OpenBMB/UltraRAG

Health v2 · maintenance only

Adoption not part of the score

5706 stars · 448 forks observed · 2026-09-24

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

UltraRAG is a low-code Python framework from OpenBMB for building complex RAG pipelines using MCP servers and YAML-based orchestration with serial, loop, and conditional control flow. It includes a visual UI for knowledge base management, workflow building, and system demos, plus support for multimodal inputs and evaluation.

Use cases

• build a RAG pipeline over my own documents
• orchestrate retrieval and generation workflows with low-code YAML
• create a visual demo of a RAG system with a knowledge base UI
• run a local DeepResearch-style agent pipeline
• evaluate and iterate on RAG experiments
• build multimodal RAG with vision-language models

When to choose

• you want to prototype or productionize RAG pipelines without writing lots of glue code
• you need MCP-based tool integration and pipeline control flow (loops, branches)
• you want a bundled UI for demos and knowledge base management
• you work in the OpenBMB/LLM ecosystem with vLLM, Qwen, or DeepSeek models

When to avoid

• you need a simple drop-in vector database or embedding library rather than a full pipeline framework
• you require a non-Python stack or managed cloud RAG service
• your use case is a single fixed retrieval flow where a few lines of code suffice

Facets

framework · maturity active

rag agent-framework mcp web-framework llm-inference data-science large-language-models machine-learning developer-tools python self-hosted cross-platform low-code mcp-framework pipeline-orchestration yaml-config multimodal knowledge-base deepresearch retrieval-pipeline retrieval-augmented-generation ai-agents docker web-server

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markdown · JSON · MCP: product_card(name="OpenBMB/UltraRAG")