auto-review-loop-llm

To automate LLM review cycles, configure an OpenAI-compatible API via llm-chat MCP server or environment variables, then trigger the loop to run iterative review phases until a positive verdict or maximum rounds are reached.

An autonomous LLM feedback loop is a multi-round process where an AI reviews research artifacts, generates scores and verdicts, implements fixes, and re-reviews. It persists state to JSON after each round to maintain traceability until a positive assessment is achieved.

Yes, you can use any OpenAI-compatible LLM API provider. The system supports MCP-based configuration and environment variables, allowing you to plug in your preferred provider to guide fixes and escalation during the autonomous review loop.

You configure the llm-chat MCP server for an AI review loop by setting your preferred LLM provider details in the server configuration or environment variables. This setup enables the autonomous review phases to execute and persist state correctly.

Use an autonomous review loop instead of manual LLM review when you need iterative, multi-round assessments with explicit scoring thresholds and state persistence. It is ideal for scaling research artifact validation without continuous human intervention.

The autonomous review loop stops before a positive verdict when the configured MAX_ROUNDS limit is reached. This constraint prevents infinite cycles, ensuring the review process terminates and outputs the final state logs even if artifacts remain unresolved.