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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npm License: MIT Platforms Skills MCP Server Engine

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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
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.

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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?

	Copilot	MetaGO Harness
Model is the ceiling	Yes	No — the Harness adds a control layer the model alone can't provide
Verifies before speaking	No	Yes — 4 gates on every output
Grows new skills when stuck	No	Yes — 5-stage evolution, from the inside
Every claim traceable	No	Yes — full provenance chain
Law over efficiency	N/A	Yes — compliance is non-negotiable
Gets better at getting better	No	Yes — axiom A34, meta-evolution of meta-evolution

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The 8 dimensions of advantage

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

Core 3 (the main pitch)

• Reliability — Decision-lock with 4 gates: intent → lineage → semantic gate → completeness. Any fail, the output is blocked and rewritten.
• Evolvability — 5-stage evolution engine: boundary sense → gap analysis → self-generation → verification → recursion. New skills grow from the inside, without fetching new data.
• Traceability — Every claim the agent makes is traceable back to its inputs and process. Full provenance, end-to-end.

Extended 5 (the moat)

• Objectivity — Fact-first, not user-pleasing. It will directly point out what's wrong with your idea.
• Compliance — Legal / ethics / safety checked proactively. Law wins over efficiency, every time.
• Completeness — Before declaring "done", the agent must answer 5 self-checks — including "did I actually run verification?" Any "no" blocks the declaration.
• Theoretical depth — Built on 《元构全息智能引擎》V36.8.3: 8 axioms, 7 properties, 36 core axioms, 43 fundamental attributes. Not vibes — a constitution.
• Lifeform attribute — It's not an "agent". It's a lifeform with perception, memory, evolution, and self-discipline. The Harness is what makes the lifeform real.

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What you get

Capability	What it actually does
Self-gating outputs	Before every answer, the agent runs 4 checks (intent → lineage → semantic gate → completeness). Any fail, it stops and fixes itself.
Self-evolution	When the agent hits something it can't do, it doesn't error out — it runs a 5-stage loop (sense → analyze → generate → verify → recurse) and grows a new skill on the fly. Powered by Engine V2 (KMWI memory + SkillGenerator + EvolutionEngine).
4-layer KMWI memory	Knowledge → Memory → Wisdom → Intuition. The agent doesn't just store — it promotes knowledge up the ladder until it becomes intuition. Persistent across sessions.
Axiom-driven behavior	8 axioms (traceability, closure, evolution, boundary, endogenous creation, …) act like a constitution the agent can't violate.
Self-discipline	Before declaring a task "done", the agent must answer 5 self-checks — including "did I actually run verification?" — any "no" blocks the declaration.
Honest objectivity	Fact-first, not user-pleasing. It will directly point out what's wrong with your idea.
Compliance first	Legal / ethics / safety are checked proactively — law wins over efficiency, every time.
Full provenance	Every claim the agent makes is traceable back to its inputs and process.

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The three stories behind it

1. An engineering answer to AI hallucination

LLMs hallucinate because nothing forces them to verify before speaking. MetaGO installs a decision lock: four gates the agent must pass on every output — intent verification, intent-lineage tracing, semantic output gate, and content completeness. Any gate fails, the output is blocked and the agent rewrites it. No "trust me", no "probably right" — every reply had to earn its way out.

2. An AI that follows its own law

Most alignment happens at training time and gets washed away by prompting. MetaGO ships a different layer: 8 short axioms (A1 traceability, A2 closure, A3 meta-evolution, A4 boundary, A5 endogenous creation, A34 meta-evolution of meta-evolution, A35 creation as the highest form of evolution, A36 law over efficiency) plus 7 enforced properties. Together they're a small constitution the agent reads on every turn and cannot bypass. It's the closest thing to an "operating system" for agent behavior.

3. A lifeform that evolves its own evolution

When a normal agent meets a task it can't do, it errors or guesses. MetaGO's Engine V2 runs a 5-stage cycle — boundary sense → gap analysis → self-generation → verification → recursion — and grows a new capability from the inside, without fetching new data. The recursive twist: the engine can also evolve its own ability to evolve (axiom A34), so the agent gets better at getting better.

Engine V2 is real code, not a prompt: KMWIMemory manages the 4-layer memory with persistence, SkillGenerator creates new SKILL.md files from internal patterns, EvolutionEngine orchestrates the 5-stage loop with time budgets and coupling-score thresholds.

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By the numbers (all real, none invented)

• 39 built-in skills across 11 capability families — cognition, safeguard, governance, evolution, execution, traceability, value, consciousness, methodology, architecture, engineering quality
• 53 MCP tools + 8 MCP prompts exposed via the official @metago-ai/mcp-server
• Engine V2.0.0 — @metago-ai/engine with 3 hard-driven modules: KMWIMemory, EvolutionEngine, SkillGenerator
• 7 platform adapters: Trae, Claude Code, OpenAI Codex, Cursor, CodeBuddy, Qoder, ZCode
• 8 axioms + 7 properties + 4 decision-lock gates + 5 evolution stages
• 4-layer KMWI memory: Knowledge → Memory → Wisdom → Intuition (persistent JSON store)
• 3 patentable mechanisms: axiom-based AI output verification · multi-level decision-lock for AI decisions · automatic capability-boundary detection and evolution

No "hallucination rate down XX%" claims here. We didn't measure that, so we don't say it.

