# Farfetch Product Search Scraper (`stealth_mode/farfetch-product-search-scraper`) Actor Scrape product listings from Farfetch search results with precision. Collect brand names, prices, stock levels, images, promotions, and 20+ fields per item — essential for price monitoring, competitive analysis, and fashion ecommerce research. - **URL**: https://apify.com/stealth\_mode/farfetch-product-search-scraper.md - **Developed by:** [Stealth mode](https://apify.com/stealth_mode) (community) - **Categories:** Automation, Developer tools, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Farfetch Product Search Scraper: Extract Luxury Fashion Data Fast --- ### What Is Farfetch? Farfetch is a global marketplace for luxury fashion, connecting boutiques and brands with customers worldwide. The platform hosts hundreds of thousands of products across clothing, footwear, accessories, and beauty. Extracting this data manually is impractical — the **Farfetch Product Search Scraper** automates collection of product search results, delivering structured data instantly. --- ### Overview The **Farfetch Product Search Scraper** extracts product listings from Farfetch search pages, capturing comprehensive item data in structured format. It serves: - **Price monitoring services** tracking luxury fashion pricing trends - **Retail analysts** benchmarking competitor product assortments - **Fashion researchers** studying designer brand availability and positioning - **Ecommerce developers** building luxury product aggregators - **Marketing teams** gathering competitive intelligence on product launches The scraper handles pagination automatically, collects high-quality product images, and gracefully manages network failures with the `ignore_url_failures` option. --- ### Input Format The scraper accepts a JSON configuration object: ```json { "urls": [ "https://www.farfetch.com/shopping/women/clothing-1/items.aspx?page=2" ], "ignore_url_failures": true, "max_items_per_url": 200 } ```` #### Input Parameters Explained | Parameter | Type | Description | Example | |---|---|---|---| | `urls` | array | List of Farfetch product search result pages to scrape | `["https://www.farfetch.com/shopping/women/clothing-1/items.aspx?page=2"]` | | `ignore_url_failures` | boolean | If `true`, scraper continues if some URLs fail; if `false`, it stops on first error | `true` | | `max_items_per_url` | integer | Maximum number of products to collect per URL (useful for testing or limiting data volume) | `200` | #### Tips for Input Configuration - **URLs:** Use Farfetch search result pages (with filters like category, gender, price range). Each URL should display a product grid. - **Pagination:** The scraper extracts items from a single page. For multi-page scraping, add separate URLs for each page number. - **Filtering:** Apply Farfetch's built-in filters (price, size, designer, condition) before copying the URL to pre-select data. - **max\_items\_per\_url:** Set to `200` for full collection, or lower (e.g., `50`) for quick test runs. *** ### Output Format #### Example Output ```json { "id": "32872187", "resource_identifier": { "__typename": "ResourceIdentifier", "path": "/shopping/women/self-portrait-pointelle-peplum-knit-mini-dress-item-32872187.aspx" }, "brand": { "__typename": "Brand", "id": "2049368", "name": "Self-Portrait" }, "short_description": "pointelle peplum knit mini dress", "label": null, "merchant_id": "12467", "promotion": null, "stock_quantity": 6, "ranking_algorithm": "OS0HWphZfjgLHk", "search_algorithm": null, "price": { "__typename": "ProductCatalogItemSalePrice", "value": { "__typename": "GenericValue", "raw": 368, "formatted": "$368" }, "currency": { "__typename": "Currency", "iso_code": "USD", "symbol": "$" }, "installments": null, "full_price_value": { "__typename": "GenericValue", "raw": 675, "formatted": "$675" }, "discount_percentage": { "__typename": "GenericValue", "raw": 45, "formatted": "45%" } }, "product_price": { "__typename": "ProductCatalogItemPrice", "full": { "__typename": "ProductFullPrice", "value": { "__typename": "GenericValue", "raw": 675, "formatted": "$675" } }, "discounts": [ { "__typename": "ProductDiscountPrice", "value": { "__typename": "GenericValue", "raw": 368, "formatted": "$368" }, "percentage": { "__typename": "GenericValue", "raw": 45, "formatted": "45%" }, "type": "SALE", "name": null } ], "final": { "__typename": "ProductFinalPrice", "value": { "__typename": "GenericValue", "raw": 