Whop App Store Scraper ⭐

From $5 / 1,000 apps — Scrapes every public app from the Whop App Store, the fastest-growing digital product marketplace with 190+ B2C and B2B apps across trading, AI, community, education, and more.

Search by sort order, category, view type, and app type. Returns app profiles, creator details, install metrics, daily active users, time-spent data, reviews, and gallery images. Up to unlimited results per run. No login required. No API key needed.

🚀 Features

• Full app store coverage — Fetches all 190+ public apps from the Whop App Store, including B2C consumer apps (178+), B2B business tools (13), and more. Nothing is missed.

• Rich app profiles — Extracts 17 structured fields per app: name, description, creator info, company details, install counts, DAU, engagement metrics, ratings, gallery images, and lifecycle status.

• Multi-axis sorting — Sort by 12 different fields: discoverable_at, created_at, total_installs_last_7_days, total_installs_last_30_days, daily_active_users, time_spent, time_spent_last_24_hours, ai_prompt_count, total_ai_cost_usd, total_ai_tokens, last_ai_prompt_at, and ai_average_rating.

• Category filtering — Filter apps by 9 categories: trading, ai, community, education, sports, travel, customer-support, social-media, and health-fitness. Narrow your crawl to exactly the market segment you care about.

• Automatic cursor-based pagination — No manual page-turning. The actor handles pagination transparently, following the GraphQL cursor until every app is collected.

• Zero authentication required — The Whop App Store's public API is fully open. No login, no API key, no cookies, no session tokens needed. Just configure and run.

• Configurable depth — Set maxPages to limit runs to a specific number of pages (24 apps per page). Perfect for quick tests, partial scrapes, or budget-conscious workflows.

• Clean normalized output — Every app is transformed into a consistent schema with predictable field names and types. Ready for CSV export, database import, or programmatic analysis.

📋 What You Get

Every scraped app returns 17 fields of structured data:

Field	Type	Description	Example
appId	string	Unique app identifier	"app_fqYWzj3RZXe1O0"
name	string	Public app name	"Community Onboarding"
appDescription	string	App description text	"Turn new members into active members in minutes"
url	string	Whop app store URL	"https://whop.com/apps/app_fqYWzj3RZXe1O0/"
iconUrl	`string	null`	App icon image URL
galleryImageUrl	`string	null`	First gallery/screenshot
creator.id	string	Creator user ID	"user_mA7XJV9qhruAh"
creator.name	string	Creator display name	"Jonas"
creator.username	string	Creator @username	"contactjonas"
creator.profilePictureUrl	`string	null`	Creator avatar URL
company.route	string	Company page slug	"cancellation-flow"
company.memberCount	integer	Company member count	208
stats.totalInstallsLast7Days	integer	Installs in the last 7 days	34
stats.dau	integer	Daily active users	16
stats.timeSpentLast24HoursInSeconds	integer	Time spent in the last 24h	3695
status	string	App lifecycle status	"hidden"
discoverableAt	string	ISO date when app went live	"2026-04-29T16:31:18.000Z"
reviews.average	`number	null`	Average review rating
reviews.counts	array	Review count per star tier	[3, 0, 0, 0, 0]

💰 Pricing

This actor costs $5 per 1,000 apps scraped. One platform compute unit (CU) on Apify processes approximately 200 app records.

Volume	Approximate Apps	Est. Compute Units	Est. Cost
Light	500 apps	2.5 CU	$12.50
Medium	5,000 apps	25 CU	$125
Heavy (full store)	10,000+ apps	50 CU	$250
Bulk (multi-run)	50,000 apps	250 CU	$1,250

Runs complete in under 30–60 seconds for a full store scrape on default settings. Costs scale linearly with the number of apps — there are no hidden fees or per-field charges.

📥 Input

All fields are optional. Run without any configuration to scrape every app in the Whop App Store.

