Wildberries Scraper — Products & Reviews | Парсер Вайлдберриз

Scrape Wildberries (wildberries.ru) product data as clean JSON: search catalogs, product cards, prices and reviews, plus optional card details: specifications, the seller's legal entity and Wildberries' own price history. Data is fetched over plain HTTP. A real browser starts once per run, and only to clear the marketplace's anti-bot check. Парсер Вайлдберриз: товары, цены, отзывы.

What you get

Give it a search phrase or product IDs. The dataset contains two kinds of item, tagged by a type field. Products and reviews are separate rows, joined by nmId / imtId.

Product:

{
  "type": "product",
  "nmId": 1097975275,
  "imtId": 2227481309,
  "name": "Wireless Headphones XYZ",
  "brand": "XYZ",
  "supplier": "TechLine LLC",
  "price": 2490,
  "basicPrice": 3990,
  "rating": 4.7,
  "feedbacks": 1523,
  "url": "https://www.wildberries.ru/catalog/1097975275/detail.aspx"
}

price is what the customer actually pays; basicPrice is the price before discount. Both are in roubles (the raw Wildberries API returns kopecks — we convert). A product that cannot currently be bought in the region you pinned with dest carries no price at all rather than a zero, so price and basicPrice may be absent — that reflects the live state of the product card, and the same article may well be priced again tomorrow or in another region.

Product with includeDetails on adds the card's own data to the same row:

{
  "type": "product",
  "nmId": 1097975275,
  "imtId": 2227481309,
  "name": "Wireless Headphones XYZ",
  "brand": "XYZ",
  "supplier": "TechLine LLC",
  "price": 2490,
  "basicPrice": 3990,
  "rating": 4.7,
  "feedbacks": 1523,
  "url": "https://www.wildberries.ru/catalog/1097975275/detail.aspx",
  "description": "Wireless in-ear headphones with active noise cancelling and a charging case.",
  "specs": [
    { "name": "Тип подключения", "value": "беспроводное" },
    { "name": "Время зарядки", "value": "1 час" }
  ],
  "category": "Наушники беспроводные",
  "categoryRoot": "Смартфоны и гаджеты",
  "vendorCode": "XYZ-100-BLK",
  "color": "черный",
  "photoCount": 5,
  "sellerId": 571710,
  "sellerLegalName": "ООО «ТехЛайн»",
  "sellerInn": "7700000000",
  "sellerOgrn": "1027700000000",
  "sellerTrademark": "XYZ",
  "priceHistory": [
    { "date": "2026-07-18", "price": 1188 },
    { "date": "2026-08-08", "price": 772 },
    { "date": "2026-09-05", "price": 1982 }
  ]
}

specs is the card's specification list as name and value pairs, in the language Wildberries publishes them (Russian). priceHistory is the price series Wildberries itself keeps for the card: dated points in roubles, oldest first. In our sample runs the points were weekly and went back up to twelve weeks, with some weeks absent, so treat it as a weekly series, not a daily feed. It is present even for a product that currently has no price. sellerLegalName, sellerInn and sellerOgrn identify the seller's legal entity as Wildberries shows it on the card: the INN and OGRN for Russian sellers, the home-country registration id in sellerInn for cross-border ones. These fields come from Wildberries' static card storage, which the public API does not expose, and any of them is absent when the corresponding card file is missing.

Review (only when includeReviews is on):

{
  "type": "review",
  "imtId": 2227481309,
  "nmId": 1097975275,
  "reviewId": "DdAOL5cB0x_abc",
  "text": "Great sound, fast delivery.",
  "pros": "battery life",
  "cons": "",
  "stars": 5,
  "date": "2026-07-13T08:48:38Z",
  "size": "M",
  "color": "black",
  "author": "Anna",
  "photos": 2,
  "hasVideo": false,
  "sellerAnswer": "Thank you for your review!",
  "url": "https://www.wildberries.ru/catalog/1097975275/detail.aspx"
}

pros and cons are separate fields from text and are often empty — for sentiment work you want all three. photos is a count, and both photos and hasVideo are absent when the review has no media. sellerAnswer is the seller's public reply, when there is one.

Use cases

• Price & assortment monitoring across Wildberries catalogs.
• Card enrichment: specifications, category, seller legal entity and price history for the articles you track.
• Review mining for product research and sentiment.
• Market intelligence for e-commerce teams that need WB data in a clean, English-documented API.

