id, content, reply_to
)
return SendResult(success=True, message_id=message_id)
except Exception as e:
logger.error("[Email] Send failed to %s: %s", chat_id, e)
return SendResult(success=False, error=str(e))
def _send_email(
self,
to_addr: str,
body: str,
reply_to_msg_id: Optional[str] = None,
) -> str:
"""Send an email via SMTP. Runs in executor thread."""
msg = MIMEMultipart()
msg["From"] = self._address
msg["To"] = to_addr
# Thread context for reply
ctx = self._thread_context.get(to_addr, {})
subject = ctx.get("subject", "Hermes Agent")
if not subject.startswith("Re:"):
subject = f"Re: {subject}"
msg["Subject"] = subject
# Threading headers
original_msg_id = reply_to_msg_id or ctx.get("message_id")
if original_msg_id:
msg["In-Reply-To"] = original_msg_id
msg["References"] = original_msg_id
msg_id = f"<hermes-{uuid.uuid4().hex[:12]}@{self._address.split('@')[1]}>"
msg["Message-ID"] = msg_id
msg.attach(MIMEText(body, "plain", "utf-8"))
smtp = smtplib.SMTP(self._smtp_host, self._smtp_port)
smtp.starttls(context=ssl.create_default_context())
smtp.login(self._address, self._password)
smtp.send_message(msg)
smtp.quit()
logger.info("[Email] Sent reply to %s (subject: %s)", to_addr, subject)
return msg_id
async def send_typing(self, chat_id: str, metadata: Optional[Dict[str, Any]] = None) -> None:
"""Email has no typing indicator — no-op."""
pass
async def send_image(
self,
chat_id: str,
image_url: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
) -> SendResult:
"""Send an image URL as part of an email body."""
text = caption or ""
text += f"\n\nImage: {image_url}"
return await self.send(chat_id, text.strip(), reply_to)
async def send_document(
self,
chat_id: str,
file_path: str,
caption: Optional[str] = None,
file_name: Optional[str] = None,
reply_to: Optional[str] = None,
) -> SendResult:
"""Send a file as an email attachment."""
try:
loop = asyncio.get_running_loop()
message_id = await loop.run_in_executor(
None,
self._send_email_with_attachment,
chat_id,
caption or "",
file_path,
file_name,
)
return SendResult(success=True, message_id=message_id)
except Exception as e:
logger.error("[Email] Send document failed: %s", e)
return SendResult(success=False, error=str(e))
def _send_email_with_attachment(
self,
to_addr: str,
body: str,
file_path: str,
file_name: Optional[str] = None,
) -> str:
"""Send an email with a file attachment via SMTP."""
msg = MIMEMultipart()
msg["From"] = self._address
msg["To"] = to_addr
ctx = self._thread_context.get(to_addr, {})
subject = ctx.get("subject", "Hermes Agent")
if not subject.startswith("Re:"):
subject = f"Re: {subject}"
msg["Subject"] = subject
original_msg_id = ctx.get("message_id")
if original_msg_id:
msg["In-Reply-To"] = original_msg_id
msg["References"] = original_msg_id
msg_id = f"<hermes-{uuid.uuid4().hex[:12]}@{self._address.split('@')[1]}>"
msg["Message-ID"] = msg_id
if body:
msg.attach(MIMEText(body, "plain", "utf-8"))
# Attach file
p = Path(file_path)
fname = file_name or p.name
with open(p, "rb") as f:
part = MIMEBase("application", "octet-stream")
part.set_payload(f.read())
encoders.encode_base64(part)
part.add_header("Content-Disposition", f"attachment; filename={fname}")
msg.attach(part)
smtp = smtplib.SMTP(self._smtp_host, self._smtp_port)
smtp.starttls(context=ssl.create_default_context())
smtp.login(self._address, self._password)
smtp.send_message(msg)
smtp.quit()
return msg_id
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
"""Return basic info about the email chat."""
ctx = self._thread_context.get(chat_id, {})
return {
"name": chat_id,
"type": "dm",
"chat_id": chat_id,
"subject": ctx.get("subject", ""),
}

View File

@@ -83,7 +83,6 @@ class HomeAssistantAdapter(BasePlatformAdapter):
self._watch_domains: Set[str] = set(extra.get("watch_domains", []))
self._watch_entities: Set[str] = set(extra.get("watch_entities", []))
self._ignore_entities: Set[str] = set(extra.get("ignore_entities", []))
self._watch_all: bool = bool(extra.get("watch_all", False))
self._cooldown_seconds: int = int(extra.get("cooldown_seconds", 30))
# Cooldown tracking: entity_id -> last_event_timestamp
@@ -116,15 +115,6 @@ class HomeAssistantAdapter(BasePlatformAdapter):
# Dedicated REST session for send() calls
self._rest_session = aiohttp.ClientSession()
# Warn if no event filters are configured
if not self._watch_domains and not self._watch_entities and not self._watch_all:
logger.warning(
"[%s] No watch_domains, watch_entities, or watch_all configured. "
"All state_changed events will be dropped. Configure filters in "
"your HA platform config to receive events.",
self.name,
)
# Start background listener
self._listen_task = asyncio.create_task(self._listen_loop())
self._running = True
@@ -267,17 +257,13 @@ class HomeAssistantAdapter(BasePlatformAdapter):
if entity_id in self._ignore_entities:
return
# Apply domain/entity watch filters (closed by default — require
# explicit watch_domains, watch_entities, or watch_all to forward)
# Apply domain/entity watch filters
domain = entity_id.split(".")[0] if "." in entity_id else ""
if self._watch_domains or self._watch_entities:
domain_match = domain in self._watch_domains if self._watch_domains else False
entity_match = entity_id in self._watch_entities if self._watch_entities else False
if not domain_match and not entity_match:
return
elif not self._watch_all:
# No filters configured and watch_all is off — drop the event
return
# Apply cooldown
now = time.time()
@@ -433,7 +419,7 @@ class HomeAssistantAdapter(BasePlatformAdapter):
except Exception as e:
return SendResult(success=False, error=str(e))
async def send_typing(self, chat_id: str, metadata=None) -> None:
async def send_typing(self, chat_id: str) -> None:
"""No typing indicator for Home Assistant."""
pass

View File

@@ -1,895 +0,0 @@
"""Matrix gateway adapter.
Connects to any Matrix homeserver (self-hosted or matrix.org) via the
matrix-nio Python SDK. Supports optional end-to-end encryption (E2EE)
when installed with ``pip install "matrix-nio[e2e]"``.
Environment variables:
MATRIX_HOMESERVER Homeserver URL (e.g. https://matrix.example.org)
MATRIX_ACCESS_TOKEN Access token (preferred auth method)
MATRIX_USER_ID Full user ID (@bot:server) — required for password login
MATRIX_PASSWORD Password (alternative to access token)
MATRIX_ENCRYPTION Set "true" to enable E2EE
MATRIX_ALLOWED_USERS Comma-separated Matrix user IDs (@user:server)
MATRIX_HOME_ROOM Room ID for cron/notification delivery
"""
from __future__ import annotations
import asyncio
import json
import logging
import mimetypes
import os
import re
import time
from pathlib import Path
from typing import Any, Dict, List, Optional, Set
from gateway.config import Platform, PlatformConfig
from gateway.platforms.base import (
BasePlatformAdapter,
MessageEvent,
MessageType,
SendResult,
)
logger = logging.getLogger(__name__)
# Matrix message size limit (4000 chars practical, spec has no hard limit
# but clients render poorly above this).
MAX_MESSAGE_LENGTH = 4000
# Store directory for E2EE keys and sync state.
_STORE_DIR = Path.home() / ".hermes" / "matrix" / "store"
# Grace period: ignore messages older than this many seconds before startup.
_STARTUP_GRACE_SECONDS = 5
def check_matrix_requirements() -> bool:
"""Return True if the Matrix adapter can be used."""
token = os.getenv("MATRIX_ACCESS_TOKEN", "")
password = os.getenv("MATRIX_PASSWORD", "")
homeserver = os.getenv("MATRIX_HOMESERVER", "")
if not token and not password:
logger.debug("Matrix: neither MATRIX_ACCESS_TOKEN nor MATRIX_PASSWORD set")
return False
if not homeserver:
logger.warning("Matrix: MATRIX_HOMESERVER not set")
return False
try:
import nio # noqa: F401
return True
except ImportError:
logger.warning(
"Matrix: matrix-nio not installed. "
"Run: pip install 'matrix-nio[e2e]'"
)
return False
class MatrixAdapter(BasePlatformAdapter):
"""Gateway adapter for Matrix (any homeserver)."""
def __init__(self, config: PlatformConfig):
super().__init__(config, Platform.MATRIX)
self._homeserver: str = (
config.extra.get("homeserver", "")
or os.getenv("MATRIX_HOMESERVER", "")
).rstrip("/")
self._access_token: str = config.token or os.getenv("MATRIX_ACCESS_TOKEN", "")
self._user_id: str = (
config.extra.get("user_id", "")
or os.getenv("MATRIX_USER_ID", "")
)
self._password: str = (
config.extra.get("password", "")
or os.getenv("MATRIX_PASSWORD", "")
)
self._encryption: bool = config.extra.get(
"encryption",
os.getenv("MATRIX_ENCRYPTION", "").lower() in ("true", "1", "yes"),
)
self._client: Any = None # nio.AsyncClient
self._sync_task: Optional[asyncio.Task] = None
self._closing = False
self._startup_ts: float = 0.0
# Cache: room_id → bool (is DM)
self._dm_rooms: Dict[str, bool] = {}
# Set of room IDs we've joined
self._joined_rooms: Set[str] = set()
# Event deduplication (bounded deque keeps newest entries)
from collections import deque
self._processed_events: deque = deque(maxlen=1000)
self._processed_events_set: set = set()
def _is_duplicate_event(self, event_id) -> bool:
"""Return True if this event was already processed. Tracks the ID otherwise."""
if not event_id:
return False
if event_id in self._processed_events_set:
return True
if len(self._processed_events) == self._processed_events.maxlen:
evicted = self._processed_events[0]
self._processed_events_set.discard(evicted)
self._processed_events.append(event_id)
self._processed_events_set.add(event_id)
return False
# ------------------------------------------------------------------
# Required overrides
# ------------------------------------------------------------------
async def connect(self) -> bool:
"""Connect to the Matrix homeserver and start syncing."""
import nio
if not self._homeserver:
logger.error("Matrix: homeserver URL not configured")
return False
# Determine store path and ensure it exists.
store_path = str(_STORE_DIR)
_STORE_DIR.mkdir(parents=True, exist_ok=True)
# Create the client.
if self._encryption:
try:
client = nio.AsyncClient(
self._homeserver,
self._user_id or "",
store_path=store_path,
)
logger.info("Matrix: E2EE enabled (store: %s)", store_path)
except Exception as exc:
logger.warning(
"Matrix: failed to create E2EE client (%s), "
"falling back to plain client. Install: "
"pip install 'matrix-nio[e2e]'",
exc,
)
client = nio.AsyncClient(self._homeserver, self._user_id or "")
else:
client = nio.AsyncClient(self._homeserver, self._user_id or "")
self._client = client
# Authenticate.
if self._access_token:
client.access_token = self._access_token
# Resolve user_id if not set.
if not self._user_id:
resp = await client.whoami()
if isinstance(resp, nio.WhoamiResponse):
self._user_id = resp.user_id
client.user_id = resp.user_id
logger.info("Matrix: authenticated as %s", self._user_id)
else:
logger.error(
"Matrix: whoami failed — check MATRIX_ACCESS_TOKEN and MATRIX_HOMESERVER"
)
await client.close()
return False
else:
client.user_id = self._user_id
logger.info("Matrix: using access token for %s", self._user_id)
elif self._password and self._user_id:
resp = await client.login(
self._password,
device_name="Hermes Agent",
)
if isinstance(resp, nio.LoginResponse):
logger.info("Matrix: logged in as %s", self._user_id)
else:
logger.error("Matrix: login failed — %s", getattr(resp, "message", resp))
await client.close()
return False
else:
logger.error("Matrix: need MATRIX_ACCESS_TOKEN or MATRIX_USER_ID + MATRIX_PASSWORD")
await client.close()
return False
# If E2EE is enabled, load the crypto store.
if self._encryption and hasattr(client, "olm"):
try:
if client.should_upload_keys:
await client.keys_upload()
logger.info("Matrix: E2EE crypto initialized")
except Exception as exc:
logger.warning("Matrix: crypto init issue: %s", exc)
# Register event callbacks.
client.add_event_callback(self._on_room_message, nio.RoomMessageText)
client.add_event_callback(self._on_room_message_media, nio.RoomMessageImage)
client.add_event_callback(self._on_room_message_media, nio.RoomMessageAudio)
client.add_event_callback(self._on_room_message_media, nio.RoomMessageVideo)
client.add_event_callback(self._on_room_message_media, nio.RoomMessageFile)
client.add_event_callback(self._on_invite, nio.InviteMemberEvent)
# If E2EE: handle encrypted events.
if self._encryption and hasattr(client, "olm"):
client.add_event_callback(
self._on_room_message, nio.MegolmEvent
)
# Initial sync to catch up, then start background sync.
self._startup_ts = time.time()
self._closing = False
# Do an initial sync to populate room state.
resp = await client.sync(timeout=10000, full_state=True)
if isinstance(resp, nio.SyncResponse):
self._joined_rooms = set(resp.rooms.join.keys())
logger.info(
"Matrix: initial sync complete, joined %d rooms",
len(self._joined_rooms),
)
# Build DM room cache from m.direct account data.
await self._refresh_dm_cache()
else:
logger.warning("Matrix: initial sync returned %s", type(resp).__name__)
# Start the sync loop.
self._sync_task = asyncio.create_task(self._sync_loop())
self._mark_connected()
return True
async def disconnect(self) -> None:
"""Disconnect from Matrix."""
self._closing = True
if self._sync_task and not self._sync_task.done():
self._sync_task.cancel()
try:
await self._sync_task
except (asyncio.CancelledError, Exception):
pass
if self._client:
await self._client.close()
self._client = None
logger.info("Matrix: disconnected")
async def send(
self,
chat_id: str,
content: str,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send a message to a Matrix room."""
import nio
if not content:
return SendResult(success=True)
formatted = self.format_message(content)
chunks = self.truncate_message(formatted, MAX_MESSAGE_LENGTH)
last_event_id = None
for chunk in chunks:
msg_content: Dict[str, Any] = {
"msgtype": "m.text",
"body": chunk,
}
# Convert markdown to HTML for rich rendering.
html = self._markdown_to_html(chunk)
if html and html != chunk:
msg_content["format"] = "org.matrix.custom.html"
msg_content["formatted_body"] = html
# Reply-to support.
if reply_to:
msg_content["m.relates_to"] = {
"m.in_reply_to": {"event_id": reply_to}
}
# Thread support: if metadata has thread_id, send as threaded reply.
thread_id = (metadata or {}).get("thread_id")
if thread_id:
relates_to = msg_content.get("m.relates_to", {})
relates_to["rel_type"] = "m.thread"
relates_to["event_id"] = thread_id
relates_to["is_falling_back"] = True
if reply_to and "m.in_reply_to" not in relates_to:
relates_to["m.in_reply_to"] = {"event_id": reply_to}
msg_content["m.relates_to"] = relates_to
resp = await self._client.room_send(
chat_id,
"m.room.message",
msg_content,
)
if isinstance(resp, nio.RoomSendResponse):
last_event_id = resp.event_id
else:
err = getattr(resp, "message", str(resp))
logger.error("Matrix: failed to send to %s: %s", chat_id, err)
return SendResult(success=False, error=err)
return SendResult(success=True, message_id=last_event_id)
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
"""Return room name and type (dm/group)."""
name = chat_id
chat_type = "group"
if self._client:
room = self._client.rooms.get(chat_id)
if room:
name = room.display_name or room.canonical_alias or chat_id
# Use DM cache.
if self._dm_rooms.get(chat_id, False):
chat_type = "dm"
elif room.member_count == 2:
chat_type = "dm"
return {"name": name, "type": chat_type}
# ------------------------------------------------------------------
# Optional overrides
# ------------------------------------------------------------------
async def send_typing(
self, chat_id: str, metadata: Optional[Dict[str, Any]] = None
) -> None:
"""Send a typing indicator."""
if self._client:
try:
await self._client.room_typing(chat_id, typing_state=True, timeout=30000)
except Exception:
pass
async def edit_message(
self, chat_id: str, message_id: str, content: str
) -> SendResult:
"""Edit an existing message (via m.replace)."""
import nio
formatted = self.format_message(content)
msg_content: Dict[str, Any] = {
"msgtype": "m.text",
"body": f"* {formatted}",
"m.new_content": {
"msgtype": "m.text",
"body": formatted,
},
"m.relates_to": {
"rel_type": "m.replace",
"event_id": message_id,
},
}
html = self._markdown_to_html(formatted)
if html and html != formatted:
msg_content["m.new_content"]["format"] = "org.matrix.custom.html"
msg_content["m.new_content"]["formatted_body"] = html
msg_content["format"] = "org.matrix.custom.html"
msg_content["formatted_body"] = f"* {html}"
resp = await self._client.room_send(chat_id, "m.room.message", msg_content)
if isinstance(resp, nio.RoomSendResponse):
return SendResult(success=True, message_id=resp.event_id)
return SendResult(success=False, error=getattr(resp, "message", str(resp)))
async def send_image(
self,
chat_id: str,
image_url: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Download an image URL and upload it to Matrix."""
try:
# Try aiohttp first (always available), fall back to httpx
try:
import aiohttp as _aiohttp
async with _aiohttp.ClientSession() as http:
async with http.get(image_url, timeout=_aiohttp.ClientTimeout(total=30)) as resp:
resp.raise_for_status()
data = await resp.read()
ct = resp.content_type or "image/png"
fname = image_url.rsplit("/", 1)[-1].split("?")[0] or "image.png"
except ImportError:
import httpx
async with httpx.AsyncClient() as http:
resp = await http.get(image_url, follow_redirects=True, timeout=30)
resp.raise_for_status()
data = resp.content
ct = resp.headers.get("content-type", "image/png")
fname = image_url.rsplit("/", 1)[-1].split("?")[0] or "image.png"
except Exception as exc:
logger.warning("Matrix: failed to download image %s: %s", image_url, exc)
return await self.send(chat_id, f"{caption or ''}\n{image_url}".strip(), reply_to)
return await self._upload_and_send(chat_id, data, fname, ct, "m.image", caption, reply_to, metadata)
async def send_image_file(
self,
chat_id: str,
image_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload a local image file to Matrix."""
return await self._send_local_file(chat_id, image_path, "m.image", caption, reply_to, metadata=metadata)
async def send_document(
self,
chat_id: str,
file_path: str,
caption: Optional[str] = None,
file_name: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload a local file as a document."""
return await self._send_local_file(chat_id, file_path, "m.file", caption, reply_to, file_name, metadata)
async def send_voice(
self,
chat_id: str,
audio_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload an audio file as a voice message."""
return await self._send_local_file(chat_id, audio_path, "m.audio", caption, reply_to, metadata=metadata)
async def send_video(
self,
chat_id: str,
video_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload a video file."""
return await self._send_local_file(chat_id, video_path, "m.video", caption, reply_to, metadata=metadata)
def format_message(self, content: str) -> str:
"""Pass-through — Matrix supports standard Markdown natively."""
# Strip image markdown; media is uploaded separately.
content = re.sub(r"!\[([^\]]*)\]\(([^)]+)\)", r"\2", content)
return content
# ------------------------------------------------------------------
# File helpers
# ------------------------------------------------------------------
async def _upload_and_send(
self,
room_id: str,
data: bytes,
filename: str,
content_type: str,
msgtype: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload bytes to Matrix and send as a media message."""
import nio
# Upload to homeserver.
resp = await self._client.upload(
data,
content_type=content_type,
filename=filename,
)
if not isinstance(resp, nio.UploadResponse):
err = getattr(resp, "message", str(resp))
logger.error("Matrix: upload failed: %s", err)
return SendResult(success=False, error=err)
mxc_url = resp.content_uri
# Build media message content.
msg_content: Dict[str, Any] = {
"msgtype": msgtype,
"body": caption or filename,
"url": mxc_url,
"info": {
"mimetype": content_type,
"size": len(data),
},
}
if reply_to:
msg_content["m.relates_to"] = {
"m.in_reply_to": {"event_id": reply_to}
}
thread_id = (metadata or {}).get("thread_id")
if thread_id:
relates_to = msg_content.get("m.relates_to", {})
relates_to["rel_type"] = "m.thread"
relates_to["event_id"] = thread_id
relates_to["is_falling_back"] = True
msg_content["m.relates_to"] = relates_to
resp2 = await self._client.room_send(room_id, "m.room.message", msg_content)
if isinstance(resp2, nio.RoomSendResponse):
return SendResult(success=True, message_id=resp2.event_id)
return SendResult(success=False, error=getattr(resp2, "message", str(resp2)))
async def _send_local_file(
self,
room_id: str,
file_path: str,
msgtype: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
file_name: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Read a local file and upload it."""
p = Path(file_path)
if not p.exists():
return await self.send(
room_id, f"{caption or ''}\n(file not found: {file_path})", reply_to
)
fname = file_name or p.name
ct = mimetypes.guess_type(fname)[0] or "application/octet-stream"
data = p.read_bytes()
return await self._upload_and_send(room_id, data, fname, ct, msgtype, caption, reply_to, metadata)
# ------------------------------------------------------------------
# Sync loop
# ------------------------------------------------------------------
async def _sync_loop(self) -> None:
"""Continuously sync with the homeserver."""
while not self._closing:
try:
await self._client.sync(timeout=30000)
except asyncio.CancelledError:
return
except Exception as exc:
if self._closing:
return
logger.warning("Matrix: sync error: %s — retrying in 5s", exc)
await asyncio.sleep(5)
# ------------------------------------------------------------------
# Event callbacks
# ------------------------------------------------------------------
async def _on_room_message(self, room: Any, event: Any) -> None:
"""Handle incoming text messages (and decrypted megolm events)."""
import nio
# Ignore own messages.
if event.sender == self._user_id:
return
# Deduplicate by event ID (nio can fire the same event more than once).
if self._is_duplicate_event(getattr(event, "event_id", None)):
return
# Startup grace: ignore old messages from initial sync.
event_ts = getattr(event, "server_timestamp", 0) / 1000.0
if event_ts and event_ts < self._startup_ts - _STARTUP_GRACE_SECONDS:
return
# Handle decrypted MegolmEvents — extract the inner event.
if isinstance(event, nio.MegolmEvent):
# Failed to decrypt.
logger.warning(
"Matrix: could not decrypt event %s in %s",
event.event_id, room.room_id,
)
return
# Skip edits (m.replace relation).
source_content = getattr(event, "source", {}).get("content", {})
relates_to = source_content.get("m.relates_to", {})
if relates_to.get("rel_type") == "m.replace":
return
body = getattr(event, "body", "") or ""
if not body:
return
# Determine chat type.
is_dm = self._dm_rooms.get(room.room_id, False)
if not is_dm and room.member_count == 2:
is_dm = True
chat_type = "dm" if is_dm else "group"
# Thread support.
thread_id = None
if relates_to.get("rel_type") == "m.thread":
thread_id = relates_to.get("event_id")
# Reply-to detection.
reply_to = None
in_reply_to = relates_to.get("m.in_reply_to", {})
if in_reply_to:
reply_to = in_reply_to.get("event_id")
# Strip reply fallback from body (Matrix prepends "> ..." lines).
if reply_to and body.startswith("> "):
lines = body.split("\n")
stripped = []
past_fallback = False
for line in lines:
if not past_fallback:
if line.startswith("> ") or line == ">":
continue
if line == "":
past_fallback = True
continue
past_fallback = True
stripped.append(line)
body = "\n".join(stripped) if stripped else body
# Message type.
msg_type = MessageType.TEXT
if body.startswith("!") or body.startswith("/"):
msg_type = MessageType.COMMAND
source = self.build_source(
chat_id=room.room_id,
chat_type=chat_type,
user_id=event.sender,
user_name=self._get_display_name(room, event.sender),
thread_id=thread_id,
)
msg_event = MessageEvent(
text=body,
message_type=msg_type,
source=source,
raw_message=getattr(event, "source", {}),
message_id=event.event_id,
reply_to_message_id=reply_to,
)
await self.handle_message(msg_event)
async def _on_room_message_media(self, room: Any, event: Any) -> None:
"""Handle incoming media messages (images, audio, video, files)."""
import nio
# Ignore own messages.
if event.sender == self._user_id:
return
# Deduplicate by event ID.
if self._is_duplicate_event(getattr(event, "event_id", None)):
return
# Startup grace.
event_ts = getattr(event, "server_timestamp", 0) / 1000.0
if event_ts and event_ts < self._startup_ts - _STARTUP_GRACE_SECONDS:
return
body = getattr(event, "body", "") or ""
url = getattr(event, "url", "")
# Convert mxc:// to HTTP URL for downstream processing.
http_url = ""
if url and url.startswith("mxc://"):
http_url = self._mxc_to_http(url)
# Determine message type from event class.
# Use the MIME type from the event's content info when available,
# falling back to category-level MIME types for downstream matching
# (gateway/run.py checks startswith("image/"), startswith("audio/"), etc.)
content_info = getattr(event, "content", {}) if isinstance(getattr(event, "content", None), dict) else {}
event_mimetype = (content_info.get("info") or {}).get("mimetype", "")
media_type = "application/octet-stream"
msg_type = MessageType.DOCUMENT
if isinstance(event, nio.RoomMessageImage):
msg_type = MessageType.PHOTO
media_type = event_mimetype or "image/png"
elif isinstance(event, nio.RoomMessageAudio):
msg_type = MessageType.AUDIO
media_type = event_mimetype or "audio/ogg"
elif isinstance(event, nio.RoomMessageVideo):
msg_type = MessageType.VIDEO
media_type = event_mimetype or "video/mp4"
elif event_mimetype:
media_type = event_mimetype
# For images, download and cache locally so vision tools can access them.
# Matrix MXC URLs require authentication, so direct URL access fails.
cached_path = None
if msg_type == MessageType.PHOTO and url:
try:
ext_map = {
"image/jpeg": ".jpg", "image/png": ".png",
"image/gif": ".gif", "image/webp": ".webp",
}
ext = ext_map.get(event_mimetype, ".jpg")
download_resp = await self._client.download(url)
if isinstance(download_resp, nio.DownloadResponse):
from gateway.platforms.base import cache_image_from_bytes
cached_path = cache_image_from_bytes(download_resp.body, ext=ext)
logger.info("[Matrix] Cached user image at %s", cached_path)
except Exception as e:
logger.warning("[Matrix] Failed to cache image: %s", e)
is_dm = self._dm_rooms.get(room.room_id, False)
if not is_dm and room.member_count == 2:
is_dm = True
chat_type = "dm" if is_dm else "group"
# Thread/reply detection.
source_content = getattr(event, "source", {}).get("content", {})
relates_to = source_content.get("m.relates_to", {})
thread_id = None
if relates_to.get("rel_type") == "m.thread":
thread_id = relates_to.get("event_id")
source = self.build_source(
chat_id=room.room_id,
chat_type=chat_type,
user_id=event.sender,
user_name=self._get_display_name(room, event.sender),
thread_id=thread_id,
)
# Use cached local path for images, HTTP URL for other media types
media_urls = [cached_path] if cached_path else ([http_url] if http_url else None)
media_types = [media_type] if media_urls else None
msg_event = MessageEvent(
text=body,
message_type=msg_type,
source=source,
raw_message=getattr(event, "source", {}),
message_id=event.event_id,
media_urls=media_urls,
media_types=media_types,
)
await self.handle_message(msg_event)
async def _on_invite(self, room: Any, event: Any) -> None:
"""Auto-join rooms when invited."""
import nio
if not isinstance(event, nio.InviteMemberEvent):
return
# Only process invites directed at us.
if event.state_key != self._user_id:
return
if event.membership != "invite":
return
logger.info(
"Matrix: invited to %s by %s — joining",
room.room_id, event.sender,
)
try:
resp = await self._client.join(room.room_id)
if isinstance(resp, nio.JoinResponse):
self._joined_rooms.add(room.room_id)
logger.info("Matrix: joined %s", room.room_id)
# Refresh DM cache since new room may be a DM.
await self._refresh_dm_cache()
else:
logger.warning(
"Matrix: failed to join %s: %s",
room.room_id, getattr(resp, "message", resp),
)
except Exception as exc:
logger.warning("Matrix: error joining %s: %s", room.room_id, exc)
# ------------------------------------------------------------------
# Helpers
# ------------------------------------------------------------------
async def _refresh_dm_cache(self) -> None:
"""Refresh the DM room cache from m.direct account data.
Tries the account_data API first, then falls back to parsing
the sync response's account_data for robustness.
"""
if not self._client:
return
dm_data: Optional[Dict] = None
# Primary: try the dedicated account data endpoint.
try:
resp = await self._client.get_account_data("m.direct")
if hasattr(resp, "content"):
dm_data = resp.content
elif isinstance(resp, dict):
dm_data = resp
except Exception as exc:
logger.debug("Matrix: get_account_data('m.direct') failed: %s — trying sync fallback", exc)
# Fallback: parse from the client's account_data store (populated by sync).
if dm_data is None:
try:
# matrix-nio stores account data events on the client object
ad = getattr(self._client, "account_data", None)
if ad and isinstance(ad, dict) and "m.direct" in ad:
event = ad["m.direct"]
if hasattr(event, "content"):
dm_data = event.content
elif isinstance(event, dict):
dm_data = event
except Exception:
pass
if dm_data is None:
return
dm_room_ids: Set[str] = set()
for user_id, rooms in dm_data.items():
if isinstance(rooms, list):
dm_room_ids.update(rooms)
self._dm_rooms = {
rid: (rid in dm_room_ids)
for rid in self._joined_rooms
}
def _get_display_name(self, room: Any, user_id: str) -> str:
"""Get a user's display name in a room, falling back to user_id."""
if room and hasattr(room, "users"):
user = room.users.get(user_id)
if user and getattr(user, "display_name", None):
return user.display_name
# Strip the @...:server format to just the localpart.
if user_id.startswith("@") and ":" in user_id:
return user_id[1:].split(":")[0]
return user_id
def _mxc_to_http(self, mxc_url: str) -> str:
"""Convert mxc://server/media_id to an HTTP download URL."""
# mxc://matrix.org/abc123 → https://matrix.org/_matrix/client/v1/media/download/matrix.org/abc123
# Uses the authenticated client endpoint (spec v1.11+) instead of the
# deprecated /_matrix/media/v3/download/ path.
if not mxc_url.startswith("mxc://"):
return mxc_url
parts = mxc_url[6:] # strip mxc://
# Use our homeserver for download (federation handles the rest).
return f"{self._homeserver}/_matrix/client/v1/media/download/{parts}"
def _markdown_to_html(self, text: str) -> str:
"""Convert Markdown to Matrix-compatible HTML.
Uses a simple conversion for common patterns. For full fidelity
a markdown-it style library could be used, but this covers the
common cases without an extra dependency.
"""
try:
import markdown
html = markdown.markdown(
text,
extensions=["fenced_code", "tables", "nl2br"],
)
# Strip wrapping <p> tags for single-paragraph messages.
if html.count("<p>") == 1:
html = html.replace("<p>", "").replace("</p>", "")
return html
except ImportError:
pass
# Minimal fallback: just handle bold, italic, code.
html = text
html = re.sub(r"\*\*(.+?)\*\*", r"<strong>\1</strong>", html)
html = re.sub(r"\*(.+?)\*", r"<em>\1</em>", html)
html = re.sub(r"`([^`]+)`", r"<code>\1</code>", html)
html = re.sub(r"\n", r"<br>", html)
return html

