Walmart Reviews Scraper

Extract Walmart product reviews at scale — ratings, review text, verified purchase status, helpfulness votes, reviewer details, and media. Provide product URLs or item IDs and get structured review data exported to JSON, CSV, or Excel. No API key or login required.

🛒 What does Walmart Reviews Scraper do?

Walmart Reviews Scraper pulls customer reviews from any Walmart product page using Walmart's own review data infrastructure. Enter one or more product URLs or item IDs and the scraper returns all available review fields: star rating, reviewer name, review title and full text, submission date, verified purchase badge, helpfulness votes, reviewer location, and attached photos.

The scraper accesses Walmart's server-rendered review pages, giving you the same data users see when browsing — no unofficial hacks, no API key needed. Sort reviews by relevance, helpfulness, date, or rating. Filter by specific star counts to target only 1-star complaints or 5-star praise.

👥 Who is Walmart Reviews Scraper for?

E-commerce businesses and brand managers

• Track your own product sentiment on Walmart continuously
• Monitor competitor product reviews to identify weaknesses and opportunities
• Detect sudden review spikes or drops that indicate quality issues

Market researchers and analysts

• Build large-scale datasets of consumer opinions for NLP/LLM training
• Analyze sentiment trends across product categories
• Compare customer satisfaction across Walmart's marketplace sellers

Product developers and UX teams

• Gather voice-of-customer data to inform roadmap decisions
• Extract feature requests and pain points from 1-star reviews
• Identify recurring complaints before they affect sales rank

Agencies and consultants

• Deliver competitive analysis reports for retail clients
• Support SEO content strategies with real review language
• Automate reputation monitoring workflows for brands selling on Walmart

✅ Why use Walmart Reviews Scraper?

• 🔓 No API key or login required — works with public Walmart review data
• 📦 Rich review data — 18+ fields per review including media URLs and user badges
• 🔢 Pagination support — scrape hundreds or thousands of reviews per product
• ⭐ Star filter — isolate 1-star complaints or 5-star endorsements instantly
• 🔄 Sort options — most relevant, most helpful, most recent, highest/lowest rated
• 🗃️ Flexible export — JSON, CSV, Excel, XML via Apify platform
• 📅 Scheduled runs — monitor review sentiment on a schedule
• 🔗 Works with Walmart Scraper — combine with our product listing actor for end-to-end research

📊 What data can you extract?

Field	Description
itemId	Walmart item ID
productName	Full product name
productUrl	Direct link to product page
reviewId	Unique review identifier
title	Review headline
reviewText	Full review body text
rating	Star rating (1–5)
date	Submission date (ISO format)
reviewerName	Display name of reviewer
isVerifiedPurchase	Whether purchase was verified
helpfulVotes	Positive helpfulness votes
negativeFeedback	Negative helpfulness votes
totalVotes	Total votes on the review
badges	User badges (e.g., "Verified Purchase", "Top Reviewer")
userLocation	Reviewer's self-reported location
recommended	Whether reviewer recommends the product
syndicationSource	If review was syndicated from another platform
mediaUrls	URLs to photos attached to the review
scrapedAt	ISO timestamp of when the review was scraped

💰 How much does it cost to scrape Walmart reviews?

Walmart Reviews Scraper uses pay-per-event (PPE) pricing — you only pay for what you extract.

Event	Cost
Run start	$0.005
Per review scraped	$0.003

Real-world cost examples:

Use case	Reviews	Cost
Quick product snapshot	20	~$0.07
Standard product audit	100	~$0.31
Deep review analysis	500	~$1.51
Full review dataset	1,000	~$3.01

Free plan estimate: Apify's free plan includes $5 in monthly credits. That's roughly 1,600 reviews per month at no cost.

Pricing scales down automatically for higher-volume Apify plans (Silver, Gold, Platinum). See the Apify pricing page for plan details.

