microsoft/graphrag

Health v2 · maintenance only

Adoption not part of the score

35699 stars · 3750 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

GraphRAG is a Python library and CLI pipeline from Microsoft Research that extracts a knowledge graph from unstructured text, builds community hierarchies with summaries, and uses those structures for graph-based retrieval-augmented generation. It improves LLM question answering over private datasets where naive vector-similarity RAG struggles to connect disparate information.

Use cases

• build a knowledge graph from private documents for LLM question answering
• answer questions requiring synthesis across disparate sources in a corpus
• improve RAG quality over enterprise research or business documents
• generate hierarchical community summaries of a large text dataset
• run graph-based retrieval instead of vector similarity search

When to choose

• your corpus is large and narrative, and baseline vector RAG fails to connect the dots
• you need global, dataset-level questions answered rather than snippet lookup
• you want a maintained, well-documented graph-based RAG pipeline with an indexer and query engine

When to avoid

• you need active feature development or community PRs - the project is in maintenance mode
• you have a small budget - LLM-based indexing is expensive
• you only need simple semantic search over short documents - baseline RAG is cheaper and simpler
• you require an officially supported Microsoft product - this is a research demonstration

Facets

library · maturity maintenance

rag llm-inference etl search-engine nlp large-language-models artificial-intelligence python cli cross-platform knowledge-graph graphrag llm gpt question-answering community-summarization microsoft-research retrieval-augmented-generation natural-language-processing data-engineering

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