Developer Builds Offline AI Agent Memory Using SQLite Instead of Vector Embeddings

A data engineer building the LoreConvo tool chose SQLite with its FTS5 full-text search extension over cloud-based vector embeddings for storing AI agent session memory. The decision was driven by concerns over network latency, per-call costs, and the lack of transparency and reproducibility that embedding-based storage introduces. Each conversation session is saved as a row in a local SQLite file, capturing transcripts, summaries, tool usage, and tags, with no user action required. Because the file resides entirely on the user's machine, it works offline and can be easily backed up, migrated, or version-controlled. The developer noted that while a hybrid approach combining full-text search with embeddings still makes sense in some scenarios, SQLite alone delivered comparable recall quality without the overhead of managed embedding services.