cheetahclaws 3.5.82

CheetahClaws: An Extensible, Python-Native Agent System for Autonomous Multi-Model Workflows

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CheetahClaws: A Fast and Easy-to-Use Agent Harness Infrastructure for Long-Horizon, Multi-Model, and Tool-Using AI Systems

Website · Scaling the Harness · Issue

Quick Install

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

After installation:

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

Other install methods: pip install | uv install | run from source | full details

🔥🔥🔥 News (Pacific Time)

• June 5, 2026 (latest, v3.5.82): User-controllable token/cost budgets — /budget $5 / /budget 200k / /budget daily $20 cap spend per session or per day, enforced before each model call; on hit the session auto-saves and you're shown how to /resume or raise the cap and continue (warns at ≥80%/95%; --budget sets it at startup). Details: docs/guides/features.md · docs/news.md.
• June 5, 2026: Adaptive Markdown streaming — live output stays correct on every device by auto-selecting a per-device tier (live in-place redraw on capable terminals incl. modern SSH emulators, append-only commit for SSH/Apple Terminal/pipes/CJK text so frames never duplicate, plain fallback); also ships a visual /context usage grid and a 1M context window for deepseek-v4-flash. Details: docs/guides/features.md · docs/news.md.
• June 4, 2026 (v3.05.81): Claude-Code-style quiet output hides per-tool execution and shows one summary line per turn (on by default), with a live spinner timer + token estimate and a ✻ Worked for… footer; /verbose overrides, toggle with /quiet. Details: docs/guides/features.md · docs/news.md.
• June 4, 2026: Context-window override — /config context_window=<N> sets the context length that drives the prompt %, /context, the compaction trigger, and the output cap consistently (distinct from max_tokens; read live, no restart). Details: docs/guides/reference.md · docs/news.md.
• June 4, 2026: Rich Live streaming keeps long responses live via a bounded tail window — redrawing only the most recent screenful and committing the full output when done, fixing duplicate/stale frames (builds on PR #133). Details: docs/guides/features.md · docs/news.md.
• May 31, 2026: QQ bot bridge — /qq connects cheetahclaws to QQ groups + C2C private chats via the official qq-botpy SDK (PR #121). Details: docs/guides/bridges.md · docs/news.md.
• May 12, 2026: Security hardening sweep — env-var bot tokens, web CSRF cookie, terminal session owner-binding, and plugin/MCP/filesystem sandboxing (two CRITICAL + HIGH rounds, 2347 tests green). Details: docs/guides/security.md · docs/news.md.
• May 12, 2026: Daemon foundation roadmap — all nine F-1…F-9 items landed: subprocess agent runners, on-crash restart policy, daemonized Telegram/Slack/WeChat bridges, and budget guardrails. Details: docs/news.md.

For more news, see here.

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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

_{Task execution in the terminal}

_{Web UI: browser chat — sidebar, tool cards, approval prompts, Markdown streaming}

_{Autonomous trading agent}

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.

Dimension	Claude Code (TypeScript)	CheetahClaws (Python)
Language	TypeScript + React/Ink	Python 3.8+
Source files / LoC	~1,332 files / ~283K	~315 files / ~90K (core; ~127K with tests)
Built-in tools / commands	44+ / 88	27 / 50+
Model providers	Anthropic only	8+ (Anthropic · OpenAI · Gemini · Kimi · Qwen · DeepSeek · MiniMax · …)
Local models	No	Yes — Ollama, LM Studio, vLLM, any OpenAI-compatible endpoint
Build step	Yes (Bun + esbuild)	No — python cheetahclaws.py
Extensibility	Closed (compile-time)	Open — register_tool() at runtime, Markdown skills, git plugins, MCP
Voice input	Proprietary WebSocket (OAuth)	Local Whisper / OpenAI — works offline

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.

