Python

utility.python Utility v0.1.0

Holds a Python script (imported from n8n). Inert until a Python runtime is enabled — convert to JavaScript to run it now.

Finding it in the library

Search the builder's node library for Python (it lives under Utility). A single click opens the in-editor docs panel shown here — description, ports, and every property, without leaving the canvas. Double-click (or drag) to add it to the workflow.

Library capture pending — regenerate with npm run shots:nodes.

Wired up in the builder

Python in a real, runnable flow — captured live from the Studio editor, exactly as it looks on your canvas. This is the same workflow used for the example input & output below.

Canvas capture pending — regenerate with npm run shots:nodes.

How it’s configured

The node’s Configure panel as it opens in the builder when you select the step — every setting laid out with real values. Click any field to edit it.

Config capture pending — regenerate with npm run shots:nodes. See the full property reference below.

Ports

Ports are the node’s contract with its neighbours. In the editor a port label renders bold when wired and italic when optional; ◈ ports accept attachment carriers rather than data wires.

Direction	Port	Label	What flows through it
Input	`input`	Input
Output	`output`	Output

How data flows through it

Python consumes the content of the incoming envelope — when it is fed directly by a trigger, the trigger’s wrapper is unwrapped at the node boundary so the node sees the actual data, not the metadata shell. Its output becomes the payload for the next node, while the envelope (trace ids, correlation, binary refs) rides along untouched. In the Runs view you always see the whole envelope for both sides of this node.

Expressions in the config

None of this node’s properties are string-typed, so {{ }} expressions don’t apply here — JSON- and code-typed fields are always taken literally.

Build it with AI

Every node in this reference is reachable through Flowdrome’s AI Copilot and the MCP tools — say what you want, and the graph surgery happens server-side. Node types resolve fuzzily, so the catalog label (Python) works as well as the exact type id (utility.python).

In the Copilot panel (or any connected AI):

add a python node after the trigger

As a step in a create_chain_workflow call:

{"type":"Python","config":{}}

Raw MCP call — add this node to a workflow with add_node

curl -s -X POST http://localhost:48170/mcp -H "content-type: application/json" -d '{ "jsonrpc": "2.0", "id": "1", "method": "tools/call", "params": { "name": "add_node", "arguments": { "workflowId": "<id>", "type": "Python" } } }'

Example input & output

Captured from a real test run of the workflow above — this is what you see in the run data panel after pressing Test workflow. This particular run fails by design (see the note below).

Input — what the node received

{
  "kind": "json",
  "contentType": "application/json",
  "payload": {
    "a": 1
  },
  "body": {
    "a": 1
  }
}

Output — what the node produced

This step raised: this node holds Python code, but the engine has no Python runtime yet — convert it to a JavaScript node to run it now.

Property reference

Every setting, with its type and default — the same fields shown configured in the panel above.

Property	Type	Default	Description
Code `code`	`code`	`"return input"`	Python imported from n8n. The engine has no Python runtime yet — this node is inert (running it errors). Convert to a JavaScript node to run the logic now.
Output ports `outputs`	`json`	`["output"]`	List of output port names the script can emit to.

Related nodes

The rest of the Utility group — the same folder you’d scan in the editor’s library.

This page is generated from the node registry by gen-node-docs.mjs on every site build — ports, properties, defaults and visibility rules cannot drift from the code. The screenshots and example data are captured from a live Flowdrome by npm run shots:nodes and npm run gen:examples.