Dify vs LLMStack
Side-by-side comparison of two AI agent tools
Short answer
- LLMStack has had no commit in 22 months; Dify is actively maintained (2,369 commits in the last 90 days).
- Dify is growing faster: +3,637 GitHub stars in the last 30 days vs +2 for LLMStack.
- Pick Dify for: production-ready platform for agentic workflow development. Pick LLMStack for: no-code multi-agent framework to build LLM Agents, workflows and applications with your data.
From GitHub data refreshed daily.
Difyfree
Production-ready platform for agentic workflow development.
LLMStackfree
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
Metrics
| Dify | LLMStack | |
|---|---|---|
| Stars | 157.8k | 2.3k |
| Star velocity /mo | 3.6k | 1.5789473684210529 |
| Commits (90d) | 2.4k | 0 |
| Releases (6m) | 9 | 0 |
| Overall score | 0.8805791472432994 | 0.16212211142476857 |
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
- +无代码可视化构建界面，非技术用户可以轻松创建复杂的AI工作流程和智能体
- +支持多种AI提供商和模型链接，可以根据不同需求组合使用最适合的模型
- +提供灵活的部署选项，既有云端托管服务，也支持本地和私有云部署
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
- -需要Docker环境支持后台作业，增加了技术部署复杂性
- -默认管理员凭据需要手动更改，存在潜在的安全风险
- -复杂工作流程的构建仍需要一定的AI和业务逻辑理解
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
- •构建连接企业内部数据的客户服务聊天机器人，自动回答常见问题并处理客户请求
- •创建跨部门的业务流程自动化，通过AI智能体处理文档分析、数据提取和决策支持
- •建立从Slack或Discord触发的内部AI助手，帮助团队进行项目管理和信息检索
FAQ
- Which is more popular, Dify or LLMStack?
- Dify has more GitHub stars (157,757 vs 2,308).
- Which is more actively developed, Dify or LLMStack?
- Dify had more commits in the last 90 days (2,369 vs 0).
- Should I use Dify or LLMStack?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.