Dimension	OpenClaw (TypeScript)	CheetahClaws (Python)
Lines of code	~245K (~10,349 files)	~90K core (~315 files)
Primary focus	Personal assistant across channels	AI coding assistant / dev tool
Architecture	Always-on Gateway daemon + apps	Zero-install terminal REPL
Messaging channels	20+ (WhatsApp · Signal · iMessage · Discord · Matrix · …)	Terminal + Telegram · WeChat · Slack · QQ bridges
Local / offline models	Limited	Full — Ollama · vLLM · LM Studio · any OpenAI-compatible
Code editing tools	Browser control, Canvas	Read · Write · Edit · Bash · Glob · Grep · NotebookEdit · GetDiagnostics
Mobile / Live Canvas	Yes (menu bar + iOS/Android, A2UI)	—
MCP support	—	Yes (stdio/SSE/HTTP)
Hackability	245K lines, harder to modify	~90K lines — agent loop in one file

If you want…	Use
A personal assistant on WhatsApp/Signal/Discord, mobile-first, browser automation + Canvas	OpenClaw
An AI coding assistant in your terminal, full offline/local models, multi-provider switching, source you can read in an afternoon	CheetahClaws

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

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Features

Feature	Details
Multi-provider	Anthropic · OpenAI · Gemini · Kimi · Qwen · Zhipu · DeepSeek · MiniMax · Ollama · LM Studio · Custom endpoint
Agent loop	Streaming API + automatic tool-use loop; the whole loop is in agent.py
27 built-in tools	Read · Write · Edit · Bash · Glob · Grep · WebFetch · WebSearch · NotebookEdit · GetDiagnostics · Memory* · Agent/SendMessage · Skill · AskUserQuestion · Task* · SleepTimer · EnterPlanMode/ExitPlanMode · (MCP + plugin tools auto-added)
MCP integration	Connect any MCP server (stdio/SSE/HTTP); tools auto-registered — see extensions guide
Plugin system	Install/enable/update plugins from git URLs or local paths; multi-scope; recommendation engine
Task management	TaskCreate/Update/Get/List, sequential IDs, dependency edges, persisted to .cheetahclaws/tasks.json
Context compression	Four cooperating layers — dynamic max_tokens cap, per-model context-window registry, two-layer snip + AI summarize at 70%, and auto-fanout for oversized tool outputs. Details
Persistent memory	Dual-scope (user + project), 4 types, confidence/source metadata, conflict detection, recency-weighted search, /memory consolidate
Multi-agent	Spawn typed sub-agents (coder/reviewer/researcher/…), git-worktree isolation, background mode
Permission system	auto / accept-all / manual / plan modes
Checkpoints & plan mode	Auto-snapshot conversation + files each turn (/checkpoint, /rewind); /plan read-only analysis mode
Slash commands & themes	50+ slash commands with Tab-complete; /theme offers 15 curated palettes
Brainstorm → Worker	/brainstorm runs an N-persona debate → todo_list.txt; /worker auto-implements the pending tasks
SSJ Developer Mode	/ssj — persistent power menu chaining Brainstorm, Worker, Review, Trading, Agent, Video/TTS, Monitor, etc.
Trading agent	/trading multi-agent analysis, backtesting, paper-trade calibration, MV portfolios. Guide
Monitor	/monitor subscribes to AI-monitored topics on a schedule (arxiv / stock / crypto / news / custom), pushes reports to bridges/console
Research (multi-source)	/research fans out to 20 sources with attention heat table, entity extraction, trend sparkline, comparison mode. Guide
Autonomous agents	/agent background loops from Markdown templates; iteration summaries pushed via bridge; stagnation-stop guard
Bridges + remote control	Telegram · WeChat · Slack · QQ — chat round-trip, slash passthrough, per-bridge job queue (!jobs/!retry/!cancel). Guide
Voice / Vision / Video / TTS	Offline Whisper /voice; /image clipboard vision (local + cloud); /video + /tts content factories. Guide
Web UI	--web — multi-user browser chat + PTY terminal. Guide
More	Tmux integration · !cmd shell escape · proactive monitoring · 3×Ctrl+C force-quit · session persistence · /cloudsave GitHub-Gist sync · cost tracking · --print non-interactive mode

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)

