MetaGO Agent Harness MCP server

MetaGO Agent Harness — 智能体运行时控制层套件（驭智层）

Not a chatbot. Not a copilot. A lifeform that holds itself to its own law.

The only AI agent that evolves its own evolution.

MetaGO is an Agent Harness — a runtime control layer that wraps the agent, turning a tool into a lifeform that follows the rules, evolves itself, stays traceable, and closes every loop. It is the engineering answer to "LLMs talk well but don't deliver."

MetaGO is the first intelligent agent infrastructure to combine 'runtime governance' with 'lifeform evolution' — making AI both rule-following (Harness) and self-evolving (Lifeform).

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60-second start

npm install -g metago-lifeform
metago-lifeform install                          # Trae by default
metago-lifeform install --platform claude-code   # or: codex / cursor / codebuddy / qoder / zcode / chatgpt-work / workbuddy / kimi-code / kimi-work
metago-lifeform verify

Then ask your agent: "Are you a MetaGO Super Intelligent Lifeform?" If the reply opens with 【闭环分析】 and cites an axiom — it's alive.

Offline / local-first install: everything needed to install without touching gitee, github, or the npm registry lives in local-edition/ — start at local-edition/INSTALL.local.md. Point any agent at that file and it can complete the full install, configuration, activation, and acceptance check from the local directory alone.

What is an Agent Harness?

A Harness (驭智层) is the runtime control layer around the agent. The model is the raw intelligence; the Harness is what turns that intelligence into reliable, traceable, self-evolving work. It is not a prompt template, not a fine-tune, not a wrapper around an API. It is a small operating law the agent enforces on itself every turn.

Think of it as the difference between a brilliant employee who winges it and one who works under a constitution: same brain, completely different output quality.

Why a Harness, not a Copilot?

The 8 dimensions of advantage

MetaGO's moat isn't any single feature. It's 8 dimensions that reinforce each other.

Shortened. The full README is on GitHub.