Martin Loop MCP server

MartinLoop

Your coding agent says it's done. MartinLoop makes it prove it.

One system to control, verify and understand coding-agent work.

MartinLoop gives AI coding agents budgets, stop conditions, rollback rules, and receipts.

Built from thousands of agent runs where the problem was not intelligence -- it was uncontrolled execution.

Get started: npx -y martin-loop@latest start Try the demo: npx -y martin-loop@latest demo

MartinLoop is part of the NVIDIA Inception program.

Start Here

Install — run npx -y martin-loop@latest start, or install it globally with npm install -g martin-loop@latest.

Governed run — define an objective, verifier, budget, and iteration cap with martin run.

Verifier — completion requires fresh verifier evidence bound to the active run and workspace. A configured verifier proves only the checks it runs; VERIFIED is not a claim that the code is bug-free or automatically safe to merge.

Budget — set a hard spend ceiling with --budget-usd and an attempt ceiling with --max-iterations.

Receipts — inspect the latest result with martin dossier --latest and validate stored integrity with martin runs verify --latest.

MCP — install @martinloop/mcp@latest in a supported host or generate host configuration with martin mcp print-config.

Documentation — continue with the quickstart, CLI reference, or MCP setup.

When --model is provided, MartinLoop passes it through unchanged. Without --model, the authenticated host runtime chooses its own default. MartinLoop does not inject a hidden fallback model.

For AI Agents

MartinLoop is the execution-control system around coding agents. The coding agent still writes the code. MartinLoop governs the run and makes completion prove itself.

Use MartinLoop when a coding task needs one or more of these controls:

• preflight readiness before agent spend
• hard budget and iteration limits
• allowed or denied edit scope
• controlled retries and stop conditions
• independent verifier commands
• recovery and rollback evidence
• a finite completion decision
• run receipts, dossiers, history, failure classification, and post-run analysis
• one control layer across Codex, Claude Code, and other coding-agent environments

Canonical lifecycle:

DEFINE
  -> PREFLIGHT
  -> CONTROL
  -> VERIFY
  -> RECOVER
  -> PROVE
  -> ANALYZE

The product-level flow is Definition of Done -> Controlled Run -> Verified Handoff.

For machine-readable context start with llms.txt, llms-full.txt, and MartinLoop for AI Agents.

One System Around the Run

Shortened. The full README is on GitHub.