How GraphRAG Outperforms Vector Search in Enterprise LLM Accuracy

In a study conducted by Diffbot, it was found that GraphRAG, which uses a knowledge graph as the retrieval substrate, significantly outperforms traditional vector search methods in enterprise large language model (LLM) accuracy, particularly for schema-bound queries such as KPIs and forecasts. The KG-LM Accuracy Benchmark demonstrated that while vector RAG approaches scored 0% on these complex queries, GraphRAG achieved a 56.2% accuracy, which further increased to over 90% with FalkorDB's 2025 SDK improvements. This performance is attributed to GraphRAG's ability to explicitly encode entity relationships, providing schema-aligned context that vector searches lack. The study suggests that for enterprise queries involving business logic, metric definitions, and schema conformity, GraphRAG is not just beneficial but necessary, as it provides the structural fidelity required for high-entity-density queries and operational analytics. The findings underscore the importance of using graph-based retrieval methods in production-grade systems to enhance the accuracy and reliability of GenAI applications, with FalkorDB offering an optimized solution with its GraphRAG SDK.

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