# Depop Scraper – Listings, Prices, Sellers & Demand (`yumitori/depop-market-intelligence`) Actor Scrape Depop by keyword and get hundreds to thousands of real listings — price, discount, sold-status, condition, size, brand, colour, seller and live like-count — plus exact demand counts and seller stats (followers, rating, items sold). No proxies, no browser, no login. - **URL**: https://apify.com/yumitori/depop-market-intelligence.md - **Developed by:** [Yakugusa Yumitori](https://apify.com/yumitori) (community) - **Categories:** E-commerce, SEO tools, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 listings 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 ## Depop Scraper – Resale Listings, Prices & Sellers #### Type a keyword → get hundreds to thousands of real Depop listings with price, sold-status, condition, size, brand, likes + exact demand counts and seller stats. ## ⚡ No proxies. No browser. No setup. Most Depop scrapers run a stealth **headless browser** (Camoufox / Playwright / Puppeteer) and make **you** bring and configure residential proxies — slow, expensive, fragile, and fiddly to set up. **This one doesn't.** Just type a keyword and run: | | **This actor** | Typical Depop scrapers | |---|---|---| | **Proxy setup** | ✅ **None — works out of the box** | ❌ You must supply & configure proxies | | **Engine** | ✅ Lightweight (no browser) | ❌ Headless browser (Camoufox/Playwright) | | **Speed** | ✅ Hundreds of listings in seconds | ❌ Browser pages load one at a time | | **Cost per run** | ✅ Low (no browser CPU/RAM) | ❌ High (heavy browser containers) | | **Demand data** | ✅ Exact live market counts | ❌ Listings only | | **Seller stats** | ✅ Followers / rating / items sold | ❌ Rarely included | | **Reliability** | ✅ No captchas, no browser flakiness | ❌ Breaks when the page UI changes | **Bottom line:** more data, faster and cheaper, with zero configuration — captured live from Depop, never estimated. --- ### 🛍️ What you get per listing | Field | Example | |---|---| | `price` / `original_price` / `discount_pct` | `50.00` / `170.00` / `71` | | `brand` | `Carhartt` | | `condition` | `used_excellent` | | `sizes` | `["L"]` | | `colours` | `["navy","blue"]` | | `like_count` | `277` ← per-listing demand signal | | `status` | `ONSALE` / `SOLD` | | `seller` + `url` | `@mila_mae` · link | | `category` / `department` / `location` | `Outerwear` / `Menswear` / `Lake Stevens, US` | | `photo` | image thumbnail | **Optional seller enrichment** adds `seller_followers`, `seller_rating`, `seller_reviews`, `seller_items_sold`, `seller_verified` to every listing — and a dedicated **Sellers** tab. Plus a per-keyword **run summary** (Key-Value store → `SUMMARY`): live demand total, department split, price min/median/max, % reduced, and top brands. --- ### 🎯 Best features - **Zero proxy setup** — it just works. No residential proxy account, no config. - **Live demand counts** — the *exact* number of listings for your keyword, split by department. Real market size, not a guess. - **Per-listing likes** — the clearest demand signal Depop exposes. Sort by it to see what's actually selling. - **Seller intelligence** — followers, rating, reviews and items-sold for the sellers behind every listing. - **Depth is a dial** — *Light* pulls a fast few-hundred sample; *Deep* pulls a few thousand. You choose coverage vs. speed. - **Captured live** — every field is read from Depop at run time, never modelled. --- ### Input | Field | Type | Default | Description | |---|---|---|---| | `searchTerms` | array | — | Keywords to harvest, e.g. `"carhartt jacket"`, `"y2k baby tee"`. | | `depth` | select | `light` | `light` ≈ a few hundred listings · `deep` ≈ a few thousand. | | `maxItemsPerTerm` | integer | `500` | Cap on listings kept per keyword. | | `enrichSellers` | boolean | `false` | Add seller followers / rating / items-sold (Sellers tab). | | `country` | string | `us` | Market for demand counts & pricing (ISO-2). | | `startUrls` | array | — | Depop search URLs (harvested like keywords) or shop URLs. | | `shopNames` | array | — | Seller handles, e.g. `"shadyvtg"` — pulls their stats directly. | **Minimal — just type a keyword:** ```json { "searchTerms": ["carhartt jacket"] } ```` **Full example:** ```json { "searchTerms": ["carhartt jacket", "y2k baby tee"], "depth": "light", "maxItemsPerTerm": 500, "enrichSellers": true, "country": "us" } ``` #### Coverage depth | Depth | Listings / keyword | |---|---| | **Light** | a few hundred | | **Deep** | a few thousand | For niche keywords, *Deep* approaches full-market coverage. For huge keywords it returns a deep, deduped, representative sample. *** ### Output Results land in two dataset views — **🛍️ Listings** and **🏪 Sellers** — plus a per-keyword **run summary** in the Key-Value store. Every record is clean JSON, ready for the API, MCP, or any downstream tool. #### 🛍️ A listing (keyword / search-URL run) Every listing is a complete record — here's one in full (`enrichSellers` on adds the `seller_*` fields): ```json { "id": 774289895, "url": "https://www.depop.com/products/chow_picky-vintage-carhartt-jacket-very-high-8a0b/", "photo": "https://media-photos.depop.com/b1/452092653/3909731495_…/P0.jpg", "brand": "Carhartt", "price": 