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Quick Install

pip install cheetahclaws

Then just run:

cheetahclaws        # start chatting!

Other install methods: one-line install script | install from source | uv install | run from source install | full install details

🖥️ Prefer a native app? A desktop build (Electron) wraps the full chat UI in a window — no terminal needed. See desktop/.

🔥🔥🔥 News (Pacific Time)

• July 9, 2026: Official Docker image + one-command publish. Pre-built image on Docker Hub (docker pull chauncygu/cheetahclaws) so you can run the Web UI without cloning; fixes a first-run PermissionError by pre-creating the .cheetahclaws/workspace dirs owned by the non-root user, makes the compose image overridable via CHEETAH_IMAGE, and adds scripts/docker-publish.sh (auto-reads the version, multi/single-arch). New docs sections: Pull from Docker Hub and Interactive setup / CLI mode. Details
• July 8, 2026: New /workspace command manages isolated working directories under ~/.cheetahclaws/workspaces (list/switch/default/create/delete) (PR #162); startup auto-switching is opt-in via workspace_auto (off by default, so launching in a project directory is unchanged), and default is now a sticky key separate from last-used. Details
• July 6, 2026 (v3.5.84): so even non-vision models can act on clipboard screenshots (error dumps, code, tables); runs only when / are installed and is fully opt-out via .

For more news, see here.

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Sponsor

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CheetahClaws

CheetahClaws: A Fast and Easy-to-Use Python native Agent Harness Infrastructure, Supporting Any Model, such as Claude, GPT, Gemini, Kimi, Qwen, Zhipu, DeepSeek, MiniMax, and local open-source models via Ollama or any OpenAI-compatible endpoint.

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Content

• Why CheetahClaws
• CheetahClaws vs OpenClaw
• Features
• Supported Models
• Installation
• Usage: Closed-Source API Models
• Usage: Open-Source Models (Local)
• Model Name Format
• Trading Agent
• Web UI
• Documentation (guides for all features)
• Contributing · FAQ · Citation

Demos

More animated demos (code review, /research, /brainstorm, /lab, Telegram/WeChat/Slack bridges) live in docs/media/.

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Why CheetahClaws

Claude Code is a powerful, production-grade AI coding assistant — but its source is a compiled ~12 MB TypeScript/Node bundle (~1,300 files, ~283K lines), tightly coupled to the Anthropic API, hard to modify, and impossible to run against a local or alternative model.

CheetahClaws reimplements the same core loop in ~90K lines of readable Python — keeping what you need, dropping what you don't, and adding multi-provider + local-model support. Full comparison: docs/guides/comparison.md.

Where Claude Code wins: richer React/Ink UI, more built-in tools, enterprise features (MDM, team permission sync, OAuth/keychain), AI-driven memory extraction, single-binary production reliability.

Where CheetahClaws wins: any-model switching (--model//model, no recompile) incl. full local/offline support; a readable agent loop in one file (agent.py, ~740 lines); zero build; runtime tool registration + MCP + git plugins + Markdown skills; task dependency graph (blocks/blocked_by); two-layer context compression; offline voice; cloud session sync; bridges to Telegram/WeChat/Slack/QQ.

Who it's for: developers who want a local/non-Anthropic coding assistant, researchers studying how agentic assistants work, and teams who need a hackable baseline — without a Node.js build chain.

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CheetahClaws vs OpenClaw

OpenClaw is another popular open-source assistant (TypeScript/Node). The two have different primary goals — OpenClaw is a personal life-assistant across messaging channels; CheetahClaws is a developer/coding tool.

Full comparison — both sides' wins + key design differences (agent loop, tool registration, context compression, memory): docs/guides/comparison.md.

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Features

Full feature reference — every row above with complete detail (context-compression layers, auto-fanout, 15 themes, the full Trading/Research/Agents writeups, …): docs/guides/features.md.

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Supported Models

Closed-Source (API)

litellm/ adapter: routes to 100+ providers behind one SDK — mainly for upstreams with awkward auth (Bedrock SigV4, Azure deployment routing, Vertex service-account JWTs). For plain OpenAI-shaped endpoints, prefer the zero-dependency custom/ adapter. Install with pip install ".[litellm]". See recipes.md.

Open-Source (Local via Ollama)

Tool calling needs a function-calling model — recommended: qwen2.5-coder, llama3.3, mistral, phi4. Models that emit tool calls as text (<tool_call>…</tool_call>, [TOOL_CALLS]…) instead of Ollama's structured field are auto-recovered, so they execute tools out of the box rather than just chatting about it. Reasoning models (deepseek-r1, qwen3, gemma4) stream native <think> blocks; enable with /verbose + /thinking.

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Installation

pip install cheetahclaws

Works on Linux, macOS, WSL2, and Android (Termux) (Python 3.10+). First run guides you through provider + API-key setup; re-run anytime with cheetahclaws --setup.

Windows: native Windows is not supported — use WSL2. Android/Termux: pkg install python git && pip install cheetahclaws.

