Walmart Product, Price and Review Data Scraper

Extract structured Walmart product data from keyword searches, direct product URLs, or item IDs. The Actor returns prices, availability, sellers, ratings, product details, images, and optional customer review records in an analysis-ready dataset.

Use the output for retail research, catalog enrichment, assortment tracking, seller analysis, shopping applications, and AI workflows that need current public Walmart product information.

Best fit

• Retail analysts comparing product assortment, pricing, and availability.
• Marketplace sellers researching competing listings and seller participation.
• Brand teams organizing ratings and review text for qualitative analysis.
• Data teams enriching catalogs from Walmart product URLs or item IDs.
• AI shopping and research agents that need structured product evidence.

A practical research scenario

A retail analyst starts with a search for wireless headphones and collects product cards with prices, ratings, stock signals, and sellers. The analyst then sends selected product URLs through detail mode to add variants, specifications, images, and descriptions. Review extraction can add the customer language behind each rating, giving the final dataset both market signals and product-level context.

This sequence keeps discovery broad and enrichment focused while preserving a stable itemId for joining records.

Quick start

Search Walmart products

{
  "mode": "search",
  "searchQuery": "wireless headphones",
  "maxProducts": 20,
  "sortBy": "bestMatch"
}

Enrich product URLs

{
  "mode": "productUrls",
  "productUrls": [
    "https://www.walmart.com/ip/JLab-Studio-2-Wireless-Headphones/13544111159"
  ],
  "includeVariants": true,
  "includeSpecifications": true,
  "includeReviews": true,
  "maxReviewsPerProduct": 10
}

Resolve Walmart item IDs

{
  "mode": "itemIds",
  "itemIds": ["13544111159", "741224561"],
  "maxProducts": 10
}

Input reference

Field	Purpose
mode	Selects search, productUrls, or itemIds.
searchQuery	Keyword used in search mode.
productUrls	Walmart product detail URLs for full enrichment.
itemIds	Walmart numeric item identifiers.
maxProducts	Upper bound for product records.
includeReviews	Adds customer review rows to the dataset.
maxReviewsPerProduct	Upper bound for reviews associated with each product.
includeVariants	Includes available product configurations.
includeSpecifications	Includes the product specification map.
minPrice, maxPrice	Applies a price range to discovered products.
brands	Keeps products matching the selected brands.
minRating	Applies a minimum displayed rating.
sortBy	Uses the supported Walmart result-ordering options.
proxyConfiguration	Controls the US browsing network used for Walmart pages.

Output data

Product records can include:

• itemId, productUrl, title, brand, model, and category
• currentPrice, listPrice, savings, currency, and onSale
• rating, reviewCount, inStock, sellerName, and sellerType
• pickupAvailable, shippingAvailable, and freeShipping
• images, variants, specifications, breadcrumbs, and badges
• scrapeSource and scrapedAt for provenance

Review rows use _type: "review" and can include the parent itemId, review text, rating, author, purchase verification, community votes, photos, pros, cons, and submission time.

{
  "itemId": "13544111159",
  "title": "JLab Studio 2 Wireless Headphones",
  "brand": "JLab",
  "currentPrice": 24.88,
  "currency": "USD",
  "rating": 4.5,
  "inStock": true,
  "sellerName": "Walmart.com",
  "scrapeSource": "product"
}

AI agent workflows

The Actor works well as a focused data tool through Apify MCP or the Apify API. Its modes make intent explicit and its product and review rows have clear provenance.

Example agent request:

Find Walmart listings for wireless headphones, return product IDs, prices, ratings, availability, and sellers, then enrich the strongest candidates with variants and specifications.

Useful routing guidance:

• Choose search for product discovery and comparison.
• Choose productUrls for richer product details and optional reviews.
• Choose itemIds when an upstream catalog already contains Walmart identifiers.
• Join product and review rows with itemId.
• Use scrapeSource when a workflow needs source provenance.

Run through the API

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~walmart-data-extractor/runs" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "search",
    "searchQuery": "wireless headphones",
    "maxProducts": 20,
    "includeReviews": false
  }'

The Apify token stays in the Authorization header. Results are available from the run's default dataset in JSON, CSV, Excel, and other supported formats.

Data sourcing and recovery

The primary path uses Playwright with US residential sessions and extracts Walmart's structured page data. Product parsing follows multiple known page layouts for search cards, details, variants, specifications, and reviews.

When the direct path reaches its recovery threshold, the Actor can route the same search, item ID, or product URL request through its configured SerpApi Walmart provider. Recovered records follow the same caps, deduplication, dataset, and billing flow. The scrapeSource field distinguishes direct and provider-backed records.

Pricing

This Actor uses Pay per event pricing with platform usage passed through. Product and optional review events are charged only when their corresponding records are written. Treat the live Pricing tab as the current source of truth for event prices and billing details.

Use maxProducts and maxReviewsPerProduct to keep each run aligned with the research brief.

Best results

• Use search mode for discovery and product URL mode for full detail enrichment.
• Keep the default US residential proxy configuration for consistent Walmart.com product context.
• Start with a focused product cap, inspect the dataset, and expand the next run around the most useful categories or brands.
• Keep variants and specifications enabled for catalog work; use compact output options for lighter monitoring feeds.
• Use review extraction on selected products when customer language is part of the analysis.

Related Actors

• Start with Google Shopping Scraper when the workflow needs cross-merchant product discovery, then compare selected offers with Walmart records.
• Use Amazon Product Intelligence alongside Walmart output for cross-market catalog and pricing research.

Builder's note

I designed this Actor around a practical two-stage retail workflow: discover many products with compact search records, then enrich a selected set with full details and reviews. The stable itemId, explicit record types, and scrapeSource field make that workflow straightforward for spreadsheets, databases, and AI agents.

Responsible use

This Actor collects publicly available Walmart product and review information. Use the dataset for legitimate research and automation in line with applicable laws, platform terms, and your organization's data-governance requirements.