{"data":{"slug":"microsoft-graphrag","name":"graphrag","tagline":"A modular graph-based Retrieval-Augmented Generation (RAG) system","github_url":"https://github.com/microsoft/graphrag","owner":"microsoft","repo":"graphrag","owner_avatar_url":"https://avatars.githubusercontent.com/u/6154722?v=4","primary_language":"Python","stars":35519,"forks":3734,"topics":["gpt","gpt-4","gpt4","graphrag","llm","llms","rag"],"archived":false,"github_pushed_at":"2026-08-14T18:16:11+00:00","maintenance_label":"Very active","stars_delta_30d":1049,"url":"https://www.graphcanon.com/tools/microsoft-graphrag","markdown_url":"https://www.graphcanon.com/tools/microsoft-graphrag.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/microsoft-graphrag","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=microsoft-graphrag","description":"A modular graph-based Retrieval-Augmented Generation (RAG) system","homepage_url":"https://microsoft.github.io/graphrag/","license":"MIT","open_issues":46,"watchers":201,"ai_summary":"GraphRAG is a retrieval-augmented generation framework built for Python that integrates graph-based data structures to improve the efficiency and relevance of information retrieval for large language models.","readme_excerpt":"# GraphRAG\n\n> [!WARNING]\n> GraphRAG is a research project that explores the functional use of graphs to form a targeted context for question answering. Since our first release in July 2024 the capabilities of frontier models have changed dramatically, and our portfolio of research projects has diversified to match. This project is largely in maintenance mode, and won't be accepting new PRs or implementing new features. We'll perform bug fixes and dependency updates as appropriate, particularly to address CVEs as they arise.\n\n👉 [Microsoft Research Blog Post](https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/)<br/>\n👉 [Read the docs](https://microsoft.github.io/graphrag)<br/>\n👉 [GraphRAG Arxiv](https://arxiv.org/pdf/2404.16130)\n\n<div align=\"left\">\n  <a href=\"https://pypi.org/project/graphrag/\">\n    <img alt=\"PyPI - Version\" src=\"https://img.shields.io/pypi/v/graphrag\">\n  </a>\n  <a href=\"https://pypi.org/project/graphrag/\">\n    <img alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dm/graphrag\">\n  </a>\n  <a href=\"https://github.com/microsoft/graphrag/issues\">\n    <img alt=\"GitHub Issues\" src=\"https://img.shields.io/github/issues/microsoft/graphrag\">\n  </a>\n  <a href=\"https://github.com/microsoft/graphrag/discussions\">\n    <img alt=\"GitHub Discussions\" src=\"https://img.shields.io/github/discussions/microsoft/graphrag\">\n  </a>\n</div>\n\n## Overview\n\nThe 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.\n\nTo 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 <a href=\"https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/\" target=\"_blank\">Microsoft Research Blog Post.</a>\n\n## Quickstart\n\nTo get started with the GraphRAG system we recommend trying the [command line quickstart](https://microsoft.github.io/graphrag/get_started/).\n\n## Repository Guidance\n\nThis 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.\n\n⚠️ _Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small._\n\n## Diving Deeper\n\n- To learn about our contribution guidelines, see [CONTRIBUTING.md](./CONTRIBUTING.md)\n- To start developing _GraphRAG_, see [DEVELOPING.md](./DEVELOPING.md)\n- Join the conversation and provide feedback in the [GitHub Discussions tab!](https://github.com/microsoft/graphrag/discussions)\n\n## Prompt Tuning\n\nUsing _GraphRAG_ with your data out of the box may not yield the best possible results.\nWe strongly recommend to fine-tune your prompts following the [Prompt Tuning Guide](https://microsoft.github.io/graphrag/prompt_tuning/overview/) in our documentation.\n\n## Versioning\n\nPlease see the [breaking changes](./breaking-changes.md) document for notes on our approach to versioning the project.\n\n_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 back them up if necessary._\n\n## Responsible AI FAQ\n\nSee [RAI_TRANSPARENCY.md](./RAI_TRANSPARENCY.md)\n\n- [What is GraphRAG?](./RAI_TRANSPARENCY.md#what-is-graphrag)\n- [What can GraphRAG do?](./RAI_TRANSPARENCY.md#what-can-graphrag-do)\n- [What are GraphRAG’s intended use(s)?](./RAI_TRANSPARENCY.md#what-are-graphrags-intended-uses)\n- [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)\n- [What ar","github_created_at":"2024-03-27T17:57:52+00:00","created_at":"2026-07-07T17:31:52.518882+00:00","updated_at":"2026-08-16T12:02:04.115536+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"gpt","name":"gpt"},{"slug":"gpt-4","name":"gpt-4"},{"slug":"graph","name":"graph"},{"slug":"llm","name":"llm"},{"slug":"rag","name":"rag"}],"trust":{"provenance":{"is_fork":false,"github_id":778431525,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-16T12:02:03.156Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":2,"days_since_push":1,"last_release_at":"2026-07-18T01:23:18Z","stars_delta_30d":1049,"open_issues_delta_30d":-15},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T10:57:57.962Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-16T12:02:03.756Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-16T12:02:03.756Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-08-16T12:02:03.756Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need to leverage graph structures to enhance the efficiency of information retrieval within a Retrieval-Augmented Generation setup."],"when_not_to_use":["If your application does not require or benefit from the specific graph-based approach GraphRAG employs; traditional RAG systems might be sufficient without the added layer of complexity introduced by","+"],"source":"enrich:decision_facts","observed_at":"2026-07-11T12:54:56.822Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"GraphRAG is a Python-based tool designed for integrating retrieval and generation processes in large language models using graph structures."}]}}