GraphCanon updated 5d · GitHub synced 5d · 36 views this month
Decision brief
GraphRAG is a Python-based tool designed for integrating retrieval and generation processes in large language models using graph structures.
Good fit when
- When you need to leverage graph structures to enhance the efficiency of information retrieval within a Retrieval-Augmented Generation setup.
Avoid when
- 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
- +
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 5d
- Provenance
- Not a fork · Organization account
- As of 5d
- Security (OSV)
- No lockfile
- As of 1mo
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Backing
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- Company
- Microsoft·GitHub org profile·1mo
- Employees
- 221,000·Wikidata (P1128 employees)·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install graphragHow it fits your stack(24)
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Evidence and technical details
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Overview
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.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 16, 2026
Categories
Graph entities
Tags
README
GraphRAG
[!WARNING] 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.
👉 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.
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.
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 GraphRAG, see DEVELOPING.md
- 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.
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.
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 ar
For agents
This page has a .md twin and JSON over the API.