ragflow

RAGFlow addresses the challenge of enhancing the performance of large language models (LLMs) by integrating Retrieval-Augmented Generation (RAG) with advanced agent capabilities. This open source tool for engineering teams is particularly beneficial for ML and AI teams looking to create a robust context layer that improves information retrieval and response accuracy. With a growth rate of 33.5% over 298 days, it demonstrates strong community interest and adoption, indicating a solid maturity level for production use. However, it may not be the right choice for teams requiring a simpler solution or those working with less complex data retrieval needs.

RAGFlow is a strong choice when teams need a powerful, production-ready solution that leverages RAG techniques for enhanced context in AI applications. Consider alternatives if your project does not require advanced agent capabilities or if you need a more straightforward implementation.

This tool is ideal for data scientists, machine learning engineers, and AI researchers who are focused on improving LLM performance. It is commonly integrated into AI-driven applications, chatbots, and knowledge management systems.

Latest commit: 2026-09-04. Over the past 286 days, this repository gained 22.6k stars (+33.5% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.