Agent Memory Persistence

Provide long-term memory persistence for AI agents with SQLite-backed storage, structured metadata, vector embeddings, semantic retrieval, lifecycle manageme...

Use this skill when an agent needs durable memory storage across sessions.

What it provides

• SQLite-backed persistence for text, metadata, and embedding vectors
• CRUD operations for memory items
• Semantic retrieval with cosine-similarity vector search
• Memory lifecycle operations including expiration cleanup
• Filters by user, session, type, and time window

Project structure

• src/MemoryStore.ts: low-level SQLite storage engine
• src/VectorIndex.ts: vector similarity search over stored embeddings
• src/MemoryManager.ts: high-level API used by agents
• src/types.ts: shared TypeScript contracts

Usage pattern

1. Create a MemoryManager with a SQLite path.
2. Write memories with content, optional metadata, and optional embedding.
3. Query memories by session/user or use searchByVector() for semantic lookup.
4. Periodically call cleanupExpired() to delete stale memories.

Notes

• Embeddings are stored as JSON arrays in SQLite.
• Vector search is implemented in TypeScript using cosine similarity, which keeps deployment simple and avoids SQLite extensions.
• If memory volume grows substantially, replace VectorIndex with an ANN index or SQLite vector extension while preserving the MemoryManager API.