经 AI Skill Hub 精选评估，UltraRAG MCP工具 获评「强烈推荐」。已获得 5.5k 颗 GitHub Star，这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.2 分，适合有一定技术背景的用户使用。
UltraRAG MCP工具 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
UltraRAG MCP工具 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/OpenBMB/UltraRAG
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"ultrarag-mcp--": {
"command": "npx",
"args": ["-y", "ultrarag"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例： 用户: 请帮我用 UltraRAG MCP工具 执行以下任务... Claude: [自动调用 UltraRAG MCP工具 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入："列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"ultrarag_mcp__": {
"command": "npx",
"args": ["-y", "ultrarag"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="./docs/ultrarag_dark.svg"> <source media="(prefers-color-scheme: light)" srcset="./docs/ultrarag.svg"> <img alt="UltraRAG" src="./docs/ultrarag.svg" width="55%"> </picture> </p>
<p align="center"> <a href="https://trendshift.io/repositories/18747" target="_blank"><img src="https://trendshift.io/api/badge/repositories/18747" alt="OpenBMB%2FUltraRAG | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </p>
<p align="center"> <a href="https://ultrarag.github.io/"><img src="https://img.shields.io/badge/Homepage-6ABED8?style=for-the-badge&logoColor=white" alt="Homepage"/></a> <a href="https://ultrarag.openbmb.cn/pages/en/getting_started/introduction"><img src="https://img.shields.io/badge/Documentation-66B89E?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation"/></a> <a href="https://modelscope.cn/datasets/UltraRAG/UltraRAG_Benchmark"><img src="https://img.shields.io/badge/Dataset-DE8EA6?style=for-the-badge&logo=databricks&logoColor=white" alt="Dataset"/></a> <a href="https://github.com/OpenBMB/UltraRAG/tree/rag-paper-daily/rag-paper-daily"><img src="https://img.shields.io/badge/Paper_Daily-A48BC8?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper Daily"/></a> </p>
<p align="center"> <a href="./docs/README_zh.md"><b>简体中文</b></a> | <b>English</b> </p>
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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.
<p align="center"> <picture> <img alt="UltraRAG Architecture" src="./docs/architecture.png" width=90%> </picture> </p>
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🚀 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. </td> <td width="50%" valign="top"> ⚡ 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. </td> </tr> <tr> <td width="50%" valign="top"> 📊 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. </td> <td width="50%" valign="top"> 🎯 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. </td> </tr> </table> Core dependenciesuv pip install -e . 📦 InstallationWe provide two installation methods: local source code installation (recommended using Method 1: Source Code InstallationWe 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: ```shell Direct installationpip install uv Full installationuv pip install -e ".[all]" On-demand installationuv pip install -e ".[retriever]" ``` Method 2: Docker Container DeploymentIf you prefer not to configure a local Python environment, you can deploy using Docker. Get Code and Images ```shell Option A: Pull from Docker Hubdocker 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 locallydocker build -t ultrarag:v0.3.0 . Verify InstallationAfter installation, run the following example command to check if the environment is normal: If you see the following output, the installation is successful: 🚀 Quick StartWe 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. 🛠️ Demo SystemsDesigned 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 AgentCPM-Report model, it can automatically perform multi-step retrieval and integration to generate tens of thousands of words of survey reports.
🎯 aiskill88 AI 点评
A 级
2026-05-20
设计思路先进的MCP框架，低代码特性降低使用门槛。星数适中但增长快，社区活跃度高，适合RAG场景快速迭代。 📚 实用指南（长尾问题）
适合谁
最佳实践
常见错误
部署方案
⚡ 核心功能
👥 适合谁
⭐ 最佳实践
⚠️ 常见错误
👥 适合人群
🎯 使用场景
⚖️ 优点与不足
✅ 优点
⚠️ 不足
⚠️ 使用须知
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。 建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。 📄 License 说明
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。 🔗 相关工具推荐
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❓ 常见问题 FAQ
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💡 AI Skill Hub 点评
AI Skill Hub 点评：UltraRAG MCP工具 的核心功能完整，质量优秀。对于Claude Desktop / Claude Code 用户来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。 🌐 原始信息
🔗 原始来源
🐙 GitHub 仓库 https://github.com/OpenBMB/UltraRAG
🌐 官方网站 https://ultrarag.github.io/
收录时间：2026-05-18 · 更新时间：2026-05-19 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。 🤖 交给 Agent 安装 · UltraRAG MCP工具选择 Agent 类型，复制安装指令后粘贴到对应客户端 claude skill install https://github.com/OpenBMB/UltraRAG
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