# Fashion Nova Scraper (`hypebridge/fashionnova-scraper`) Actor Extracts product data from Fashion Nova using homepage URLs, category pages, search pages, and direct product URLs. It is built for product research, merchandising analysis, catalog monitoring, and structured exports you can use in spreadsheets, BI tools, or downstream automations. - **URL**: https://apify.com/hypebridge/fashionnova-scraper.md - **Developed by:** [Hypebridge](https://apify.com/hypebridge) (community) - **Categories:** E-commerce, Automation, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / result 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 ### What does Fashion Nova Scraper do? **Fashion Nova Scraper** extracts product data from [Fashion Nova](https://www.fashionnova.com/) using homepage URLs, category pages, search pages, and direct product URLs. It is built for product research, merchandising analysis, catalog monitoring, and structured exports you can use in spreadsheets, BI tools, or downstream automations. It focuses on publicly visible product data. It does not require an official Fashion Nova API. ### Why scrape Fashion Nova? - **Track product assortments**: monitor what is currently listed in categories like dresses, shoes, or matching sets. - **Research pricing and merchandising**: compare titles, prices, availability, variants, and collection placement across product groups. - **Build structured catalogs**: export Fashion Nova product data into JSON, CSV, Excel, or other pipelines for internal analysis. - **Capture search results**: run keyword searches such as `white heels` or `maxi dress` and save the matching products. - **Collect product detail pages at scale**: enrich direct product URLs with descriptions, media, options, and variant data. ### What data can Fashion Nova Scraper extract? | Field | Type | Description | | --- | --- | --- | | `url` | String | Canonical Fashion Nova product URL | | `title` | String | Product title | | `vendor` | String | Product brand or vendor | | `productType` | String | Product type or category label | | `availableForSale` | Boolean | Whether the product is currently available | | `minPriceAmount` | String | Lowest product price found | | `maxPriceAmount` | String | Highest product price found | | `currencyCode` | String | Currency code such as `USD` | | `imageUrl` | String | Primary product image URL | | `description` | String | Product description text | | `variants` | Array | Product variants, option values, and related pricing | | `reviewCount` | Number | Review count when available | The full field list is available in the **Output** tab. ### How to scrape Fashion Nova 1. [Create a free Apify account](https://apify.com) if you do not already have one. 2. Open **Fashion Nova Scraper** and click **Try for free**. 3. Add one or more inputs: - homepage URL like `https://www.fashionnova.com/` - collection URL like `https://www.fashionnova.com/collections/dresses` - search URL like `https://www.fashionnova.com/search?query=white+heels&division=women` - direct product URL 4. Set **Max items** to control how many products are returned. 5. Start the run and download the results from the **Dataset** tab in JSON, CSV, Excel, XML, RSS, or HTML. ### How much will it cost? Fashion Nova Scraper is designed for usage-based runs, so the easiest way to control cost is to limit scope before scaling up. Practical cost controls: - Start with a small `maxItems` value such as `10` or `20` - Run one collection or one search URL first - Only enable optional review and merchant policy fields when you actually need them Current pricing is shown directly on the actor page in Apify Console. ### Input See the **Input** tab for the full schema. Key fields: | Parameter | Type | Default | Description | | --- | --- | --- | --- | | `startUrls` | Array | `[]` | Homepage, category, search, or product URLs to scrape | | `enrichProductDetail` | Boolean | `true` | Return richer product detail data instead of list-only snapshots | | `maxItems` | Number | `50` | Maximum number of products to return. Use `0` for unlimited | | `maxConcurrency` | Number | `15` | Request concurrency | | `debugMode` | Boolean | `false` | Save debugging artifacts for failed requests | `startUrls` is required. #### Example input ```json { "startUrls": [ { "url": "https://www.fashionnova.com/search?query=white+heels&division=women" }, { "url": "https://www.fashionnova.com/products/phone-calls-pinstripe-mini-dress-fncolorname-blue-combo" } ], "maxItems": 20 } ```` ### Output Results are saved to the **Dataset** tab and can be downloaded in multiple formats. Each item includes: - **Product identity**: `id`, `gid`, `handle`, `url`, `title` - **Catalog metadata**: `vendor`, `productType`, `tags`, `availableForSale`, `publishedAt` - **Pricing and media**: `minPriceAmount`, `maxPriceAmount`, `currencyCode`, `imageUrl`, `images`, `media` - **Detail fields**: `description`, `options`, `variants`, `collections`, `seo` - **Review and policy fields**: `averageRating`, `reviewCount`, `merchantReturnPolicy`, `shippingDetails` - **Run context**: `sourceLabel`, `sourceUrl`, `collectionHandle`, `searchTerm`, `scrapedAt` #### Sample output ```json { "id": "6540558499964", "handle": "lindsay-striped-skirt-set-pink-combo", "url": "https://www.fashionnova.com/products/lindsay-striped-skirt-set-pink-combo", "title": "Lindsay Striped Polo Skirt Set - Pink/combo", "vendor": "Fashion Nova", "productType": "Matching Sets", "availableForSale": true, "minPriceAmount": "34.99", "maxPriceAmount": "34.99", "currencyCode": "USD", "imageUrl": "https://cdn.shopify.com/example.jpg", "description": "Buy Lindsay Striped Polo Skirt Set in Pink/combo from Fashion Nova.", "sourceLabel": "COLLECTION", "sourceUrl": "https://www.fashionnova.com/collections/matching-sets", "reviewCount": null, "scrapedAt": "2026-04-22T18:42:11.000Z" } ``` ### Tips - Use the homepage URL if you want a quick sample of currently featured Fashion Nova categories. - Use direct product URLs when you need guaranteed product detail output for specific items. - Use search URLs when you are validating demand for keywords like `white heels`, `mini dress`, or `cargo pants`. - Review