MetaHarness

Mint a custom AI agent harness from any repo.

npx metaharness · open the Studio →

(Repo: ruvnet/metaharness · CLI: metaharness · Library: @ruvnet/agent-harness-generator)

Explore the full animated walkthrough → · 12 chapters · 96-second full tour · 32-second preview

Earlier 20-chapter capability tour · Open the Studio

GenerateA harness for your repoGovernTools with explicit policy EvolveEvaluate harness changesShipYour name. Your package.

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What this is

Every serious repo deserves its own agent. A repo-aware CLI, a repo-aware coding agent, a local MCP server, memory scoped to the project, skills generated from the actual file layout, governance policy, release verification, witness-signed provenance.

metaharness mints those, on demand, from a GitHub URL or a blank slate. It is not another agent framework. It is a factory for agent frameworks.

The model is replaceable. The harness is the product.

What it gives you

In under 60 seconds, in your browser, with nothing leaving your machine:

• A custom AI agent harness for your repo (or any repo)
• Recommended agents, skills, slash commands, MCP tools
• A scoped memory namespace + governance policy
• Witness-signed provenance + release gates
• Drops into Claude Code, OpenAI Codex, pi.dev, Hermes, OpenClaw, RVM, or Prime Agent — pick one or all

Output is an npm-publishable .zip with your name on it, your branding, your npx <your-name> CLI.

New

• Bounded contrastive harness development: @metaharness/darwin/contrastive adds scoped proposals, actual probes, selective composition and metered experiment memory behind immutable caller policy. API guide. Independent paired confirmation adds unseen-task, statistical and regression checks. Public intent-classification evidence remains separate from general agent performance claims.

Watch Darwin evolve a harness · Explore model routing · Review experimental extensions

• Run an experimental ARC-AGI-3 harness from the ChatGPT UI. @metaharness/arc-agi-3 owns exact observations, persistent evidence-backed memory, belief-state exploration, guarded actions, supervisor interventions, checkpoints, and hash-chained receipts. @metaharness/arc-agi-3-chatgpt exposes that controller as a remote MCP server and MCP Apps canvas for ChatGPT Developer Mode. ChatGPT is the OpenAI reasoning host: neither package uses the OpenAI API or an OPENAI_API_KEY. An opt-in ARC-specific AVO loop adds governed candidate selection, lineage, memory, blocking supervision, and an explicitly separate retrodiction arm. The frozen synthetic mechanism gate passes, and one actor-declared-clean single-game smoke favored AVO 3.2676 to 0.3968, but that non-competition result is not claim-eligible. All packages remain private and experimental; no ARC performance claim is made until an official closed scorecard satisfies the frozen controlled-ablation gate in ADR-254.
• Governed autonomous variation for difficult engineering work. @metaharness/avo lets an agent repeatedly inspect, edit, execute real tools, evaluate, repair/revert, branch, consult structured RVF memory, and commit—while MetaHarness retains immutable capabilities, budgets, promotion, quarantine, rollback, and signed replay receipts. Simple work stays on Darwin's fast path. The runtime has a deterministic 205-action RVF interruption proof; the stronger “AVO-class” claim remains blocked on the preregistered 100-task unseen SWE-bench gate in ADR-251. ADR-276 now enforces that boundary at publication against the exact tag SHA and npm tarball, protected claim semantics, measured cost, lineage roots, and two independent graders.
• Run your harness on Prime Agent — and borrow its best ideas. The 10th host (@metaharness/host-prime-agent, --host prime-agent) emits your tools as project-scoped, Python-backed Prime Agent skills (.prime/agent/skills/ — the host has no MCP) plus an install runbook. Fail-closed: Prime Agent can't enforce a deny-list itself, so a non-empty deny-list ships a prominent SANDBOX-REQUIRED.md instead of silently dropping your posture. From its design we also shipped RefineMutator — an evidence-backed proposer in @metaharness/darwin that must cite the failing traces behind each edit or propose nothing — and an opt-in --sessions crash-recoverable JSONL session log whose Rust and TS (wasm) replays produce identical state hashes. Its PTC ("kernel as the only tool") claim is honestly deferred behind a pre-registered A/B in evals-toolcall. See ADR-246 / ADR-247. ($0)
• Score any repo before you build it. npx metaharness score <repo> reads the repo (never runs it) and prints a one-screen report card — how well a harness fits, how likely it is to build, how safe the tools are, and the rough cost per run — so you know what you'll get before scaffolding.
• Pick the cheapest model that's good enough. @metaharness/router routes each request to the right model from your own results — same quality, far less spend. Works out of