50.0, "original_price": 170.0, "currency": "USD", "is_reduced": true, "discount_pct": 71, "status": "ONSALE", "condition": "used_good", "sizes": ["M"], "colours": ["navy", "blue"], "like_count": 61, "category": "Outerwear", "department": "Menswear", "product_type": "jackets", "description": "Vintage carhartt jacket very high quality and nice fade #90s #sick #workwear", "location": "Lake Stevens, United States", "country": "US", "username": "chow_picky", "search_term": "carhartt jacket", "seller": "chow_picks", "seller_followers": 27, "seller_rating": 5.0, "seller_reviews": 4, "seller_items_sold": 26, "seller_verified": false } ``` #### 🏪 A seller (shop-URL / `shopNames` run) ```json { "record_type": "seller", "seller": "shadyvtg", "seller_followers": 3053, "seller_rating": 3.95, "seller_reviews": 1674, "seller_items_sold": 10073, "seller_verified": true, "seller_url": "https://www.depop.com/shadyvtg/" } ``` #### 📊 Run summary (Key-Value store → `SUMMARY`) One entry per keyword — the market at a glance: ```json { "keyword": "carhartt jacket", "demand_total": 69514, "departments": { "menswear": 60362, "womenswear": 7604, "kidswear": 1442, "everything_else": 106 }, "harvested": 500, "price_min": 8.0, "price_median": 62.0, "price_max": 480.0, "reduced_count": 168, "top_brands": { "Carhartt": 451, "Carhartt WIP": 15, "Dickies": 13, "Nike": 6 } } ``` *** ### Use cases - **Resellers & vintage dealers** — price your inventory against the live market (median - distribution by condition and size); see which brands and styles get the most likes. - **Sourcing** — find high-demand, under-supplied niches before you buy stock. - **Brands & brand protection** — monitor the secondhand volume and pricing of your label, spot grey-market activity, and gauge brand health on the resale market. - **Market researchers & analysts** — measure demand via listing volume + engagement, benchmark resale prices against retail, and build datasets for fashion/circular-economy research. - **Competitor analysis** — see who the top sellers are for any keyword and how their catalogs are priced. *** ### Good to know — results mirror Depop's own search Your results are **exactly what Depop returns** for that keyword — faithful and unfiltered. So you may see listings whose `brand` isn't the literal word you typed. That's expected and correct: - **Style & aesthetic keywords aren't brands** — searching `y2k`, `vintage`, `streetwear` or `cottagecore` returns *every* brand tagged that way (American Vintage, Mossimo, Southpole…) plus lots of **`Other`** (unbranded thrift/vintage — which is most of that market). That spread *is* the real picture for a style search. - **Sub-brands & parent brands** — e.g. searching `jordan` also returns `Nike`-branded listings, because Air Jordan is a Nike line and sellers tag it either way. Searching `carhartt` returns `Carhartt WIP` too. - **Keyword in the title** — Depop matches your term against titles, tags and descriptions, so a listing that *mentions* your keyword can carry a different brand. This is what you want for **accurate market data** — if we silently dropped those, your price and demand numbers would be wrong (often a large share of a model's market is tagged under the parent brand). **Want just one exact brand?** Everything you need is already in the data — filter it in one click: 1. Open the **Storage → Dataset** tab (or export to CSV / Google Sheets). 2. Filter or sort by the **`brand`** column (e.g. keep only `brand = "Jordan"`). 3. The `top_brands` block in the run **summary** shows the full brand breakdown so you know what's in there before you filter. *** ### FAQ **Is scraping Depop legal?** This actor only collects **public** listing and shop data — pages anyone can view without logging in — and never bypasses access controls or touches private/account data. That's the lower-risk category, but you're responsible for using the data in line with Depop's Terms and any privacy rules (GDPR / CCPA) that apply to you. **Do I need a Depop login or account?** No. Everything comes from public pages — no login, no cookies, no account. **How fresh is the data?** Every field is read from Depop **live at run time** — never cached or estimated. Depop has no fixed refresh schedule, so you set the cadence: run it on the Apify **scheduler** (e.g. daily for price/inventory tracking, weekly for trends). **Can I get sold items and seller stats?** Yes — to the extent Depop shows them publicly, which this actor reads directly. Each listing carries its **status** (`ONSALE` / `SOLD`), and seller enrichment pulls **followers, rating, reviews and items-sold** from public shop pages. Most tools can't reliably surface these — we read them straight off the page. **Which markets does it cover?