Youtu-GraphRAG – A Graph-Retrieval-Augmented Generation Framework Open-Sourced by Tencent Youtu Lab

What is Youtu-GraphRAG?

Youtu-GraphRAG is a graph-retrieval-augmented generation (Graph-RAG) framework open-sourced by Tencent Youtu Lab. By organizing knowledge into a graph and integrating it with large language models (LLMs) for retrieval and reasoning, the framework helps models provide more accurate answers to complex questions while reducing “hallucinations.” It features multi-hop reasoning, support for knowledge-intensive tasks, and domain scalability. Through innovations such as graph-based pattern construction, community detection, and proxy retrieval, Youtu-GraphRAG significantly reduces token costs and improves accuracy. It supports seamless domain transfer and a variety of application scenarios, serving as a valuable complement to LLM applications.

Key Features of Youtu-GraphRAG

• Complex Reasoning and Multi-Hop QA: Breaks down complex questions into sub-questions, retrieves and reasons step by step within the knowledge graph, and generates precise answers.

• Knowledge-Intensive Tasks: Handles tasks requiring large amounts of structured or domain-specific knowledge, enhancing the model’s understanding through graph-based knowledge organization.

• Domain Scalability: Supports seamless domain transfer; knowledge graphs can be quickly adapted to new domains with minimal adjustments.

• Efficient Retrieval and Reasoning: Optimized retrieval strategies and iterative reasoning mechanisms reduce token usage, suitable for cost-sensitive scenarios.

• Visualization and Explainability: Provides a graphical interface to display knowledge graph construction and reasoning paths, improving interpretability.

Technical Principles of Youtu-GraphRAG

• Graph-Pattern Guided Knowledge Tree Construction: Automatically builds hierarchical knowledge trees based on seed graph patterns, including entity types, relationships, and attribute types. The tree has four layers: attribute, relation, keyword, and community, supporting top-down filtering and bottom-up reasoning.

• Dual-Aware Community Detection: Employs a novel community detection algorithm that combines structural topology and subgraph semantics for hierarchical community partitioning. Each community generates a summary to enhance knowledge abstraction.

• Proxy Retrieval and Iterative Reasoning: Complex questions are decomposed into sub-questions for parallel retrieval in the knowledge graph. Results are processed through Iterative Reasoning Chains (IRCoT) to construct final answers step by step.

• Unified Configuration Management: Centralized configuration system using a YAML file allows runtime parameter overrides, enabling seamless domain transfer and reducing manual intervention.

• Optimized Retrieval Strategies: Uses enhanced prompting, indexing, and retrieval strategies to lower token costs and improve efficiency. Supports parallel processing of sub-questions to further accelerate reasoning.

• Fair Anonymous Dataset “AnonyRAG”: Provides multilingual (Chinese and English) anonymized datasets to evaluate Graph-RAG’s real-world retrieval performance while preventing knowledge leakage.

Project Links

• GitHub Repository: https://github.com/TencentCloudADP/youtu-graphrag

• arXiv Paper: https://arxiv.org/pdf/2508.19855

Application Scenarios of Youtu-GraphRAG

• Complex Question-Answering Systems: Handles multi-step reasoning tasks, such as academic research or technical consulting, providing precise answers via graph-based retrieval and reasoning.

• Enterprise Knowledge Management: Integrates internal knowledge bases, enabling rapid responses to complex employee or customer queries and improving knowledge-sharing efficiency.

• Intelligent Customer Service: Provides accurate solutions in customer support scenarios through efficient retrieval and reasoning, enhancing user satisfaction.

• Healthcare Consultation: Assists doctors or patients in querying complex medical information, offering reasoning and advice based on professional knowledge.

• Legal Consultation: Supports legal professionals and users with knowledge retrieval and case reasoning to solve complex legal problems.