Knowledge & Documents🔴Developer

GraphRAG

Name: GraphRAG
Brand: GraphRAG
Availability: InStock

Microsoft's graph-based retrieval augmented generation for complex document understanding and multi-hop reasoning.

Starting atFree

💡

In Plain English

Microsoft's approach to AI-powered document search using knowledge graphs — understands relationships between concepts for deeper answers.

Overview

GraphRAG is Microsoft Research's open-source, modular graph-based Retrieval-Augmented Generation system, designed to solve a fundamental weakness of traditional vector-based RAG: the inability to answer global, holistic, or multi-hop questions that require reasoning across an entire corpus rather than retrieving isolated passages. Released under the MIT license on GitHub at microsoft/graphrag, the project introduces a structured pipeline that uses an LLM to extract entities, relationships, and claims from unstructured source documents, builds a knowledge graph from those extractions, and then runs hierarchical community detection (using the Leiden algorithm) to partition that graph into clusters of semantically related entities. For each community, GraphRAG pre-generates summaries at multiple levels of abstraction, producing a 'community hierarchy' that the system can query at retrieval time.

At query time, GraphRAG offers two primary search modes that target different question types. Local Search answers entity-centric questions by traversing the neighborhood of relevant entities in the graph, pulling in related entities, relationships, and source text chunks. Global Search answers corpus-wide, thematic, or summarization-style questions ('What are the major themes across these reports?') by performing a map-reduce over the community summaries — something pure vector search cannot do well because no single chunk contains the answer. A more recent DRIFT search mode blends local and global approaches for better performance on mixed questions.

The pipeline is implemented in Python and exposed as a CLI plus a configurable indexing engine (graphrag init, graphrag index, graphrag query). It supports OpenAI, Azure OpenAI, and other LLM backends via configuration, and stores artifacts as Parquet files that integrate with downstream analytics, vector stores like LanceDB, or visualization tools. The project is research-driven: it is positioned as a data pipeline and reference implementation for building on top of, not a turnkey production service. Microsoft also maintains a managed Azure offering, Azure AI Search with GraphRAG patterns, for teams that want a hosted version. GraphRAG is best understood as the canonical example of 'graph-augmented RAG' — a category that has rapidly become a standard pattern for enterprise knowledge work where context, provenance, and global reasoning matter more than raw retrieval latency.

🎨

Vibe Coding Friendly?

▼

Difficulty:intermediate

Suitability for vibe coding depends on your experience level and the specific use case.

Learn about Vibe Coding →

Was this helpful?

Key Features

Graph-Based Knowledge Extraction+

Uses LLMs to extract entities, relationships, and claims from documents, building a structured knowledge graph that captures semantic connections traditional chunking misses.

Use Case:

Analyzing a corpus of research papers to understand how different concepts and findings relate across publications.

Global Search+

Synthesizes answers from community summaries across the entire dataset, enabling holistic questions that vanilla RAG cannot handle.

Use Case:

Asking 'What are the key regulatory trends?' across thousands of policy documents.

Local Search+

Combines graph neighborhood traversal with vector similarity for precise, context-rich answers to specific questions.

Use Case:

Finding detailed information about a specific entity and all its relationships within the knowledge base.

Community Detection+

Applies the Leiden algorithm to identify clusters of related entities, generating hierarchical summaries at multiple abstraction levels.

Use Case:

Automatically organizing a large knowledge base into thematic groups for exploration.

Customizable Extraction Prompts+

Entity and relationship extraction prompts can be tuned for specific domains, improving accuracy for specialized corpora.

Use Case:

Configuring extraction for medical literature to focus on drug interactions, symptoms, and treatment protocols.

Structured Output Artifacts+

Produces inspectable Parquet files containing entities, relationships, communities, and summaries for debugging and analysis.

Use Case:

Auditing the knowledge graph to verify extraction quality before deploying to production.

Pricing Plans

Plan 1

Free

Plan 2

Azure consumption-based

See Full Pricing →Free vs Paid →Is it worth it? →

Ready to get started with GraphRAG?