368, "formatted": "$368" }, "type": "SALE" }, "currency": { "__typename": "Currency", "iso_code": "USD", "symbol": "$" }, "installments": null, "promo_offer": null }, "variation_properties": [ { "__typename": "ProductCatalogItemScaledSizeVariationProperty", "order": 1, "total_items": 2, "values": [ { "__typename": "ProductCatalogItemScaledSizeVariationPropertyValue", "id": "S", "order": 1, "description": "S" }, { "__typename": "ProductCatalogItemScaledSizeVariationPropertyValue", "id": "L", "order": 2, "description": "L" } ] } ], "visualization_experience": { "__typename": "VisualizationExperience", "is_for_members": false }, "tags": [], "external_triggers": null, "delivery_methods": [], "attributes": [], "return_detail": null, "availability_types": [ "AVAILABLE_FOR_PURCHASE" ], "available_promotion_ids": [], "images": [ { "__typename": "ProductCatalogItemImage", "order": 1, "size480": { "__typename": "Image", "url": "https://cdn-images.farfetch-contents.com/32/87/21/87/32872187_65525248_480.jpg", "alt": "Self-Portrait pointelle peplum knit mini dress" } }, { "__typename": "ProductCatalogItemImage", "order": 2, "size480": { "__typename": "Image", "url": "https://cdn-images.farfetch-contents.com/32/87/21/87/32872187_65509655_480.jpg", "alt": "Self-Portrait pointelle peplum knit mini dress" } } ], "from_url": "https://www.farfetch.com/shopping/women/clothing-1/items.aspx?page=2" } ``` Each product extracted returns a detailed record with 20+ fields: #### Core Identification | Field | Description | Example Use | |---|---|---| | `ID` | Unique Farfetch product identifier | Database deduplication | | `Resource Identifier` | Alternative product reference code | System integration | | `Brand` | Designer or brand name | Price comparison by brand | | `Short Description` | Brief product name and style | Display in reports | | `Label` | Product category label or tag | Filtering and segmentation | #### Pricing & Inventory | Field | Description | Example Use | |---|---|---| | `Price` | Current listing price (may include currency) | Price tracking and alerts | | `Product Price` | Structured price data (base amount) | Financial analysis | | `Stock Quantity` | Available units in inventory | Demand signal detection | | `Promotion` | Active discount or promotional offer | Sales trend analysis | | `Available Promotion IDs` | List of applicable promotions | Marketing campaign tracking | #### Merchant & Logistics | Field | Description | Example Use | |---|---|---| | `Merchant ID` | Seller/vendor identifier on Farfetch | Multi-merchant analysis | | `Delivery Methods` | Shipping options (standard, express, etc.) | Logistics cost modeling | | `Return Detail` | Return policy information | Customer service insights | | `Availability Types` | Stock status flags (in stock, pre-order, etc.) | Fulfillment planning | #### Product Details & Experience | Field | Description | Example Use | |---|---|---| | `Variation Properties` | Size, color, material options | SKU-level granularity | | `Attributes` | Additional product specs (fit, length, etc.) | Attribute-based filtering | | `Tags` | Curated product tags or keywords | Content organization | | `External Triggers` | Third-party data or alerts (e.g., restocks) | Automated notifications | #### Visual & Ranking | Field | Description | Example Use | |---|---|---| | `Images` | Product photo URLs (multiple images per item) | Catalog builds and displays | | `Ranking Algorithm` | Farfetch's relevance/popularity score | Search quality analysis | | `Search Algorithm` | Search result position data | Visibility tracking | | `Visualization Experience` | Display format indicators | UX research | *** ### How to Use 1. **Find search pages** — Navigate to Farfetch and use filters to build your desired product search (e.g., women's clothing, price range $500-$2000). Copy the URL. 2. **Add multiple pages** — For pagination, manually add URLs for page 1, 2, 3, etc., or let the scraper handle single pages at a time. 3. **Configure limits** — Set `max_items_per_url` based on your needs: - `20–50` for quick tests - `100–200` for bulk collection 4. **Enable resilience** — Keep `ignore_url_failures: true` to avoid interruptions from temporary network issues. 