Version 0.8.0 (2025-06-27)
1、Overview
We are excited to announce the official release of KAG version 0.8. This update focuses on continuously enhancing the consistency, rigor, and accuracy of large model knowledge base-driven reasoning and question-answering. It also introduces several significant new features and capabilities.
First, we have upgraded the capabilities of the KAG knowledge base. We have expanded support for two modes: private domain knowledge bases (including structured and unstructured data) and public domain knowledge bases. This includes the ability to integrate public web data sources such as LBS and WebSearch via the MCP protocol. Additionally, we have improved the management of private domain knowledge base indexing, incorporating multiple foundational index types such as Outline, Summary, KnowledgeUnit, AtomicQuery, Chunk, and Table. This supports developers in customizing indexes and synchronizing them with product interfaces. Users can select the most appropriate index type based on their specific scenarios, achieving a balance between construction costs and business outcomes.
Second, we have optimized the product experience and refined the system integration interfaces. On the product front, we have decoupled the knowledge base from applications. The knowledge base now manages private domain data (both structured and unstructured) and public domain data, while applications can link to multiple knowledge bases. Based on the index types used during knowledge base construction, the system automatically adapts the corresponding retrieval engine to recall data. Both applications and knowledge bases independently manage model dependencies and task scheduling, enhancing flexibility.
In terms of system integration, we have refined the KAG recall and Q&A interfaces, adding support for embedding reasoning and Q&A pages within business applications. We have fully embraced the MCP protocol, enabling seamless integration of KAG reasoning and Q&A into agent workflows (based on the MCP protocol).
Third, KAG has successfully adapted to the KAG-Thinker model. Through optimizations such as broad decomposition and deep solving of complex problems, knowledge boundary determination, and noise-resistant retrieval results, the stability of the KAG framework's reasoning paradigm and the rigor of its reasoning logic have been significantly improved under the guidance of iterative thinking paradigms. Further details on the release and usage of the KAG-Thinker model will be provided in our upcoming announcements.
In addition to the aforementioned framework and product optimizations, we have also enhanced Q&A efficiency and the stability of streaming outputs. This update is based on the Qwen2.5-72B foundation model and has achieved performance alignment across various RAG frameworks and selected KG datasets. The overall benchmark results of this release are illustrated in Figures 1~3, with detailed metrics available in the open_benchmark section.
2、Framework Enhancements
2.1、Configurable Index Management
Index extraction and retrieval are core capabilities of knowledge base applications. KAG v0.8 has upgraded its architecture to support configurable management of index construction and retrieval. Each index type is equipped with its own extractor (Extractor) and retriever (Retriever), managed through the IndexManager.
KAG comes with built-in foundational index types such as KnowledgeUnit (an enhanced version of graph triples), Outline, Summary, Chunk, AtomicQuery, and Table, along with their corresponding extractors and retrievers. During the knowledge base construction phase, the platform invokes the appropriate extractor based on the index types selected by the user to perform index extraction. In the application phase, based on the knowledge bases associated with the application, the platform automatically calls the corresponding retriever to complete the recall of graph/chunk/doc data and integrates with the KAG-Solver pipeline to enable reasoning and question-answering.
Developers can extend the IndexManager to implement or combine custom extractors and retrievers. After packaging KAG with these custom components and replacing the corresponding installation package in the openspg-server image, users can select the custom index types on the product page during knowledge base construction. This enables seamless integration of custom index types with the product.
3、Product and platform optimization
3.1、System Integration
This release offers three methods to integrate KAG into business systems: HttpAPI, MCP Protocol, and Frontend Page Embedding.
The KAG HttpAPI provides two types of interfaces: recall and reasoning & Q&A. Developers can choose to use KAG as a retrieval source or as a complete reasoning and Q&A capability.
The KAG MCP Protocol offers interfaces for reasoning and Q&A, which can be embedded into agent applications like Cursor.
In this release, the reasoning and Q&A functionality has been upgraded to an independent page, allowing developers to embed this page into their business systems.
3.2、User Experience Optimization
This release addresses several community concerns, including response latency, streaming output stability, model configuration management, and task scheduling reliability. We greatly appreciate the community's support and patience with KAG.
4、Future Plans
In upcoming versions, we will continue to focus on enhancing large models' ability to leverage external knowledge bases. Our goal is to achieve bidirectional enhancement and seamless integration between large models and symbolic knowledge, improving the factuality, rigor, and consistency of reasoning and Q&A in professional scenarios. We will also keep releasing updates to push the boundaries of capability and drive adoption in vertical domains.