View File

@@ -1,682 +0,0 @@
"""Mattermost gateway adapter.
Connects to a self-hosted (or cloud) Mattermost instance via its REST API
(v4) and WebSocket for real-time events. No external Mattermost library
required — uses aiohttp which is already a Hermes dependency.
Environment variables:
MATTERMOST_URL Server URL (e.g. https://mm.example.com)
MATTERMOST_TOKEN Bot token or personal-access token
MATTERMOST_ALLOWED_USERS Comma-separated user IDs
MATTERMOST_HOME_CHANNEL Channel ID for cron/notification delivery
"""
from __future__ import annotations
import asyncio
import json
import logging
import os
import re
import time
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from gateway.config import Platform, PlatformConfig
from gateway.platforms.base import (
BasePlatformAdapter,
MessageEvent,
MessageType,
SendResult,
)
logger = logging.getLogger(__name__)
# Mattermost post size limit (server default is 16383, but 4000 is the
# practical limit for readable messages — matching OpenClaw's choice).
MAX_POST_LENGTH = 4000
# Channel type codes returned by the Mattermost API.
_CHANNEL_TYPE_MAP = {
"D": "dm",
"G": "group",
"P": "group", # private channel → treat as group
"O": "channel",
}
# Reconnect parameters (exponential backoff).
_RECONNECT_BASE_DELAY = 2.0
_RECONNECT_MAX_DELAY = 60.0
_RECONNECT_JITTER = 0.2
def check_mattermost_requirements() -> bool:
"""Return True if the Mattermost adapter can be used."""
token = os.getenv("MATTERMOST_TOKEN", "")
url = os.getenv("MATTERMOST_URL", "")
if not token:
logger.debug("Mattermost: MATTERMOST_TOKEN not set")
return False
if not url:
logger.warning("Mattermost: MATTERMOST_URL not set")
return False
try:
import aiohttp # noqa: F401
return True
except ImportError:
logger.warning("Mattermost: aiohttp not installed")
return False
class MattermostAdapter(BasePlatformAdapter):
"""Gateway adapter for Mattermost (self-hosted or cloud)."""
def __init__(self, config: PlatformConfig):
super().__init__(config, Platform.MATTERMOST)
self._base_url: str = (
config.extra.get("url", "")
or os.getenv("MATTERMOST_URL", "")
).rstrip("/")
self._token: str = config.token or os.getenv("MATTERMOST_TOKEN", "")
self._bot_user_id: str = ""
self._bot_username: str = ""
# aiohttp session + websocket handle
self._session: Any = None # aiohttp.ClientSession
self._ws: Any = None # aiohttp.ClientWebSocketResponse
self._ws_task: Optional[asyncio.Task] = None
self._reconnect_task: Optional[asyncio.Task] = None
self._closing = False
# Reply mode: "thread" to nest replies, "off" for flat messages.
self._reply_mode: str = (
config.extra.get("reply_mode", "")
or os.getenv("MATTERMOST_REPLY_MODE", "off")
).lower()
# Dedup cache: post_id → timestamp (prevent reprocessing)
self._seen_posts: Dict[str, float] = {}
self._SEEN_MAX = 2000
self._SEEN_TTL = 300 # 5 minutes
# ------------------------------------------------------------------
# HTTP helpers
# ------------------------------------------------------------------
def _headers(self) -> Dict[str, str]:
return {
"Authorization": f"Bearer {self._token}",
"Content-Type": "application/json",
}
async def _api_get(self, path: str) -> Dict[str, Any]:
"""GET /api/v4/{path}."""
import aiohttp
url = f"{self._base_url}/api/v4/{path.lstrip('/')}"
try:
async with self._session.get(url, headers=self._headers()) as resp:
if resp.status >= 400:
body = await resp.text()
logger.error("MM API GET %s%s: %s", path, resp.status, body[:200])
return {}
return await resp.json()
except aiohttp.ClientError as exc:
logger.error("MM API GET %s network error: %s", path, exc)
return {}
async def _api_post(
self, path: str, payload: Dict[str, Any]
) -> Dict[str, Any]:
"""POST /api/v4/{path} with JSON body."""
import aiohttp
url = f"{self._base_url}/api/v4/{path.lstrip('/')}"
try:
async with self._session.post(
url, headers=self._headers(), json=payload
) as resp:
if resp.status >= 400:
body = await resp.text()
logger.error("MM API POST %s%s: %s", path, resp.status, body[:200])
return {}
return await resp.json()
except aiohttp.ClientError as exc:
logger.error("MM API POST %s network error: %s", path, exc)
return {}
async def _api_put(
self, path: str, payload: Dict[str, Any]
) -> Dict[str, Any]:
"""PUT /api/v4/{path} with JSON body."""
import aiohttp
url = f"{self._base_url}/api/v4/{path.lstrip('/')}"
try:
async with self._session.put(
url, headers=self._headers(), json=payload
) as resp:
if resp.status >= 400:
body = await resp.text()
logger.error("MM API PUT %s%s: %s", path, resp.status, body[:200])
return {}
return await resp.json()
except aiohttp.ClientError as exc:
logger.error("MM API PUT %s network error: %s", path, exc)
return {}
async def _upload_file(
self, channel_id: str, file_data: bytes, filename: str, content_type: str = "application/octet-stream"
) -> Optional[str]:
"""Upload a file and return its file ID, or None on failure."""
import aiohttp
url = f"{self._base_url}/api/v4/files"
form = aiohttp.FormData()
form.add_field("channel_id", channel_id)
form.add_field(
"files",
file_data,
filename=filename,
content_type=content_type,
)
headers = {"Authorization": f"Bearer {self._token}"}
async with self._session.post(url, headers=headers, data=form) as resp:
if resp.status >= 400:
body = await resp.text()
logger.error("MM file upload → %s: %s", resp.status, body[:200])
return None
data = await resp.json()
infos = data.get("file_infos", [])
return infos[0]["id"] if infos else None
# ------------------------------------------------------------------
# Required overrides
# ------------------------------------------------------------------
async def connect(self) -> bool:
"""Connect to Mattermost and start the WebSocket listener."""
import aiohttp
if not self._base_url or not self._token:
logger.error("Mattermost: URL or token not configured")
return False
self._session = aiohttp.ClientSession()
self._closing = False
# Verify credentials and fetch bot identity.
me = await self._api_get("users/me")
if not me or "id" not in me:
logger.error("Mattermost: failed to authenticate — check MATTERMOST_TOKEN and MATTERMOST_URL")
await self._session.close()
return False
self._bot_user_id = me["id"]
self._bot_username = me.get("username", "")
logger.info(
"Mattermost: authenticated as @%s (%s) on %s",
self._bot_username,
self._bot_user_id,
self._base_url,
)
# Start WebSocket in background.
self._ws_task = asyncio.create_task(self._ws_loop())
self._mark_connected()
return True
async def disconnect(self) -> None:
"""Disconnect from Mattermost."""
self._closing = True
if self._ws_task and not self._ws_task.done():
self._ws_task.cancel()
try:
await self._ws_task
except (asyncio.CancelledError, Exception):
pass
if self._reconnect_task and not self._reconnect_task.done():
self._reconnect_task.cancel()
if self._ws:
await self._ws.close()
self._ws = None
if self._session and not self._session.closed:
await self._session.close()
logger.info("Mattermost: disconnected")
async def send(
self,
chat_id: str,
content: str,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send a message (or multiple chunks) to a channel."""
if not content:
return SendResult(success=True)
formatted = self.format_message(content)
chunks = self.truncate_message(formatted, MAX_POST_LENGTH)
last_id = None
for chunk in chunks:
payload: Dict[str, Any] = {
"channel_id": chat_id,
"message": chunk,
}
# Thread support: reply_to is the root post ID.
if reply_to and self._reply_mode == "thread":
payload["root_id"] = reply_to
data = await self._api_post("posts", payload)
if not data or "id" not in data:
return SendResult(success=False, error="Failed to create post")
last_id = data["id"]
return SendResult(success=True, message_id=last_id)
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
"""Return channel name and type."""
data = await self._api_get(f"channels/{chat_id}")
if not data:
return {"name": chat_id, "type": "channel"}
ch_type = _CHANNEL_TYPE_MAP.get(data.get("type", "O"), "channel")
display_name = data.get("display_name") or data.get("name") or chat_id
return {"name": display_name, "type": ch_type}
# ------------------------------------------------------------------
# Optional overrides
# ------------------------------------------------------------------
async def send_typing(
self, chat_id: str, metadata: Optional[Dict[str, Any]] = None
) -> None:
"""Send a typing indicator."""
await self._api_post(
f"users/{self._bot_user_id}/typing",
{"channel_id": chat_id},
)
async def edit_message(
self, chat_id: str, message_id: str, content: str
) -> SendResult:
"""Edit an existing post."""
formatted = self.format_message(content)
data = await self._api_put(
f"posts/{message_id}/patch",
{"message": formatted},
)
if not data or "id" not in data:
return SendResult(success=False, error="Failed to edit post")
return SendResult(success=True, message_id=data["id"])
async def send_image(
self,
chat_id: str,
image_url: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Download an image and upload it as a file attachment."""
return await self._send_url_as_file(
chat_id, image_url, caption, reply_to, "image"
)
async def send_image_file(
self,
chat_id: str,
image_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload a local image file."""
return await self._send_local_file(
chat_id, image_path, caption, reply_to
)
async def send_document(
self,
chat_id: str,
file_path: str,
caption: Optional[str] = None,
file_name: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload a local file as a document."""
return await self._send_local_file(
chat_id, file_path, caption, reply_to, file_name
)
async def send_voice(
self,
chat_id: str,
audio_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload an audio file."""
return await self._send_local_file(
chat_id, audio_path, caption, reply_to
)
async def send_video(
self,
chat_id: str,
video_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload a video file."""
return await self._send_local_file(
chat_id, video_path, caption, reply_to
)
def format_message(self, content: str) -> str:
"""Mattermost uses standard Markdown — mostly pass through.
Strip image markdown into plain links (files are uploaded separately).
"""
# Convert ![alt](url) to just the URL — Mattermost renders
# image URLs as inline previews automatically.
content = re.sub(r"!\[([^\]]*)\]\(([^)]+)\)", r"\2", content)
return content
# ------------------------------------------------------------------
# File helpers
# ------------------------------------------------------------------
async def _send_url_as_file(
self,
chat_id: str,
url: str,
caption: Optional[str],
reply_to: Optional[str],
kind: str = "file",
) -> SendResult:
"""Download a URL and upload it as a file attachment."""
import aiohttp
try:
async with self._session.get(url, timeout=aiohttp.ClientTimeout(total=30)) as resp:
if resp.status >= 400:
# Fall back to sending the URL as text.
return await self.send(chat_id, f"{caption or ''}\n{url}".strip(), reply_to)
file_data = await resp.read()
ct = resp.content_type or "application/octet-stream"
# Derive filename from URL.
fname = url.rsplit("/", 1)[-1].split("?")[0] or f"{kind}.png"
except Exception as exc:
logger.warning("Mattermost: failed to download %s: %s", url, exc)
return await self.send(chat_id, f"{caption or ''}\n{url}".strip(), reply_to)
file_id = await self._upload_file(chat_id, file_data, fname, ct)
if not file_id:
return await self.send(chat_id, f"{caption or ''}\n{url}".strip(), reply_to)
payload: Dict[str, Any] = {
"channel_id": chat_id,
"message": caption or "",
"file_ids": [file_id],
}
if reply_to and self._reply_mode == "thread":
payload["root_id"] = reply_to
data = await self._api_post("posts", payload)
if not data or "id" not in data:
return SendResult(success=False, error="Failed to post with file")
return SendResult(success=True, message_id=data["id"])
async def _send_local_file(
self,
chat_id: str,
file_path: str,
caption: Optional[str],
reply_to: Optional[str],
file_name: Optional[str] = None,
) -> SendResult:
"""Upload a local file and attach it to a post."""
import mimetypes
p = Path(file_path)
if not p.exists():
return await self.send(
chat_id, f"{caption or ''}\n(file not found: {file_path})", reply_to
)
fname = file_name or p.name
ct = mimetypes.guess_type(fname)[0] or "application/octet-stream"
file_data = p.read_bytes()
file_id = await self._upload_file(chat_id, file_data, fname, ct)
if not file_id:
return SendResult(success=False, error="File upload failed")
payload: Dict[str, Any] = {
"channel_id": chat_id,
"message": caption or "",
"file_ids": [file_id],
}
if reply_to and self._reply_mode == "thread":
payload["root_id"] = reply_to
data = await self._api_post("posts", payload)
if not data or "id" not in data:
return SendResult(success=False, error="Failed to post with file")
return SendResult(success=True, message_id=data["id"])
# ------------------------------------------------------------------
# WebSocket
# ------------------------------------------------------------------
async def _ws_loop(self) -> None:
"""Connect to the WebSocket and listen for events, reconnecting on failure."""
delay = _RECONNECT_BASE_DELAY
while not self._closing:
try:
await self._ws_connect_and_listen()
# Clean disconnect — reset delay.
delay = _RECONNECT_BASE_DELAY
except asyncio.CancelledError:
return
except Exception as exc:
if self._closing:
return
logger.warning("Mattermost WS error: %s — reconnecting in %.0fs", exc, delay)
if self._closing:
return
# Exponential backoff with jitter.
import random
jitter = delay * _RECONNECT_JITTER * random.random()
await asyncio.sleep(delay + jitter)
delay = min(delay * 2, _RECONNECT_MAX_DELAY)
async def _ws_connect_and_listen(self) -> None:
"""Single WebSocket session: connect, authenticate, process events."""
# Build WS URL: https:// → wss://, http:// → ws://
ws_url = re.sub(r"^http", "ws", self._base_url) + "/api/v4/websocket"
logger.info("Mattermost: connecting to %s", ws_url)
self._ws = await self._session.ws_connect(ws_url, heartbeat=30.0)
# Authenticate via the WebSocket.
auth_msg = {
"seq": 1,
"action": "authentication_challenge",
"data": {"token": self._token},
}
await self._ws.send_json(auth_msg)
logger.info("Mattermost: WebSocket connected and authenticated")
async for raw_msg in self._ws:
if self._closing:
return
if raw_msg.type in (
raw_msg.type.TEXT,
raw_msg.type.BINARY,
):
try:
event = json.loads(raw_msg.data)
except (json.JSONDecodeError, TypeError):
continue
await self._handle_ws_event(event)
elif raw_msg.type in (
raw_msg.type.ERROR,
raw_msg.type.CLOSE,
raw_msg.type.CLOSING,
raw_msg.type.CLOSED,
):
logger.info("Mattermost: WebSocket closed (%s)", raw_msg.type)
break
async def _handle_ws_event(self, event: Dict[str, Any]) -> None:
"""Process a single WebSocket event."""
event_type = event.get("event")
if event_type != "posted":
return
data = event.get("data", {})
raw_post_str = data.get("post")
if not raw_post_str:
return
try:
post = json.loads(raw_post_str)
except (json.JSONDecodeError, TypeError):
return
# Ignore own messages.
if post.get("user_id") == self._bot_user_id:
return
# Ignore system posts.
if post.get("type"):
return
post_id = post.get("id", "")
# Dedup.
self._prune_seen()
if post_id in self._seen_posts:
return
self._seen_posts[post_id] = time.time()
# Build message event.
channel_id = post.get("channel_id", "")
channel_type_raw = data.get("channel_type", "O")
chat_type = _CHANNEL_TYPE_MAP.get(channel_type_raw, "channel")
# For DMs, user_id is sufficient. For channels, check for @mention.
message_text = post.get("message", "")
# Mention-only mode: skip channel messages that don't @mention the bot.
# DMs (type "D") are always processed.
if channel_type_raw != "D":
mention_patterns = [
f"@{self._bot_username}",
f"@{self._bot_user_id}",
]
has_mention = any(
pattern.lower() in message_text.lower()
for pattern in mention_patterns
)
if not has_mention:
logger.debug(
"Mattermost: skipping non-DM message without @mention (channel=%s)",
channel_id,
)
return
# Resolve sender info.
sender_id = post.get("user_id", "")
sender_name = data.get("sender_name", "").lstrip("@") or sender_id
# Thread support: if the post is in a thread, use root_id.
thread_id = post.get("root_id") or None
# Determine message type.
file_ids = post.get("file_ids") or []
msg_type = MessageType.TEXT
if message_text.startswith("/"):
msg_type = MessageType.COMMAND
# Download file attachments immediately (URLs require auth headers
# that downstream tools won't have).
media_urls: List[str] = []
media_types: List[str] = []
for fid in file_ids:
try:
file_info = await self._api_get(f"files/{fid}/info")
fname = file_info.get("name", f"file_{fid}")
ext = Path(fname).suffix or ""
mime = file_info.get("mime_type", "application/octet-stream")
import aiohttp
dl_url = f"{self._base_url}/api/v4/files/{fid}"
async with self._session.get(
dl_url,
headers={"Authorization": f"Bearer {self._token}"},
timeout=aiohttp.ClientTimeout(total=30),
) as resp:
if resp.status < 400:
file_data = await resp.read()
from gateway.platforms.base import cache_image_from_bytes, cache_document_from_bytes
if mime.startswith("image/"):
local_path = cache_image_from_bytes(file_data, ext or ".png")
media_urls.append(local_path)
media_types.append(mime)
elif mime.startswith("audio/"):
from gateway.platforms.base import cache_audio_from_bytes
local_path = cache_audio_from_bytes(file_data, ext or ".ogg")
media_urls.append(local_path)
media_types.append(mime)
else:
local_path = cache_document_from_bytes(file_data, fname)
media_urls.append(local_path)
media_types.append(mime)
else:
logger.warning("Mattermost: failed to download file %s: HTTP %s", fid, resp.status)
except Exception as exc:
logger.warning("Mattermost: error downloading file %s: %s", fid, exc)
source = self.build_source(
chat_id=channel_id,
chat_type=chat_type,
user_id=sender_id,
user_name=sender_name,
thread_id=thread_id,
)
msg_event = MessageEvent(
text=message_text,
message_type=msg_type,
source=source,
raw_message=post,
message_id=post_id,
media_urls=media_urls if media_urls else None,
media_types=media_types if media_types else None,
)
await self.handle_message(msg_event)
def _prune_seen(self) -> None:
"""Remove expired entries from the dedup cache."""
if len(self._seen_posts) < self._SEEN_MAX:
return
now = time.time()
self._seen_posts = {
pid: ts
for pid, ts in self._seen_posts.items()
if now - ts < self._SEEN_TTL
}

