# Wildberries Seller Scraper: Supplier Intel & Stats (`getascraper/wildberries-seller-scraper`) Actor Build full seller dossiers from Wildberries: the company behind each store (name, INN tax ID, address, trademark) plus catalog stats (product count, brands, price range, total reviews, average rating, stock). Profile sellers by ID, store URL, or keyword. Fast and reliable, no login. - **URL**: https://apify.com/getascraper/wildberries-seller-scraper.md - **Developed by:** [GetAScraper](https://apify.com/getascraper) (community) - **Categories:** E-commerce, Automation, Developer tools - **Stats:** 4 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.25 / 1,000 result rows 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
Profile any Wildberries seller: legal entity plus catalog intelligence. Turn a seller ID, store URL, or keyword into a full dossier: the registered company (name, tax ID, address, trademark) plus catalog stats like product count, brands, price range, reviews, rating, and stock. Built for competitor research, sourcing, and lead generation.
Wildberries (Вайлдберриз) suite   •  Products, prices, ranks, reviews, and sellers from Wildberries
 Price & Rank Tracker
Daily price and rank monitoring 		 Product Search
Search, catalog and live prices 		 Reviews Scraper
Ratings, text and buyer photos 		 Seller Scraper
➤ You are here
**Get the full company profile and catalog intelligence behind any Wildberries seller in seconds.** Turn a seller ID, store URL, or search keyword into a complete dossier: the registered company (name, tax ID, legal address, trademark) plus catalog stats like product count, brands carried, price range, total reviews, average rating, and stock levels. Built for competitor research, sourcing, and lead generation on Russia's largest marketplace. Runs fast and reliably with no login, and exports clean data you can open in Excel, Google Sheets, or your own tools. ### 🔍 What does Wildberries Seller Scraper do? This Actor profiles sellers on [Wildberries](https://www.wildberries.ru), the largest online marketplace in Russia and the CIS. For each seller it combines two layers of public data into one row: the **company record** (display name, full legal name, tax IDs, registered address, trademark) and **catalog statistics** computed across the seller's products (how many products they list, which brands they carry, their price range, total customer reviews, average rating, and units in stock). Most scrapers only tag each product with a seller name. This one returns a true per-seller intelligence record, so you get the whole picture of a store in a single line of data.
⚡ A whole store in one row. No account or login required. Get a seller's legal entity plus catalog stats (products, brands, price band, reviews, inventory) in a single dossier, ready to schedule.
### 💡 Why use Wildberries Seller Scraper? "I source products to resell, and I need to size up a competitor fast. One run tells me how many products a seller lists, their price band, and how many reviews they pull, so I know in seconds whether they are a serious player." "I build prospect lists for a B2B service. I feed in a category keyword and get back every seller behind the top products, complete with their legal company name and tax ID for outreach and verification." "I vet suppliers before placing wholesale orders. Seeing the registered legal entity and address next to their catalog size and ratings saves me from dealing with fly-by-night stores." "I track a market over time. I schedule this weekly and watch how a seller's product count, pricing, and review totals move, which is a clean demand signal for the whole category." ### 🚀 How to use Wildberries Seller Scraper 1. Open the input form. 2. Add one or more sources: seller IDs, seller store URLs, or search keywords. 3. Optional: turn on "Include product rows" for a full per-product breakdown, or set a region for local pricing. 4. Click Start. When the run finishes, download the data as JSON, CSV, Excel, or HTML, or pull it from your account. ### ⚙️ Input At least one source is required: seller IDs, seller URLs, or search keywords. | Field | Type | Required | Description | |---|---|---|---| | `sellerIds` | array of IDs | No | Wildberries seller IDs (the number in a seller store URL). | | `sellerUrls` | array of URLs | No | Seller store page URLs. The ID is read from each automatically. | | `searchQueries` | array of text | No | Product keywords. The Actor finds the sellers behind matching products and profiles them. | | `includeProducts` | boolean | No | Also return one row per product in each seller's catalog. | | `maxProductsPerSeller` | integer | No | How many catalog products to scan per seller when computing stats. Higher is more accurate and costs more. | | `maxSellersPerSearch` | integer | No | Cap on how many distinct sellers to profile per keyword. | | `maxItems` | integer | No | Hard cap on total rows returned. | | `region` | enum | No | Region used for catalog pricing and availability. | ### 📦 Output Each seller becomes one dossier row. You can download the