RAG y conocimiento
llmware
Unified framework for enterprise RAG pipelines with small, specialized models - local, private and secure.
★ 14,8k estrellasApache-2.0
Ver la guía →UltraRAG es un proyecto de RAG y conocimiento de código abierto de OpenBMB (OpenBMB/UltraRAG en GitHub). Tiene 5.717 estrellas en GitHub y 450 bifurcaciones, se publica bajo la licencia Apache-2.0 y se actualizó por última vez en GitHub el 7 de octubre de 2026.
A low-code MCP framework for building complex, innovative RAG pipelines — visibly, not as a black box.
Map the retrieval and generation steps you need — corpus processing, retriever, reranking, generation, evaluation.
Clone the repo and sync dependencies with uv, choosing core, full or per-module extras.
Point the pipeline at your embedding and LLM providers (for example OpenAI, Qwen or local vLLM) and prepare your corpus.
Execute a bundled example experiment YAML, then open the UI to inspect each step's output.
Compose retrieval, generation and evaluation steps in a few dozen lines of YAML instead of hand-writing orchestration code.
Built as a low-code MCP framework, so pipeline components interact through the Model Context Protocol.
Designed against black-box development: pipeline logic and intermediate results stay inspectable, including in the web UI.
Install only the modules you need with extras — retriever, generation, or the full stack with corpus processing and evaluation.
Ships multimodal support and example pipelines including a lightweight DeepResearch setup.
Ready Docker images (base CPU, base GPU and full GPU) are published on Docker Hub for a fast start.
Clone UltraRAG (shallow) and enter the directory.
git clone https://github.com/OpenBMB/UltraRAG.git --depth 1
cd UltraRAGCreate the environment with uv; add --all-extras for the full feature set.
uv syncExecute the hello-world experiment to verify the installation end to end.
ultrarag run examples/experiments/sayhello.yamlUltraRAG is maintained by OpenBMB, the team behind MiniCPM and related open models.
Yes — it is open source under the Apache-2.0 license.
It depends on your pipeline: hosted models (OpenAI and similar) need your key, while local models served with vLLM do not.
Those are general application frameworks; UltraRAG specializes in declarative, MCP-based RAG pipelines where every reasoning step remains visible and swappable.
Clone the repo, run uv sync, and execute the sayhello example pipeline.
Unified framework for enterprise RAG pipelines with small, specialized models - local, private and secure.
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Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.