What is Graph RAG?
Graph RAG is retrieval-augmented generation that retrieves from a knowledge graph: a structured network of entities and the relationships between them. The system finds the entities a question names, walks the edges connecting them to related facts, and passes that resolved subgraph to the model as context together with the path it traversed.
The distinction bites on questions whose answer lives in more than one document. When a policy update supersedes clause 4 of a fee schedule, a graph traversal knows the two records are linked and returns the resolved answer; a plain similarity search returns both documents and lets the model guess.
How Graph RAG works
Graph RAG runs as four stages, one offline and three at answer time.
Ingestion comes first. A model or a rules pass reads the corpus and extracts entities (products, plans, policies, clauses, regions) along with the typed relationships between them, writing both into a knowledge graph. Most builds also compute a vector embedding for every node and every source passage, so the graph and the text index point at the same material.
Query time starts with entity linking. The question is parsed for the entities it names and each one is matched to a node, usually with fuzzy or embedding-based matching, because customers almost never use the canonical label. Traversal follows: the system walks one, two, or three hops out from those nodes, collecting connected facts and the edges that justify including them.
The last stage is generation, mechanically identical to ordinary retrieval-augmented generation. The assembled subgraph becomes the context window, and the model writes an answer traceable to specific nodes and edges rather than to a ranked pile of paragraphs.
Types of Graph RAG
Entity-centric (local) Graph RAG: Retrieval starts at the entities the question names and expands a fixed number of hops outward, collecting connected facts, which keeps context small.
Community-summary (global) Graph RAG: The graph is clustered ahead of time and each cluster carries a written summary, so corpus-wide questions read summaries, at real indexing cost.
Hybrid graph and vector retrieval: Both retrievers run and their results are merged and reranked before generation, covering questions the graph schema never modeled.
Ontology-first Graph RAG: The graph is built against a curated schema drawn from systems you already trust, such as a catalog or billing model, so edges are reliable and coverage stays narrow.
Extraction-first Graph RAG: A language model reads unstructured documents and proposes entities and relationships, buying fast coverage in exchange for a standing review burden on precision.
Graph RAG vs vector RAG vs agentic RAG
Teams evaluating retrieval architectures conflate these three, partly because most production stacks already contain two of them. Vector RAG retrieves passages by embedding similarity and returns whatever text sits closest to the question. Agentic RAG plans a sequence of searches, reads the results, and decides what to look up next. Graph RAG resolves the entities in a question and traverses typed relationships to assemble facts that no single passage contains. The three differ in what the retrieval step knows about structure, and they compose: an agent can call graph traversal as one of its tools.
What it retrieves | What you must build | Strongest on | Main cost | Choose it when | |
|---|---|---|---|---|---|
Graph RAG | Entities, typed edges, and the passages attached to them | Extraction pipeline, graph store, and a schema | Multi-hop, superseded, and cross-document questions | Indexing and schema maintenance | Answers depend on how facts relate |
Vector RAG | Top-k passages ranked by embedding similarity | An embedding index over chunked documents | Questions a single passage already answers | Chunking quality and recall tuning | The corpus is prose and questions are direct |
Agentic RAG | Whatever its tools return across several planned searches | A planner, tool definitions, and step limits | Ambiguous questions that need decomposition | Latency and unpredictable token spend | A single retrieval pass leaves gaps |
Start with vector retrieval, and add graph traversal when your failure log fills with questions whose answer spans two documents: supersessions, entitlements, dependency chains. Add an agent layer once one retrieval pass, graph or vector, keeps missing the question entirely.
Why Graph RAG matters for customer experience
Support questions rarely stay inside one document. The entitlement lives in the plan record, the exception lives in a policy addendum, and the deadline lives in a regional annex. With similarity-only retrieval the model receives three plausible passages and no signal about which one governs, so it answers from the most fluent one. Customers meet that as a confident wrong policy, and agents meet it as a rebuttal they write by hand.
Graph traversal supplies the missing signal: the edge recording that this addendum overrides that clause for this plan tier. It is why grounding and retrieval-augmented generation get discussed together, since a citation is only as honest as the structure behind it.
The tradeoff is real. A graph encodes an opinion about how your domain is organised, and that opinion has to be maintained by someone; teams unwilling to staff it get better returns from careful chunking. Drawing the line early between retrieving a policy and executing a structured action saves rework later.
How is Graph RAG measured?
No public leaderboard scores Graph RAG against a private support corpus, so the measurement rig is something you build and keep. External benchmarks move fast enough to date quickly: the Stanford HAI AI Index reported one-year gains of roughly 19 to 67 percentage points across MMMU, GPQA, and SWE-bench in its 2025 edition, so a retrieval evaluation run last year says little about this year’s stack.
Four internal measurements carry the decision. Retrieval hit rate: on a labeled set of real questions, did traversal return the passage that actually governs. Multi-hop accuracy: the same measurement restricted to questions whose answer spans two or more documents, which is where the graph earns its cost. Graph precision: sampled edges reviewed by someone who knows the domain, because one wrong edge propagates into every answer touching it. End-to-end latency at the hop depth you intend to ship.
