24f549a692
Complete Signal adapter using signal-cli daemon HTTP API. Based on PR #268 by ibhagwan, rebuilt on current main with bug fixes. Architecture: - SSE streaming for inbound messages with exponential backoff (2s→60s) - JSON-RPC 2.0 for outbound (send, typing, attachments, contacts) - Health monitor detects stale SSE connections (120s threshold) - Phone number redaction in all logs and global redact.py Features: - DM and group message support with separate access policies - DM policies: pairing (default), allowlist, open - Group policies: disabled (default), allowlist, open - Attachment download with magic-byte type detection - Typing indicators (8s refresh interval) - 100MB attachment size limit, 8000 char message limit - E.164 phone + UUID allowlist support Integration: - Platform.SIGNAL enum in gateway/config.py - Signal in _is_user_authorized() allowlist maps (gateway/run.py) - Adapter factory in _create_adapter() (gateway/run.py) - user_id_alt/chat_id_alt fields in SessionSource for UUIDs - send_message tool support via httpx JSON-RPC (not aiohttp) - Interactive setup wizard in 'hermes gateway setup' - Connectivity testing during setup (pings /api/v1/check) - signal-cli detection and install guidance Bug fixes from PR #268: - Timestamp reads from envelope_data (not outer wrapper) - Uses httpx consistently (not aiohttp in send_message tool) - SIGNAL_DEBUG scoped to signal logger (not root) - extract_images regex NOT modified (preserves group numbering) - pairing.py NOT modified (no cross-platform side effects) - No dual authorization (adapter defers to run.py for user auth) - Wildcard uses set membership ('*' in set, not list equality) - .zip default for PK magic bytes (not .docx) No new Python dependencies — uses httpx (already core). External requirement: signal-cli daemon (user-installed). Tests: 30 new tests covering config, init, helpers, session source, phone redaction, authorization, and send_message integration. Co-authored-by: ibhagwan <ibhagwan@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, 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 setup # configure your LLM provider
hermes # start chatting!
Getting Started
hermes # Interactive CLI — start a conversation
hermes model # Switch provider or model
hermes setup # Re-run the setup wizard
hermes gateway # Start the messaging gateway (Telegram, Discord, etc.)
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, 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 |
Contributing
We welcome contributions! See the Contributing Guide for development setup, code style, and PR process.
Quick start for contributors:
git clone --recurse-submodules https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
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
Community
- 💬 Discord
- 📚 Skills Hub
- 🐛 Issues
- 💡 Discussions
License
MIT — see LICENSE.
Built by Nous Research.