View Pricing Options →

Getting Started with GraphRAG

1**Install GraphRAG**: `pip install graphrag` and ensure you have Python 3.10+ with sufficient disk space for graph artifacts
2**Configure LLM access**: Set up OpenAI API keys or configure Azure OpenAI endpoints for the entity extraction and summarization pipeline
3**Prepare documents**: Organize your document corpus into a single directory — GraphRAG works best with 100+ documents containing interconnected information
4**Run indexing**: Execute `python -m graphrag.index --root ./your-data` to build the knowledge graph (expect significant LLM token usage for large corpora)
5**Test queries**: Try both local search for specific questions and global search for holistic understanding to compare GraphRAG's capabilities with traditional RAG

Ready to start? Try GraphRAG →

Best Use Cases

🎯

Enterprise knowledge management with complex relationships: Organizations with large document repositories where information spans multiple documents and understanding relationships between concepts is critical — like connecting customer complaints to product features to engineering decisions across thousands of documents.

⚡

Research corpus analysis for holistic insights: Academic and industry researchers analyzing large bodies of literature to identify trends, gaps, and connections between studies that no single paper explicitly discusses — enabling meta-analysis and novel research directions.

🔧

Legal document understanding for case preparation: Law firms analyzing discovery documents, case law, and regulatory materials where precedents, citations, and legal relationships between entities determine case outcomes and require multi-document synthesis.

🚀

Complex multi-hop question answering across domains: Applications requiring answers that combine information from multiple sources and reasoning chains — like 'How do supply chain disruptions in Asia affect European manufacturing costs?' across economic reports, trade data, and industry analysis.

Limitations & What It Can't Do

We believe in transparent reviews. Here's what GraphRAG doesn't handle well:

⚠GraphRAG is a research-grade pipeline rather than a managed product. Indexing is LLM-call-heavy and can be costly and slow on large corpora; incremental updates are supported but less mature than full re-indexing. Extraction quality is bounded by the underlying LLM and by domain-specific prompt tuning — out-of-the-box entity types may miss specialized vocabulary in fields like medicine or law. The system has no built-in auth, multi-tenancy, UI, or hosted query endpoint; teams must build those layers themselves or adopt the separate Azure-hosted GraphRAG accelerator. Latency for global search is higher than for plain vector RAG because it map-reduces over community summaries. Finally, the project moves quickly: APIs, config schemas, and default prompts have changed across releases, so pinning a version is recommended for production deployments.

Pros & Cons

✓ Pros

✓Answers global/thematic questions across an entire corpus that vector RAG fundamentally cannot — community summaries enable map-reduce reasoning over the whole dataset.
✓Strong provenance and explainability: every answer can be traced back to specific entities, relationships, and source text chunks in the graph.
✓Modular indexing pipeline with swappable LLM, embedding, and storage backends (OpenAI, Azure OpenAI, local models via config) — outputs land as Parquet for easy downstream use.
✓Backed by Microsoft Research with active development, published papers, and a managed Azure path (`graphrag-accelerator`) for teams that outgrow the OSS pipeline.
✓DRIFT search and hierarchical community summaries give meaningfully better results than naive RAG on multi-hop and synthesis-heavy benchmarks reported by the team.
✓MIT-licensed and self-hostable, with no vendor lock-in for the indexing or query stack.

✗ Cons

✗Indexing cost is high: building the graph requires many LLM calls per document (entity extraction, claim extraction, community summarization), which can become expensive on large corpora.
✗Initial setup has a steeper learning curve than vector RAG — you must understand entity extraction prompts, community levels, and the local/global/DRIFT trade-offs to get good results.
✗Updating the index incrementally is harder than with a vector store; re-indexing or running the incremental update pipeline is non-trivial for fast-changing data.
✗Quality of the resulting graph depends heavily on the underlying LLM and on prompt tuning for the source domain — out-of-the-box extraction can miss domain-specific entity types.
✗Positioned as a research/reference pipeline rather than a turnkey product, so production concerns (auth, multi-tenancy, observability, scaling) are left to the integrator.

Frequently Asked Questions

How is GraphRAG different from traditional vector RAG?+

Traditional RAG retrieves the top-k most similar text chunks for a query, which works well for narrow, fact-lookup questions but fails on global or multi-hop questions where the answer is spread across many documents. GraphRAG builds a knowledge graph of entities, relationships, and claims, then uses hierarchical community summaries to enable global reasoning ('summarize the main themes') and local graph traversal for entity-centric questions, in addition to standard chunk retrieval.

What are Local Search, Global Search, and DRIFT Search?+

Local Search answers questions about specific entities by traversing their graph neighborhood and pulling in related text. Global Search answers corpus-wide, summarization-style questions by map-reducing over pre-computed community summaries. DRIFT Search is a newer hybrid mode that combines local entity context with global community context to better handle questions that span both granularities.

