GraphRAG Explained: Architecture, Components, Use Cases, and RAG Comparison

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 13th, 2026 | 13 min read

Quick answer: What is GraphRAG?

GraphRAG is a retrieval-augmented generation (RAG) approach that uses a knowledge graph as its primary memory instead of only free-text chunks and vector search. It represents entities and relationships as a graph, then retrieves subgraphs relevant to a query and uses them as structured context for an LLM.

  • Graph-based memory: Stores knowledge as nodes and edges, not just text chunks.
  • Structured retrieval: Pulls subgraphs and associated text as context for the LLM.
  • Better reasoning: Makes paths and relationships explicit, improving multi-step answers.
  • Explainability: Graph structure plus metadata makes it easier to show evidence and provenance.
  • Hybrid design: Usually paired with embeddings and classic RAG for robustness.

Below: how GraphRAG works, GraphRAG vs vanilla RAG.

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How GraphRAG works (architecture and flow)

At a high level, GraphRAG adds a graph indexing and retrieval layer between your raw data and the LLM. The system extracts entities and relationships from documents, builds a graph, and attaches text snippets and metadata to graph elements. At query time, it runs graph queries to fetch a small, relevant subgraph plus supporting text, as in Microsoft’s GraphRAG system.

High-level architecture at a glance


A typical GraphRAG stack has four main layers. The data ingestion layer parses documents, logs, tables, or APIs and normalizes them into a canonical format. The graph construction layer runs entity extraction and linking, then writes nodes, edges, and properties to a graph store or metadata lake, similar to graph modeling approaches documented in the Neo4j developer blog.

The retrieval and reasoning layer translates user questions into graph queries, runs traversals and ranking, and fetches supporting text. Finally, the LLM orchestration layer builds prompts that include the retrieved subgraph, textual evidence, and provenance, then generates answers and citations, following patterns described in the GraphRAG repository.

Step-by-step flow (from query to answer)


You can think of a GraphRAG interaction as a pipeline.

  1. User query: The user asks a question in natural language.
  2. Query understanding: A lightweight LLM or rules extract entities, intent, and constraints.
  3. Graph retrieval: The system runs graph traversals and filters to fetch candidate nodes and edges.

Those candidates are then enriched with linked documents, metrics, or lineage records. A reranking step selects the most relevant context for the final prompt. The LLM receives a compact representation of the subgraph plus human-readable evidence and returns an answer with references.

What retrieved context looks like in GraphRAG


In vanilla RAG, retrieved context is usually a list of text chunks, often stored in a vector database. In GraphRAG, context is a subgraph, plus attached text and metadata such as lineage, owners, and quality scores, as you see in many examples from the Neo4j developer blog.

Concretely, the prompt may include:

  • A list of key entities with their types and properties.
  • A set of relationships (edges) showing how those entities connect.
  • Short evidence snippets or metrics linked to relevant nodes and edges.

This structure helps the LLM see both “what” and “how it all connects,” which is crucial for enterprise scenarios that span systems like your data catalog and data lineage.

Where embeddings fit (and why GraphRAG is usually hybrid)


Purely symbolic graphs struggle with fuzzy matches, synonyms, and long-tail phrasing. To handle this, most real-world GraphRAG systems use embeddings at several points in the pipeline, as outlined in the LlamaIndex docs.

Common hybrid patterns include:

  • Using vector search to find candidate entities or documents, then jumping into graph traversal.
  • Storing embeddings as properties on nodes or edges to support semantic similarity search in the graph, a pattern Neo4j highlights in its developer blog.
  • Using a classic RAG path as a fallback when the graph is sparse or incomplete.

This hybrid approach combines the precision and explainability of graphs with the robustness of dense retrieval.

Core components of a GraphRAG system

You can implement GraphRAG with many tools, but the conceptual building blocks are similar. The key is to design a knowledge representation and retrieval pipeline that fits your data and use cases. This is also where modern metadata platforms and knowledge graphs, such as Atlan’s Metadata Lakehouse, become powerful foundations.

Knowledge representation: property graph vs RDF


Most implementations choose between property graphs and RDF/knowledge-graph standards. Property graphs (like Neo4j’s model) treat nodes and edges as first-class objects with arbitrary key–value properties. They are often easier for application teams to evolve and query via languages like Cypher, as described in the Neo4j developer blog.

RDF-based graphs use triples and schemas like OWL to enforce semantics and interoperability across systems. They shine when you need reasoning over ontologies and cross-organization data exchange. For many enterprise GraphRAG pilots, a property-graph-first approach is a practical starting point, with the option to align to a more formal ontology later.

Graph storage and querying


Under the hood, you need a graph store capable of handling your scale and query patterns. That could be a native graph database, a graph layer on top of your warehouse, or an active metadata platform that exposes a graph API over lineage, glossary, and catalog assets, as discussed in the Neo4j developer blog.

Key capabilities include:

  • Efficient traversals across multiple hops and relationship types.
  • Flexible predicates to filter by time, domain, data quality, or governance tags.
  • Rich indexing over properties and text attached to nodes and edges.

Many teams start by mapping existing metadata from tools like their data catalog) and business glossary into a unified graph.

