Code step

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When to use it

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How it works

def run(ctx):
    contact = ctx["Lookup contact"]["body"]
    return {
        "display_name": f"{contact['first_name']} {contact['last_name']}",
        "is_premium": contact.get("tier") in ("gold", "platinum"),
    }

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Inspector

• Configuration — Draft your Python code directly in the provided editor. For an enhanced experience, click the purple Open IDE button to access a full-screen coding environment.
  • Here, you can write and edit your Python logic with greater visibility.
  • Specify any external Python packages your code requires—these will be installed into the sandboxed environment automatically.
  
  • Here you can write your pythion code
  • Enter packages that needs to be installed in the sandbox
• Output — After saving your code in the editor, this tab displays the structure of the data returned by your run function. You can reference these output fields as variables in downstream steps using the workflow context.
  

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Accessing context

def run(ctx):
    # Access trigger/input data
    email = ctx["input"]["customer_email"]
    
    # Access upstream step output
    contact = ctx["Lookup contact"]["body"]
    status = ctx["Lookup contact"]["status_code"]
    
    return {"email": email, "name": contact["first_name"]}

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Examples

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Reshape an HTTP response

def run(ctx):
    contact = ctx["Lookup contact"]["body"]
    return {
        "display_name": f"{contact['first_name']} {contact['last_name']}",
        "tags": [t["name"] for t in contact.get("tags", [])],
        "is_premium": contact.get("tier") in ("gold", "platinum"),
    }

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Call a third-party SDK

import stripe

def run(ctx):
    stripe.api_key = ctx["input"]["stripe_key"]
    customer = stripe.Customer.retrieve(ctx["input"]["customer_id"])
    return {
        "email": customer.email,
        "balance": customer.balance,
    }

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Limits

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Considerations

• Don’t cache state between runs. Each run gets a fresh sandbox.
• Let exceptions propagate. Caught-and-swallowed exceptions hide bugs. If you handle an error, return a structured {"failed": True, "reason": "..."} and branch on it with a Condition step.
• Avoid heavy packages on the critical path. pandas and numpy add cold-start latency. For simple data shaping, write it without dependencies.