dataset as JSON, CSV, Excel, or HTML. ```json { "recordType": "seller", "supplierId": "1418867", "supplierName": "OOO TAYKONG", "supplierFullName": "OBSHCHESTVO S OGRANICHENNOY OTVETSTVENNOSTYU TAYKONG", "inn": "0800011139", "kpp": "771401001", "ogrn": "1230800005523", "legalAddress": "125315, Moscow, proezd Bolshoy Koptevsky, 3, str. 1", "trademark": "JOYCITY", "sellerUrl": "https://www.wildberries.ru/seller/1418867", "productCount": 622, "scannedProducts": 200, "brandCount": 1, "brands": ["JOYCITY"], "priceMinRub": 388, "priceMaxRub": 3163, "priceAvgRub": 1559, "totalReviews": 477847, "avgRating": 5, "totalInventory": 10119, "inStockProducts": 200, "scrapedAt": "2026-06-29T19:42:30.995Z" } ```` ### 📊 Data table | Field | Type | Description | |---|---|---| | `supplierId` | string | Wildberries seller ID. | | `supplierName` | string | Seller display name. | | `supplierFullName` | string | Full registered legal name. | | `inn` | string | Tax identification number, when published. | | `kpp` | string | Tax registration reason code, when published. | | `ogrn` | string | State registration number, when published. | | `legalAddress` | string | Registered legal address. | | `trademark` | string | Registered trademark. | | `sellerUrl` | string | Link to the seller store page. | | `productCount` | integer | Total products in the seller's catalog. | | `scannedProducts` | integer | Products actually scanned for the stats below. | | `brandCount` | integer | Number of distinct brands the seller carries. | | `brands` | array | List of brands carried. | | `priceMinRub` | integer | Lowest product price in rubles. | | `priceMaxRub` | integer | Highest product price in rubles. | | `priceAvgRub` | integer | Average product price in rubles. | | `totalReviews` | integer | Sum of review counts across products, a demand signal. | | `avgRating` | number | Average product rating. | | `totalInventory` | integer | Total units in stock across scanned products. | | `inStockProducts` | integer | Number of scanned products currently in stock. | | `scrapedAt` | string | Timestamp of the run. | With product rows enabled, extra rows carry per-product `name`, `brand`, `priceRub`, `rating`, `feedbacks`, and `productUrl`. ### 💰 How much does it cost to scrape Wildberries sellers? Pricing is pay per result, so you only pay for the data you actually get. There are no subscriptions and no monthly fees, and runs that return nothing cost nothing. Each seller dossier scans that seller's catalog, so you can lower the products scanned per seller for cheaper, faster runs when you only need headline stats. ### ✨ Tips - Start with a smaller products-per-seller value for fast headline stats, then raise it when you need precise price and inventory aggregates on large catalogs. - Use search keywords to map every seller in a category in one run. - Turn on product rows only when you need the full catalog, since it multiplies the number of rows returned. ### ❓ FAQ #### Is it legal to scrape Wildberries seller data? This Actor collects publicly available marketplace and business-registry information that Wildberries already shows on its own pages. You are responsible for following Wildberries' terms and any data-protection laws that apply to how you use the data. #### Why are some legal fields empty for a seller? Wildberries publishes different details for different sellers. Sole proprietors, for example, often have no registration code or legal address. Missing values are left out rather than filled with fake placeholders, so every field you see is real. #### Do I need an account or login for the target site? No. You do not need any Wildberries login or cookies. Just provide sellers or keywords and run it. #### Can I get every product for a seller, not just the stats? Yes. Turn on the product rows option and the Actor returns one row per product in the seller's catalog alongside the dossier. #### Found a problem or need another field? Open a ticket on the Issues tab of this Actor. Custom data fields and tailored solutions are available on request. # Actor input Schema ## `sellerIds` (type: `array`): Wildberries supplier IDs (the numeric ID in a seller URL, e.g. '1000168'). ## `sellerUrls` (type: `array`): Wildberries seller page URLs (e.g. 'https://www.wildberries.ru/seller/1000168'). The ID is extracted automatically. ## `searchQueries` (type: `array`): Russian or English product keywords. The Actor finds the sellers behind matching products, then profiles them. ## `includeProducts` (type: `boolean`): Also emit one row per product in each seller's catalog (in addition to the seller dossier row). ## `maxProductsPerSeller` (type: `integer`): How many catalog products to scan per seller when computing aggregates (0 = all). Higher = more accurate stats, more cost. ## `maxSellersPerSearch` (type: `integer`): When using search queries, cap how many distinct sellers to profile per keyword. ## `maxItems` (type: `integer`): Hard cap on total dataset rows across all sellers (0 = unlimited). ## `region` (type: `string`): Region used for catalog pricing and