Why a simple key-value store may beat a vector database for agent memory

A developer building a memory API for AI agents found that most agent memory failures stem from an inability to recall specific, named facts across sessions rather than from fuzzy semantic search limitations. The author distinguishes between two types of memory: semantic memory, which retrieves relevant content from large unstructured text using embeddings and vector databases, and named memory, which stores and retrieves discrete facts by a fixed key. Many common agent use cases, such as remembering a chosen database, a deployment script, or a client's name, fall into the named memory category and require only reliable key-value storage. The author argues that developers reflexively reach for vector databases because tutorials default to them, even when the underlying problem is not semantic at all. The piece also cautions that vector databases remain the correct choice when retrieval must be fuzzy or when agents query large unstructured document corpora.