Provider	Example models	Context	API Key Env
Anthropic	claude-opus-4-6 · claude-sonnet-4-6 · claude-haiku-4-5-20251001	200k	ANTHROPIC_API_KEY
OpenAI	gpt-4o · gpt-4.1 · gpt-5 · o3 · o4-mini	128–200k	OPENAI_API_KEY
Google	gemini-2.5-pro · gemini-2.0-flash · gemini-1.5-pro	1–2M	GEMINI_API_KEY
Moonshot (Kimi)	moonshot-v1-8k / -32k / -128k	8–128k	MOONSHOT_API_KEY
Alibaba (Qwen)	qwen-max · qwen-plus · qwen-turbo · qwq-32b	32k–1M	DASHSCOPE_API_KEY
Zhipu (GLM)	glm-4-plus · glm-4 · glm-4-flash (free tier)	128k	ZHIPU_API_KEY
DeepSeek	deepseek-chat · deepseek-reasoner	64k	DEEPSEEK_API_KEY
MiniMax	MiniMax-Text-01 · MiniMax-VL-01 · abab6.5s-chat	256k–1M	MINIMAX_API_KEY
AWS Bedrock / Azure / Vertex (via litellm)	litellm/<provider>/<model>	varies	provider-specific

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)

Model	Size	Strengths	Pull
qwen2.5-coder	7B / 32B	Best for coding	ollama pull qwen2.5-coder
llama3.3 / llama3.2	70B / 3B–11B	General purpose	ollama pull llama3.3
deepseek-r1	7B–70B	Reasoning, math	ollama pull deepseek-r1
mistral / mixtral	7B / 8x7B	Fast / strong MoE	ollama pull mistral
phi4 · gemma3 · codellama	14B · 4–27B · 7–34B	Reasoning / open / code	ollama pull phi4
llava · llama3.2-vision	7–13B · 11B	Vision	ollama pull llava

Tool calling needs a function-calling model — recommended: qwen2.5-coder, llama3.3, mistral, phi4. Reasoning models (deepseek-r1, qwen3, gemma4) stream native <think> blocks; enable with /verbose + /thinking.

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Installation

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

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 is a full-modal AI inference platform with an OpenAI-compatible API — DeepSeek, Qwen, GLM, Kimi, MiniMax and more behind one endpoint. It plugs into 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

deepseek-ai/deepseek-v4-pro is a reasoning model; any other Atlas chat model id works the same way.

All Atlas Cloud chat models (59)

• Anthropic (Claude): anthropic/claude-haiku-4.5-20251001, anthropic/claude-opus-4.8, anthropic/claude-sonnet-4.6
• OpenAI (GPT): openai/gpt-5.4, openai/gpt-5.5
• Google (Gemini): google/gemini-3.1-flash-lite, google/gemini-3.1-pro-preview, google/gemini-3.5-flash
• Qwen: qwen/qwen2.5-7b-instruct, Qwen/Qwen3-235B-A22B-Instruct-2507, qwen/qwen3-235b-a22b-thinking-2507, qwen/qwen3-30b-a3b, Qwen/Qwen3-30B-A3B-Instruct-2507, qwen/qwen3-30b-a3b-thinking-2507, qwen/qwen3-32b, qwen/qwen3-8b, Qwen/Qwen3-Coder, qwen/qwen3-coder-next, qwen/qwen3-max-2026-01-23, Qwen/Qwen3-Next-80B-A3B-Instruct, Qwen/Qwen3-Next-80B-A3B-Thinking, Qwen/Qwen3-VL-235B-A22B-Instruct, qwen/qwen3-vl-235b-a22b-thinking, qwen/qwen3-vl-30b-a3b-instruct, qwen/qwen3-vl-30b-a3b-thinking, qwen/qwen3-vl-8b-instruct, qwen/qwen3.5-122b-a10b, qwen/qwen3.5-27b, qwen/qwen3.5-35b-a3b, qwen/qwen3.5-397b-a17b, qwen/qwen3.6-35b-a3b, qwen/qwen3.6-plus
• DeepSeek: deepseek-ai/deepseek-ocr, deepseek-ai/deepseek-r1-0528, deepseek-ai/DeepSeek-V3-0324, deepseek-ai/DeepSeek-V3.1, deepseek-ai/DeepSeek-V3.1-Terminus, deepseek-ai/deepseek-v3.2, deepseek-ai/DeepSeek-V3.2-Exp, deepseek-ai/deepseek-v4-flash, deepseek-ai/deepseek-v4-pro
• Moonshot (Kimi): moonshotai/Kimi-K2-Instruct, moonshotai/Kimi-K2-Instruct-0905, moonshotai/Kimi-K2-Thinking, moonshotai/kimi-k2.5, moonshotai/kimi-k2.6
• Zhipu (GLM): zai-org/GLM-4.6, zai-org/glm-4.7, zai-org/glm-5, zai-org/glm-5-turbo, zai-org/glm-5.1, zai-org/glm-5v-turbo
• MiniMax: MiniMaxAI/MiniMax-M2, minimaxai/minimax-m2.1, minimaxai/minimax-m2.5, minimaxai/minimax-m2.7
• xAI: xai/grok-4.3
• Kwaipilot: kwaipilot/kat-coder-pro-v2
• Other: owl

