07927f6bf2
* fix: Home Assistant event filtering now closed by default Previously, when no watch_domains or watch_entities were configured, ALL state_changed events passed through to the agent, causing users to be flooded with notifications for every HA entity change. Now events are dropped by default unless the user explicitly configures: - watch_domains: list of domains to monitor (e.g. climate, light) - watch_entities: list of specific entity IDs to monitor - watch_all: true (new option — opt-in to receive all events) A warning is logged at connect time if no filters are configured, guiding users to set up their HA platform config. All 49 gateway HA tests + 52 HA tool tests pass. * docs: update Home Assistant integration documentation - homeassistant.md: Fix event filtering docs to reflect closed-by-default behavior. Add watch_all option. Replace Python dict config example with YAML. Fix defaults table (was incorrectly showing 'all'). Add required configuration warning admonition. - environment-variables.md: Add HASS_TOKEN and HASS_URL to Messaging section. - messaging/index.md: Add Home Assistant to description, architecture diagram, platform toolsets table, and Next Steps links. * fix(terminal): strip provider env vars from background and PTY subprocesses Extends the env var blocklist from #1157 to also cover the two remaining leaky paths in process_registry.py: - spawn_local() PTY path (line 156) - spawn_local() background Popen path (line 197) Both were still using raw os.environ, leaking provider vars to background processes and interactive PTY sessions. Now uses the same dynamic _HERMES_PROVIDER_ENV_BLOCKLIST from local.py. Explicit env_vars passed to spawn_local() still override the blocklist, matching the existing behavior for callers that intentionally need these. Gap identified by PR #1004 (@PeterFile). * feat(delegate): add observability metadata to subagent results Enrich delegate_task results with metadata from the child AIAgent: - model: which model the child used - exit_reason: completed | interrupted | max_iterations - tokens.input / tokens.output: token counts - tool_trace: per-tool-call trace with byte sizes and ok/error status Tool trace uses tool_call_id matching to correctly pair parallel tool calls with their results, with a fallback for messages without IDs. Cherry-picked from PR #872 by @omerkaz, with fixes: - Fixed parallel tool call trace pairing (was always updating last entry) - Removed redundant 'iterations' field (identical to existing 'api_calls') - Added test for parallel tool call trace correctness Co-authored-by: omerkaz <omerkaz@users.noreply.github.com> * feat(stt): add free local whisper transcription via faster-whisper Replace OpenAI-only STT with a dual-provider system mirroring the TTS architecture (Edge TTS free / ElevenLabs paid): STT: faster-whisper local (free, default) / OpenAI Whisper API (paid) Changes: - tools/transcription_tools.py: Full rewrite with provider dispatch, config loading, local faster-whisper backend, and OpenAI API backend. Auto-downloads model (~150MB for 'base') on first voice message. Singleton model instance reused across calls. - pyproject.toml: Add faster-whisper>=1.0.0 as core dependency - hermes_cli/config.py: Expand stt config to match TTS pattern with provider selection and per-provider model settings - agent/context_compressor.py: Fix .strip() crash when LLM returns non-string content (dict from llama.cpp, None). Fixes #1100 partially. - tests/: 23 new tests for STT providers + 2 for compressor fix - docs/: Updated Voice & TTS page with STT provider table, model sizes, config examples, and fallback behavior Fallback behavior: - Local not installed → OpenAI API (if key set) - OpenAI key not set → local whisper (if installed) - Neither → graceful error message to user Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com> --------- Co-authored-by: omerkaz <omerkaz@users.noreply.github.com> Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com>
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), z.ai/GLM, Kimi/Moonshot, MiniMax, 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, and WSL2. The installer handles everything — Python, Node.js, dependencies, and the hermes command. No prerequisites except git.
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
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:
git clone 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 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:
git submodule update --init tinker-atropos uv pip install -e "./tinker-atropos"
Community
- 💬 Discord
- 📚 Skills Hub
- 🐛 Issues
- 💡 Discussions
License
MIT — see LICENSE.
Built by Nous Research.