Python Code Execution

Run custom Python scripts directly inside your workflow. Use it for data transformation, complex logic, reshaping payloads, or anything that can't be handled by a standard module.

1. Add the Module

Add the Python Code module to your workflow as an action node.

2. Pass Data In Context

In the module configuration panel, add context variables under the Context section. Each entry takes a Name and a Value, use the variable picker to map values from upstream nodes.

Each context entry becomes a key on WORKFLOW_CONTEXT inside your script:

def main(WORKFLOW_CONTEXT):
    amount = WORKFLOW_CONTEXT["amount"]

3. Write Your Script

Your script must define a main function. Stacksync automatically injects WORKFLOW_CONTEXT as the argument containing all your context values.

def main(WORKFLOW_CONTEXT):
    amount = WORKFLOW_CONTEXT["amount"]
    return {
        "amount": amount,
    }

4. Return Data

Whatever main returns becomes the node's output, available to downstream nodes via standard node referencing.

Reference in downstream nodes:

{{ module_id.amount }}

Debugging: print() output is captured and surfaced under metadata.stdout in the execution logs. Use it for debugging — it does not pass data downstream.

Available Libraries

Library	Use case
pandas	Data manipulation and transformation
numpy	Numerical operations
phonenumbers	Phone number parsing and formatting
json	JSON parsing
os	OS-level utilities
Python 3.10 standard library	All built-in modules

Limits

Limit	Value
Timeout	300s (5 min)
Memory	700 MB
Filesystem	Read-only, no writes
Runtime packages	Pre-installed only

Watch out: Large pandas DataFrames can hit the 700 MB memory limit. Process data in chunks where possible.