Walmart Scraper & API: Products, Search, Reviews

One actor for everything on walmart.com: full product details, search and category results, and all customer reviews. Paste any Walmart URL, type a keyword or give item IDs; mix them freely in one run.

It reads the data Walmart already embeds in its pages over plain HTTP instead of driving a browser, so it is fast (hundreds of rows per minute) and cheap. Pages Walmart blocks are retried on fresh IPs, then on US residential IPs (at our cost), and you only pay for rows that actually arrive.

Why this one

• All reviews, not just the first pages. Products with 4,000+ written reviews come back complete. Filter by star rating (e.g. only 1 and 2 stars), sort by newest, most helpful, highest or lowest rating.
• Past Walmart's page limit. Walmart stops paging after 10 pages. When a search or category has more matches, the actor splits it into price ranges automatically and de-duplicates.
• Full product record. Price, was-price, savings, rollback/clearance, stock, seller (name, ID, rating, type, Walmart-fulfilled), other offers, UPC, model, specifications, description, highlights, every image, variants with their own price and stock, delivery date, pickup, return policy, rating breakdown 1-5 stars, recommended %, review aspects and Walmart's AI review summary.
• One input, every page type. Product (/ip/...), search (/search?q=...), category and browse (/cp/..., /browse/...), brand and seller pages, review pages (/reviews/product/...).
• Pay only for results, three clear prices: product, search row, review. Failed pages are never charged; they are listed in the key-value store record FAILED.

Input

Field	What it does
Walmart URLs	Any mix of product, search, category, brand, seller and review URLs
Search terms	Keywords, as typed in Walmart's search box
Product IDs	Item IDs, the number at the end of a product URL (5113175776)
Max products per search / category	Default 50, 0 = everything
Open every search result for full details	Off = fast summary rows; on = full product record per result
Also scrape reviews	Reviews for every product found
Max reviews per product	Default 100, 0 = all
Review order / star filter	Relevance, helpful, newest, oldest, highest, lowest; any set of 1-5 stars
Search sort, min/max price	Best match, price low/high, best sellers, rating, new
Max results in total	Hard cap on rows in the run, protects your budget
Include raw Walmart data	Adds the complete original JSON (hundreds of fields) under raw

Examples

All 1- and 2-star reviews of one product, newest first:

{
  "startUrls": [{ "url": "https://www.walmart.com/ip/5113175776" }],
  "includeReviews": true,
  "maxReviewsPerProduct": 0,
  "reviewsStarFilter": ["1", "2"],
  "reviewsSort": "submission-desc"
}

Price monitoring: full details for the top 200 results of two searches:

{
  "searchTerms": ["air fryer", "robot vacuum"],
  "maxItemsPerSearch": 200,
  "scrapeProductDetails": true
}

A whole category (every TV), fast summary rows:

{
  "startUrls": [{ "url": "https://www.walmart.com/browse/electronics/tvs/3944_1060825_447913" }],
  "maxItemsPerSearch": 0
}

Output

Every row has a type: product, searchResult or review. The dataset has a table view for each.

product (shortened):

{
  "type": "product",
  "usItemId": "5113175776",
  "name": "Acer Chromebook 315 15.6 inch Laptop Intel Processor N4500 4GB RAM 64GB eMMC Moonstone Purple",
  "brand": "Acer",
  "upc": "195133229036",
  "model": "CB315-4H-C0VN",
  "price": 239.54,
  "wasPrice": null,
  "availability": "IN_STOCK",
  "rating": 4.3,
  "reviewCount": 20702,
  "ratingBreakdown": { "5": 15130, "4": 2129, "3": 983, "2": 557, "1": 1903 },
  "recommendedPercentage": 83,
  "reviewSummaryAI": "Customers are pleased with the laptop's affordable price...",
  "seller": { "name": "VeloCore Tech US", "id": 103122918, "type": "EXTERNAL", "rating": 4.7, "reviewCount": 34, "fulfilledByWalmart": true },
  "otherOffersCount": 7,
  "deliveryDate": "2026-11-07T22:59:00.000Z",
  "returnPolicy": { "returnable": true, "freeReturns": true, "windowDays": 30 },
  "categories": ["Electronics", "Computers, Laptops and Tablets", "Laptops"],
  "images": ["https://i5.walmartimages.com/..."],
  "specifications": { "Processor": "Intel Celeron", "RAM memory": "4 GB", "Screen size": "15.6 in" },
  "variants": [{ "usItemId": "5113175776", "attributes": ["hard_drive_capacity-64gb", "actual_color-moonstonepurple"], "price": 239.54, "availability": "IN_STOCK" }]
}

searchResult: usItemId, name, price, wasPrice, rating, reviewCount, availability, isSponsored, sellerName, badges, fulfillmentType, deliveryOptions, image, url, position, source.

review: reviewId, rating, title, text, date (YYYY-MM-DD), userNickname, verifiedPurchase, incentivized, helpfulVotes, unhelpfulVotes, variant (e.g. color/size bought), photos, videos, sellerName, fulfilledBy, syndicationSource, sellerResponses, plus usItemId and productName.

Use it from code (API)

Call it as a Walmart API from any language. One HTTP request returns the rows as JSON:

curl -s -X POST \
  "https://api.apify.com/v2/acts/mvkdata~walmart-scraper-fast/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"searchTerms": ["air fryer"], "maxItemsPerSearch": 100}'

Ready-to-run Python, JavaScript and curl examples (reviews to CSV, daily price monitor): github.com/mvkdata/walmart-scraper. Step-by-step guide: How to scrape every Walmart review.

Pricing

Pay per result, cheaper with higher Apify plans. No start fee. You pay only for rows in your dataset; blocked or failed pages cost nothing.

FAQ

Why does reviewCount differ from the number of reviews I get? reviewCount counts star ratings; many shoppers rate without writing text. Walmart only lists written reviews, and the actor returns all of them.

Can I scrape walmart.ca or Walmart Mexico? Not yet: walmart.com (US) only.

Is scraping Walmart legal? The actor collects publicly visible data only, without logging in. Reviews contain public nicknames; check your own use case and local rules (e.g. GDPR) before storing personal data.