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.

Hosted sync (optional) — governed work is local-first. Configure MARTIN_API_TOKEN and MARTIN_TELEMETRY_ENDPOINT, then use martin sync status and martin sync flush to send preserved evidence to a dashboard later. See the quickstart.

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

Teams should not need to stitch together a separate script or point tool for every part of coding-agent execution. MartinLoop connects the control path around the agent from preflight through post-run evidence.

MartinLoop does not replace Git, GitHub, CI, dedicated security scanners, observability platforms, code review, or the coding agent itself. It gives those workflows one governed execution record to inspect.

Why MartinLoop

AI coding agents are useful, but unbounded retry loops are expensive.

A task that looked like a small fix can become dozens of attempts, a blown token budget, and a diff nobody trusts. MartinLoop gives every run an explicit contract: objective, verifier, budget, scope, receipts, and a clear stop condition.

Use it when AI coding work needs to stay bounded, inspectable, and safe to review before it becomes expensive or destructive.

Why Teams Adopt MartinLoop

• It turns agent behavior into inspectable run receipts you can actually review.
• It enforces hard stop conditions before runaway retries spend more money.
• It adds rollback-aware rules so failed attempts do not silently leave unsafe changes behind.
• It helps teams compare outcomes across agents under one governed flow.

Teams use MartinLoop when they need governed agent execution that can be reviewed and trusted.

2-Minute Install Path

npx -y martin-loop@latest start
npx -y martin-loop@latest demo
cd martin-loop-demo
npm install
npx -y martin-loop@latest run "Summarize the demo workspace and prove tests still pass" --verify "npm test" --budget-usd 2 --max-iterations 1

Quick Start

Try MartinLoop in a disposable demo workspace:

npx -y martin-loop@latest start
npx -y martin-loop@latest demo
npx -y martin-loop@latest --version
cd martin-loop-demo
npm install
npx -y martin-loop@latest run "Summarize the demo workspace and prove tests still pass" --verify "npm test" --budget-usd 2 --max-iterations 1
npx -y martin-loop@latest dossier --latest
npx -y martin-loop@latest share --latest

Optional global install:

npm install -g martin-loop
martin-loop --version

If this flow is useful, open an issue with feedback so we can keep improving the public experience.

start prints the first-run guided path. run auto-checks doctor, session-start, and preflight, then executes when the environment is ready. Use --proof only when you intentionally want an explicit no-spend lane.

Inspect-first flow:

npx -y martin-loop@latest doctor
npx -y martin-loop@latest session-start
npx -y martin-loop@latest preflight "Summarize the demo workspace and prove tests still pass" --verify "npm test"

share --latest writes three files into the selected run directory under share/: run-receipt.json, run-receipt.md, and proof-card.svg.

Release notes for MartinLoop 0.6.5: MartinLoop 0.6.5.

Release notes for MartinLoop 0.6.6: MartinLoop 0.6.6.

Release notes for MartinLoop 0.6.7: MartinLoop 0.6.7.

Release notes for MartinLoop 0.6.8: MartinLoop 0.6.8.

Model and Engine Support

MartinLoop governs the job independently of the coding worker.

• Native agent CLIs: Codex, Claude Code, Gemini CLI
• OpenAI-compatible endpoints: Kimi K2, NVIDIA Nemotron, DeepSeek, Qwen/Qwen Coder, Mistral/Codestral, OpenRouter/Together/Fireworks routes, Ollama, LM Studio, llama.cpp, and other compatible endpoints
• Use --engine openai for OpenAI-compatible model endpoints.

The worker changes; MartinLoop's budget, scope, verifier, receipt, and integrity contract does not.

More detail: Model and engine support

The Run From Start to Handoff

MartinLoop's terminal presentation is built around the governed lifecycle, not around a single verifier command.

Governed Run Plan shows the configured finish line before work starts, including the task, budget posture, verifier plan, scope, and execution boundaries.

Controlled Run keeps the coding agent working inside those boundaries while MartinLoop tracks attempts, cost, stop conditions, and recovery state.

Verified Handoff closes the loop with one authoritative outcome:

• VERIFIED when the configured evidence supports the Definition of Done
• STOPPED when a configured hard boundary ends the run
• NEEDS REVIEW when completion cannot be established from the available evidence

The handoff can include verifier steps, scope state, attempt count, cost provenance, unresolved evidence, recovery state, receipt integrity, and the next safe action. The exact fields depend on what the run actually established.

Visual Proof

MartinLoop turns an AI coding run into an inspectable execution record: budget used, verifier result, changed files, rollback evidence, and final receipt.