View File

@@ -1,766 +0,0 @@
"""Signal messenger platform adapter.
Connects to a signal-cli daemon running in HTTP mode.
Inbound messages arrive via SSE (Server-Sent Events) streaming.
Outbound messages and actions use JSON-RPC 2.0 over HTTP.
Based on PR #268 by ibhagwan, rebuilt with bug fixes.
Requires:
- signal-cli installed and running: signal-cli daemon --http 127.0.0.1:8080
- SIGNAL_HTTP_URL and SIGNAL_ACCOUNT environment variables set
"""
import asyncio
import base64
import json
import logging
import os
import random
import re
import time
from datetime import datetime, timezone
from pathlib import Path
from typing import Dict, List, Optional, Any
from urllib.parse import unquote
import httpx
from gateway.config import Platform, PlatformConfig
from gateway.platforms.base import (
BasePlatformAdapter,
MessageEvent,
MessageType,
SendResult,
cache_image_from_bytes,
cache_audio_from_bytes,
cache_document_from_bytes,
cache_image_from_url,
)
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
SIGNAL_MAX_ATTACHMENT_SIZE = 100 * 1024 * 1024 # 100 MB
MAX_MESSAGE_LENGTH = 8000 # Signal message size limit
TYPING_INTERVAL = 8.0 # seconds between typing indicator refreshes
SSE_RETRY_DELAY_INITIAL = 2.0
SSE_RETRY_DELAY_MAX = 60.0
HEALTH_CHECK_INTERVAL = 30.0 # seconds between health checks
HEALTH_CHECK_STALE_THRESHOLD = 120.0 # seconds without SSE activity before concern
# E.164 phone number pattern for redaction
_PHONE_RE = re.compile(r"\+[1-9]\d{6,14}")
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _redact_phone(phone: str) -> str:
"""Redact a phone number for logging: +15551234567 -> +155****4567."""
if not phone:
return "<none>"
if len(phone) <= 8:
return phone[:2] + "****" + phone[-2:] if len(phone) > 4 else "****"
return phone[:4] + "****" + phone[-4:]
def _parse_comma_list(value: str) -> List[str]:
"""Split a comma-separated string into a list, stripping whitespace."""
return [v.strip() for v in value.split(",") if v.strip()]
def _guess_extension(data: bytes) -> str:
"""Guess file extension from magic bytes."""
if data[:4] == b"\x89PNG":
return ".png"
if data[:2] == b"\xff\xd8":
return ".jpg"
if data[:4] == b"GIF8":
return ".gif"
if len(data) >= 12 and data[:4] == b"RIFF" and data[8:12] == b"WEBP":
return ".webp"
if data[:4] == b"%PDF":
return ".pdf"
if len(data) >= 8 and data[4:8] == b"ftyp":
return ".mp4"
if data[:4] == b"OggS":
return ".ogg"
if len(data) >= 2 and data[0] == 0xFF and (data[1] & 0xE0) == 0xE0:
return ".mp3"
if data[:2] == b"PK":
return ".zip"
return ".bin"
def _is_image_ext(ext: str) -> bool:
return ext.lower() in (".jpg", ".jpeg", ".png", ".gif", ".webp")
def _is_audio_ext(ext: str) -> bool:
return ext.lower() in (".mp3", ".wav", ".ogg", ".m4a", ".aac")
_EXT_TO_MIME = {
".jpg": "image/jpeg", ".jpeg": "image/jpeg", ".png": "image/png",
".gif": "image/gif", ".webp": "image/webp",
".ogg": "audio/ogg", ".mp3": "audio/mpeg", ".wav": "audio/wav",
".m4a": "audio/mp4", ".aac": "audio/aac",
".mp4": "video/mp4", ".pdf": "application/pdf", ".zip": "application/zip",
}
def _ext_to_mime(ext: str) -> str:
"""Map file extension to MIME type."""
return _EXT_TO_MIME.get(ext.lower(), "application/octet-stream")
def _render_mentions(text: str, mentions: list) -> str:
"""Replace Signal mention placeholders (\\uFFFC) with readable @identifiers.
Signal encodes @mentions as the Unicode object replacement character
with out-of-band metadata containing the mentioned user's UUID/number.
"""
if not mentions or "\uFFFC" not in text:
return text
# Sort mentions by start position (reverse) to replace from end to start
# so indices don't shift as we replace
sorted_mentions = sorted(mentions, key=lambda m: m.get("start", 0), reverse=True)
for mention in sorted_mentions:
start = mention.get("start", 0)
length = mention.get("length", 1)
# Use the mention's number or UUID as the replacement
identifier = mention.get("number") or mention.get("uuid") or "user"
replacement = f"@{identifier}"
text = text[:start] + replacement + text[start + length:]
return text
def check_signal_requirements() -> bool:
"""Check if Signal is configured (has URL and account)."""
return bool(os.getenv("SIGNAL_HTTP_URL") and os.getenv("SIGNAL_ACCOUNT"))
# ---------------------------------------------------------------------------
# Signal Adapter
# ---------------------------------------------------------------------------
class SignalAdapter(BasePlatformAdapter):
"""Signal messenger adapter using signal-cli HTTP daemon."""
platform = Platform.SIGNAL
def __init__(self, config: PlatformConfig):
super().__init__(config, Platform.SIGNAL)
extra = config.extra or {}
self.http_url = extra.get("http_url", "http://127.0.0.1:8080").rstrip("/")
self.account = extra.get("account", "")
self.ignore_stories = extra.get("ignore_stories", True)
# Parse allowlists — group policy is derived from presence of group allowlist
group_allowed_str = os.getenv("SIGNAL_GROUP_ALLOWED_USERS", "")
self.group_allow_from = set(_parse_comma_list(group_allowed_str))
# HTTP client
self.client: Optional[httpx.AsyncClient] = None
# Background tasks
self._sse_task: Optional[asyncio.Task] = None
self._health_monitor_task: Optional[asyncio.Task] = None
self._typing_tasks: Dict[str, asyncio.Task] = {}
self._running = False
self._last_sse_activity = 0.0
self._sse_response: Optional[httpx.Response] = None
# Normalize account for self-message filtering
self._account_normalized = self.account.strip()
# Track recently sent message timestamps to prevent echo-back loops
# in Note to Self / self-chat mode (mirrors WhatsApp recentlySentIds)
self._recent_sent_timestamps: set = set()
self._max_recent_timestamps = 50
logger.info("Signal adapter initialized: url=%s account=%s groups=%s",
self.http_url, _redact_phone(self.account),
"enabled" if self.group_allow_from else "disabled")
# ------------------------------------------------------------------
# Lifecycle
# ------------------------------------------------------------------
async def connect(self) -> bool:
"""Connect to signal-cli daemon and start SSE listener."""
if not self.http_url or not self.account:
logger.error("Signal: SIGNAL_HTTP_URL and SIGNAL_ACCOUNT are required")
return False
self.client = httpx.AsyncClient(timeout=30.0)
# Health check — verify signal-cli daemon is reachable
try:
resp = await self.client.get(f"{self.http_url}/api/v1/check", timeout=10.0)
if resp.status_code != 200:
logger.error("Signal: health check failed (status %d)", resp.status_code)
return False
except Exception as e:
logger.error("Signal: cannot reach signal-cli at %s: %s", self.http_url, e)
return False
self._running = True
self._last_sse_activity = time.time()
self._sse_task = asyncio.create_task(self._sse_listener())
self._health_monitor_task = asyncio.create_task(self._health_monitor())
logger.info("Signal: connected to %s", self.http_url)
return True
async def disconnect(self) -> None:
"""Stop SSE listener and clean up."""
self._running = False
if self._sse_task:
self._sse_task.cancel()
try:
await self._sse_task
except asyncio.CancelledError:
pass
if self._health_monitor_task:
self._health_monitor_task.cancel()
try:
await self._health_monitor_task
except asyncio.CancelledError:
pass
# Cancel all typing tasks
for task in self._typing_tasks.values():
task.cancel()
self._typing_tasks.clear()
if self.client:
await self.client.aclose()
self.client = None
logger.info("Signal: disconnected")
# ------------------------------------------------------------------
# SSE Streaming (inbound messages)
# ------------------------------------------------------------------
async def _sse_listener(self) -> None:
"""Listen for SSE events from signal-cli daemon."""
url = f"{self.http_url}/api/v1/events?account={self.account}"
backoff = SSE_RETRY_DELAY_INITIAL
while self._running:
try:
logger.debug("Signal SSE: connecting to %s", url)
async with self.client.stream(
"GET", url,
headers={"Accept": "text/event-stream"},
timeout=None,
) as response:
self._sse_response = response
backoff = SSE_RETRY_DELAY_INITIAL # Reset on successful connection
self._last_sse_activity = time.time()
logger.info("Signal SSE: connected")
buffer = ""
async for chunk in response.aiter_text():
if not self._running:
break
buffer += chunk
while "\n" in buffer:
line, buffer = buffer.split("\n", 1)
line = line.strip()
if not line:
continue
# Parse SSE data lines
if line.startswith("data:"):
data_str = line[5:].strip()
if not data_str:
continue
self._last_sse_activity = time.time()
try:
data = json.loads(data_str)
await self._handle_envelope(data)
except json.JSONDecodeError:
logger.debug("Signal SSE: invalid JSON: %s", data_str[:100])
except Exception:
logger.exception("Signal SSE: error handling event")
except asyncio.CancelledError:
break
except httpx.HTTPError as e:
if self._running:
logger.warning("Signal SSE: HTTP error: %s (reconnecting in %.0fs)", e, backoff)
except Exception as e:
if self._running:
logger.warning("Signal SSE: error: %s (reconnecting in %.0fs)", e, backoff)
if self._running:
# Add 20% jitter to prevent thundering herd on reconnection
jitter = backoff * 0.2 * random.random()
await asyncio.sleep(backoff + jitter)
backoff = min(backoff * 2, SSE_RETRY_DELAY_MAX)
self._sse_response = None
# ------------------------------------------------------------------
# Health Monitor
# ------------------------------------------------------------------
async def _health_monitor(self) -> None:
"""Monitor SSE connection health and force reconnect if stale."""
while self._running:
await asyncio.sleep(HEALTH_CHECK_INTERVAL)
if not self._running:
break
elapsed = time.time() - self._last_sse_activity
if elapsed > HEALTH_CHECK_STALE_THRESHOLD:
logger.warning("Signal: SSE idle for %.0fs, checking daemon health", elapsed)
try:
resp = await self.client.get(
f"{self.http_url}/api/v1/check", timeout=10.0
)
if resp.status_code == 200:
# Daemon is alive but SSE is idle — update activity to
# avoid repeated warnings (connection may just be quiet)
self._last_sse_activity = time.time()
logger.debug("Signal: daemon healthy, SSE idle")
else:
logger.warning("Signal: health check failed (%d), forcing reconnect", resp.status_code)
self._force_reconnect()
except Exception as e:
logger.warning("Signal: health check error: %s, forcing reconnect", e)
self._force_reconnect()
def _force_reconnect(self) -> None:
"""Force SSE reconnection by closing the current response."""
if self._sse_response and not self._sse_response.is_stream_consumed:
try:
asyncio.create_task(self._sse_response.aclose())
except Exception:
pass
self._sse_response = None
# ------------------------------------------------------------------
# Message Handling
# ------------------------------------------------------------------
async def _handle_envelope(self, envelope: dict) -> None:
"""Process an incoming signal-cli envelope."""
# Unwrap nested envelope if present
envelope_data = envelope.get("envelope", envelope)
# Handle syncMessage: extract "Note to Self" messages (sent to own account)
# while still filtering other sync events (read receipts, typing, etc.)
is_note_to_self = False
if "syncMessage" in envelope_data:
sync_msg = envelope_data.get("syncMessage")
if sync_msg and isinstance(sync_msg, dict):
sent_msg = sync_msg.get("sentMessage")
if sent_msg and isinstance(sent_msg, dict):
dest = sent_msg.get("destinationNumber") or sent_msg.get("destination")
sent_ts = sent_msg.get("timestamp")
if dest == self._account_normalized:
# Check if this is an echo of our own outbound reply
if sent_ts and sent_ts in self._recent_sent_timestamps:
self._recent_sent_timestamps.discard(sent_ts)
return
# Genuine user Note to Self — promote to dataMessage
is_note_to_self = True
envelope_data = {**envelope_data, "dataMessage": sent_msg}
if not is_note_to_self:
return
# Extract sender info
sender = (
envelope_data.get("sourceNumber")
or envelope_data.get("sourceUuid")
or envelope_data.get("source")
)
sender_name = envelope_data.get("sourceName", "")
sender_uuid = envelope_data.get("sourceUuid", "")
if not sender:
logger.debug("Signal: ignoring envelope with no sender")
return
# Self-message filtering — prevent reply loops (but allow Note to Self)
if self._account_normalized and sender == self._account_normalized and not is_note_to_self:
return
# Filter stories
if self.ignore_stories and envelope_data.get("storyMessage"):
return
# Get data message — also check editMessage (edited messages contain
# their updated dataMessage inside editMessage.dataMessage)
data_message = (
envelope_data.get("dataMessage")
or (envelope_data.get("editMessage") or {}).get("dataMessage")
)
if not data_message:
return
# Check for group message
group_info = data_message.get("groupInfo")
group_id = group_info.get("groupId") if group_info else None
is_group = bool(group_id)
# Group message filtering — derived from SIGNAL_GROUP_ALLOWED_USERS:
# - No env var set → groups disabled (default safe behavior)
# - Env var set with group IDs → only those groups allowed
# - Env var set with "*" → all groups allowed
# DM auth is fully handled by run.py (_is_user_authorized)
if is_group:
if not self.group_allow_from:
logger.debug("Signal: ignoring group message (no SIGNAL_GROUP_ALLOWED_USERS)")
return
if "*" not in self.group_allow_from and group_id not in self.group_allow_from:
logger.debug("Signal: group %s not in allowlist", group_id[:8] if group_id else "?")
return
# Build chat info
chat_id = sender if not is_group else f"group:{group_id}"
chat_type = "group" if is_group else "dm"
# Extract text and render mentions
text = data_message.get("message", "")
mentions = data_message.get("mentions", [])
if text and mentions:
text = _render_mentions(text, mentions)
# Process attachments
attachments_data = data_message.get("attachments", [])
media_urls = []
media_types = []
if attachments_data and not getattr(self, "ignore_attachments", False):
for att in attachments_data:
att_id = att.get("id")
att_size = att.get("size", 0)
if not att_id:
continue
if att_size > SIGNAL_MAX_ATTACHMENT_SIZE:
logger.warning("Signal: attachment too large (%d bytes), skipping", att_size)
continue
try:
cached_path, ext = await self._fetch_attachment(att_id)
if cached_path:
# Use contentType from Signal if available, else map from extension
content_type = att.get("contentType") or _ext_to_mime(ext)
media_urls.append(cached_path)
media_types.append(content_type)
except Exception:
logger.exception("Signal: failed to fetch attachment %s", att_id)
# Build session source
source = self.build_source(
chat_id=chat_id,
chat_name=group_info.get("groupName") if group_info else sender_name,
chat_type=chat_type,
user_id=sender,
user_name=sender_name or sender,
user_id_alt=sender_uuid if sender_uuid else None,
chat_id_alt=group_id if is_group else None,
)
# Determine message type from media
msg_type = MessageType.TEXT
if media_types:
if any(mt.startswith("audio/") for mt in media_types):
msg_type = MessageType.VOICE
elif any(mt.startswith("image/") for mt in media_types):
msg_type = MessageType.PHOTO
# Parse timestamp from envelope data (milliseconds since epoch)
ts_ms = envelope_data.get("timestamp", 0)
if ts_ms:
try:
timestamp = datetime.fromtimestamp(ts_ms / 1000, tz=timezone.utc)
except (ValueError, OSError):
timestamp = datetime.now(tz=timezone.utc)
else:
timestamp = datetime.now(tz=timezone.utc)
# Build and dispatch event
event = MessageEvent(
source=source,
text=text or "",
message_type=msg_type,
media_urls=media_urls,
media_types=media_types,
timestamp=timestamp,
)
logger.debug("Signal: message from %s in %s: %s",
_redact_phone(sender), chat_id[:20], (text or "")[:50])
await self.handle_message(event)
# ------------------------------------------------------------------
# Attachment Handling
# ------------------------------------------------------------------
async def _fetch_attachment(self, attachment_id: str) -> tuple:
"""Fetch an attachment via JSON-RPC and cache it. Returns (path, ext)."""
result = await self._rpc("getAttachment", {
"account": self.account,
"attachmentId": attachment_id,
})
if not result:
return None, ""
# Handle dict response (signal-cli returns {"data": "base64..."})
if isinstance(result, dict):
result = result.get("data")
if not result:
logger.warning("Signal: attachment response missing 'data' key")
return None, ""
# Result is base64-encoded file content
raw_data = base64.b64decode(result)
ext = _guess_extension(raw_data)
if _is_image_ext(ext):
path = cache_image_from_bytes(raw_data, ext)
elif _is_audio_ext(ext):
path = cache_audio_from_bytes(raw_data, ext)
else:
path = cache_document_from_bytes(raw_data, ext)
return path, ext
# ------------------------------------------------------------------
# JSON-RPC Communication
# ------------------------------------------------------------------
async def _rpc(self, method: str, params: dict, rpc_id: str = None) -> Any:
"""Send a JSON-RPC 2.0 request to signal-cli daemon."""
if not self.client:
logger.warning("Signal: RPC called but client not connected")
return None
if rpc_id is None:
rpc_id = f"{method}_{int(time.time() * 1000)}"
payload = {
"jsonrpc": "2.0",
"method": method,
"params": params,
"id": rpc_id,
}
try:
resp = await self.client.post(
f"{self.http_url}/api/v1/rpc",
json=payload,
timeout=30.0,
)
resp.raise_for_status()
data = resp.json()
if "error" in data:
logger.warning("Signal RPC error (%s): %s", method, data["error"])
return None
return data.get("result")
except Exception as e:
logger.warning("Signal RPC %s failed: %s", method, e)
return None
# ------------------------------------------------------------------
# Sending
# ------------------------------------------------------------------
async def send(
self,
chat_id: str,
content: str,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send a text message."""
await self._stop_typing_indicator(chat_id)
params: Dict[str, Any] = {
"account": self.account,
"message": content,
}
if chat_id.startswith("group:"):
params["groupId"] = chat_id[6:]
else:
params["recipient"] = [chat_id]
result = await self._rpc("send", params)
if result is not None:
self._track_sent_timestamp(result)
return SendResult(success=True)
return SendResult(success=False, error="RPC send failed")
def _track_sent_timestamp(self, rpc_result) -> None:
"""Record outbound message timestamp for echo-back filtering."""
ts = rpc_result.get("timestamp") if isinstance(rpc_result, dict) else None
if ts:
self._recent_sent_timestamps.add(ts)
if len(self._recent_sent_timestamps) > self._max_recent_timestamps:
self._recent_sent_timestamps.pop()
async def send_typing(self, chat_id: str, metadata=None) -> None:
"""Send a typing indicator."""
params: Dict[str, Any] = {
"account": self.account,
}
if chat_id.startswith("group:"):
params["groupId"] = chat_id[6:]
else:
params["recipient"] = [chat_id]
await self._rpc("sendTyping", params, rpc_id="typing")
async def send_image(
self,
chat_id: str,
image_url: str,
caption: Optional[str] = None,
**kwargs,
) -> SendResult:
"""Send an image. Supports http(s):// and file:// URLs."""
await self._stop_typing_indicator(chat_id)
# Resolve image to local path
if image_url.startswith("file://"):
file_path = unquote(image_url[7:])
else:
# Download remote image to cache
try:
file_path = await cache_image_from_url(image_url)
except Exception as e:
logger.warning("Signal: failed to download image: %s", e)
return SendResult(success=False, error=str(e))
if not file_path or not Path(file_path).exists():
return SendResult(success=False, error="Image file not found")
# Validate size
file_size = Path(file_path).stat().st_size
if file_size > SIGNAL_MAX_ATTACHMENT_SIZE:
return SendResult(success=False, error=f"Image too large ({file_size} bytes)")
params: Dict[str, Any] = {
"account": self.account,
"message": caption or "",
"attachments": [file_path],
}
if chat_id.startswith("group:"):
params["groupId"] = chat_id[6:]
else:
params["recipient"] = [chat_id]
result = await self._rpc("send", params)
if result is not None:
self._track_sent_timestamp(result)
return SendResult(success=True)
return SendResult(success=False, error="RPC send with attachment failed")
async def send_document(
self,
chat_id: str,
file_path: str,
caption: Optional[str] = None,
filename: Optional[str] = None,
**kwargs,
) -> SendResult:
"""Send a document/file attachment."""
await self._stop_typing_indicator(chat_id)
if not Path(file_path).exists():
return SendResult(success=False, error="File not found")
params: Dict[str, Any] = {
"account": self.account,
"message": caption or "",
"attachments": [file_path],
}
if chat_id.startswith("group:"):
params["groupId"] = chat_id[6:]
else:
params["recipient"] = [chat_id]
result = await self._rpc("send", params)
if result is not None:
self._track_sent_timestamp(result)
return SendResult(success=True)
return SendResult(success=False, error="RPC send document failed")
# ------------------------------------------------------------------
# Typing Indicators
# ------------------------------------------------------------------
async def _start_typing_indicator(self, chat_id: str) -> None:
"""Start a typing indicator loop for a chat."""
if chat_id in self._typing_tasks:
return # Already running
async def _typing_loop():
try:
while True:
await self.send_typing(chat_id)
await asyncio.sleep(TYPING_INTERVAL)
except asyncio.CancelledError:
pass
self._typing_tasks[chat_id] = asyncio.create_task(_typing_loop())
async def _stop_typing_indicator(self, chat_id: str) -> None:
"""Stop a typing indicator loop for a chat."""
task = self._typing_tasks.pop(chat_id, None)
if task:
task.cancel()
try:
await task
except asyncio.CancelledError:
pass
# ------------------------------------------------------------------
# Chat Info
# ------------------------------------------------------------------
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
"""Get information about a chat/contact."""
if chat_id.startswith("group:"):
return {
"name": chat_id,
"type": "group",
"chat_id": chat_id,
}
# Try to resolve contact name
result = await self._rpc("getContact", {
"account": self.account,
"contactAddress": chat_id,
})
name = chat_id
if result and isinstance(result, dict):
name = result.get("name") or result.get("profileName") or chat_id
return {
"name": name,
"type": "dm",
"chat_id": chat_id,
}

View File

@@ -9,9 +9,7 @@ Uses slack-bolt (Python) with Socket Mode for:
"""
import asyncio
import logging
import os
import re
from typing import Dict, List, Optional, Any
try:
@@ -35,16 +33,11 @@ from gateway.platforms.base import (
MessageEvent,
MessageType,
SendResult,
SUPPORTED_DOCUMENT_TYPES,
cache_document_from_bytes,
cache_image_from_url,
cache_audio_from_url,
)
logger = logging.getLogger(__name__)
def check_slack_requirements() -> bool:
"""Check if Slack dependencies are available."""
return SLACK_AVAILABLE
@@ -66,31 +59,28 @@ class SlackAdapter(BasePlatformAdapter):
- Typing indicators (not natively supported by Slack bots)
"""
MAX_MESSAGE_LENGTH = 39000 # Slack API allows 40,000 chars; leave margin
MAX_MESSAGE_LENGTH = 4000 # Slack's limit is higher but mrkdwn can inflate
def __init__(self, config: PlatformConfig):
super().__init__(config, Platform.SLACK)
self._app: Optional[AsyncApp] = None
self._handler: Optional[AsyncSocketModeHandler] = None
self._bot_user_id: Optional[str] = None
self._user_name_cache: Dict[str, str] = {} # user_id → display name
async def connect(self) -> bool:
"""Connect to Slack via Socket Mode."""
if not SLACK_AVAILABLE:
logger.error(
"[Slack] slack-bolt not installed. Run: pip install slack-bolt",
)
print("[Slack] slack-bolt not installed. Run: pip install slack-bolt")
return False
bot_token = self.config.token
app_token = os.getenv("SLACK_APP_TOKEN")
if not bot_token:
logger.error("[Slack] SLACK_BOT_TOKEN not set")
print("[Slack] SLACK_BOT_TOKEN not set")
return False
if not app_token:
logger.error("[Slack] SLACK_APP_TOKEN not set")
print("[Slack] SLACK_APP_TOKEN not set")
return False
try:
@@ -106,13 +96,6 @@ class SlackAdapter(BasePlatformAdapter):
async def handle_message_event(event, say):
await self._handle_slack_message(event)
# Acknowledge app_mention events to prevent Bolt 404 errors.
# The "message" handler above already processes @mentions in
# channels, so this is intentionally a no-op to avoid duplicates.
@self._app.event("app_mention")
async def handle_app_mention(event, say):
pass
# Register slash command handler
@self._app.command("/hermes")
async def handle_hermes_command(ack, command):
@@ -124,22 +107,19 @@ class SlackAdapter(BasePlatformAdapter):
asyncio.create_task(self._handler.start_async())
self._running = True
logger.info("[Slack] Connected as @%s (Socket Mode)", bot_name)
print(f"[Slack] Connected as @{bot_name} (Socket Mode)")
return True
except Exception as e: # pragma: no cover - defensive logging
logger.error("[Slack] Connection failed: %s", e, exc_info=True)
except Exception as e:
print(f"[Slack] Connection failed: {e}")
return False
async def disconnect(self) -> None:
"""Disconnect from Slack."""
if self._handler:
try:
await self._handler.close_async()
except Exception as e: # pragma: no cover - defensive logging
logger.warning("[Slack] Error while closing Socket Mode handler: %s", e, exc_info=True)
await self._handler.close_async()
self._running = False
logger.info("[Slack] Disconnected")
print("[Slack] Disconnected")
async def send(
self,
@@ -153,40 +133,27 @@ class SlackAdapter(BasePlatformAdapter):
return SendResult(success=False, error="Not connected")
try:
# Convert standard markdown → Slack mrkdwn
formatted = self.format_message(content)
kwargs = {
"channel": chat_id,
"text": content,
}
# Split long messages, preserving code block boundaries
chunks = self.truncate_message(formatted, self.MAX_MESSAGE_LENGTH)
# Reply in thread if thread_ts is available
if reply_to:
kwargs["thread_ts"] = reply_to
elif metadata and metadata.get("thread_ts"):
kwargs["thread_ts"] = metadata["thread_ts"]
thread_ts = self._resolve_thread_ts(reply_to, metadata)
last_result = None
# reply_broadcast: also post thread replies to the main channel.
# Controlled via platform config: gateway.slack.reply_broadcast
broadcast = self.config.extra.get("reply_broadcast", False)
for i, chunk in enumerate(chunks):
kwargs = {
"channel": chat_id,
"text": chunk,
}
if thread_ts:
kwargs["thread_ts"] = thread_ts
# Only broadcast the first chunk of the first reply
if broadcast and i == 0:
kwargs["reply_broadcast"] = True
last_result = await self._app.client.chat_postMessage(**kwargs)
result = await self._app.client.chat_postMessage(**kwargs)
return SendResult(
success=True,
message_id=last_result.get("ts") if last_result else None,
raw_response=last_result,
message_id=result.get("ts"),
raw_response=result,
)
except Exception as e: # pragma: no cover - defensive logging
logger.error("[Slack] Send error: %s", e, exc_info=True)
except Exception as e:
print(f"[Slack] Send error: {e}")
return SendResult(success=False, error=str(e))
async def edit_message(
@@ -205,258 +172,12 @@ class SlackAdapter(BasePlatformAdapter):
text=content,
)
return SendResult(success=True, message_id=message_id)
except Exception as e: # pragma: no cover - defensive logging
logger.error(
"[Slack] Failed to edit message %s in channel %s: %s",
message_id,
chat_id,
e,
exc_info=True,
)
except Exception as e:
return SendResult(success=False, error=str(e))
async def send_typing(self, chat_id: str, metadata=None) -> None:
"""Show a typing/status indicator using assistant.threads.setStatus.
Displays "is thinking..." next to the bot name in a thread.
Requires the assistant:write or chat:write scope.
Auto-clears when the bot sends a reply to the thread.
"""
if not self._app:
return
thread_ts = None
if metadata:
thread_ts = metadata.get("thread_id") or metadata.get("thread_ts")
if not thread_ts:
return # Can only set status in a thread context
try:
await self._app.client.assistant_threads_setStatus(
channel_id=chat_id,
thread_ts=thread_ts,
status="is thinking...",
)
except Exception as e:
# Silently ignore — may lack assistant:write scope or not be
# in an assistant-enabled context. Falls back to reactions.
logger.debug("[Slack] assistant.threads.setStatus failed: %s", e)
def _resolve_thread_ts(
self,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> Optional[str]:
"""Resolve the correct thread_ts for a Slack API call.
Prefers metadata thread_id (the thread parent's ts, set by the
gateway) over reply_to (which may be a child message's ts).
"""
if metadata:
if metadata.get("thread_id"):
return metadata["thread_id"]
if metadata.get("thread_ts"):
return metadata["thread_ts"]
return reply_to
async def _upload_file(
self,
chat_id: str,
file_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Upload a local file to Slack."""
if not self._app:
return SendResult(success=False, error="Not connected")
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
result = await self._app.client.files_upload_v2(
channel=chat_id,
file=file_path,
filename=os.path.basename(file_path),
initial_comment=caption or "",
thread_ts=self._resolve_thread_ts(reply_to, metadata),
)
return SendResult(success=True, raw_response=result)
# ----- Markdown → mrkdwn conversion -----
def format_message(self, content: str) -> str:
"""Convert standard markdown to Slack mrkdwn format.
Protected regions (code blocks, inline code) are extracted first so
their contents are never modified. Standard markdown constructs
(headers, bold, italic, links) are translated to mrkdwn syntax.
"""
if not content:
return content
placeholders: dict = {}
counter = [0]
def _ph(value: str) -> str:
"""Stash value behind a placeholder that survives later passes."""
key = f"\x00SL{counter[0]}\x00"
counter[0] += 1
placeholders[key] = value
return key
text = content
# 1) Protect fenced code blocks (``` ... ```)
text = re.sub(
r'(```(?:[^\n]*\n)?[\s\S]*?```)',
lambda m: _ph(m.group(0)),
text,
)
# 2) Protect inline code (`...`)
text = re.sub(r'(`[^`]+`)', lambda m: _ph(m.group(0)), text)
# 3) Convert markdown links [text](url) → <url|text>
text = re.sub(
r'\[([^\]]+)\]\(([^)]+)\)',
lambda m: _ph(f'<{m.group(2)}|{m.group(1)}>'),
text,
)
# 4) Convert headers (## Title) → *Title* (bold)
def _convert_header(m):
inner = m.group(1).strip()
# Strip redundant bold markers inside a header
inner = re.sub(r'\*\*(.+?)\*\*', r'\1', inner)
return _ph(f'*{inner}*')
text = re.sub(
r'^#{1,6}\s+(.+)$', _convert_header, text, flags=re.MULTILINE
)
# 5) Convert bold: **text** → *text* (Slack bold)
text = re.sub(
r'\*\*(.+?)\*\*',
lambda m: _ph(f'*{m.group(1)}*'),
text,
)
# 6) Convert italic: _text_ stays as _text_ (already Slack italic)
# Single *text* → _text_ (Slack italic)
text = re.sub(
r'(?<!\*)\*([^*\n]+)\*(?!\*)',
lambda m: _ph(f'_{m.group(1)}_'),
text,
)
# 7) Convert strikethrough: ~~text~~ → ~text~
text = re.sub(
r'~~(.+?)~~',
lambda m: _ph(f'~{m.group(1)}~'),
text,
)
# 8) Convert blockquotes: > text → > text (same syntax, just ensure
# no extra escaping happens to the > character)
# Slack uses the same > prefix, so this is a no-op for content.
# 9) Restore placeholders in reverse order
for key in reversed(list(placeholders.keys())):
text = text.replace(key, placeholders[key])
return text
# ----- Reactions -----
async def _add_reaction(
self, channel: str, timestamp: str, emoji: str
) -> bool:
"""Add an emoji reaction to a message. Returns True on success."""
if not self._app:
return False
try:
await self._app.client.reactions_add(
channel=channel, timestamp=timestamp, name=emoji
)
return True
except Exception as e:
# Don't log as error — may fail if already reacted or missing scope
logger.debug("[Slack] reactions.add failed (%s): %s", emoji, e)
return False
async def _remove_reaction(
self, channel: str, timestamp: str, emoji: str
) -> bool:
"""Remove an emoji reaction from a message. Returns True on success."""
if not self._app:
return False
try:
await self._app.client.reactions_remove(
channel=channel, timestamp=timestamp, name=emoji
)
return True
except Exception as e:
logger.debug("[Slack] reactions.remove failed (%s): %s", emoji, e)
return False
# ----- User identity resolution -----
async def _resolve_user_name(self, user_id: str) -> str:
"""Resolve a Slack user ID to a display name, with caching."""
if not user_id:
return ""
if user_id in self._user_name_cache:
return self._user_name_cache[user_id]
if not self._app:
return user_id
try:
result = await self._app.client.users_info(user=user_id)
user = result.get("user", {})
# Prefer display_name → real_name → user_id
profile = user.get("profile", {})
name = (
profile.get("display_name")
or profile.get("real_name")
or user.get("real_name")
or user.get("name")
or user_id
)
self._user_name_cache[user_id] = name
return name
except Exception as e:
logger.debug("[Slack] users.info failed for %s: %s", user_id, e)
self._user_name_cache[user_id] = user_id
return user_id
async def send_image_file(
self,
chat_id: str,
image_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send a local image file to Slack by uploading it."""
try:
return await self._upload_file(chat_id, image_path, caption, reply_to, metadata)
except FileNotFoundError:
return SendResult(success=False, error=f"Image file not found: {image_path}")
except Exception as e: # pragma: no cover - defensive logging
logger.error(
"[%s] Failed to send local Slack image %s: %s",
self.name,
image_path,
e,
exc_info=True,
)
text = f"🖼️ Image: {image_path}"
if caption:
text = f"{caption}\n{text}"
return await self.send(chat_id, text, reply_to=reply_to, metadata=metadata)
async def send_typing(self, chat_id: str) -> None:
"""Slack doesn't have a direct typing indicator API for bots."""
pass
async def send_image(
self,
@@ -464,7 +185,6 @@ class SlackAdapter(BasePlatformAdapter):
image_url: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send an image to Slack by uploading the URL as a file."""
if not self._app:
@@ -483,18 +203,12 @@ class SlackAdapter(BasePlatformAdapter):
content=response.content,
filename="image.png",
initial_comment=caption or "",
thread_ts=self._resolve_thread_ts(reply_to, metadata),
thread_ts=reply_to,
)
return SendResult(success=True, raw_response=result)
except Exception as e: # pragma: no cover - defensive logging
logger.warning(
"[Slack] Failed to upload image from URL %s, falling back to text: %s",
image_url,
e,
exc_info=True,
)
except Exception as e:
# Fall back to sending the URL as text
text = f"{caption}\n{image_url}" if caption else image_url
return await self.send(chat_id=chat_id, content=text, reply_to=reply_to)
@@ -505,102 +219,24 @@ class SlackAdapter(BasePlatformAdapter):
audio_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
**kwargs,
) -> SendResult:
"""Send an audio file to Slack."""
if not self._app:
return SendResult(success=False, error="Not connected")
try:
return await self._upload_file(chat_id, audio_path, caption, reply_to, metadata)
except FileNotFoundError:
return SendResult(success=False, error=f"Audio file not found: {audio_path}")
except Exception as e: # pragma: no cover - defensive logging
logger.error(
"[Slack] Failed to send audio file %s: %s",
audio_path,
e,
exc_info=True,
result = await self._app.client.files_upload_v2(
channel=chat_id,
file=audio_path,
filename=os.path.basename(audio_path),
initial_comment=caption or "",
thread_ts=reply_to,
)
return SendResult(success=True, raw_response=result)
except Exception as e:
return SendResult(success=False, error=str(e))
async def send_video(
self,
chat_id: str,
video_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send a video file to Slack."""
if not self._app:
return SendResult(success=False, error="Not connected")
if not os.path.exists(video_path):
return SendResult(success=False, error=f"Video file not found: {video_path}")
try:
result = await self._app.client.files_upload_v2(
channel=chat_id,
file=video_path,
filename=os.path.basename(video_path),
initial_comment=caption or "",
thread_ts=self._resolve_thread_ts(reply_to, metadata),
)
return SendResult(success=True, raw_response=result)
except Exception as e: # pragma: no cover - defensive logging
logger.error(
"[%s] Failed to send video %s: %s",
self.name,
video_path,
e,
exc_info=True,
)
text = f"🎬 Video: {video_path}"
if caption:
text = f"{caption}\n{text}"
return await self.send(chat_id, text, reply_to=reply_to, metadata=metadata)
async def send_document(
self,
chat_id: str,
file_path: str,
caption: Optional[str] = None,
file_name: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send a document/file attachment to Slack."""
if not self._app:
return SendResult(success=False, error="Not connected")
if not os.path.exists(file_path):
return SendResult(success=False, error=f"File not found: {file_path}")
display_name = file_name or os.path.basename(file_path)
try:
result = await self._app.client.files_upload_v2(
channel=chat_id,
file=file_path,
filename=display_name,
initial_comment=caption or "",
thread_ts=self._resolve_thread_ts(reply_to, metadata),
)
return SendResult(success=True, raw_response=result)
except Exception as e: # pragma: no cover - defensive logging
logger.error(
"[%s] Failed to send document %s: %s",
self.name,
file_path,
e,
exc_info=True,
)
text = f"📎 File: {file_path}"
if caption:
text = f"{caption}\n{text}"
return await self.send(chat_id, text, reply_to=reply_to, metadata=metadata)
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
"""Get information about a Slack channel."""
if not self._app:
@@ -614,13 +250,7 @@ class SlackAdapter(BasePlatformAdapter):
"name": channel.get("name", chat_id),
"type": "dm" if is_dm else "group",
}
except Exception as e: # pragma: no cover - defensive logging
logger.error(
"[Slack] Failed to fetch chat info for %s: %s",
chat_id,
e,
exc_info=True,
)
except Exception:
return {"name": chat_id, "type": "unknown"}
# ----- Internal handlers -----
@@ -639,22 +269,13 @@ class SlackAdapter(BasePlatformAdapter):
text = event.get("text", "")
user_id = event.get("user", "")
channel_id = event.get("channel", "")
thread_ts = event.get("thread_ts") or event.get("ts")
ts = event.get("ts", "")
# Determine if this is a DM or channel message
channel_type = event.get("channel_type", "")
is_dm = channel_type == "im"
# Build thread_ts for session keying.
# In channels: fall back to ts so each top-level @mention starts a
# new thread/session (the bot always replies in a thread).
# In DMs: only use the real thread_ts — top-level DMs should share
# one continuous session, threaded DMs get their own session.
if is_dm:
thread_ts = event.get("thread_ts") # None for top-level DMs
else:
thread_ts = event.get("thread_ts") or ts # ts fallback for channels
# In channels, only respond if bot is mentioned
if not is_dm and self._bot_user_id:
if f"<@{self._bot_user_id}>" not in text:
@@ -684,8 +305,8 @@ class SlackAdapter(BasePlatformAdapter):
media_urls.append(cached)
media_types.append(mimetype)
msg_type = MessageType.PHOTO
except Exception as e: # pragma: no cover - defensive logging
logger.warning("[Slack] Failed to cache image from %s: %s", url, e, exc_info=True)
except Exception as e:
print(f"[Slack] Failed to cache image: {e}", flush=True)
elif mimetype.startswith("audio/") and url:
try:
ext = "." + mimetype.split("/")[-1].split(";")[0]
@@ -695,63 +316,8 @@ class SlackAdapter(BasePlatformAdapter):
media_urls.append(cached)
media_types.append(mimetype)
msg_type = MessageType.VOICE
except Exception as e: # pragma: no cover - defensive logging
logger.warning("[Slack] Failed to cache audio from %s: %s", url, e, exc_info=True)
elif url:
# Try to handle as a document attachment
try:
original_filename = f.get("name", "")
ext = ""
if original_filename:
_, ext = os.path.splitext(original_filename)
ext = ext.lower()
# Fallback: reverse-lookup from MIME type
if not ext and mimetype:
mime_to_ext = {v: k for k, v in SUPPORTED_DOCUMENT_TYPES.items()}
ext = mime_to_ext.get(mimetype, "")
if ext not in SUPPORTED_DOCUMENT_TYPES:
continue # Skip unsupported file types silently
# Check file size (Slack limit: 20 MB for bots)
file_size = f.get("size", 0)
MAX_DOC_BYTES = 20 * 1024 * 1024
if not file_size or file_size > MAX_DOC_BYTES:
logger.warning("[Slack] Document too large or unknown size: %s", file_size)
continue
# Download and cache
raw_bytes = await self._download_slack_file_bytes(url)
cached_path = cache_document_from_bytes(
raw_bytes, original_filename or f"document{ext}"
)
doc_mime = SUPPORTED_DOCUMENT_TYPES[ext]
media_urls.append(cached_path)
media_types.append(doc_mime)
msg_type = MessageType.DOCUMENT
logger.debug("[Slack] Cached user document: %s", cached_path)
# Inject text content for .txt/.md files (capped at 100 KB)
MAX_TEXT_INJECT_BYTES = 100 * 1024
if ext in (".md", ".txt") and len(raw_bytes) <= MAX_TEXT_INJECT_BYTES:
try:
text_content = raw_bytes.decode("utf-8")
display_name = original_filename or f"document{ext}"
display_name = re.sub(r'[^\w.\- ]', '_', display_name)
injection = f"[Content of {display_name}]:\n{text_content}"
if text:
text = f"{injection}\n\n{text}"
else:
text = injection
except UnicodeDecodeError:
pass # Binary content, skip injection
except Exception as e: # pragma: no cover - defensive logging
logger.warning("[Slack] Failed to cache document from %s: %s", url, e, exc_info=True)
# Resolve user display name (cached after first lookup)
user_name = await self._resolve_user_name(user_id)
except Exception as e:
print(f"[Slack] Failed to cache audio: {e}", flush=True)
# Build source
source = self.build_source(
@@ -759,7 +325,6 @@ class SlackAdapter(BasePlatformAdapter):
chat_name=channel_id, # Will be resolved later if needed
chat_type="dm" if is_dm else "group",
user_id=user_id,
user_name=user_name,
thread_id=thread_ts,
)
@@ -774,26 +339,22 @@ class SlackAdapter(BasePlatformAdapter):
reply_to_message_id=thread_ts if thread_ts != ts else None,
)
# Add 👀 reaction to acknowledge receipt
await self._add_reaction(channel_id, ts, "eyes")
await self.handle_message(msg_event)
# Replace 👀 with ✅ when done
await self._remove_reaction(channel_id, ts, "eyes")
await self._add_reaction(channel_id, ts, "white_check_mark")
async def _handle_slash_command(self, command: dict) -> None:
"""Handle /hermes slash command."""
text = command.get("text", "").strip()
user_id = command.get("user_id", "")
channel_id = command.get("channel_id", "")
# Map subcommands to gateway commands — derived from central registry.
# Also keep "compact" as a Slack-specific alias for /compress.
from hermes_cli.commands import slack_subcommand_map
subcommand_map = slack_subcommand_map()
subcommand_map["compact"] = "/compress"
# Map subcommands to gateway commands
subcommand_map = {
"new": "/reset", "reset": "/reset",
"status": "/status", "stop": "/stop",
"help": "/help",
"model": "/model", "personality": "/personality",
"retry": "/retry", "undo": "/undo",
}
first_word = text.split()[0] if text else ""
if first_word in subcommand_map:
# Preserve arguments after the subcommand
@@ -837,16 +398,3 @@ class SlackAdapter(BasePlatformAdapter):
else:
from gateway.platforms.base import cache_image_from_bytes
return cache_image_from_bytes(response.content, ext)
async def _download_slack_file_bytes(self, url: str) -> bytes:
"""Download a Slack file and return raw bytes."""
import httpx
bot_token = self.config.token
async with httpx.AsyncClient(timeout=30.0, follow_redirects=True) as client:
response = await client.get(
url,
headers={"Authorization": f"Bearer {bot_token}"},
)
response.raise_for_status()
return response.content

