UltraRAG — Overview
UltraRAG is a lightweight framework for building Retrieval-Augmented Generation (RAG) systems, designed around the Model Context Protocol (MCP) architecture. It decouples core RAG components into atomic MCP Servers (e.g., retriever, generation, corpus management, evaluation) and uses an MCP Client to orchestrate these servers into complex pipelines. The project targets researchers and developers who want to rapidly prototype, evaluate, debug, and deploy RAG applications with minimal code.
Key Design Principles
- Modularization: core functions are split into independent MCP Servers so each capability can be developed, tested, and deployed separately.
- Low-code orchestration: pipelines and complex control flow (sequences, conditionals, loops) are defined in YAML, enabling precise orchestration without substantial custom code.
- Visual IDE and debugging: a Pipeline Builder provides bi-directional sync between canvas-based visual construction and code editing, plus tools to inspect intermediate outputs for error attribution.
- Reproducible research: built-in unified evaluation workflows and benchmark integrations make experimentation, metric management, and baseline comparisons easier.
Major Features
- Inference orchestration supporting sequential flows, conditional branches, and loops via YAML configuration.
- Atomic MCP Servers: retriever, generator (LLM connector), corpus ingestion, evaluation, and other tools are pluggable and reusable.
- UltraRAG UI: visual pipeline builder, online parameter/prompt tuning, integrated assistant for design and prompt generation, and one-click conversion of pipelines into an interactive web conversational demo.
- Knowledge base management: tools for building and querying custom corpora and vector stores, and connectors for common vector DBs and embedding/generation models.
- Unified evaluation: pre-built evaluation workflows and dataset integrations to run standard RAG benchmarks and compare results in a consistent way.
Installation & Deployment
UltraRAG offers two main installation methods:
- Source installation (recommended): uses the uv environment manager to create reproducible Python environments and manage extras (retriever, generation, etc.).
- Docker deployment: prebuilt images and instructions to run the UI and services in containers (GPU/CPU images provided).
After installation, example commands let you validate the setup and try simple pipelines (e.g., ultrarag run examples/sayhello.yaml).
Typical Usage Scenarios
- Research experiments: rapid setup of RAG experiments, integrated evaluation, and visualization to debug intermediate reasoning steps.
- Prototype & demos: quickly build interactive conversational demos from pipeline logic and deploy them as web UIs.
- Industrial prototyping: modular servers allow swapping retrievers, vector stores, and LLM backends for production-like testing and deployment.
- Deep research pipelines: multi-step retrieval and report-generation workflows (e.g., DeepResearch) that combine retrieval, multi-hop reasoning, and LLM integration.
Extensibility & Community
UltraRAG encourages contributions via the usual GitHub workflow (fork → issues → PRs). It integrates with community resources (model hubs, dataset pages, and connectors) and provides channels for discussion (Discord, WeChat, Feishu). The project is developed and maintained by OpenBMB in collaboration with academic and open-source partners.
Where it fits
UltraRAG is positioned between low-level libraries and full-stack applications: it provides a structured, modular foundation for orchestration and evaluation while minimizing boilerplate through low-code YAML orchestration and a visual IDE. It is especially useful when you need transparent, debuggable RAG pipelines and a fast path from algorithm to interactive demo.
Links & Resources
- Repository: https://github.com/OpenBMB/UltraRAG
- Official docs / site: https://ultrarag.openbmb.cn/
- Example datasets and benchmarks are linked from the documentation and repository.
