🎥 Demo

https://github.com/user-attachments/assets/d86a41a8-4181-4e3a-af0e-dc453a6b8594

📖 Overview

MetaClaw is an agent that meta-learns and evolves in the wild. Just talk to your agent as you normally would — MetaClaw turns every live conversation into a learning signal, enabling the agent to continuously improve through real-world deployment rather than offline training alone.

Under the hood, it places your model behind a proxy that intercepts interactions from your personal agent (OpenClaw, CoPaw, IronClaw, PicoClaw, ZeroClaw, NanoClaw, NemoClaw, or any OpenAI-compatible client), injects relevant skills at each turn, and meta-learns from accumulated experience. For Anthropic-native agents like NanoClaw, MetaClaw also exposes a /v1/messages Anthropic-compatible endpoint so the full pipeline works without any agent-side changes. Skills are summarized automatically after each session; with RL enabled, a meta-learning scheduler defers weight updates to idle windows so the agent is never interrupted during active use.

No GPU cluster required. MetaClaw works with any OpenAI-compatible LLM API out of the box, and uses a Tinker-compatible backend for cloud-based LoRA training. Tinker is the default reference path; MinT and Weaver can be enabled through separate compatibility packages when needed.

🤖 Key Features

One-click deployment

Configure once with metaclaw setup, then metaclaw start brings up the proxy, injects skills, and wires your chosen personal agent (OpenClaw, CoPaw, or IronClaw) automatically. No manual shell scripts needed.

Three operating modes

| Mode | Default | What it does | |------|---------|--------------| | skills_only | | Proxy your LLM API. Skills injected and auto-summarized after each session. No GPU/Tinker required. | | rl | | Skills + RL training (GRPO). Trains immediately when a batch is full. Optional OPD for teacher distillation. | | auto | ✅ | Skills + RL + smart scheduler. RL weight updates only run during sleep/idle/meeting windows. |

Long-term memory

MetaClaw can persist facts, preferences, and project history across sessions and inject relevant context at each turn — so your agent remembers what you've told it, even weeks later.

Asynchronous by design

Serving, reward modeling, and training are fully decoupled. The agent continues responding while scoring and optimization run in parallel.

🚀 Quick Start

1. Install

OpenClaw (one-click): use the v0.4.0 release—run the snippet below, then metaclaw setup and metaclaw start. More detail (Windows, mirrors, config, troubleshooting): extensions/metaclaw-openclaw/README.md.

curl -LO https://github.com/aiming-lab/MetaClaw/releases/download/v0.4.0/metaclaw-plugin.zip
unzip metaclaw-plugin.zip -d ~/.openclaw/extensions
openclaw plugins enable metaclaw-openclaw && openclaw gateway restart

pip (PyPI or this repo):

pip install -e .                        # skills_only mode (lightweight)
pip install -e ".[rl]"                  # + RL training support (torch, transformers, tinker)
pip install -e ".[evolve]"              # + skill evolution via OpenAI-compatible LLM
pip install -e ".[scheduler]"           # + Google Calendar integration for scheduler
pip install -e ".[rl,evolve,scheduler]" # recommended for full RL + scheduler setup

(Optional) WeChat integration uses the official @tencent-weixin/openclaw-weixin plugin. MetaClaw auto-installs it when WeChat is enabled:

metaclaw config wechat.enabled true
metaclaw start

The plugin is installed automatically on metaclaw start. You can also install it manually:

npx -y @tencent-weixin/openclaw-weixin-cli@latest install

To switch WeChat accounts (re-login with a new QR code):

metaclaw start --wechat-relogin

If you want to run rl.backend=mint, install the MinT compatibility package separately in the same environment, for example mindlab-toolkit. Similarly, for rl.backend=weaver, install nex-weaver separately. MetaClaw keeps these dependencies out of the default package so RL users can choose Tinker, MinT, or Weaver explicitly.

2. Configure

metaclaw setup

The interactive wizard will ask you to:

1. Choose your personal agent — openclaw, copaw, ironclaw, picoclaw, zeroclaw, nanoclaw, nemoclaw, or none (MetaClaw will auto-configure it on start)
2. Choose your auth method — api_key (direct API) or oauth_token (CLI subprocess)
3. Choose your LLM provider:
   • api_key: Kimi, Qwen, OpenAI, Volcano Engine, or custom → enter API base + API key
   • oauth_token: Anthropic (Claude Code), OpenAI Codex, or Gemini CLI → paste OAuth token
4. Enter your model ID and optionally enable RL training

MetaClaw's RL path can switch explicitly between tinker, mint, and weaver. auto is the recommended default and will infer the backend from credentials, base URLs, or environment variables when the corresponding package is installed.

Tinker:

metaclaw config rl.backend tinker
metaclaw config rl.api_key sk-...
metac