CrabRAG Demo: Graph Memory Beats Vector Search with Multi-hop Reasoning
AI Engineer · youtube · 2026-07-23
Stephen Chin explored why AI agents need graph memory rather than just more tokens. He demonstrated two agents with identical data: the vector database agent failed to find specific details, while the graph agent successfully traced connections and flagged exposed ports running out-of-date software.
He noted that most assistants store memory as markdown files, consuming over 100,000 tokens per round. Because similarity in vector space is not a true relationship, multi-hop questions tend to hallucinate. Graph databases, by storing entities and edges and seeding searches with vectors, provide precise, explainable, and auditable answers.
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