What Is GraphRAG? How It Works and When to Use It Over Vector RAG (2026)

GraphRAG is retrieval-augmented generation where the retrieval step queries a knowledge graph instead of (or alongside) a vector index. Instead of pulling the top-k most similar text chunks, the system pulls entities, relationships, and the paths connecting them, then hands that structured context to the LLM. Microsoft Research introduced the term in April 2024 with a paper on query-focused summarization, and the pattern has since spread into LangChain, LlamaIndex, and Neo4j's own tooling.

The pitch is simple: vector search finds chunks that sound similar to your query. It does not know that "Acme's CFO" and "the person who signed the Q3 filing" are the same entity, or that a supplier three hops away from a flagged vendor might matter to a compliance question. A graph stores those connections explicitly, so the retrieval step can follow them.

That said, GraphRAG is not a universal upgrade over vector RAG. It costs more to build, more to maintain, and it solves a specific class of problem. If your questions are single-fact lookups, a vector index answers them faster and cheaper. Graph structure earns its cost when the answer depends on relationships between entities that a similarity score cannot see.

What is GraphRAG, exactly?

GraphRAG is a retrieval-augmented generation architecture that builds a knowledge graph from your source documents (entities as nodes, relationships as edges) and queries that graph at inference time, instead of or in addition to a vector similarity search.

Standard RAG has two steps: embed the corpus into a vector store, then at query time retrieve the chunks closest to the query embedding. GraphRAG adds a middle step. During ingestion, an LLM (or a smaller extraction model) reads each document and pulls out entities and the relationships between them: "Company A acquired Company B in 2023," "Person X reports to Person Y." Those get written into a graph database. At query time, retrieval can traverse the graph, not just rank chunks by cosine similarity.

Microsoft's original implementation goes a step further and builds community summaries: it clusters the graph into groups of related entities, then has an LLM pre-summarize each cluster. That lets the system answer broad, corpus-level questions ("what are the main themes across these 10,000 documents?") that a chunk-based vector search handles badly, because no single chunk contains the whole answer.

How GraphRAG actually works

Three stages, in order: extraction, graph construction, and query-time traversal.

Entity and relationship extraction. An LLM (or a fine-tuned NER model for cheaper, higher-volume extraction) reads each document chunk and outputs a list of entities and the relationships between them, typically as structured triples: subject, predicate, object. "Sarah Chen, works at, Acme Corp." Extraction quality is the single biggest failure point in a GraphRAG build. If the extraction model merges two different "John Smith" entities, or misses a relationship because it's phrased unusually, the graph is wrong and every downstream query inherits that error.

Graph construction. Extracted triples get written into a graph database, commonly Neo4j, or into a graph layer on top of a vector store (LlamaIndex's PropertyGraphIndex and LangChain's graph modules both support this). Entity resolution happens here too: deciding that "Sarah Chen," "S. Chen," and "the VP of Engineering" mentioned in different documents refer to the same node. This step is where most of the engineering time goes, not the querying.

Query-time traversal. When a query comes in, the system identifies relevant entities in the question, then traverses the graph outward from those entities, some number of hops, to pull connected context. That context (a subgraph, or a set of paths) gets serialized into text and passed to the LLM alongside or instead of vector-retrieved chunks. Multi-hop questions, "which vendors used by our flagged supplier also work with our top competitor," are exactly what this step is built for.

GraphRAG vs vector RAG

Most production GraphRAG systems are not graph-only. They run a vector index and a graph side by side, using the graph for relationship queries and the vector index for everything else, then combine both at retrieval time. Pure graph-only retrieval is rare outside of research demos.

When graph structure earns its keep

Graph structure is worth the extra build and maintenance cost when your actual query pattern is relational, not just topical.

Signs it's worth it:

• Questions that require connecting facts across multiple documents ("which contracts reference a clause that also appears in our terminated vendor agreements")
• A domain with a genuine, stable entity model: people, organizations, products, transactions, with relationships that matter to the answer
• Corpus-wide questions where no single chunk contains the full answer, and you need a summary built from patterns across the whole document set
• Compliance, fraud, or due-diligence workloads where the relationship between two entities is the finding, not a side detail

Signs it's overhead:

• Most queries are single-fact or definitional ("what is our refund policy") where a chunk match is a direct hit
• The corpus doesn't have a real entity model (marketing copy, support tickets with no consistent entity structure)
• You need this working in weeks, not months, and don't have the eng time for entity resolution tuning
• Your document set changes fast enough that graph maintenance becomes a full-time job on its own

A useful gut check: pull 20 real questions your users actually ask. If most of them are answerable from a single retrieved chunk, you don't need a graph. If more than a handful require connecting two or three separate facts, GraphRAG is worth prototyping.

