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.

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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!

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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 →

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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

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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.

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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"

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Community

• 💬 Discord
• 📚 Skills Hub
• 🐛 Issues
• 💡 Discussions

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License

MIT — see LICENSE.

Built by Nous Research.