Integrate the Streamlabs API with the Python API

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

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Popular Streamlabs and Python Actions#

Create Donation with the Streamlabs API

Create a donation for the authenticated user. See the documentation

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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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Send Alert with the Streamlabs API

Sends an alert to the stream overlay with a custom message, image, and sound. See the documentation

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Send Test Alert with the Streamlabs API

Send a test alert to the stream overlay in StreamLabs. See the documentation

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Overview of Streamlabs#

The Streamlabs API opens doors to automating and enhancing live streaming experiences. By tapping into Streamlabs' functionalities, you can automate alerts, manage donations, and interact with your audience in real time. Augment your streaming workflow by integrating with other services to cut down on manual processes, respond to events as they happen, and personalize the interaction with your viewers.

Connect Streamlabs#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    streamlabs: {
      type: "app",
      app: "streamlabs",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://streamlabs.com/api/v1.0/user`,
      params: {
        access_token: `${this.streamlabs.$auth.oauth_access_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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