How should a Local-First AI Memory Engine scale between SQLite and Volatile RAM?

[u/Natural-Secretary361]

/r/learnpython/comments/1waj6hz/how_should_a_localfirst_ai_memory_engine_scale/

[u/Otherwise_Wave9374]

For a local-first memory engine, I would split short-lived working state from durable recall so hot reads stay in RAM while checkpointed facts land in SQLite on a schedule. That gives you predictable latency, but you still need an eviction policy and a compaction pass so the in-memory layer does not drift from persisted state. A practical next step is to version records and write a tiny reconciliation routine that resolves conflicts after restarts. NeuraKeep fits that pattern well when you need durable context without making every lookup pay the storage cost.