aedb773f0d
Two changes to prevent unnecessary Anthropic prompt cache misses in the gateway, where a fresh AIAgent is created per user message: 1. Reuse stored system prompt for continuing sessions: When conversation_history is non-empty, load the system prompt from the session DB instead of rebuilding from disk. The model already has updated memory in its conversation history (it wrote it!), so re-reading memory from disk produces a different system prompt that breaks the cache prefix. 2. Stabilize Honcho context per session: - Only prefetch Honcho context on the first turn (empty history) - Bake Honcho context into the cached system prompt and store to DB - Remove the per-turn Honcho injection from the API call loop This ensures the system message is identical across all turns in a session. Previously, re-fetching Honcho could return different context on each turn, changing the system message and invalidating the cache. Both changes preserve the existing behavior for compression (which invalidates the prompt and rebuilds from scratch) and for the CLI (where the same AIAgent persists and the cached prompt is already stable across turns). Tests: 2556 passed (6 new)
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 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, 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 |
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.