How AI agents change Graph RAG
An AI agent turns retrieval output into an action input, and that changes what the graph is for. When an agent resolves a customer to an account node, the traversal hands back identifiers (order ID, plan ID, entitlement code) that the agent can pass straight into an API call. Retrieval stops being a reading exercise and becomes the lookup step of an execution path.
The consequence is that edge correctness now has money attached. A mislabeled relationship between a plan and a refund window no longer produces a slightly wrong paragraph; it produces a refund issued against the wrong policy. Orchestration frameworks such as LangGraph make the loop explicit by modeling retrieval, tool calls, and checks as nodes with defined transitions. Multi-step cases show the pattern clearly, as in these complex travel change requests, where entitlement, fare rule, and fee schedule all have to resolve before anything is promised.
Implementing Graph RAG
Judge an implementation on five axes. Coverage: which entity types and relationships the graph actually models, and what share of live questions touch something outside it. Integration surface: whether the graph reads current records from a CRM, billing, or order system, or a snapshot taken at index time. Ownership: someone has to own the ontology, adjudicate a disputed edge, and decide when a relationship type is retired.
Governance and security come next. Regulated buyers commonly raise SOC 2 Type II for the retrieval infrastructure and ISO 42001 for AI management practice, and the question both attach to Graph RAG is whether you can reconstruct, months later, which nodes and edges produced a given answer.
The constraint teams underestimate is permission-aware traversal. A graph merges facts from systems with different access rules, and a two-hop path can surface an entity the asker was never entitled to see unless entitlements are evaluated at every hop.
Graph RAG and semantic retrieval
Graph RAG describes what the retriever knows about structure; agentic RAG describes who decides that a retrieval should happen at all, which is why the two appear together in production stacks with a planner calling traversal as one tool among several.
Semantic search remains load-bearing underneath both, because a traversal has to start somewhere and the entry node is usually found by matching the meaning of a customer’s phrasing to a canonical entity label.
What does Graph RAG mean in plain terms?
Think of Graph RAG as a librarian who has read the collection. A similarity-based system hands you the five documents that mention your question and wishes you luck. A graph-based one knows the March memo amended the January table, and tells you what applies now. The RAG in the name stands for retrieval-augmented generation, the practice of fetching source material at answer time and writing the reply from it.
Without the graph, ask whether your plan still covers something after a March update and the system returns the update and the old coverage table, both accurate, and lets the model choose between them. With the graph, the relationship between the two is stored, so there is nothing to choose.
The tradeoff: a person has to decide what an edge means and keep deciding as the business shifts. When the ontology drifts away from how products are actually sold, traversal returns answers that are wrong with unusual confidence.
Common Graph RAG mistakes
Four failure patterns recur.
Encoding contradictions is the first. Extraction is faithful, so when two documents disagree about a fee, the graph gains two edges and no rule for choosing between them. The graph made the conflict machine-readable without resolving it, and resolution is editorial work nobody scheduled.
Over-traversing is the second. Raising hop depth feels like raising recall, and each extra hop pulls in loosely related nodes that dilute the context window, lengthen latency, and give the model more material to misread.
Treating the ontology as a project is the third. Schemas are built once, celebrated, and left alone while new plan types and regions ship weekly, until traversal is answering about a product structure that no longer exists.
The fourth is evaluating on single-hop questions. If the test set is questions one passage already answers, the graph shows no measurable gain, the investment looks unjustified, and the multi-hop cases that motivated it never enter the numbers.
Frequently Asked Questions
What is the difference between Graph RAG and vector RAG?
Graph RAG and vector RAG differ in what the retriever understands. Vector RAG ranks text chunks by embedding similarity and returns the closest ones. Graph RAG resolves the entities in a question and follows stored relationships between them, so it can assemble an answer that spans several documents and knows which fact supersedes another.
What is a multi-hop question?
A multi-hop question requires combining facts from two or more places before an answer exists. Asking whether a customer’s plan qualifies for an expedited refund after a policy change is multi-hop: the plan tier, the refund rule, and the change notice each live separately. Similarity search returns all three and resolves none of them.
Do you need a knowledge graph to use Graph RAG?
A knowledge graph is the defining component, so yes. It does not have to be built from scratch, though. Most teams derive an initial graph from structured systems they already run, such as a product catalog, billing schema, or entitlement table, then extend it with entities extracted from policy documents and help content.
Graph RAG vs agentic RAG: which one should you use?
Graph RAG and agentic RAG answer different questions and often ship together. Graph RAG improves what a single retrieval call returns by using structure. Agentic RAG improves coverage by planning several retrieval calls and reacting to results. Use graph traversal when answers span linked records, and add agent planning when one pass keeps missing.
When is Graph RAG not worth building?
Graph RAG is poor value when the corpus is small, mostly prose, and the questions are direct, because well-chunked passages already answer them. It also underperforms when nobody owns the schema, since an unmaintained graph returns confident answers built on relationships that stopped being true several product releases ago.
How does Graph RAG reduce hallucinations?
Graph RAG narrows what the model can plausibly say by handing it a resolved set of facts and the relationships joining them. The model has less room to invent a bridge between two documents because the bridge is supplied or absent. Wrong edges still produce wrong answers, so graph precision has to be audited.