Is GraphRAG free to use?+

Yes — the GraphRAG codebase at github.com/microsoft/graphrag is open source under the MIT license. However, the indexing pipeline makes many LLM API calls (entity extraction, claim extraction, community summarization), so you pay the underlying LLM provider (OpenAI, Azure OpenAI, etc.) for compute. Indexing a large corpus can be significantly more expensive upfront than building a plain vector index.

Which LLMs and storage backends does GraphRAG support?+

GraphRAG supports OpenAI and Azure OpenAI for both chat completion and embeddings out of the box, configured via settings.yaml. Other providers can be wired in through the modular LLM interface. Outputs are stored as Parquet files; vector embeddings can be stored in LanceDB (default), Azure AI Search, or Cosmos DB. The graph itself can be exported to GraphML or Neo4j for visualization.

When should I use GraphRAG instead of LlamaIndex or LangChain?+

Use GraphRAG when your use case requires global reasoning, multi-hop questions, or strong provenance across a fixed or slow-changing corpus — for example, intelligence analysis, regulatory document review, or research synthesis. Use LlamaIndex or LangChain when you need a general-purpose orchestration framework, fast incremental indexing, or simpler entity-lookup retrieval. Many teams use GraphRAG as one retriever component inside a larger LlamaIndex/LangChain pipeline.

🦞

New to AI tools?

Read practical guides for choosing and using AI tools

Read Guides →

Get updates on GraphRAG and 370+ other AI tools

Weekly insights on the latest AI tools, features, and trends delivered to your inbox.

What's New in 2026

Through late 2025 and into 2026, the GraphRAG project has matured beyond its initial research drop: DRIFT Search is now a first-class retrieval mode alongside Local and Global; the indexing engine has been refactored for better incremental updates via graphrag update; configuration moved to a cleaner YAML schema with explicit LLM and embedding provider blocks; and the project added official support for additional vector stores and Azure-native artifact storage. The Azure GraphRAG Solution Accelerator has been kept in step, providing a deployable reference architecture. Community adoption has accelerated, with GraphRAG-style patterns (entities + communities + hierarchical summaries) becoming a standard option in frameworks like LlamaIndex and emerging as the dominant approach for explainable, multi-hop enterprise RAG.

Alternatives to GraphRAG

LlamaIndex

AI Agent Builders

LlamaIndex: Build and optimize RAG pipelines with advanced indexing and agent retrieval for LLM applications.

LangChain

AI Agent Builders

The industry-standard framework for building production-ready LLM applications with comprehensive tool integration, agent orchestration, and enterprise observability through LangSmith.

Unstructured

Document AI

Document ETL engine that converts messy PDFs, Word files, and images into AI-ready structured data with intelligent chunking.

Cognee

AI Memory & Search

Open-source framework that builds knowledge graphs from your data so AI systems can analyze and reason over connected information rather than isolated text chunks.

View All Alternatives & Detailed Comparison →

User Reviews

No reviews yet. Be the first to share your experience!

Quick Info

Try GraphRAG Today

Get started with GraphRAG and see if it's the right fit for your needs.

Get Started →

Need help choosing the right AI stack?

Take our 60-second quiz to get personalized tool recommendations

Find Your Perfect AI Stack →

Want a faster launch?

Explore 20 ready-to-deploy AI agent templates for sales, support, dev, research, and operations.

Browse Agent Templates →

More about GraphRAG

Pricing Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Overview

Key Features

Graph-Based Knowledge Extraction+

Uses LLMs to extract entities, relationships, and claims from documents, building a structured knowledge graph that captures semantic connections traditional chunking misses.

Use Case:

Analyzing a corpus of research papers to understand how different concepts and findings relate across publications.

Global Search+

Synthesizes answers from community summaries across the entire dataset, enabling holistic questions that vanilla RAG cannot handle.

Use Case:

Asking 'What are the key regulatory trends?' across thousands of policy documents.

Local Search+

Combines graph neighborhood traversal with vector similarity for precise, context-rich answers to specific questions.

Use Case:

Finding detailed information about a specific entity and all its relationships within the knowledge base.

Community Detection+

Applies the Leiden algorithm to identify clusters of related entities, generating hierarchical summaries at multiple abstraction levels.

Use Case:

Automatically organizing a large knowledge base into thematic groups for exploration.

