About ragflow
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
RAGFlow is a pioneering open-source Retrieval-Augmented Generation (RAG) engine that elevates AI applications by seamlessly integrating advanced RAG methodologies with dynamic Agent capabilities. This fusion creates a robust context layer for Large Language Models (LLMs), enabling more accurate, context-aware, and intelligent responses. Unlike basic RAG systems, RAGFlow enhances document understanding through sophisticated parsing and deep research features, supporting tools like DeepSeek-R1 and GraphRAG. Its open-source nature, hosted on GitHub and built with Python, ensures full transparency, customization, and community-driven innovation. By combining retrieval-augmented generation with agentic workflows, RAGFlow delivers superior performance in AI search and context engineering, making it an invaluable, free solution for developers and researchers aiming to build next-generation AI applications with enhanced reliability and depth.
Common Use Cases
- Enhancing customer support chatbots with accurate, context-rich answers from internal knowledge bases.
- Powering academic research tools for deep document analysis and literature review summarization.
- Building intelligent enterprise search engines that retrieve and synthesize information from vast document repositories.
- Developing AI assistants for legal or medical professionals to quickly parse and understand complex documents.
- Creating content generation platforms that produce well-researched articles by leveraging retrieved external sources.
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Key Features
- Python
- Open Source
- GitHub Hosted
How to Get Started
Usage Statistics
Active Users
76,996
API Calls
8,649,000
Additional Information
Category
Generative AI
Pricing
Free
Last Updated
4/3/2026
