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What is GraphRAG? How it works and when to use it
GraphRAG is retrieval-augmented generation that pulls context from a knowledge graph instead of only from text chunks that look similar. It follows typed relationships between entities, so answers can cross several facts and cite where each one came from.
What is GraphRAG?
Traditional RAG feeds an LLM the text chunks that look most similar to a prompt and asks it to synthesize an answer. The model fills in what it can’t find — that’s the hallucination tax.
GraphRAG replaces the lookup. Instead of retrieving by embedding similarity, it traverses explicit entity relationships in your knowledge graph. The path from question to answer is declared, not inferred — across structured systems, unstructured content, and world knowledge alike.
Decentralized GraphRAG with Fluree pushes enterprise accuracy past 95%, with cited answers, row-level governance, and no vector index to keep fresh.
Six reasons graph traversal
beats fuzzy similarity.
Traverse explicit paths, not guess at similarity
GraphRAG follows typed relationships through your knowledge graph — the exact path from entity to entity to entity. Vector RAG retrieves chunks that look similar and asks the model to fill the gaps.
- “What projects has Alice worked on with people who reported to Bob?” is one traversal
- Accuracy stays stable as entity count grows; vector-only systems degrade
- Deterministic paths → deterministic answers
Which GraphRAG do people mean?
The word covers two different builds. They solve different problems, so it helps to know which one you are evaluating.
A graph generated from documents
An LLM reads your text, extracts entities and relationships, groups them into communities, and writes summaries to search. This is the pattern from the 2024 paper behind Microsoft GraphRAG. It suits broad questions across a document set. Its README warns that indexing can be expensive and says the project is largely in maintenance mode.
A governed graph of your business
Entities come from your systems and documents, modeled against an ontology, with policy on every fact. Retrieval runs queries against that graph. It suits questions that need exact joins, access control, and an audit trail. This is what Fluree builds, and it takes more modeling up front than pointing an LLM at a folder.
Three pillars the enterprise can’t ship without.
Accuracy is the headline. Connectivity, verifiability, and embedded governance are what make GraphRAG production-ready across finance, healthcare, legal, and operations.
Universal Connectivity
Every system. One retrieval surface.
- Connects databases, SaaS, files, and content in place — no rip-and-replace integration.
- One semantic view across every source so retrieval reaches the whole business at once.
Why they can't
Vector pipelines see only the text they’ve been fed. When the answer lives across CRM, billing, product, and support systems, fragmented embeddings leave the model guessing at joins that were never expressed.
100% Verifiable Accuracy
Trace every answer to the row it came from.
- Every answer carries its source — systems queried, records retrieved, relationships traversed, timestamps included.
- In finance, healthcare, legal, and ops, explainability is not optional — it’s governance.
Why they can't
Vector systems return a relevance score, not a proof — no path from the answer back to its sources. Audit, compliance, and trust all break down.
Embedded Security
Policy enforced in the graph itself.
- Every node carries its access rules — the model never sees unauthorized data.
- Users and agents get a governed slice instead of a dangerous all-access retrieval layer.
Why they can't
Vector stores treat security as a downstream filter. Sensitive content leaks into shared embeddings, row-level controls disappear, and governance becomes a brittle layer of patches over a fundamentally ungoverned index.
Vector RAG vs.
GraphRAG.
A direct comparison of retrieval approaches across the capabilities that matter for enterprise AI deployments.
Weighing vector databases for RAG? See seven vector search options compared — Pinecone, Qdrant, Milvus, Chroma, pgvector, Elasticsearch, and Fluree, every claim sourced.
The GraphRAG playbook.
Webinars, whitepapers, and articles that walk through the architecture, the evidence, and the production path.
The Future of RAG — Graph-Native AI with Fluree and MCP
GraphRAG and MCP, side by side — grounding agents in a governed knowledge graph.
Watch replay
GraphRAG vs. Vector RAG: When Knowledge Graphs Outperform Semantic Search
The evidence that explicit relationships beat embedding similarity on multi-hop, governed queries.
Read the articleThe complete guide to retrieval, knowledge graphs & LLMs.
Download whitepaper
GraphRAG & Knowledge Graphs: Making Your Data AI-Ready for 2026
What production-ready GraphRAG looks like — and the data foundations teams need to get there.
Read the articleRecognized by Gartner
Gartner, “Cool Vendors in Data Management: GenAI Disrupts Traditional Technologies,” 30 May 2024. GARTNER and COOL VENDORS are registered trademarks of Gartner, Inc. and/or its affiliates and are used herein with permission. Gartner does not endorse any vendor, product or service depicted in its research publications.
Stop hallucinating. Start citing.
Every AI answer in production should trace back to the row it came from. Fluree’s GraphRAG makes that the default — governed, deterministic, and ready for audit.
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