# MetaClaw **Repository Path**: hzz0818/MetaClaw ## Basic Information - **Project Name**: MetaClaw - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-05-10 - **Last Updated**: 2026-05-10 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

# Just talk to your agent — it learns and *EVOLVES*.

Inspired by how brains learn. Meta-learn and evolve your 🦞 from every conversation in the wild. No GPU required.
MetaClaw Architecture

[🇨🇳 中文](./assets/README_ZH.md) • [🇯🇵 日本語](./assets/README_JA.md) • [🇰🇷 한국어](./assets/README_KO.md) • [🇫🇷 Français](./assets/README_FR.md) • [🇩🇪 Deutsch](./assets/README_DE.md) • [🇪🇸 Español](./assets/README_ES.md) • [🇧🇷 Português](./assets/README_PT.md) • [🇷🇺 Русский](./assets/README_RU.md) • [🇮🇹 Italiano](./assets/README_IT.md) • [🇻🇳 Tiếng Việt](./assets/README_VI.md) • [🇦🇪 العربية](./assets/README_AR.md) • [🇮🇳 हिन्दी](./assets/README_HI.md)
[Overview](#-overview) • [Quick Start](#-quick-start) • [Multi-Claw Support](#-multi-claw-support) • [Configuration](#️-configuration) • [Skills Mode](#-skills-mode) • [RL Mode](#-rl-mode) • [Auto Mode](#-auto-mode-default) • [Memory](#-memory) • [Citation](#-citation)

---

### Two commands. That's it.

```bash 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](https://arxiv.org/pdf/2603.17187)" is out! **🏆 Ranked No. 1** on [HuggingFace Daily Papers](https://huggingface.co/papers/2603.17187)! 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](https://www.thinkingmachines.ai/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](https://github.com/aiming-lab/MetaClaw/releases/tag/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`](./extensions/metaclaw-openclaw/README.md). ```bash 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): ```bash 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`](https://github.com/nicepkg/openclaw-weixin) plugin. MetaClaw auto-installs it when WeChat is enabled: ```bash metaclaw config wechat.enabled true metaclaw start ``` The plugin is installed automatically on `metaclaw start`. You can also install it manually: ```bash npx -y @tencent-weixin/openclaw-weixin-cli@latest install ``` To switch WeChat accounts (re-login with a new QR code): ```bash 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`](https://github.com/MindLab-Research/mindlab-toolkit). Similarly, for `rl.backend=weaver`, install [`nex-weaver`](https://github.com/nex-agi/weaver) separately. MetaClaw keeps these dependencies out of the default package so RL users can choose Tinker, MinT, or Weaver explicitly. ### 2. Configure ```bash 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**: ```bash metaclaw config rl.backend tinker metaclaw config rl.api_key sk-... metaclaw config rl.model moonshotai/Kimi-K2.5 ``` **MinT**: ```bash metaclaw config rl.backend mint metaclaw config rl.api_key sk-mint-... metaclaw config rl.base_url https://mint.macaron.xin/ metaclaw config rl.model Qwen/Qwen3-4B-Instruct-2507 ``` **Weaver**: ```bash metaclaw config rl.backend weaver metaclaw config rl.api_key sk-... metaclaw config rl.base_url https://weaver-console.nex-agi.cn metaclaw config rl.model Qwen/Qwen3-8B ``` Legacy aliases `rl.tinker_api_key` and `rl.tinker_base_url` are still accepted for backward compatibility. ### 3. Start ```bash metaclaw start ``` That's it. MetaClaw starts the proxy, automatically configures your chosen personal agent to use it, and restarts the gateway. Open your agent and start chatting — skills are injected at every turn, and the session is automatically summarized into new skills when you're done. --- ## 🦞 Multi-Claw Support MetaClaw works as a transparent proxy in front of any personal agent that supports an OpenAI-compatible LLM backend. The `claw_type` setting tells MetaClaw which agent to auto-configure when it starts. | `claw_type` | Agent | What MetaClaw does on `start` | |---|---|---| | `openclaw` | [OpenClaw](https://openclaw.ai) | Runs `openclaw config set models.providers.metaclaw …` + `gateway restart`. Uses the `anthropic-messages` API format so memory plugins (Hindsight, mem0, memory-lancedb) receive `event.rawMessage` correctly. | | `copaw` | [CoPaw](https://github.com/agentscope-ai/CoPaw) | Patches `~/.copaw/config.json` → `models.default` → `openai_compatible` pointing at the proxy port. CoPaw's ConfigWatcher hot-reloads automatically. | | `ironclaw` | [IronClaw](https://github.com/nearai/ironclaw) | Patches `~/.ironclaw/.env` → `LLM_BACKEND=openai_compatible` + `LLM_BASE_URL/MODEL/API_KEY`. Runs `ironclaw service restart`. | | `picoclaw` | [PicoClaw](https://github.com/sipeed/picoclaw) | Injects a `metaclaw` entry into `~/.picoclaw/config.json` `model_list` and sets it as the default model. Runs `picoclaw gateway restart`. | | `zeroclaw` | [ZeroClaw](https://github.com/zeroclaw-labs/zeroclaw) | Patches `~/.zeroclaw/config.toml` → `provider = "openai-compatible"` + `base_url/model/api_key`. Runs `zeroclaw service restart`. | | `nanoclaw` | [NanoClaw](https://github.com/qwibitai/nanoclaw) | Patches nanoclaw's `.env` → `ANTHROPIC_BASE_URL` pointing at the proxy's `/v1/messages` Anthropic-compatible endpoint. Restarts via `launchctl` (macOS) or `systemctl --user` (Linux). | | `nemoclaw` | [NemoClaw](https://github.com/NVIDIA/NemoClaw) | Registers a `metaclaw` provider in OpenShell via `openshell provider create` and sets it as the active inference route via `openshell inference set`. Persists config to `~/.nemoclaw/config.json`. | | `hermes` | [Hermes Agent](https://github.com/NousResearch/hermes-agent) | Injects a `metaclaw` entry into `~/.hermes/config.yaml` `custom_providers` and sets `model.provider: custom:metaclaw`. Runs `hermes gateway restart`. | | `none` | — | Skips auto-configuration. Point your agent at the proxy manually. | ### Setup Pick your agent during `metaclaw setup` (the first question in the wizard): ``` Personal agent to configure (openclaw/copaw/ironclaw/picoclaw/zeroclaw/nanoclaw/nemoclaw/hermes/none) [openclaw]: ``` Or set it directly at any time: ```bash metaclaw config claw_type copaw # switch to CoPaw metaclaw config claw_type ironclaw # switch to IronClaw metaclaw config claw_type picoclaw # switch to PicoClaw metaclaw config claw_type zeroclaw # switch to ZeroClaw metaclaw config claw_type nanoclaw # switch to NanoClaw metaclaw config claw_type nemoclaw # switch to NemoClaw metaclaw config claw_type hermes # switch to Hermes Agent metaclaw config claw_type none # manual / custom agent ``` Then run `metaclaw start` as usual — the proxy comes up and the chosen agent is wired automatically. ### Manual wiring (claw_type=none) Point any OpenAI-compatible client at the MetaClaw proxy: ``` base_url: http://127.0.0.1:30000/v1 api_key: metaclaw # or whatever proxy.api_key is set to model: ``` For Anthropic-native clients (e.g. the Claude SDK or NanoClaw's credential proxy), use the Anthropic-compatible endpoint instead: ``` ANTHROPIC_BASE_URL: http://127.0.0.1:30000 ANTHROPIC_API_KEY: metaclaw ``` --- ## ⚙️ Configuration Configuration lives in `~/.metaclaw/config.yaml`, created by `metaclaw setup`. **CLI commands:** ``` metaclaw setup # Interactive first-time configuration wizard metaclaw start # Start MetaClaw (default: auto mode) metaclaw start --mode rl # Force RL mode (no scheduler) for this session metaclaw start --mode skills_only # Force skills-only mode for this session metaclaw stop # Stop a running MetaClaw instance metaclaw status # Check proxy health, running mode, and scheduler state metaclaw config show # View current configuration metaclaw config KEY VALUE # Set a config value metaclaw config llm.oauth_token TOKEN # Store OAuth token for current CLI provider metaclaw auth paste-token --provider anthropic # Store OAuth token (anthropic | openai-codex | gemini) metaclaw auth status # Show all stored auth profiles metaclaw uninstall # Remove all MetaClaw data, OpenClaw extension, and pip package ``` When you start MetaClaw, the command waits until the local proxy becomes healthy before returning. Use `metaclaw status` to verify readiness and `metaclaw stop` to stop the background process.