Input	Type	Required	Default	Description
orderBy	string	No	discoverable_at	Sort field — discoverable_at, created_at, total_installs_last_7_days, total_installs_last_30_days, daily_active_users, time_spent, time_spent_last_24_hours, ai_prompt_count, total_ai_cost_usd, total_ai_tokens, last_ai_prompt_at, ai_average_rating
direction	string	No	desc	Sort direction — desc (descending) or asc (ascending)
viewType	string	No	hub	Store section — hub (all apps, 190+), discover (featured, 34), dash, dashboard, analytics, skills, openapi
appType	string	No	b2c_app	App type — b2c_app (178 apps), b2b_app (13 apps), company_app, component
category	string	No	"" (all)	Category filter — trading, ai, community, education, sports, travel, customer-support, social-media, health-fitness
maxPages	integer	No	null (all)	Stop after N pages (24 apps per page). Leave empty for unlimited.

Example Input (JSON)

{
  "orderBy": "total_installs_last_7_days",
  "direction": "desc",
  "appType": "b2c_app",
  "viewType": "hub",
  "category": "ai",
  "maxPages": 3
}

Example Output Item (JSON)

{
  "appId": "app_fqYWzj3RZXe1O0",
  "name": "Community Onboarding",
  "appDescription": "Turn new members into active members in minutes",
  "creator": {
    "id": "user_mA7XJV9qhruAh",
    "name": "Jonas",
    "username": "contactjonas",
    "profilePictureUrl": "https://assets-2-prod.whop.com/public/uploads/2026-05-21/1f6b0d03-81f7-4f8e-bd26-a28da6d5921f/image.png"
  },
  "company": {
    "route": "cancellation-flow",
    "memberCount": 208
  },
  "iconUrl": "https://img-v2-prod.whop.com/XsThZ-w3KdgZLtWT34us-V0vH954nkN8szcocT8YFMA/resize:fill/width:180/height:180/enlarge:true/plain/https://assets-2-prod.whop.com/public/uploads/user_19624863/image/apps/2026-04-25/c1a77155-f498-4ff2-9f58-32fd135a745e.png",
  "stats": {
    "totalInstallsLast7Days": 34,
    "dau": 16,
    "timeSpentLast24HoursInSeconds": 3695
  },
  "status": "hidden",
  "discoverableAt": "2026-04-29T16:31:18.000Z",
  "reviews": {
    "average": 5,
    "counts": [3, 0, 0, 0, 0]
  },
  "galleryImageUrl": "https://img-v2-prod.whop.com/JYoZrKeDag8cVZ2TOGErTW1mjG-49QmME365eYgRqCc/plain/https://assets-2-prod.whop.com/public/uploads/user_19624863/image/access_passes/2026-05-04/51e79962-c528-4990-966d-b8d47a825aea.png",
  "url": "https://whop.com/apps/app_fqYWzj3RZXe1O0/"
}

💡 Use Cases

Market Intelligence for Whop Competitors & Investors

If you're building on Whop, competing with a Whop-based product, or evaluating the Whop ecosystem for investment, this dataset is your primary source of competitive intelligence. By running the actor regularly (e.g., weekly), you can track which apps are gaining installs, which creators are growing their DAU, and which categories are attracting the most new products. The stats.totalInstallsLast7Days field tells you what's trending right now, while stats.dau reveals which apps have genuine stickiness versus one-hit wonders. Cross-reference with reviews.average and reviews.counts to identify apps with strong product-market fit. Monitor the discoverableAt field to spot new entrants the moment they hit the store. For investors, a weekly scrape of the full store tells you which verticals — AI tools, trading bots, community platforms — are seeing the most supply growth and user adoption, giving you signal before anyone else.

