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MartinLoopSecure your AI agents with limits, proof checks, and complete visibility

Control AI coding agents with spend limits, proof checks, safety rules, and full audit trails. MartinLoop wraps Claude, Codex & OpenCode for secure, visible automation.

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More About MartinLoop

MartinLoop — The OS for AI Coding Agents

MartinLoop is the open-source control plane that transforms chaotic AI coding agents into governed, cost-controlled workflows. It gives engineering teams real spending limits, verified task completion, and complete audit trails—turning autonomous coding from a budget risk into a measurable asset.

Product Highlights

  • Real Budget Caps: Set dollar limits before any run starts; MartinLoop tracks spend in real time and stops before the ceiling is hit
  • Intelligent Failure Handling: 12-class failure taxonomy routes syntax errors, hallucinations, and logic bugs to targeted fixes—not blind retries
  • Verified Completion: Tasks only mark complete when tests pass, with JSONL run records your finance team can actually read
  • Cross-Model Compatibility: Works seamlessly with Claude, Codex, Cursor, and open-source models—no vendor lock-in
  • Open Source Core: Apache 2.0 licensed, free forever; fork, self-host, and audit the entire runtime

Use Cases

  • Nightly Code Generation: Run AI agents overnight with hard budget limits, waking to completed, tested, and verified code
  • Cost Governance for Finance: Provide leadership with clear cost-per-task metrics and ROI per agent, turning budget reviews into expansion conversations
  • Compliance-Ready Workflows: Generate inspectable receipts for every decision, approval, and action—essential for regulated industries
  • Multi-Model Agent Fleets: Govern mixed model environments with unified controls regardless of underlying provider

Target Audience

MartinLoop serves engineering leaders, platform teams, and CTOs who are already deploying AI coding agents but need to eliminate runaway costs, unverified outputs, and audit gaps. It's built for organizations where autonomous coding must be both ambitious and accountable.

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