aiai-agentai-agentsanthropicchatgptclaudeclaude-codeclawdbotcodexhermeshermes-agentllmmoltbotnous-researchopenaiopenclaw
1b8a1c7d5e
The _generate_summary() method assumed message content is always a
string (msg.get('content') or ''). When content is a multimodal list
(e.g. [{type: 'text', text: '...'}, {type: 'image_url', ...}]), this
produced mangled output: len() returned the list length instead of
character count, and slicing produced list items instead of substrings.
Add _content_to_text() helper that safely converts any content format
to plain text:
- str → returned as-is
- None → empty string
- list (multimodal) → text parts joined, images replaced with [image]
- dict/other → JSON serialization with str() fallback
This ensures multimodal conversations compress correctly instead of
producing garbled summaries.
Inspired by PR #776 by @kshitijk4poor.
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| .github | ||
| .plans | ||
| agent | ||
| assets | ||
| cron | ||
| datagen-config-examples | ||
| docs/skins | ||
| environments | ||
| gateway | ||
| hermes_cli | ||
| honcho_integration | ||
| landingpage | ||
| mini-swe-agent@07aa6a7385 | ||
| optional-skills | ||
| plans | ||
| scripts | ||
| skills | ||
| tests | ||
| tinker-atropos@65f084ee80 | ||
| tools | ||
| website | ||
| .env.example | ||
| .gitignore | ||
| .gitmodules | ||
| AGENTS.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| README.md | ||
| batch_runner.py | ||
| cli-config.yaml.example | ||
| cli.py | ||
| hermes | ||
| hermes_constants.py | ||
| hermes_state.py | ||
| hermes_time.py | ||
| mini_swe_runner.py | ||
| model_tools.py | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| requirements.txt | ||
| rl_cli.py | ||
| run_agent.py | ||
| setup-hermes.sh | ||
| toolset_distributions.py | ||
| toolsets.py | ||
| trajectory_compressor.py | ||
| utils.py | ||
| uv.lock | ||
README.md
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.