Knowledge graph RAG: structured retrieval for AI agents

This post introduces Knowledge Graph RAG as an advancement over traditional vector RAG. It details how modeling data as entities and relationships (nodes and edges) allows for structured, multi-hop retrieval, which is crucial for answering complex agent queries that span multiple documents or facts. The post outlines a four-step pattern for Knowledge Graph RAG (data extraction, query entity linking, graph traversal, response generation) and highlights the limitations of vector search for relationship-path queries, whole-corpus questions, and dense domains. It also addresses the critical 'freshness problem' in knowledge graphs, where stale data can lead to incorrect agent reasoning, and emphasizes the need for near-real-time updates. The post positions structured retrieval as a key component of context engineering for AI agents, complementing short-term and long-term memory tiers.