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 one or several star ratings to target only 1- and 2-star complaints or 5-star praise. Monitor new reviews with a date cutoff ("only reviews since 30 days ago"), and get brand replies, Top Reviewer flags, seller and fulfillment, plus the product's average rating and total review count on every row.

Pair price monitoring with review monitoring

Use Walmart Scraper to track Walmart prices, was-prices, savings, sellers, fulfillment, and deal badges, then run Walmart Reviews Scraper on the same item IDs to understand the sentiment behind changes in conversion or product rank. Price datasets show what changed; review fields such as rating, reviewText, isVerifiedPurchase, helpfulVotes, date, and recommendation signals explain whether customers are reacting to quality, value, delivery, or seller issues.

Walmart Reviews Scraper is the review-monitoring layer of your e-commerce workflow — it does not scrape product prices itself.

👥 Who is it 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 — one or several ratings, e.g. only 1- and 2-star complaints
• 📅 Date cutoff for monitoring — reviewsSince: "7 days" returns only new reviews, so scheduled runs pay only for what's new
• 💬 Brand replies — the brand's public response to each review, when there is one
• 📈 Product context on every row — average rating, total reviews and recommend percentage
• ⚡ Server API — one product's reviews over a single HTTP request, no run to start
• 🔄 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 of the reviewed variant (can differ from the one you entered)
requestedItemId	The item ID from your input
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	Review badges (e.g., "Verified Purchase")
isTopReviewer	Whether Walmart marks the reviewer as a Top Reviewer
reviewerReviewCount	Reviewer's review-count milestone (e.g., 50), when Walmart shows it
userLocation	Reviewer's self-reported location (Walmart rarely shows it now; usually empty)
recommended	Whether reviewer recommends the product (often null: Walmart asks only some reviewers)
syndicationSource	If review was syndicated from another platform
mediaUrls	URLs to photos attached to the review
sellerName	Seller of the reviewed purchase
fulfilledBy	Who fulfilled the purchase
brandResponse	The brand's public reply to the review, if any
brandResponder	Brand team that replied (e.g., "TeamAcer")
productAverageRating	Product average rating at scrape time
productTotalReviews	Product total review count at scrape time
productRecommendedPercentage	Share of reviewers who recommend the product
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

From 2026-11-28 the price will be lower: $1.80 per 1,000 reviews on the Free and Starter plans, $1.62 on Scale, and $1.44 on Business and above; the run start stays $0.005. For example, 100 reviews will cost about $0.19 on Starter.

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, a Star filter, or Only reviews since for monitoring
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",
  "filterByStars": ["1", "2"]
}

Weekly monitoring: only reviews from the last 7 days (schedule this run weekly):

{
  "productUrls": ["https://www.walmart.com/ip/5113175776"],
  "maxReviewsPerProduct": 500,
  "reviewsSince": "7 days"
}

⚙️ 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)
filterByStars	String[]	none	Several star ratings, e.g. ["1","2"]; combined with filterByStar
reviewsSince	String	none	Only reviews on or after a date (2026-01-31) or within a period ("30 days", "2 weeks", "3 months"). Reads newest first and stops at the first older review
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
• https://www.walmart.com/reviews/product/12345678
• 12345678 (bare item ID)
• API callers may also send { "url": "..." } objects

📤 Output examples

Single review object:

{
  "itemId": "3193557250",
  "requestedItemId": "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"],
  "isTopReviewer": true,
  "reviewerReviewCount": 50,
  "userLocation": "",
  "recommended": null,
  "syndicationSource": null,
  "mediaUrls": [
    "https://i5.walmartimages.com/dfw/6e29e393-df87/k2-_af02033a.jpg"
  ],
  "sellerName": "Walmart.com",
  "fulfilledBy": "Walmart",
  "brandResponse": "Thank you for your review! We are glad the Chromebook works well for school. Regards, Acer America",
  "brandResponder": "TeamAcer",
  "productAverageRating": 4.3,
  "productTotalReviews": 20781,
  "productRecommendedPercentage": 83,
  "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 — filterByStars: ["1","2"] isolates complaints, 5 shows your best-case testimonials
• Monitor only new reviews — schedule a daily run with reviewsSince: "1 day" (or weekly with "7 days") so each run returns and charges only new reviews
• 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.

🛠️ API usage

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.

⚖️ Legality

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 scrapes three products at a time. Each product is scraped to your maxReviewsPerProduct limit. If a run gets close to its timeout, it stops cleanly, keeps every review it found, and its status message says how many it got and why it stopped.

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. The Actor recognizes Walmart's explicit zero-review or unpublished-product response and handles it gracefully. In a multi-product run, one empty product does not prevent other products from being processed.

What happens if Walmart blocks every requested product or changes its page format? The run fails with an actionable extraction error when every valid requested product exhausts its fetch retries or cannot be parsed and no reviews are emitted. This is intentional: scheduled and API workflows can retry or alert instead of treating a technical failure as a successful empty result. A mixed run remains successful when at least one product emits useful reviews, and the logs identify products that failed.

I'm getting blocked / an extraction error — 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. Retry a failed run; if every product continues to fail, check proxy access and the run log for a Walmart bot challenge or page-format change.

Diagnostic privacy

Failed operations send sanitized diagnostic input, exceptions, and Actor/build/run IDs to our private GlitchTip service for repair. Secret fields and URL query strings are removed; reports are retained for 30 days. No AI models process your input or reviews. Public review text, reviewer nicknames, and review metadata are stored in your Apify dataset. Apify storage and logs follow your account's retention settings; delete these stores when no longer needed. Proxy and diagnostic service costs are included, not charged separately.

This is an independent tool, not affiliated with or endorsed by Walmart.

🛒 Related 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

Server API

Walmart Reviews Scraper is also a Server Actor. Besides normal runs, it answers short HTTP requests directly, without starting a run for each one. Copy the server URL from the Actor's Server tab (do not build it from the Actor name) and authenticate with your Apify token. Normal Actor runs keep the full input and dataset output; use them for many products or more reviews.

$curl -sS "$SERVER_URL/reviews?product=5113175776&maxReviews=20&stars=1,2" -H "Authorization: Bearer $APIFY_TOKEN"

Method and path	Input	Output and charge
GET /	None	ready: true, the routes and limits; no scraping or charge
GET /reviews	product (walmart.com/ip/... URL, review page URL or numeric item ID), optional maxReviews (1–60, default 20), sort (relevancy, helpful, submission-desc, rating-desc, rating-asc), stars (comma-separated 1–5, e.g. 1,2), since (2026-01-31 or 30 days; sorts newest first)	{ok,count,results} with the same review records as the dataset; the usual per-review price, charged only when reviews are returned; no start fee

Each request has a 60-second deadline and a 512 KiB response cap, and one server works on at most four requests at once. Nothing is cached between callers. Errors use { "ok": false, "error": { "code": "...", "message": "...", "retryable": false } }: 400 invalid input (INVALID_PRODUCT, INVALID_LIMIT, INVALID_STARS, INVALID_INPUT), 402 spending limit, 404 no matching reviews or unknown route, 405 wrong method, 413 size limit, 502 Walmart blocked the request or charging failed, 503 server busy or stopping, 504 timeout. Retry only when retryable is true. A successful answer can carry warning: { code: "CHARGE_INCOMPLETE" } when only part of its charge went through; the results are still complete and you are charged only for what went through. The Server tab shows the full OpenAPI description for each route and status.