App Store Reviews Scraper

Extract Apple App Store reviews for any iOS app, across any of Apple's 175 country storefronts — filtered by star rating and keyword, returned as clean structured JSON. No API key, no login, no scraping infrastructure to babysit.

Point it at an App Store URL or a numeric app ID, pick your storefronts, and get back real reviews with rating, title, body, author, app version, date, and helpful-votes — plus the app's overall rating and total ratings count on every row. The reliability wedge: Apple's public review feed silently returns empty results depending on the requesting client and CDN node — the single most common complaint about other App Store review scrapers. This Actor detects empty-but-valid responses and retries through rotating sessions until it gets real data, so you get reviews for storefronts where other scrapers hand you an empty file.

You give it	You get back
App Store URLs or numeric app IDs (one or many)	Real user reviews as structured JSON rows
Country storefronts (us, gb, de, jp, …)	Separate review streams per app per storefront
A star-rating range (e.g. 1–3 for complaints only)	Only reviews inside that range
A keyword (crash, subscription, refund)	Only reviews mentioning that word or phrase
Nothing else — no Apple account, no key	rating, title, text, author, appVersion, date, votedHelpful + app overall rating & ratings count

What does the App Store Reviews Scraper do?

It pulls the most recent customer reviews for any iOS app directly from Apple's public customerreviews RSS feed and turns them into a flat, analysis-ready dataset. For each app you provide, it walks every storefront you list, fetches up to ~500 of the most recent reviews per app per storefront (Apple's own feed limit), applies your rating and keyword filters, and pushes each surviving review to the dataset as a clean JSON row. It also does an itunes.apple.com/lookup call per app per storefront so every review row carries the app's name, developer, overall rating, and total ratings count — meaning a single run is enough for a full report, with no joins on your side.

Mix apps and storefronts freely: 3 apps × 5 countries produces 15 independent review streams in one run. The whole thing runs headless on Apify — you never touch a browser, a proxy list, or Apple's feed quirks.

Who is it for?

Audience	What they pull
ASO specialists	Star-rating drivers and recurring complaint themes per storefront to feed keyword and metadata decisions
Product managers	Version-tagged reviews to see exactly what a release changed in user sentiment
Indie iOS devs	Their own app's fresh 1–2★ reviews across every market they ship to, ready to triage
Market researchers	Cross-competitor review corpora to size demand and surface unmet needs before a build
AI / data teams	Stable, HTML-free JSON rows to feed LLM sentiment clustering and theme-extraction pipelines

Why use this scraper?

• Reliable across all storefronts. Empty-feed detection plus session rotation means you get data for storefronts (notably the US feed) where naive scrapers return an empty result. This is the reason to pick this Actor over the free alternatives.
• Star-rating filter, built in. Set the range to 1–3 stars and collect only complaints — an instant competitor pain-point dataset. Set it to 5 to study what fans love.
• Keyword filter, applied server-side. Keep only reviews mentioning subscription, crash, ads, or refund. You don't pay to download and store noise you'll throw away.
• No API key, no login, no cookies. It reads Apple's public feeds. There is nothing to authenticate.
• Clean JSON for pipelines. Flat schema, stable field names, no HTML — drop it straight into an LLM, a warehouse, or a notebook.
• Built by an indie iOS dev who actually uses it. This is the exact data source behind real ASO and product-research decisions on shipping apps, not a generic scraper someone spun up and forgot.

What data can you extract?

