SQL Outperforms Vector Databases for Most AI Agent Memory Use Cases

A developer analysis argues that vector databases have become the default for AI agent memory in 2024, but are often unnecessary and overly complex for most workloads. Vector stores introduce operational overhead, unpredictable recall, and debugging challenges that SQL-based solutions avoid. For structured, time-filtered, or tag-based memory retrieval, classic SQL offers deterministic and explainable queries in a single step. The analysis demonstrates that SQLite can store both structured metadata and raw embeddings, handling brute-force semantic similarity for datasets under 100,000 items without sacrificing speed. The conclusion is that vector databases are best suited for large-scale pure semantic search, while SQL remains the more practical choice for layered or episodic agent memory.