About Graphrag Toolkit
Graphrag Toolkit is an MCP server published by awslabs in the Data category: python toolkit for building graph-enhanced GenAI applications.
The repository has 450 stars and 106 forks, with the last commit 6 days ago. Recent commit activity is the strongest signal that reported issues will get attention.
Install
git clone https://github.com/awslabs/graphrag-toolkitThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Ask AI
Ask AI about Graphrag Toolkit
Powered by Claude · Grounded in docs
Security checks
Static checks derived from the repository 4 days ago. Heuristics on the source, not an audit.
- ✓README presentThe repository ships documentation.
- ✓License declaredUsable under a stated license.
- ✓Tests presentA test suite exists in the repository.
- ✓Dependencies pinnedA lockfile pins dependency versions.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
1 check has not been run yet.
Releases
README
GraphRAG Toolkit
Check out the graphrag-toolkit Docs found here: Start here!
The graphrag-toolkit is a collection of Python tools for building graph-enhanced Generative AI applications.
Lexical Graph
The lexical-graph provides a framework for automating the construction of a hierarchical lexical graph from unstructured data, and composing question-answering strategies that query this graph when answering user questions.
Additional Resources
- Introducing the GraphRAG Toolkit [Blog Post] GraphRAG Toolkit launch blog post.
- AWS re:Invent 2025 - Deep Dive into Deloitte's Amazon Neptune GenAI Security Intelligence Center [Video] Discusses the design of the GraphRAG Toolkit and shows how Deloitte use the lexical graph in its security intelligence center.
- VectorDB vs GraphDB for Gen AI Agents [Video] Discussion on the differences between vector search and graph search, how they work, and how to use them together to enhance the accuracy of GenAI applications and more. Includes examples that use the GraphRAG Toolkit.
- Leveraging VectorDB and GraphDB: Enhancing Gen AI Applications with Hybrid Queries [Blog Post] Companion blog post to the VectorDB vs GraphDB video above.
- Use GraphRAG with Amazon Neptune to improve generative AI applications [Code Sample] Jupyter notebook-based self-guided workshop that allows you to explore the GraphRAG Toolkit features.
- RAG Explorer [Code Sample] Interactive UI-based app that uses the GraphRAG Toolkit to compare GraphRAG and Vector RAG responses.
- Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval [Article] Describes the deisgn of the hierarchical lexical graph model.
BYOKG-RAG
BYOKG-RAG is a novel approach to Knowledge Graph Question Answering (KGQA) that combines the power of Large Language Models (LLMs) with structured knowledge graphs. The system allows users to bring their own knowledge graph and perform complex question answering over it.
Security
See CONTRIBUTING for more information.
License
This project is licensed under the Apache-2.0 License.
README fetched from the repository 4 days ago. The original is authoritative.