How Much Memory Does Your Agent Need? — A Practical Memory Store Selection Guide

Short summary

Most AI agents don't need a vector database — ByteDance data shows vector retrieval serves only 3.7% of memory requests while 96.3% are key-value lookups and state reads. The article categorizes agent memory into four types (session state, domain knowledge, error history, semantic) and compares memory(), SQLite, ChromaDB, and PostgreSQL across latency, capacity, and operational cost. A three-layer architecture is proposed that covers session state, domain knowledge, and error history without unnecessary dependencies.

• •Vector retrieval accounts for only 3.7% of agent memory requests at ByteDance
• •Four memory types: session state, domain knowledge, error history, semantic memory
• •SQLite handles 870K records at <8ms latency with zero tuning — sufficient for 99% of agent projects
• •Three-layer architecture: memory()/STATE.md → SQLite → ChromaDB, each covering a distinct need

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