auto-review-loop-llm

An autonomous review loop automates iterative artifact evaluation by feeding prior round results back into LLM prompts, suggesting fixes, and re-reviewing until a positive verdict or max round limit is reached.

Yes, you can use an OpenAI-compatible LLM to automatically evaluate project artifacts and suggest fixes by operating within a controlled loop that repeatedly assesses and re-reviews until readiness criteria are met.

You do not strictly need an MCP server; the review loop supports LLM integration via the llm-chat MCP server or direct API calls, requiring only environment credentials for your chosen OpenAI-compatible service.

The review loop handles state persistence by recording each round's results to support auditing, traceability, and restart from the last saved state, ensuring continuous iterative evaluation without losing progress.

If the review loop does not reach a positive verdict, it stops after hitting the MAX_ROUNDS limit, ending the iterative review-implement-re-evaluate cycle and returning the final round's suggestions.

Use an autonomous LLM-driven review loop when reducing manual back-and-forth in evaluating research artifact quality and readiness is critical, requiring repeated automated assessment and fix suggestions.