Give coding agents more work. Watch them less. Ship more.
MartinLoop lets you give coding agents real software jobs without babysitting every step.
Your coding agent still writes the code. MartinLoop keeps the job focused, bounded, and accountable — with limits, stop conditions, verification, recovery, and a clear outcome when the run ends.
Run coding agents without babysitting them. Keep jobs focused, bounded, checked and accountable from start to finish. Finally run your agents swarms overnight and get your time back.
2026-11-05
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Trust your coding agents with real work.
Built from thousands of agent runs where the problem was not just intelligence — it was whether you could give the agent a real job and confidently walk away.
Measure your loop tax:npx -y martin-loop@latest audit 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
Audit first — run npx -y martin-loop@latest audit to see how much API-equivalent Claude Code spend went into fix-and-retry loops. Add --offline for zero network access or --share for a shareable summary.
Install / onboard — 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.
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 system around the coding job.
The coding agent still writes the code. MartinLoop keeps the job, limits, verification, recovery, outcome, and history consistent around the work.
Use MartinLoop when a coding task needs:
clear job and success criteria
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 consistent workflow across supported coding-agent runtimes
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.
Stage
MartinLoop role
Define
Capture the objective, verifier, budget, scope, and finish line.
Preflight
Check readiness and required workflow evidence before agent spend.
Control
Enforce budgets, attempts, path boundaries, policy, and stop conditions while the coding agent works.
Verify
Run configured checks and bind the evidence to the active run and workspace.
Recover
Preserve recovery and rollback state when another attempt or human review is required.
Prove
Produce the authoritative VERIFIED, STOPPED, or NEEDS REVIEW handoff plus receipts.
Analyze
Inspect run history, cost provenance, failure classes, dossiers, and shareable evidence after execution.
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.
Change the worker. Keep the job.
MartinLoop is model-agnostic by design. The job, limits, verification, recovery, and outcome are separate from the worker underneath.
Native coding-agent CLIs and OpenAI-compatible model runtimes use different execution paths, so MartinLoop only describes a worker as fully supported when that execution path has been publicly released and validated.
Why MartinLoop
AI coding agents can create more software than ever. The problem is trusting them with bigger jobs without turning yourself into their full-time manager.
Agents drift. They retry. They break unrelated things. They say "done" too early. And a run that was supposed to save time can create even more work to review.
MartinLoop keeps the job accountable outside the agent itself: clear boundaries, hard limits, configured checks, recovery information, and an explicit outcome.
The goal is simple:
Give the agent more responsibility without giving it unlimited freedom.
Why Teams Adopt MartinLoop
Less babysitting — give agents larger jobs without watching every step.
Clear boundaries — keep scope, attempts, and spend inside the limits you set.
Checks before "done" — configured verification decides whether the evidence passed.
Understand failures — know why the run stopped instead of digging through logs.
Recover cleanly — preserve enough evidence to retry, repair, roll back, or hand off.
One workflow across agents — keep the job consistent as the worker underneath changes.
MartinLoop reads Claude Code session history locally and reports how much API-equivalent agent spend happened inside fix-and-retry loops, how often verifier commands failed, the longest retry chain, and sessions that ended red. Session contents stay local. Use --offline to disable even the public pricing lookup, and --share to create a screenshot-ready SVG plus Markdown summary.
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 enable --engine codex
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.
The result is local simulation evidence only: it is non-governed, is not persisted to the Swarm run store, and does not evaluate signed receipt integrity. Inspect demo output through the demo --swarm human view or demo --swarm --json, plus the local files created under --dir. Do not use swarm status, swarm inspect, swarm dossier, swarm verify, or swarm share for demo output.
For real persisted Swarm evidence, use swarm plan and swarm run. Their stored runs are the only inputs to swarm status, swarm inspect, swarm dossier, swarm verify, and swarm share.
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.
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.
Ungoverned agents can retry until cost and scope drift. MartinLoop adds budget caps, verifier gates, and audit evidence so the run has a clear stop condition.
MartinLoop Arcade
Long governed runs do not have to mean staring at a spinner. In an interactive terminal, MartinLoop Arcade can be offered while the coding agent continues working in the background.
Arcade is presentation-only. It cannot change the agent, budget, verifier, policy decision, run outcome, or receipt evidence. It stays out of JSON, CI, non-interactive, and other machine-readable execution paths.
Use --arcade to offer Arcade immediately for a supported interactive run, or --no-arcade to suppress it for that run.
Proof Receipts
Proof receipts are local share bundles for governed AI coding runs. They show the task, spend, budget, verifier result, receipt integrity, and any evidence boundary that should not be rounded into confidence.
This real governed run spent $0.51 against a $3.00 budget. The verifier passed and the receipt integrity was signed, but the proof stayed at EVIDENCE_BOUNDARY because rollback evidence was not recorded.