Full config reference (click to expand)

```yaml mode: auto # "auto" | "rl" | "skills_only" claw_type: openclaw # "openclaw" | "copaw" | "ironclaw" | "picoclaw" | "zeroclaw" | "nanoclaw" | "nemoclaw" | "hermes" | "none" llm: auth_method: api_key # "api_key" | "oauth_token" provider: kimi # kimi | qwen | openai | minimax | novita | openrouter | volcengine | custom model_id: moonshotai/Kimi-K2.5 api_base: https://api.moonshot.cn/v1 api_key: sk-... # oauth_token example (token stored via `metaclaw auth paste-token`): # auth_method: oauth_token # provider: anthropic # anthropic | openai-codex | gemini # model_id: claude-sonnet-4-6 proxy: port: 30000 api_key: "" # optional bearer token for the local MetaClaw proxy skills: enabled: true dir: ~/.metaclaw/skills # your skill library retrieval_mode: template # template | embedding top_k: 6 task_specific_top_k: 10 # cap task-specific skills (default 10) auto_evolve: true # auto-summarize skills after each session rl: enabled: false # set to true to enable RL training backend: auto # "auto" | "tinker" | "mint" | "weaver" model: moonshotai/Kimi-K2.5 api_key: "" base_url: "" # optional backend endpoint, e.g. https://mint.macaron.xin/ for MinT or https://weaver-console.nex-agi.cn for Weaver tinker_api_key: "" # legacy alias for api_key tinker_base_url: "" # legacy alias for base_url prm_url: https://api.openai.com/v1 prm_model: gpt-5.2 prm_api_key: "" lora_rank: 32 batch_size: 4 resume_from_ckpt: "" # optional checkpoint path to resume training evolver_api_base: "" # leave empty to reuse llm.api_base evolver_api_key: "" evolver_model: gpt-5.2 opd: enabled: false # set to true to enable OPD (teacher distillation) teacher_url: "" # teacher model base URL (OpenAI-compatible /v1/completions) teacher_model: "" # teacher model name (e.g., Qwen/Qwen3-32B) teacher_api_key: "" # teacher model API key kl_penalty_coef: 1.0 # KL penalty coefficient for OPD max_context_tokens: 20000 # prompt token cap before truncation; 0 = no truncation (recommended # for skills_only mode with large-context cloud models) context_window: 0 # context window advertised to the agent (e.g. OpenClaw compaction # threshold); 0 = auto (200 000 in skills_only, 32 768 in rl/auto) scheduler: # v0.3: meta-learning scheduler (auto-enabled in auto mode) enabled: false # auto mode enables this automatically; set manually for rl mode sleep_start: "23:00" sleep_end: "07:00" idle_threshold_minutes: 30 min_window_minutes: 15 calendar: enabled: false credentials_path: "" token_path: "" ```

--- ## 💪 Skills Mode **`metaclaw start --mode skills_only`** The lightest mode. No GPU, no RL backend needed. MetaClaw places your LLM behind a proxy that injects relevant skills at every turn, then auto-summarizes new skills after each conversation. For OpenAI-compatible custom providers, set `llm.api_base` to the full chat API base (usually ending in `/v1`, for example `https://your-gateway.example/v1`). In `skills_only` mode, MetaClaw reuses that same endpoint for prompt compression and related helper LLM calls unless you configure a separate evolver endpoint. Skills are short Markdown instructions stored in `~/.metaclaw/skills/` as individual `SKILL.md` files. The library grows automatically with your usage. To pre-load the built-in skill bank (40+ skills across coding, security, agentic tasks, etc.): ```bash cp -r memory_data/skills/* ~/.metaclaw/skills/ ``` --- ## 🔬 RL Mode **`metaclaw start --mode rl`** Everything in Skills Mode, plus continuous RL fine-tuning from live conversations. Each conversation turn is tokenized and submitted as a training sample. A judge LLM (PRM) scores responses asynchronously, and a Tinker-compatible backend (Tinker cloud, MinT, or Weaver) runs LoRA fine-tuning with hot-swapped weights. **Tinker**: ```bash metaclaw config rl.backend tinker metaclaw config rl.api_key sk-... metaclaw config rl.model moonshotai/Kimi-K2.5 metaclaw config rl.prm_url https://api.openai.com/v1 metaclaw config rl.prm_api_key sk-... metaclaw start --mode rl ``` **MinT**: ```bash metaclaw config rl.backend mint metaclaw config rl.api_key sk-mint-... metaclaw config rl.base_url https://mint.macaron.xin/ metaclaw config rl.model Qwen/Qwen3-4B-Instruct-2507 metaclaw config rl.prm_url https://api.openai.com/v1 metaclaw config rl.prm_api_key sk-... metaclaw start --mode rl ``` **Weaver**: ```bash metaclaw config rl.backend weaver metaclaw config rl.api_key sk-... metaclaw config rl.base_url https://weaver-console.nex-agi.cn metaclaw config rl.model Qwen/Qwen3-8B metaclaw config rl.prm_url https://api.openai.com/v1 metaclaw config rl.prm_api_key sk-... metaclaw start --mode rl ``` A dedicated evolver LLM also extracts new skills from failed episodes, feeding them back into the skill library. **Programmatic rollout** (no OpenClaw TUI needed): set `openclaw_env_data_dir` to a directory of JSONL task files: ```json {"task_id": "task_1", "instruction": "Register the webhook at https://example.com/hook"} ``` ### On-Policy Distillation (OPD) OPD is an optional add-on for RL Mode. It distills a larger teacher model into the student on-policy: the student generates responses as usual, and the teacher provides per-token log-probabilities on those same responses. A KL penalty steers the student toward the teacher's distribution. ```bash metaclaw config opd.enabled true metaclaw config opd.teacher_url http://localhost:8082/v1 metaclaw config opd.teacher_model Qwen/Qwen3-32B metaclaw config opd.kl_penalty_coef 1.0 ``` The teacher must be served behind an OpenAI-compatible `/v1/completions` endpoint (e.g., vLLM, SGLang). OPD can be combined with PRM scoring, both run asynchronously. See `examples/run_conversation_opd.py` and `scripts/run_openclaw_tinker_opd.sh`. --- ## 🧠 Auto Mode (Default) **`metaclaw start`** Everything in RL Mode, plus a meta-learning scheduler that defers weight updates to user-inactive windows so the agent is never interrupted during active use. This is the default mode. The RL weight hot-swap step pauses the agent for several minutes. Instead of training immediately when a batch is full (like RL Mode does), auto mode waits for an appropriate window. Three conditions trigger an update window (any one is sufficient): - **Sleep hours**: configurable start/end time (e.g., 23:00 to 07:00) - **Keyboard inactivity**: triggers after N minutes of idle time - **Google Calendar events**: detects meetings so updates can run while you're away ```bash metaclaw config scheduler.sleep_start "23:00" metaclaw config scheduler.sleep_end "07:00" metaclaw config scheduler.idle_threshold_minutes 30 # Optional: Google Calendar integration pip install -e ".[scheduler]" metaclaw config scheduler.calendar.enabled true metaclaw config scheduler.calendar.credentials_path ~/.metaclaw/client_secrets.json ``` If the user returns mid-update, the partial batch is saved and resumed at the next window. Each `ConversationSample` is tagged with a `skill_generation` version. When skill evolution bumps the generation, the RL buffer is flushed so only post-evolution samples are used for gradient updates (MAML support/query set separation). --- ## 🧠 Memory MetaClaw v0.4.0 adds a long-term memory layer that runs alongside skills. Where skills capture *how* to do things, memory captures *what* has happened — user preferences, project state, recurring context, and cross-session facts. ### How it works At the end of each session, MetaClaw extracts structured memory units from the conversation and stores them locally. On the next turn, relevant memories are retrieved and injected