fashion >99% Success Rate

Farfetch Scraper

Extract luxury fashion product data from Farfetch including multi-currency pricing, size/fit variations, and product recommendations.

TypeScript Cheerio Crawlee Global

Try on Apify

Key Features

Category and product detail page scraping

Multi-currency pricing — auto-detects based on proxy location

Optional size/fit variation extraction

Up to 90 recommended products per item

Full media galleries and detailed descriptions

Brand, category, and extra info extraction

Use Cases

Luxury fashion market intelligence
Cross-platform price comparison for designer brands
Fashion trend analysis and product discovery
Competitive pricing for multi-brand retailers
Product recommendation engine training data

Input Parameters

Parameter	Type	Required	Description
`startUrls`	array	Yes	Farfetch product or category URLs
`proxy`	object	No	Proxy config — location affects currency
`maxRequestsPerCrawl`	number	No	Request limit (default: 100)
`maxConcurrency`	number	No	Parallel requests (default: 5)
`withSizeFit`	boolean	No	Include size/fit data (default: false)
`withRecommends`	boolean	No	Include recommendations (default: false)

Output Example

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{
  "source": "https://www.farfetch.com/shopping/...",
  "brand": "Gucci",
  "title": "GG Marmont Matelasse Shoulder Bag",
  "description": "Crafted from matelasse leather...",
  "details": ["Made in Italy", "100% Calf Leather"],
  "categories": ["Women", "Bags", "Shoulder Bags"],
  "options": [
    { "name": "Size", "values": ["One Size"] }
  ],
  "variants": [
    {
      "sku": "FF-GU-001",
      "price": 229000,
      "currency": "USD",
      "inStock": true
    }
  ],
  "medias": [
    { "type": "image", "url": "https://..." }
  ]
}

Frequently Asked Questions

How does multi-currency pricing work?

Farfetch displays prices based on your location. The scraper uses your proxy location to determine which currency is returned. Use a US proxy for USD, UK proxy for GBP, etc.

How many recommended products can be extracted?

Up to 90 recommended products per item when withRecommends is enabled. This is useful for building product graphs and recommendation datasets.