Vinted Search Scraper

Extract Vinted second-hand fashion listings at scale through the platform's official mobile API — the same backend the Vinted app uses. That means no headless browser, no proxies, and no DataDome/CAPTCHA breakage: clean, structured JSON with prices, brands, sizes, condition, photos, seller info and engagement metrics, across 20+ country marketplaces.

Search by keyword, or paste any Vinted search / catalog / brand / member URL and the actor reads its filters automatically.

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Who is this for?

• Resellers & flippers — spot underpriced items, track what sells, source inventory across countries.
• Price & market researchers — monitor second-hand prices, brand demand and condition mix over time.
• Brands & analysts — measure how a label performs on the resale market (volume, price bands, engagement).
• PropTech / e-commerce devs — a stable JSON schema to power dashboards, price-comparison tools and alerts.

Output fields

Each listing is one dataset row:

Field	Type	Description
id	integer	Vinted listing ID
url	string	Canonical listing URL
title	string	Listing title
description	string	Full text description (when Include full description is on)
price	string	Item price
currency	string	Price currency (e.g. USD, EUR, GBP)
total_item_price	string	Total incl. Buyer Protection
service_fee	string	Buyer Protection fee
brand	string	Brand name
size	string	Size label
condition	string	New with tags / Very good / Good …
favourite_count	integer	Number of favourites (likes)
view_count	integer	Number of views
is_promoted	boolean	Promoted (bumped) listing
seller_id	integer	Seller user ID
seller_login	string	Seller username
seller_business	boolean	Business (pro) seller flag
seller_profile_url	string	Seller profile URL
photo	string	Main photo URL
photos	array	All photo URLs
photo_count	integer	Number of photos
scraped_at	string	Scrape timestamp (UTC, ISO-8601)

High-value use cases

1. Sourcing & arbitrage — pull the newest listings for a brand + size under a price cap, sort by price, and surface deals to relist.
2. Price benchmarking — snapshot a category daily and chart median price by condition to price your own inventory correctly.
3. Brand demand tracking — measure favourites/views per brand to see what's heating up before it sells out.
4. Competitor monitoring — watch a specific seller or catalog and export new listings to your CRM or a Google Sheet.
5. Dataset building — assemble a clean second-hand fashion dataset (title, price, brand, condition, images) for ML or analytics.

Input parameters

Field	Type	Default	Description
searchQueries	array	["nike air max"]	Free-text keywords; each is scraped in turn
startUrls	array	–	Vinted search/catalog/brand/member URLs (filters read from the URL)
country	string	com	Marketplace: com, fr, de, co.uk, es, it, pl, nl, …
conditions	array	–	Condition filter (New with tags, Very good, …)
order	string	newest_first	newest_first, relevance, price_low_to_high, price_high_to_low
priceFrom / priceTo	integer	–	Price range in the marketplace currency
currency	string	–	ISO currency for the price filter (USD, EUR, GBP, PLN…)
catalogIds	array	–	Catalog/category IDs (from /catalog/1904-shoes)
brandIds	array	–	Brand IDs (from /brand/53-nike)
sizeIds / colorIds	array	–	Size / color IDs
includeDescription	boolean	false	Also fetch each listing's full description
maxItems	integer	100	Stop after this many listings

Tip — going past ~1,000 results: Vinted caps deep pagination per query at about 1,000 items. To collect more, split the job by catalogIds, brandIds or price bands (e.g. 0–20, 20–50, 50+).

Example input

{
    "searchQueries": ["nike air max", "levis 501"],
    "country": "com",
    "conditions": ["6", "2"],
    "priceTo": 80,
    "order": "newest_first",
    "includeDescription": false,
    "maxItems": 200
}

Example output

{
    "id": 9511053552,
    "url": "https://www.vinted.com/items/9511053552-nike-air-max-1-sc-field-purple",
    "title": "Nike Air Max 1 SC \u201cField Purple\u201d Men's 10.5 New",
    "price": "227.69",
    "currency": "USD",
    "total_item_price": "236.34",
    "service_fee": "8.65",
    "brand": "Nike",
    "size": "10.5",
    "condition": "New with tags",
    "favourite_count": 3,
    "view_count": 41,
    "is_promoted": false,
    "seller_id": 258105024,
    "seller_login": "vacantshowroom",
    "seller_business": false,
    "photo": "https://images1.vinted.net/…/f800/…jpeg",
    "photos": ["https://images1.vinted.net/…"],
    "photo_count": 5,
    "scraped_at": "2026-07-28T21:14:00Z"
}

Scheduling & integrations

• Schedule runs (hourly/daily) from the Apify Console to keep a fresh feed.
• Webhooks can push each finished run to your endpoint, Make, or Zapier.
• Export to JSON, CSV, Excel, or Google Sheets, or pull from the Dataset API.

Use it from an AI agent (MCP)

The dataset is clean and typed, so it plugs straight into AI workflows. Apify Actors are callable over the Model Context Protocol, letting assistants like Claude or Cursor run this scraper and reason over the results — "find Nike Air Max under $60 in Very good condition and summarise the price range."

FAQ

Is there a result limit? Vinted caps deep pagination at ~1,000 items per query. Partition by catalog/brand/price to gather more.

How fresh is the data? Every run hits Vinted live, so results reflect the marketplace at run time. Sort by newest_first for the latest listings.

Does it need proxies? No. It uses Vinted's mobile API with an anonymous token, so it runs proxy-free and avoids the anti-bot walls the website enforces.

Can I scrape a specific country? Yes — set country, or paste a URL from that marketplace (e.g. vinted.fr) and the host is detected automatically.

Can I get item descriptions? Yes — enable Include full description. It adds one request per item, so it's slower but returns the full listing text.

Is this affiliated with Vinted? No. This is an independent tool that reads publicly available listing data. Respect Vinted's Terms and applicable laws.

It stopped mid-run — do I lose progress? No. Progress is checkpointed to the key-value store and resumes automatically on the same input.