View File

@@ -1,271 +0,0 @@
"""SMS (Twilio) platform adapter.
Connects to the Twilio REST API for outbound SMS and runs an aiohttp
webhook server to receive inbound messages.
Shares credentials with the optional telephony skill — same env vars:
- TWILIO_ACCOUNT_SID
- TWILIO_AUTH_TOKEN
- TWILIO_PHONE_NUMBER (E.164 from-number, e.g. +15551234567)
Gateway-specific env vars:
- SMS_WEBHOOK_PORT (default 8080)
- SMS_ALLOWED_USERS (comma-separated E.164 phone numbers)
- SMS_ALLOW_ALL_USERS (true/false)
- SMS_HOME_CHANNEL (phone number for cron delivery)
"""
import asyncio
import base64
import json
import logging
import os
import re
import urllib.parse
from typing import Any, Dict, List, Optional
from gateway.config import Platform, PlatformConfig
from gateway.platforms.base import (
BasePlatformAdapter,
MessageEvent,
MessageType,
SendResult,
)
logger = logging.getLogger(__name__)
TWILIO_API_BASE = "https://api.twilio.com/2010-04-01/Accounts"
MAX_SMS_LENGTH = 1600 # ~10 SMS segments
DEFAULT_WEBHOOK_PORT = 8080
# E.164 phone number pattern for redaction
_PHONE_RE = re.compile(r"\+[1-9]\d{6,14}")
def _redact_phone(phone: str) -> str:
"""Redact a phone number for logging: +15551234567 -> +1555***4567."""
if not phone:
return "<none>"
if len(phone) <= 8:
return phone[:2] + "***" + phone[-2:] if len(phone) > 4 else "****"
return phone[:5] + "***" + phone[-4:]
def check_sms_requirements() -> bool:
"""Check if SMS adapter dependencies are available."""
try:
import aiohttp # noqa: F401
except ImportError:
return False
return bool(os.getenv("TWILIO_ACCOUNT_SID") and os.getenv("TWILIO_AUTH_TOKEN"))
class SmsAdapter(BasePlatformAdapter):
"""
Twilio SMS <-> Hermes gateway adapter.
Each inbound phone number gets its own Hermes session (multi-tenant).
Replies are always sent from the configured TWILIO_PHONE_NUMBER.
"""
MAX_MESSAGE_LENGTH = MAX_SMS_LENGTH
def __init__(self, config: PlatformConfig):
super().__init__(config, Platform.SMS)
self._account_sid: str = os.environ["TWILIO_ACCOUNT_SID"]
self._auth_token: str = os.environ["TWILIO_AUTH_TOKEN"]
self._from_number: str = os.getenv("TWILIO_PHONE_NUMBER", "")
self._webhook_port: int = int(
os.getenv("SMS_WEBHOOK_PORT", str(DEFAULT_WEBHOOK_PORT))
)
self._runner = None
self._http_session: Optional["aiohttp.ClientSession"] = None
def _basic_auth_header(self) -> str:
"""Build HTTP Basic auth header value for Twilio."""
creds = f"{self._account_sid}:{self._auth_token}"
encoded = base64.b64encode(creds.encode("ascii")).decode("ascii")
return f"Basic {encoded}"
# ------------------------------------------------------------------
# Required abstract methods
# ------------------------------------------------------------------
async def connect(self) -> bool:
import aiohttp
from aiohttp import web
if not self._from_number:
logger.error("[sms] TWILIO_PHONE_NUMBER not set — cannot send replies")
return False
app = web.Application()
app.router.add_post("/webhooks/twilio", self._handle_webhook)
app.router.add_get("/health", lambda _: web.Response(text="ok"))
self._runner = web.AppRunner(app)
await self._runner.setup()
site = web.TCPSite(self._runner, "0.0.0.0", self._webhook_port)
await site.start()
self._http_session = aiohttp.ClientSession()
self._running = True
logger.info(
"[sms] Twilio webhook server listening on port %d, from: %s",
self._webhook_port,
_redact_phone(self._from_number),
)
return True
async def disconnect(self) -> None:
if self._http_session:
await self._http_session.close()
self._http_session = None
if self._runner:
await self._runner.cleanup()
self._runner = None
self._running = False
logger.info("[sms] Disconnected")
async def send(
self,
chat_id: str,
content: str,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
import aiohttp
formatted = self.format_message(content)
chunks = self.truncate_message(formatted)
last_result = SendResult(success=True)
url = f"{TWILIO_API_BASE}/{self._account_sid}/Messages.json"
headers = {
"Authorization": self._basic_auth_header(),
}
session = self._http_session or aiohttp.ClientSession()
try:
for chunk in chunks:
form_data = aiohttp.FormData()
form_data.add_field("From", self._from_number)
form_data.add_field("To", chat_id)
form_data.add_field("Body", chunk)
try:
async with session.post(url, data=form_data, headers=headers) as resp:
body = await resp.json()
if resp.status >= 400:
error_msg = body.get("message", str(body))
logger.error(
"[sms] send failed to %s: %s %s",
_redact_phone(chat_id),
resp.status,
error_msg,
)
return SendResult(
success=False,
error=f"Twilio {resp.status}: {error_msg}",
)
msg_sid = body.get("sid", "")
last_result = SendResult(success=True, message_id=msg_sid)
except Exception as e:
logger.error("[sms] send error to %s: %s", _redact_phone(chat_id), e)
return SendResult(success=False, error=str(e))
finally:
# Close session only if we created a fallback (no persistent session)
if not self._http_session and session:
await session.close()
return last_result
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
return {"name": chat_id, "type": "dm"}
# ------------------------------------------------------------------
# SMS-specific formatting
# ------------------------------------------------------------------
def format_message(self, content: str) -> str:
"""Strip markdown — SMS renders it as literal characters."""
content = re.sub(r"\*\*(.+?)\*\*", r"\1", content, flags=re.DOTALL)
content = re.sub(r"\*(.+?)\*", r"\1", content, flags=re.DOTALL)
content = re.sub(r"__(.+?)__", r"\1", content, flags=re.DOTALL)
content = re.sub(r"_(.+?)_", r"\1", content, flags=re.DOTALL)
content = re.sub(r"```[a-z]*\n?", "", content)
content = re.sub(r"`(.+?)`", r"\1", content)
content = re.sub(r"^#{1,6}\s+", "", content, flags=re.MULTILINE)
content = re.sub(r"\[([^\]]+)\]\([^\)]+\)", r"\1", content)
content = re.sub(r"\n{3,}", "\n\n", content)
return content.strip()
# ------------------------------------------------------------------
# Twilio webhook handler
# ------------------------------------------------------------------
async def _handle_webhook(self, request) -> "aiohttp.web.Response":
from aiohttp import web
try:
raw = await request.read()
# Twilio sends form-encoded data, not JSON
form = urllib.parse.parse_qs(raw.decode("utf-8"))
except Exception as e:
logger.error("[sms] webhook parse error: %s", e)
return web.Response(
text='<?xml version="1.0" encoding="UTF-8"?><Response></Response>',
content_type="application/xml",
status=400,
)
# Extract fields (parse_qs returns lists)
from_number = (form.get("From", [""]))[0].strip()
to_number = (form.get("To", [""]))[0].strip()
text = (form.get("Body", [""]))[0].strip()
message_sid = (form.get("MessageSid", [""]))[0].strip()
if not from_number or not text:
return web.Response(
text='<?xml version="1.0" encoding="UTF-8"?><Response></Response>',
content_type="application/xml",
)
# Ignore messages from our own number (echo prevention)
if from_number == self._from_number:
logger.debug("[sms] ignoring echo from own number %s", _redact_phone(from_number))
return web.Response(
text='<?xml version="1.0" encoding="UTF-8"?><Response></Response>',
content_type="application/xml",
)
logger.info(
"[sms] inbound from %s -> %s: %s",
_redact_phone(from_number),
_redact_phone(to_number),
text[:80],
)
source = self.build_source(
chat_id=from_number,
chat_name=from_number,
chat_type="dm",
user_id=from_number,
user_name=from_number,
)
event = MessageEvent(
text=text,
message_type=MessageType.TEXT,
source=source,
raw_message=form,
message_id=message_sid,
)
# Non-blocking: Twilio expects a fast response
asyncio.create_task(self.handle_message(event))
# Return empty TwiML — we send replies via the REST API, not inline TwiML
return web.Response(
text='<?xml version="1.0" encoding="UTF-8"?><Response></Response>',
content_type="application/xml",
)

File diff suppressed because it is too large Load Diff

View File

@@ -1,557 +0,0 @@
"""Generic webhook platform adapter.
Runs an aiohttp HTTP server that receives webhook POSTs from external
services (GitHub, GitLab, JIRA, Stripe, etc.), validates HMAC signatures,
transforms payloads into agent prompts, and routes responses back to the
source or to another configured platform.
Configuration lives in config.yaml under platforms.webhook.extra.routes.
Each route defines:
- events: which event types to accept (header-based filtering)
- secret: HMAC secret for signature validation (REQUIRED)
- prompt: template string formatted with the webhook payload
- skills: optional list of skills to load for the agent
- deliver: where to send the response (github_comment, telegram, etc.)
- deliver_extra: additional delivery config (repo, pr_number, chat_id)
Security:
- HMAC secret is required per route (validated at startup)
- Rate limiting per route (fixed-window, configurable)
- Idempotency cache prevents duplicate agent runs on webhook retries
- Body size limits checked before reading payload
- Set secret to "INSECURE_NO_AUTH" to skip validation (testing only)
"""
import asyncio
import hashlib
import hmac
import json
import logging
import re
import subprocess
import time
from typing import Any, Dict, List, Optional
try:
from aiohttp import web
AIOHTTP_AVAILABLE = True
except ImportError:
AIOHTTP_AVAILABLE = False
web = None # type: ignore[assignment]
from gateway.config import Platform, PlatformConfig
from gateway.platforms.base import (
BasePlatformAdapter,
MessageEvent,
MessageType,
SendResult,
)
logger = logging.getLogger(__name__)
DEFAULT_HOST = "0.0.0.0"
DEFAULT_PORT = 8644
_INSECURE_NO_AUTH = "INSECURE_NO_AUTH"
def check_webhook_requirements() -> bool:
"""Check if webhook adapter dependencies are available."""
return AIOHTTP_AVAILABLE
class WebhookAdapter(BasePlatformAdapter):
"""Generic webhook receiver that triggers agent runs from HTTP POSTs."""
def __init__(self, config: PlatformConfig):
super().__init__(config, Platform.WEBHOOK)
self._host: str = config.extra.get("host", DEFAULT_HOST)
self._port: int = int(config.extra.get("port", DEFAULT_PORT))
self._global_secret: str = config.extra.get("secret", "")
self._routes: Dict[str, dict] = config.extra.get("routes", {})
self._runner = None
# Delivery info keyed by session chat_id — consumed by send()
self._delivery_info: Dict[str, dict] = {}
# Reference to gateway runner for cross-platform delivery (set externally)
self.gateway_runner = None
# Idempotency: TTL cache of recently processed delivery IDs.
# Prevents duplicate agent runs when webhook providers retry.
self._seen_deliveries: Dict[str, float] = {}
self._idempotency_ttl: int = 3600 # 1 hour
# Rate limiting: per-route timestamps in a fixed window.
self._rate_counts: Dict[str, List[float]] = {}
self._rate_limit: int = int(config.extra.get("rate_limit", 30)) # per minute
# Body size limit (auth-before-body pattern)
self._max_body_bytes: int = int(
config.extra.get("max_body_bytes", 1_048_576)
) # 1MB
# ------------------------------------------------------------------
# Lifecycle
# ------------------------------------------------------------------
async def connect(self) -> bool:
# Validate routes at startup — secret is required per route
for name, route in self._routes.items():
secret = route.get("secret", self._global_secret)
if not secret:
raise ValueError(
f"[webhook] Route '{name}' has no HMAC secret. "
f"Set 'secret' on the route or globally. "
f"For testing without auth, set secret to '{_INSECURE_NO_AUTH}'."
)
app = web.Application()
app.router.add_get("/health", self._handle_health)
app.router.add_post("/webhooks/{route_name}", self._handle_webhook)
self._runner = web.AppRunner(app)
await self._runner.setup()
site = web.TCPSite(self._runner, self._host, self._port)
await site.start()
self._mark_connected()
route_names = ", ".join(self._routes.keys()) or "(none configured)"
logger.info(
"[webhook] Listening on %s:%d — routes: %s",
self._host,
self._port,
route_names,
)
return True
async def disconnect(self) -> None:
if self._runner:
await self._runner.cleanup()
self._runner = None
self._mark_disconnected()
logger.info("[webhook] Disconnected")
async def send(
self,
chat_id: str,
content: str,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Deliver the agent's response to the configured destination.
chat_id is ``webhook:{route}:{delivery_id}`` — we pop the delivery
info stored during webhook receipt so it doesn't leak memory.
"""
delivery = self._delivery_info.pop(chat_id, {})
deliver_type = delivery.get("deliver", "log")
if deliver_type == "log":
logger.info("[webhook] Response for %s: %s", chat_id, content[:200])
return SendResult(success=True)
if deliver_type == "github_comment":
return await self._deliver_github_comment(content, delivery)
# Cross-platform delivery (telegram, discord, etc.)
if self.gateway_runner and deliver_type in (
"telegram",
"discord",
"slack",
"signal",
"sms",
):
return await self._deliver_cross_platform(
deliver_type, content, delivery
)
logger.warning("[webhook] Unknown deliver type: %s", deliver_type)
return SendResult(
success=False, error=f"Unknown deliver type: {deliver_type}"
)
async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
return {"name": chat_id, "type": "webhook"}
# ------------------------------------------------------------------
# HTTP handlers
# ------------------------------------------------------------------
async def _handle_health(self, request: "web.Request") -> "web.Response":
"""GET /health — simple health check."""
return web.json_response({"status": "ok", "platform": "webhook"})
async def _handle_webhook(self, request: "web.Request") -> "web.Response":
"""POST /webhooks/{route_name} — receive and process a webhook event."""
route_name = request.match_info.get("route_name", "")
route_config = self._routes.get(route_name)
if not route_config:
return web.json_response(
{"error": f"Unknown route: {route_name}"}, status=404
)
# ── Auth-before-body ─────────────────────────────────────
# Check Content-Length before reading the full payload.
content_length = request.content_length or 0
if content_length > self._max_body_bytes:
return web.json_response(
{"error": "Payload too large"}, status=413
)
# ── Rate limiting ────────────────────────────────────────
now = time.time()
window = self._rate_counts.setdefault(route_name, [])
window[:] = [t for t in window if now - t < 60]
if len(window) >= self._rate_limit:
return web.json_response(
{"error": "Rate limit exceeded"}, status=429
)
window.append(now)
# Read body
try:
raw_body = await request.read()
except Exception as e:
logger.error("[webhook] Failed to read body: %s", e)
return web.json_response({"error": "Bad request"}, status=400)
# Validate HMAC signature (skip for INSECURE_NO_AUTH testing mode)
secret = route_config.get("secret", self._global_secret)
if secret and secret != _INSECURE_NO_AUTH:
if not self._validate_signature(request, raw_body, secret):
logger.warning(
"[webhook] Invalid signature for route %s", route_name
)
return web.json_response(
{"error": "Invalid signature"}, status=401
)
# Parse payload
try:
payload = json.loads(raw_body)
except json.JSONDecodeError:
# Try form-encoded as fallback
try:
import urllib.parse
payload = dict(
urllib.parse.parse_qsl(raw_body.decode("utf-8"))
)
except Exception:
return web.json_response(
{"error": "Cannot parse body"}, status=400
)
# Check event type filter
event_type = (
request.headers.get("X-GitHub-Event", "")
or request.headers.get("X-GitLab-Event", "")
or payload.get("event_type", "")
or "unknown"
)
allowed_events = route_config.get("events", [])
if allowed_events and event_type not in allowed_events:
logger.debug(
"[webhook] Ignoring event %s for route %s (allowed: %s)",
event_type,
route_name,
allowed_events,
)
return web.json_response(
{"status": "ignored", "event": event_type}
)
# Format prompt from template
prompt_template = route_config.get("prompt", "")
prompt = self._render_prompt(
prompt_template, payload, event_type, route_name
)
# Inject skill content if configured.
# We call build_skill_invocation_message() directly rather than
# using /skill-name slash commands — the gateway's command parser
# would intercept those and break the flow.
skills = route_config.get("skills", [])
if skills:
try:
from agent.skill_commands import (
build_skill_invocation_message,
get_skill_commands,
)
skill_cmds = get_skill_commands()
for skill_name in skills:
cmd_key = f"/{skill_name}"
if cmd_key in skill_cmds:
skill_content = build_skill_invocation_message(
cmd_key, user_instruction=prompt
)
if skill_content:
prompt = skill_content
break # Load the first matching skill
else:
logger.warning(
"[webhook] Skill '%s' not found", skill_name
)
except Exception as e:
logger.warning("[webhook] Skill loading failed: %s", e)
# Build a unique delivery ID
delivery_id = request.headers.get(
"X-GitHub-Delivery",
request.headers.get("X-Request-ID", str(int(time.time() * 1000))),
)
# ── Idempotency ─────────────────────────────────────────
# Skip duplicate deliveries (webhook retries).
now = time.time()
# Prune expired entries
self._seen_deliveries = {
k: v
for k, v in self._seen_deliveries.items()
if now - v < self._idempotency_ttl
}
if delivery_id in self._seen_deliveries:
logger.info(
"[webhook] Skipping duplicate delivery %s", delivery_id
)
return web.json_response(
{"status": "duplicate", "delivery_id": delivery_id},
status=200,
)
self._seen_deliveries[delivery_id] = now
# Use delivery_id in session key so concurrent webhooks on the
# same route get independent agent runs (not queued/interrupted).
session_chat_id = f"webhook:{route_name}:{delivery_id}"
# Store delivery info for send() — consumed (popped) on delivery
deliver_config = {
"deliver": route_config.get("deliver", "log"),
"deliver_extra": self._render_delivery_extra(
route_config.get("deliver_extra", {}), payload
),
"payload": payload,
}
self._delivery_info[session_chat_id] = deliver_config
# Build source and event
source = self.build_source(
chat_id=session_chat_id,
chat_name=f"webhook/{route_name}",
chat_type="webhook",
user_id=f"webhook:{route_name}",
user_name=route_name,
)
event = MessageEvent(
text=prompt,
message_type=MessageType.TEXT,
source=source,
raw_message=payload,
message_id=delivery_id,
)
logger.info(
"[webhook] %s event=%s route=%s prompt_len=%d delivery=%s",
request.method,
event_type,
route_name,
len(prompt),
delivery_id,
)
# Non-blocking — return 202 Accepted immediately
asyncio.create_task(self.handle_message(event))
return web.json_response(
{
"status": "accepted",
"route": route_name,
"event": event_type,
"delivery_id": delivery_id,
},
status=202,
)
# ------------------------------------------------------------------
# Signature validation
# ------------------------------------------------------------------
def _validate_signature(
self, request: "web.Request", body: bytes, secret: str
) -> bool:
"""Validate webhook signature (GitHub, GitLab, generic HMAC-SHA256)."""
# GitHub: X-Hub-Signature-256 = sha256=<hex>
gh_sig = request.headers.get("X-Hub-Signature-256", "")
if gh_sig:
expected = "sha256=" + hmac.new(
secret.encode(), body, hashlib.sha256
).hexdigest()
return hmac.compare_digest(gh_sig, expected)
# GitLab: X-Gitlab-Token = <plain secret>
gl_token = request.headers.get("X-Gitlab-Token", "")
if gl_token:
return hmac.compare_digest(gl_token, secret)
# Generic: X-Webhook-Signature = <hex HMAC-SHA256>
generic_sig = request.headers.get("X-Webhook-Signature", "")
if generic_sig:
expected = hmac.new(
secret.encode(), body, hashlib.sha256
).hexdigest()
return hmac.compare_digest(generic_sig, expected)
# No recognised signature header but secret is configured → reject
logger.debug(
"[webhook] Secret configured but no signature header found"
)
return False
# ------------------------------------------------------------------
# Prompt rendering
# ------------------------------------------------------------------
def _render_prompt(
self,
template: str,
payload: dict,
event_type: str,
route_name: str,
) -> str:
"""Render a prompt template with the webhook payload.
Supports dot-notation access into nested dicts:
``{pull_request.title}`` → ``payload["pull_request"]["title"]``
"""
if not template:
truncated = json.dumps(payload, indent=2)[:4000]
return (
f"Webhook event '{event_type}' on route "
f"'{route_name}':\n\n```json\n{truncated}\n```"
)
def _resolve(match: re.Match) -> str:
key = match.group(1)
value: Any = payload
for part in key.split("."):
if isinstance(value, dict):
value = value.get(part, f"{{{key}}}")
else:
return f"{{{key}}}"
if isinstance(value, (dict, list)):
return json.dumps(value, indent=2)[:2000]
return str(value)
return re.sub(r"\{([a-zA-Z0-9_.]+)\}", _resolve, template)
def _render_delivery_extra(
self, extra: dict, payload: dict
) -> dict:
"""Render delivery_extra template values with payload data."""
rendered: Dict[str, Any] = {}
for key, value in extra.items():
if isinstance(value, str):
rendered[key] = self._render_prompt(value, payload, "", "")
else:
rendered[key] = value
return rendered
# ------------------------------------------------------------------
# Response delivery
# ------------------------------------------------------------------
async def _deliver_github_comment(
self, content: str, delivery: dict
) -> SendResult:
"""Post agent response as a GitHub PR/issue comment via ``gh`` CLI."""
extra = delivery.get("deliver_extra", {})
repo = extra.get("repo", "")
pr_number = extra.get("pr_number", "")
if not repo or not pr_number:
logger.error(
"[webhook] github_comment delivery missing repo or pr_number"
)
return SendResult(
success=False, error="Missing repo or pr_number"
)
try:
result = subprocess.run(
[
"gh",
"pr",
"comment",
str(pr_number),
"--repo",
repo,
"--body",
content,
],
capture_output=True,
text=True,
timeout=30,
)
if result.returncode == 0:
logger.info(
"[webhook] Posted comment on %s#%s", repo, pr_number
)
return SendResult(success=True)
else:
logger.error(
"[webhook] gh pr comment failed: %s", result.stderr
)
return SendResult(success=False, error=result.stderr)
except FileNotFoundError:
logger.error(
"[webhook] 'gh' CLI not found — install GitHub CLI for "
"github_comment delivery"
)
return SendResult(
success=False, error="gh CLI not installed"
)
except Exception as e:
logger.error("[webhook] github_comment delivery error: %s", e)
return SendResult(success=False, error=str(e))
async def _deliver_cross_platform(
self, platform_name: str, content: str, delivery: dict
) -> SendResult:
"""Route response to another platform (telegram, discord, etc.)."""
if not self.gateway_runner:
return SendResult(
success=False,
error="No gateway runner for cross-platform delivery",
)
try:
target_platform = Platform(platform_name)
except ValueError:
return SendResult(
success=False, error=f"Unknown platform: {platform_name}"
)
adapter = self.gateway_runner.adapters.get(target_platform)
if not adapter:
return SendResult(
success=False,
error=f"Platform {platform_name} not connected",
)
# Use home channel if no specific chat_id in deliver_extra
extra = delivery.get("deliver_extra", {})
chat_id = extra.get("chat_id", "")
if not chat_id:
home = self.gateway_runner.config.get_home_channel(target_platform)
if home:
chat_id = home.chat_id
else:
return SendResult(
success=False,
error=f"No chat_id or home channel for {platform_name}",
)
return await adapter.send(chat_id, content)

