UltraRAG

Latest News 🔥

• [2026.01.20] 🎉 AgentCPM-Report Model Released! DeepResearch is finally localized: 8B on-device writing agent AgentCPM-Report is open-sourced 👉 |🤗 Model|

• [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.

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: From "Chat Demo" to "Full-Stack Development"

UltraRAG UI breaks through the boundaries of traditional chat interfaces and evolves into a visual RAG full-process integrated development environment (IDE) that integrates orchestration, debugging, and demonstration.

The system has a built-in powerful Pipeline Builder that supports bidirectional real-time synchronization between 'canvas drag-and-drop' and 'code editing', and allows online fine-tuning of Pipeline parameters and Prompts. It also innovatively introduces an intelligent AI assistant that deeply assists in the entire development process of Pipeline structure design, parameter tuning, and Prompt generation. The completed logic flow can be converted with one click into an interactive chat system and seamlessly integrates knowledge base management components, supporting users to build their own knowledge bases for document Q&A, truly achieving a one-stop closed loop from underlying logic construction, data governance to upper-layer application delivery.

https://github.com/user-attachments/assets/03a2045e-26ca-4426-bc18-cfc3e9520ea2

Key Highlights

• 🚀 Low-Code Orchestration of Complex Workflows

  • Inference Orchestration: Natively supports control structures such as sequential, loop, and conditional branches. Developers only need to write YAML configuration files to implement complex iterative RAG logic in dozens of lines of code.

• ⚡ Modular Extension and Reproduction

  • Atomic Servers: Based on the MCP architecture, functions are decoupled into independent Servers. New features only need to be registered as function-level Tools to seamlessly integrate into workflows, achieving extremely high reusability.

• 📊 Unified Evaluation and Benchmark Comparison

  • Research Efficiency: Built-in standardized evaluation workflows, ready-to-use mainstream research benchmarks. Through unified metric management and baseline integration, significantly improves experiment reproducibility and comparison efficiency.

• ✨ Rapid Interactive Prototype Generation

  • One-Click Delivery: Say goodbye to tedious UI development. With just one command, Pipeline logic can be instantly converted into an interactive conversational Web UI, shortening the distance from algorithm to demonstration.

Installation

We provide two installation methods: local source code installation (recommended using uv for package management) and Docker container deployment

Method 1: Source Code Installation (Recommended)

We strongly recommend using uv to manage Python environments and dependencies, as it can greatly improve installation speed.

Prepare Environment

If you haven't installed uv yet, please execute:

## Direct installation
pip install uv
## Download
curl -LsSf https://astral.sh/uv/install.sh | sh

Download Source Code

git clone https://github.com/OpenBMB/UltraRAG.git --depth 1
cd UltraRAG

Install Dependencies

Please choose one synchronization method according to your usage scenario:

• Core dependencies: If you only need to run basic core functions, such as only using UltraRAG UI:

  uv sync
  

• Full installation: If you want to fully experience UltraRAG's retrieval, generation, corpus processing, and evaluation functions, please run:

  uv sync --extra retriever --extra generation --extra corpus --extra evaluation
  

• On-demand installation: If you only need to run specific modules, keep the corresponding --extra as needed, for example:

  uv sync --extra retriever   # Retrieval module only
  uv sync --extra generation  # Generation module only
  

Method 2: Docker Container Deployment

If you don't want to configure a local Python environment, you can use Docker to start with one click.

# 1. Download code
git clone https://github.com/OpenBMB/UltraRAG.git --depth 1
cd UltraRAG
# 2. Build image
docker build -t ultrarag:latest .
# 3. Start container (port 5050 is automatically mapped)
docker run -it --gpus all -p 5050:5050 ultrarag:latest

Note: After the container starts, UltraRAG UI will run automatically. You can directly access http://localhost:5050 in your browser to use it.

Verify Installation

After installation, run the following example command to check if the environment is normal:

ultrarag run examples/sayhello.yaml

If you see the following output, the installation is successful:

Hello, UltraRAG v3!

Quick Start

We provide complete tutorial examples from beginner to advanced. Whether you are conducting academic research or building industrial applications, you can find guidance here. Welcome to visit the Documentation for more details.

Research Experiments

Designed for researchers, providing data, experimental workflows, and visualization analysis tools.

• Getting Started: Learn how to quickly run standard RAG experimental workflows based on UltraRAG.
• Evaluation Data: Download the most commonly used public evaluation datasets in the RAG field and large-scale retrieval corpora, directly for research benchmark testing.
• Case Analysis: Provides a visual Case Study interface to deeply track each intermediate output of the workflow, assisting in analysis and error attribution.
• Code Integration: Learn how to directly call UltraRAG components in Python code to achieve more flexible customized development.

Demo Systems

Designed for developers and end users, providing complete UI interaction and complex application cases.

• Quick Start: Learn how to start UltraRAG UI and familiarize yourself with various advanced configurations in administrator mode.
• Deployment Guide: Detailed production environment deployment tutorials, covering the setup of Retriever, Generation models (LLM), and Milvus vector database.
• Deep Research: Flagship case, deploy a Deep Research Pipeline. Combined with the SurveyCP