Why I gave my code review agent a graph
**# Why I gave my code review agent a graph By a student builder who got tired of watching AI hallucinate code reviews. Like almost every computer science student working with generative AI over the past year, I started with what felt like the obvious playbook: Retrieval-Augmented Generation (RAG) and raw prompt stuffing. Whenever my classmates or I submitted a pull request for a team project, we…
By a student builder who grew weary of AI-generated code reviews that hallucinated results, I decided to give my code review agent a graph structure. After trying Retrieval-Augmented Generation (RAG) and raw prompt stuffing, which failed to accurately predict downstream impacts of code changes, I realized the limitations of treating source code as flat text chunks.
I switched to a graph-based approach using FalkorDB, a graph database powered by Cypher queries. This change allowed the agent to perform fast, accurate computations of potential code impact using relationships like CALLS, IMPORTS, and CONTAINS. Tests showed that the graph-based agent could identify downstream blast radii and missing connections with remarkable speed and accuracy, eliminating hallucinations and providing mathematical proofs for its answers.
The project also revealed architectural insights like identifying high-risk functions and improving test coverage by analyzing call chains and test dependencies.
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