How knowledge graphs complement vector search in AI memory systems

Most AI memory systems rely on chunking text, embedding it, and retrieving the closest matches to a query. That approach struggles with factual questions that have a single current answer, a history of past answers, and links to other entities. A knowledge graph instead stores entities and typed, directed relationships, such as a service's database or owner, which allows precise queries. Vector search remains useful for fuzzy recall and returning original evidence, but it cannot tell which fact is current or enumerate scattered facts. The article describes how the Palace memory server for coding agents uses a knowledge graph alongside vector search, keeping the graph deliberately simple.