Teknium af22421e87 feat(dashboard): page-scoped plugin slots for built-in pages (#15658)
* fix(terminal): three-layer defense against watch_patterns notification spam

Background processes that stack notify_on_complete=True with watch_patterns
can flood the user with duplicate, delayed notifications — matches deliver
asynchronously via the completion queue and continue arriving minutes after
the process has exited. The docstring warning against this (PR #12113) has
proven insufficient; agents still misuse the combination.

Three layered defenses, each sufficient on its own:

1. Mutual exclusion (terminal_tool.py): When both flags are set on a
   background process, drop watch_patterns with a warning. notify_on_complete
   wins because 'let me know when it's done' is the more useful signal and
   fires exactly once. Extracted as _resolve_notification_flag_conflict() so
   the rule is testable in isolation.

2. Suppress-after-exit (process_registry.py): _check_watch_patterns() now
   bails the moment session.exited is True. Post-exit chunks (buffered reads
   draining after the process is gone) no longer produce notifications. This
   is the fix flagged as future work in session 20260418_020302_79881c.

3. Global circuit breaker (process_registry.py): Per-session rate limits don't
   catch the sibling-flood case — N concurrent processes can each stay under
   8/10s and still collectively spam. New WATCH_GLOBAL_MAX_PER_WINDOW=15 cap
   trips a 30-second cooldown across ALL sessions, emits a single
   watch_overflow_tripped event, silently counts dropped events, and emits a
   watch_overflow_released summary when the cooldown ends.

Also updates the tool schema + docstring to document the new behavior.

Tests: 8 new tests covering all three fixes (suppress-after-exit x2,
mutual-exclusion resolver x4, global breaker trip/cooldown/release x2).
All 60 tests across test_watch_patterns.py, test_notify_on_complete.py,
test_terminal_tool.py pass.

Real-world trigger: self-inflicted in session 20260425_051924 — three
concurrent hermes-sweeper review subprocesses each set watch_patterns=
['failed validation', 'errored'] AND notify_on_complete=True, then iterated
over multiple items, producing enough matches per process to defeat the
per-session cap while staying under the global cap that didn't yet exist.

* fix(terminal): aggressive 1-per-15s watch_patterns rate limit + strike-3 promotion

Per Teknium's direction, the watch_patterns rate limit is now much more
aggressive and self-healing.

## New rule — per session

- HARD cap: 1 watch-match notification per 15 seconds per process.
- Any match arriving inside the cooldown window is dropped and counts as
  ONE strike for that window (many drops in the same window still = 1 strike).
- After 3 consecutive strike windows, watch_patterns is permanently disabled
  for the session and the session is auto-promoted to notify_on_complete
  semantics — exactly one notification when the process actually exits.
- A cooldown window that expires with zero drops resets the consecutive
  strike counter — healthy cadence is forgiven.

## Schema + docstring rewritten

The tool schema description now gives the model explicit guidance:
- notify_on_complete is 'the right choice for almost every long-running task'
- watch_patterns is for RARE one-shot signals on LONG-LIVED processes
- Do NOT use watch_patterns with loops/batch jobs — error patterns fire every
  iteration and will hit the strike limit fast
- Mutual exclusion is stated on both parameter descriptions
- 1/15s cooldown and 3-strike promotion are stated in the watch_patterns
  description so the model sees the contract every turn

## Removed

- WATCH_MAX_PER_WINDOW (8/10s) and WATCH_OVERLOAD_KILL_SECONDS (45) — the
  new 1/15s limit subsumes both; keeping them would double-count.
- _watch_window_hits / _watch_window_start / _watch_overload_since fields
  on ProcessSession. Replaced by _watch_last_emit_at / _watch_cooldown_until
  / _watch_strike_candidate / _watch_consecutive_strikes.

## Kept

- Global circuit breaker across all sessions (15/10s → 30s cooldown) as a
  secondary safety net for concurrent siblings. Still valuable when 20
  short-lived processes each fire once — none individually violates the
  per-session limit.
- Suppress-after-exit guard.
- Mutual exclusion resolver at the tool entry point.

## Tests

- 6 new tests in TestPerSessionRateLimit covering: first match delivers,
  second in cooldown suppressed, multi-drop = single strike, 3 strikes
  disables + promotes, clean window resets counter, suppressed count
  carried to next emit.
- Global circuit breaker tests rewritten to use fresh sessions instead of
  hacking removed per-window fields.
- 50/50 watch_patterns + notify_on_complete tests pass.
- 60/60 including test_terminal_tool.py pass.

* feat(dashboard): page-scoped plugin slots for built-in pages

Dashboard plugins can now inject components into specific built-in
pages (Sessions, Analytics, Logs, Cron, Skills, Config, Env, Docs,
Chat) without overriding the whole route.

Previously, plugins could only:
  1. Add new tabs (tab.path)
  2. Replace whole built-in pages (tab.override)
  3. Inject into global shell slots (header-*, footer-*, pre-main, ...)

None of those let a plugin add a banner, card, or widget to an
existing page. The new <page>:top / <page>:bottom slots close that
gap, reusing the existing registerSlot() API.

