Beamer

Improve user engagement with an easy to use newsfeed and changelog for your website. Announce relevant news and updates with Beamer.

Integrate the Beamer API with the Python API

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

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 Beamer

The Beamer API enables you to automate client communication and project management tasks. With Beamer, you can create notifications, announcements, and alerts that keep your team and clients updated on project status, milestones, and deadlines. When integrated with Pipedream, Beamer unleashes the power of serverless workflows to connect with myriad apps, allowing for sophisticated automations that can streamline communications, synchronize updates across platforms, and trigger actions based on specific events or conditions.

Connect Beamer

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    beamer: {
      type: "app",
      app: "beamer",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      method: "post",
      url: `https://api.getbeamer.com/v0/ping`,
      headers: {
        "Beamer-Api-Key": `${this.beamer.$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

1
2
3
4
5
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}}