I accidentally turned LLM memory into program analysis

The author, a vulnerability researcher, found that LLM agents lose track of established facts during long investigations, leading to hallucinations. To solve this, they built Lemmalog, a Datalog engine that maintains a structured knowledge base. The LLM extracts facts from messy sources, while Lemmalog derives conclusions and tracks dependencies, automatically invalidating affected results when facts change. This approach provides provenance for conclusions and distinguishes between relevant past information and current truth, improving reliability in complex security research.