Graph RAG vs Vector RAG

Vector retrieval-augmented generation (RAG) finds information based on semantic similarity (locating chunks that "sound like" the query), while Graph RAG traverses structured relationships between entities (finding information that is "connected to" the query), and understanding when each approach excels determines the quality of your agent's knowledge retrieval. Vector RAG handles open-ended questions well when the answer lives in a specific passage, but it struggles when the answer requires connecting multiple pieces of information across a knowledge base, which is exactly where Graph RAG excels through multi-hop traversal and explicit entity relationships. The emerging best practice combines both: vector search handles initial discovery and graph traversal handles structured exploration, with each approach covering the other's weaknesses.