Visual Editor
Build data pipelines by dragging nodes onto a canvas and connecting them — no code required. Each node is one operation (read a file, filter rows, join two tables), and you can inspect the data after every step.
Try it in your browser first
Flowfile Lite runs this same canvas entirely in your browser at demo.flowfile.org — a lightweight subset with no backend, databases, scheduler, or AI.
Three concepts
- Nodes — operations, grouped into six palette categories: Input, Transform, Combine, Aggregate, Output, and Machine Learning.
- Connections — drag between node handles to define how data flows, left to right.
- Execution modes — Development materializes every node so you can preview all intermediate data; Performance executes only what outputs need, with query optimization across nodes.
If you haven't built a flow yet, the Quickstart walks through a complete one in five steps.
In this section
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Canvas mechanics: create, connect, configure, run, save.
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The Excel-like expression language used in Formula and Filter nodes.
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Every node, per category, with configuration tables.
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Run arbitrary Python in Docker-isolated kernels as a node.
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Missing a node you need? Build your own — visually, no Python file to write by hand — and it joins the palette with its own settings form.
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Complete flows with data, expected results, and downloads.
Settings covers theme and user management.
Visual or Python?
Both build the same flow graph, so this is a preference, not a commitment: any visual flow exports to Python, and any Python pipeline opens on the canvas. The canvas shines for exploring unfamiliar data and for handing work to colleagues who don't code; the Python API fits automation, version control, and logic that outgrows node settings.