Project GraphRAG
LLM-Derived Knowledge Graphs
GraphRAG
GraphRAG (Graphs + Retrieval Augmented Generation) is a technique for richly understanding text datasets by combining text extraction, network analysis, and LLM prompting and summarization into a single end-to-end system.
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GraphRAG: Unlocking LLM discovery on narrative private data
2月 13, 2024 | Jonathan Larson, Steven Truitt
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GraphRAG: New tool for complex data discovery now on GitHub
7月 2, 2024 | Darren Edge, Ha Trinh, Steven Truitt, Jonathan Larson
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GraphRAG: Improving global search via dynamic community selection
11月 15, 2024 | Bryan Li, Ha Trinh, Darren Edge, Jonathan Larson
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LazyGraphRAG: Setting a new standard for quality and cost
11月 25, 2024 | Darren Edge, Ha Trinh, Jonathan Larson
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Moving to GraphRAG 1.0 – Streamlining ergonomics for developers and users
12月 16, 2024 | Nathan Evans, Alonso Guevara Fernández, Joshua Bradley
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Claimify: Extracting high-quality claims from language model outputs
3月 19, 2025 | Dasha Metropolitansky
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BenchmarkQED: Automated benchmarking of RAG systems
6月 5, 2025 | Darren Edge, Ha Trinh, Andres Morales Esquivel, Jonathan Larson
Microsoft Discovery
GraphRAG and LazyGraphRAG technology is now available through Microsoft Discovery (opens in new tab), an agentic platform for scientific research built in Azure.
Open Source
The GraphRAG library is available on GitHub (opens in new tab). The GraphRAG solution accelerator repository, an API experience hosted on Azure, has been archived but can still be referenced below. BenchmarkQED, a set of tools for evaluating RAG based systems, is also available on GitHub (opens in new tab).
GraphRAG Library (opens in new tab)