Entity extraction, linking, and disambiguation


GraphRAG depends on good entities and relationships. You typically run an NER (named entity recognition) or schema-aware extractor over documents and tables, then link mentions to canonical entities in your graph, similar to structured extraction flows in the LlamaIndex documentation.

Disambiguation is the hard part. You need to distinguish “Apple” the company from “apple” the fruit, or “revenue” in different business units. This is where an enterprise data glossary and governance layer help by defining canonical terms, owners, and allowed meanings that can guide linking and resolve conflicts, often enforced via data governance.

Retrieval strategy: traversal, scoring, and reranking


Once the graph is built, retrieval is a three-step game.

  1. Seed selection: Map the query to seed nodes via entity matching, keyword search, or semantic search.
  2. Traversal: Walk the graph outward using patterns like k-hop neighborhoods, path queries, or graph algorithms, as commonly demonstrated in the Neo4j developer blog.
  3. Scoring and reranking: Rank candidate nodes, edges, and evidence using features like distance, edge types, recency, or usage.

Modern frameworks such as Microsoft’s GraphRAG system and LlamaIndex’s property-graph tooling provide building blocks for this, including flexible retrievers and rerankers.

Evidence and provenance layer


GraphRAG’s biggest advantage in enterprises is traceability. Every node and edge can carry pointers to underlying tables, dashboards, owners, and lineage paths. That makes it natural to attach evidence and citations to LLM answers.

A solid evidence layer will tie graph elements to:

  • Source systems and datasets (e.g., tables already cataloged in your data catalog.
  • Lineage paths from raw data to metrics and reports via data lineage.
  • Owners, stewards, and policies from active metadata and governance tooling such as active metadata and data governance.

GraphRAG vs vanilla RAG (and hybrid approaches)

GraphRAG is not a replacement for classic RAG in all cases. Instead, it is a specialization that shines when relationships and multi-hop reasoning matter. Most robust systems mix both, routing queries between them based on intent and data coverage.

Retrieval unit: chunks vs entities and relationships


In vanilla RAG, the retrieval unit is typically a chunk of text, sometimes with light metadata like document ID or section title. Embeddings make it easy to find semantically similar chunks, but the system is largely ignorant of deep structure, as many RAG framework overviews (for example, the LlamaIndex documentation) point out.

In GraphRAG, the retrieval unit is often an entity, relationship, or small subgraph. Text snippets and embeddings are attached as properties. This lets you query for “all systems feeding this KPI” or “paths between two business concepts,” not just paragraphs that mention certain keywords.

Reasoning behavior: implicit vs explicit paths


Vanilla RAG relies on the LLM to infer relationships implicitly from unstructured text. It can answer many questions, but multi-hop reasoning becomes brittle when the needed facts are spread across several documents, as noted in work like “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”.

GraphRAG makes paths explicit. You can encode patterns like “customer → subscription → invoice → payment” or “dataset → dbt model → dashboard.” The LLM then reads a concise description of these paths instead of rediscovering them from scratch, which improves consistency and reduces hallucinations in complex workflows, consistent with the design of Microsoft’s GraphRAG repository.

Quality and failure modes comparison


Vanilla RAG often fails quietly by retrieving partially relevant chunks or missing a crucial hop, leading to confident but incorrect answers. It can also struggle to explain how it reached a conclusion, a challenge highlighted in the broader RAG literature such as Lewis et al., 2020.

GraphRAG’s failures are more structural: missing entities, incomplete relationships, or noisy linking. The upside is that these issues are observable and debuggable through the graph itself. You can inspect missing edges, review glossary terms, or analyze lineage gaps instead of staring at black-box embedding scores, a workflow that aligns with practices described in the Neo4j developer blog.

Cost and complexity tradeoffs


You pay for GraphRAG’s power with higher implementation complexity. You need entity extraction, a graph store, and an ingestion pipeline that keeps the graph fresh. That is more work than dropping documents into a vector database and calling it a day.

However, for high-stakes enterprise use cases, this upfront cost often pays off through better answer quality, explainability, and governance. Long-context LLMs and platforms like Atlan’s Metadata Lakehouse and active metadata can reduce some of this complexity by centralizing metadata and graph-like relationships.

When to use GraphRAG (and when not to)

GraphRAG is not a silver bullet for every chatbot or Q&A interface. It is particularly well-suited to environments where data relationships are central to the question. Before you invest, it is worth running a simple decision process that factors in use cases, data readiness, and governance needs.

Best-fit use cases


GraphRAG shines in multi-hop, relationship-heavy scenarios. For example, “Why did this KPI drop?” requires tracing metrics back through models, pipelines, and source systems; “Which customers are impacted by this policy change?” needs joining contracts, regions, and product usage.

Other strong fits include:

  • Regulatory and risk analysis that depends on traceable audit trails.
  • Knowledge management over complex domains like healthcare, finance, or supply chains.
  • Data and AI observability use cases that integrate catalog, lineage, and quality signals.

Decision checklist


A simple checklist can help you decide whether GraphRAG belongs in your architecture.

