Plasmic

Visually build pages and content on any tech stack. Empower the whole team to ship incredibly fast

Integrate the Plasmic API with the Python API

Setup the Plasmic API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Plasmic 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 Plasmic

The Plasmic API enables you to tap into the powerful visual design features of Plasmic within Pipedream workflows. With it, you can automate the fetching, updating, and publishing of Plasmic projects and their components. This opens up possibilities for dynamic content management, design collaboration automation, and streamlined deployment processes. You can use the Plasmic API to integrate with various services, trigger updates across platforms, or synchronize design changes in real-time.

Connect Plasmic

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    plasmic: {
      type: "app",
      app: "plasmic",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://codegen.plasmic.app/api/v1/loader/html/preview/${this.plasmic.$auth.project_id}/{your_component_name}`,
      headers: {
        "x-plasmic-api-project-tokens": `${this.plasmic.$auth.project_id}:${this.plasmic.$auth.project_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

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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}}