The best return experience, for your customers and for your team.
Emit new event when a label is updated. See the documentation
Emit new event when a new return is created. See the documentation
Emit new event when the status of a return has been updated. See the documentation
Cancels a pending return request in Loop. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Flags a particular return as important inside Loop. Requires return ID as a mandatory prop. See the documentation
Starts the processing of a return inside Loop. Return ID is a required prop to initiate the process. See the documentation
The Loop Returns API enables merchants to automate and streamline their returns and exchanges process. It offers endpoints that allow you to initiate returns, update return states, and manage return-related data, all programmable to fit into your existing e-commerce and customer service workflows. With Pipedream, you can trigger workflows based on events in Loop, or use actions to call the Loop API directly, automating tasks like syncing return data with customer service platforms, updating inventory management systems, or even issuing refunds.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
loop_returns: {
type: "app",
app: "loop_returns",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.loopreturns.com/api/v1/allowlists`,
headers: {
"accept": `application/json`,
"X-Authorization": `${this.loop_returns.$auth.api_key}`,
},
})
},
})
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:
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}}