Creator Discovery & Partnership Sourcing

Are you a Whop creator looking for collaboration partners, or a brand seeking influencers to promote your product? The actor surfaces every creator on the platform along with their app, install counts, and engagement metrics. Use creator.name, creator.username, and creator.profilePictureUrl to build a creator directory. Then layer on stats.totalInstallsLast7Days to gauge reach and stats.dau to understand audience activity levels. Filter by category to find creators in your niche — for example, trading for financial tool creators, community for engagement-focused products, or ai for cutting-edge AI app builders. Export the dataset to a CRM or spreadsheet and prioritize outreach to creators with high install velocity and active user bases. The company.memberCount field also tells you how large each creator's team is, helping you estimate their operational maturity.

Competitive Benchmarking for Whop Creators

If you already operate an app on Whop, you need to know where you stand. Run the actor to get a complete snapshot of the competitive landscape. Filter by your category and sort by total_installs_last_7_days or daily_active_users to see who's ahead of you and by how much. Examine the apps above you in the rankings: What do their descriptions say? What's their reviews.average? How does their stats.timeSpentLast24HoursInSeconds compare to yours? If a competitor with similar stats.dau has vastly higher engagement time, their onboarding or feature set may be better. Use this data to set performance targets: "We need to reach 50 installs/week to break into the top 10 in the AI category." Export the full dataset, diff it against last week's scrape, and measure your growth against the market. The status field also reveals which apps have gone hidden or inactive — opportunities to capture their users.

Category Trend Analysis for Product Strategy

Whop's category structure — trading, AI, community, education, sports, travel, customer-support, social-media, health-fitness — maps directly to the most active digital product markets of 2026. Run the actor with category set to each vertical and compare the results. How many apps are in the ai category vs trading? What's the average reviews.average per category? Which category has the highest median stats.dau? This analysis helps you decide where to build next. For example, if the education category has only 11 apps but they all have high engagement time, it's an underserved market with proven demand. If community has 51 apps but low per-app installs, the category may be saturated and you need a differentiated angle. Present these findings as a strategy deck using the exported JSON — the structured schema makes pivot tables and dashboards trivial to build.

Data Science & Research on Marketplace Economics

The Whop App Store is a fascinating two-sided marketplace where creators build and sell apps to end-users. For researchers studying platform economics, this actor provides a complete, structured dataset of every product on the platform. Analyze the distribution of installs vs. ratings — do highly-rated apps always get more installs, or is there a discoverability problem? Study the relationship between company.memberCount and stats.dau — do team-backed apps outperform solo creators? Track discoverableAt dates to measure the platform's growth trajectory: how many new apps launched per month? Which categories are growing fastest? The data is clean JSON, ready for pandas, R, or any analysis tool. Run monthly scrapes to build a longitudinal dataset that lets you model marketplace dynamics, creator retention, and category lifecycles.

Automated Monitoring & Alerting Pipeline

Combine this actor with Apify's scheduling and webhook features to build an automated Whop market monitoring system. Schedule the actor to run daily and compare the latest output against a baseline. Set up alerts for: new apps from top creators (check creator.id in new results), apps that crossed 100+ daily_active_users, categories that gained 5+ new apps in a week, or apps whose status changed from active to hidden (possible shutdown or acquisition). Pipe the output into a Google Sheet via Apify's Google Sheets integration, or into a PostgreSQL database for querying. With the unlimited results per run, you can run this pipeline every day and build a real-time market intelligence feed that would cost thousands with traditional research tools.

❓ Frequently Asked Questions

How do I search for specific types of apps?

Use the category field to filter by vertical — for example, set "category": "trading" to get only trading apps, or "category": "ai" for AI tools. Combine with orderBy and direction to sort results. For example, to find the most installed AI apps: orderBy: "total_installs_last_7_days", direction: "desc", category: "ai". Use appType to switch between B2C apps (178+, consumer-facing) and B2B apps (13, business tools).

How many apps can I scrape in one run?

There is no hard limit. The actor paginates through every page until it reaches the end of the dataset. With default settings (B2C apps, hub view type), you'll get 190+ apps in one run. Set maxPages to cap the number of pages if you only need a subset — each page returns up to 24 apps. A full store scrape typically returns around 8–10 pages.

Do I need a Whop account or API key?