Changes
- web/src/plugins/slots.ts: 18 new KNOWN_SLOT_NAMES entries
  (sessions:top, sessions:bottom, analytics:top, ..., chat:bottom),
  grouped under "Shell-wide" vs "Page-scoped" in the docblock
- web/src/pages/*: each built-in page now renders
    <PluginSlot name="<page>:top" />
  as the first child of its outer wrapper and
    <PluginSlot name="<page>:bottom" />
  as the last child -- zero visual cost when no plugin registers
- plugins/example-dashboard: registers a demo banner into
  sessions:top via registerSlot(), with matching slots entry in
  the manifest -- so freshly-setup users can see what page-scoped
  slots look like without writing any plugin code
- website/docs: new "Page-scoped slots" table in the plugin
  authoring guide, with a worked example
- tests/hermes_cli/test_web_server.py: round-trip test for
  colon-bearing slot names (sessions:top, analytics:bottom, ...)

Validation
- npm run build: clean (tsc -b + vite build, 2761 modules)
- scripts/run_tests.sh tests/hermes_cli/test_web_server.py::TestDashboardPluginManifestExtensions: 5/5 pass
2026-04-25 06:55:35 -07:00
2026-02-25 11:53:44 -08:00
2026-04-10 00:46:37 -04:00
2026-04-22 20:02:46 -07:00
2026-04-23 15:08:41 -07:00
2026-04-11 15:30:37 -04:00
2026-04-10 00:46:37 -04:00
2026-03-07 13:43:08 -08:00
2026-04-24 12:51:04 -04:00

Hermes Agent

Hermes Agent ☤

Documentation Discord License: MIT Built by Nous Research

The self-improving AI agent built by Nous Research. It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. It's not tied to your laptop — talk to it from Telegram while it works on a cloud VM.

Use any model you want — Nous Portal, OpenRouter (200+ models), NVIDIA NIM (Nemotron), Xiaomi MiMo, z.ai/GLM, Kimi/Moonshot, MiniMax, Hugging Face, OpenAI, or your own endpoint. Switch with hermes model — no code changes, no lock-in.

A real terminal interfaceFull TUI with multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, and streaming tool output.
Lives where you doTelegram, Discord, Slack, WhatsApp, Signal, and CLI — all from a single gateway process. Voice memo transcription, cross-platform conversation continuity.
A closed learning loopAgent-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. Honcho dialectic user modeling. Compatible with the agentskills.io open standard.
Scheduled automationsBuilt-in cron scheduler with delivery to any platform. Daily reports, nightly backups, weekly audits — all in natural language, running unattended.
Delegates and parallelizesSpawn isolated subagents for parallel workstreams. Write Python scripts that call tools via RPC, collapsing multi-step pipelines into zero-context-cost turns.
Runs anywhere, not just your laptopSix terminal backends — local, Docker, SSH, Daytona, Singularity, and Modal. Daytona and Modal offer serverless persistence — your agent's environment hibernates when idle and wakes on demand, costing nearly nothing between sessions. Run it on a $5 VPS or a GPU cluster.
Research-readyBatch trajectory generation, Atropos RL environments, trajectory compression for training the next generation of tool-calling models.

Quick Install

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

Works on Linux, macOS, WSL2, and Android via Termux. The installer handles the platform-specific setup for you.

Android / Termux: The tested manual path is documented in the Termux guide. On Termux, Hermes installs a curated .[termux] extra because the full .[all] extra currently pulls Android-incompatible voice dependencies.

Windows: Native Windows is not supported. Please install WSL2 and run the command above.

After installation:

source ~/.bashrc    # reload shell (or: source ~/.zshrc)
hermes              # start chatting!

Getting Started

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 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 →

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> /<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 and the Messaging Gateway guide.


Documentation

All documentation lives at hermes-agent.nousresearch.com/docs:

Section What's Covered
Quickstart Install → setup → first conversation in 2 minutes
CLI Usage Commands, keybindings, personalities, sessions
Configuration Config file, providers, models, all options
Messaging Gateway Telegram, Discord, Slack, WhatsApp, Signal, Home Assistant
Security Command approval, DM pairing, container isolation
Tools & Toolsets 40+ tools, toolset system, terminal backends
Skills System Procedural memory, Skills Hub, creating skills
Memory Persistent memory, user profiles, best practices
MCP Integration Connect any MCP server for extended capabilities
Cron Scheduling Scheduled tasks with platform delivery
Context Files Project context that shapes every conversation
Architecture Project structure, agent loop, key classes
Contributing Development setup, PR process, code style
CLI Reference All commands and flags
Environment Variables Complete env var reference

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:

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 for development setup, code style, and PR process.

Quick start for contributors — clone and go with setup-hermes.sh:

git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
./setup-hermes.sh     # installs uv, creates venv, installs .[all], symlinks ~/.local/bin/hermes
./hermes              # auto-detects the venv, no need to `source` first

Manual path (equivalent to the above):

curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv venv --python 3.11
source venv/bin/activate
uv pip install -e ".[all,dev]"
scripts/run_tests.sh

RL Training (optional): The RL/Atropos integration (environments/) ships via the atroposlib and tinker dependencies pulled in by .[all,dev] — no submodule setup required.


Community


License

MIT — see LICENSE.

Built by Nous Research.

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