Alternative: one-line install script

curl -fsSL https://raw.githubusercontent.com/SafeRL-Lab/cheetahclaws/main/scripts/install.sh | bash

After installation, reload your shell so cheetahclaws is on PATH:

source ~/.zshrc     # macOS
# or: source ~/.bashrc   # Linux
cheetahclaws        # start chatting!

Alternative: install with pip from source code

git clone https://github.com/SafeRL-Lab/cheetahclaws.git
cd cheetahclaws
pip install .                       # then: cheetahclaws
git pull && pip install --force-reinstall .   # to update

Optional extras

pip install ".[voice]"      # voice input (sounddevice + faster-whisper)
pip install ".[vision]"     # clipboard image capture (Pillow)
pip install ".[autosuggest]"# typing-time slash autosuggest (prompt_toolkit)
pip install ".[browser]"    # headless browser (playwright); then: playwright install chromium
pip install ".[files]"      # PDF + Excel reading (pymupdf, openpyxl)
pip install ".[ocr]"        # image OCR (pytesseract)
pip install ".[trading]"    # trading agent (yfinance, rank-bm25)
pip install ".[qq]"         # QQ bot bridge (qq-botpy)
pip install ".[litellm]"    # AWS Bedrock / Azure / Vertex auth via litellm
pip install ".[all]"        # everything above

Alternative: install with uv

git clone https://github.com/SafeRL-Lab/cheetahclaws.git && cd cheetahclaws
uv tool install ".[all]"            # minimal: uv tool install .
uv tool install ".[all]" --reinstall   # update   ·   uv tool uninstall cheetahclaws

Alternative: run directly from source (no install)

git clone https://github.com/SafeRL-Lab/cheetahclaws.git && cd cheetahclaws
pip install -r requirements.txt
python cheetahclaws.py              # changes take effect immediately

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Usage: Closed-Source API Models

Every cloud provider follows the same pattern — export its API key (see the Supported Models table for the env-var name), then select a model:

export ANTHROPIC_API_KEY=sk-ant-...     # or OPENAI_API_KEY / GEMINI_API_KEY / DEEPSEEK_API_KEY / …
cheetahclaws                            # default model
cheetahclaws --model gpt-4o             # pick any model
cheetahclaws --model deepseek-chat --thinking --verbose

Provider get-key pages: Anthropic · OpenAI · Gemini · Kimi · Qwen · Zhipu · DeepSeek · MiniMax.

AWS Bedrock / Azure / Vertex use the litellm/<provider>/<model> form (pip install ".[litellm]") — full env-var recipes in recipes.md.

Full per-provider guide — every provider's get-key page + example model commands, plus Bedrock/Azure/Vertex env-var recipes: docs/guides/usage.md.

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Usage: Open-Source Models (Local)

Ollama (recommended)

curl -fsSL https://ollama.com/install.sh | sh   # install
ollama pull qwen2.5-coder                        # pull a tool-calling model
ollama serve                                     # http://localhost:11434 (auto-starts on macOS)
cheetahclaws --model ollama/qwen2.5-coder        # run (use `ollama list` to see local models)

LM Studio

Download LM Studio, grab a GGUF model, start its Local Server (port 1234), then:

cheetahclaws --model lmstudio/<model-name>

vLLM / self-hosted OpenAI-compatible server

python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen2.5-Coder-32B-Instruct --port 8000 \
    --enable-auto-tool-choice --tool-call-parser hermes

export CUSTOM_BASE_URL=http://localhost:8000/v1
export CUSTOM_API_KEY=token-abc123      # any non-empty string if the server has no auth
cheetahclaws --model custom/Qwen2.5-Coder-32B-Instruct

The name after custom/ must match the server's --served-model-name. For the Web UI, --web --model custom/<name> persists the model before the server starts. Remote server? Point CUSTOM_BASE_URL at its IP.

Full local-model guide — Ollama step-by-step, LM Studio, vLLM + Web UI: docs/guides/usage.md.

Atlas Cloud (hosted, OpenAI-compatible)

🎁 Atlas Cloud serves DeepSeek, Qwen, GLM, Kimi, MiniMax and more behind one OpenAI-compatible endpoint, via the zero-dependency custom/ adapter:

export CUSTOM_BASE_URL=https://api.atlascloud.ai/v1
export CUSTOM_API_KEY=your_atlascloud_api_key
cheetahclaws --model custom/deepseek-ai/deepseek-v4-pro

Any Atlas chat model id works the same way — full list of all 59 models: docs/guides/usage.md.

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Model Name Format

Three equivalent forms are accepted:

cheetahclaws --model gpt-4o                  # 1. auto-detect by prefix
cheetahclaws --model ollama/qwen2.5-coder    # 2. provider/model
cheetahclaws --model kimi:moonshot-v1-32k    # 3. provider:model

Auto-detection by prefix: claude-→anthropic · gpt-/o1/o3→openai · gemini-→gemini · moonshot-/kimi-→kimi · qwen/qwq-→qwen · glm-→zhipu · deepseek-→deepseek · MiniMax-/abab→minimax · llama/mistral/phi/gemma/mixtral/codellama→ollama.