availability. ## `debugLogging` (type: `boolean`): Print granular tracing tags (SEARCH, PROFILE, CATALOG, EMIT) to the run logs. ## `proxyConfiguration` (type: `object`): Proxy is required: Wildberries returns 429 on direct catalog requests. Datacenter proxies (the default) are fast, cheap, and work reliably here. Switch to Residential only if a very large run gets rate-limited. ## Actor input object example ```json { "sellerIds": [], "sellerUrls": [], "searchQueries": [ "кроссовки" ], "includeProducts": false, "maxProductsPerSeller": 1000, "maxSellersPerSearch": 10, "maxItems": 1000, "region": "MOSCOW", "debugLogging": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "sellerIds": [], "sellerUrls": [], "searchQueries": [ "кроссовки" ], "includeProducts": false, "maxProductsPerSeller": 1000, "maxSellersPerSearch": 10, "maxItems": 1000, "region": "MOSCOW", "debugLogging": false, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("getascraper/wildberries-seller-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 = { "sellerIds": [], "sellerUrls": [], "searchQueries": ["кроссовки"], "includeProducts": False, "maxProductsPerSeller": 1000, "maxSellersPerSearch": 10, "maxItems": 1000, "region": "MOSCOW", "debugLogging": False, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("getascraper/wildberries-seller-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 '{ "sellerIds": [], "sellerUrls": [], "searchQueries": [ "кроссовки" ], "includeProducts": false, "maxProductsPerSeller": 1000, "maxSellersPerSearch": 10, "maxItems": 1000, "region": "MOSCOW", "debugLogging": false, "proxyConfiguration": { "useApifyProxy": true } }' | apify call getascraper/wildberries-seller-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getascraper/wildberries-seller-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wildberries Seller Scraper: Supplier Intel & Stats", "description": "Build full seller dossiers from Wildberries: the company behind each store (name, INN tax ID, address, trademark) plus catalog stats (product count, brands, price range, total reviews, average rating, stock). Profile sellers by ID, store URL, or keyword. Fast and reliable, no login.", "version": "1.0", "x-build-id": "IKXhWlbsOy8zlDnet" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getascraper~wildberries-seller-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getascraper-wildberries-seller-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getascraper~wildberries-seller-scraper/runs": { "post": { "operationId": "runs-sync-getascraper-wildberries-seller-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getascraper~wildberries-seller-scraper/run-sync": { "post": { "operationId": "run-sync-getascraper-wildberries-seller-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "sellerIds": { "title": "Seller IDs", "type": "array", "description": "Wildberries supplier IDs (the numeric ID in a seller URL, e.g. '1000168').", "default": [] }, "sellerUrls": { "title": "Seller URLs", "type": "array", "description": "Wildberries seller page URLs (e.g. 'https://www.wildberries.ru/seller/1000168'). The ID is extracted automatically.", "default": [], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "searchQueries": { "title": "Search queries", "type": "array", "description": "Russian or English product keywords. The Actor finds the sellers behind matching products, then profiles them.", "default": [] }, "includeProducts": { "title": "Include product rows", "type": "boolean", "description": "Also emit one row per product in each seller's catalog (in addition to the seller dossier row).", "default": false }, "maxProductsPerSeller": { "title": "Max products per seller", "minimum": 0, "maximum": 100000, "type": "integer", "description": "How many catalog products to scan per seller when computing aggregates (0 = all). Higher = more accurate stats, more cost.", "default": 1000 }, "maxSellersPerSearch": { "title": "Max sellers per search query", "minimum": 1, "maximum": 1000, "type": "integer", "description": "When using search queries, cap how many distinct sellers to profile per keyword.", "default": 10 }, "maxItems": { "title": "Maximum total rows", "minimum": 0, "maximum": 1000000, "type": "integer", "description": "Hard cap on total dataset rows across all sellers (0 = unlimited).", "default": 1000 }, "region": { "title": "Warehouse region", "enum": [ "MOSCOW", "KRASNODAR", "NOVOSIBIRSK", "EKATERINBURG", "KAZAHSTAN", "ST_PETERSBURG" ], "type": "string", "description": "Region used for catalog pricing and availability.", "default": "MOSCOW" }, "debugLogging": { "title": "Verbose debug logs", "type": "boolean", "description": "Print granular tracing tags (SEARCH, PROFILE, CATALOG, EMIT) to the run logs.", "default": false }, "proxyConfiguration": { "title": "Proxy setup", "type": "object", "description": "Proxy is required: Wildberries returns 429 on direct catalog requests. Datacenter proxies (the default) are fast, cheap, and work reliably here. Switch to Residential only if a very large run gets rate-limited.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```