Entity Graph Retrieval Helps AI Agents Find Contextually Related Memories

A technique called entity graph retrieval addresses a key limitation of semantic search in AI agent memory systems: its inability to surface memories linked by related entities rather than similar words. The approach, demonstrated in a system called Norax, works by extracting named entities from stored memories, connecting co-occurring entities with weighted edges, and grouping them into clusters using the Louvain community detection algorithm. When a query arrives, the system identifies which entity community it belongs to and boosts memory results that share entities or community membership with the query. For example, a query mentioning 'Colby' would automatically surface wallet and payment memories because those entities cluster together, even without keyword overlap. The method requires no model training or external API calls, making it a low-cost addition that meaningfully improves how AI agents retrieve contextually relevant information.