Chaindesk vs Flowise
Side-by-side comparison of two AI agent tools
Short answer
- Chaindesk has had no commit in 27 months; Flowise is actively maintained (24 commits in the last 90 days).
- Flowise is growing faster: +686 GitHub stars in the last 30 days vs +4 for Chaindesk.
- Pick Chaindesk for: the no-code platform for building custom LLM Agents. Pick Flowise for: build AI Agents, Visually.
From GitHub data refreshed daily.
Chaindeskfree
The no-code platform for building custom LLM Agents
Flowisefree
Build AI Agents, Visually
Metrics
| Chaindesk | Flowise | |
|---|---|---|
| Stars | 3.0k | 55.5k |
| Star velocity /mo | 3.9473684210526314 | 685.578947368421 |
| Commits (90d) | 0 | 24 |
| Releases (6m) | 0 | 3 |
| Overall score | 0.1751288334186123 | 0.5711753668616123 |
Pros
- +No-code approach potentially makes LLM agent creation accessible to non-developers
- +Moderate GitHub community interest with 2940 stars
- +Focuses specifically on custom LLM agents rather than general AI tools
- +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
Cons
- -Extremely limited documentation makes evaluation difficult
- -Unclear what specific features or capabilities are actually provided
- -Cannot assess reliability, performance, or production readiness from available information
- -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
Use Cases
- •Building chatbots or conversational agents without coding
- •Creating custom AI assistants for specific business needs
- •Prototyping LLM-powered applications through visual interfaces
- •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
FAQ
- Which is more popular, Chaindesk or Flowise?
- Flowise has more GitHub stars (55,486 vs 2,965).
- Which is more actively developed, Chaindesk or Flowise?
- Flowise had more commits in the last 90 days (24 vs 0).
- Should I use Chaindesk or Flowise?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.