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
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
1{
2 "source": "https://www.farfetch.com/shopping/...",
3 "brand": "Gucci",
4 "title": "GG Marmont Matelasse Shoulder Bag",
5 "description": "Crafted from matelasse leather...",
6 "details": ["Made in Italy", "100% Calf Leather"],
7 "categories": ["Women", "Bags", "Shoulder Bags"],
8 "options": [
9 { "name": "Size", "values": ["One Size"] }
10 ],
11 "variants": [
12 {
13 "sku": "FF-GU-001",
14 "price": 229000,
15 "currency": "USD",
16 "inStock": true
17 }
18 ],
19 "medias": [
20 { "type": "image", "url": "https://..." }
21 ]
22}
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.
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