ragflow (infiniflow/ragflow) is an open-source AI project on GitHub. Repository summary: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs Its focus includes retrieval-augmented generation. It is suitable for extension, integration, and iterative delivery in real workflows.
License
Apache-2.0
Stars
81,413
Homepage
https://ragflow.io/Features
- Core capability: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
- Supports vector retrieval and retrieval-augmented reasoning
- Repository: infiniflow/ragflow
- Primary language: Python
- Open-source license: Apache-2.0
- GitHub traction: about 81,409 stars
Use Cases
- Builds enterprise knowledge Q&A and document retrieval systems
- Build internal AI workflow prototypes with ragflow
- Validate ragflow in production-like engineering scenarios
- Building enterprise process automation
- Cross-system collaborative task execution
- Integrating operations data pipelines
FAQ
Teams should first define integration boundaries and call patterns, then map repository capabilities into concrete interfaces, parameters, and access rules. GitHub repository: https://github.com/infiniflow/ragflow. Community traction is around 81,409 stars. License: Apache-2.0.
It usually works as an execution component or capability layer, with common deployment fits such as: Builds enterprise knowledge Q&A and document retrieval systems, Build internal AI workflow prototypes with ragflow, Validate ragflow in production-like engineering scenarios.