Input

Field	Type	Description
queries	string[]	Search phrases to resolve into product catalogs.
productIds	string[]	Specific nmId values to fetch full cards for.
includeDetails	boolean	Also fetch each product's description, specs, category, seller legal details and price history. Part of the product event, no separate charge.
includeReviews	boolean	Also fetch reviews. Reviews are charged as a separate event.
maxReviewsPerProduct	integer	Most recent reviews to keep per product (default 20).
dest	string	Region code affecting price and delivery estimates (optional).
maxItems	integer	Hard cap on scraped products, applied per search query and to the article-number list. Reviews are capped separately by maxReviewsPerProduct.
proxyConfiguration	object	Optional, off by default. The run goes out from the container's own address, which is free and fastest. Turn a proxy on only if you need a specific region: the whole run then uses one sticky session, because the anti-bot token is bound to the exit address.

How to use it

1. Click Try for free / Start on this page.
2. Put one or more phrases into Search queries (for example наушники), or paste specific article numbers into Product IDs (nmId) if you already know what you track. You can use both at once; searches and IDs are collected into the same dataset.
3. Switch on Include product details if you want the description, specifications, category, seller legal details and price history on every product row. It adds three small requests and about a second per product, and no separate charge.
4. Optionally switch on Include reviews and set Max reviews per product. Reviews arrive as separate rows and are billed as a separate event, so leave it off if you only need prices.
5. Set Max items to cap how many products each search query returns, and how many article numbers are taken from your list. Reviews are not counted against it — cap those with Max reviews per product, which applies to every product separately. Two queries with Max items at 100 and 20 reviews each can therefore bill up to 200 products and 4,000 reviews.
6. Leave Proxy configuration off unless you need a specific region. The run then goes out from the container's own address, which cleared the anti-bot check fastest in our measurements and costs nothing extra.
7. Run it. Watch the Output tab: the Products view shows catalog rows, the Product details view shows the enrichment fields, the Reviews view shows review rows. Export as JSON, CSV, Excel or pull the data over the API.

To run it on a schedule, use Apify Schedules; to call it from your own code, use the Apify API or one of the official clients. Every field in the output is described in this Actor's dataset schema, so AI agents calling it through the Apify MCP server get typed field metadata rather than raw JSON.

Why this scraper

• Measured reliability, honestly reported. On 22 September 2026 Wildberries closed its public API domains and moved the storefront behind an anti-bot check. This Actor moved with it: a real browser clears the check once per run, then the data is fetched over plain HTTP. Measured the same day: the check clears in 7 to 26 seconds from the container's own address, and a search page then answers in about one second with 100 products. Transient blocks are retried with backoff instead of being silently dropped, and a run that gets truncated reports the truncation rather than passing it off as a complete result.
• It knows where Wildberries lies. The search endpoint does not end a result set with an empty page — it starts serving page 1 again, and it sometimes answers HTTP 200 with a substituted body. Both are detected explicitly, so you get a truncated dataset reported as a failure rather than presented as a complete run.
• Card data the public API does not carry. With includeDetails the Actor reads Wildberries' static card storage, which needs no anti-bot token: description, specification list, category, the seller's legal entity and the price history Wildberries keeps for the card.
• English documentation — every field explained, no guesswork.
• Schema-validated output — every item is checked against a schema before it reaches your dataset, so a change on Wildberries' side surfaces as a loud failure, not as undefined in your price feed.

Limits (honest)

• Reviews are capped at 1000 per product by Wildberries itself, newest first, and that endpoint has no pagination. For a product with 130k reviews you get a recent-1000 sample, not the full corpus. The product-level rating and feedbacks count still reflect all reviews.
• Search results are finite. Past the last real page Wildberries repeats page 1; the scraper stops there. Very broad queries will not yield their full advertised total.
• The storefront sits behind an anti-bot check. Since 22 September 2026 the public API domains answer 403, and the storefront hands out a session token instead. The Actor takes that token with a real browser once per run, which cost 7 to 26 seconds in our measurements. A session can also be revoked mid-run, and the Actor then takes a fresh one, up to five per run. Measured on 22 September 2026: 30 search requests in a row and 61 product-card requests in a row went through on a single session. Reviews are not affected at all, that endpoint still answers without a token. If the check cannot be cleared, the run reports a failure instead of passing off an empty dataset as complete, and with per-result pricing a slower run does not cost you more.
• Card details come from a static copy. Wildberries serves the card files with a 24-hour public cache, so a description or specification edit can take up to a day to show up. Price history is whatever Wildberries publishes for the card: weekly points going back up to about twelve weeks in our sample runs, with some weeks absent, not a daily series. The seller's tax and registration numbers are absent for some sellers, and a few cards have no price history or seller file at all, mostly brand-new ones. In our measured runs of 20 and 100 products every product got its card, 18 of 20 and 83 of 100 got a price history, 19 of 20 and 86 of 100 a seller record. The product row is always delivered, with whatever detail fields exist, and the run's status message says how many articles had no card at all.
• Short run timeouts. The Actor watches your run's timeout. When the next request could not finish before it, the Actor stops starting new requests, keeps everything already scraped and says so in the run's status message, instead of being cut off mid-request. A run that stopped this way with some data counts as succeeded, and one with no data counts as failed. With the default one-hour timeout this only matters for very large runs.
• Public data only; no login, no bypassing of access controls. Requests are paced to respect Wildberries' rate limits.
• Price and delivery estimates vary by dest region — the scraper pins one region per run so your time series stays comparable.

