Why I Built Local-First Agent Memory

Short summary

The author argues that embedding-based agent memory introduces hidden costs — cloud latency, opaque storage, and model-update fragility — and demonstrates a local-first alternative using SQLite with FTS5 full-text search. Stored as a single file, the approach delivers sub-second keyword queries with deterministic ranking, offline operation, and easy backup. A hybrid approach may still make sense for some scenarios, but for many agent memory use cases, FTS5 is a practical, lightweight alternative.

• •Embedding-only agent memory adds latency, cost, and opacity that local-first storage avoids
• •SQLite + FTS5 provides sub-second keyword search with deterministic, inspectable ranking
• •Single-file storage enables easy backup, migration, and version control of agent session memory

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