Integrate the Chatfuel (Dashboard API) API with the Python API

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

Popular Chatfuel (Dashboard API) 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.

Try it

Overview of Chatfuel (Dashboard API)#

Chatfuel’s Dashboard API opens a realm of possibilities for automating and streamlining chatbot interactions and management. With this API, you can programmatically update content, retrieve analytics, manage users, and automate messaging. This empowers you to dynamically adjust chat flows based on user behavior or external triggers, analyze user interactions for insights, and personalize the chat experience at scale.

Connect Chatfuel (Dashboard API)#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    chatfuel_dashboard_api_: {
      type: "app",
      app: "chatfuel_dashboard_api_",
    }
  },
  async run({steps, $}) {
    const data = {
      "title": `YOUR_BOT_TITLE`,
    }
    return await axios($, {
      method: "post",
      url: `https://dashboard.chatfuel.com/api/bots`,
      headers: {
        Authorization: `Bearer ${this.chatfuel_dashboard_api_.$auth.api_token}`,
      },
      data,
    })
  },
})

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}}