View File

@@ -26,8 +26,6 @@ _IS_WINDOWS = platform.system() == "Windows"
from pathlib import Path
from typing import Dict, List, Optional, Any
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
@@ -134,9 +132,8 @@ class WhatsAppAdapter(BasePlatformAdapter):
)
self._session_path: Path = Path(config.extra.get(
"session_path",
get_hermes_home() / "whatsapp" / "session"
Path.home() / ".hermes" / "whatsapp" / "session"
))
self._reply_prefix: Optional[str] = config.extra.get("reply_prefix")
self._message_queue: asyncio.Queue = asyncio.Queue()
self._bridge_log_fh = None
self._bridge_log: Optional[Path] = None
@@ -182,32 +179,10 @@ class WhatsAppAdapter(BasePlatformAdapter):
# Ensure session directory exists
self._session_path.mkdir(parents=True, exist_ok=True)
# Check if bridge is already running and connected
import aiohttp
import asyncio
try:
async with aiohttp.ClientSession() as session:
async with session.get(
f"http://127.0.0.1:{self._bridge_port}/health",
timeout=aiohttp.ClientTimeout(total=2)
) as resp:
if resp.status == 200:
data = await resp.json()
bridge_status = data.get("status", "unknown")
if bridge_status == "connected":
print(f"[{self.name}] Using existing bridge (status: {bridge_status})")
self._mark_connected()
self._bridge_process = None # Not managed by us
asyncio.create_task(self._poll_messages())
return True
else:
print(f"[{self.name}] Bridge found but not connected (status: {bridge_status}), restarting")
except Exception:
pass # Bridge not running, start a new one
# Kill any orphaned bridge from a previous gateway run
_kill_port_process(self._bridge_port)
await asyncio.sleep(1)
import time
time.sleep(1)
# Start the bridge process in its own process group.
# Route output to a log file so QR codes, errors, and reconnection
@@ -216,14 +191,6 @@ class WhatsAppAdapter(BasePlatformAdapter):
self._bridge_log = self._session_path.parent / "bridge.log"
bridge_log_fh = open(self._bridge_log, "a")
self._bridge_log_fh = bridge_log_fh
# Build bridge subprocess environment.
# Pass WHATSAPP_REPLY_PREFIX from config.yaml so the Node bridge
# can use it without the user needing to set a separate env var.
bridge_env = os.environ.copy()
if self._reply_prefix is not None:
bridge_env["WHATSAPP_REPLY_PREFIX"] = self._reply_prefix
self._bridge_process = subprocess.Popen(
[
"node",
@@ -235,7 +202,6 @@ class WhatsAppAdapter(BasePlatformAdapter):
stdout=bridge_log_fh,
stderr=bridge_log_fh,
preexec_fn=None if _IS_WINDOWS else os.setsid,
env=bridge_env,
)
# Wait for the bridge to connect to WhatsApp.
@@ -254,7 +220,7 @@ class WhatsAppAdapter(BasePlatformAdapter):
try:
async with aiohttp.ClientSession() as session:
async with session.get(
f"http://127.0.0.1:{self._bridge_port}/health",
f"http://localhost:{self._bridge_port}/health",
timeout=aiohttp.ClientTimeout(total=2)
) as resp:
if resp.status == 200:
@@ -286,7 +252,7 @@ class WhatsAppAdapter(BasePlatformAdapter):
try:
async with aiohttp.ClientSession() as session:
async with session.get(
f"http://127.0.0.1:{self._bridge_port}/health",
f"http://localhost:{self._bridge_port}/health",
timeout=aiohttp.ClientTimeout(total=2)
) as resp:
if resp.status == 200:
@@ -306,7 +272,7 @@ class WhatsAppAdapter(BasePlatformAdapter):
# Start message polling task
asyncio.create_task(self._poll_messages())
self._mark_connected()
self._running = True
print(f"[{self.name}] Bridge started on port {self._bridge_port}")
return True
@@ -324,23 +290,6 @@ class WhatsAppAdapter(BasePlatformAdapter):
pass
self._bridge_log_fh = None
async def _check_managed_bridge_exit(self) -> Optional[str]:
"""Return a fatal error message if the managed bridge child exited."""
if self._bridge_process is None:
return None
returncode = self._bridge_process.poll()
if returncode is None:
return None
message = f"WhatsApp bridge process exited unexpectedly (code {returncode})."
if not self.has_fatal_error:
logger.error("[%s] %s", self.name, message)
self._set_fatal_error("whatsapp_bridge_exited", message, retryable=True)
self._close_bridge_log()
await self._notify_fatal_error()
return self.fatal_error_message or message
async def disconnect(self) -> None:
"""Stop the WhatsApp bridge and clean up any orphaned processes."""
if self._bridge_process:
@@ -365,11 +314,11 @@ class WhatsAppAdapter(BasePlatformAdapter):
self._bridge_process.kill()
except Exception as e:
print(f"[{self.name}] Error stopping bridge: {e}")
else:
# Bridge was not started by us, don't kill it
print(f"[{self.name}] Disconnecting (external bridge left running)")
self._mark_disconnected()
# Also kill any orphaned bridge processes on our port
_kill_port_process(self._bridge_port)
self._running = False
self._bridge_process = None
self._close_bridge_log()
print(f"[{self.name}] Disconnected")
@@ -384,9 +333,6 @@ class WhatsAppAdapter(BasePlatformAdapter):
"""Send a message via the WhatsApp bridge."""
if not self._running:
return SendResult(success=False, error="Not connected")
bridge_exit = await self._check_managed_bridge_exit()
if bridge_exit:
return SendResult(success=False, error=bridge_exit)
try:
import aiohttp
@@ -400,7 +346,7 @@ class WhatsAppAdapter(BasePlatformAdapter):
payload["replyTo"] = reply_to
async with session.post(
f"http://127.0.0.1:{self._bridge_port}/send",
f"http://localhost:{self._bridge_port}/send",
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as resp:
@@ -432,14 +378,11 @@ class WhatsAppAdapter(BasePlatformAdapter):
"""Edit a previously sent message via the WhatsApp bridge."""
if not self._running:
return SendResult(success=False, error="Not connected")
bridge_exit = await self._check_managed_bridge_exit()
if bridge_exit:
return SendResult(success=False, error=bridge_exit)
try:
import aiohttp
async with aiohttp.ClientSession() as session:
async with session.post(
f"http://127.0.0.1:{self._bridge_port}/edit",
f"http://localhost:{self._bridge_port}/edit",
json={
"chatId": chat_id,
"messageId": message_id,
@@ -466,9 +409,6 @@ class WhatsAppAdapter(BasePlatformAdapter):
"""Send any media file via bridge /send-media endpoint."""
if not self._running:
return SendResult(success=False, error="Not connected")
bridge_exit = await self._check_managed_bridge_exit()
if bridge_exit:
return SendResult(success=False, error=bridge_exit)
try:
import aiohttp
@@ -487,7 +427,7 @@ class WhatsAppAdapter(BasePlatformAdapter):
async with aiohttp.ClientSession() as session:
async with session.post(
f"http://127.0.0.1:{self._bridge_port}/send-media",
f"http://localhost:{self._bridge_port}/send-media",
json=payload,
timeout=aiohttp.ClientTimeout(total=120),
) as resp:
@@ -553,19 +493,17 @@ class WhatsAppAdapter(BasePlatformAdapter):
file_name or os.path.basename(file_path),
)
async def send_typing(self, chat_id: str, metadata=None) -> None:
async def send_typing(self, chat_id: str) -> None:
"""Send typing indicator via bridge."""
if not self._running:
return
if await self._check_managed_bridge_exit():
return
try:
import aiohttp
async with aiohttp.ClientSession() as session:
await session.post(
f"http://127.0.0.1:{self._bridge_port}/typing",
f"http://localhost:{self._bridge_port}/typing",
json={"chatId": chat_id},
timeout=aiohttp.ClientTimeout(total=5)
)
@@ -576,15 +514,13 @@ class WhatsAppAdapter(BasePlatformAdapter):
"""Get information about a WhatsApp chat."""
if not self._running:
return {"name": "Unknown", "type": "dm"}
if await self._check_managed_bridge_exit():
return {"name": chat_id, "type": "dm"}
try:
import aiohttp
async with aiohttp.ClientSession() as session:
async with session.get(
f"http://127.0.0.1:{self._bridge_port}/chat/{chat_id}",
f"http://localhost:{self._bridge_port}/chat/{chat_id}",
timeout=aiohttp.ClientTimeout(total=10)
) as resp:
if resp.status == 200:
@@ -608,14 +544,10 @@ class WhatsAppAdapter(BasePlatformAdapter):
return
while self._running:
bridge_exit = await self._check_managed_bridge_exit()
if bridge_exit:
print(f"[{self.name}] {bridge_exit}")
break
try:
async with aiohttp.ClientSession() as session:
async with session.get(
f"http://127.0.0.1:{self._bridge_port}/messages",
f"http://localhost:{self._bridge_port}/messages",
timeout=aiohttp.ClientTimeout(total=30)
) as resp:
if resp.status == 200:
@@ -627,10 +559,6 @@ class WhatsAppAdapter(BasePlatformAdapter):
except asyncio.CancelledError:
break
except Exception as e:
bridge_exit = await self._check_managed_bridge_exit()
if bridge_exit:
print(f"[{self.name}] {bridge_exit}")
break
print(f"[{self.name}] Poll error: {e}")
await asyncio.sleep(5)
@@ -681,11 +609,6 @@ class WhatsAppAdapter(BasePlatformAdapter):
print(f"[{self.name}] Failed to cache image: {e}", flush=True)
cached_urls.append(url)
media_types.append("image/jpeg")
elif msg_type == MessageType.PHOTO and os.path.isabs(url):
# Local file path — bridge already downloaded the image
cached_urls.append(url)
media_types.append("image/jpeg")
print(f"[{self.name}] Using bridge-cached image: {url}", flush=True)
elif msg_type == MessageType.VOICE and url.startswith(("http://", "https://")):
try:
cached_path = await cache_audio_from_url(url, ext=".ogg")
@@ -712,3 +635,4 @@ class WhatsAppAdapter(BasePlatformAdapter):
except Exception as e:
print(f"[{self.name}] Error building event: {e}")
return None

File diff suppressed because it is too large Load Diff

View File

@@ -8,11 +8,9 @@ Handles:
- Dynamic system prompt injection (agent knows its context)
"""
import hashlib
import logging
import os
import json
import re
import uuid
from pathlib import Path
from datetime import datetime, timedelta
@@ -21,41 +19,6 @@ from typing import Dict, List, Optional, Any
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# PII redaction helpers
# ---------------------------------------------------------------------------
_PHONE_RE = re.compile(r"^\+?\d[\d\-\s]{6,}$")
def _hash_id(value: str) -> str:
"""Deterministic 12-char hex hash of an identifier."""
return hashlib.sha256(value.encode("utf-8")).hexdigest()[:12]
def _hash_sender_id(value: str) -> str:
"""Hash a sender ID to ``user_<12hex>``."""
return f"user_{_hash_id(value)}"
def _hash_chat_id(value: str) -> str:
"""Hash the numeric portion of a chat ID, preserving platform prefix.
``telegram:12345`` → ``telegram:<hash>``
``12345`` → ``<hash>``
"""
colon = value.find(":")
if colon > 0:
prefix = value[:colon]
return f"{prefix}:{_hash_id(value[colon + 1:])}"
return _hash_id(value)
def _looks_like_phone(value: str) -> bool:
"""Return True if *value* looks like a phone number (E.164 or similar)."""
return bool(_PHONE_RE.match(value.strip()))
from .config import (
Platform,
GatewayConfig,
@@ -82,8 +45,6 @@ class SessionSource:
user_name: Optional[str] = None
thread_id: Optional[str] = None # For forum topics, Discord threads, etc.
chat_topic: Optional[str] = None # Channel topic/description (Discord, Slack)
user_id_alt: Optional[str] = None # Signal UUID (alternative to phone number)
chat_id_alt: Optional[str] = None # Signal group internal ID
@property
def description(self) -> str:
@@ -107,7 +68,7 @@ class SessionSource:
return ", ".join(parts)
def to_dict(self) -> Dict[str, Any]:
d = {
return {
"platform": self.platform.value,
"chat_id": self.chat_id,
"chat_name": self.chat_name,
@@ -117,11 +78,6 @@ class SessionSource:
"thread_id": self.thread_id,
"chat_topic": self.chat_topic,
}
if self.user_id_alt:
d["user_id_alt"] = self.user_id_alt
if self.chat_id_alt:
d["chat_id_alt"] = self.chat_id_alt
return d
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "SessionSource":
@@ -134,8 +90,6 @@ class SessionSource:
user_name=data.get("user_name"),
thread_id=data.get("thread_id"),
chat_topic=data.get("chat_topic"),
user_id_alt=data.get("user_id_alt"),
chat_id_alt=data.get("chat_id_alt"),
)
@classmethod
@@ -183,21 +137,7 @@ class SessionContext:
}
_PII_SAFE_PLATFORMS = frozenset({
Platform.WHATSAPP,
Platform.SIGNAL,
Platform.TELEGRAM,
})
"""Platforms where user IDs can be safely redacted (no in-message mention system
that requires raw IDs). Discord is excluded because mentions use ``<@user_id>``
and the LLM needs the real ID to tag users."""
def build_session_context_prompt(
context: SessionContext,
*,
redact_pii: bool = False,
) -> str:
def build_session_context_prompt(context: SessionContext) -> str:
"""
Build the dynamic system prompt section that tells the agent about its context.
@@ -205,15 +145,7 @@ def build_session_context_prompt(
- Where messages are coming from
- What platforms are connected
- Where it can deliver scheduled task outputs
When *redact_pii* is True **and** the source platform is in
``_PII_SAFE_PLATFORMS``, phone numbers are stripped and user/chat IDs
are replaced with deterministic hashes before being sent to the LLM.
Platforms like Discord are excluded because mentions need real IDs.
Routing still uses the original values (they stay in SessionSource).
"""
# Only apply redaction on platforms where IDs aren't needed for mentions
redact_pii = redact_pii and context.source.platform in _PII_SAFE_PLATFORMS
lines = [
"## Current Session Context",
"",
@@ -224,25 +156,7 @@ def build_session_context_prompt(
if context.source.platform == Platform.LOCAL:
lines.append(f"**Source:** {platform_name} (the machine running this agent)")
else:
# Build a description that respects PII redaction
src = context.source
if redact_pii:
# Build a safe description without raw IDs
_uname = src.user_name or (
_hash_sender_id(src.user_id) if src.user_id else "user"
)
_cname = src.chat_name or _hash_chat_id(src.chat_id)
if src.chat_type == "dm":
desc = f"DM with {_uname}"
elif src.chat_type == "group":
desc = f"group: {_cname}"
elif src.chat_type == "channel":
desc = f"channel: {_cname}"
else:
desc = _cname
else:
desc = src.description
lines.append(f"**Source:** {platform_name} ({desc})")
lines.append(f"**Source:** {platform_name} ({context.source.description})")
# Channel topic (if available - provides context about the channel's purpose)
if context.source.chat_topic:
@@ -252,31 +166,8 @@ def build_session_context_prompt(
if context.source.user_name:
lines.append(f"**User:** {context.source.user_name}")
elif context.source.user_id:
uid = context.source.user_id
if redact_pii:
uid = _hash_sender_id(uid)
lines.append(f"**User ID:** {uid}")
lines.append(f"**User ID:** {context.source.user_id}")
# Platform-specific behavioral notes
if context.source.platform == Platform.SLACK:
lines.append("")
lines.append(
"**Platform notes:** You are running inside Slack. "
"You do NOT have access to Slack-specific APIs — you cannot search "
"channel history, pin/unpin messages, manage channels, or list users. "
"Do not promise to perform these actions. If the user asks, explain "
"that you can only read messages sent directly to you and respond."
)
elif context.source.platform == Platform.DISCORD:
lines.append("")
lines.append(
"**Platform notes:** You are running inside Discord. "
"You do NOT have access to Discord-specific APIs — you cannot search "
"channel history, pin messages, manage roles, or list server members. "
"Do not promise to perform these actions. If the user asks, explain "
"that you can only read messages sent directly to you and respond."
)
# Connected platforms
platforms_list = ["local (files on this machine)"]
for p in context.connected_platforms:
@@ -290,8 +181,7 @@ def build_session_context_prompt(
lines.append("")
lines.append("**Home Channels (default destinations):**")
for platform, home in context.home_channels.items():
hc_id = _hash_chat_id(home.chat_id) if redact_pii else home.chat_id
lines.append(f" - {platform.value}: {home.name} (ID: {hc_id})")
lines.append(f" - {platform.value}: {home.name} (ID: {home.chat_id})")
# Delivery options for scheduled tasks
lines.append("")
@@ -301,10 +191,7 @@ def build_session_context_prompt(
if context.source.platform == Platform.LOCAL:
lines.append("- `\"origin\"` → Local output (saved to files)")
else:
_origin_label = context.source.chat_name or (
_hash_chat_id(context.source.chat_id) if redact_pii else context.source.chat_id
)
lines.append(f"- `\"origin\"` → Back to this chat ({_origin_label})")
lines.append(f"- `\"origin\"` → Back to this chat ({context.source.chat_name or context.source.chat_id})")
# Local always available
lines.append("- `\"local\"` → Save to local files only (~/.hermes/cron/output/)")
@@ -343,20 +230,11 @@ class SessionEntry:
# Token tracking
input_tokens: int = 0
output_tokens: int = 0
cache_read_tokens: int = 0
cache_write_tokens: int = 0
total_tokens: int = 0
estimated_cost_usd: float = 0.0
cost_status: str = "unknown"
# Last API-reported prompt tokens (for accurate compression pre-check)
last_prompt_tokens: int = 0
# Set when a session was created because the previous one expired;
# consumed once by the message handler to inject a notice into context
was_auto_reset: bool = False
auto_reset_reason: Optional[str] = None # "idle" or "daily"
reset_had_activity: bool = False # whether the expired session had any messages
def to_dict(self) -> Dict[str, Any]:
result = {
@@ -369,12 +247,7 @@ class SessionEntry:
"chat_type": self.chat_type,
"input_tokens": self.input_tokens,
"output_tokens": self.output_tokens,
"cache_read_tokens": self.cache_read_tokens,
"cache_write_tokens": self.cache_write_tokens,
"total_tokens": self.total_tokens,
"last_prompt_tokens": self.last_prompt_tokens,
"estimated_cost_usd": self.estimated_cost_usd,
"cost_status": self.cost_status,
}
if self.origin:
result["origin"] = self.origin.to_dict()
@@ -390,8 +263,8 @@ class SessionEntry:
if data.get("platform"):
try:
platform = Platform(data["platform"])
except ValueError as e:
logger.debug("Unknown platform value %r: %s", data["platform"], e)
except ValueError:
pass
return cls(
session_key=data["session_key"],
@@ -404,56 +277,22 @@ class SessionEntry:
chat_type=data.get("chat_type", "dm"),
input_tokens=data.get("input_tokens", 0),
output_tokens=data.get("output_tokens", 0),
cache_read_tokens=data.get("cache_read_tokens", 0),
cache_write_tokens=data.get("cache_write_tokens", 0),
total_tokens=data.get("total_tokens", 0),
last_prompt_tokens=data.get("last_prompt_tokens", 0),
estimated_cost_usd=data.get("estimated_cost_usd", 0.0),
cost_status=data.get("cost_status", "unknown"),
)
def build_session_key(source: SessionSource, group_sessions_per_user: bool = True) -> str:
def build_session_key(source: SessionSource) -> str:
"""Build a deterministic session key from a message source.
This is the single source of truth for session key construction.
DM rules:
- DMs include chat_id when present, so each private conversation is isolated.
- thread_id further differentiates threaded DMs within the same DM chat.
- Without chat_id, thread_id is used as a best-effort fallback.
- Without thread_id or chat_id, DMs share a single session.
Group/channel rules:
- chat_id identifies the parent group/channel.
- user_id/user_id_alt isolates participants within that parent chat when available when
``group_sessions_per_user`` is enabled.
- thread_id differentiates threads within that parent chat.
- Without participant identifiers, or when isolation is disabled, messages fall back to one
shared session per chat.
- Without identifiers, messages fall back to one session per platform/chat_type.
WhatsApp DMs include chat_id (multi-user), other DMs do not (single owner).
"""
platform = source.platform.value
if source.chat_type == "dm":
if source.chat_id:
if source.thread_id:
return f"agent:main:{platform}:dm:{source.chat_id}:{source.thread_id}"
if platform == "whatsapp" and source.chat_id:
return f"agent:main:{platform}:dm:{source.chat_id}"
if source.thread_id:
return f"agent:main:{platform}:dm:{source.thread_id}"
return f"agent:main:{platform}:dm"
participant_id = source.user_id_alt or source.user_id
key_parts = ["agent:main", platform, source.chat_type]
if source.chat_id:
key_parts.append(source.chat_id)
if source.thread_id:
key_parts.append(source.thread_id)
if group_sessions_per_user and participant_id:
key_parts.append(str(participant_id))
return ":".join(key_parts)
return f"agent:main:{platform}:{source.chat_type}:{source.chat_id}"
class SessionStore:
@@ -472,9 +311,7 @@ class SessionStore:
self._entries: Dict[str, SessionEntry] = {}
self._loaded = False
self._has_active_processes_fn = has_active_processes_fn
# on_auto_reset is deprecated — memory flush now runs proactively
# via the background session expiry watcher in GatewayRunner.
self._pre_flushed_sessions: set = set() # session_ids already flushed by watcher
self._on_auto_reset = on_auto_reset # callback(old_entry) before auto-reset
# Initialize SQLite session database
self._db = None
@@ -494,14 +331,10 @@ class SessionStore:
if sessions_file.exists():
try:
with open(sessions_file, "r", encoding="utf-8") as f:
with open(sessions_file, "r") as f:
data = json.load(f)
for key, entry_data in data.items():
try:
self._entries[key] = SessionEntry.from_dict(entry_data)
except (ValueError, KeyError):
# Skip entries with unknown/removed platform values
continue
self._entries[key] = SessionEntry.from_dict(entry_data)
except Exception as e:
print(f"[gateway] Warning: Failed to load sessions: {e}")
@@ -509,85 +342,27 @@ class SessionStore:
def _save(self) -> None:
"""Save sessions index to disk (kept for session key -> ID mapping)."""
import tempfile
self.sessions_dir.mkdir(parents=True, exist_ok=True)
sessions_file = self.sessions_dir / "sessions.json"
data = {key: entry.to_dict() for key, entry in self._entries.items()}
fd, tmp_path = tempfile.mkstemp(
dir=str(self.sessions_dir), suffix=".tmp", prefix=".sessions_"
)
try:
with os.fdopen(fd, "w", encoding="utf-8") as f:
json.dump(data, f, indent=2)
f.flush()
os.fsync(f.fileno())
os.replace(tmp_path, sessions_file)
except BaseException:
try:
os.unlink(tmp_path)
except OSError as e:
logger.debug("Could not remove temp file %s: %s", tmp_path, e)
raise
with open(sessions_file, "w") as f:
json.dump(data, f, indent=2)
def _generate_session_key(self, source: SessionSource) -> str:
"""Generate a session key from a source."""
return build_session_key(
source,
group_sessions_per_user=getattr(self.config, "group_sessions_per_user", True),
)
return build_session_key(source)
def _is_session_expired(self, entry: SessionEntry) -> bool:
"""Check if a session has expired based on its reset policy.
Works from the entry alone — no SessionSource needed.
Used by the background expiry watcher to proactively flush memories.
Sessions with active background processes are never considered expired.
"""
if self._has_active_processes_fn:
if self._has_active_processes_fn(entry.session_key):
return False
policy = self.config.get_reset_policy(
platform=entry.platform,
session_type=entry.chat_type,
)
if policy.mode == "none":
return False
now = datetime.now()
if policy.mode in ("idle", "both"):
idle_deadline = entry.updated_at + timedelta(minutes=policy.idle_minutes)
if now > idle_deadline:
return True
if policy.mode in ("daily", "both"):
today_reset = now.replace(
hour=policy.at_hour,
minute=0, second=0, microsecond=0,
)
if now.hour < policy.at_hour:
today_reset -= timedelta(days=1)
if entry.updated_at < today_reset:
return True
return False
def _should_reset(self, entry: SessionEntry, source: SessionSource) -> Optional[str]:
def _should_reset(self, entry: SessionEntry, source: SessionSource) -> bool:
"""
Check if a session should be reset based on policy.
Returns the reset reason ("idle" or "daily") if a reset is needed,
or None if the session is still valid.
Sessions with active background processes are never reset.
"""
if self._has_active_processes_fn:
session_key = self._generate_session_key(source)
if self._has_active_processes_fn(session_key):
return None
return False
policy = self.config.get_reset_policy(
platform=source.platform,
@@ -595,14 +370,14 @@ class SessionStore:
)
if policy.mode == "none":
return None
return False
now = datetime.now()
if policy.mode in ("idle", "both"):
idle_deadline = entry.updated_at + timedelta(minutes=policy.idle_minutes)
if now > idle_deadline:
return "idle"
return True
if policy.mode in ("daily", "both"):
today_reset = now.replace(
@@ -615,9 +390,9 @@ class SessionStore:
today_reset -= timedelta(days=1)
if entry.updated_at < today_reset:
return "daily"
return True
return None
return False
def has_any_sessions(self) -> bool:
"""Check if any sessions have ever been created (across all platforms).
@@ -659,20 +434,18 @@ class SessionStore:
if session_key in self._entries and not force_new:
entry = self._entries[session_key]
reset_reason = self._should_reset(entry, source)
if not reset_reason:
if not self._should_reset(entry, source):
entry.updated_at = now
self._save()
return entry
else:
# Session is being auto-reset. The background expiry watcher
# should have already flushed memories proactively; discard
# the marker so it doesn't accumulate.
# Session is being auto-reset — flush memories before destroying
was_auto_reset = True
auto_reset_reason = reset_reason
# Track whether the expired session had any real conversation
reset_had_activity = entry.total_tokens > 0
self._pre_flushed_sessions.discard(entry.session_id)
if self._on_auto_reset:
try:
self._on_auto_reset(entry)
except Exception as e:
logger.debug("Auto-reset callback failed: %s", e)
if self._db:
try:
self._db.end_session(entry.session_id, "session_reset")
@@ -680,8 +453,6 @@ class SessionStore:
logger.debug("Session DB operation failed: %s", e)
else:
was_auto_reset = False
auto_reset_reason = None
reset_had_activity = False
# Create new session
session_id = f"{now.strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}"
@@ -696,8 +467,6 @@ class SessionStore:
platform=source.platform,
chat_type=source.chat_type,
was_auto_reset=was_auto_reset,
auto_reset_reason=auto_reset_reason,
reset_had_activity=reset_had_activity,
)
self._entries[session_key] = entry
@@ -720,16 +489,7 @@ class SessionStore:
self,
session_key: str,
input_tokens: int = 0,
output_tokens: int = 0,
cache_read_tokens: int = 0,
cache_write_tokens: int = 0,
last_prompt_tokens: int = None,
model: str = None,
estimated_cost_usd: Optional[float] = None,
cost_status: Optional[str] = None,
cost_source: Optional[str] = None,
provider: Optional[str] = None,
base_url: Optional[str] = None,
output_tokens: int = 0
) -> None:
"""Update a session's metadata after an interaction."""
self._ensure_loaded()
@@ -739,36 +499,13 @@ class SessionStore:
entry.updated_at = datetime.now()
entry.input_tokens += input_tokens
entry.output_tokens += output_tokens
entry.cache_read_tokens += cache_read_tokens
entry.cache_write_tokens += cache_write_tokens
if last_prompt_tokens is not None:
entry.last_prompt_tokens = last_prompt_tokens
if estimated_cost_usd is not None:
entry.estimated_cost_usd += estimated_cost_usd
if cost_status:
entry.cost_status = cost_status
entry.total_tokens = (
entry.input_tokens
+ entry.output_tokens
+ entry.cache_read_tokens
+ entry.cache_write_tokens
)
entry.total_tokens = entry.input_tokens + entry.output_tokens
self._save()
if self._db:
try:
self._db.update_token_counts(
entry.session_id,
input_tokens=input_tokens,
output_tokens=output_tokens,
cache_read_tokens=cache_read_tokens,
cache_write_tokens=cache_write_tokens,
estimated_cost_usd=estimated_cost_usd,
cost_status=cost_status,
cost_source=cost_source,
billing_provider=provider,
billing_base_url=base_url,
model=model,
entry.session_id, input_tokens, output_tokens
)
except Exception as e:
logger.debug("Session DB operation failed: %s", e)
@@ -818,49 +555,7 @@ class SessionStore:
logger.debug("Session DB operation failed: %s", e)
return new_entry
def switch_session(self, session_key: str, target_session_id: str) -> Optional[SessionEntry]:
"""Switch a session key to point at an existing session ID.
Used by ``/resume`` to restore a previously-named session.
Ends the current session in SQLite (like reset), but instead of
generating a fresh session ID, re-uses ``target_session_id`` so the
old transcript is loaded on the next message.
"""
self._ensure_loaded()
if session_key not in self._entries:
return None
old_entry = self._entries[session_key]
# Don't switch if already on that session
if old_entry.session_id == target_session_id:
return old_entry
# End the current session in SQLite
if self._db:
try:
self._db.end_session(old_entry.session_id, "session_switch")
except Exception as e:
logger.debug("Session DB end_session failed: %s", e)
now = datetime.now()
new_entry = SessionEntry(
session_key=session_key,
session_id=target_session_id,
created_at=now,
updated_at=now,
origin=old_entry.origin,
display_name=old_entry.display_name,
platform=old_entry.platform,
chat_type=old_entry.chat_type,
)
self._entries[session_key] = new_entry
self._save()
return new_entry
def list_sessions(self, active_minutes: Optional[int] = None) -> List[SessionEntry]:
"""List all sessions, optionally filtered by activity."""
self._ensure_loaded()
@@ -879,17 +574,10 @@ class SessionStore:
"""Get the path to a session's legacy transcript file."""
return self.sessions_dir / f"{session_id}.jsonl"
def append_to_transcript(self, session_id: str, message: Dict[str, Any], skip_db: bool = False) -> None:
"""Append a message to a session's transcript (SQLite + legacy JSONL).
Args:
skip_db: When True, only write to JSONL and skip the SQLite write.
Used when the agent already persisted messages to SQLite
via its own _flush_messages_to_session_db(), preventing
the duplicate-write bug (#860).
"""
# Write to SQLite (unless the agent already handled it)
if self._db and not skip_db:
def append_to_transcript(self, session_id: str, message: Dict[str, Any]) -> None:
"""Append a message to a session's transcript (SQLite + legacy JSONL)."""
# Write to SQLite
if self._db:
try:
self._db.append_message(
session_id=session_id,
@@ -904,7 +592,7 @@ class SessionStore:
# Also write legacy JSONL (keeps existing tooling working during transition)
transcript_path = self.get_transcript_path(session_id)
with open(transcript_path, "a", encoding="utf-8") as f:
with open(transcript_path, "a") as f:
f.write(json.dumps(message, ensure_ascii=False) + "\n")
def rewrite_transcript(self, session_id: str, messages: List[Dict[str, Any]]) -> None:
@@ -931,7 +619,7 @@ class SessionStore:
# JSONL: overwrite the file
transcript_path = self.get_transcript_path(session_id)
with open(transcript_path, "w", encoding="utf-8") as f:
with open(transcript_path, "w") as f:
for msg in messages:
f.write(json.dumps(msg, ensure_ascii=False) + "\n")
@@ -953,17 +641,11 @@ class SessionStore:
return []
messages = []
with open(transcript_path, "r", encoding="utf-8") as f:
with open(transcript_path, "r") as f:
for line in f:
line = line.strip()
if line:
try:
messages.append(json.loads(line))
except json.JSONDecodeError:
logger.warning(
"Skipping corrupt line in transcript %s: %s",
session_id, line[:120],
)
messages.append(json.loads(line))
return messages

