dify vs agno
Side-by-side comparison of two AI agent tools
difyfree
Production-ready platform for agentic workflow development.
agnoopen-source
Build, run, manage agentic software at scale.
Metrics
| dify | agno | |
|---|---|---|
| Stars | 135.1k | 39.1k |
| Star velocity /mo | 3.1k | 562.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8149565873457701 | 0.768704835232136 |
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
- +Production-ready runtime with built-in scalability, session isolation, and native tracing capabilities
- +Comprehensive monitoring and management through AgentOS UI for testing, debugging, and production oversight
- +Simple development experience - build sophisticated agents with memory and tools in approximately 20 lines of Python code
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
- -Python-focused platform with limited examples for other programming languages
- -Requires multiple dependencies and proper configuration of API keys and database connections
- -May have a learning curve for implementing complex multi-agent workflows and team coordination
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
- •Building production AI agents with persistent state, memory, and custom tool integrations for customer service or automation
- •Creating multi-agent teams and workflows for complex business processes that require coordination between specialized agents
- •Enterprise deployment of AI agents with comprehensive monitoring, user session management, and production-grade reliability requirements