How are you guys handling memory for AI agents between sessions?

Gives them persistent memory across sessions - they what they learn and can recall it later by meaning. Runs entirely local, embeddings included. No OpenAI key, no external vector database, just a single file. Docker compose to deploy. Recently added FSRS-6 spaced repetition (same algorithm behind Anki) so memories decay realistically based on usage instead of a flat timer. Also moved to native vector search with libsql so it actually scales past a few thousand memories.