Knowledge graph RAG: structured retrieval for AI agents

What this post added

Introduces knowledge graph RAG as a method to improve AI agent retrieval by modeling data as entities and relationships, enabling multi-hop retrieval. Contrasts knowledge graph RAG with vector RAG, detailing a four-step pattern (data extraction, query entity linking, graph traversal, response generation). Highlights limitations of vector search for relationship-path queries, whole-corpus questions, and dense domains. Discusses the freshness problem in knowledge graphs due to batch indexing and proposes incremental, near-real-time updates. Outlines a tiered agent memory stack (short-term working memory, long-term memory, entity relationships/multi-hop queries) for structured retrieval.