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A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines

MCP Servers5.7k stars435 forksPythonApache-2.0Updated 2d ago
Editor's note

UltraRAG is a Python-based MCP framework developed by Tsinghua University's THUNLP lab, Northeastern University, OpenBMB, and AI9Stars that lets developers build retrieval-augmented generation pipelines by composing independent MCP Servers for core components such as Retriever and Generator, then orchestrating them through an MCP Client using YAML configuration files. The framework supports sequential, loop, and conditional branch control structures, enabling iterative RAG logic with minimal code. It connects to LLMs including GPT, DeepSeek, Qwen, and vLLM-served models, and works with embedding models from Hugging Face sentence-transformers. A built-in Flask-based UI functions as a visual RAG IDE with a Pipeline Builder that synchronizes canvas construction and code editing in real time, plus a knowledge base management module for document question-answering. A standout feature is the AgentCPM-Report integration, an 8B on-device writing agent for localized deep research. Researchers prototyping novel RAG architectures and engineers building production retrieval systems both benefit from the low-code workflow and transparent reasoning visibility introduced in version 3.0.

ClaudeWave Trust Score
100/100
Verified
Passed
  • Open-source license (Apache-2.0)
  • Actively maintained (<30d)
  • Healthy fork ratio
  • Clear description
  • Topics declared
  • Mature repo (>1y old)
Last scanned: 6/11/2026
Install in Claude Code / Claude Desktop
Method: UVX (Python) · ultrarag
Claude Code CLI
claude mcp add ultrarag -- uvx ultrarag
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "ultrarag": {
      "command": "uvx",
      "args": ["ultrarag"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Package name inferred from the repository name. Verify it exists on PyPI, or clone https://github.com/OpenBMB/UltraRAG and follow its README.
Use cases

MCP Servers overview

README preview not available. Visit the repo on GitHub for full documentation.
deepseekdemoeasyembeddingflaskgpthuggingface-transformersllmmcpmultimodalopenaiqwenragsentence-transformersuivllmvlm

What people ask about UltraRAG

What is OpenBMB/UltraRAG?

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OpenBMB/UltraRAG is mcp servers for the Claude AI ecosystem. A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines It has 5.7k GitHub stars and was last updated 2d ago.

How do I install UltraRAG?

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You can install UltraRAG by cloning the repository (https://github.com/OpenBMB/UltraRAG) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is OpenBMB/UltraRAG safe to use?

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Our security agent has analyzed OpenBMB/UltraRAG and assigned a Trust Score of 100/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.

Who maintains OpenBMB/UltraRAG?

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OpenBMB/UltraRAG is maintained by OpenBMB. The last recorded GitHub activity is from 2d ago, with 28 open issues.

Are there alternatives to UltraRAG?

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Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.

Deploy UltraRAG to your cloud

Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.

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