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Your First Graph

This walkthrough builds a tiny pipeline that feeds an explicit tensor through a couple of operations — enough to learn the Start node execution model that every CodefyUI graph relies on.

1. Add an input​

Drag a TensorInput node (Data category) onto the canvas. Set its value_mode to explicit and fill the inline grid editor with the numbers you want the pipeline to see.

2. Wire up some operations​

Connect it through any chain of tensor-op nodes, for example:

TensorInput → Reshape → Softmax → Print

Drag from each output port to the next input port. The edges validate types as you connect.

3. Add a Start node​

:::warning Every graph needs a Start node Drag a Start node onto the canvas and connect its trigger output (the diamond handle on the right side) to the first node you want executed — typically the TensorInput.

Without a Start → first-node trigger edge, the graph is treated as a draft and Run rejects it with a "No start node defined" toast. Only nodes reachable from a Start are executed. :::

This trigger-based routing is what lets you keep scratch nodes on the canvas without running them, and it enables conditional branches (e.g. a Switch node) where only one path executes.

4. Run it​

Click Run. Watch per-node progress stream into the Execution Log, and the Print node's output appear there too. See Running Graphs for what happens during execution.

5. Inspect what flowed​

Open the Settings popover, switch Record outputs ON, and run again. Now click any node to open the Teaching Inspector and see the exact tensor — shape, dtype, min/max/mean, and values — at every step.

Next steps​

  • Load a real example instead of building from scratch — see the Examples Gallery (e.g. Train CNN on MNIST).
  • Browse every node you can drop on the canvas in the Node Reference.