README

Less Code, Lower Barrier, Faster Deployment







   
   
   
  



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Latest News 🔥

- [2026.01.23] 🎉 UltraRAG 3.0 Released: Say no to "black box" development—make every line of reasoning logic clearly visible 👉 📖 Blog
- [2026.01.20] 🎉 AgentCPM-Report Model Released! DeepResearch is finally localized: 8B on-device writing agent AgentCPM-Report is open-sourced 👉 🤗 Model


Previous News


- [2025.11.11] 🎉 UltraRAG 2.1 Released: Enhanced knowledge ingestion & multimodal support, with a more complete unified evaluation system!
- [2025.09.23] New daily RAG paper digest, updated every day 👉 📖 Papers
- [2025.09.09] Released a Lightweight DeepResearch Pipeline local setup tutorial 👉 📺 bilibili · 📖 Blog
- [2025.09.01] Released a step-by-step UltraRAG installation and full RAG walkthrough video 👉 📺 bilibili · 📖 Blog
- [2025.08.28] 🎉 UltraRAG 2.0 Released! UltraRAG 2.0 is fully upgraded: build a high-performance RAG with just a few dozen lines of code, empowering researchers to focus on ideas and innovation! We have preserved the UltraRAG v2 code, which can be viewed at v2.
- [2025.01.23] UltraRAG Released! Enabling large models to better comprehend and utilize knowledge bases. The UltraRAG 1.0 code is still available at v1.



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 💡 About UltraRAG

UltraRAG is the first lightweight RAG development framework based on the Model Context Protocol (MCP) architecture design, jointly launched by THUNLP at Tsinghua University, NEUIR at Northeastern University, OpenBMB, and AI9stars.

Designed for research exploration and industrial prototyping, UltraRAG standardizes core RAG components (Retriever, Generation, etc.) as independent MCP Servers, combined with the powerful workflow orchestration capabilities of the MCP Client. Developers can achieve precise orchestration of complex control structures such as conditional branches and loops simply through YAML configuration.


  
    
  


 🖥️ UltraRAG UI

UltraRAG UI transcends the boundaries of traditional chat interfaces, evolving into a visual RAG Integrated Development Environment (IDE) that combines orchestration, debugging, and demonstration.

The system features a powerful built-in Pipeline Builder that supports bidirectional real-time synchronization between "Canvas Construction" and "Code Editing," allowing for granular online adjustments of pipeline parameters and prompts. Furthermore, it introduces an Intelligent AI Assistant to empower the entire development lifecycle, from pipeline structural design to parameter tuning and prompt generation. Once constructed, logic flows can be converted into interactive dialogue systems with a single click. The system seamlessly integrates Knowledge Base Management components, enabling users to build custom knowledge bases for document Q&A. This truly realizes a one-stop closed loop, spanning from underlying logic construction and data governance to final application deployment.


  
    
  
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https://github.com/use