Ask yourself:

  1. Do your questions regularly span 3+ hops across systems or entities?
  2. Do you need explainable paths and provenance, not just answers?
  3. Do you already curate metadata in tools like a business glossary or data glossary?
  4. Are you ready to invest in graph modeling, not just chunking documents?

If you answer “yes” to most of these, GraphRAG is worth piloting alongside your existing RAG stack.

When GraphRAG is overkill


If your primary goal is simple document Q&A or FAQ-style chat, classic RAG is usually enough. For example, answering “What is our vacation policy?” or “How do I submit expenses?” from a few handbooks does not justify a full graph pipeline.

GraphRAG may also be overkill if your data estate is small, poorly governed, or extremely volatile. In those cases, invest first in foundational capabilities like a data catalog and data governance, then revisit GraphRAG once you have stable metadata to work with.

Data prerequisites


Successful GraphRAG projects start from solid metadata and governance. You need reasonably consistent identifiers, clear domain boundaries, and at least a minimal business glossary to avoid “garbage in, garbage out.”

Helpful prerequisites include:

  • A catalog of tables, dashboards, and pipelines with owners and tags, via a system like Atlan’s data catalog.
  • Lineage or dependency information between assets, for example from data lineage.
  • A governed glossary of key terms and metrics across domains, such as Atlan’s data glossary.

Common pitfalls and how to avoid them

GraphRAG introduces new pitfalls beyond those in vanilla RAG. The good news is that most of them are systemic issues that can be monitored and improved over time. A strong evaluation and metadata strategy is critical to staying ahead of them.

Entity linking and ambiguity errors


Poor entity linking creates subtle but dangerous errors. The system might link a mention of “ARR” to the wrong metric definition or confuse customers with similar names.

Mitigations include:

  • Using domain-specific glossaries and canonical IDs to guide linking, via a business glossary.
  • Adding human-in-the-loop review for high-impact entities or relationships.
  • Tracking linking confidence scores and surfacing low-confidence areas for curation.

Over-expansion and context bloat


Graph traversals can easily explode in size. A naive “2-hop from all seed nodes” query might pull thousands of nodes and overwhelms the LLM context window.

To avoid this:

  • Limit hop counts and filter by edge types, recency, or importance scores.
  • Use graph algorithms like PageRank or community detection to prioritize key nodes.
  • Combine graph retrieval with long-context models judiciously.

Stale graphs and missing relationships


A GraphRAG system is only as good as its freshness. If your pipelines add new tables or dashboards but the graph is updated monthly, users will quickly hit gaps and lose trust.

Best practices include:

  • Incremental ingestion from upstream systems with change data capture signals.
  • Treating the graph as an operational metadata product, not a static model.
  • Using active metadata platforms like active metadata to automatically ingest lineage, schema changes, and usage data.

Weak provenance and unverifiable answers


If you do not capture strong provenance, GraphRAG answers may still feel like “AI guesses.” Users want to know which datasets, queries, or dashboards back up a conclusion.

Make provenance a first-class design goal:

  • Attach dataset IDs, report URLs, and query hashes as properties on nodes and edges.
  • Include explicit “sources” sections in prompts with structured references.
  • Log and expose evidence to downstream tools so users can drill from an answer to the underlying asset in one click via your data catalog.

Evaluation and hallucination reduction


GraphRAG can reduce hallucinations, but it does not eliminate them automatically. You still need offline evaluation sets, online feedback, and targeted tests for complex multi-hop scenarios.

Effective evaluation strategies:

  • Build labeled multi-hop question sets drawn from real incidents or governance tasks.
  • Measure faithfulness to retrieved subgraphs, not just surface-level relevance.
  • Use graph-based audits to inspect where missing edges or low-quality entities correlate with bad answers, then fix those upstream.

Implementation options and a practical GraphRAG example

You do not need to build everything from scratch to pilot GraphRAG. You can start small using existing frameworks and your current metadata, then deepen the graph and retrieval logic as you learn.

Three implementation patterns (choose based on what you have)


Here are three practical starting patterns.

  1. Metadata-first GraphRAG: Use your catalog, lineage, and glossary as the initial graph, then attach evidence from warehouses and BI tools. This is often the fastest path if you already run a platform like Atlan’s Metadata Lakehouse.
  2. Application-domain graph: Start with a focused domain like fraud, supply chain, or support tickets. Model key entities and flows in a Neo4j or similar graph and integrate with RAG later.
  3. Framework-led pilot: Use open-source GraphRAG implementations from Microsoft’s GraphRAG system or LlamaIndex’s property-graph index, plugging them into your existing RAG stack.

Tooling and system design choices


Your choices fall into a few categories.

  • Graph store: Neo4j, a cloud graph service, or a metadata store that exposes a graph API.
  • RAG framework: LlamaIndex or LangChain with graph extensions, or custom orchestration.
  • LLM: Long-context models like GPT-4o, Claude, or Gemini 2.5 Pro can absorb larger subgraphs and reduce retrieval complexity.
  • Metadata source: Your existing data catalog, lineage tool, or governance platform.

Choose based on what you already have and where your team has expertise. The important part is to start simple and iterate based on real user questions.

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