Every review is one dataset item with this shape:

{
    "appId": "6448311069",
    "appName": "ChatGPT",
    "developer": "OpenAI OpCo, LLC",
    "country": "us",
    "reviewId": "12008765432",
    "rating": 1,
    "title": "Latest update broke sync",
    "text": "After the update my chats no longer sync between devices…",
    "author": "jsmith42",
    "appVersion": "1.2026.188",
    "date": "2026-07-20T18:45:39-07:00",
    "votedHelpful": 3,
    "appOverallRating": 4.74,
    "appTotalRatings": 1424044
}

Field	Type	Description
appId	string	Numeric App Store ID the review belongs to
appName	string	App title, from Apple's lookup
developer	string	Developer / publisher name
country	string	ISO storefront code the review came from
reviewId	string	Apple's unique review identifier (deduplication key)
rating	number	Star rating, 1–5
title	string	Review headline
text	string	Full review body
author	string	Reviewer's display nickname
appVersion	string	App version the review was written against
date	string	Review timestamp (ISO 8601)
votedHelpful	number	Net "helpful" vote sum on the review
appOverallRating	number	App's current average rating in that storefront
appTotalRatings	number	App's total number of ratings in that storefront

How to scrape App Store reviews

1. Open the Actor and paste one or more App Store URLs (like https://apps.apple.com/us/app/chatgpt/id6448311069) or plain numeric IDs (6448311069) into Apps.
2. List the storefronts you care about in Country storefronts — us, gb, de, jp, and so on. Leave it as us for a quick test.
3. Optionally set a rating range and a keyword to narrow the dataset.
4. Set Max reviews per app per country to control volume and cost.
5. Click Start. When the run finishes, export the dataset as JSON, CSV, Excel, or pull it via the Apify API.

No proxies to configure and no Apple credentials — the Actor handles fetching, retries, and feed quirks for you.

How much does it cost to scrape App Store reviews

You are billed per review collected, so cost scales with volume, not with time. The Max reviews per app per country cap is your direct cost control — lower it for cheap monitoring runs, raise it (up to Apple's ~500 ceiling) for deep research. Filters help too: because the rating and keyword filters run before data is stored, reviews that don't pass your filters don't add to the count. A daily monitoring run over a handful of your own apps costs a fraction of a broad multi-competitor research pull. Check the Actor's pricing panel on Apify for the current per-item rate.

How to monitor App Store reviews for your app

Set up a Scheduled run in Apify: add your own app IDs, list every storefront you ship to, set maxRating to ★★★ 3 so you only capture reviews that need attention, and schedule it daily. Each run returns the latest critical reviews per market with the app version attached, so you can spot a bad release immediately. Because reviewId is stable, you can deduplicate against previous runs and pipe only genuinely new 1–2★ reviews into Slack, email, or a webhook using Apify integrations — a lightweight review-alerting system without standing up any backend.

How to export App Store reviews to CSV or Google Sheets

Every run writes to an Apify dataset, which you can export in one click as CSV, JSON, Excel, HTML, or RSS from the run's Storage tab. For Google Sheets, either export CSV and import it, or connect the Apify Google Sheets integration to push each run's reviews into a spreadsheet automatically. Programmatic pulls use the Apify API / SDK — GET the dataset items in the format you want and drop them into your warehouse or notebook. The flat, HTML-free schema means no cleanup step before analysis.

App Store Reviews API alternative

Apple does not offer a public API for reading arbitrary apps' customer reviews — the App Store Connect API only exposes reviews for apps you own. This Actor is the practical alternative: it reads Apple's public review feed for any app, returns a stable JSON schema you can code against, and is callable programmatically through the Apify API, the Apify client SDKs (JS/Python), or an MCP server for AI agents. You get API-shaped, versionable output for competitor and market data that Apple's own API will never give you.

Is it legal to scrape App Store reviews?

App Store reviews are public data that anyone can read without logging in, and this Actor only reads Apple's publicly served feeds — it performs no login, uses no private API, and collects no personal account data beyond the public reviewer nickname Apple already displays. That said, how you use the data is on you: respect Apple's Terms of Service, honor GDPR/CCPA and other applicable privacy law when handling any personal data, and consult your own legal counsel for your specific use case. This is general information, not legal advice.