No. The Whop App Store's GraphQL API is publicly accessible. The actor queries the same API endpoint that the Whop frontend uses, and no authentication is required. You can run the actor immediately without any sign-up, login, or token configuration.

Does this scrape apps from all regions and languages?

The Whop App Store serves a global audience. The API returns all apps regardless of region or language. Descriptions are primarily in English, but the actor captures whatever content the creator has published. There are no geo-restrictions on the API endpoint.

How fresh is the data?

Each run fetches live data directly from the Whop GraphQL API in real-time. The totalInstallsLast7Days, stats.dau, and timeSpentLast24HoursInSeconds fields reflect the most current metrics at the time of the request. We recommend scheduling daily runs if you need up-to-the-day install and engagement tracking.

Can I export the data to CSV, Excel, or a database?

Yes. Apify provides built-in export options for every dataset run. After the actor completes, you can download the results as JSON, CSV, XML, Excel, or HTML directly from the Apify Console. You can also use Apify's integrations to push data to Google Sheets, Airtable, PostgreSQL, BigQuery, or any webhook endpoint — all without writing any code.

How is this different from building a custom scraper for Whop?

Building a custom scraper for Whop would require: reverse-engineering the GraphQL API, writing cursor-based pagination logic, handling rate limits, normalizing nested responses into flat fields, and maintaining the code as the API evolves. This actor does all of that out of the box. Additionally, the Whop frontend renders dynamic data that a basic HTML scraper could never capture — fields like stats.dau, totalInstallsLast7Days, and reviews.average only exist in the GraphQL response, not in the DOM. A custom scraper would need a headless browser and significant development effort to match this actor's output.

What if the API changes or breaks?

This actor is actively maintained. The GraphQL query is designed to mirror the exact request the Whop frontend sends, so it's resilient to minor schema changes. If the API endpoint, query structure, or field naming changes, the actor will be updated promptly. You can check the GitHub repository for the latest changes and report any issues.

📚 Technical Details

How It Works

The actor sends a single GraphQL POST request to Whop's internal fetchDiscoverPublicApps endpoint — the same endpoint the Whop frontend uses to load the app store. It requests 24 apps per page with cursor-based pagination, parsing the pageInfo.hasNextPage and pageInfo.endCursor fields to determine whether to continue. Each raw app response is normalized into a consistent schema that flattens nested objects (creator, company, stats, reviews) into predictable fields. The actor pushes each normalized app to the Apify dataset immediately, so partial results are available even if a run is interrupted.

Error Handling

• HTTP errors — If the GraphQL API returns a non-2xx status code, the actor throws with the response body for debugging. This is not expected under normal operation.
• GraphQL errors — If the API returns errors in the response body (e.g., for invalid input), the actor logs the full error payload and stops.
• Network failures — The actor relies on Node.js fetch with default timeouts. For production use at high frequency, consider wrapping the fetch call in a retry-with-backoff loop (the codebase can be extended with a lightweight retry utility).
• Empty pages — If a page returns zero nodes but indicates there are more pages (a theoretical edge case), the loop exits gracefully rather than spinning forever.

Data Integrity

• Null safety — Every field access is guarded with optional chaining and nullish coalescing. If the API omits a field, the output sets it to null or 0 rather than crashing.
• Date formatting — Whop stores discoverableAt as a Unix timestamp (seconds since epoch). The actor converts it to standard ISO 8601 format for portability.
• Schema consistency — All app objects follow the exact same field structure regardless of app type, category, or status. No missing keys, no inconsistent types.
• Pagination guarantees — The actor respects maxPages exactly and stops cleanly at the boundary. If maxPages is not set, it continues until hasNextPage is false, ensuring complete coverage.

SEO Keywords

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⚠️ Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Whop Inc., Whop.com, or any of their subsidiaries. All trademarks are the property of their respective owners.

This Actor accesses only publicly available app store data on whop.com/apps. You are solely responsible for ensuring your use complies with the site's Terms of Service and applicable laws.