kotaemon vs ragflow

Side-by-side comparison of two AI agent tools

Short answer

  • ragflow is growing faster: +2,402 GitHub stars in the last 30 days vs +90 for kotaemon.
  • Pick kotaemon for: an open-source RAG-based tool for chatting with your documents. Pick ragflow for: open-source RAG engine combining knowledge retrieval and agent capabilities for LLMs.

From GitHub data refreshed daily.

k
kotaemonopen-source

An open-source RAG-based tool for chatting with your documents.

ragflowopen-source

Open-source RAG engine combining knowledge retrieval and agent capabilities for LLMs

Metrics

kotaemonragflow
Stars25.8k91.6k
Star velocity /mo902.4k
Commits (90d)02.7k
Releases (6m)110
Overall score0.31781794146163940.9098521001650974

Pros

    • +结合了先进的RAG技术和Agent能力，提供比传统RAG更强大的功能
    • +开源且拥有活跃社区支持，GitHub星数超过7.6万，可信度高
    • +提供云服务和Docker容器化部署，支持多种部署方式

    Cons

      • -作为相对复杂的RAG系统，可能需要一定的技术背景才能充分配置和优化
      • -大规模部署可能需要相当的计算资源和存储空间

      Use Cases

        • •企业知识库问答系统，基于内部文档为员工提供智能查询服务
        • •智能客服系统，结合产品文档和FAQ提供准确的客户支持
        • •研究助手应用，帮助研究人员从大量学术文献中检索相关信息
        View kotaemon DetailsView ragflow Details

        FAQ

        Which is more popular, kotaemon or ragflow?
        ragflow has more GitHub stars (91,619 vs 25,800).
        Which is more actively developed, kotaemon or ragflow?
        ragflow had more commits in the last 90 days (2,666 vs 0).
        Should I use kotaemon or ragflow?
        Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.