summary and merchant policy fields are collected automatically when Fashion Nova exposes them publicly. - Set `maxItems` before large runs so you can validate the output shape before scaling. ### FAQ #### Can I scrape the Fashion Nova homepage? Yes. A homepage URL such as `https://www.fashionnova.com/` is supported and can be used as a discovery entry point. #### Can I scrape direct product URLs? Yes. Direct Fashion Nova product URLs are supported and return enriched product detail records. #### Can I scrape Fashion Nova search results? Yes. Pass a Fashion Nova search URL such as `https://www.fashionnova.com/search?query=white+heels&division=women` as a start URL. #### Does it return reviews and merchant policies? Yes. The actor automatically returns review summary fields and merchant policy fields when those details are publicly available for the product. Availability still varies by product. #### Is scraping Fashion Nova legal? This actor is intended for publicly available data only. You are responsible for making sure your use complies with applicable laws, the site's terms, and your internal data policies. ### Support - Bugs or feature requests: use the **Issues** tab on the actor page - Programmatic usage: use the **API** tab in Apify Console > **Disclaimer**: This actor is intended for ethical extraction of publicly available data only. Your results may still contain personal data or business-sensitive data depending on the source pages you scrape. You are responsible for ensuring your use complies with GDPR, CCPA, platform terms, and any other applicable laws or policies. # Actor input Schema ## `startUrls` (type: `array`): Fashion Nova homepage, collection URLs, search URLs, or direct product URLs. ## `enrichProductDetail` (type: `boolean`): Enrich discovered products. Disable for faster list-only snapshots. ## `maxItems` (type: `integer`): Maximum number of product records to produce across the full run. Set to 0 for unlimited. ## `maxConcurrency` (type: `integer`): Crawler concurrency for bootstrap, listing, and product batch requests. ## `proxyConfiguration` (type: `object`): Optional proxy settings. Start without a proxy first and only add one if Fashion Nova begins throttling or blocking your runs. ## `debugMode` (type: `boolean`): Save request context and response bodies from failed requests to the key-value store. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.fashionnova.com/" } ], "enrichProductDetail": true, "maxItems": 100, "maxConcurrency": 15, "proxyConfiguration": { "useApifyProxy": false }, "debugMode": false } ``` # Actor output Schema ## `defaultView` (type: `string`): Complete dataset with every extracted field ## `overviewView` (type: `string`): Dataset view focused on the main product fields ## `files` (type: `string`): Key-value store artifacts such as failed request context, failed response bodies, and bootstrap config when debug mode is enabled # 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 = { "startUrls": [ { "url": "https://www.fashionnova.com/" } ], "enrichProductDetail": true, "maxItems": 100, "maxConcurrency": 15, "proxyConfiguration": { "useApifyProxy": false }, "debugMode": false }; // Run the Actor and wait for it to finish const run = await client.actor("hypebridge/fashionnova-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 = { "startUrls": [{ "url": "https://www.fashionnova.com/" }], "enrichProductDetail": True, "maxItems": 100, "maxConcurrency": 15, "proxyConfiguration": { "useApifyProxy": False }, "debugMode": False, } # Run the Actor and wait for it to finish run = client.actor("hypebridge/fashionnova-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 '{ "startUrls": [ { "url": "https://www.fashionnova.com/" } ], "enrichProductDetail": true, "maxItems": 100, "maxConcurrency": 15, "proxyConfiguration": { "useApifyProxy": false }, "debugMode": false }' | apify call hypebridge/fashionnova-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=hypebridge/fashionnova-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fashion Nova Scraper", "description": "Extracts product data from Fashion Nova using homepage URLs, category pages, search pages, and direct product URLs. It is built for product research, merchandising analysis, catalog monitoring, and structured exports you can use in spreadsheets, BI tools, or downstream automations.", "version": "0.0", "x-build-id": "HXRpUZVp4KXf1RfwH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/hypebridge~fashionnova-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-hypebridge-fashionnova-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/hypebridge~fashionnova-scraper/runs": { "post": { "operationId": "runs-sync-hypebridge-fashionnova-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/hypebridge~fashionnova-scraper/run-sync": { "post": { "operationId": "run-sync-hypebridge-fashionnova-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "startUrls": { "title": "Start URLs", "type": "array", "description": "Fashion Nova homepage, collection URLs, search URLs, or direct product URLs.", "default": [], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "enrichProductDetail": { "title": "Enrich product detail", "type": "boolean", "description": "Enrich discovered products. Disable for faster list-only snapshots.", "default": true }, "maxItems": { "title": "Max items", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Maximum number of product records to produce across the full run. Set to 0 for unlimited.", "default": 50 }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 25, "type": "integer", "description": "Crawler concurrency for bootstrap, listing, and product batch requests.", "default": 15 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional proxy settings. Start without a proxy first and only add one if Fashion Nova begins throttling or blocking your runs.", "default": { "useApifyProxy": false } }, "debugMode": { "title": "Debug mode", "type": "boolean", "description": "Save request context and response bodies from failed requests to the key-value store.", "default": false } } }, "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 } } } } } } } } } } ```