# Fashionphile Luxury Handbag Listings Scraper (`jungle_synthesizer/fashionphile-luxury-handbag-listings-scraper`) Actor Scrapes all pre-owned luxury handbag, jewelry, and accessories listings from Fashionphile. Returns structured product data including brand, model, condition, price, compare-at price, inventory status, and full tag facets for resale market analysis and cross-platform price comparison. - **URL**: https://apify.com/jungle\_synthesizer/fashionphile-luxury-handbag-listings-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Fashionphile Luxury Handbag Listings Scraper Scrapes all pre-owned luxury handbag, jewelry, watch, and accessories listings from Fashionphile — one of the three dominant US luxury consignment platforms. Returns structured product data including brand, model, condition, price, compare-at price, inventory status, and full tag facets. ### What it does Paginates Fashionphile's Shopify storefront JSON API (`/products.json`) until all listings are collected. No browser automation is required — the endpoint returns clean structured data directly. Optional filters let you narrow to a specific brand (e.g. Louis Vuitton, Chanel, Hermes) or product type (Handbags, Jewelry, Watches, Accessories, Shoes). ### Use cases - **Resale arbitrage** — Monitor Fashionphile prices to find items listed below Rebag or The RealReal for the same model. Cross-platform comparison is the most common buyer intent ("fashionphile vs rebag", 70 searches/mo; "fashionphile vs real real", 140 searches/mo). - **Market research** — Track secondary-market pricing by brand, model, and condition over time. Luxury brands and appraisers use secondary-market data to benchmark authentication and insurance valuations. - **Inventory monitoring** — Track which Hermes/Chanel/LV models are moving fastest by watching `available` status and `inventory_quantity` across runs. - **Price intelligence** — Compare `price_usd` vs `compare_at_price_usd` to identify markdown depth across brands. ### Input parameters | Field | Type | Description | Default | |-------|------|-------------|---------| | `maxItems` | integer | Maximum listings to return. Omit to scrape the full catalog (~25k–40k items). | 10 | | `brand` | string | Filter to a specific designer (e.g. `Louis Vuitton`, `Chanel`, `Hermes`, `Cartier`). Case-sensitive — match Fashionphile's vendor names. | (all brands) | | `productType` | string | Filter by category: `Handbags`, `Jewelry`, `Watches`, `Accessories`, `Shoes`. | (all types) | ### Output schema Each record maps to one Fashionphile listing (one unique consignment item): | Field | Type | Description | |-------|------|-------------| | `listing_id` | string | Shopify product ID | | `variant_id` | string | Shopify variant ID (one per item) | | `sku` | string | Fashionphile internal SKU | | `title` | string | Listing title (e.g. `Epi Speedy 25 Black`) | | `brand` | string | Designer brand (e.g. `Louis Vuitton`, `Chanel`, `Hermes`) | | `product_type` | string | Category (Bags, Jewelry, Watches, Accessories, Shoes) | | `model` | string | Model name parsed from title or tags | | `condition` | string | Condition grade where available (Excellent / Very Good / Good) | | `color` | string | Color extracted from tags or title | | `material` | string | Leather/material type where available (Epi, Caviar, Togo, etc.) | | `price_usd` | number | Current listing price in USD | | `compare_at_price_usd` | number | Original/compare-at price where present | | `available` | boolean | Whether the item is in stock | | `inventory_quantity` | integer | Inventory count (typically 0 or 1 for consignment items) | | `tags` | string[] | Shopify tags (brand, category, material facets) | | `handle` | string | URL slug | | `product_url` | string | Full URL to the listing | | `image_urls` | string[] | Product image URLs | | `published_at` | string | ISO timestamp of listing publication | | `updated_at` | string | ISO timestamp of last update | | `scraped_at` | string | ISO timestamp of scrape | ### Sample output ```json { "listing_id": "16012364841263", "variant_id": "58001832739119", "sku": "1884572", "title": "Taurillon Clemence Birkin 35 Rose Jaipur", "brand": "Hermes", "product_type": "Bags", "model": "Taurillon Clemence Birkin", "condition": "", "color": "", "material": "Clemence", "price_usd": 10500.0, "compare_at_price_usd": 10500.0, "available": false, "inventory_quantity": 0, "tags": ["tiktok-market-template-shoulder-bags", "US"], "handle": "hermes-taurillon-clemence-birkin-35-rose-jaipur-1884572", "product_url": "https://www.fashionphile.com/p/hermes-taurillon-clemence-birkin-35-rose-jaipur-1884572", "image_urls": ["https://cdn.fashionphile.com/..."], "published_at": "2026-05-15T00:00:00-07:00", "updated_at": "2026-05-28T00:00:00-07:00", "scraped_at": "2026-05-28T17:12:24.714Z" } ```` ### Technical notes - **No proxy required.** The Fashionphile Shopify products.json endpoint is publicly accessible with no anti-bot protection. - **Pagination.** The scraper uses Shopify's standard `?page=N&limit=250` pattern and stops when an empty page is returned. The full catalog takes around 100–160 API calls. - **Condition field.** Fashionphile's products.json does not consistently expose condition in structured fields — the `condition` value is populated where available from tags. For the most reliable condition data, check the Fashionphile product page directly using the `product_url` field. - **Filters are additive.** Providing both `brand` and `productType` returns items matching both constraints. - **Rate limiting.** The scraper uses a conservative 400ms delay between requests to avoid overloading Fashionphile's servers. ### Cross-platform comparison Fashionphile is part of the US luxury resale triopoly alongside Rebag and The RealReal. This actor shares an output schema with the companion Rebag Luxury Listings Scraper, enabling direct cross-platform joins on `brand` + model text for arbitrage and market research workflows. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of records to scrape ## `brand` (type: `string`): Filter listings by brand/designer (e.g. "Louis Vuitton", "Chanel", "Hermes"). Leave blank to scrape all brands. ## `productType` (type: `string`): Filter by product category: Handbags, Jewelry, Watches, Accessories, Shoes. Leave blank to scrape all types. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "brand": "", "productType": "" }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/fashionphile-luxury-handbag-listings-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "brand": "", "productType": "", } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/fashionphile-luxury-handbag-listings-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "brand": "", "productType": "" }' | apify call jungle_synthesizer/fashionphile-luxury-handbag-listings-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/fashionphile-luxury-handbag-listings-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fashionphile Luxury Handbag Listings Scraper", "description": "Scrapes all pre-owned luxury handbag, jewelry, and accessories listings from Fashionphile. Returns structured product data including brand, model, condition, price, compare-at price, inventory status, and full tag facets for resale market analysis and cross-platform price comparison.", "version": "0.1", "x-build-id": "toBKYEF90WpQvHNsZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~fashionphile-luxury-handbag-listings-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-fashionphile-luxury-handbag-listings-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/jungle_synthesizer~fashionphile-luxury-handbag-listings-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-fashionphile-luxury-handbag-listings-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/jungle_synthesizer~fashionphile-luxury-handbag-listings-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-fashionphile-luxury-handbag-listings-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": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape" }, "brand": { "title": "Brand Filter", "type": "string", "description": "Filter listings by brand/designer (e.g. \"Louis Vuitton\", \"Chanel\", \"Hermes\"). Leave blank to scrape all brands." }, "productType": { "title": "Product Type Filter", "type": "string", "description": "Filter by product category: Handbags, Jewelry, Watches, Accessories, Shoes. Leave blank to scrape all types." } } }, "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 } } } } } } } } } } ```