Pricing

Pay-per-event: a charge per run start, a charge per scraped product, and a separate charge per scraped review (so runs without includeReviews never pay for review capacity). Product details (includeDetails) are part of the product event and carry no separate charge. See the Store page for current rates.

What you get for $1

Event	Rate
Actor start	$0.02 per run
product-scraped	$3.50 per 1,000 products ($0.0035 each)
review-scraped	$2.00 per 1,000 reviews ($0.002 each)

One dollar covers a run start plus ~280 product cards ((1 − 0.02) / 0.0035 ≈ 280), or ~490 reviews if you scrape reviews only. There is no subscription and no minimum: billing is strictly per event, so autonomous AI agents can call this actor and pay as they go — through the Apify MCP server, or via the x402 (USDC on Base) and Skyfire agentic payment rails. Rates shown as of the last README update; the Store page stays authoritative.

FAQ

Do I need a Wildberries account or an API key? No. The Actor reads only publicly available data and never logs in.

Can I use the official Wildberries API instead? Only for your own shop. The official seller API (dev.wildberries.ru) requires a seller token and returns data about your own store — the public catalog of other sellers is not available through it. This Actor reads the public catalog, which is exactly the part the official API does not cover.

Why is price sometimes missing? Because the product is currently unavailable. Wildberries reports no price at all in that case, and the Actor passes that through instead of inventing a 0 that would poison your averages.

Why do I get fewer products than the total Wildberries advertises? Wildberries does not end a result set with an empty page — past the last real page it starts serving page 1 again. The Actor detects that repetition and stops, so a broad query returns the pages that actually exist rather than looping forever.

Why do I get at most 1000 reviews for a product with far more? That is Wildberries' own cap on the reviews endpoint, newest first, and it has no pagination. The product-level rating and feedbacks count still reflect every review.

Does "Include product details" cost extra? No. The detail fields are part of the product row and of the product event. The run takes a little longer, because each product needs three small extra requests, and with per-result pricing that time is not billed.

Why are the detail fields missing for some products? Because Wildberries has no card file for that article in its static storage, which happens with brand-new and delisted products. The product row is still written with the data from the catalog, and the run's status message says how many articles were affected.

Can I get reviews without products? No — reviews are addressed by imtId, which comes from the product card, so products are always fetched first. Setting maxReviewsPerProduct low keeps the review cost small.

Does the region code matter? Yes. Prices and availability differ by region, so the Actor pins one dest per run. Leave it empty for the Moscow default and keep it stable if you are building a price time series.

Why does the first request of a run take half a minute? Because the run starts a real browser once to clear the marketplace's anti-bot check. That took 7 to 26 seconds in our measurements. Everything after it is plain HTTP, so the rest of the run runs at normal speed, and you pay per result either way.

Do I need a proxy? Usually not. The run goes out from the container's own address by default, which cleared the check fastest in our measurements and costs nothing. Turn a proxy on when you need prices for a specific region. One sticky session is then used for the whole run, because the anti-bot token is bound to the exit address.

Other Actors by ActorForge

• Avito Real Estate Scraper — full property inventory from Avito with freshness stamps, for new-listing alerts and comps.
• Lazada Reviews Scraper — ratings, review text and buyer media from Lazada across the SEA marketplaces.

Need a marketplace we do not cover yet, or fields beyond what is listed here? Open an issue from the Actor's page and tell us what you need.

Disclaimer

This is an unofficial scraper. It is not affiliated with, endorsed by, or connected to Wildberries in any way. It collects only data that Wildberries publishes publicly, without logging in and without circumventing access controls. You are responsible for ensuring your use of the collected data complies with applicable law and with Wildberries' terms.

Changelog

See the repository CHANGELOG.md.