Customizable Extraction Prompts+

Entity and relationship extraction prompts can be tuned for specific domains, improving accuracy for specialized corpora.

Use Case:

Configuring extraction for medical literature to focus on drug interactions, symptoms, and treatment protocols.

Structured Output Artifacts+

Produces inspectable Parquet files containing entities, relationships, communities, and summaries for debugging and analysis.

Use Case:

Auditing the knowledge graph to verify extraction quality before deploying to production.

Getting Started with GraphRAG

1**Install GraphRAG**: `pip install graphrag` and ensure you have Python 3.10+ with sufficient disk space for graph artifacts

2**Configure LLM access**: Set up OpenAI API keys or configure Azure OpenAI endpoints for the entity extraction and summarization pipeline

3**Prepare documents**: Organize your document corpus into a single directory — GraphRAG works best with 100+ documents containing interconnected information

4**Run indexing**: Execute `python -m graphrag.index --root ./your-data` to build the knowledge graph (expect significant LLM token usage for large corpora)

5**Test queries**: Try both local search for specific questions and global search for holistic understanding to compare GraphRAG's capabilities with traditional RAG

Best Use Cases

🎯

Enterprise knowledge management with complex relationships: Organizations with large document repositories where information spans multiple documents and understanding relationships between concepts is critical — like connecting customer complaints to product features to engineering decisions across thousands of documents.

⚡

Research corpus analysis for holistic insights: Academic and industry researchers analyzing large bodies of literature to identify trends, gaps, and connections between studies that no single paper explicitly discusses — enabling meta-analysis and novel research directions.

🔧

Legal document understanding for case preparation: Law firms analyzing discovery documents, case law, and regulatory materials where precedents, citations, and legal relationships between entities determine case outcomes and require multi-document synthesis.

🚀

Complex multi-hop question answering across domains: Applications requiring answers that combine information from multiple sources and reasoning chains — like 'How do supply chain disruptions in Asia affect European manufacturing costs?' across economic reports, trade data, and industry analysis.

Limitations & What It Can't Do

We believe in transparent reviews. Here's what GraphRAG doesn't handle well:

⚠GraphRAG is a research-grade pipeline rather than a managed product. Indexing is LLM-call-heavy and can be costly and slow on large corpora; incremental updates are supported but less mature than full re-indexing. Extraction quality is bounded by the underlying LLM and by domain-specific prompt tuning — out-of-the-box entity types may miss specialized vocabulary in fields like medicine or law. The system has no built-in auth, multi-tenancy, UI, or hosted query endpoint; teams must build those layers themselves or adopt the separate Azure-hosted GraphRAG accelerator. Latency for global search is higher than for plain vector RAG because it map-reduces over community summaries. Finally, the project moves quickly: APIs, config schemas, and default prompts have changed across releases, so pinning a version is recommended for production deployments.

Pros & Cons

✓ Pros

✓Answers global/thematic questions across an entire corpus that vector RAG fundamentally cannot — community summaries enable map-reduce reasoning over the whole dataset.
✓Strong provenance and explainability: every answer can be traced back to specific entities, relationships, and source text chunks in the graph.
✓Modular indexing pipeline with swappable LLM, embedding, and storage backends (OpenAI, Azure OpenAI, local models via config) — outputs land as Parquet for easy downstream use.
✓Backed by Microsoft Research with active development, published papers, and a managed Azure path (`graphrag-accelerator`) for teams that outgrow the OSS pipeline.
✓DRIFT search and hierarchical community summaries give meaningfully better results than naive RAG on multi-hop and synthesis-heavy benchmarks reported by the team.
✓MIT-licensed and self-hostable, with no vendor lock-in for the indexing or query stack.

✗ Cons

✗Indexing cost is high: building the graph requires many LLM calls per document (entity extraction, claim extraction, community summarization), which can become expensive on large corpora.
✗Initial setup has a steeper learning curve than vector RAG — you must understand entity extraction prompts, community levels, and the local/global/DRIFT trade-offs to get good results.
✗Updating the index incrementally is harder than with a vector store; re-indexing or running the incremental update pipeline is non-trivial for fast-changing data.
✗Quality of the resulting graph depends heavily on the underlying LLM and on prompt tuning for the source domain — out-of-the-box extraction can miss domain-specific entity types.
✗Positioned as a research/reference pipeline rather than a turnkey product, so production concerns (auth, multi-tenancy, observability, scaling) are left to the integrator.