Agent Memory Persistence

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name: agent-memory-persistence description: Provide long-term memory persistence for AI agents with SQLite-backed storage, structured metadata, vector embeddings, semantic retrieval, lifecycle management, and queries by user, session, and time.

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Agent Memory Persistence

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.

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.