LLMFlows vs ragflow
Side-by-side comparison of two AI agent tools
Short answer
- LLMFlows has had no commit in 36 months; ragflow is actively maintained (2,665 commits in the last 90 days).
- ragflow is growing faster: +2,412 GitHub stars in the last 30 days vs +0 for LLMFlows.
- Pick LLMFlows for: lLMFlows - Simple, Explicit and Transparent LLM Apps. Pick ragflow for: open-source RAG engine combining knowledge retrieval and agent capabilities for LLMs.
From GitHub data refreshed daily.
LLMFlowsopen-source
LLMFlows - Simple, Explicit and Transparent LLM Apps
ragflowopen-source
Open-source RAG engine combining knowledge retrieval and agent capabilities for LLMs
Metrics
| LLMFlows | ragflow | |
|---|---|---|
| Stars | 708 | 91.6k |
| Star velocity /mo | 0.15873015873015872 | 2.4k |
| Commits (90d) | 0 | 2.7k |
| Releases (6m) | 0 | 10 |
| Overall score | 0.1431426946004791 | 0.9150811116917444 |
Pros
- +Complete transparency with no hidden prompts or LLM calls, making debugging and monitoring straightforward
- +Minimalistic design with clear abstractions that don't compromise on flexibility or capabilities
- +Explicit API design that promotes clean, readable code and easy maintenance of complex LLM workflows
- +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
- -Relatively small community with 707 GitHub stars, which may limit community support and resources
- -Minimalistic approach might require more manual setup compared to more feature-rich frameworks
- -Limited built-in integrations compared to larger LLM frameworks, requiring more custom implementation
- -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
- •Building transparent chatbots where every LLM interaction needs to be traceable and debuggable
- •Creating question-answering systems that combine multiple LLMs with vector stores for document retrieval
- •Developing AI agents with complex multi-step workflows that require explicit control over each LLM call
- •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
FAQ
- Which is more popular, LLMFlows or ragflow?
- ragflow has more GitHub stars (91,600 vs 708).
- Which is more actively developed, LLMFlows or ragflow?
- ragflow had more commits in the last 90 days (2,665 vs 0).
- Should I use LLMFlows 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.