🚀 How to scrape Walmart product reviews

1. Go to Walmart Reviews Scraper on Apify Store
2. Click Try for free
3. In the Product URLs or item IDs field, paste one or more Walmart product URLs
   • Example: https://www.walmart.com/ip/Acer-Chromebook-315/5113175776
   • Or just the item ID: 5113175776
4. Set Max reviews per product (start with 20–50 for a quick test)
5. Optionally choose a Sort and Star filter
6. Click Start — results appear in the dataset tab within seconds

Example inputs for different scenarios:

Scrape 100 most recent reviews for a specific item:

{
  "productUrls": ["https://www.walmart.com/ip/5113175776"],
  "maxReviewsPerProduct": 100,
  "sort": "submission-desc"
}

Scrape only 1-star reviews to find complaints:

{
  "productUrls": [
    "https://www.walmart.com/ip/ProductName/5113175776",
    "https://www.walmart.com/ip/OtherProduct/1444893965"
  ],
  "maxReviewsPerProduct": 200,
  "sort": "relevancy",
  "filterByStar": 1
}

⚙️ Input parameters

Parameter	Type	Default	Description
productUrls	String[]	—	Required. List of Walmart product URLs or numeric item IDs
maxReviewsPerProduct	Integer	50	Max reviews to scrape per product (1–1000)
sort	String	relevancy	Sort order: relevancy, helpful, submission-desc, rating-desc, rating-asc
filterByStar	Integer	none	Filter to reviews with this star rating (1–5)
maxRequestRetries	Integer	5	Retry attempts per failed request

Supported product URL formats:

• https://www.walmart.com/ip/Product-Name/12345678
• https://www.walmart.com/ip/12345678
• 12345678 (bare item ID)

📤 Output examples

Single review object:

{
  "itemId": "5113175776",
  "productName": "Acer Chromebook 315 15.6-inch Laptop",
  "productUrl": "https://www.walmart.com/ip/5113175776",
  "reviewId": "416117133",
  "title": "Great value for the price",
  "reviewText": "I needed a basic laptop for school work and this Chromebook is perfect. Very fast, easy to set up, and the battery life is excellent.",
  "rating": 5,
  "date": "2026-02-10",
  "reviewerName": "Dionne",
  "isVerifiedPurchase": true,
  "helpfulVotes": 12,
  "negativeFeedback": 1,
  "totalVotes": 13,
  "badges": ["Verified Purchase", "Top Reviewer"],
  "userLocation": "Texas",
  "recommended": true,
  "syndicationSource": null,
  "mediaUrls": [
    "https://i5.walmartimages.com/dfw/6e29e393-df87/k2-_af02033a.jpg"
  ],
  "scrapedAt": "2026-04-03T09:41:01.751Z"
}

💡 Tips for best results

• Start small — test with 20 reviews before running large batches
• Use item IDs for reliability — extract the numeric ID from the URL (e.g., 5113175776 from /ip/Product/5113175776)
• Filter by star to focus research — filterByStar: 1 isolates complaints, 5 shows your best-case testimonials
• Combine with Walmart Scraper — use Walmart Scraper to find item IDs from search results, then feed them into this actor for deep review analysis
• Schedule sentiment monitoring — run daily or weekly on key products to detect rating changes early
• Export to CSV for Excel analysis — click "Export" → CSV in the dataset view for pivot tables and sentiment analysis

🔗 Integrations

Walmart Reviews Scraper → Google Sheets Automatically sync new reviews into a Google Sheet using Apify's Google Sheets integration. Great for team dashboards tracking product sentiment without coding.

Walmart Reviews Scraper → Slack alerts Set up a webhook to post 1-star reviews to a Slack channel in real time. Useful for customer success teams who want to respond quickly to public complaints.

Walmart Reviews Scraper → Make/Zapier Chain the scraper into automated workflows: extract reviews → filter negative ones → create support tickets in Zendesk or Jira.

Scheduled sentiment monitoring Run the scraper daily on your top 10 products. Connect the dataset to a Looker Studio dashboard showing review volume, average rating, and sentiment trends over time.

LLM training datasets Use the actor's scheduling + dataset API to collect rolling review datasets for fine-tuning sentiment models or training product-specific NLP classifiers.

🛠️ Using the Apify API

Integrate Walmart Reviews Scraper into your workflow using the Apify API.

