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Graphrag

A Python-based research project and modular data pipeline that uses graph structures to form targeted context for Retrieval-Augmented Generation question answering.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Graphrag — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when question answering benefits from structured relationships across a document collection, and when you can evaluate indexing expense and tune prompts for your data.

Who it is for

  • Developers building graph-based RAG systems
  • Teams exploring knowledge-graph-backed research assistants
  • Organizations prototyping question answering over private or domain-specific documents

Documented uses

  • Extract structured information from unstructured text
  • Build graph-based retrieval context for questions
  • Explore documents through relationships represented as knowledge-graph memory
  • Prototype domain-specific RAG applications
Before you choose

Requirements & limits

The repository is public, accessible, open source, and MIT licensed. It is described as a research project rather than an officially supported Microsoft offering.

  • Graph-based context formation is the defining technical approach.
  • The project is written in Python and is suitable for self-hosted or cloud-oriented development.
  • Indexing may be expensive; the documentation recommends starting small.
  • Prompt tuning may be needed for useful results with custom data.
  • The repository is in maintenance mode, prioritizing bug fixes and dependency updates over new features.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start with the GraphRAG command-line quickstart

Begin evaluating the GraphRAG system using its documented command-line quickstart.

Before you start

  • A copy of the GraphRAG repository or its documented installation path
  • Access to the GraphRAG documentation
  • A small dataset suitable for initial indexing

Steps

  1. Read the GraphRAG documentation and command-line quickstart.

  2. Start with a small dataset, because indexing can be an expensive operation.

  3. Review the prompt-tuning guidance before judging results on your data.

Expected outcome

An initial evaluation path for GraphRAG's graph-based retrieval and question-answering workflow.

Guide limitations

  • The evidence does not provide installation commands or execution details.
  • Indexing costs and results depend on the data and configuration.
  • The project recommends prompt tuning for use with custom data.
Documentation 1 ↗Documentation 2 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
GraphragExtract structured information from unstructured textCloud, Self-hostedThe repository is public, accessible, open source, and MIT licensed. It is described as a research project rather than an officially supported Microsoft offering.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
microsoft/graphrag · Official homepage title · retained in its source language
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