App Review Scraper — Google Play & App Store Monitor

Scrape and monitor app reviews on Google Play and the App Store in one run: rating, text, author, app version, helpful votes and dates — in clean, schema-documented JSON. Optional monitor mode returns only reviews posted since your last run.

No LLM, no logins. App Store reviews come from the official Apple RSS feed (up to 500 per app, newest first); Google Play reviews are parsed from the live store page.

1 app = 1 watch. Schedule it with monitorMode: true and every run is a fresh-review feed for your app — or your competitors' apps.

AI agent quick start

Actor tool ID: dev-hoss/app-review-intelligence

• Send apps mixing stores freely: Google Play package names (com.twitter.android) and/or App Store ids (333903271).
• Read the type field: review (the data), app-summary (per-app coverage: count, avg rating, mode), error, info.
• Monitor mode: keep the same stateKey per watchlist; first run records a baseline (firstRunReportsAll: true seeds everything); subsequent runs emit only new reviews with isNew: true.
• Unavailable apps emit an error record — not a failed run.

Quick start

{
    "apps": ["com.twitter.android", "333903271"],
    "maxReviewsPerApp": 100,
    "minStars": 1
}

Input

Field	Type	Default	Description
apps	array (required)	—	1–50 apps: Play package names, App Store ids, or full store URLs.
maxReviewsPerApp	integer	100	Cap per app. App Store: up to 500 (official RSS, 10 pages). Play: what the store page renders.
minStars	integer	1	Only reviews with at least this many stars. Use 2–3 to focus on critical feedback.
country	string	us	App Store storefront / Play locale (us, gb, de, jp…).
monitorMode	boolean	false	Emit only reviews new since the previous run.
stateKey	string	default	Separate watchlists in monitor mode.
firstRunReportsAll	boolean	false	Monitor mode: first run emits everything instead of a silent baseline.
concurrency	integer	3	Apps in parallel (both stores rate-limit; keep low).

Output

{
    "type": "review",
    "store": "app-store",
    "appId": "333903271",
    "appName": "X",
    "country": "us",
    "author": "Bitetaeat lifestyle",
    "rating": 5,
    "title": null,
    "text": "Truly the most genuine social media platform there can be!",
    "reviewDate": "2026-09-10T18:22:04.000Z",
    "version": "11.13",
    "thumbsUp": null,
    "isNew": null,
    "checkedAt": "2026-09-12T14:00:00.000Z"
}

Plus app-summary per app (reviews fetched, new count, average rating, mode), error records for unavailable apps, and an info record when nothing matched your filters.

Honest limitations

• App Store: official RSS, newest first — the best data source of the two.
• Google Play: reviews visible on the static store page (a handful per app, "most relevant" order). Pagination requires unofficial APIs that break often — not in this Actor's scope. If you need deep Play history, this Actor is the wrong tool.
• No sentiment scoring, no LLM — clean data out, you analyze it your way.

Pricing

Pay per event: $0.004 per review fetched ($4 / 1,000) + $0.02 per app checked + $0.01 Actor Start. Platform usage included. A daily monitor over 10 apps ≈ 300 reviews/day ≈ $36/month at full depth — typically far less in monitor mode.

Use with AI agents

POST https://api.apify.com/acts/dev-hoss~app-review-intelligence/run-sync-get-dataset-items?token=YOUR_API_TOKEN
Content-Type: application/json
{"apps": ["com.twitter.android", "333903271"], "maxReviewsPerApp": 50}

Also works with Apify's hosted MCP server (mcp.apify.com).

Who is this for?

• App developers & PMs — monitor your own and competitors' reviews without another SaaS seat
• ASO agencies — client reporting across both stores with one tool
• Product research — version-pinned complaints and feature requests
• AI agents — reputation-monitoring feeds with structured, schema-documented output