Flowise vs LLMStack
Side-by-side comparison of two AI agent tools
Short answer
- LLMStack has had no commit in 22 months; Flowise is actively maintained (24 commits in the last 90 days).
- Flowise is growing faster: +690 GitHub stars in the last 30 days vs +2 for LLMStack.
- Pick Flowise for: build AI Agents, Visually. Pick LLMStack for: no-code multi-agent framework to build LLM Agents, workflows and applications with your data.
From GitHub data refreshed daily.
Flowisefree
Build AI Agents, Visually
LLMStackfree
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
Metrics
| Flowise | LLMStack | |
|---|---|---|
| Stars | 55.5k | 2.3k |
| Star velocity /mo | 690.4761904761905 | 1.746031746031746 |
| Commits (90d) | 24 | 0 |
| Releases (6m) | 3 | 0 |
| Overall score | 0.593432940642942 | 0.17396362626861472 |
Pros
- +可视化拖拽界面，降低AI智能体开发门槛，无需编程背景即可使用
- +支持多种部署选项，包括本地安装、Docker容器和云端服务，适应不同使用场景
- +活跃的开源社区支持，GitHub上51k+星标显示了强大的用户基础和持续维护
- +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Node.js 18.15.0+运行环境，对系统环境有一定技术要求
- -复杂的多模块架构可能对简单用例造成过度工程化
- -文档和功能细节有限，可能需要额外学习成本
- -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
- •企业级AI客服机器人快速搭建，通过可视化流程设计对话逻辑
- •数据分析工作流自动化，连接多个AI模型进行复合分析任务
- •教育培训场景中的AI助手原型开发，用于概念验证和演示
- •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, Flowise or LLMStack?
- Flowise has more GitHub stars (55,494 vs 2,309).
- Which is more actively developed, Flowise or LLMStack?
- Flowise had more commits in the last 90 days (24 vs 0).
- Should I use Flowise or LLMStack?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.