Walmart Reviews Scraper

Scrape Walmart.com customer reviews for any product — by product URL, item ID, or keyword search. The actor deep-paginates every review per product (not just the first page) and returns one clean JSON record per review, plus an optional per-product rating summary.

Built on the same battle-tested anti-bot stack as our walmart-data-extractor (real Chromium + Apify Residential US + session rotation), with a managed SerpApi fallback so a transient Walmart block returns real reviews instead of an empty run. MCP-ready for AI agents.

What you get

• Every review, deep-paginated — up to your maxReviewsPerProduct cap, not the ~10 a single page shows.
• Rich per-review fields — rating, title, body text, author, verified-purchaser flag, helpful / unhelpful votes, customer photos, structured pros/cons, syndication flag, and submission date.
• Per-product summary row — overall rating, total review count, and a 1–5 star histogram (toggle with includeProductSummary).
• Sort & filter — newest, most helpful, highest/lowest rating, or most relevant; plus an exact client-side star-rating filter (e.g. mine only 1-star complaints).

When to use this actor

• Brand & CX teams tracking Walmart review sentiment over time.
• Walmart Marketplace sellers monitoring competitor SKU complaints.
• Researchers and AI shopping agents pulling a structured review corpus for a product.

When NOT to use it: if you want product attributes (price, stock, seller, variants, specs) rather than reviews, use the walmart-data-extractor actor instead. This actor is reviews-first.

Output

Each review is one dataset record (_type: "review"):

Field	Type	Description
reviewId	string	Stable Walmart review ID
itemId	string	Walmart numeric item ID the review belongs to
productName	string	Product the review is for
productUrl	string	Canonical product page URL
rating	number	Star rating this reviewer gave (1–5)
title	string	Review headline
text	string	Full review body
author	string	Reviewer display name
verifiedPurchaser	boolean	Verified-purchaser flag
helpfulVotes	number	"Helpful" upvotes
unhelpfulVotes	number	"Not helpful" downvotes
photos	string[]	Customer-uploaded photo URLs
pros / cons	string[]	Structured pros/cons when present
syndicated	boolean	Syndicated from a partner site
submittedAt	string	Submission time
reviewSource	string	scraped or serpapi
scrapedAt	string	ISO 8601 timestamp

When includeProductSummary is on, one extra row per product (_type: "product") carries overallRating, totalReviewCount, ratingBreakdown ({ "1"..."5": n }), and brand.

Example review record

{
  "_type": "review",
  "reviewId": "d1f0e2a3-1234-5678-9abc-def012345678",
  "itemId": "1820546583",
  "productName": "Apple AirPods Pro (2nd Generation)",
  "productUrl": "https://www.walmart.com/ip/1820546583",
  "rating": 5,
  "title": "Best earbuds I've owned",
  "text": "Noise cancellation is incredible and they pair instantly...",
  "author": "MusicFan22",
  "verifiedPurchaser": true,
  "helpfulVotes": 14,
  "unhelpfulVotes": 1,
  "photos": ["https://i5.walmartimages.com/asr/review-photo.jpeg"],
  "pros": ["Sound quality", "Battery life"],
  "cons": [],
  "syndicated": false,
  "submittedAt": "2026-03-14T09:22:00.000Z",
  "reviewSource": "scraped",
  "scrapedAt": "2026-06-18T07:30:00.000Z"
}

Pricing (pay per event)

Event	Price
Actor start	$0.00005
Review extracted	$0.003 / review
Product summary row	$0.001 / product

Both pay-per-event and usage-based billing are available — pick whichever fits your job at run time. The actor logs the worst-case cost cap at the start of every run, before any charge fires.

Input

Three auto-routed modes:

1. Keyword search → top products' reviews

{
  "mode": "search",
  "searchQuery": "airpods pro",
  "maxProducts": 5,
  "maxReviewsPerProduct": 100,
  "sortReviews": "mostRecent"
}

2. Direct product URLs

{
  "mode": "productUrls",
  "productUrls": ["https://www.walmart.com/ip/Apple-AirPods-Pro-2/1820546583"],
  "maxReviewsPerProduct": 200
}

3. Walmart item IDs

{
  "mode": "itemIds",
  "itemIds": ["1820546583"],
  "ratingFilter": "1",
  "maxReviewsPerProduct": 50
}

Key options: maxReviewsPerProduct (1–1000), sortReviews (mostRecent | mostHelpful | highestRating | lowestRating | mostRelevant), ratingFilter (all | 5…1), includeProductSummary (default true). Item IDs are the numbers at the end of a Walmart product URL.

How it works

Walmart runs Akamai + PerimeterX (a 9/10-difficulty anti-bot stack), so raw HTTP / Cheerio gets blocked at the TLS-fingerprint level. This actor uses PlaywrightCrawler + real Chromium through Apify Residential proxies pinned to US, with a fingerprint pool, a cookie-persistent session pool, and SessionError-driven session rotation: a transient block retires the flagged session and retries on a fresh US residential IP with exponential backoff (~1.5s → 30s).

Reviews are parsed from Walmart's server-rendered __NEXT_DATA__ JSON — no fragile DOM scraping. The reviews surface is deep-paginated until your cap or the product runs out of reviews; a global dedup guard stops cleanly when a page returns nothing new.

SerpApi fallback: if every rotated session is blocked and the live scrape yields zero reviews, the targeted item IDs are routed to SerpApi's managed walmart_product_reviews engine so the run still returns real data. Recovered reviews go through the same cap/charge/dedup path and the run exits SUCCEEDED.

Using it from an AI agent (MCP)

The actor is exposed as apify--walmart-reviews-scraper in the Apify MCP server. Give the agent a product URL or item ID and ask for reviews — it returns structured JSON it can summarize, classify, or aggregate. Narrow maxReviewsPerProduct and ratingFilter to keep token use and cost predictable.

FAQ

Does it need login or cookies? No. Reviews are public; no Walmart account is required.

How many reviews can I get per product? Up to 1000 via maxReviewsPerProduct. Most products expose far fewer than that.

Why did a run return a single diagnostic row? Walmart blocked every rotated session and the SerpApi fallback was unavailable for that input — usually a transient block. Retry, or lower maxProducts.

Can I get product price/specs too? Use the companion walmart-data-extractor actor for full product fields.

Legal

This actor collects only publicly available review data from Walmart.com. Use it in compliance with Walmart's Terms of Service and applicable laws (including data-protection regulations such as GDPR/CCPA where relevant). You are responsible for how you use the scraped data. Do not use it to republish copyrighted review text verbatim at scale, harass reviewers, or process personal data unlawfully. This actor is not affiliated with or endorsed by Walmart Inc.