Running Python in workflows


Pipedream supports Python v3.12 in workflows. Run any Python code, use any PyPI package, connect to APIs, and more.

Adding a Python code step

  1. Click the + icon to add a new step
  2. Click Custom Code
  3. In the new step, select the python language runtime in language dropdown

Python Code Step Structure

A new Python Code step will have the following structure:

def handler(pd: "pipedream"):
  # Reference data from previous steps
  # Return data for use in future steps
  return {"foo": {"test": True}}

You can also perform more complex operations, including leveraging your connected accounts to make authenticated API requests, accessing Data Stores and installing PyPI packages.

Logging and debugging

You can use print at any time in a Python code step to log information as the script is running.

The output for the print logs will appear in the Results section just beneath the code editor.

Python print log output in the results

Using third party packages

You can use any packages from PyPI in your Pipedream workflows. This includes popular choices such as:

To use a PyPI package, just include it in your step's code:

import requests

And that's it. No need to update a requirements.txt or specify elsewhere in your workflow of which packages you need. Pipedream will automatically install the dependency for you.

If your package's import name differs from its PyPI package name

Pipedream's package installation uses the pipreqs package to detect package imports and install the associated package for you. Some packages, like python-telegram-bot, use an import name that differs from their PyPI name:

pip install python-telegram-bot


import telegram

Use the built in magic comment system to resolve these mismatches:

# pipedream add-package python-telegram-bot
import telegram

Pinning package versions

Each time you deploy a workflow with Python code, Pipedream downloads the PyPi packages you import in your step. By default, Pipedream deploys the latest version of the PyPi package each time you deploy a change.

There are many cases where you may want to specify the version of the packages you're using. If you'd like to use a specific version of a package in a workflow, you can add that version in a magic comment, for example:

# pipedream add-package pandas==2.0.0
import pandas

Currently, you cannot use different versions of the same package in different steps in a workflow.

Making an HTTP request

We recommend using the popular requests HTTP client package available in Python to send HTTP requests.

No need to run pip install, just import requests at the top of your step's code and it's available for your code to use.

See the Making HTTP Requests with Python docs for more information.

Returning HTTP responses

You can return HTTP responses from HTTP-triggered workflows using the pd.respond() method:

def handler(pd: "pipedream"):
    "status": 200,
    "body": {
      "message": "Everything is ok"

Please note to always include at least the body and status keys in your pd.respond argument. The body must also be a JSON serializable object or dictionary.

Unlike the Node.js equivalent, the Python pd.respond helper does not yet support responding with Streams.


Don't forget to configure your workflow's HTTP trigger to allow a custom response. Otherwise your workflow will return the default response.

Sharing data between steps

A step can accept data from other steps in the same workflow, or pass data downstream to others.

Using data from another step

In Python steps, data from the initial workflow trigger and other steps are available in the pd.steps object.

In this example, we'll pretend this data is coming into our workflow's HTTP trigger via POST request.

// POST <our-workflows-endpoint>
  "id": 1,
  "name": "Bulbasaur",
  "type": "plant"

In our Python step, we can access this data in the pd.steps object passed into the handler. Specifically, this data from the POST request into our workflow is available in the trigger dictionary item.

def handler(pd: "pipedream"):
  # retrieve the data from the HTTP request in the initial workflow trigger
  pokemon_name = pd.steps["trigger"]["event"]["name"]
  pokemon_type = pd.steps["trigger"]["event"]["type"]
  print(f"{pokemon_name} is a {pokemon_type} type Pokemon")

Sending data downstream to other steps

To share data created, retrieved, transformed or manipulated by a step to others downstream, return the data in the handler function:

# This step is named "code" in the workflow
import requests
def handler(pd: "pipedream"):
  r = requests.get("")
  # Store the JSON contents into a variable called "pokemon"
  pokemon = r.json()
  # Expose the data to other steps in the "pokemon" key from this step
  return {
    "pokemon": pokemon

Now this pokemon data is accessible to downstream steps within pd.steps["code"]["pokemon"]

You can only export JSON-serializable data from steps. Things like:

  • strings
  • numbers
  • lists
  • dictionaries

Using environment variables

You can leverage any environment variables defined in your Pipedream account in a Python step. This is useful for keeping your secrets out of code as well as keeping them flexible to swap API keys without having to update each step individually.

To access them, use the os module.

import os
def handler(pd: "pipedream"):
  token = os.environ["AIRTABLE_API_KEY"]

Or an even more useful example, using the stored environment variable to make an authenticated API request.

Using API key authentication

If an particular service requires you to use an API key, you can pass it via the headers of the request.

This proves your identity to the service so you can interact with it:

import requests
import os
def handler(pd: "pipedream"):
  token = os.environ["AIRTABLE_API_KEY"]
  url = ""
  headers = { "Authorization": f"Bearer {token}"}
  r = requests.get(url, headers=headers)

There are 2 different ways of using the os module to access your environment variables.

os.environ["ENV_NAME_HERE"] will raise an error that stops your workflow if that key doesn't exist in your Pipedream account.

Whereas os.environ.get("ENV_NAME_HERE") will not throw an error and instead returns an empty string.

If your code relies on the presence of a environment variable, consider using os.environ["ENV_NAME_HERE"] instead.

Handling errors

You may need to exit a workflow early. In a Python step, just a raise an error to halt a step's execution.

raise NameError("Something happened that should not. Exiting early.")

All exceptions from your Python code will appear in the logs area of the results.

Ending a workflow early

Sometimes you want to end your workflow early, or otherwise stop or cancel the execution of a workflow under certain conditions. For example:

  • You may want to end your workflow early if you don't receive all the fields you expect in the event data.
  • You only want to run your workflow for 5% of all events sent from your source.
  • You only want to run your workflow for users in the United States. If you receive a request from outside the U.S., you don't want the rest of the code in your workflow to run.
  • You may use the user_id contained in the event to look up information in an external API. If you can't find data in the API tied to that user, you don't want to proceed.

In any code step, calling return pd.flow.exit() will end the execution of the workflow immediately. No remaining code in that step, and no code or destination steps below, will run for the current event.


It's a good practice to use return pd.flow.exit() to immediately exit the workflow. In contrast, pd.flow.exit() on its own will end the workflow only after executing all remaining code in the step.

def handler(pd: "pipedream"):
  return pd.flow.exit("reason")
  print("This code will not run, since pd.flow.exit() was called above it")

You can pass any string as an argument to pd.flow.exit():

def handler(pd: "pipedream"):
  return pd.flow.exit("Exiting early. Goodbye.")
  print("This code will not run, since pd.flow.exit() was called above it")

Or exit the workflow early within a conditional:

import random
def handler(pd: "pipedream"):
  # Flip a coin, running pd.flow.exit() for 50% of events
  if random.randint(0, 100) <= 50:
    return pd.flow.exit("reason")
  print("This code will only run 50% of the time");

File storage

You can also store and read files with Python steps. This means you can upload photos, retrieve datasets, accept files from an HTTP request and more.

The /tmp directory is accessible from your workflow steps for saving and retrieving files.

You have full access to read and write both files in /tmp.

See the Working with the filesystem in Python docs for more information.