# Wildberries Reviews Scraper: Ratings, Text & Photos (`getascraper/wildberries-reviews-scraper`) Actor Scrape every customer review from Wildberries.ru products: star ratings, review text, pros and cons, size and fit feedback, reviewer photos, and seller replies. Input a product URL, NM-ID, or search keyword. Uses the private feedbacks API, no browser needed. - **URL**: https://apify.com/getascraper/wildberries-reviews-scraper.md - **Developed by:** [GetAScraper](https://apify.com/getascraper) (community) - **Categories:** E-commerce, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.94 / 1,000 reviews 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
Extract every Wildberries customer review at scale. Get star ratings, full review text, pros and cons, size and fit feedback, buyer photos, and seller replies for any product on wildberries.ru. Built for sentiment analysis, product research, and brand monitoring.
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
➤ You are here 		 Seller Scraper
Supplier intel and shop stats
Extract every **customer review from Wildberries.ru** at scale: star ratings, full review text, pros and cons, size and fit feedback, reviewer photos, and seller replies. Feed it a **product URL, NM-ID, or search keyword** and get a clean, structured dataset in seconds. Built for sentiment analysis, product research, brand monitoring, and AI training data. No browser, no manual copying. ### 🔍 What does Wildberries Reviews Scraper do? This Actor scrapes product reviews from [Wildberries](https://www.wildberries.ru), Russia's largest online marketplace. Give it any combination of search keywords, product page URLs, or product NM-IDs, and it returns every review for those products: rating, written feedback, pros and cons, fit feedback, customer photos, helpfulness votes, and the seller's reply. It runs on the Apify platform with API access, scheduling, integrations (Make, Zapier, Google Sheets), proxy rotation, and run monitoring built in.
⚡ Every review, structured. No account or login required. Pull ratings, text, pros and cons, fit feedback, and buyer photos for any product, ready for sentiment analysis or AI training data.
### 💡 Why use Wildberries Reviews Scraper? - **Sentiment & product research** - analyze what customers love and complain about across thousands of reviews. - **Brand & reputation monitoring** - track ratings and feedback for your products or a competitor's. - **Retail & sourcing intel** - read real buyer experiences before stocking a product. - **AI / NLP training data** - collect labeled, rating-tagged review text at volume. - **Fit & sizing insight** - the size feedback field is gold for apparel and footwear sellers. ### 🚀 How to use Wildberries Reviews Scraper 1. Open the **Input** tab. 2. Add **search keywords** (e.g. `кроссовки`), paste **product URLs**, or enter **NM-IDs**. 3. Optionally set filters: rating range, only-with-photos, only-with-text, or a date range. 4. Click **Start**. Download the results as JSON, CSV, Excel, or HTML, or pull them via the API. ### ⚙️ Input | Field | Description | |---|---| | `searchQueries` | Keywords to find products, then scrape their reviews | | `productUrls` | Direct Wildberries product page URLs | | `nmIds` | Specific product NM-IDs | | `minRating` / `maxRating` | Keep only reviews within a star range (1-5) | | `onlyWithText` | Skip rating-only reviews | | `onlyWithPhotos` | Keep only reviews with customer photos | | `dateFrom` / `dateTo` | Restrict to a date window (YYYY-MM-DD) | | `sortBy` | newest, oldest, highest, lowest, or most helpful | | `maxProductsPerSearch` | Cap products pulled per search keyword | | `maxReviewsPerProduct` | Cap reviews per product (0 = all) | | `maxItems` | Hard cap on total reviews (0 = unlimited) | ### 📦 Output You can download the dataset in various formats such as JSON, HTML, CSV, or Excel. Each row is one review: ```json { "reviewId": "abc123", "nmId": "585408655", "imtId": "12345678", "productName": "Кроссовки беговые", "brand": "Nike", "rating": 5, "text": "Отличные кроссовки, очень удобные!", "pros": "Удобные, легкие", "cons": "Маломерят", "sizeFeedback": "runs small", "color": "черный", "size": "42", "authorName": "Анна", "createdDate": "2026-05-14T08:21:00Z", "votesUp": 12, "votesDown": 0, "sellerReply": "Спасибо за отзыв!", "photoUrls": ["https://feedback01.wbbasket.ru/.../fs.webp"], "productUrl": "https://www.wildberries.ru/catalog/585408655/detail.aspx" } ```` ### 📊 Output data fields | Field | Description | |---|---| | `reviewId` | Unique review identifier | | `nmId` / `imtId` | Product NM-ID and root product ID | | `productName`, `brand` | Product identity | | `rating` | Star rating, 1-5 | | `text`, `pros`, `cons` | Written feedback | | `sizeFeedback` | Fit: true to size / runs small / runs large | | `color`, `size` | Variant