Depop Scraper

Extract listings from Depop — the global peer-to-peer marketplace for vintage, streetwear, and second-hand fashion with 30M+ registered users across 9 countries.

First Depop scraper on Apify Store. No competition in this niche.

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Features

• 🔍 Search anything — keyword, category, price range filters
• 🌍 9 country domains — US, UK, France, Italy, Spain, Germany, Australia, New Zealand, Canada
• 📊 Rich data — price, brand, size, condition, category, photos, seller info, likes count, views
• 🔄 Smart sorting — newest, oldest, price low→high, price high→low
• 🛡️ Anti-bot resistant — headless browser with stealth plugins handles Depop blocking
• 📦 Multi-strategy extraction — API interception, __NEXT_DATA__ parsing, DOM fallback
• 💰 Pay-per-event — you only pay for what you use

Pricing

Event	Price
Start	$0.50 per run
Per item	$0.0020 per listing ($2.00 / 1,000 items)

A typical search for "vintage dress" (960 results) would cost **$2.42**. A small search (50 results) costs ~$0.60.

Quick Start

Via Apify Console

1. Go to Depop Scraper in Apify Console
2. Click Run and fill in the fields:
   • search — what to look for (e.g. "Levi's 501")
   • country — Depop domain (e.g. com, co.uk, fr)
   • maxItems — how many listings to scrape
3. Wait for the run to complete
4. Download results as JSON, CSV, or Excel

Via API

curl -X POST "https://api.apify.com/v2/acts/serene_trombone~depop-scraper/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "search": "vintage dress",
    "country": "com",
    "maxItems": 100,
    "order": "price_low_to_high"
  }'

Via Apify SDK (Node.js)

import { Actor } from 'apify';

const run = await Actor.call('serene_trombone/depop-scraper', {
  search: 'Nike Air Max',
  country: 'co.uk',
  maxItems: 50,
});

const { items } = await Actor.openDataset(run.defaultDatasetId);
console.log(items);

Via Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("serene_trombone/depop-scraper").call(
    run_input={
        "search": "Levi's jeans",
        "country": "com",
        "maxItems": 100,
    }
)
dataset = client.dataset(run["defaultDatasetId"])
items = dataset.list_items().items
print(items)

Input Parameters

Parameter	Type	Default	Description
search	string	required	Search keyword or phrase
country	string	com	Depop domain: com, co.uk, fr, it, es, de, au, nz, ca
maxItems	number	100	Max listings to scrape (1–100,000)
category	string	—	Category filter (e.g. tops, dresses, shoes)
minPrice	number	—	Minimum price in local currency
maxPrice	number	—	Maximum price in local currency
order	string	newest	Sort: newest, oldest, price_low_to_high, price_high_to_low
proxyConfiguration	object	Apify proxy	Proxy configuration (recommended)

Output Schema

Each scraped listing returns the following fields:

#	Field	Type	Description
1	id	string | number	Unique Depop item ID
2	title	string | null	Listing title
3	brand	string | null	Brand name (e.g. Levi's, Nike, Zara)
4	price	number | null	Item price
5	currency	string | null	Currency code (USD, GBP, EUR, etc.)
6	size	string | null	Size label (e.g. "M", "UK 8", "One Size")
7	condition	string | null	Item condition (e.g. "New", "Used - Good")
8	category	string | null	Product category
9	description	string | null	Full item description (detail mode)
10	material	string | null	Material composition (when available)
11	colour	string | null	Item colour (when available)
12	image	string | null	Main photo URL
13	images	string[]	All photo URLs
14	url	string | null	Direct link to the listing on Depop
15	likesCount	number	Number of likes
16	viewCount	number	Number of views (detail mode)
17	sellerId	number | null	Depop user ID of the seller
18	sellerUsername	string | null	Seller's username
19	sellerAvatar	string | null	Seller's profile picture URL
20	sellerBio	string | null	Seller's bio text (detail mode)
21	country	string	Country code (e.g. com, co.uk)
22	domain	string	Domain scraped (e.g. www.depop.com)
23	scrapedAt	string	ISO 8601 timestamp of when the item was scraped

Sample Output

{
  "id": 123456789,
  "title": "Vintage 90s Levi's 501 Jeans",
  "brand": "Levi's",
  "price": 45.00,
  "currency": "USD",
  "size": "W30 L32",
  "condition": "Used - Good",
  "category": "Jeans",
  "image": "https://media.depop.com/products/.../image.jpg",
  "images": [
    "https://media.depop.com/products/.../image1.jpg",
    "https://media.depop.com/products/.../image2.jpg"
  ],
  "url": "https://www.depop.com/products/seller123-vintage-501/",
  "likesCount": 12,
  "viewCount": 234,
  "sellerId": 987654,
  "sellerUsername": "vintage_seller",
  "sellerAvatar": "https://media.depop.com/users/.../avatar.jpg",
  "sellerBio": "curating vintage since 2020",
  "country": "com",
  "domain": "www.depop.com",
  "scrapedAt": "2026-07-04T12:00:00.000Z"
}

Use Cases

• Reseller research — find underpriced items, track trending styles
• Brand monitoring — track second-hand value of brands over time
• Market analysis — fashion economics, pricing trends, vintage market research
• Competitive intelligence — monitor competitor listings and pricing
• Data collection — AI/ML training datasets for fashion models, size recommender systems
• Price tracking — monitor price drops and newly listed items

Rate Limits & Anti-Bot

Depop uses bot detection and may block aggressive scraping. This Actor handles it with:

• ✅ Headless Playwright browser (full JS rendering)
• ✅ Session rotation with Apify proxy
• ✅ Retry logic with backoff
• ✅ Stealth plugin to avoid detection
• ✅ API interception for efficient data extraction
• ✅ Multi-strategy fallback (API → __NEXT_DATA__ → DOM)

Using the Actor without Apify proxy may result in blocking.

Technical Details

The scraper uses a three-layer extraction strategy:

1. API Interception — Captures JSON responses from Depop's internal API as the page loads
2. __NEXT_DATA__ parsing — Extracts Next.js server-rendered data payload
3. DOM fallback — Parses product cards from the rendered HTML as last resort

Stack: Crawlee (PlaywrightCrawler) · Node.js 20 · Apify SDK

Changelog

v1.0 — 2026-07-04

• Initial release
• 9 country domain support
• Full search/filter pipeline
• Multi-strategy data extraction
• API response interception
• Deduplication by item ID

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Built with 🛠️ by Meester Bot · Not affiliated with Depop