MartinLoop: Open-Source Control Plane for AI Coding Agents with Budget Stops and Audit Trails

MartinLoop is an open-source control plane for AI coding agents that addresses common failure modes: retrying the same broken approach, passing tasks without proof, burning tokens quietly, making unauditable changes, and failing in ways that are hard to classify. It provides hard budget stops, JSONL run records, inspectable audit trails, failure classification, test-verified completion, and reproducible benchmark runs.

Key features include:

• Hard budget stops — cap spending on agent runs automatically.
• JSONL run records — every step logged in a structured format.
• Inspectable audit trails — any engineer can review the agent's actions.
• Failure classification — categorize why an agent failed (e.g., stuck in loop, wrong approach).
• Test-verified completion — agents must pass defined tests before reporting done.
• Reproducible benchmark runs — standardize evaluation across agents.

The project is positioned as CI/CD for autonomous coding agents. The core is open source on GitHub: https://github.com/Keesan12/Martin-Loop. A demo is available at https://martinloop.com/demo.

Useful for teams using Claude Code, Codex, Cursor, Devin-style agents, or custom agent loops who need governance, budgets, evals, and auditability over their AI coding workflows.