Generate your own receipt after a governed run:
npx -y martin-loop@latest run "Summarize the demo workspace and prove tests still pass" --proof --verify "npm test"
npx -y martin-loop@latest runs verify --latest
npx -y martin-loop@latest share --latest
For deterministic installs, pin the package line ([email protected]) or use martin-loop@latest. Plain npx martin-loop can resolve a stale local cache on some machines.
Expected share bundle outputs:
share/run-receipt.json
share/run-receipt.md
share/proof-card.svg
See It In Action
The point is not that every governed run is always cheaper. The point is that every run becomes inspectable and enforceable: budget policy, verifier result, stop reason, and evidence are explicit.
For a deterministic public repro lane, use the benchmark workspace and compare governed execution to unbounded retry behavior:
A Ralph-style loop is the failure mode where an AI coding agent keeps trying without knowing when continuing is unsafe, uneconomical, or unlikely to succeed.
MartinLoop keeps the useful part of the loop, then adds brakes:
stop before budget overspend
classify unsafe or invalid actions before execution
write an audit record for every attempt
preserve rollback and verifier evidence for review
reduce runaway context growth with compact run summaries
Failure Taxonomy (13 Runtime Classes)
When a coding agent fails, "something went wrong" is not useful enough.
MartinLoop classifies runtime failures into 13 canonical classes so a failed run can tell you what happened, what needs attention, and what future runs can learn from.
Defaults come from martin.config.yaml; CLI flags can override them. Budget preflight blocks attempts that would exceed policy.
Agent adapters
Claude CLI, Codex CLI, Gemini CLI, and direct-provider adapters normalize execution results.
Safety and verification
Scope checks, verifier command checks, prompt integrity, and grounding decide whether work can continue.
Persistence
JSONL run records, evidence summaries, and repo-backed artifacts make every run inspectable later. Each loop record is locally signed (HMAC, per-runs-root key) and dossier/runs get/runs verify/challenge/badge report an integrity verdict (verified / tamper_detected / unsigned) so post-hoc edits to a record are detectable, not just inspectable.
Trust Boundaries
Cost and token outputs always include provenance (actual, calculated, estimated, or unavailable).
For Codex specifically, MartinLoop reports authoritative usage only when the host exposes it; otherwise MartinLoop labels usage as estimated and avoids presenting it as settled accounting.
Receipt integrity must be verified before a run is treated as trustworthy evidence for external review.
--budget <n> Hard cost cap in USD
--budget-usd <n> Alias for --budget
--soft-limit-usd <n> Soft budget threshold in USD
--verify <cmd> Verifier command after each attempt
--proof Run verifier-only evidence checks without claiming governed execution
--max-iterations <n> Maximum number of attempts
--max-tokens <n> Maximum token budget
--engine <name> Adapter to use: claude, codex, gemini, or openai
--cwd <path> Repo root for the run
--allow-path <glob> Restrict writes to this path pattern; repeatable
--deny-path <glob> Block this path pattern; repeatable
--runs-dir <path> Override the local Martin runs root
Examples below use npx martin-loop so they work without a global install. If you install martin-loop globally, the martin alias works too.
Use martin-loop share --latest after dossier when you want a redacted bundle you can hand to another person without sending raw run-store files.
The root martin-loop package, standalone @martinloop/mcp package, plugin metadata, and MCPB product version are aligned at 0.8.10. The MCPB manifest schema remains 0.3.
The public MCP release train labels are:
0.1.4 operator foundation
0.2.0 cockpit expansion
0.2.5 public MCP package line
0.2.7 usability and review release
0.3.0 host adoption and onboarding release
0.3.1 review and handoff release
0.5.3 execution-control and host-compatibility release
0.5.5 governed-autonomous execution and proof-surface release
0.5.6 hosted run sync, fail-closed rollback, and verified-completion hardening
The standalone MCP registry/server identifier is io.github.Keesan12/martin-loop.
Issues, bug reports, workflow feedback, and focused pull requests are welcome. Public-facing docs should stay concise, user-centered, and accurate.
git checkout -b feat/your-feature
pnpm lint
pnpm test
git commit -m "feat: describe what you built"
git push -u origin feat/your-feature
Telemetry & Privacy
MartinLoop sends minimal anonymous usage data to help improve reliability and prioritize development. On a fresh interactive install, telemetry is enabled by default but remains blocked until the one-time disclosure is shown. After the disclosure, the same run may send the allowlisted anonymous events unless you opt out.
What is sent:
Random installation ID (generated locally, never linked to your identity)
Per-process session ID
CLI version, Node version, OS and architecture
Event name and timestamp
Command category, run duration, success/failure category
Whether a receipt was generated; whether recovery occurred
Run coding agents without babysitting them. Keep jobs focused, bounded, checked and accountable from start to finish. Finally run your agents swarms overnight and get your time back.
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