--- title: "graphrag" type: "tool" slug: "microsoft-graphrag" canonical_url: "https://www.graphcanon.com/tools/microsoft-graphrag" github_url: "https://github.com/microsoft/graphrag" homepage_url: "https://microsoft.github.io/graphrag/" stars: 34239 forks: 3621 primary_language: "Python" license: "MIT" categories: ["evaluation-observability", "llm-frameworks"] tags: ["knowledge-graph-memory-structures", "data-pipeline-and-transformation-suite", "llm", "rag", "gpt-4", "gpt", "graph-based-retrieval-augmented-generation"] updated_at: "2026-07-07T18:20:25.053098+00:00" --- # graphrag > A modular graph-based Retrieval-Augmented Generation (RAG) system GraphRAG is a data pipeline and transformation suite that uses LLMs to extract structured data from unstructured text, enhancing the performance of LLMs on private data. ## Facts - Repository: https://github.com/microsoft/graphrag - Homepage: https://microsoft.github.io/graphrag/ - Stars: 34,239 · Forks: 3,621 · Open issues: 158 · Watchers: 195 - Primary language: Python - License: MIT - Last pushed: 2026-06-22T07:23:02+00:00 ## Categories - [Evaluation & Observability](/categories/evaluation-observability.md) - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags knowledge graph memory structures, data pipeline and transformation suite, llm, rag, gpt-4, gpt, graph-based retrieval-augmented generation ## Related tools - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 144,575) - [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. (★ 116,702) - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711) - [TradingAgents](/tools/tauricresearch-tradingagents.md) - TradingAgents: Multi-Agents LLM Financial Trading Framework (★ 91,610) ## README (excerpt) ```text # GraphRAG 👉 [Microsoft Research Blog Post](https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/)
👉 [Read the docs](https://microsoft.github.io/graphrag)
👉 [GraphRAG Arxiv](https://arxiv.org/pdf/2404.16130)
PyPI - Version PyPI - Downloads GitHub Issues GitHub Discussions
## 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. 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. ## Quickstart To get started with the GraphRAG system we recommend trying the [command line quickstart](https://microsoft.github.io/graphrag/get_started/). ## 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](./CONTRIBUTING.md) - To start developing _GraphRAG_, see [DEVELOPING.md](./DEVELOPING.md) - Join the conversation and provide feedback in the [GitHub Discussions tab!](https://github.com/microsoft/graphrag/discussions) ## 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](https://microsoft.github.io/graphrag/prompt_tuning/overview/) in our documentation. ## Versioning Please see the [breaking changes](./breaking-changes.md) document for notes on our approach to versioning the project. *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.* ## Responsible AI FAQ See [RAI_TRANSPARENCY.md](./RAI_TRANSPARENCY.md) - [What is GraphRAG?](./RAI_TRANSPARENCY.md#what-is-graphrag) - [What can GraphRAG do?](./RAI_TRANSPARENCY.md#what-can-graphrag-do) - [What are GraphRAG’s intended use(s)?](./RAI_TRANSPARENCY.md#what-are-graphrags-intended-uses) - [How was GraphRAG evaluated? What metrics are used to measure performance?](./RAI_TRANSPARENCY.md#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?](./RAI_TRANSPARENCY.md#what-are-the-limitations-of-graphrag-how-can-users-minimize-the-impact-of-graphrags-limitations-when-using-the-system) - [What operational factors and settings allow for effective and responsible use of GraphRAG?](./RAI_TRANSPARENCY.md#what-operational-factors-and-settings-allow-for-effective-and-responsible-use-of-graphrag) ## Trademarks This project may contain trademarks or logos ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/microsoft-graphrag`](/api/graphcanon/tools/microsoft-graphrag) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_