Reviewed comparison
Flowise vs Langflow
Flowise and Langflow both use visual graphs to build agents and RAG applications, with different implementation ecosystems, licensing boundaries, and deployment workflows.
Decision summary
Flowise or Langflow?
Choose Flowise when its JavaScript ecosystem, Agentflows, and hosted or enterprise options fit the team. Choose Langflow when Python extensibility and publishing flows as APIs or MCP servers are central.
Choose Flowise when
- You prefer a Node.js-based platform and broad packaged integrations
- Managed cloud or enterprise options are part of the roadmap
- Chatflow and Agentflow abstractions match the application
Choose Langflow when
- Your team develops primarily in Python
- Custom Python components need to stay close to the visual workflow
- Publishing flows as APIs and MCP tools is a primary requirement
Visual builders improve iteration but can hide dependency, secret, and version complexity. Establish export, testing, review, and rollback practices before treating either graph as production application code.
Side-by-side comparison
Differences are highlighted. Verify version-specific requirements in the official documentation.
| Criterion | Flowise FlowiseAI | Langflow Langflow |
|---|---|---|
| Product and deployment | ||
| Primary role | Agents and Automation | Agents and Automation |
| Deployment | Desktop / localSelf-hostedManaged cloud | Desktop / localSelf-hosted |
| Open source | Yes | Yes |
| License | Apache-2.0 community; commercial enterprise components | MIT |
| Pricing model | Open-source community edition; paid cloud and enterprise | Free and open source |
| Setup difficulty | Medium | Medium |
| Audience | PersonalTeamEnterprise | PersonalTeamEnterprise |
| Compatibility | ||
| Platforms | macOSWindowsLinuxDocker | macOSWindowsLinuxDocker |
| Accelerators | Uses connected model runtime | Uses connected model runtime |
| Install methods | Node.js packageDockerManaged cloud | Desktop applicationPython packageDocker |
| Model formats | Uses connected model runtime | Uses connected model runtime |
| Integrations | OllamaOpenAI-compatible APIsVector databasesMCP servers | Model providersVector databasesMCP clientsPython components |
| Capabilities | ||
| Web interface | Yes | Yes |
| API | Yes | Yes |
| Model management | No | No |
| Multi-user | Yes | Yes |
| Multi-GPU | No | No |
| Multi-node | No | No |
| RAG | Yes | Yes |
| Agents | Yes | Yes |
| Image generation | No | No |
| Tool calling | Yes | Yes |
Flowise is best for
- Visual agent and RAG prototyping
- Publishing workflows as APIs
- Teams needing many ready-made integrations
Important limitations
- Complex flows can be hard to version and test
- Enterprise code has separate licensing
Langflow is best for
- Visual agent workflows with Python extensibility
- Turning flows into APIs and MCP tools
- Interactive workflow debugging
Important limitations
- Production governance needs additional process
- Workflow portability depends on components and secrets