The real tradeoffs

Cost. Extraction runs an LLM call (or several) per document chunk during ingestion, on top of the embedding cost vector RAG already pays. For a large corpus, that's a real bill before you've answered a single query.

Latency. Graph traversal at query time is slower than a single vector similarity search, especially for multi-hop queries. If your product needs sub-second responses, budget for it or cache aggressively.

Maintenance. Entity resolution is never fully solved. New documents introduce new entity mentions that need to be matched against the existing graph, and that matching degrades over time without ongoing tuning. Vector RAG's maintenance story is simpler: re-embed the new chunk, done.

Extraction quality is the bottleneck, not the graph database. Teams that struggle with GraphRAG usually aren't fighting Neo4j. They're fighting an extraction pipeline that inconsistently identifies the same entity across documents, which quietly corrupts the graph.

None of this means skip GraphRAG. It means treat it as a deliberate architecture choice for a specific query pattern, not a default upgrade to bolt onto an existing RAG pipeline because the term is getting attention.

Where this fits for a real RAG build

Most teams evaluating GraphRAG are already running a vector-based RAG pipeline and asking whether a graph layer solves a specific problem they're hitting, usually multi-hop questions or entity-relationship queries their current setup answers badly. That's a scoping question before it's a tooling question: what are the actual queries failing today, and does a graph fix them, or does better chunking and metadata filtering fix them for less money.

Once a graph layer looks worth it, the next questions are architectural: which extraction method, which graph store, how to combine graph retrieval with your existing vector search, and whether to build or buy. Our GraphRAG implementation guide covers those decisions.

If you're architecting a RAG pipeline from scratch and want that scoping done properly before committing to graph infrastructure, that's the kind of build our RAG pipeline architecture and development work covers.

FAQ

What is GraphRAG in simple terms?

GraphRAG is retrieval-augmented generation that retrieves from a knowledge graph, entities and their relationships, instead of only ranking text chunks by similarity. It answers questions that depend on how facts connect, not just which text sounds similar to the query.

How is GraphRAG different from a knowledge graph?

A knowledge graph is a data structure: nodes for entities, edges for relationships. GraphRAG is the retrieval architecture that builds a knowledge graph from your documents and queries it at inference time to feed an LLM. The graph is the data; GraphRAG is what you do with it at query time.

Does GraphRAG replace vector RAG?

No. Most production systems run both, using the vector index for chunk-level similarity search and the graph for relationship and multi-hop queries, then combine the results. Pure graph-only retrieval is uncommon outside research settings.

Is GraphRAG more expensive than vector RAG?

Yes, on both ingestion and maintenance. Entity and relationship extraction runs an LLM pass over your corpus that vector RAG doesn't need, and keeping entity resolution accurate as new documents arrive is ongoing work. Query-time traversal is also typically slower than a single vector similarity search.

What tools support GraphRAG?

Neo4j has native GraphRAG tooling, LlamaIndex ships a PropertyGraphIndex, and LangChain has graph-retrieval modules. Microsoft's original GraphRAG implementation, released as open source in 2024, is also a common starting point for the community-summarization pattern.

When should I not use GraphRAG?

When your actual queries are single-fact lookups or general semantic search, and your corpus doesn't have a stable, meaningful entity model. In that case a vector index alone is faster to build, cheaper to run, and answers the same questions just as well.

How long does a GraphRAG build take?

Longer than a vector RAG build of the same corpus, mainly because entity resolution needs tuning against real documents, not a fixed timeline. A working prototype can come together in a few weeks; getting entity resolution accurate enough for production use is usually the phase that takes longer than teams expect.

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Sources: Microsoft Research, "From Local to Global: A Graph RAG Approach to Query-Focused Summarization", Microsoft GraphRAG open-source project, Neo4j, "What Is GraphRAG?", LlamaIndex PropertyGraphIndex documentation