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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 an error toast: "No entry points defined. Drag a Start node from the palette and connect it to the node you want to start execution from." The executable set includes each triggered node, its downstream data flow, any upstream nodes that feed data into that set, and internal roots in any reached preset or subgraph container. :::

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​

Record node outputs is on by default (Settings → Recording & Inspection), so the run you just made already captured every node's output. 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.