Just talk to your agent — it learns and EVOLVES.

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Overview • Quick Start • Multi-Claw Support • Configuration • Skills Mode • RL Mode • Auto Mode • Memory • Citation

Two commands. That's it.

metaclaw setup              # one-time config wizard
metaclaw start              # default: auto mode — skills + scheduled RL training
metaclaw start --mode rl    # RL without scheduler (trains immediately on full batch)
metaclaw start --mode skills_only  # skills only, no RL (no Tinker needed)

🔥 News

• [04/11/2026] v0.4.1 — Incremental memory ingestion: the memory layer now extracts and persists turns every N turns (default 5) instead of only at session end, shrinking the mid-session memory blackout window.

• [03/25/2026] v0.4.0 — Contexture layer: MetaClaw now persists cross-session memory for users and projects. Relevant facts, preferences, and project history are automatically retrieved and injected into prompts. Includes adaptive memory policy, background consolidation, and an optional memory sidecar service.

• [03/24/2026] v0.3.3 — One-click OpenClaw plugin: MetaClaw now ships as a native OpenClaw extension — drop the folder into OpenClaw's extensions, run one command, and everything is set up automatically.

• [03/18/2026] Our technical report "MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild" is out! 🏆 Ranked No. 1 on HuggingFace Daily Papers! Check it out!

• [03/16/2026] v0.3.2 — Multi-claw support: IronClaw, PicoClaw, ZeroClaw, CoPaw, NanoClaw, and NemoClaw now supported alongside OpenClaw. NanoClaw connected via new /v1/messages Anthropic-compatible endpoint; NemoClaw via OpenShell inference routing. Added OpenRouter as a supported LLM platform.

• [03/13/2026] v0.3.1 — MinT backend support: RL training now works with both Tinker and MinT. Configurable via rl.backend (auto/tinker/mint).

• [03/13/2026] v0.3 — Continual meta-learning support: slow RL updates now only run during sleep hours, idle time, or Google Calendar meetings. Added support/query set separation to prevent stale reward signals from polluting model updates.

• [03/11/2026] v0.2 — One-click deployment via metaclaw CLI. Skills enabled by default, RL is now opt-in.

• [03/09/2026] We release MetaClaw — Just talk to your agent and let it evolve automatically. NO GPU deployment required; just plug into the API.

🎥 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:

• Choose your personal agent — openclaw, copaw, ironclaw, picoclaw, zeroclaw, nanoclaw, nemoclaw, or none (MetaClaw will auto-configure it on start)

• Choose your auth method — api_key (direct API) or oauth_token (CLI subprocess)

• 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

• 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