Input configuration

Field	Key	Type	Default	Description
Apps	appIds	array (required)	—	App Store URLs or plain numeric app IDs. Mix freely; one dataset covers all of them.
Country storefronts	countries	array	["us"]	ISO storefront codes to scrape (us, gb, de, jp, br, fr, …). Each is scraped separately.
Max reviews per app per country	maxReviewsPerApp	integer	100	Cap per app per storefront, 1–500. You're billed per review collected, so this is your cost control.
Min rating	minRating	string (1–5)	1	Keep reviews rated at least this many stars.
Max rating	maxRating	string (1–5)	5	Keep reviews rated at most this many stars. Set to 3 for critical-only competitor research.
Keyword filter	keyword	string (nullable)	—	Keep only reviews mentioning this word or phrase in the title or body (case-insensitive). Leave empty for everything.

FAQ

How many reviews can I get per app per country? Apple's public feed exposes up to ~500 of the most recent reviews per app per storefront, sorted by recency. That's the hard ceiling from Apple — historic archives beyond that are not available from Apple at all. To go wider, add more storefronts.

Do the rating and keyword filters affect billing? Yes, in your favor. Filters are applied before data is stored, so you're only billed for reviews that pass your filters and land in the dataset — not for everything Apple returned.

Do I need an Apple account or API key? No. No login, no cookies, no API key, no quota. The Actor reads public feeds only.

Which countries are supported? All 175 Apple App Store storefronts. Just pass the ISO country codes you want — us, gb, de, jp, fr, br, in, kr, and so on. Unknown or unavailable storefronts for a given app are skipped with a warning rather than failing the run.

Can I scrape several apps at once? Yes. Add multiple URLs or IDs, and multiple storefronts — every app × storefront combination becomes its own review stream in the same dataset.

What if a storefront returns an empty feed? That's exactly the failure mode this Actor is built to beat: it detects empty-but-valid responses and retries through rotating sessions before giving up, so you get real data where naive scrapers return nothing.

Integrations

Connect the Actor to the tools you already use through Apify integrations:

• App Store reviews → Google Sheets — auto-append each run's reviews to a spreadsheet for ASO tracking and team review.
• App Store reviews → Slack / Discord — get pinged the moment a new 1-star review lands for your app.
• App Store reviews → Make / Zapier — route reviews into a CRM, a support queue, or an email digest.
• App Store reviews → BigQuery / Snowflake — pipe reviews into your warehouse via Airbyte for sentiment analysis at scale.
• Scheduled runs & webhooks — run daily to track rating drivers over time, or POST results the moment a run completes.

Use with AI agents via MCP

This Actor is callable from AI assistants through Apify's Model Context Protocol (MCP) server. That lets an LLM agent (Claude, or any MCP-capable assistant) run the scraper as a tool — "pull the latest 1-star reviews for these three apps in the US and Germany and cluster the complaints" — and receive the structured JSON rows back inline, with no glue code. Point your MCP client at Apify's MCP server, expose this Actor, and the agent can trigger runs and read the resulting dataset directly. The stable, HTML-free schema is designed for exactly this kind of agent consumption.

Related scrapers

Part of the datascoutlab data toolkit — combine them for cross-platform review intelligence and market research:

• Google Play Reviews Scraper — the Android counterpart, with deep review history and developer replies. Pair it with this Actor for full cross-platform coverage of any app.
• YouTube Outlier Finder — surface breakout videos on small, fast-growing channels before a topic goes mainstream.
• Google Trends Scraper — reliable interest-over-time data for demand sizing and trend validation.
• Telegram Channel Scraper — messages, view counts, and keyword search across public Telegram channels.

Disclaimer

This Actor is not affiliated with, endorsed by, or sponsored by Apple Inc. Apple, App Store, and related marks are trademarks of Apple Inc. The Actor collects only publicly available review data from Apple's public feeds. You are responsible for using the collected data in compliance with Apple's Terms of Service, applicable data-protection law (including GDPR and CCPA), and any other regulations that apply to your use case. This is not legal advice.

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