Executive Summary
For AI agents needing long-term memory, always-true facts like user preferences and project decisions are better stored in structured markdown files loaded at startup rather than embedded in a vector database. This approach avoids lossy similarity retrieval, allows easy hand-editing, reduces operational complexity, and prevents silent truncation of context. The method uses a capped index file with pointers to topic files, keeping always-on context small and reliable, while retrieval-augmented generation (RAG) handles large, variable corpora.
ResultReliable loading of must-have context every time without lossy retrieval or silent truncation; easier maintenance by hand-editing files; reduced operational complexity
Implementation ComplexityMedium effort
Best forSoftware development / AI tooling / Developer / AI engineer / Structured markdown files with capped index plus retrieval-augmented generation (RAG)