👉 Microsoft Research Blog Post
👉 Read the docs
👉 GraphRAG Arxiv

Overview

The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs. GraphRAG is available in both Python and .NET 10 C#, with identical functionality and configuration format across both implementations.

To learn more about GraphRAG and how it can be used to enhance your LLM's ability to reason about your private data, please visit the Microsoft Research Blog Post.

Implementations

Quickstart

Python

To get started with the Python implementation, we recommend trying the command line quickstart.

pip install graphrag
graphrag init
# Add your documents to ./input/
graphrag index
graphrag query "What are the top themes in this story?"

.NET

For the .NET implementation, see the full .NET Getting Started Guide.

git clone https://github.com/microsoft/graphrag.git
cd graphrag/dotnet
dotnet build
dotnet run --project src/GraphRag -- init --root ./my-project
# Add your documents to ./my-project/input/
dotnet run --project src/GraphRag -- index --root ./my-project
dotnet run --project src/GraphRag -- query --root ./my-project --method local --query "What are the top themes?"

Repository Structure

graphrag/
├── packages/               ← Python monorepo (8 packages)
│   ├── graphrag/           ← Main Python package (CLI, indexing, query)
│   ├── graphrag-common/    ← Shared utilities
│   ├── graphrag-storage/   ← Storage backends
│   ├── graphrag-cache/     ← Caching layer
│   ├── graphrag-chunking/  ← Text chunking
│   ├── graphrag-input/     ← Document ingestion
│   ├── graphrag-llm/       ← LLM abstraction
│   └── graphrag-vectors/   ← Vector stores
├── dotnet/                 ← .NET 10 implementation
│   ├── src/                ← 8 core libraries + 15 strategy plugins + SearchApp
│   ├── tests/              ← Unit + integration tests
│   └── docs/               ← .NET-specific documentation
├── docs/                   ← MkDocs documentation site (Python-focused)
├── tests/                  ← Python test suite
└── scripts/                ← Build & CI scripts

Repository Guidance

This repository presents a methodology for using knowledge graph memory structures to enhance LLM outputs. Please note that the provided code serves as a demonstration and is not an officially supported Microsoft offering.

⚠️ Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.

Diving Deeper

• To learn about our contribution guidelines, see CONTRIBUTING.md
• To start developing the Python implementation, see DEVELOPING.md
• For .NET development, see the .NET Getting Started Guide and .NET Project Overview
• For the .NET Search App, see the Search App Getting Started Guide
• Join the conversation and provide feedback in the GitHub Discussions tab!

Prompt Tuning

Using GraphRAG with your data out of the box may not yield the best possible results. We strongly recommend to fine-tune your prompts following the Prompt Tuning Guide in our documentation.

Versioning

Please see the breaking changes document for notes on our approach to versioning the project.

Python: Always run graphrag init --root [path] --force between minor version bumps to ensure you have the latest config format. Run the provided migration notebook between major version bumps if you want to avoid re-indexing prior datasets. Note that this will overwrite your configuration and prompts, so backup if necessary.

.NET: The .NET implementation shares the same settings.yaml configuration format. See dotnet/docs/getting-started.md for version-specific details.

Responsible AI FAQ

See RAI_TRANSPARENCY.md

• What is GraphRAG?
• What can GraphRAG do?
• What are GraphRAG’s intended use(s)?
• How was GraphRAG evaluated? What metrics are used to measure performance?
• What are the limitations of GraphRAG? How can users minimize the impact of GraphRAG’s limitations when using the system?
• What operational factors and settings allow for effective and responsible use of GraphRAG?

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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