UltraRAG

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](https://github.com/OpenBMB/UltraRAG/blob/page/project/blog/en/ultrarag3_0.md) - **[2026.01.20]** AgentCPM-Report Model Released! DeepResearch is finally localized: 8B on-device writing agent AgentCPM-Report is open-sourced [ Model](https://huggingface.co/openbmb/AgentCPM-Report) 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](https://github.com/OpenBMB/UltraRAG/tree/rag-paper-daily/rag-paper-daily) - **[2025.09.09]** Released a Lightweight DeepResearch Pipeline local setup tutorial [ bilibili](https://www.bilibili.com/video/BV1p8JfziEwM) · [ Blog](https://github.com/OpenBMB/UltraRAG/blob/page/project/blog/en/01_build_light_deepresearch.md) - **[2025.09.01]** Released a step-by-step UltraRAG installation and full RAG walkthrough video [ bilibili](https://www.bilibili.com/video/BV1B9apz4E7K/?share_source=copy_web&vd_source=7035ae721e76c8149fb74ea7a2432710) · [ Blog](https://github.com/OpenBMB/UltraRAG/blob/page/project/blog/en/00_Installing_and_Running_RAG.md) - **[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](https://github.com/OpenBMB/UltraRAG/tree/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](https://github.com/OpenBMB/UltraRAG/tree/v1). --- ## About UltraRAG UltraRAG is the first lightweight RAG development framework based on the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/docs/getting-started/intro) architecture design, jointly launched by [THUNLP](https://nlp.csai.tsinghua.edu.cn/) at Tsinghua University, [NEUIR](https://neuir.github.io) at Northeastern University, [OpenBMB](https://www.openbmb.cn/home), and [AI9stars](https://github.com/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. https://github.com/user-attachments/assets/fcf437b7-8b79-42f2-bf4e-e3b7c2a896b9 ### ✨ 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 We strongly recommend using [uv](https://github.com/astral-sh/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: ```shell ## Direct installation pip install uv==0.12.0 ## Download curl -LsSf https://astral.sh/uv/0.12.0/install.sh | sh ``` **Download Source Code** ```shell git clone https://github.com/OpenBMB/UltraRAG.git --depth 1 cd UltraRAG ``` **Install Dependencies** Choose one of the following modes to install dependencies based on your use case: **A: Create a New Environment** Use `uv sync` to automatically create a virtual environment and synchronize dependencies: - Core dependencies: If you only need to run basic core functions, such as only using UltraRAG UI: ```shell uv sync ``` - Full installation: If you want to fully experience UltraRAG's retrieval, generation, corpus processing, and evaluation functions, please run: ```shell uv sync --all-extras ``` This is the recommended team setup: every MCP server shares the same `.venv` and `uv.lock`. Linux GPU dependencies are locked to CUDA 12.9, including the official vLLM cu129 wheel. - On-demand installation: If you only need to run specific modules, keep the corresponding `--extra` as needed, for example: ```shell uv sync --extra retriever # Retrieval module only uv sync --extra generation # Generation module only ``` Once installed, activate the virtual environment: ```shell # Windows CMD .venv\Scripts\activate.bat # Windows Powershell .venv\Scripts\Activate.ps1 # macOS / Linux source .venv/bin/activate ``` **B: Install into an Existing Environment** To install UltraRAG into your currently active Python environment, use `uv pip`: ```shell # Core dependencies uv pip install -e . # Full installation uv pip install -e ".[all]" # On-demand installation uv pip install -e ".[retriever]" ``` ### Method 2: Docker Container Deployment If you prefer not to configure a local Python environment, you can deploy using Docker. **Get Code and Images** ```shell # 1. Clone the repository git clone https://github.com/OpenBMB/UltraRAG.git --depth 1 cd UltraRAG # 2. Prepare the image (choose one) # Option A: Pull from Docker Hub docker pull hdxin2002/ultrarag:v0.3.0-base-cpu # Base version (CPU) docker pull hdxin2002/ultrarag:v0.3.0-base-gpu # Base version (GPU) docker pull hdxin2002/ultrarag:v0.3.0 # Full version (GPU) # Option B: Build locally docker build -t ultrarag:v0.3.0 . ``` **Start the Container** ```shell # Start the container (Port 5050 is mapped by default) docker run -it --gpus all -p 5050:5050 ``` 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: ```shell ultrarag run examples/experiments/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](https://ultrarag.openbmb.cn/pages/en/getting_started/introduction) for more details. ### Research Experiments Designed for researchers, providing data, experimental workflows, and visualization analysis tools. - [Getting Started](https://ultrarag.openbmb.cn/pages/en/getting_started/quick_start): Learn how to quickly run standard RAG experimental workflows based on UltraRAG. - [Evaluation Data](https://ultrarag.openbmb.cn/pages/en/develop_guide/dataset): Download the most commonly used public evaluation datasets in the RAG field and large-scale retrieval corpora, directly for research benchmark testing. - [Case Analysis](https://ultrarag.openbmb.cn/pages/en/develop_guide/case_study): Provides a visual Case Study interface to deeply track each intermediate output of the workflow, assisting in analysis and error attribution. - [Structured Debugging Guide](./docs/debug_rag_workflows_zh.md) (Chinese): When answers look suspicious, retrieval hits are unstable, the reasoning chain drifts, or post-deployment behavior is abnormal, troubleshoot across four layers — input & retrieval, reasoning & planning, state & context, and deployment & runtime. - [Code Integration](https://ultrarag.openbmb.cn/pages/en/develop_guide/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](https://ultrarag.openbmb.cn/pages/en/ui/start): Learn how to start UltraRAG UI and familiarize yourself with various advanced configurations in administrator mode. - [Deployment Guide](https://ultrarag.openbmb.cn/pages/en/ui/prepare): Detailed production environment deployment tutorials, covering the setup of Retriever, Generation models (LLM), and Milvus vector database. - [Deep Research](https://ultrarag.openbmb.cn/pages/en/demo/deepresearch): Flagship case, deploy a Deep Research Pipeline. Combined with the AgentCPM-Report model, it can automatically perform multi-step retrieval and integration to generate tens of thousands of words of survey reports. ## Contributing Thanks to the following contributors for their code submissions and testing. We also welcome new members to join us in collect