Apple Ads Library Scraper avatar

Apple Ads Library Scraper

Under maintenance
Try for free

No credit card required

Go to Store
This Actor is under maintenance.

This Actor may be unreliable while under maintenance. Would you like to try a similar Actor instead?

See alternative Actors
Apple Ads Library Scraper

Apple Ads Library Scraper

sameh.jarour/apple-ads-library-scraper
Try for free

No credit card required

The Apple Ads Library Scraper is a key tool for Mobile Growth Managers and User Acquisition Managers to monitor key competitors' insights as they plan to launch their Apple Search ads on the Apple store.

You can access the Apple Ads Library Scraper programmatically from your own Python applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {}
9
10# Run the Actor and wait for it to finish
11run = client.actor("sameh.jarour/apple-ads-library-scraper").call(run_input=run_input)
12
13# Fetch and print Actor results from the run's dataset (if there are any)
14print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
15for item in client.dataset(run["defaultDatasetId"]).iterate_items():
16    print(item)
17
18# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Apple Ads Library Scraper API in Python

The Apify API client for Python is the official library that allows you to use Apple Ads Library Scraper API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

pip install apify-client

Other API clients include:

Developer
Maintained by Community

Actor Metrics

  • 1 monthly user

  • 0 No bookmarks yet

  • >99% runs succeeded

  • Created in Jan 2025

  • Modified a month ago