Dify vs LangChain
Side-by-side comparison of two AI agent tools
Short answer
- Dify is growing faster: +3,652 GitHub stars in the last 30 days vs +142 for LangChain.
- Pick Dify for: production-ready platform for agentic workflow development. Pick LangChain for: the agent engineering platform.
From GitHub data refreshed daily.
Difyfree
Production-ready platform for agentic workflow development.
LangChainopen-source
The agent engineering platform
Metrics
| Dify | LangChain | |
|---|---|---|
| Stars | 157.7k | 18.2k |
| Star velocity /mo | 3.7k | 142.06349206349208 |
| Commits (90d) | 2.3k | 182 |
| Releases (6m) | 9 | 10 |
| Overall score | 0.8905087884899539 | 0.7045885680298942 |
Pros
- +生产级稳定性和企业级功能支持，适合大规模部署应用
- +可视化工作流编辑器，大幅降低 AI 应用开发门槛
- +活跃的开源社区和丰富的生态系统，持续更新迭代
- +模型互操作性强，支持轻松切换不同LLM模型，适应技术发展变化
- +集成生态丰富，提供大量模型提供商、工具和向量存储的现成集成
- +生产就绪特性完备，内置监控、评估和调试支持，便于部署可靠的应用
Cons
- -学习曲线存在，需要时间熟悉平台的各种组件和配置
- -复杂工作流的性能优化需要深入了解平台机制
- -自部署版本需要一定的运维能力和资源投入
- -框架抽象层可能引入额外的性能开销和复杂性
- -依赖众多外部服务和集成，可能存在版本兼容性问题
- -对于简单LLM调用场景可能过于复杂，学习曲线较陡峭
Use Cases
- •企业客服机器人和智能助手的快速开发与部署
- •复杂业务流程的自动化处理，如文档分析、数据处理等
- •知识库问答系统和内容生成应用的构建
- •构建需要实时数据增强的RAG应用，连接多种数据源和外部系统
- •快速原型开发LLM应用，测试不同模型和工作流而无需重构
- •开发复杂的代理系统和可控制的AI工作流程，支持多步骤推理
FAQ
- Which is more popular, Dify or LangChain?
- Dify has more GitHub stars (157,730 vs 18,245).
- Which is more actively developed, Dify or LangChain?
- Dify had more commits in the last 90 days (2,338 vs 182).
- Should I use Dify or LangChain?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.