Integrate the Labs64 NetLicensing API with the Python API

Setup the Labs64 NetLicensing API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Labs64 NetLicensing and Python remarkably fast. Free for developers.

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Popular Labs64 NetLicensing 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 Labs64 NetLicensing#

The Labs64 NetLicensing API is a sophisticated software licensing service that enables you to manage product licenses and configurations seamlessly. Using Pipedream, you can tap into this power to automate license creation, validation, and tracking. This enables you to integrate licensing operations into your sales, deployment, and customer support workflows, ensuring consistent and automated management of software licenses across customer lifecycles.

Connect Labs64 NetLicensing#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    labs64_netlicensing: {
      type: "app",
      app: "labs64_netlicensing",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://go.netlicensing.io/core/v2/rest/product`,
      headers: {
        "Accept": `application/json`,
      },
      auth: {
        username: `apiKey`,
        password: `${this.labs64_netlicensing.$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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