Node.js example:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('automation-lab/walmart-reviews-scraper').call({
    productUrls: ['https://www.walmart.com/ip/5113175776'],
    maxReviewsPerProduct: 100,
    sort: 'submission-desc',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Scraped ${items.length} reviews`);

Python example:

from apify_client import ApifyClient

client = ApifyClient(token='YOUR_API_TOKEN')

run = client.actor('automation-lab/walmart-reviews-scraper').call(run_input={
    'productUrls': ['https://www.walmart.com/ip/5113175776'],
    'maxReviewsPerProduct': 100,
    'sort': 'submission-desc',
})

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(f"{item['rating']}⭐ {item['title'][:60]}")

cURL example:

curl -X POST "https://api.apify.com/v2/acts/automation-lab~walmart-reviews-scraper/runs" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "productUrls": ["https://www.walmart.com/ip/5113175776"],
    "maxReviewsPerProduct": 50,
    "sort": "relevancy"
  }'

🤖 Use with AI agents via MCP

Walmart Reviews Scraper is available as a tool for AI assistants that support the Model Context Protocol (MCP).

Add the Apify MCP server to your AI client — this gives you access to all Apify actors, including this one:

Setup for Claude Code

$claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/walmart-reviews-scraper"

Setup for Claude Desktop, Cursor, or VS Code

Add this to your MCP config file:

{
    "mcpServers": {
        "apify": {
            "url": "https://mcp.apify.com?tools=automation-lab/walmart-reviews-scraper"
        }
    }
}

Your AI assistant will use OAuth to authenticate with your Apify account on first use.

Example prompts

Once connected, try asking your AI assistant:

• "Use automation-lab/walmart-reviews-scraper to scrape the 50 most recent reviews for Walmart item 5113175776 and summarize the main complaints"
• "Get all 1-star reviews for this Walmart product https://www.walmart.com/ip/5113175776 and identify the top 3 recurring issues"
• "Scrape 100 reviews for each of these 5 Walmart products and compare their average ratings"

Learn more in the Apify MCP documentation.

⚖️ Is it legal to scrape Walmart reviews?

Scraping publicly visible review data is generally considered legal in most jurisdictions. Walmart product reviews are publicly accessible to any visitor — no login, subscription, or API key is required to read them.

This scraper only accesses public data and follows responsible scraping practices:

• 🐢 Rate-limited requests with delays between pages
• 🔄 Retry logic instead of hammer-and-spam
• 🚫 No login, no credentials, no private data

Always review Walmart's Terms of Service before large-scale scraping. For commercial use of scraped data, consult your legal team. This tool is intended for research, competitive analysis, and personal use cases.

❓ FAQ

How many reviews can I scrape per product? Walmart products can have thousands of reviews. The maxReviewsPerProduct parameter controls the limit. Start with 50–100 for testing. For large-scale extractions (1,000+ reviews), set a higher limit and ensure your Apify plan has sufficient credits.

How much does it cost to scrape 500 reviews? With default PPE pricing: $0.005 (start) + 500 × $0.003 (reviews) = $1.51 total. On the Apify free plan ($5 credits), you can scrape ~1,600 reviews per month.

Can I scrape reviews for multiple products at once? Yes — add multiple product URLs to the productUrls array. The actor processes them sequentially. Each product is scraped to your maxReviewsPerProduct limit.

Why are some reviews missing or the count is lower than expected? Walmart may not return all reviews on some products — they typically display verified purchase reviews and hide reviews flagged for policy violations. If you see fewer reviews than Walmart's total count, try different sort orders (e.g., submission-desc) to get different review subsets.

Why do some items return 0 reviews? Brand-new products, discontinued items, or items that only recently moved to Walmart's catalog may have 0 public reviews. Items with very few reviews (1–2) may also behave differently. The actor handles these gracefully and moves on.

I'm getting blocked / 0 results — what should I do? This scraper requires the Apify proxy (RESIDENTIAL group) to bypass Walmart's anti-bot protection. Make sure you're running on the Apify platform (not just triggering from a local CLI without proper proxy setup). Cloud runs use residential proxies automatically.

🛒 Other Walmart and e-commerce scrapers

Looking for more data? Check out our other automation-lab actors:

• Walmart Scraper — Scrape Walmart product listings from search results
• Amazon Reviews Scraper — Extract Amazon product reviews by ASIN
• Google Maps Reviews — Scrape local business reviews from Google Maps
• Airbnb Reviews — Extract guest reviews from Airbnb listings
• Booking Reviews Scraper — Scrape hotel reviews from Booking.com
• TripAdvisor Reviews Scraper — Extract restaurant and attraction reviews