GraphRAG

GraphRAG is a retrieval-augmented generation approach that structures knowledge as a graph of entities and relationships rather than flat text chunks, enabling multi-hop reasoning and synthesis across interconnected documents. Microsoft released an open-source implementation in July 2024.

GraphRAG (Graph Retrieval-Augmented Generation) extends standard RAG by first extracting a knowledge graph from the source corpus. Nodes represent entities—people, organizations, concepts, events—and edges represent named relationships between them. At query time, the system traverses this graph structure to retrieve relevant connected subgraphs, rather than relying solely on embedding similarity to find independent text chunks. This structural awareness allows the system to follow chains of relationships and aggregate information distributed across many source documents.

The GraphRAG pipeline operates in two phases. During indexing, an LLM processes all source documents to extract named entities and relationships, storing them as a graph, often in a graph database such as Neo4j or as in-memory structures. Community detection algorithms such as Leiden clustering group related nodes into thematic communities, and LLM-generated summaries are produced for each community. During querying, the system executes either local queries—finding closely connected entity nodes via vector similarity—or global queries—aggregating over community summaries to answer broad thematic questions spanning the whole corpus. Retrieved subgraphs or summaries are passed as context to an LLM for final answer generation.

Standard dense-vector RAG handles lookup queries effectively but struggles with questions requiring synthesis across many documents or multi-hop relational reasoning—identifying indirect business relationships, tracing the provenance of a claim across dozens of sources, or summarizing a theme that emerges only from reading many documents together. GraphRAG handles these holistic and relational queries significantly better. The main tradeoff is higher indexing cost, since LLM-based entity extraction must process the entire corpus upfront, and ongoing graph storage overhead.

Microsoft published the GraphRAG framework as open source in July 2024, accompanied by research demonstrating substantial improvements over naive RAG on complex corpus-level question answering benchmarks. As of 2026, GraphRAG principles have been integrated into enterprise knowledge management products and document intelligence platforms from multiple vendors. Hybrid architectures combining dense vector retrieval for simple lookup queries with graph traversal for relational queries are emerging as the practical standard for large-scale enterprise knowledge bases.