microsoft/graphrag

Project overview

GraphRAG uses LLMs to extract structured data from unstructured text and applies knowledge graph memory structures to enhance LLM reasoning over private data. It is a Python library and demonstration project that requires CLI usage, prompt tuning, and involves potentially expensive indexing operations.

Project type

RAG · Data Processing

Use cases

Knowledge Q&A

Deployment

Refer to project documentation

License

MIT

Best for

• Users who need to enhance LLM reasoning over private data and are comfortable with coding, CLI tools, and prompt tuning.

Key capabilities

• Extracts meaningful, structured data from unstructured text using the power of LLMs.
• Uses knowledge graph memory structures to enhance LLM outputs.
• Supports fine-tuning prompts to yield better results for specific data.
• Provides a command line quickstart and CLI commands for initialization.

Limitations and risks

• GraphRAG indexing can be an expensive operation due to high associated costs.
• The provided code serves as a demonstration and is not an officially supported Microsoft offering.
• Using GraphRAG with your data out of the box may not yield the best possible results without prompt tuning.
• There is a high cost risk associated with the potentially expensive indexing operations.

Getting started

• Setup is considered hard, requiring users to read all documentation to understand the process and costs, complete prompt tuning for best results, and use coding or CLI tools. The first success path involves installing the graphrag package via PyPI, running 'graphrag init --root [path] --force', and executing the command line quickstart.

Evidence and sources

• GitHub project description: A modular graph-based Retrieval-Augmented Generation (RAG) system
• README: 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.
• README: 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…
• README: ⚠️ *Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.*
• README: 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:/…

Reviewed at: 2026-07-17T10:38:44Z