5. **Run and export** — Launch the scraper and download results as JSON, CSV, or Excel. **Best practices:** - Test with a single URL and small `max_items_per_url` (20–50) first. - Use Farfetch's search filters to pre-select products (e.g., "In Stock Only," designer name) before scraping. - Monitor image URLs — they may have expiration or CDN parameters. *** ### Use Cases & Applications - **Price intelligence:** Build real-time pricing dashboards tracking luxury fashion across hundreds of products. - **Competitive benchmarking:** Monitor competing retailers' Farfetch strategies, brand mix, and promotional activity. - **Product research:** Analyze design trends, material usage, and color/size availability in designer collections. - **Inventory alerts:** Track stock levels and out-of-stock signals for pre-order or limited-edition items. - **Content curation:** Collect product data (descriptions, images, attributes) for fashion blogs or influencer tools. - **Market analysis:** Study price elasticity, promotional depth, and seasonal inventory patterns in luxury fashion. The scraper transforms static search pages into actionable datasets for fashion ecommerce, retail technology, and market research. *** ### Conclusion The **Farfetch Product Search Scraper** is a powerful tool for anyone analyzing luxury fashion ecommerce. With 20+ fields per product and reliable handling of large result sets, it delivers the granular data needed for pricing, competitive, and market analysis. Start scraping Farfetch search results today and unlock data-driven insights in luxury retail. # Actor input Schema ## `urls` (type: `array`): Add the URLs of the product search list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list. ## `ignore_url_failures` (type: `boolean`): If true, the scraper will continue running even if some URLs fail to be scraped. ## `max_items_per_url` (type: `integer`): The maximum number of items to scrape per URL. ## Actor input object example ```json { "urls": [ "https://www.farfetch.com/shopping/women/clothing-1/items.aspx?page=2" ], "ignore_url_failures": true, "max_items_per_url": 20 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://www.farfetch.com/shopping/women/clothing-1/items.aspx?page=2" ], "ignore_url_failures": true, "max_items_per_url": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("stealth_mode/farfetch-product-search-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "urls": ["https://www.farfetch.com/shopping/women/clothing-1/items.aspx?page=2"], "ignore_url_failures": True, "max_items_per_url": 20, } # Run the Actor and wait for it to finish run = client.actor("stealth_mode/farfetch-product-search-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://www.farfetch.com/shopping/women/clothing-1/items.aspx?page=2" ], "ignore_url_failures": true, "max_items_per_url": 20 }' | apify call stealth_mode/farfetch-product-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=stealth_mode/farfetch-product-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Farfetch Product Search Scraper", "description": "Scrape product listings from Farfetch search results with precision. Collect brand names, prices, stock levels, images, promotions, and 20+ fields per item — essential for price monitoring, competitive analysis, and fashion ecommerce research.", "version": "0.0", "x-build-id": "IzcVhhyKJvp4HZp5V" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stealth_mode~farfetch-product-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stealth_mode-farfetch-product-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/stealth_mode~farfetch-product-search-scraper/runs": { "post": { "operationId": "runs-sync-stealth_mode-farfetch-product-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/stealth_mode~farfetch-product-search-scraper/run-sync": { "post": { "operationId": "run-sync-stealth_mode-farfetch-product-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "urls": { "title": "URLs of the product search list urls to scrape", "type": "array", "description": "Add the URLs of the product search list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list.", "items": { "type": "string" } }, "ignore_url_failures": { "title": "Continue running even if some URLs fail to be scraped", "type": "boolean", "description": "If true, the scraper will continue running even if some URLs fail to be scraped." }, "max_items_per_url": { "title": "Max items per URL", "type": "integer", "description": "The maximum number of items to scrape per URL." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```