into the prompt alongside skills — so the agent knows what you've worked on before, without you having to repeat yourself. Memory runs entirely in the background. There is nothing new to configure for basic use; it activates automatically when `memory.enabled` is set to `true`. ```bash metaclaw config memory.enabled true ``` ### Memory types | Type | What it captures | |------|-----------------| | `episodic` | Specific past events and actions | | `semantic` | General facts about the user or project | | `preference` | Stated or inferred user preferences | | `project_state` | Current goals, open tasks, recent decisions | | `working_summary` | Rolling summary of recent activity | ### Configuration ```yaml memory: enabled: false top_k: 5 # memories injected per turn max_tokens: 800 # token budget for memory block retrieval_mode: hybrid # keyword | semantic | hybrid consolidation_interval: 10 # consolidate every N sessions store_path: ~/.metaclaw/memory # local storage path ``` ### Memory sidecar (optional) For deployments that require process isolation, MetaClaw ships with a standalone memory sidecar service (`openclaw-metaclaw-memory`). When configured, the main proxy delegates all memory reads and writes to the sidecar over a local HTTP API. ```bash metaclaw config memory.sidecar_url http://127.0.0.1:30001 ``` --- ## 📊 Benchmark The MetaClaw-Bench evaluates how well AI agents learn and adapt from multi-day interaction histories. It ships two dataset variants (30-day full and 12-day small) with a CLI that covers the full pipeline — validation, inference, scoring, and reporting. See [`benchmark/README.md`](benchmark/README.md) for setup and usage. --- ## 🗑️ Uninstall ```bash metaclaw uninstall ``` This removes everything in one step: stops the running instance, cleans MetaClaw references from `~/.openclaw/openclaw.json`, deletes `~/.openclaw/extensions/metaclaw-openclaw/`, deletes `~/.metaclaw/`, uninstalls the pip package, and restarts the OpenClaw gateway. You will be prompted to confirm before anything is deleted. After uninstall, remove the source repo manually if you cloned it: ```bash rm -rf /path/to/MetaClaw ``` --- ## 📚 Citation ```bibtex @article{xia2026metaclaw, title={MetaClaw: Just Talk An Agent That Meta-Learns and Evolves in the Wild}, author={Xia, Peng and Chen, Jianwen and Yang, Xinyu and Tu, Haoqin and Liu, Jiaqi and Xiong, Kaiwen and Han, Siwei and Qiu, Shi and Ji, Haonian and Zhou, Yuyin and Zheng, Zeyu and Xie, Cihang and Yao, Huaxiu}, journal={arXiv preprint arXiv:2603.17187}, year={2026} } ``` --- ## 🙏 Acknowledgements MetaClaw builds on top of the following open-source projects: - [OpenClaw](https://openclaw.ai) – the primary supported personal agent. - [CoPaw](https://github.com/agentscope-ai/CoPaw) – multi-channel personal agent support. - [IronClaw](https://github.com/nearai/ironclaw) – Rust-native personal agent support. - [NanoClaw](https://github.com/qwibitai/nanoclaw) – container-isolated Anthropic-native personal agent. - [NemoClaw](https://github.com/NVIDIA/NemoClaw) – NVIDIA OpenShell-sandboxed personal agent with NIM inference. - [SkillRL](https://github.com/aiming-lab/SkillRL) – our skill-augmented RL framework. - [Tinker](https://www.thinkingmachines.ai/tinker/) – used for online RL training. - [MinT](https://github.com/MindLab-Research/mindlab-toolkit) – alternative backend for online RL training. - [Weaver](https://github.com/nex-agi/weaver) – alternative backend for online RL training. - [OpenClaw-RL](https://github.com/Gen-Verse/OpenClaw-RL) – inspiration for our RL design. - [awesome-openclaw-skills](https://github.com/VoltAgent/awesome-openclaw-skills) – provides the foundation for our skill bank. --- ## 📄 License This project is licensed under the [MIT License](LICENSE).