** Set the `country` input (ISO-2, e.g. `us`, `gb`, `au`) — demand counts and pricing reflect that market. *** ### Notes - Public Depop data only — no login, no personal/private data, no account access. - Junk/placeholder listings (e.g. "ISO" posts, sentinel prices) are filtered out. - Prices are in the market's currency (set by `country`). # Actor input Schema ## `searchTerms` (type: `array`): Keywords to harvest, e.g. "carhartt jacket", "y2k baby tee", "stussy". Each returns deduped active listings with full attributes + live demand counts. ## `startUrls` (type: `array`): Paste Depop URLs instead of (or with) keywords. A search URL (depop.com/search/?q=…) is harvested like a keyword. A shop URL (depop.com/username) — or a product URL (depop.com/products/username-…) — returns that seller's public stats (followers, rating, items sold). ## `shopNames` (type: `array`): Seller usernames to pull public stats for (followers, rating, reviews, items sold). e.g. "shadyvtg". One billed record each. ## `depth` (type: `string`): Light ≈ a few hundred listings/keyword in ~45s (price × sort × colour). Deep ≈ a few thousand/keyword in ~2–3 min (full price × colour cross-product). Depth is a cost/coverage dial. ## `maxItemsPerTerm` (type: `integer`): Hard cap on deduped listings kept per keyword (after junk-filtering). Each kept listing is a billed event. ## `enrichSellers` (type: `boolean`): Add the seller's followers / rating / reviews / items-sold to every listing (one extra page per unique seller, cached). Off = faster, listings only. ## `country` (type: `string`): Country context for demand counts and pricing (ISO-2, e.g. us, gb, au). ## Actor input object example ```json { "searchTerms": [ "carhartt jacket" ], "startUrls": [], "shopNames": [], "depth": "light", "maxItemsPerTerm": 500, "enrichSellers": false, "country": "us" } ``` # Actor output Schema ## `listings` (type: `string`): All harvested listings and seller records. ## `summary` (type: `string`): Per-keyword demand, price stats, top brands. # 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 = { "searchTerms": [ "carhartt jacket" ], "startUrls": [], "shopNames": [] }; // Run the Actor and wait for it to finish const run = await client.actor("yumitori/depop-market-intelligence").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 = { "searchTerms": ["carhartt jacket"], "startUrls": [], "shopNames": [], } # Run the Actor and wait for it to finish run = client.actor("yumitori/depop-market-intelligence").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 '{ "searchTerms": [ "carhartt jacket" ], "startUrls": [], "shopNames": [] }' | apify call yumitori/depop-market-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=yumitori/depop-market-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Depop Scraper – Listings, Prices, Sellers & Demand", "description": "Scrape Depop by keyword and get hundreds to thousands of real listings — price, discount, sold-status, condition, size, brand, colour, seller and live like-count — plus exact demand counts and seller stats (followers, rating, items sold). No proxies, no browser, no login.", "version": "0.1", "x-build-id": "f7v3lRigoxCPERZWj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/yumitori~depop-market-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-yumitori-depop-market-intelligence", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/yumitori~depop-market-intelligence/runs": { "post": { "operationId": "runs-sync-yumitori-depop-market-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/yumitori~depop-market-intelligence/run-sync": { "post": { "operationId": "run-sync-yumitori-depop-market-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchTerms": { "title": "Search keywords", "uniqueItems": true, "type": "array", "description": "Keywords to harvest, e.g. \"carhartt jacket\", \"y2k baby tee\", \"stussy\". Each returns deduped active listings with full attributes + live demand counts.", "items": { "type": "string" } }, "startUrls": { "title": "Depop URLs (search / shop / product)", "uniqueItems": true, "type": "array", "description": "Paste Depop URLs instead of (or with) keywords. A search URL (depop.com/search/?q=…) is harvested like a keyword. A shop URL (depop.com/username) — or a product URL (depop.com/products/username-…) — returns that seller's public stats (followers, rating, items sold).", "items": { "type": "string" } }, "shopNames": { "title": "Shop names / @handles", "type": "array", "description": "Seller usernames to pull public stats for (followers, rating, reviews, items sold). e.g. \"shadyvtg\". One billed record each.", "items": { "type": "string" } }, "depth": { "title": "Coverage depth", "enum": [ "light", "deep" ], "type": "string", "description": "Light ≈ a few hundred listings/keyword in ~45s (price × sort × colour). Deep ≈ a few thousand/keyword in ~2–3 min (full price × colour cross-product). Depth is a cost/coverage dial.", "default": "light" }, "maxItemsPerTerm": { "title": "Max listings per keyword", "minimum": 10, "maximum": 5000, "type": "integer", "description": "Hard cap on deduped listings kept per keyword (after junk-filtering). Each kept listing is a billed event.", "default": 500 }, "enrichSellers": { "title": "Enrich with seller stats", "type": "boolean", "description": "Add the seller's followers / rating / reviews / items-sold to every listing (one extra page per unique seller, cached). Off = faster, listings only.", "default": false }, "country": { "title": "Market (country)", "type": "string", "description": "Country context for demand counts and pricing (ISO-2, e.g. us, gb, au).", "default": "us" } } }, "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 } } } } } } } } } } ```