Knowledge Graph RAG 2026: GraphRAG vs Vector RAG - When Graphs Actually Win (35k Stars)

Vector search misses questions that connect multiple documents - like who worked with whom across a decade of reports. Graph RAG was built for those questions.

## Knowledge Graph RAG: Answering Questions Vector Search Cannot Regular RAG retrieves chunks by semantic similarity. That works when the answer lives inside one chunk. It fails when the answer requires connecting facts scattered across many documents - relationships, hierarchies, and multi-hop reasoning. Knowledge graph RAG solves this by building a graph of entities and relationships first, then answering questions by traversing it. The most famous implementation is Microsoft's GraphRAG, released in 2024, with 35,146 GitHub stars by August 2026. ## How GraphRAG Works GraphRAG processes a corpus in two passes: 1. **Entity extraction.** An LLM reads every document chunk and extracts entities (people, companies, concepts) and their relationships, building a knowledge graph. 2. **Community detection.** The graph is partitioned into communities using hierarchical clustering. Each community gets an LLM-generated summary. At query time, GraphRAG matches the question to relevant communities, reads their summaries, and answers with a global view instead of a local chunk match. That is why it shines on questions like What themes connect all our customer complaints? - a question that touches the whole corpus, not one passage. ## When Graphs Win (and When They Don't) | Question type | Better fit | |---------------|-----------| | Single fact lookup (What is X's price?) | Vector RAG (cheaper, faster) | | Multi-document relationships (Who partnered with whom?) | Graph RAG | | Global corpus themes (What are the top risks?) | Graph RAG | | Real-time Q&A on a small doc set | Vector RAG | The honest trade-off: GraphRAG indexing is expensive (LLM extraction over every chunk) and slower at query time. For a 100-page manual, plain vector RAG is the right call. For a 10,000-document corporate corpus where questions are analytical, GraphRAG earns its cost. ## Getting Started ```bash pip install graphrag # index: graphrag index --root ./ragproject # query: graphrag query --root ./ragproject --method global --query "..." ``` Neo4j, the leading graph database, also has its own GraphRAG integrations, and both work with local LLMs if you want to keep everything private. ## FAQ **Is GraphRAG better than vector RAG?** It is different: better at global/multi-hop questions, slower and costlier to build. **Can I run GraphRAG locally?** Yes - it works with local LLMs like Ollama, just slower. **What models does it need?** Extraction quality depends on the LLM; GPT-4-class models give the best graphs. **Do I need Neo4j?** No - GraphRAG can use a lightweight local graph; Neo4j is one option, not a requirement.