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* 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.
Hermes Agent ☤
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 interface | Full TUI with multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, and streaming tool output. |
| Lives where you do | Telegram, Discord, Slack, WhatsApp, Signal, and CLI — all from a single gateway process. Voice memo transcription, cross-platform conversation continuity. |
| A closed learning loop | 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. Honcho dialectic user modeling. Compatible with the agentskills.io open standard. |
| Scheduled automations | Built-in cron scheduler with delivery to any platform. Daily reports, nightly backups, weekly audits — all in natural language, running unattended. |
| Delegates and parallelizes | Spawn 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 laptop | Six 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-ready | Batch 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
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 theatroposlibandtinkerdependencies pulled in by.[all,dev]— no submodule setup required.
Community
- 💬 Discord
- 📚 Skills Hub
- 🐛 Issues
- 🔌 HermesClaw — Community WeChat bridge: Run Hermes Agent and OpenClaw on the same WeChat account.
License
MIT — see LICENSE.
Built by Nous Research.