View File

@@ -11,17 +11,9 @@ that will be useful when we add named profiles (multiple agents running
concurrently under distinct configurations).
"""
import hashlib
import json
import os
import sys
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Optional
_GATEWAY_KIND = "hermes-gateway"
_RUNTIME_STATUS_FILE = "gateway_state.json"
_LOCKS_DIRNAME = "gateway-locks"
from typing import Optional
def _get_pid_path() -> Path:
@@ -30,199 +22,11 @@ def _get_pid_path() -> Path:
return home / "gateway.pid"
def _get_runtime_status_path() -> Path:
"""Return the persisted runtime health/status file path."""
return _get_pid_path().with_name(_RUNTIME_STATUS_FILE)
def _get_lock_dir() -> Path:
"""Return the machine-local directory for token-scoped gateway locks."""
override = os.getenv("HERMES_GATEWAY_LOCK_DIR")
if override:
return Path(override)
state_home = Path(os.getenv("XDG_STATE_HOME", Path.home() / ".local" / "state"))
return state_home / "hermes" / _LOCKS_DIRNAME
def _utc_now_iso() -> str:
return datetime.now(timezone.utc).isoformat()
def _scope_hash(identity: str) -> str:
return hashlib.sha256(identity.encode("utf-8")).hexdigest()[:16]
def _get_scope_lock_path(scope: str, identity: str) -> Path:
return _get_lock_dir() / f"{scope}-{_scope_hash(identity)}.lock"
def _get_process_start_time(pid: int) -> Optional[int]:
"""Return the kernel start time for a process when available."""
stat_path = Path(f"/proc/{pid}/stat")
try:
# Field 22 in /proc/<pid>/stat is process start time (clock ticks).
return int(stat_path.read_text().split()[21])
except (FileNotFoundError, IndexError, PermissionError, ValueError, OSError):
return None
def _read_process_cmdline(pid: int) -> Optional[str]:
"""Return the process command line as a space-separated string."""
cmdline_path = Path(f"/proc/{pid}/cmdline")
try:
raw = cmdline_path.read_bytes()
except (FileNotFoundError, PermissionError, OSError):
return None
if not raw:
return None
return raw.replace(b"\x00", b" ").decode("utf-8", errors="ignore").strip()
def _looks_like_gateway_process(pid: int) -> bool:
"""Return True when the live PID still looks like the Hermes gateway."""
cmdline = _read_process_cmdline(pid)
if not cmdline:
return False
patterns = (
"hermes_cli.main gateway",
"hermes_cli/main.py gateway",
"hermes gateway",
"gateway/run.py",
)
return any(pattern in cmdline for pattern in patterns)
def _record_looks_like_gateway(record: dict[str, Any]) -> bool:
"""Validate gateway identity from PID-file metadata when cmdline is unavailable."""
if record.get("kind") != _GATEWAY_KIND:
return False
argv = record.get("argv")
if not isinstance(argv, list) or not argv:
return False
cmdline = " ".join(str(part) for part in argv)
patterns = (
"hermes_cli.main gateway",
"hermes_cli/main.py gateway",
"hermes gateway",
"gateway/run.py",
)
return any(pattern in cmdline for pattern in patterns)
def _build_pid_record() -> dict:
return {
"pid": os.getpid(),
"kind": _GATEWAY_KIND,
"argv": list(sys.argv),
"start_time": _get_process_start_time(os.getpid()),
}
def _build_runtime_status_record() -> dict[str, Any]:
payload = _build_pid_record()
payload.update({
"gateway_state": "starting",
"exit_reason": None,
"platforms": {},
"updated_at": _utc_now_iso(),
})
return payload
def _read_json_file(path: Path) -> Optional[dict[str, Any]]:
if not path.exists():
return None
try:
raw = path.read_text().strip()
except OSError:
return None
if not raw:
return None
try:
payload = json.loads(raw)
except json.JSONDecodeError:
return None
return payload if isinstance(payload, dict) else None
def _write_json_file(path: Path, payload: dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload))
def _read_pid_record() -> Optional[dict]:
pid_path = _get_pid_path()
if not pid_path.exists():
return None
raw = pid_path.read_text().strip()
if not raw:
return None
try:
payload = json.loads(raw)
except json.JSONDecodeError:
try:
return {"pid": int(raw)}
except ValueError:
return None
if isinstance(payload, int):
return {"pid": payload}
if isinstance(payload, dict):
return payload
return None
def write_pid_file() -> None:
"""Write the current process PID and metadata to the gateway PID file."""
_write_json_file(_get_pid_path(), _build_pid_record())
def write_runtime_status(
*,
gateway_state: Optional[str] = None,
exit_reason: Optional[str] = None,
platform: Optional[str] = None,
platform_state: Optional[str] = None,
error_code: Optional[str] = None,
error_message: Optional[str] = None,
) -> None:
"""Persist gateway runtime health information for diagnostics/status."""
path = _get_runtime_status_path()
payload = _read_json_file(path) or _build_runtime_status_record()
payload.setdefault("platforms", {})
payload.setdefault("kind", _GATEWAY_KIND)
payload["pid"] = os.getpid()
payload["start_time"] = _get_process_start_time(os.getpid())
payload["updated_at"] = _utc_now_iso()
if gateway_state is not None:
payload["gateway_state"] = gateway_state
if exit_reason is not None:
payload["exit_reason"] = exit_reason
if platform is not None:
platform_payload = payload["platforms"].get(platform, {})
if platform_state is not None:
platform_payload["state"] = platform_state
if error_code is not None:
platform_payload["error_code"] = error_code
if error_message is not None:
platform_payload["error_message"] = error_message
platform_payload["updated_at"] = _utc_now_iso()
payload["platforms"][platform] = platform_payload
_write_json_file(path, payload)
def read_runtime_status() -> Optional[dict[str, Any]]:
"""Read the persisted gateway runtime health/status information."""
return _read_json_file(_get_runtime_status_path())
"""Write the current process PID to the gateway PID file."""
pid_path = _get_pid_path()
pid_path.parent.mkdir(parents=True, exist_ok=True)
pid_path.write_text(str(os.getpid()))
def remove_pid_file() -> None:
@@ -233,157 +37,24 @@ def remove_pid_file() -> None:
pass
def acquire_scoped_lock(scope: str, identity: str, metadata: Optional[dict[str, Any]] = None) -> tuple[bool, Optional[dict[str, Any]]]:
"""Acquire a machine-local lock keyed by scope + identity.
Used to prevent multiple local gateways from using the same external identity
at once (e.g. the same Telegram bot token across different HERMES_HOME dirs).
"""
lock_path = _get_scope_lock_path(scope, identity)
lock_path.parent.mkdir(parents=True, exist_ok=True)
record = {
**_build_pid_record(),
"scope": scope,
"identity_hash": _scope_hash(identity),
"metadata": metadata or {},
"updated_at": _utc_now_iso(),
}
existing = _read_json_file(lock_path)
if existing:
try:
existing_pid = int(existing["pid"])
except (KeyError, TypeError, ValueError):
existing_pid = None
if existing_pid == os.getpid() and existing.get("start_time") == record.get("start_time"):
_write_json_file(lock_path, record)
return True, existing
stale = existing_pid is None
if not stale:
try:
os.kill(existing_pid, 0)
except (ProcessLookupError, PermissionError):
stale = True
else:
current_start = _get_process_start_time(existing_pid)
if (
existing.get("start_time") is not None
and current_start is not None
and current_start != existing.get("start_time")
):
stale = True
# Check if process is stopped (Ctrl+Z / SIGTSTP) — stopped
# processes still respond to os.kill(pid, 0) but are not
# actually running. Treat them as stale so --replace works.
if not stale:
try:
_proc_status = Path(f"/proc/{existing_pid}/status")
if _proc_status.exists():
for _line in _proc_status.read_text().splitlines():
if _line.startswith("State:"):
_state = _line.split()[1]
if _state in ("T", "t"): # stopped or tracing stop
stale = True
break
except (OSError, PermissionError):
pass
if stale:
try:
lock_path.unlink(missing_ok=True)
except OSError:
pass
else:
return False, existing
try:
fd = os.open(lock_path, os.O_CREAT | os.O_EXCL | os.O_WRONLY)
except FileExistsError:
return False, _read_json_file(lock_path)
try:
with os.fdopen(fd, "w", encoding="utf-8") as handle:
json.dump(record, handle)
except Exception:
try:
lock_path.unlink(missing_ok=True)
except OSError:
pass
raise
return True, None
def release_scoped_lock(scope: str, identity: str) -> None:
"""Release a previously-acquired scope lock when owned by this process."""
lock_path = _get_scope_lock_path(scope, identity)
existing = _read_json_file(lock_path)
if not existing:
return
if existing.get("pid") != os.getpid():
return
if existing.get("start_time") != _get_process_start_time(os.getpid()):
return
try:
lock_path.unlink(missing_ok=True)
except OSError:
pass
def release_all_scoped_locks() -> int:
"""Remove all scoped lock files in the lock directory.
Called during --replace to clean up stale locks left by stopped/killed
gateway processes that did not release their locks gracefully.
Returns the number of lock files removed.
"""
lock_dir = _get_lock_dir()
removed = 0
if lock_dir.exists():
for lock_file in lock_dir.glob("*.lock"):
try:
lock_file.unlink(missing_ok=True)
removed += 1
except OSError:
pass
return removed
def get_running_pid() -> Optional[int]:
"""Return the PID of a running gateway instance, or ``None``.
Checks the PID file and verifies the process is actually alive.
Cleans up stale PID files automatically.
"""
record = _read_pid_record()
if not record:
remove_pid_file()
pid_path = _get_pid_path()
if not pid_path.exists():
return None
try:
pid = int(record["pid"])
except (KeyError, TypeError, ValueError):
remove_pid_file()
return None
try:
pid = int(pid_path.read_text().strip())
os.kill(pid, 0) # signal 0 = existence check, no actual signal sent
except (ProcessLookupError, PermissionError):
return pid
except (ValueError, ProcessLookupError, PermissionError):
# Stale PID file — process is gone
remove_pid_file()
return None
recorded_start = record.get("start_time")
current_start = _get_process_start_time(pid)
if recorded_start is not None and current_start is not None and current_start != recorded_start:
remove_pid_file()
return None
if not _looks_like_gateway_process(pid):
if not _record_looks_like_gateway(record):
remove_pid_file()
return None
return pid
def is_gateway_running() -> bool:
"""Check if the gateway daemon is currently running."""

View File

@@ -14,10 +14,8 @@ import time
from pathlib import Path
from typing import Optional
from hermes_cli.config import get_hermes_home
CACHE_PATH = get_hermes_home() / "sticker_cache.json"
CACHE_PATH = Path(os.path.expanduser("~/.hermes/sticker_cache.json"))
# Vision prompt for describing stickers -- kept concise to save tokens
STICKER_VISION_PROMPT = (

View File

@@ -1,202 +0,0 @@
"""Gateway streaming consumer — bridges sync agent callbacks to async platform delivery.
The agent fires stream_delta_callback(text) synchronously from its worker thread.
GatewayStreamConsumer:
1. Receives deltas via on_delta() (thread-safe, sync)
2. Queues them to an asyncio task via queue.Queue
3. The async run() task buffers, rate-limits, and progressively edits
a single message on the target platform
Design: Uses the edit transport (send initial message, then editMessageText).
This is universally supported across Telegram, Discord, and Slack.
Credit: jobless0x (#774, #1312), OutThisLife (#798), clicksingh (#697).
"""
from __future__ import annotations
import asyncio
import logging
import queue
import time
from dataclasses import dataclass
from typing import Any, Optional
logger = logging.getLogger("gateway.stream_consumer")
# Sentinel to signal the stream is complete
_DONE = object()
@dataclass
class StreamConsumerConfig:
"""Runtime config for a single stream consumer instance."""
edit_interval: float = 0.3
buffer_threshold: int = 40
cursor: str = ""
class GatewayStreamConsumer:
"""Async consumer that progressively edits a platform message with streamed tokens.
Usage::
consumer = GatewayStreamConsumer(adapter, chat_id, config, metadata=metadata)
# Pass consumer.on_delta as stream_delta_callback to AIAgent
agent = AIAgent(..., stream_delta_callback=consumer.on_delta)
# Start the consumer as an asyncio task
task = asyncio.create_task(consumer.run())
# ... run agent in thread pool ...
consumer.finish() # signal completion
await task # wait for final edit
"""
def __init__(
self,
adapter: Any,
chat_id: str,
config: Optional[StreamConsumerConfig] = None,
metadata: Optional[dict] = None,
):
self.adapter = adapter
self.chat_id = chat_id
self.cfg = config or StreamConsumerConfig()
self.metadata = metadata
self._queue: queue.Queue = queue.Queue()
self._accumulated = ""
self._message_id: Optional[str] = None
self._already_sent = False
self._edit_supported = True # Disabled on first edit failure (Signal/Email/HA)
self._last_edit_time = 0.0
self._last_sent_text = "" # Track last-sent text to skip redundant edits
@property
def already_sent(self) -> bool:
"""True if at least one message was sent/edited — signals the base
adapter to skip re-sending the final response."""
return self._already_sent
def on_delta(self, text: str) -> None:
"""Thread-safe callback — called from the agent's worker thread."""
if text:
self._queue.put(text)
def finish(self) -> None:
"""Signal that the stream is complete."""
self._queue.put(_DONE)
async def run(self) -> None:
"""Async task that drains the queue and edits the platform message."""
# Platform message length limit — leave room for cursor + formatting
_raw_limit = getattr(self.adapter, "MAX_MESSAGE_LENGTH", 4096)
_safe_limit = max(500, _raw_limit - len(self.cfg.cursor) - 100)
try:
while True:
# Drain all available items from the queue
got_done = False
while True:
try:
item = self._queue.get_nowait()
if item is _DONE:
got_done = True
break
self._accumulated += item
except queue.Empty:
break
# Decide whether to flush an edit
now = time.monotonic()
elapsed = now - self._last_edit_time
should_edit = (
got_done
or (elapsed >= self.cfg.edit_interval
and len(self._accumulated) > 0)
or len(self._accumulated) >= self.cfg.buffer_threshold
)
if should_edit and self._accumulated:
# Split overflow: if accumulated text exceeds the platform
# limit, finalize the current message and start a new one.
while (
len(self._accumulated) > _safe_limit
and self._message_id is not None
):
split_at = self._accumulated.rfind("\n", 0, _safe_limit)
if split_at < _safe_limit // 2:
split_at = _safe_limit
chunk = self._accumulated[:split_at]
await self._send_or_edit(chunk)
self._accumulated = self._accumulated[split_at:].lstrip("\n")
self._message_id = None
self._last_sent_text = ""
display_text = self._accumulated
if not got_done:
display_text += self.cfg.cursor
await self._send_or_edit(display_text)
self._last_edit_time = time.monotonic()
if got_done:
# Final edit without cursor
if self._accumulated and self._message_id:
await self._send_or_edit(self._accumulated)
return
await asyncio.sleep(0.05) # Small yield to not busy-loop
except asyncio.CancelledError:
# Best-effort final edit on cancellation
if self._accumulated and self._message_id:
try:
await self._send_or_edit(self._accumulated)
except Exception:
pass
except Exception as e:
logger.error("Stream consumer error: %s", e)
async def _send_or_edit(self, text: str) -> None:
"""Send or edit the streaming message."""
try:
if self._message_id is not None:
if self._edit_supported:
# Skip if text is identical to what we last sent
if text == self._last_sent_text:
return
# Edit existing message
result = await self.adapter.edit_message(
chat_id=self.chat_id,
message_id=self._message_id,
content=text,
)
if result.success:
self._already_sent = True
self._last_sent_text = text
else:
# Edit not supported by this adapter — stop streaming,
# let the normal send path handle the final response.
# Without this guard, adapters like Signal/Email would
# flood the chat with a new message every edit_interval.
logger.debug("Edit failed, disabling streaming for this adapter")
self._edit_supported = False
else:
# Editing not supported — skip intermediate updates.
# The final response will be sent by the normal path.
pass
else:
# First message — send new
result = await self.adapter.send(
chat_id=self.chat_id,
content=text,
metadata=self.metadata,
)
if result.success and result.message_id:
self._message_id = result.message_id
self._already_sent = True
self._last_sent_text = text
else:
# Initial send failed — disable streaming for this session
self._edit_supported = False
except Exception as e:
logger.error("Stream send/edit error: %s", e)

View File

@@ -11,5 +11,4 @@ Provides subcommands for:
- hermes cron - Manage cron jobs
"""
__version__ = "0.4.0"
__release_date__ = "2026.3.18"
__version__ = "v1.0.0"

