The Python function action lets you write custom Python scripts for data transformations, parsing, and payload enrichment within your workflows.

Python environment

The Python function action runs in a restricted execution environment with the following characteristics:

• Python version: 3.12.8
• Available libraries: In addition to the Python standard library, the following packages are available:
  • rsa (version 4.9)
  • python-dateutil (version 2.8.2)
• Network access: Restricted

Script structure

All Python scripts must define a main function that accepts a ctx parameter of type Context. For example:

from execution_context import Context

def main(*, ctx: Context):
  # Use ctx to access Trigger or Steps data
  workflow_name = ctx["WorkflowName"]
  return f"Running workflow {workflow_name!r}"

The ctx object provides access to all workflow context variables, similar to the $ variable in JavaScript expressions. Use dictionary-style access (for example, ctx["Steps"]["Step_name"]["variable"]) to reference values from previous steps.

Add a Python function action

In the workflow canvas:

1. Click + to add a workflow step.
2. Search for Python.
3. Select the Python action to add it to your workflow.

Write Python scripts with AI

You can use Bits AI to help write Python scripts inside a workflow step.

To write a script with Bits AI:

1. Add a Python step to your workflow.
2. In the Inputs section, click Write Code with AI.
3. Enter a custom prompt or select one of the sample prompts.
4. Optionally, click Test script to generate a preview of the workflow step.
5. To save the script, click Accept changes. To continue editing the script, click Reject changes.
6. Click the X to close the AI dialog.
7. Enter a Description.
8. Click Save.

Script examples

Parse and transform JSON data

This example parses a JSON string from a previous step and extracts specific fields.

from execution_context import Context
import json

def main(*, ctx: Context):
    # Get JSON string from previous step
    json_string = ctx["Steps"]["Get_data"]["output"]

    # Parse and transform
    data = json.loads(json_string)
    return {
        "user_ids": [user["id"] for user in data["users"]],
        "total_count": len(data["users"])
    }

Work with dates and timestamps

This example uses the python-dateutil library to perform date calculations.

from execution_context import Context
from dateutil import parser, relativedelta
from datetime import datetime

def main(*, ctx: Context):
    # Parse a date string
    start_date = parser.parse(ctx["Trigger"]["date_string"])

    # Calculate date 30 days in the future
    future_date = start_date + relativedelta.relativedelta(days=30)

    return {
        "start": start_date.isoformat(),
        "end": future_date.isoformat(),
        "days_difference": 30
    }

Cryptographic operations

This example uses the rsa library to encrypt a message.

from execution_context import Context
import rsa
import base64

def main(*, ctx: Context):
    # Get message from workflow context
    message = ctx["Steps"]["Compose_message"]["text"]

    # Generate RSA key pair
    (public_key, private_key) = rsa.newkeys(512)

    # Encrypt message
    encrypted = rsa.encrypt(message.encode(), public_key)

    return {
        "encrypted_message": base64.b64encode(encrypted).decode(),
        "public_key": public_key.save_pkcs1().decode()
    }