RAG и базы знаний
llmware
Unified framework for enterprise RAG pipelines with small, specialized models - local, private and secure.
★ 14,8 тыс. звёздApache-2.0
Смотреть руководство →UltraRAG — проект с открытым исходным кодом из категории «RAG и базы знаний» от OpenBMB (OpenBMB/UltraRAG на GitHub). У него 5 717 звёзд на GitHub и 450 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 7 октября 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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Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.