View File

@@ -19,12 +19,10 @@ import json
import logging
import os
import shutil
import shlex
import stat
import base64
import hashlib
import subprocess
import threading
import time
import uuid
import webbrowser
@@ -46,10 +44,6 @@ try:
import fcntl
except Exception:
fcntl = None
try:
import msvcrt
except Exception:
msvcrt = None
# =============================================================================
# Constants
@@ -67,8 +61,6 @@ DEFAULT_AGENT_KEY_MIN_TTL_SECONDS = 30 * 60 # 30 minutes
ACCESS_TOKEN_REFRESH_SKEW_SECONDS = 120 # refresh 2 min before expiry
DEVICE_AUTH_POLL_INTERVAL_CAP_SECONDS = 1 # poll at most every 1s
DEFAULT_CODEX_BASE_URL = "https://chatgpt.com/backend-api/codex"
DEFAULT_GITHUB_MODELS_BASE_URL = "https://api.githubcopilot.com"
DEFAULT_COPILOT_ACP_BASE_URL = "acp://copilot"
CODEX_OAUTH_CLIENT_ID = "app_EMoamEEZ73f0CkXaXp7hrann"
CODEX_OAUTH_TOKEN_URL = "https://auth.openai.com/oauth/token"
CODEX_ACCESS_TOKEN_REFRESH_SKEW_SECONDS = 120
@@ -111,20 +103,6 @@ PROVIDER_REGISTRY: Dict[str, ProviderConfig] = {
auth_type="oauth_external",
inference_base_url=DEFAULT_CODEX_BASE_URL,
),
"copilot": ProviderConfig(
id="copilot",
name="GitHub Copilot",
auth_type="api_key",
inference_base_url=DEFAULT_GITHUB_MODELS_BASE_URL,
api_key_env_vars=("COPILOT_GITHUB_TOKEN", "GH_TOKEN", "GITHUB_TOKEN"),
),
"copilot-acp": ProviderConfig(
id="copilot-acp",
name="GitHub Copilot ACP",
auth_type="external_process",
inference_base_url=DEFAULT_COPILOT_ACP_BASE_URL,
base_url_env_var="COPILOT_ACP_BASE_URL",
),
"zai": ProviderConfig(
id="zai",
name="Z.AI / GLM",
@@ -145,244 +123,21 @@ PROVIDER_REGISTRY: Dict[str, ProviderConfig] = {
id="minimax",
name="MiniMax",
auth_type="api_key",
inference_base_url="https://api.minimax.io/anthropic",
inference_base_url="https://api.minimax.io/v1",
api_key_env_vars=("MINIMAX_API_KEY",),
base_url_env_var="MINIMAX_BASE_URL",
),
"anthropic": ProviderConfig(
id="anthropic",
name="Anthropic",
auth_type="api_key",
inference_base_url="https://api.anthropic.com",
api_key_env_vars=("ANTHROPIC_API_KEY", "ANTHROPIC_TOKEN", "CLAUDE_CODE_OAUTH_TOKEN"),
),
"alibaba": ProviderConfig(
id="alibaba",
name="Alibaba Cloud (DashScope)",
auth_type="api_key",
inference_base_url="https://dashscope-intl.aliyuncs.com/apps/anthropic",
api_key_env_vars=("DASHSCOPE_API_KEY",),
base_url_env_var="DASHSCOPE_BASE_URL",
),
"minimax-cn": ProviderConfig(
id="minimax-cn",
name="MiniMax (China)",
auth_type="api_key",
inference_base_url="https://api.minimaxi.com/anthropic",
inference_base_url="https://api.minimaxi.com/v1",
api_key_env_vars=("MINIMAX_CN_API_KEY",),
base_url_env_var="MINIMAX_CN_BASE_URL",
),
"deepseek": ProviderConfig(
id="deepseek",
name="DeepSeek",
auth_type="api_key",
inference_base_url="https://api.deepseek.com/v1",
api_key_env_vars=("DEEPSEEK_API_KEY",),
base_url_env_var="DEEPSEEK_BASE_URL",
),
"ai-gateway": ProviderConfig(
id="ai-gateway",
name="AI Gateway",
auth_type="api_key",
inference_base_url="https://ai-gateway.vercel.sh/v1",
api_key_env_vars=("AI_GATEWAY_API_KEY",),
base_url_env_var="AI_GATEWAY_BASE_URL",
),
"opencode-zen": ProviderConfig(
id="opencode-zen",
name="OpenCode Zen",
auth_type="api_key",
inference_base_url="https://opencode.ai/zen/v1",
api_key_env_vars=("OPENCODE_ZEN_API_KEY",),
base_url_env_var="OPENCODE_ZEN_BASE_URL",
),
"opencode-go": ProviderConfig(
id="opencode-go",
name="OpenCode Go",
auth_type="api_key",
inference_base_url="https://opencode.ai/zen/go/v1",
api_key_env_vars=("OPENCODE_GO_API_KEY",),
base_url_env_var="OPENCODE_GO_BASE_URL",
),
"kilocode": ProviderConfig(
id="kilocode",
name="Kilo Code",
auth_type="api_key",
inference_base_url="https://api.kilo.ai/api/gateway",
api_key_env_vars=("KILOCODE_API_KEY",),
base_url_env_var="KILOCODE_BASE_URL",
),
}
# =============================================================================
# Kimi Code Endpoint Detection
# =============================================================================
# Kimi Code (platform.kimi.ai) issues keys prefixed "sk-kimi-" that only work
# on api.kimi.com/coding/v1. Legacy keys from platform.moonshot.ai work on
# api.moonshot.ai/v1 (the default). Auto-detect when user hasn't set
# KIMI_BASE_URL explicitly.
KIMI_CODE_BASE_URL = "https://api.kimi.com/coding/v1"
def _resolve_kimi_base_url(api_key: str, default_url: str, env_override: str) -> str:
"""Return the correct Kimi base URL based on the API key prefix.
If the user has explicitly set KIMI_BASE_URL, that always wins.
Otherwise, sk-kimi- prefixed keys route to api.kimi.com/coding/v1.
"""
if env_override:
return env_override
if api_key.startswith("sk-kimi-"):
return KIMI_CODE_BASE_URL
return default_url
def _gh_cli_candidates() -> list[str]:
"""Return candidate ``gh`` binary paths, including common Homebrew installs."""
candidates: list[str] = []
resolved = shutil.which("gh")
if resolved:
candidates.append(resolved)
for candidate in (
"/opt/homebrew/bin/gh",
"/usr/local/bin/gh",
str(Path.home() / ".local" / "bin" / "gh"),
):
if candidate in candidates:
continue
if os.path.isfile(candidate) and os.access(candidate, os.X_OK):
candidates.append(candidate)
return candidates
def _try_gh_cli_token() -> Optional[str]:
"""Return a token from ``gh auth token`` when the GitHub CLI is available."""
for gh_path in _gh_cli_candidates():
try:
result = subprocess.run(
[gh_path, "auth", "token"],
capture_output=True,
text=True,
timeout=5,
)
except (FileNotFoundError, subprocess.TimeoutExpired) as exc:
logger.debug("gh CLI token lookup failed (%s): %s", gh_path, exc)
continue
if result.returncode == 0 and result.stdout.strip():
return result.stdout.strip()
return None
_PLACEHOLDER_SECRET_VALUES = {
"*",
"**",
"***",
"changeme",
"your_api_key",
"your-api-key",
"placeholder",
"example",
"dummy",
"null",
"none",
}
def has_usable_secret(value: Any, *, min_length: int = 4) -> bool:
"""Return True when a configured secret looks usable, not empty/placeholder."""
if not isinstance(value, str):
return False
cleaned = value.strip()
if len(cleaned) < min_length:
return False
if cleaned.lower() in _PLACEHOLDER_SECRET_VALUES:
return False
return True
def _resolve_api_key_provider_secret(
provider_id: str, pconfig: ProviderConfig
) -> tuple[str, str]:
"""Resolve an API-key provider's token and indicate where it came from."""
if provider_id == "copilot":
# Use the dedicated copilot auth module for proper token validation
try:
from hermes_cli.copilot_auth import resolve_copilot_token
token, source = resolve_copilot_token()
if token:
return token, source
except ValueError as exc:
logger.warning("Copilot token validation failed: %s", exc)
except Exception:
pass
return "", ""
for env_var in pconfig.api_key_env_vars:
val = os.getenv(env_var, "").strip()
if has_usable_secret(val):
return val, env_var
return "", ""
# =============================================================================
# Z.AI Endpoint Detection
# =============================================================================
# Z.AI has separate billing for general vs coding plans, and global vs China
# endpoints. A key that works on one may return "Insufficient balance" on
# another. We probe at setup time and store the working endpoint.
ZAI_ENDPOINTS = [
# (id, base_url, default_model, label)
("global", "https://api.z.ai/api/paas/v4", "glm-5", "Global"),
("cn", "https://open.bigmodel.cn/api/paas/v4", "glm-5", "China"),
("coding-global", "https://api.z.ai/api/coding/paas/v4", "glm-4.7", "Global (Coding Plan)"),
("coding-cn", "https://open.bigmodel.cn/api/coding/paas/v4", "glm-4.7", "China (Coding Plan)"),
]
def detect_zai_endpoint(api_key: str, timeout: float = 8.0) -> Optional[Dict[str, str]]:
"""Probe z.ai endpoints to find one that accepts this API key.
Returns {"id": ..., "base_url": ..., "model": ..., "label": ...} for the
first working endpoint, or None if all fail.
"""
for ep_id, base_url, model, label in ZAI_ENDPOINTS:
try:
resp = httpx.post(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
json={
"model": model,
"stream": False,
"max_tokens": 1,
"messages": [{"role": "user", "content": "ping"}],
},
timeout=timeout,
)
if resp.status_code == 200:
logger.debug("Z.AI endpoint probe: %s (%s) OK", ep_id, base_url)
return {
"id": ep_id,
"base_url": base_url,
"model": model,
"label": label,
}
logger.debug("Z.AI endpoint probe: %s returned %s", ep_id, resp.status_code)
except Exception as exc:
logger.debug("Z.AI endpoint probe: %s failed: %s", ep_id, exc)
return None
# =============================================================================
# Error Types
# =============================================================================
@@ -467,64 +222,31 @@ def _auth_lock_path() -> Path:
return _auth_file_path().with_suffix(".lock")
_auth_lock_holder = threading.local()
@contextmanager
def _auth_store_lock(timeout_seconds: float = AUTH_LOCK_TIMEOUT_SECONDS):
"""Cross-process advisory lock for auth.json reads+writes. Reentrant."""
# Reentrant: if this thread already holds the lock, just yield.
if getattr(_auth_lock_holder, "depth", 0) > 0:
_auth_lock_holder.depth += 1
try:
yield
finally:
_auth_lock_holder.depth -= 1
return
"""Cross-process advisory lock for auth.json reads+writes."""
lock_path = _auth_lock_path()
lock_path.parent.mkdir(parents=True, exist_ok=True)
if fcntl is None and msvcrt is None:
_auth_lock_holder.depth = 1
try:
with lock_path.open("a+") as lock_file:
if fcntl is None:
yield
finally:
_auth_lock_holder.depth = 0
return
return
# On Windows, msvcrt.locking needs the file to have content and the
# file pointer at position 0. Ensure the lock file has at least 1 byte.
if msvcrt and (not lock_path.exists() or lock_path.stat().st_size == 0):
lock_path.write_text(" ", encoding="utf-8")
with lock_path.open("r+" if msvcrt else "a+") as lock_file:
deadline = time.time() + max(1.0, timeout_seconds)
while True:
try:
if fcntl:
fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
else:
lock_file.seek(0)
msvcrt.locking(lock_file.fileno(), msvcrt.LK_NBLCK, 1)
fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
break
except (BlockingIOError, OSError, PermissionError):
except BlockingIOError:
if time.time() >= deadline:
raise TimeoutError("Timed out waiting for auth store lock")
time.sleep(0.05)
_auth_lock_holder.depth = 1
try:
yield
finally:
_auth_lock_holder.depth = 0
if fcntl:
fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
elif msvcrt:
try:
lock_file.seek(0)
msvcrt.locking(lock_file.fileno(), msvcrt.LK_UNLCK, 1)
except (OSError, IOError):
pass
fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
def _load_auth_store(auth_file: Optional[Path] = None) -> Dict[str, Any]:
@@ -679,14 +401,6 @@ def resolve_provider(
"glm": "zai", "z-ai": "zai", "z.ai": "zai", "zhipu": "zai",
"kimi": "kimi-coding", "moonshot": "kimi-coding",
"minimax-china": "minimax-cn", "minimax_cn": "minimax-cn",
"claude": "anthropic", "claude-code": "anthropic",
"github": "copilot", "github-copilot": "copilot",
"github-models": "copilot", "github-model": "copilot",
"github-copilot-acp": "copilot-acp", "copilot-acp-agent": "copilot-acp",
"aigateway": "ai-gateway", "vercel": "ai-gateway", "vercel-ai-gateway": "ai-gateway",
"opencode": "opencode-zen", "zen": "opencode-zen",
"go": "opencode-go", "opencode-go-sub": "opencode-go",
"kilo": "kilocode", "kilo-code": "kilocode", "kilo-gateway": "kilocode",
}
normalized = _PROVIDER_ALIASES.get(normalized, normalized)
@@ -715,20 +429,15 @@ def resolve_provider(
except Exception as e:
logger.debug("Could not detect active auth provider: %s", e)
if has_usable_secret(os.getenv("OPENAI_API_KEY")) or has_usable_secret(os.getenv("OPENROUTER_API_KEY")):
if os.getenv("OPENAI_API_KEY") or os.getenv("OPENROUTER_API_KEY"):
return "openrouter"
# Auto-detect API-key providers by checking their env vars
for pid, pconfig in PROVIDER_REGISTRY.items():
if pconfig.auth_type != "api_key":
continue
# GitHub tokens are commonly present for repo/tool access but should not
# hijack inference auto-selection unless the user explicitly chooses
# Copilot/GitHub Models as the provider.
if pid == "copilot":
continue
for env_var in pconfig.api_key_env_vars:
if has_usable_secret(os.getenv(env_var, "")):
if os.getenv(env_var, "").strip():
return pid
return "openrouter"
@@ -1270,19 +979,6 @@ def fetch_nous_models(
continue
model_ids.append(mid)
# Sort: prefer opus > pro > haiku/flash > sonnet (sonnet is cheap/fast,
# users who want the best model should see opus first).
def _model_priority(mid: str) -> tuple:
low = mid.lower()
if "opus" in low:
return (0, mid)
if "pro" in low and "sonnet" not in low:
return (1, mid)
if "sonnet" in low:
return (3, mid)
return (2, mid)
model_ids.sort(key=_model_priority)
return list(dict.fromkeys(model_ids))
@@ -1595,18 +1291,18 @@ def get_api_key_provider_status(provider_id: str) -> Dict[str, Any]:
api_key = ""
key_source = ""
api_key, key_source = _resolve_api_key_provider_secret(provider_id, pconfig)
for env_var in pconfig.api_key_env_vars:
val = os.getenv(env_var, "").strip()
if val:
api_key = val
key_source = env_var
break
env_url = ""
base_url = pconfig.inference_base_url
if pconfig.base_url_env_var:
env_url = os.getenv(pconfig.base_url_env_var, "").strip()
if provider_id == "kimi-coding":
base_url = _resolve_kimi_base_url(api_key, pconfig.inference_base_url, env_url)
elif env_url:
base_url = env_url
else:
base_url = pconfig.inference_base_url
if env_url:
base_url = env_url
return {
"configured": bool(api_key),
@@ -1618,36 +1314,6 @@ def get_api_key_provider_status(provider_id: str) -> Dict[str, Any]:
}
def get_external_process_provider_status(provider_id: str) -> Dict[str, Any]:
"""Status snapshot for providers that run a local subprocess."""
pconfig = PROVIDER_REGISTRY.get(provider_id)
if not pconfig or pconfig.auth_type != "external_process":
return {"configured": False}
command = (
os.getenv("HERMES_COPILOT_ACP_COMMAND", "").strip()
or os.getenv("COPILOT_CLI_PATH", "").strip()
or "copilot"
)
raw_args = os.getenv("HERMES_COPILOT_ACP_ARGS", "").strip()
args = shlex.split(raw_args) if raw_args else ["--acp", "--stdio"]
base_url = os.getenv(pconfig.base_url_env_var, "").strip() if pconfig.base_url_env_var else ""
if not base_url:
base_url = pconfig.inference_base_url
resolved_command = shutil.which(command) if command else None
return {
"configured": bool(resolved_command or base_url.startswith("acp+tcp://")),
"provider": provider_id,
"name": pconfig.name,
"command": command,
"args": args,
"resolved_command": resolved_command,
"base_url": base_url,
"logged_in": bool(resolved_command or base_url.startswith("acp+tcp://")),
}
def get_auth_status(provider_id: Optional[str] = None) -> Dict[str, Any]:
"""Generic auth status dispatcher."""
target = provider_id or get_active_provider()
@@ -1655,8 +1321,6 @@ def get_auth_status(provider_id: Optional[str] = None) -> Dict[str, Any]:
return get_nous_auth_status()
if target == "openai-codex":
return get_codex_auth_status()
if target == "copilot-acp":
return get_external_process_provider_status(target)
# API-key providers
pconfig = PROVIDER_REGISTRY.get(target)
if pconfig and pconfig.auth_type == "api_key":
@@ -1679,18 +1343,18 @@ def resolve_api_key_provider_credentials(provider_id: str) -> Dict[str, Any]:
api_key = ""
key_source = ""
api_key, key_source = _resolve_api_key_provider_secret(provider_id, pconfig)
for env_var in pconfig.api_key_env_vars:
val = os.getenv(env_var, "").strip()
if val:
api_key = val
key_source = env_var
break
env_url = ""
base_url = pconfig.inference_base_url
if pconfig.base_url_env_var:
env_url = os.getenv(pconfig.base_url_env_var, "").strip()
if provider_id == "kimi-coding":
base_url = _resolve_kimi_base_url(api_key, pconfig.inference_base_url, env_url)
elif env_url:
base_url = env_url.rstrip("/")
else:
base_url = pconfig.inference_base_url
if env_url:
base_url = env_url.rstrip("/")
return {
"provider": provider_id,
@@ -1700,46 +1364,6 @@ def resolve_api_key_provider_credentials(provider_id: str) -> Dict[str, Any]:
}
def resolve_external_process_provider_credentials(provider_id: str) -> Dict[str, Any]:
"""Resolve runtime details for local subprocess-backed providers."""
pconfig = PROVIDER_REGISTRY.get(provider_id)
if not pconfig or pconfig.auth_type != "external_process":
raise AuthError(
f"Provider '{provider_id}' is not an external-process provider.",
provider=provider_id,
code="invalid_provider",
)
base_url = os.getenv(pconfig.base_url_env_var, "").strip() if pconfig.base_url_env_var else ""
if not base_url:
base_url = pconfig.inference_base_url
command = (
os.getenv("HERMES_COPILOT_ACP_COMMAND", "").strip()
or os.getenv("COPILOT_CLI_PATH", "").strip()
or "copilot"
)
raw_args = os.getenv("HERMES_COPILOT_ACP_ARGS", "").strip()
args = shlex.split(raw_args) if raw_args else ["--acp", "--stdio"]
resolved_command = shutil.which(command) if command else None
if not resolved_command and not base_url.startswith("acp+tcp://"):
raise AuthError(
f"Could not find the Copilot CLI command '{command}'. "
"Install GitHub Copilot CLI or set HERMES_COPILOT_ACP_COMMAND/COPILOT_CLI_PATH.",
provider=provider_id,
code="missing_copilot_cli",
)
return {
"provider": provider_id,
"api_key": "copilot-acp",
"base_url": base_url.rstrip("/"),
"command": resolved_command or command,
"args": args,
"source": "process",
}
# =============================================================================
# External credential detection
# =============================================================================
@@ -1771,20 +1395,8 @@ def detect_external_credentials() -> List[Dict[str, Any]]:
# CLI Commands — login / logout
# =============================================================================
def _update_config_for_provider(
provider_id: str,
inference_base_url: str,
default_model: Optional[str] = None,
) -> Path:
"""Update config.yaml and auth.json to reflect the active provider.
When *default_model* is provided the function also writes it as the
``model.default`` value. This prevents a race condition where the
gateway (which re-reads config per-message) picks up the new provider
before the caller has finished model selection, resulting in a
mismatched model/provider (e.g. ``anthropic/claude-opus-4.6`` sent to
MiniMax's API).
"""
def _update_config_for_provider(provider_id: str, inference_base_url: str) -> Path:
"""Update config.yaml and auth.json to reflect the active provider."""
# Set active_provider in auth.json so auto-resolution picks this provider
with _auth_store_lock():
auth_store = _load_auth_store()
@@ -1813,20 +1425,7 @@ def _update_config_for_provider(
model_cfg = {}
model_cfg["provider"] = provider_id
if inference_base_url and inference_base_url.strip():
model_cfg["base_url"] = inference_base_url.rstrip("/")
else:
# Clear stale base_url to prevent contamination when switching providers
model_cfg.pop("base_url", None)
# When switching to a non-OpenRouter provider, ensure model.default is
# valid for the new provider. An OpenRouter-formatted name like
# "anthropic/claude-opus-4.6" will fail on direct-API providers.
if default_model:
cur_default = model_cfg.get("default", "")
if not cur_default or "/" in cur_default:
model_cfg["default"] = default_model
model_cfg["base_url"] = inference_base_url.rstrip("/")
config["model"] = model_cfg
config_path.write_text(yaml.safe_dump(config, sort_keys=False))
@@ -1934,20 +1533,17 @@ def _prompt_model_selection(model_ids: List[str], current_model: str = "") -> Op
def _save_model_choice(model_id: str) -> None:
"""Save the selected model to config.yaml (single source of truth).
The model is stored in config.yaml only — NOT in .env. This avoids
conflicts in multi-agent setups where env vars would stomp each other.
"""
from hermes_cli.config import save_config, load_config
"""Save the selected model to config.yaml and .env."""
from hermes_cli.config import save_config, load_config, save_env_value
config = load_config()
# Always use dict format so provider/base_url can be stored alongside
# Handle both string and dict model formats
if isinstance(config.get("model"), dict):
config["model"]["default"] = model_id
else:
config["model"] = {"default": model_id}
config["model"] = model_id
save_config(config)
save_env_value("LLM_MODEL", model_id)
def login_command(args) -> None:

View File

@@ -6,9 +6,7 @@ Pure display functions with no HermesCLI state dependency.
import json
import logging
import os
import shutil
import subprocess
import threading
import time
from pathlib import Path
from typing import Dict, List, Any, Optional
@@ -27,7 +25,7 @@ logger = logging.getLogger(__name__)
# ANSI building blocks for conversation display
# =========================================================================
_GOLD = "\033[1;38;2;255;215;0m" # True-color #FFD700 bold
_GOLD = "\033[1;33m"
_BOLD = "\033[1m"
_DIM = "\033[2m"
_RST = "\033[0m"
@@ -38,33 +36,11 @@ def cprint(text: str):
_pt_print(_PT_ANSI(text))
# =========================================================================
# Skin-aware color helpers
# =========================================================================
def _skin_color(key: str, fallback: str) -> str:
"""Get a color from the active skin, or return fallback."""
try:
from hermes_cli.skin_engine import get_active_skin
return get_active_skin().get_color(key, fallback)
except Exception:
return fallback
def _skin_branding(key: str, fallback: str) -> str:
"""Get a branding string from the active skin, or return fallback."""
try:
from hermes_cli.skin_engine import get_active_skin
return get_active_skin().get_branding(key, fallback)
except Exception:
return fallback
# =========================================================================
# ASCII Art & Branding
# =========================================================================
from hermes_cli import __version__ as VERSION, __release_date__ as RELEASE_DATE
from hermes_cli import __version__ as VERSION
HERMES_AGENT_LOGO = """[bold #FFD700]██╗ ██╗███████╗██████╗ ███╗ ███╗███████╗███████╗ █████╗ ██████╗ ███████╗███╗ ██╗████████╗[/]
[bold #FFD700]██║ ██║██╔════╝██╔══██╗████╗ ████║██╔════╝██╔════╝ ██╔══██╗██╔════╝ ██╔════╝████╗ ██║╚══██╔══╝[/]
@@ -102,22 +78,27 @@ COMPACT_BANNER = """
# =========================================================================
def get_available_skills() -> Dict[str, List[str]]:
"""Return skills grouped by category, filtered by platform and disabled state.
"""Scan ~/.hermes/skills/ and return skills grouped by category."""
import os
Delegates to ``_find_all_skills()`` from ``tools/skills_tool`` which already
handles platform gating (``platforms:`` frontmatter) and respects the
user's ``skills.disabled`` config list.
"""
try:
from tools.skills_tool import _find_all_skills
all_skills = _find_all_skills() # already filtered
except Exception:
return {}
hermes_home = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
skills_dir = hermes_home / "skills"
skills_by_category = {}
if not skills_dir.exists():
return skills_by_category
for skill_file in skills_dir.rglob("SKILL.md"):
rel_path = skill_file.relative_to(skills_dir)
parts = rel_path.parts
if len(parts) >= 2:
category = parts[0]
skill_name = parts[-2]
else:
category = "general"
skill_name = skill_file.parent.name
skills_by_category.setdefault(category, []).append(skill_name)
skills_by_category: Dict[str, List[str]] = {}
for skill in all_skills:
category = skill.get("category") or "general"
skills_by_category.setdefault(category, []).append(skill["name"])
return skills_by_category
@@ -140,9 +121,7 @@ def check_for_updates() -> Optional[int]:
repo_dir = hermes_home / "hermes-agent"
cache_file = hermes_home / ".update_check"
# Must be a git repo — fall back to project root for dev installs
if not (repo_dir / ".git").exists():
repo_dir = Path(__file__).parent.parent.resolve()
# Must be a git repo
if not (repo_dir / ".git").exists():
return None
@@ -189,30 +168,6 @@ def check_for_updates() -> Optional[int]:
return behind
# =========================================================================
# Non-blocking update check
# =========================================================================
_update_result: Optional[int] = None
_update_check_done = threading.Event()
def prefetch_update_check():
"""Kick off update check in a background daemon thread."""
def _run():
global _update_result
_update_result = check_for_updates()
_update_check_done.set()
t = threading.Thread(target=_run, daemon=True)
t.start()
def get_update_result(timeout: float = 0.5) -> Optional[int]:
"""Get result of prefetched check. Returns None if not ready."""
_update_check_done.wait(timeout=timeout)
return _update_result
# =========================================================================
# Welcome banner
# =========================================================================
@@ -228,17 +183,6 @@ def _format_context_length(tokens: int) -> str:
return str(tokens)
def _display_toolset_name(toolset_name: str) -> str:
"""Normalize internal/legacy toolset identifiers for banner display."""
if not toolset_name:
return "unknown"
return (
toolset_name[:-6]
if toolset_name.endswith("_tools")
else toolset_name
)
def build_welcome_banner(console: Console, model: str, cwd: str,
tools: List[dict] = None,
enabled_toolsets: List[str] = None,
@@ -273,44 +217,28 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
layout_table.add_column("left", justify="center")
layout_table.add_column("right", justify="left")
# Resolve skin colors once for the entire banner
accent = _skin_color("banner_accent", "#FFBF00")
dim = _skin_color("banner_dim", "#B8860B")
text = _skin_color("banner_text", "#FFF8DC")
session_color = _skin_color("session_border", "#8B8682")
# Use skin's custom caduceus art if provided
try:
from hermes_cli.skin_engine import get_active_skin
_bskin = get_active_skin()
_hero = _bskin.banner_hero if hasattr(_bskin, 'banner_hero') and _bskin.banner_hero else HERMES_CADUCEUS
except Exception:
_bskin = None
_hero = HERMES_CADUCEUS
left_lines = ["", _hero, ""]
left_lines = ["", HERMES_CADUCEUS, ""]
model_short = model.split("/")[-1] if "/" in model else model
if model_short.endswith(".gguf"):
model_short = model_short[:-5]
if len(model_short) > 28:
model_short = model_short[:25] + "..."
ctx_str = f" [dim {dim}]·[/] [dim {dim}]{_format_context_length(context_length)} context[/]" if context_length else ""
left_lines.append(f"[{accent}]{model_short}[/]{ctx_str} [dim {dim}]·[/] [dim {dim}]Nous Research[/]")
left_lines.append(f"[dim {dim}]{cwd}[/]")
ctx_str = f" [dim #B8860B]·[/] [dim #B8860B]{_format_context_length(context_length)} context[/]" if context_length else ""
left_lines.append(f"[#FFBF00]{model_short}[/]{ctx_str} [dim #B8860B]·[/] [dim #B8860B]Nous Research[/]")
left_lines.append(f"[dim #B8860B]{cwd}[/]")
if session_id:
left_lines.append(f"[dim {session_color}]Session: {session_id}[/]")
left_lines.append(f"[dim #8B8682]Session: {session_id}[/]")
left_content = "\n".join(left_lines)
right_lines = [f"[bold {accent}]Available Tools[/]"]
right_lines = ["[bold #FFBF00]Available Tools[/]"]
toolsets_dict: Dict[str, list] = {}
for tool in tools:
tool_name = tool["function"]["name"]
toolset = _display_toolset_name(get_toolset_for_tool(tool_name) or "other")
toolset = get_toolset_for_tool(tool_name) or "other"
toolsets_dict.setdefault(toolset, []).append(tool_name)
for item in unavailable_toolsets:
toolset_id = item.get("id", item.get("name", "unknown"))
display_name = _display_toolset_name(toolset_id)
display_name = f"{toolset_id}_tools" if not toolset_id.endswith("_tools") else toolset_id
if display_name not in toolsets_dict:
toolsets_dict[display_name] = []
for tool_name in item.get("tools", []):
@@ -328,7 +256,7 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
if name in disabled_tools:
colored_names.append(f"[red]{name}[/]")
else:
colored_names.append(f"[{text}]{name}[/]")
colored_names.append(f"[#FFF8DC]{name}[/]")
tools_str = ", ".join(colored_names)
if len(", ".join(sorted(tool_names))) > 45:
@@ -347,13 +275,13 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
elif name in disabled_tools:
colored_names.append(f"[red]{name}[/]")
else:
colored_names.append(f"[{text}]{name}[/]")
colored_names.append(f"[#FFF8DC]{name}[/]")
tools_str = ", ".join(colored_names)
right_lines.append(f"[dim {dim}]{toolset}:[/] {tools_str}")
right_lines.append(f"[dim #B8860B]{toolset}:[/] {tools_str}")
if remaining_toolsets > 0:
right_lines.append(f"[dim {dim}](and {remaining_toolsets} more toolsets...)[/]")
right_lines.append(f"[dim #B8860B](and {remaining_toolsets} more toolsets...)[/]")
# MCP Servers section (only if configured)
try:
@@ -364,12 +292,12 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
if mcp_status:
right_lines.append("")
right_lines.append(f"[bold {accent}]MCP Servers[/]")
right_lines.append("[bold #FFBF00]MCP Servers[/]")
for srv in mcp_status:
if srv["connected"]:
right_lines.append(
f"[dim {dim}]{srv['name']}[/] [{text}]({srv['transport']})[/] "
f"[dim {dim}]—[/] [{text}]{srv['tools']} tool(s)[/]"
f"[dim #B8860B]{srv['name']}[/] [#FFF8DC]({srv['transport']})[/] "
f"[dim #B8860B]—[/] [#FFF8DC]{srv['tools']} tool(s)[/]"
)
else:
right_lines.append(
@@ -378,7 +306,7 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
)
right_lines.append("")
right_lines.append(f"[bold {accent}]Available Skills[/]")
right_lines.append("[bold #FFBF00]Available Skills[/]")
skills_by_category = get_available_skills()
total_skills = sum(len(s) for s in skills_by_category.values())
@@ -392,9 +320,9 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
skills_str = ", ".join(skill_names)
if len(skills_str) > 50:
skills_str = skills_str[:47] + "..."
right_lines.append(f"[dim {dim}]{category}:[/] [{text}]{skills_str}[/]")
right_lines.append(f"[dim #B8860B]{category}:[/] [#FFF8DC]{skills_str}[/]")
else:
right_lines.append(f"[dim {dim}]No skills installed[/]")
right_lines.append("[dim #B8860B]No skills installed[/]")
right_lines.append("")
mcp_connected = sum(1 for s in mcp_status if s["connected"]) if mcp_status else 0
@@ -402,11 +330,11 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
if mcp_connected:
summary_parts.append(f"{mcp_connected} MCP servers")
summary_parts.append("/help for commands")
right_lines.append(f"[dim {dim}]{' · '.join(summary_parts)}[/]")
right_lines.append(f"[dim #B8860B]{' · '.join(summary_parts)}[/]")
# Update check — use prefetched result if available
# Update check — show if behind origin/main
try:
behind = get_update_result(timeout=0.5)
behind = check_for_updates()
if behind and behind > 0:
commits_word = "commit" if behind == 1 else "commits"
right_lines.append(
@@ -419,20 +347,14 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
right_content = "\n".join(right_lines)
layout_table.add_row(left_content, right_content)
agent_name = _skin_branding("agent_name", "Hermes Agent")
title_color = _skin_color("banner_title", "#FFD700")
border_color = _skin_color("banner_border", "#CD7F32")
outer_panel = Panel(
layout_table,
title=f"[bold {title_color}]{agent_name} v{VERSION} ({RELEASE_DATE})[/]",
border_style=border_color,
title=f"[bold #FFD700]Hermes Agent {VERSION}[/]",
border_style="#CD7F32",
padding=(0, 2),
)
console.print()
term_width = shutil.get_terminal_size().columns
if term_width >= 95:
_logo = _bskin.banner_logo if _bskin and hasattr(_bskin, 'banner_logo') and _bskin.banner_logo else HERMES_AGENT_LOGO
console.print(_logo)
console.print()
console.print(HERMES_AGENT_LOGO)
console.print()
console.print(outer_panel)