the reviewer bought | | `authorName`, `authorCountry` | Reviewer identity | | `createdDate` | When the review was posted | | `votesUp`, `votesDown` | Helpfulness votes | | `sellerReply`, `sellerReplyDate` | Seller's response, if any | | `photoUrls` | Customer photo URLs | | `bables` | Auto-generated review tags | | `productUrl` | Link to the product | ### 💰 How much does it cost to scrape Wildberries reviews? This Actor uses pay-per-result pricing at **$3.99 per 1,000 reviews**. A run pulling 5,000 reviews costs about $20. Use `maxItems`, `maxReviewsPerProduct`, and `maxProductsPerSearch` to control spend. ### ✨ Tips - Use `minRating: 1, maxRating: 2` to mine complaints and quality issues fast. - Set `onlyWithPhotos: true` for visual reviews when researching real-world product condition. - For broad keywords, keep `maxProductsPerSearch` low to avoid scraping reviews for hundreds of products. ### ❓ FAQ, disclaimers, and support **Is scraping Wildberries reviews legal?** This Actor collects publicly available review data. You are responsible for complying with Wildberries' Terms of Service and applicable laws (including data-protection rules) in your use of the data. Do not use it to collect or process personal data unlawfully. **Why are some products missing reviews?** Products with zero reviews return nothing. The Actor also tries a fallback API shard automatically. **Found a bug or need a field added?** Open an issue on the Actor's **Issues** tab. Custom solutions are available on request. # Actor input Schema ## `searchQueries` (type: `array`): Russian or English product keywords (e.g. 'iphone', 'кроссовки'). The Actor finds matching products, then scrapes their reviews. Leave empty if using Product URLs or NM-IDs. ## `productUrls` (type: `array`): Direct Wildberries product page URLs (e.g. 'https://www.wildberries.ru/catalog/585408655/detail.aspx'). The NM-ID is extracted automatically. ## `nmIds` (type: `array`): Scrape reviews for specific products by their Wildberries NM-ID (the numeric part of the product URL, e.g. '585408655'). ## `minRating` (type: `integer`): Only include reviews with a star rating at or above this value (1-5). Set to 1 for all reviews; set higher to skip low ratings. ## `maxRating` (type: `integer`): Only include reviews with a star rating at or below this value (1-5). Set to 2 with min rating 1 to mine complaints. ## `onlyWithText` (type: `boolean`): Skip rating-only reviews that have no written feedback. ## `onlyWithPhotos` (type: `boolean`): Return only reviews that include customer photos. ## `dateFrom` (type: `string`): Only include reviews created on or after this date. Leave empty for no lower bound. ## `dateTo` (type: `string`): Only include reviews created on or before this date. Leave empty for no upper bound. ## `sortBy` (type: `string`): Order reviews within each product before applying limits. ## `maxProductsPerSearch` (type: `integer`): When using search queries, cap how many matching products to pull reviews for. Controls cost on broad keywords. ## `maxReviewsPerProduct` (type: `integer`): Maximum reviews to return for each product (0 = all available reviews). ## `maxItems` (type: `integer`): Hard cap on total reviews across all products (0 = unlimited). Defaults to 1000. ## `debugLogging` (type: `boolean`): Print granular tracing tags (RESOLVE, FEEDBACKS, EMIT) to the run logs. ## `proxyConfiguration` (type: `object`): Proxy is required: Wildberries returns 429 on direct requests. Datacenter proxies (the default) are fast, cheap, and work reliably here. Switch to Residential only if a very large run starts getting rate-limited. ## Actor input object example ```json { "searchQueries": [ "кроссовки" ], "productUrls": [], "nmIds": [], "minRating": 1, "maxRating": 5, "onlyWithText": false, "onlyWithPhotos": false, "dateFrom": "", "dateTo": "", "sortBy": "newest", "maxProductsPerSearch": 10, "maxReviewsPerProduct": 0, "maxItems": 1000, "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 = { "searchQueries": [ "кроссовки" ], "productUrls": [], "nmIds": [], "minRating": 1, "maxRating": 5, "onlyWithText": false, "onlyWithPhotos": false, "dateFrom": "", "dateTo": "", "sortBy": "newest", "maxProductsPerSearch": 10, "maxReviewsPerProduct": 0, "maxItems": 1000, "debugLogging": false, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("getascraper/wildberries-reviews-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 = { "searchQueries": ["кроссовки"], "productUrls": [], "nmIds": [], "minRating": 1, "maxRating": 5, "onlyWithText": False, "onlyWithPhotos": False, "dateFrom": "", "dateTo": "", "sortBy": "newest", "maxProductsPerSearch": 10, "maxReviewsPerProduct": 0, "maxItems": 1000, "debugLogging": False, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("getascraper/wildberries-reviews-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 '{ "searchQueries": [ "кроссовки" ], "productUrls": [], "nmIds": [], "minRating": 1, "maxRating": 5, "onlyWithText": false, "onlyWithPhotos": false, "dateFrom": "", "dateTo": "", "sortBy": "newest", "maxProductsPerSearch": 10, "maxReviewsPerProduct": 0, "maxItems": 1000, "debugLogging": false, "proxyConfiguration": { "useApifyProxy": true } }' | apify call getascraper/wildberries-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getascraper/wildberries-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wildberries Reviews Scraper: Ratings, Text & Photos", "description": "Scrape every customer review from Wildberries.ru products: star ratings, review text, pros and cons, size and fit feedback, reviewer photos, and seller replies. Input a product URL, NM-ID, or search keyword. Uses the private feedbacks API, no browser needed.", "version": "1.0", "x-build-id": "4HvgaIcmOLN9uZQHq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getascraper~wildberries-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getascraper-wildberries-reviews-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getascraper~wildberries-reviews-scraper/runs": { "post": { "operationId": "runs-sync-getascraper-wildberries-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getascraper~wildberries-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-getascraper-wildberries-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQueries": { "title": "Search queries", "type": "array", "description": "Russian or English product keywords (e.g. 'iphone', 'кроссовки'). The Actor finds matching products, then scrapes their reviews. Leave empty if using Product URLs or NM-IDs.", "default": [] }, "productUrls": { "title": "Product URLs", "type": "array", "description": "Direct Wildberries product page URLs (e.g. 'https://www.wildberries.ru/catalog/585408655/detail.aspx'). The NM-ID is extracted automatically.", "default": [], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "nmIds": { "title": "Product NM-IDs", "type": "array", "description": "Scrape reviews for specific products by their Wildberries NM-ID (the numeric part of the product URL, e.g. '585408655').", "default": [] }, "minRating": { "title": "Minimum rating", "minimum": 1, "maximum": 5, "type": "integer", "description": "Only include reviews with a star rating at or above this value (1-5). Set to 1 for all reviews; set higher to skip low ratings.", "default": 1 }, "maxRating": { "title": "Maximum rating", "minimum": 1, "maximum": 5, "type": "integer", "description": "Only include reviews with a star rating at or below this value (1-5). Set to 2 with min rating 1 to mine complaints.", "default": 5 }, "onlyWithText": { "title": "Only reviews with text", "type": "boolean", "description": "Skip rating-only reviews that have no written feedback.", "default": false }, "onlyWithPhotos": { "title": "Only reviews with photos", "type": "boolean", "description": "Return only reviews that include customer photos.", "default": false }, "dateFrom": { "title": "Date from (YYYY-MM-DD)", "type": "string", "description": "Only include reviews created on or after this date. Leave empty for no lower bound.", "default": "" }, "dateTo": { "title": "Date to (YYYY-MM-DD)", "type": "string", "description": "Only include reviews created on or before this date. Leave empty for no upper bound.", "default": "" }, "sortBy": { "title": "Sort reviews by", "enum": [ "newest", "oldest", "highest", "lowest", "helpful" ], "type": "string", "description": "Order reviews within each product before applying limits.", "default": "newest" }, "maxProductsPerSearch": { "title": "Max products per search query", "minimum": 1, "maximum": 1000, "type": "integer", "description": "When using search queries, cap how many matching products to pull reviews for. Controls cost on broad keywords.", "default": 10 }, "maxReviewsPerProduct": { "title": "Max reviews per product", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Maximum reviews to return for each product (0 = all available reviews).", "default": 0 }, "maxItems": { "title": "Maximum total reviews", "minimum": 0, "maximum": 1000000, "type": "integer", "description": "Hard cap on total reviews across all products (0 = unlimited). Defaults to 1000.", "default": 1000 }, "debugLogging": { "title": "Verbose debug logs", "type": "boolean", "description": "Print granular tracing tags (RESOLVE, FEEDBACKS, EMIT) to the run logs.", "default": false }, "proxyConfiguration": { "title": "Proxy setup", "type": "object", "description": "Proxy is required: Wildberries returns 429 on direct requests. Datacenter proxies (the default) are fast, cheap, and work reliably here. Switch to Residential only if a very large run starts getting 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 } } } } } } } } } } ```