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Trading Agent

A built-in AI trading analysis + backtesting module (pip install "cheetahclaws[trading]").

/trading analyze NVDA            # 5-phase pipeline: data → Bull/Bear debate → Judge → Risk panel → PM decision
/trading backtest AAPL dual_ma   # backtest a strategy (or let AI pick); Sharpe/Sortino/Calmar/drawdown/win-rate

4 strategies (dual_ma, rsi_mean_reversion, bollinger_breakout, macd_crossover), BM25 memory of past situations, US/HK/A-share + crypto markets with no-API-key data fallbacks. Guided sub-menu via /ssj → Trading.

Full guide: docs/guides/trading.md

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Web UI

A production-ready browser interface — real user accounts (bcrypt + JWT), SQLite-backed history, ops endpoints — served by Python stdlib + ten vanilla-JS modules (no Node.js / React / build step).

pip install 'cheetahclaws[web]'
cheetahclaws --web                  # auto-picks a free port (tries 8080)
cheetahclaws --web --port 9000 --host 0.0.0.0   # bind explicitly / open to LAN
cheetahclaws --web --no-auth        # skip login (localhost dev only)

Open http://localhost:<port>/chat — first account becomes admin. Includes streaming chat (WS) + SSE slash commands, persistent sessions with folders/search/Markdown export, tool cards, inline permission approval, settings panel, light/dark/system theme, and /health + /metrics endpoints. A full xterm.js PTY terminal lives at / (100% CLI parity).

Full guide: docs/guides/web-ui.md · Docker / home server: docs/guides/docker.md · Native desktop app: desktop/README.md

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Documentation

Detailed guides live in docs/guides/ to keep this README focused:

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Quick Reference

cheetahclaws [OPTIONS] [PROMPT]

  -p, --print          Non-interactive: run prompt and exit
  -m, --model MODEL    Override model (e.g. gpt-4o, ollama/llama3.3)
  --accept-all         Auto-approve all operations (no permission prompts)
  --verbose            Show thinking blocks and per-turn token counts
  --show-tools         Show each tool call instead of a per-turn summary
                       (alias: --no-quiet; compact summary is the default)
  --thinking           Enable Extended Thinking (Claude only)
  --web                Start web server (Chat UI + PTY terminal in browser)
  --port / --host      Web server port / host (default 8080 / 127.0.0.1)
  --no-auth            Disable web password (local use only)
  --version / -h       Print version / show help

cheetahclaws                                          # interactive REPL, default model
cheetahclaws -m ollama/deepseek-r1:32b                # pick a model
cheetahclaws -p "Write a Python fibonacci function"   # non-interactive
cheetahclaws --accept-all -p "Init a pyproject.toml"  # CI / automation
cheetahclaws --web --port 8008 --no-auth              # browser chat + terminal

See the Reference Guide for all 50+ slash commands, tools, and config options.

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Contributing

We welcome contributions! See the Contributing Guide for architecture, conventions, and the PR checklist.

git clone https://github.com/SafeRL-Lab/cheetahclaws.git && cd cheetahclaws
pip install -r requirements.txt && pip install pytest
python -m pytest tests/ -x -q       # 341+ tests should pass
python cheetahclaws.py              # run the REPL

Building a plugin? See the Plugin Authoring Guide and the example template.

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FAQ

A few common questions — the full FAQ is in docs/guides/faq.md.

Q: How do I add an MCP server?

/mcp add git uvx mcp-server-git          # or create .mcp.json in your project, then /mcp reload

Q: Tool calls don't work with my local Ollama model (it just keeps describing what it would do instead of doing it). CheetahClaws now auto-recovers tool calls that local models emit as text (<tool_call>…</tool_call>, [TOOL_CALLS]…) instead of in Ollama's structured field, so most function-calling models execute tools out of the box. For best reliability use a tool-calling model — qwen2.5-coder, llama3.3, mistral, or phi4. Small models are also weaker at agentic tool use than cloud models, so expect them to need clearer, more concrete prompts.

Q: After installing on macOS, cheetahclaws: command not found and no ~/.zshrc was created. Reload your shell first: source ~/.zshrc (zsh) or source ~/.bash_profile (bash). The installer creates ~/.zshrc if missing, symlinks the binary into ~/.local/bin, and adds it to PATH. If you installed an older version, either re-run the installer or add this line yourself: echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc && source ~/.zshrc.

More — remote vLLM, API cost (/cost), multiple keys per session, default model across projects, piping input, voice setup, garbled-text fixes — are all answered in docs/guides/faq.md.

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Citation

If you find the repository useful, please cite the study

@article{gu2026model,
  title={From Model Scaling to System Scaling: Scaling the Harness in Agentic AI},
  author={Gu, Shangding},
  journal={arXiv preprint arXiv:2605.26112},
  year={2026}
}

@article{cheetahclaws2026,
  title={CheetahClaws: Agent Harness Infrastructure for Long-Horizon, Multi-Model, and Tool-Using AI Systems},
  author={CheetahClaws Team},
  journal={github},
  year={2026}
}

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Thanks to all contributors: