Sumanth (@Sumanth_077) “I built a self-evolving code review agent! Most code review agents use the same” — TopicDigg

I built a self-evolving code review agent! Most code review agents use the same prompt every time they run. They can review a diff and generate comments, but they do not remember what your team accepted, rejected, or corrected in previous reviews. That means the same mistakes can keep showing up across review cycles. This project adds persistent experiential memory to the review loop. Before every review, the agent retrieves relevant team rules and similar past review trajectories from memory, then uses that context alongside the current diff to generate review comments. After the review, the engineer can accept, reject, or edit every comment. That feedback becomes the learning signal. Accepted comments reinforce useful rules. Rejected comments create lessons about what should not be flagged. Edited comments refine the team’s preferred convention, scope, or wording. The agent adapts non-parametrically. Instead of fine-tuning the underlying model, it improves by storing review outcomes as reusable memory and retrieving the most relevant experience before the next review. Here is how the loop works: • Retrieve: search memory for relevant insights and similar past reviews before generating comments • Review: generate structured feedback using the current diff plus retrieved memory • Human feedback: pause the workflow and collect an accept, reject, or edit decision for every comment • Reflect: convert those decisions into reusable natural-language rules with rationale, polarity, scope, and confidence • Persist: store both the learned insights and complete review trajectory for future retrieval Over time, the agent builds a memory of how your team actually reviews code without retraining the underlying model. The full workflow runs locally with LangGraph, Ollama, BGE embeddings, and a vector database for long-term memory. The interesting part is that every review leaves behind experience that can be retrieved and reused on the next one. Github Repo: