Flowise vs LobeHub
| Tagline | Drag-and-drop UI to build LLM-powered flows, chatbots, and AI agents visually | Modern AI chat framework with multi-provider support and MCP marketplace |
| Category | AI & LLM Tools | AI & LLM Tools |
| Replaces | ChatGPT, OpenAI API | ChatGPT, OpenAI API |
| GitHub stars | 35k | 79k |
| Language | TypeScript | Nodejs |
| License | Apache-2.0 | ⊘ Proprietary |
| Self-host difficulty | 2/5 Easy | 3/5 Moderate |
| Deploy options | Docker Docker Compose Manual | Docker Docker Compose Manual |
| Managed hosting | ||
| Last updated | 1 month ago | 5 days ago |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Flowise
- Visual canvas can become unmanageable for complex production pipelines
- No built-in fine-tuning or model training support
- Enterprise auth (SSO, RBAC) requires paid managed plan
LobeHub
- Core codebase is proprietary; community can contribute but cannot freely fork for commercial use
- Multi-user/team account management is limited in the self-hosted version compared to the cloud offering
- RAG and knowledge-base features are less mature than dedicated tools like AnythingLLM or Onyx
- Persistent conversation sync across devices requires the cloud service or custom backend setup
Bottom line
Choose Flowise if you want the lower-effort setup; choose LobeHub for the larger community and ecosystem. LobeHub has seen more recent development. Open each guide below for deploy steps and the full feature gap.
Flowise
Drag-and-drop UI to build LLM-powered flows, chatbots, and AI agents visually