Flowise vs harbor
Side-by-side comparison of two AI agent tools
Short answer
- Flowise is growing faster: +686 GitHub stars in the last 30 days vs +110 for harbor.
- Pick Flowise for: build AI Agents, Visually. Pick harbor for: one command brings a complete pre-wired LLM stack with hundreds of services to explore.
From GitHub data refreshed daily.
Flowisefree
Build AI Agents, Visually
harboropen-source
One command brings a complete pre-wired LLM stack with hundreds of services to explore.
Metrics
| Flowise | harbor | |
|---|---|---|
| Stars | 55.5k | 3.2k |
| Star velocity /mo | 685.578947368421 | 109.73684210526316 |
| Commits (90d) | 24 | 369 |
| Releases (6m) | 3 | 10 |
| Downloads (30d, npm + PyPI) | — | 176 |
| Overall score | 0.5711753668616123 | 0.6841431587002718 |
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
- +一键部署完整LLM技术栈，极大简化环境搭建
- +提供数百个预配置服务，覆盖AI开发全流程
- +支持多语言环境（NPM和PyPI），适配不同开发栈
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
- -文档信息有限，具体功能和配置选项不够清晰
- -可能存在资源占用较大的问题（数百个服务）
- -对Docker环境有依赖，需要一定的容器化基础
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
- •AI研究人员快速搭建实验环境进行模型测试
- •开发团队建立统一的LLM开发和测试环境
- •教育场景中为学生提供完整的AI开发实践平台
FAQ
- Which is more popular, Flowise or harbor?
- Flowise has more GitHub stars (55,486 vs 3,237).
- Which is more actively developed, Flowise or harbor?
- harbor had more commits in the last 90 days (369 vs 24).
- Should I use Flowise or harbor?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.