Deep Dive into GraphRAG: Six open source projects for building more powerful knowledge-enhanced question answering systems

Written by

Silas Grey

Updated on:June-20th-2025

A few days ago, I shared an article titled Knowledge Graph + Vector Database: Building a Smarter RAG System, which introduced how to combine knowledge graph and RAG system to improve the accuracy of search content. Then a reader asked me if I had any related open source project recommendations. I sorted them out and found that there are currently 6 graph RAG projects with relatively high attention. You can pay attention to them and learn about them.

Microsoft GraphRAG

GraphRAG, officially released by Microsoft, is a retrieval enhancement generation system built on the basis of LLM and graph machine learning. It can automatically build a knowledge graph from private documents, use the graph structure to perform multi-hop reasoning during query, and improve the performance of complex question and answer.

It supports graph structure reasoning and chained multi-round queries, provides a complete end-to-end data pipeline, and is compatible with enterprise-level deployments such as Azure. The project is available through the GitHub address (microsoft/graphrag ) , and is suitable for scenarios such as internal document question and answer, complex information extraction, and semantic search , helping users to efficiently process and analyze massive data .

Nano-GraphRAG

Nano-GraphRAG is a GraphRAG implementation focused on simplification, with only about 1,100 lines of code, but retains core capabilities and is compatible with multiple local/cloud models.

Nano-GraphRAG has a streamlined architecture and supports asynchronous processing. It also integrates the vector library Faiss, the graph database Neo4j, and the large model calling tool Ollama by default. It has two query modes: global and local, and supports incremental updates and customized templates.

Relatively speaking, because Nano-GraphRAG has less code and is easy to deploy, it is more suitable for local knowledge graph construction and for lightweight semantic question answering services.

DIGIMON

DIGIMON (also known as GraphRAG), developed by JayLZhou et al., is a platform for studying the effects of various GraphRAG algorithms, and is particularly suitable for academic researchers and algorithm teams.

DIGIMON supports multiple variants (such as LGraph RAG , Hippo RAG , etc.) , implements fast strategy switching through YAML configuration , and comes with evaluation scripts and datasets, while being compatible with local and OpenAI models . This project is more suitable for scenarios such as graph question answering algorithm research and multi- hop QA effect evaluation .

Fast GraphRAG

Fast GraphRAG was released by CircleMind AI, focusing on high performance and high interpretability , significantly reducing computing overhead while maintaining query accuracy.

The fast -graph rag project supports PageRank -style graph traversal search, incremental updates and automatic graph refinement, asynchronous reasoning, and type safety . The official documentation states that it can save about 6 times the reasoning cost. This project is more suitable for enterprise deployments that require cost-effective and low-latency knowledge question-answering systems .

LightRAG

LightRAG was developed by the University of Hong Kong. It is a RAG system with a graph visualization interface that supports parsing of multiple file types and is suitable for teaching, demonstration, and rapid development.

The Light RAG project supports document uploading, entity recognition, graph construction, visual query path , multi-format export , and integrates Ollama , local models and graph databases , providing a Web front-end and RAG dialogue interface . The project is more suitable for scenarios such as education , knowledge display, and general RAG service deployment .

HuixiangDou2

HuixiangDou2 (also known as ROGRAG), launched by the team from East China Normal University, focuses on knowledge enhancement in multi-round question-answering scenarios, supports a two-layer retrieval strategy, and emphasizes Chinese capabilities and robustness.

The Hu ixiangDou 2 project supports Group Chat scenarios , document layer and graph layer dual retrieval enhancement , supports Chinese (zh _cn ) interfaces by default , and is adapted to customer service and technical support question and answer systems. This project is more suitable for scenarios such as customer service systems and intelligent assistants in Chinese environments .

Finally, to summarize, for industrial-level enterprise deployment, we recommend Microsoft Graph RAG and Fast Graph RAG ; for lightweight or local testing , we recommend Nano - Graph RAG and Light RAG ; for graph question-answering research , we choose DIGIMON ; for Chinese multi- round question-answering , we recommend Huixiang Dou 2 (ROGRAG ) . These projects can be selected according to specific scenarios to optimize the performance of knowledge graphs and question - answering systems .