Integrate the PDFMonkey API with the Go API

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

Popular PDFMonkey and Go Actions#

Run Go Code with the Go API

Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.

Try it

Overview of PDFMonkey#

The PDFMonkey API on Pipedream allows you to automate the creation of PDF documents from dynamic data sources. You can generate invoices, reports, tickets, or any customized document based on templates you define. With Pipedream’s serverless platform, you can trigger PDF generation from a multitude of events, such as form submissions, scheduled times, or changes in a database, and then perform actions like sending these PDFs via email, storing them in cloud storage, or updating records in a CRM.

Connect PDFMonkey#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    pdfmonkey: {
      type: "app",
      app: "pdfmonkey",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.pdfmonkey.io/api/v1/current_user`,
      headers: {
        Authorization: `Bearer ${this.pdfmonkey.$auth.api_key}`,
      },
    })
  },
})

Overview of Go#

You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.

Connect Go#

package main

import (
	"fmt"

	pd "github.com/PipedreamHQ/pipedream-go"
)

func main() {
	// Access previous step data using pd.Steps
	fmt.Println(pd.Steps)

	// Export data using pd.Export
	data := make(map[string]interface{})
	data["name"] = "Luke"
	pd.Export("data", data)
}