View File

@@ -8,10 +8,8 @@ with the TUI.
import queue
import time as _time
import getpass
from hermes_cli.banner import cprint, _DIM, _RST
from hermes_cli.config import save_env_value_secure
def clarify_callback(cli, question, choices):
@@ -35,7 +33,7 @@ def clarify_callback(cli, question, choices):
cli._clarify_deadline = _time.monotonic() + timeout
cli._clarify_freetext = is_open_ended
if hasattr(cli, "_app") and cli._app:
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
while True:
@@ -47,13 +45,13 @@ def clarify_callback(cli, question, choices):
remaining = cli._clarify_deadline - _time.monotonic()
if remaining <= 0:
break
if hasattr(cli, "_app") and cli._app:
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
cli._clarify_state = None
cli._clarify_freetext = False
cli._clarify_deadline = 0
if hasattr(cli, "_app") and cli._app:
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
cprint(f"\n{_DIM}(clarify timed out after {timeout}s — agent will decide){_RST}")
return (
@@ -73,7 +71,7 @@ def sudo_password_callback(cli) -> str:
cli._sudo_state = {"response_queue": response_queue}
cli._sudo_deadline = _time.monotonic() + timeout
if hasattr(cli, "_app") and cli._app:
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
while True:
@@ -81,7 +79,7 @@ def sudo_password_callback(cli) -> str:
result = response_queue.get(timeout=1)
cli._sudo_state = None
cli._sudo_deadline = 0
if hasattr(cli, "_app") and cli._app:
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
if result:
cprint(f"\n{_DIM} ✓ Password received (cached for session){_RST}")
@@ -92,188 +90,56 @@ def sudo_password_callback(cli) -> str:
remaining = cli._sudo_deadline - _time.monotonic()
if remaining <= 0:
break
if hasattr(cli, "_app") and cli._app:
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
cli._sudo_state = None
cli._sudo_deadline = 0
if hasattr(cli, "_app") and cli._app:
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
cprint(f"\n{_DIM} ⏱ Timeout — continuing without sudo{_RST}")
return ""
def prompt_for_secret(cli, var_name: str, prompt: str, metadata=None) -> dict:
"""Prompt for a secret value through the TUI (e.g. API keys for skills).
Returns a dict with keys: success, stored_as, validated, skipped, message.
The secret is stored in ~/.hermes/.env and never exposed to the model.
"""
if not getattr(cli, "_app", None):
if not hasattr(cli, "_secret_state"):
cli._secret_state = None
if not hasattr(cli, "_secret_deadline"):
cli._secret_deadline = 0
try:
value = getpass.getpass(f"{prompt} (hidden, Enter to skip): ")
except (EOFError, KeyboardInterrupt):
value = ""
if not value:
cprint(f"\n{_DIM} ⏭ Secret entry cancelled{_RST}")
return {
"success": True,
"reason": "cancelled",
"stored_as": var_name,
"validated": False,
"skipped": True,
"message": "Secret setup was skipped.",
}
stored = save_env_value_secure(var_name, value)
cprint(f"\n{_DIM} ✓ Stored secret in ~/.hermes/.env as {var_name}{_RST}")
return {
**stored,
"skipped": False,
"message": "Secret stored securely. The secret value was not exposed to the model.",
}
timeout = 120
response_queue = queue.Queue()
cli._secret_state = {
"var_name": var_name,
"prompt": prompt,
"metadata": metadata or {},
"response_queue": response_queue,
}
cli._secret_deadline = _time.monotonic() + timeout
# Avoid storing stale draft input as the secret when Enter is pressed.
if hasattr(cli, "_clear_secret_input_buffer"):
try:
cli._clear_secret_input_buffer()
except Exception:
pass
elif hasattr(cli, "_app") and cli._app:
try:
cli._app.current_buffer.reset()
except Exception:
pass
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
while True:
try:
value = response_queue.get(timeout=1)
cli._secret_state = None
cli._secret_deadline = 0
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
if not value:
cprint(f"\n{_DIM} ⏭ Secret entry cancelled{_RST}")
return {
"success": True,
"reason": "cancelled",
"stored_as": var_name,
"validated": False,
"skipped": True,
"message": "Secret setup was skipped.",
}
stored = save_env_value_secure(var_name, value)
cprint(f"\n{_DIM} ✓ Stored secret in ~/.hermes/.env as {var_name}{_RST}")
return {
**stored,
"skipped": False,
"message": "Secret stored securely. The secret value was not exposed to the model.",
}
except queue.Empty:
remaining = cli._secret_deadline - _time.monotonic()
if remaining <= 0:
break
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cli._secret_state = None
cli._secret_deadline = 0
if hasattr(cli, "_clear_secret_input_buffer"):
try:
cli._clear_secret_input_buffer()
except Exception:
pass
elif hasattr(cli, "_app") and cli._app:
try:
cli._app.current_buffer.reset()
except Exception:
pass
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cprint(f"\n{_DIM} ⏱ Timeout — secret capture cancelled{_RST}")
return {
"success": True,
"reason": "timeout",
"stored_as": var_name,
"validated": False,
"skipped": True,
"message": "Secret setup timed out and was skipped.",
}
def approval_callback(cli, command: str, description: str) -> str:
"""Prompt for dangerous command approval through the TUI.
Shows a selection UI with choices: once / session / always / deny.
When the command is longer than 70 characters, a "view" option is
included so the user can reveal the full text before deciding.
Uses cli._approval_lock to serialize concurrent requests (e.g. from
parallel delegation subtasks) so each prompt gets its own turn.
"""
lock = getattr(cli, "_approval_lock", None)
if lock is None:
import threading
cli._approval_lock = threading.Lock()
lock = cli._approval_lock
timeout = 60
response_queue = queue.Queue()
choices = ["once", "session", "always", "deny"]
with lock:
timeout = 60
response_queue = queue.Queue()
choices = ["once", "session", "always", "deny"]
if len(command) > 70:
choices.append("view")
cli._approval_state = {
"command": command,
"description": description,
"choices": choices,
"selected": 0,
"response_queue": response_queue,
}
cli._approval_deadline = _time.monotonic() + timeout
cli._approval_state = {
"command": command,
"description": description,
"choices": choices,
"selected": 0,
"response_queue": response_queue,
}
cli._approval_deadline = _time.monotonic() + timeout
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
while True:
try:
result = response_queue.get(timeout=1)
cli._approval_state = None
cli._approval_deadline = 0
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
return result
except queue.Empty:
remaining = cli._approval_deadline - _time.monotonic()
if remaining <= 0:
break
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
while True:
try:
result = response_queue.get(timeout=1)
cli._approval_state = None
cli._approval_deadline = 0
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
return result
except queue.Empty:
remaining = cli._approval_deadline - _time.monotonic()
if remaining <= 0:
break
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cli._approval_state = None
cli._approval_deadline = 0
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cprint(f"\n{_DIM} ⏱ Timeout — denying command{_RST}")
return "deny"
cli._approval_state = None
cli._approval_deadline = 0
if hasattr(cli, '_app') and cli._app:
cli._app.invalidate()
cprint(f"\n{_DIM} ⏱ Timeout — denying command{_RST}")
return "deny"

View File

@@ -1,135 +0,0 @@
"""Shared curses-based multi-select checklist for Hermes CLI.
Used by both ``hermes tools`` and ``hermes skills`` to present a
toggleable list of items. Falls back to a numbered text UI when
curses is unavailable (Windows without curses, piped stdin, etc.).
"""
from typing import List, Set
from hermes_cli.colors import Colors, color
def curses_checklist(
title: str,
items: List[str],
pre_selected: Set[int],
) -> Set[int]:
"""Multi-select checklist. Returns set of **selected** indices.
Args:
title: Header text shown at the top of the checklist.
items: Display labels for each row.
pre_selected: Indices that start checked.
Returns:
The indices the user confirmed as checked. On cancel (ESC/q),
returns ``pre_selected`` unchanged.
"""
try:
import curses
selected = set(pre_selected)
result = [None]
def _ui(stdscr):
curses.curs_set(0)
if curses.has_colors():
curses.start_color()
curses.use_default_colors()
curses.init_pair(1, curses.COLOR_GREEN, -1)
curses.init_pair(2, curses.COLOR_YELLOW, -1)
curses.init_pair(3, 8, -1) # dim gray
cursor = 0
scroll_offset = 0
while True:
stdscr.clear()
max_y, max_x = stdscr.getmaxyx()
# Header
try:
hattr = curses.A_BOLD | (curses.color_pair(2) if curses.has_colors() else 0)
stdscr.addnstr(0, 0, title, max_x - 1, hattr)
stdscr.addnstr(
1, 0,
" ↑↓ navigate SPACE toggle ENTER confirm ESC cancel",
max_x - 1, curses.A_DIM,
)
except curses.error:
pass
# Scrollable item list
visible_rows = max_y - 3
if cursor < scroll_offset:
scroll_offset = cursor
elif cursor >= scroll_offset + visible_rows:
scroll_offset = cursor - visible_rows + 1
for draw_i, i in enumerate(
range(scroll_offset, min(len(items), scroll_offset + visible_rows))
):
y = draw_i + 3
if y >= max_y - 1:
break
check = "" if i in selected else " "
arrow = "" if i == cursor else " "
line = f" {arrow} [{check}] {items[i]}"
attr = curses.A_NORMAL
if i == cursor:
attr = curses.A_BOLD
if curses.has_colors():
attr |= curses.color_pair(1)
try:
stdscr.addnstr(y, 0, line, max_x - 1, attr)
except curses.error:
pass
stdscr.refresh()
key = stdscr.getch()
if key in (curses.KEY_UP, ord("k")):
cursor = (cursor - 1) % len(items)
elif key in (curses.KEY_DOWN, ord("j")):
cursor = (cursor + 1) % len(items)
elif key == ord(" "):
selected.symmetric_difference_update({cursor})
elif key in (curses.KEY_ENTER, 10, 13):
result[0] = set(selected)
return
elif key in (27, ord("q")):
result[0] = set(pre_selected)
return
curses.wrapper(_ui)
return result[0] if result[0] is not None else set(pre_selected)
except Exception:
pass # fall through to numbered fallback
# ── Numbered text fallback ────────────────────────────────────────────
selected = set(pre_selected)
print(color(f"\n {title}", Colors.YELLOW))
print(color(" Toggle by number, Enter to confirm.\n", Colors.DIM))
while True:
for i, label in enumerate(items):
check = "" if i in selected else " "
print(f" {i + 1:3}. [{check}] {label}")
print()
try:
raw = input(color(" Number to toggle, 's' to save, 'q' to cancel: ", Colors.DIM)).strip()
except (KeyboardInterrupt, EOFError):
return set(pre_selected)
if raw.lower() == "s" or raw == "":
return selected
if raw.lower() == "q":
return set(pre_selected)
try:
idx = int(raw) - 1
if 0 <= idx < len(items):
selected.symmetric_difference_update({idx})
except ValueError:
print(color(" Invalid input", Colors.DIM))

View File

@@ -1,311 +0,0 @@
"""hermes claw — OpenClaw migration commands.
Usage:
hermes claw migrate # Interactive migration from ~/.openclaw
hermes claw migrate --dry-run # Preview what would be migrated
hermes claw migrate --preset full --overwrite # Full migration, overwrite conflicts
"""
import importlib.util
import logging
import sys
from pathlib import Path
from hermes_cli.config import get_hermes_home, get_config_path, load_config, save_config
from hermes_cli.setup import (
Colors,
color,
print_header,
print_info,
print_success,
print_warning,
print_error,
prompt_yes_no,
prompt_choice,
)
logger = logging.getLogger(__name__)
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
_OPENCLAW_SCRIPT = (
PROJECT_ROOT
/ "optional-skills"
/ "migration"
/ "openclaw-migration"
/ "scripts"
/ "openclaw_to_hermes.py"
)
# Fallback: user may have installed the skill from the Hub
_OPENCLAW_SCRIPT_INSTALLED = (
get_hermes_home()
/ "skills"
/ "migration"
/ "openclaw-migration"
/ "scripts"
/ "openclaw_to_hermes.py"
)
def _find_migration_script() -> Path | None:
"""Find the openclaw_to_hermes.py script in known locations."""
for candidate in [_OPENCLAW_SCRIPT, _OPENCLAW_SCRIPT_INSTALLED]:
if candidate.exists():
return candidate
return None
def _load_migration_module(script_path: Path):
"""Dynamically load the migration script as a module."""
spec = importlib.util.spec_from_file_location("openclaw_to_hermes", script_path)
if spec is None or spec.loader is None:
return None
mod = importlib.util.module_from_spec(spec)
# Register in sys.modules so @dataclass can resolve the module
# (Python 3.11+ requires this for dynamically loaded modules)
sys.modules[spec.name] = mod
try:
spec.loader.exec_module(mod)
except Exception:
sys.modules.pop(spec.name, None)
raise
return mod
def claw_command(args):
"""Route hermes claw subcommands."""
action = getattr(args, "claw_action", None)
if action == "migrate":
_cmd_migrate(args)
else:
print("Usage: hermes claw migrate [options]")
print()
print("Commands:")
print(" migrate Migrate settings from OpenClaw to Hermes")
print()
print("Run 'hermes claw migrate --help' for migration options.")
def _cmd_migrate(args):
"""Run the OpenClaw → Hermes migration."""
source_dir = Path(getattr(args, "source", None) or Path.home() / ".openclaw")
dry_run = getattr(args, "dry_run", False)
preset = getattr(args, "preset", "full")
overwrite = getattr(args, "overwrite", False)
migrate_secrets = getattr(args, "migrate_secrets", False)
workspace_target = getattr(args, "workspace_target", None)
skill_conflict = getattr(args, "skill_conflict", "skip")
# If using the "full" preset, secrets are included by default
if preset == "full":
migrate_secrets = True
print()
print(
color(
"┌─────────────────────────────────────────────────────────┐",
Colors.MAGENTA,
)
)
print(
color(
"│ ⚕ Hermes — OpenClaw Migration │",
Colors.MAGENTA,
)
)
print(
color(
"└─────────────────────────────────────────────────────────┘",
Colors.MAGENTA,
)
)
# Check source directory
if not source_dir.is_dir():
print()
print_error(f"OpenClaw directory not found: {source_dir}")
print_info("Make sure your OpenClaw installation is at the expected path.")
print_info(f"You can specify a custom path: hermes claw migrate --source /path/to/.openclaw")
return
# Find the migration script
script_path = _find_migration_script()
if not script_path:
print()
print_error("Migration script not found.")
print_info("Expected at one of:")
print_info(f" {_OPENCLAW_SCRIPT}")
print_info(f" {_OPENCLAW_SCRIPT_INSTALLED}")
print_info("Make sure the openclaw-migration skill is installed.")
return
# Show what we're doing
hermes_home = get_hermes_home()
print()
print_header("Migration Settings")
print_info(f"Source: {source_dir}")
print_info(f"Target: {hermes_home}")
print_info(f"Preset: {preset}")
print_info(f"Mode: {'dry run (preview only)' if dry_run else 'execute'}")
print_info(f"Overwrite: {'yes' if overwrite else 'no (skip conflicts)'}")
print_info(f"Secrets: {'yes (allowlisted only)' if migrate_secrets else 'no'}")
if skill_conflict != "skip":
print_info(f"Skill conflicts: {skill_conflict}")
if workspace_target:
print_info(f"Workspace: {workspace_target}")
print()
# For execute mode (non-dry-run), confirm unless --yes was passed
if not dry_run and not getattr(args, "yes", False):
if not prompt_yes_no("Proceed with migration?", default=True):
print_info("Migration cancelled.")
return
# Ensure config.yaml exists before migration tries to read it
config_path = get_config_path()
if not config_path.exists():
save_config(load_config())
# Load and run the migration
try:
mod = _load_migration_module(script_path)
if mod is None:
print_error("Could not load migration script.")
return
selected = mod.resolve_selected_options(None, None, preset=preset)
ws_target = Path(workspace_target).resolve() if workspace_target else None
migrator = mod.Migrator(
source_root=source_dir.resolve(),
target_root=hermes_home.resolve(),
execute=not dry_run,
workspace_target=ws_target,
overwrite=overwrite,
migrate_secrets=migrate_secrets,
output_dir=None,
selected_options=selected,
preset_name=preset,
skill_conflict_mode=skill_conflict,
)
report = migrator.migrate()
except Exception as e:
print()
print_error(f"Migration failed: {e}")
logger.debug("OpenClaw migration error", exc_info=True)
return
# Print results
_print_migration_report(report, dry_run)
def _print_migration_report(report: dict, dry_run: bool):
"""Print a formatted migration report."""
summary = report.get("summary", {})
migrated = summary.get("migrated", 0)
skipped = summary.get("skipped", 0)
conflicts = summary.get("conflict", 0)
errors = summary.get("error", 0)
total = migrated + skipped + conflicts + errors
print()
if dry_run:
print_header("Dry Run Results")
print_info("No files were modified. This is a preview of what would happen.")
else:
print_header("Migration Results")
print()
# Detailed items
items = report.get("items", [])
if items:
# Group by status
migrated_items = [i for i in items if i.get("status") == "migrated"]
skipped_items = [i for i in items if i.get("status") == "skipped"]
conflict_items = [i for i in items if i.get("status") == "conflict"]
error_items = [i for i in items if i.get("status") == "error"]
if migrated_items:
label = "Would migrate" if dry_run else "Migrated"
print(color(f"{label}:", Colors.GREEN))
for item in migrated_items:
kind = item.get("kind", "unknown")
dest = item.get("destination", "")
if dest:
dest_short = str(dest).replace(str(Path.home()), "~")
print(f" {kind:<22s}{dest_short}")
else:
print(f" {kind}")
print()
if conflict_items:
print(color(f" ⚠ Conflicts (skipped — use --overwrite to force):", Colors.YELLOW))
for item in conflict_items:
kind = item.get("kind", "unknown")
reason = item.get("reason", "already exists")
print(f" {kind:<22s} {reason}")
print()
if skipped_items:
print(color(f" ─ Skipped:", Colors.DIM))
for item in skipped_items:
kind = item.get("kind", "unknown")
reason = item.get("reason", "")
print(f" {kind:<22s} {reason}")
print()
if error_items:
print(color(f" ✗ Errors:", Colors.RED))
for item in error_items:
kind = item.get("kind", "unknown")
reason = item.get("reason", "unknown error")
print(f" {kind:<22s} {reason}")
print()
# Summary line
parts = []
if migrated:
action = "would migrate" if dry_run else "migrated"
parts.append(f"{migrated} {action}")
if conflicts:
parts.append(f"{conflicts} conflict(s)")
if skipped:
parts.append(f"{skipped} skipped")
if errors:
parts.append(f"{errors} error(s)")
if parts:
print_info(f"Summary: {', '.join(parts)}")
else:
print_info("Nothing to migrate.")
# Output directory
output_dir = report.get("output_dir")
if output_dir:
print_info(f"Full report saved to: {output_dir}")
if dry_run:
print()
print_info("To execute the migration, run without --dry-run:")
print_info(f" hermes claw migrate --preset {report.get('preset', 'full')}")
elif migrated:
print()
print_success("Migration complete!")
# Warn if API keys were skipped (migrate_secrets not enabled)
skipped_keys = [
i for i in report.get("items", [])
if i.get("kind") == "provider-keys" and i.get("status") == "skipped"
]
if skipped_keys:
print()
print(color(" ⚠ API keys were NOT migrated (secrets migration is disabled by default).", Colors.YELLOW))
print(color(" Your OPENROUTER_API_KEY and other provider keys must be added manually.", Colors.YELLOW))
print()
print_info("To migrate API keys, re-run with:")
print_info(" hermes claw migrate --migrate-secrets")
print()
print_info("Or add your key manually:")
print_info(" hermes config set OPENROUTER_API_KEY sk-or-v1-...")

View File

@@ -254,7 +254,6 @@ def _wayland_save(dest: Path) -> bool:
)
if not dest.exists() or dest.stat().st_size == 0:
dest.unlink(missing_ok=True)
return False
# BMP needs conversion to PNG (common in WSLg where only BMP
@@ -286,27 +285,20 @@ def _convert_to_png(path: Path) -> bool:
logger.debug("Pillow BMP→PNG conversion failed: %s", e)
# Fall back to ImageMagick convert
tmp = path.with_suffix(".bmp")
try:
tmp = path.with_suffix(".bmp")
path.rename(tmp)
r = subprocess.run(
["convert", str(tmp), "png:" + str(path)],
capture_output=True, timeout=5,
)
tmp.unlink(missing_ok=True)
if r.returncode == 0 and path.exists() and path.stat().st_size > 0:
tmp.unlink(missing_ok=True)
return True
else:
# Convert failed — restore the original file
tmp.rename(path)
except FileNotFoundError:
logger.debug("ImageMagick not installed — cannot convert BMP to PNG")
if tmp.exists() and not path.exists():
tmp.rename(path)
except Exception as e:
logger.debug("ImageMagick BMP→PNG conversion failed: %s", e)
if tmp.exists() and not path.exists():
tmp.rename(path)
# Can't convert — BMP is still usable as-is for most APIs
return path.exists() and path.stat().st_size > 0

View File

@@ -18,36 +18,6 @@ DEFAULT_CODEX_MODELS: List[str] = [
"gpt-5.1-codex-mini",
]
_FORWARD_COMPAT_TEMPLATE_MODELS: List[tuple[str, tuple[str, ...]]] = [
("gpt-5.3-codex", ("gpt-5.2-codex",)),
("gpt-5.4", ("gpt-5.3-codex", "gpt-5.2-codex")),
("gpt-5.3-codex-spark", ("gpt-5.3-codex", "gpt-5.2-codex")),
]
def _add_forward_compat_models(model_ids: List[str]) -> List[str]:
"""Add Clawdbot-style synthetic forward-compat Codex models.
If a newer Codex slug isn't returned by live discovery, surface it when an
older compatible template model is present. This mirrors Clawdbot's
synthetic catalog / forward-compat behavior for GPT-5 Codex variants.
"""
ordered: List[str] = []
seen: set[str] = set()
for model_id in model_ids:
if model_id not in seen:
ordered.append(model_id)
seen.add(model_id)
for synthetic_model, template_models in _FORWARD_COMPAT_TEMPLATE_MODELS:
if synthetic_model in seen:
continue
if any(template in seen for template in template_models):
ordered.append(synthetic_model)
seen.add(synthetic_model)
return ordered
def _fetch_models_from_api(access_token: str) -> List[str]:
"""Fetch available models from the Codex API. Returns visible models sorted by priority."""
@@ -77,14 +47,14 @@ def _fetch_models_from_api(access_token: str) -> List[str]:
if item.get("supported_in_api") is False:
continue
visibility = item.get("visibility", "")
if isinstance(visibility, str) and visibility.strip().lower() in ("hide", "hidden"):
if isinstance(visibility, str) and visibility.strip().lower() == "hide":
continue
priority = item.get("priority")
rank = int(priority) if isinstance(priority, (int, float)) else 10_000
sortable.append((rank, slug))
sortable.sort(key=lambda x: (x[0], x[1]))
return _add_forward_compat_models([slug for _, slug in sortable])
return [slug for _, slug in sortable]
def _read_default_model(codex_home: Path) -> Optional[str]:
@@ -124,10 +94,12 @@ def _read_cache_models(codex_home: Path) -> List[str]:
if not isinstance(slug, str) or not slug.strip():
continue
slug = slug.strip()
if "codex" not in slug.lower():
continue
if item.get("supported_in_api") is False:
continue
visibility = item.get("visibility")
if isinstance(visibility, str) and visibility.strip().lower() in ("hide", "hidden"):
if isinstance(visibility, str) and visibility.strip().lower() == "hidden":
continue
priority = item.get("priority")
rank = int(priority) if isinstance(priority, (int, float)) else 10_000
@@ -155,7 +127,7 @@ def get_codex_model_ids(access_token: Optional[str] = None) -> List[str]:
if access_token:
api_models = _fetch_models_from_api(access_token)
if api_models:
return _add_forward_compat_models(api_models)
return api_models
# Fall back to local sources
default_model = _read_default_model(codex_home)
@@ -170,4 +142,4 @@ def get_codex_model_ids(access_token: Optional[str] = None) -> List[str]:
if model_id not in ordered:
ordered.append(model_id)
return _add_forward_compat_models(ordered)
return ordered

Some files were not shown because too many files have changed in this diff Show More