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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

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Documentation

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

Guide	What's inside
Features (full)	The complete feature table — every row with full detail (context compression, auto-fanout, themes, Trading/Research/Agents writeups)
Usage (all providers)	Per-provider setup + example commands: Anthropic/OpenAI/Gemini/Kimi/Qwen/Zhipu/DeepSeek/MiniMax/litellm, and local Ollama/LM Studio/vLLM
Web UI	Chat UI, PTY terminal, API endpoints, settings, auth, SSE streaming
Docker / Home Server	Dockerfile + compose: web UI + bridges in one container, host Ollama, workspace mount
Reference	CLI, 50+ commands, 33 built-in tools, session search, error classification, tool cache
Extensions	Memory, Skills, Sub-Agents, MCP servers, Plugins, Monitor, Autonomous Agents
Bridges	Telegram, WeChat, Slack, QQ setup + remote control from your phone
Security & env vars	Threat model, CHEETAHCLAWS_* vars, bot-token handling, Bash denylist, fs sandbox, CSRF
Voice & Video	Offline Whisper voice input, Video factory, TTS factory
Trading	Multi-agent analysis, backtesting, BM25 memory, data fallbacks, SSJ integration
Advanced	Brainstorm, SSJ, Tmux, proactive monitoring, checkpoints, plan mode, sessions, cloud sync
Comparison	Full positioning vs Claude Code and OpenClaw — at-a-glance tables, both sides' wins, key design differences
Recipes	12 step-by-step examples: code review, remote control, research, bug fix, browse, email, PDF/Excel
FAQ	The full FAQ (MCP, models/providers, CLI/scripting, voice)
Plugin Authoring · Example	Build a plugin: tools, commands, skills, MCP; starter template
Research Lab	/lab start <topic> — autonomous multi-agent paper writing with sandboxed experiments
Agent OS · RFC index	The cc_kernel/ layer + all design notes (RFC 0001-0032)
Contributing	Project structure, architecture guide, PR checklist

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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. Not all models support function calling — use qwen2.5-coder, llama3.3, mistral, or phi4.

Q: How do I connect to a remote GPU server running vLLM?

/config custom_base_url=http://your-server-ip:8000/v1
/config custom_api_key=your-token
/model custom/your-model-name

Q: How do I check my API cost? Run /cost (shows input/output tokens + estimated USD).

Q: Can I use multiple API keys in one session? Yes — set all keys upfront (env or /config), then switch models freely; each call uses the active provider's key.

Q: How do I set a default model across projects? Add keys to ~/.bashrc/~/.zshrc and set { "model": "claude-sonnet-4-6" } in ~/.cheetahclaws/config.json.

Q: Can I pipe input to cheetahclaws?

cat error.log | cheetahclaws -p "What is causing this error?"

Q: How do I set up voice input? pip install sounddevice faster-whisper numpy, then /voice in the REPL (downloads a ~150 MB Whisper model on first use). See the full FAQ for languages + keyterm tuning.

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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: