Langflow vs Kedro
Side-by-side comparison to help you choose the best tool.
Langflow
freemiumLangflow is an open-source, low-code visual builder for creating AI agents and RAG pipelines built on top of LangChain. Its drag-and-drop canvas lets developers and AI teams compose LangChain components visually - connecting LLMs, vector stores, tools, and memory - without writing boilerplate code. Langflow is popular for rapidly prototyping complex AI pipelines that can then be deployed as APIs.
Kedro
freeKedro is an open-source Python system for creating reproducible, maintainable, and modular data science code with pipeline orchestration. Developed by McKinsey QuantumBlack and donated to the Linux Foundation, it brings software engineering best practices like modularity and testing to data science projects. Kedro provides a standardised project structure, a data catalogue, and pipeline visualisation.
| Feature | Langflow | Kedro |
|---|---|---|
| Pricing | freemium | free |
| Category | - | - |
| Rating | 4.4 | 4.2 |
| Best For | AI engineers who want to prototype LangChain-powered agents and RAG pipelines visually without writing glue code | Data science teams who want to apply software engineering best practices to their projects |
| Views | 4 | 5 |
Pros
- Makes LangChain accessible without writing boilerplate
- Fast prototyping of complex AI pipelines
- Active open-source community
Cons
- Still maturing — some components can be buggy
- Production deployments may need additional engineering
Pros
- Excellent code organisation and modularity
- Strong software engineering principles
- Good documentation
Cons
- Learning curve for data scientists unfamiliar with software engineering
- Less real-time monitoring than alternatives
- Visual LangChain pipeline builder
- Drag-and-drop component composition
- RAG pipeline design
- One-click API deployment
- Open-source & self-hostable
- Modular pipeline nodes
- Data catalogue abstraction
- Project templating
- Pipeline visualisation
- Plugin ecosystem