graphrag-toolkit
Health Pass
- License — License: Apache-2.0
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Community trust — 377 GitHub stars
Code Pass
- Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Pass
- Permissions — No dangerous permissions requested
This Python toolkit provides a collection of frameworks for building graph-enhanced generative AI applications, specifically focusing on automating lexical graph construction and performing question-answering over custom knowledge graphs.
Security Assessment
The automated audit scan of 12 files found no dangerous code patterns, hardcoded secrets, or dangerous permission requests. As a GenAI and graph database integration framework, the tool inherently makes network requests to communicate with external databases (like Amazon Neptune) and LLM providers. It does not appear to execute unauthorized shell commands. Because it processes unstructured data to build graphs and relies on external APIs, it will handle and transmit your input data to those services. Overall risk is rated as Low, assuming you trust your configured endpoints.
Quality Assessment
The project is developed under the official `awslabs` organization and is highly active, with its most recent code push occurring today. It has earned a solid community trust baseline with 377 GitHub stars. Furthermore, the repository is properly maintained, completely transparent, and operates under the permissive Apache-2.0 license, making it safe for commercial and personal use.
Verdict
Safe to use.
Python toolkit for building graph-enhanced GenAI applications
GraphRAG Toolkit
The graphrag-toolkit is a collection of Python tools for building graph-enhanced Generative AI applications.
Installation instructions and requirements are detailed separately with each tool.
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.
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