Integrate the Action Builder API with the Python API

Setup the Action Builder API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Action Builder and Python remarkably fast. Free for developers.

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Popular Action Builder and Python Actions#

Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

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Overview of Action Builder#

The Action Builder API on Pipedream paves the way for crafting bespoke serverless workflows tailored to specific automation needs. Essentially, it's a playground for developers to create custom actions, integrate with a myriad of services, and automate tasks that were once manual and time-consuming. The API allows you to design, execute, and orchestrate actions that trigger on specific events or schedules, manipulate data with inbuilt functions, and connect disparate systems with ease, fostering a seamless data flow and enhanced productivity.

Connect Action Builder#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    action_builder: {
      type: "app",
      app: "action_builder",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://${this.action_builder.$auth.organization}.actionbuilder.org/api/rest/v1/campaigns`,
      headers: {
        "Content-Type": `application/json`,
        "OSDI-API-Token": `${this.action_builder.$auth.api_key}`,
      },
    })
  },
})

Overview of Python#

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Connect Python#

def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}

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