App Store Reviews Scraper

Scrape Apple App Store reviews for any iOS, iPadOS or macOS app in any country. Paste an App Store URL or app ID and get every review as clean JSON, CSV or Excel: star rating, title, text, app version, date and helpful votes. Works in all 175 storefronts. No login, no cookies, no proxies, no personal data.

What you get

Field	Example
rating	5
title	Finally works offline
text	The last update fixed sync on iPad…
version	578.1
date	2026-09-12T14:03:11-07:00
voteCount / voteSum	12 / 9
country	us
appName, developer, primaryGenre, averageRating, ratingCount, currentVersion	app metadata attached to every row
reviewUrl, reviewId, appId, sort, scrapedAt	for deduplication and audit

Reviewer names are deliberately not collected, so the output contains no personal data and can be stored and shared freely.

Input

{
  "apps": ["https://apps.apple.com/us/app/facebook/id284882215", "310633997"],
  "countries": ["us", "gb", "de"],
  "sort": "mostrecent",
  "maxReviewsPerApp": 1500,
  "includeAppMetadata": true
}

• apps – App Store URLs or numeric IDs. Mixed input is fine.
• countries – storefront codes (us, gb, de, fr, jp, br, in, …). Apple exposes up to 500 reviews per country per sort order, so add countries to get more.
• sort – mostrecent (newest 500) or mosthelpful (top 500 by helpful votes). Run both to widen coverage.
• maxReviewsPerApp – hard stop per app, summed across countries.
• includeAppMetadata – attach app name, developer, genre, average rating, rating count and current version to every row (free).

Pricing

Pay per review: $3 per 1,000 reviews. You are charged only for reviews actually saved to the dataset. A run that finds nothing costs nothing beyond Apify platform usage, which is tiny because the actor uses plain HTTP and about 256 MB of memory.

How to use the data

• Product research – read what users praise or hate about competitors before you build.
• Release monitoring – schedule a daily run with sort: mostrecent and alert on new 1-star reviews mentioning a bug.
• Sentiment and topic analysis – feed text into an LLM or your own classifier; version lets you compare releases.
• Localization QA – compare rating distributions across countries.
• AI agents – this actor works over MCP; ask your agent for "the latest 200 App Store reviews of app X in Germany".

Reliability

The actor reads Apple's public customer-reviews feed and the public lookup API. There is no login, no anti-bot layer and no proxy cost. Every request is retried with exponential backoff, pages are de-duplicated by review ID, and results are saved page by page so partial runs still return data.

Limits

• Apple caps the public feed at 500 reviews per country per sort order. Use more countries and both sort orders to widen coverage.
• Apps with no reviews in a given storefront return zero rows for that country, which is normal.

Integrations

Export to JSON, CSV, Excel or XML, push to Google Sheets, Slack, Zapier, Make or your own webhook from the Integrations tab, or call the actor via the Apify API and SDKs (Node.js, Python).

Legal

The actor collects only publicly available review content published by Apple and stores no reviewer identities. You are responsible for how you use the data.

Support

Open an issue on the actor's Issues tab. Bugs are usually fixed within a day.