Langflow vs LLMStack
Side-by-side comparison of two AI agent tools
Short answer
- LLMStack has had no commit in 22 months; Langflow is actively maintained (842 commits in the last 90 days).
- Langflow is growing faster: +1,446 GitHub stars in the last 30 days vs +2 for LLMStack.
- Pick Langflow for: langflow is a powerful tool for building and deploying AI-powered agents and workflows. Pick LLMStack for: no-code multi-agent framework to build LLM Agents, workflows and applications with your data.
From GitHub data refreshed daily.
Langflowopen-source
Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
LLMStackfree
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
Metrics
| Langflow | LLMStack | |
|---|---|---|
| Stars | 155.5k | 2.3k |
| Star velocity /mo | 1.4k | 1.5789473684210529 |
| Commits (90d) | 842 | 0 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 40.1K | — |
| Overall score | 0.8648447396988407 | 0.16212211142476857 |
Pros
- +可视化拖拽界面让非技术用户也能快速构建AI工作流
- +支持多种部署方式包括API、MCP服务器和桌面应用，集成灵活性极高
- +内置对所有主流LLM和向量数据库的支持，生态系统完整
- +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
- -需要Python 3.10-3.13环境，对非Python用户有技术门槛
- -复杂的企业级功能可能对简单用例过于繁重
- -学习曲线较陡，充分利用所有功能需要时间投入
- -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
- •构建多代理协作系统处理复杂业务流程和决策
- •将AI工作流部署为API服务供其他应用程序调用
- •快速原型制作和可视化测试AI工作流的效果和逻辑
- •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, Langflow or LLMStack?
- Langflow has more GitHub stars (155,471 vs 2,308).
- Which is more actively developed, Langflow or LLMStack?
- Langflow had more commits in the last 90 days (842 vs 0).
- Should I use Langflow or LLMStack?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.