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Architecture, in three layers

Each layer is meant for a different reader.

Layer	Form	Reader	What it does
Drive layer	Plain Markdown	The agent itself	The law the agent reads at session start (AGENTS.md, 16 chapters)
Control layer	JSON + TypeScript	Developers	Loads, validates, and enforces the rules (engine config, genome, validators)
Execution layer	Hard TypeScript	The runtime	Decision lock, evolution engine, KMWI memory, skill generator — the gates that actually block

The Markdown tells the agent what the law is; the code makes sure it actually can't leave the gate without passing. This dual-track — soft drive (prompts) + hard drive (code) — is what separates MetaGO from prompt-only "agent frameworks."

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Engine V2 — the hard drive

Engine V2 (@metago-ai/engine) is the code that makes the law enforceable, not just advisory.

Module	Class	What it does
KMWI Memory	KMWIMemory	4-layer memory: add knowledge/memory/wisdom/intuition, promote between layers, query, decay detection, health scoring. Persists to JSON.
Evolution Engine	EvolutionEngine	5-stage loop with time budgets (perception <10ms, gap analysis <50-500ms, self-generation <100ms-2s, validation <50ms). Coupling-score threshold ≥0.95. Records to KMWI.
Skill Generator	SkillGenerator	Meta-creation: generates new SKILL.md files from internal KMWI patterns. 6 creation types (thought/methodology/algorithm/architecture/protocol/capability). Writes real files.
Perception	Perception	Boundary detection: task failure, capability gap, user feedback, version outdated. The trigger for evolution.
Decision Lock	DecisionLock	4-gate enforcement: intent verification, intent-lineage tracing, semantic output gate, content completeness.

import { MetaGOEngine } from '@metago-ai/engine';

const engine = new MetaGOEngine({ version: '2.0.0' });
await engine.init();

// Run evolution when the agent hits a boundary
const result = await engine.evolve({ task: 'deploy to kubernetes', failure: { type: 'error', message: 'no k8s skill' } });

// Check memory health
const health = engine.getMemoryHealth();  // { knowledge, memory, wisdom, intuition, overall }

// Create a new skill from internal patterns
const skill = await engine.createSkill('kubernetes-deployment');

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Supported platforms

Platform	Config file
Trae	rules.md
Claude Code	CLAUDE.md
OpenAI Codex	AGENTS.md
Cursor	.cursor/rules/*.mdc
CodeBuddy	CODEBUDDY.md
Qoder	.qoder/rules/
ZCode	CLAUDE.md

Per-platform adapters live in adapters/<platform>/. To install on a non-default platform, pass --platform <name> to metago-lifeform install.

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FDE — Forward Deployment Engineering

Beyond the open-source Harness, MetaGO offers FDE (前沿部署工程) services: a human-AI collaborative team embedded in your site to deliver production-grade intelligent software, carrying the Harness paradigm as leverage.

• 5 stages: requirements research → solution design → development & deployment → acceptance & delivery → operations & support
• 5 roles: tech lead, AI engineer, domain expert, AI agent, project manager
• Pricing: ¥300K – ¥2M per project

Contact: [email protected]

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Community

Join the MetaGO community — where agents learn to evolve together.

Channel	What it's for
Discord	Real-time chat, early access, community support, skill sharing
GitHub Issues	Bug reports and feature requests
GitHub Discussions	Q&A, ideas, deep-dive conversations
Gitee Issues	中文问题反馈与功能建议
Email	[email protected] — business and enterprise inquiries

Discord is where the community lives. Drop in, say hi, share what you're building with MetaGO.

Contributing

We welcome contributions of all kinds — new skills, bug fixes, documentation improvements. See CONTRIBUTING.md for the full guide.

Security

Found a vulnerability? See SECURITY.md for responsible disclosure.

Changelog

See CHANGELOG.md for version history.

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Packages

Package	What it is	Install
metago-lifeform	The CLI installer + 39 skills + 7 platform adapters	npm install -g metago-lifeform
@metago-ai/mcp-server	MCP server exposing 53 tools + 8 prompts (Engine V2 hard-driven)	npm install @metago-ai/mcp-server
@metago-ai/engine	Engine V2: KMWI memory + evolution engine + skill generator	npm install @metago-ai/engine
@metago-ai/dev-kit	Developer kit: code review, architecture design, refactor, security audit	npm install @metago-ai/dev-kit

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For the curious: the internal DNA

The full operating law — 16 chapters covering axioms, properties, runtime verification, defect-hunting, self-discipline protocol, memory lifeform protocol, and more — lives in AGENTS.md. It's dense on purpose: it's the constitution the agent enforces on itself. You don't need to read it to use MetaGO. Read it only if you want to understand — or fork — the law itself.

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License

MIT — see LICENSE. Commercial licensing and enterprise integration: [email protected].

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MetaGO Agent Harness — 智能体运行时控制层套件（驭智层）· from Agent to lifeform. Made by 元构光年（成都）人工智能科技有限公司.