Integrate the Leadfeeder API with the Python API

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

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Popular Ways to Connect Leadfeeder with Python#

Run Python Code with Python API on New Lead Created from Leadfeeder API

Leadfeeder + Python

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Run Python Code with Python API on New Recurring Lead from Leadfeeder API

Leadfeeder + Python

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Popular Leadfeeder and Python Triggers#

New Lead Created from the Leadfeeder API

Emit new event when a new lead is created. See the docs

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New Recurring Lead from the Leadfeeder API

Emit new event when a lead is recurred. See the docs

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Popular Leadfeeder and Python Actions#

Find Lead with the Leadfeeder API

Retrieves a specific lead. See the docs.

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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 Leadfeeder#

Leadfeeder is a tool that reveals the companies visiting your website and how they interact with your content. By harnessing the power of the Leadfeeder API on Pipedream, you can automate the process of capturing lead data and integrating it directly into your sales and marketing workflows. This can range from enriching CRM records to triggering custom email campaigns based on visitor behavior, enabling a more dynamic and responsive engagement with potential customers.

Connect Leadfeeder#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    leadfeeder: {
      type: "app",
      app: "leadfeeder",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.leadfeeder.com/accounts`,
      headers: {
        "Authorization": `Token ${this.leadfeeder.$auth.api_token}`,
      },
    })
  },
})

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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