GraphRAG vs Competitors: Side-by-Side Comparisons [2026]

Traditional RAG retrieves the top-k most similar text chunks for a query, which works well for narrow, fact-lookup questions but fails on global or multi-hop questions where the answer is spread across many documents. GraphRAG builds a knowledge graph of entities, relationships, and claims, then uses hierarchical community summaries to enable global reasoning ('summarize the main themes') and local graph traversal for entity-centric questions, in addition to standard chunk retrieval.

Local Search answers questions about specific entities by traversing their graph neighborhood and pulling in related text. Global Search answers corpus-wide, summarization-style questions by map-reducing over pre-computed community summaries. DRIFT Search is a newer hybrid mode that combines local entity context with global community context to better handle questions that span both granularities.

Yes — the GraphRAG codebase at github.com/microsoft/graphrag is open source under the MIT license. However, the indexing pipeline makes many LLM API calls (entity extraction, claim extraction, community summarization), so you pay the underlying LLM provider (OpenAI, Azure OpenAI, etc.) for compute. Indexing a large corpus can be significantly more expensive upfront than building a plain vector index.

GraphRAG supports OpenAI and Azure OpenAI for both chat completion and embeddings out of the box, configured via settings.yaml. Other providers can be wired in through the modular LLM interface. Outputs are stored as Parquet files; vector embeddings can be stored in LanceDB (default), Azure AI Search, or Cosmos DB. The graph itself can be exported to GraphML or Neo4j for visualization.

Use GraphRAG when your use case requires global reasoning, multi-hop questions, or strong provenance across a fixed or slow-changing corpus — for example, intelligence analysis, regulatory document review, or research synthesis. Use LlamaIndex or LangChain when you need a general-purpose orchestration framework, fast incremental indexing, or simpler entity-lookup retrieval. Many teams use GraphRAG as one retriever component inside a larger LlamaIndex/LangChain pipeline.