{"data":{"slug":"openbmb-ultrarag","name":"UltraRAG","tagline":"A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines","github_url":"https://github.com/OpenBMB/UltraRAG","owner":"OpenBMB","repo":"UltraRAG","owner_avatar_url":"https://avatars.githubusercontent.com/u/89920203?v=4","primary_language":"Python","stars":5670,"forks":437,"topics":["deepseek","demo","easy","embedding","flask","gpt","huggingface-transformers","llm","mcp","multimodal","openai","qwen","rag","sentence-transformers","ui","vllm","vlm"],"archived":false,"github_pushed_at":"2026-08-17T03:24:42+00:00","maintenance_label":"Very active","stars_delta_30d":18,"url":"https://www.graphcanon.com/tools/openbmb-ultrarag","markdown_url":"https://www.graphcanon.com/tools/openbmb-ultrarag.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/openbmb-ultrarag","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=openbmb-ultrarag","description":"A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines","homepage_url":"https://ultrarag.github.io/","license":"Apache-2.0","open_issues":18,"watchers":39,"ai_summary":"UltraRAG offers a framework to build complex retrieval-augmented generation pipelines with low-code requirements.","readme_excerpt":"## 📦 Installation\n\nWe provide two installation methods: local source code installation (recommended using `uv` for package management) and Docker container deployment.\n\n---\n\n### Method 1: Source Code Installation\n\nWe strongly recommend using [uv](https://github.com/astral-sh/uv) to manage Python environments and dependencies, as it can greatly improve installation speed.\n\n**Prepare Environment**\n\nIf you haven't installed uv yet, please execute:\n\n```shell\n\n---\n\n## Direct installation\npip install uv==0.12.0\n\n---\n\n# Full installation\nuv pip install -e \".[all]\"\n\n---\n\n# On-demand installation\nuv pip install -e \".[retriever]\"\n```\n\n---\n\n### Method 2: Docker Container Deployment\n\nIf you prefer not to configure a local Python environment, you can deploy using Docker.\n\n**Get Code and Images**\n\n```shell\n\n---\n\n# Option A: Pull from Docker Hub\ndocker pull hdxin2002/ultrarag:v0.3.0-base-cpu # Base version (CPU)\ndocker pull hdxin2002/ultrarag:v0.3.0-base-gpu # Base version (GPU)\ndocker pull hdxin2002/ultrarag:v0.3.0          # Full version (GPU)\n\n---\n\n### Verify Installation\n\nAfter installation, run the following example command to check if the environment is normal:\n\n```shell\nultrarag run examples/experiments/sayhello.yaml\n```\n\nIf you see the following output, the installation is successful:\n\n```\nHello, UltraRAG v3!\n```\n\n---\n\n## 🚀 Quick Start\n\nWe provide complete tutorial examples from beginner to advanced. Whether you are conducting academic research or building industrial applications, you can find guidance here. Welcome to visit the [Documentation](https://ultrarag.openbmb.cn/pages/en/getting_started/introduction) for more details.","github_created_at":"2025-01-16T10:56:02+00:00","created_at":"2026-07-07T17:37:53.269115+00:00","updated_at":"2026-08-18T18:01:25.403177+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"deepseek","name":"deepseek"},{"slug":"demo","name":"demo"},{"slug":"easy","name":"easy"},{"slug":"embedding","name":"embedding"},{"slug":"flask","name":"flask"},{"slug":"huggingface-transformers","name":"huggingface-transformers"},{"slug":"llm","name":"llm"},{"slug":"mcp","name":"mcp"}],"trust":{"provenance":{"is_fork":false,"github_id":917628875,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-18T18:01:24.536Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":1,"last_release_at":"2026-04-09T04:41:05Z","stars_delta_30d":18,"open_issues_delta_30d":-7},"security_summary":{"status":"findings","scanner":"mcp_manifest@v1","low_count":2,"high_count":0,"last_scan_at":"2026-07-11T11:10:35.795Z","medium_count":0,"scan_profile":"mcp_manifest","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-18T18:01:25.003Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-18T18:01:25.003Z","managed_saas":false},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-18T18:01:25.003Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-18T18:01:25.003Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-18T18:01:25.003Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-18T18:01:25.003Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["You require a straightforward setup with uv package manager or Docker support","Need to create complex RAG pipelines without extensive coding knowledge"],"when_not_to_use":["Prefer tools that do not rely on specific package managers like uv","Require more customization in pipeline creation beyond what low-code environments offer"],"source":"enrich:decision_facts","observed_at":"2026-07-14T18:56:56.255Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"UltraRAG is a low-code framework for building retrieval-augmented generation pipelines with Python."},{"label":"License detail","value":"Apache-2.0 license provides freedom with conditions for use, modification, and distribution."}]}}