Walmart Reviews Scraper - All Reviews by URL, ID or Keyword

What is Walmart Reviews Scraper - All Reviews by URL, ID or Keyword?

Use this Apify Actor to scrape Walmart.com customer reviews for a product selected by product URL, Walmart item ID, or keyword search. It accepts one or many product targets, deep-paginates review pages for each product up to your cap, and returns one record per review with fields such as rating, title, text, author, verified purchaser flag, helpful votes, photos, pros, cons, and submitted date. When enabled, it also adds one per-product summary row with overall rating, total review count, brand, and a 1-5 star breakdown.

The Actor is MCP-ready and usable through Apify MCP, so AI agents can work with structured review data without extra parsing.

Best fit and connected workflows

This Actor fits workflows where the review corpus matters more than the storefront listing:

• Review monitoring for a specific Walmart product over time.
• Competitive research across product URLs, item IDs, or keyword-discovered products.
• AI workflows that need review text, star rating, verified purchase status, and engagement signals in a structured dataset.
• Downstream analysis that pairs product-level data with review-level detail.

For a broader Walmart product record first, use the related upstream Actor Walmart Product, Price and Review Data Scraper. It provides the parent product data, then this Actor adds the review layer for the same Walmart item.

Practical scenario

Maya, a marketplace analyst, starts with a Walmart item URL for a wireless earbuds product. She runs this Actor in productUrls mode, sets maxReviewsPerProduct to focus on recent feedback, and keeps includeProductSummary enabled. The dataset returns review rows with rating, title, text, author, verifiedPurchaser, helpfulVotes, and submittedAt, plus a summary row with overallRating, totalReviewCount, and ratingBreakdown.

She uses the summary row to compare sentiment at a glance, then reads the newest 1-star reviews to identify a repeated complaint. Her next step is to pass the dataset into a reporting workflow or to combine it with product metadata from the companion Walmart data extractor.

Input

Choose the input shape that matches how you want to identify products.

Field	Type	Description
mode	string	Selects the lookup path: search, productUrls, or itemIds.
searchQuery	string	Keyword search used when mode is search. The top products are resolved automatically.
productUrls	array of strings	Walmart product page URLs to scrape in productUrls mode.
itemIds	array of strings	Raw Walmart item IDs to scrape in itemIds mode.
maxProducts	integer	Upper bound on how many search results get scraped in search mode.
maxReviewsPerProduct	integer	Upper bound on reviews per product.
sortReviews	string	Review order: mostRecent, mostHelpful, highestRating, lowestRating, or mostRelevant.
ratingFilter	string	Exact star bucket to keep: all, 5, 4, 3, 2, or 1.
verifiedOnly	boolean	Keep only verified-purchase reviews. Skipped reviews are never charged. Defaults to false.
includeProductSummary	boolean	Adds one summary row per product when enabled.
proxyConfiguration	object	Apify proxy settings, with Residential US as the default.

Focused example:

{
  "mode": "productUrls",
  "productUrls": [
    "https://www.walmart.com/ip/Apple-AirPods-Pro-2/1820546583"
  ],
  "maxReviewsPerProduct": 25,
  "sortReviews": "mostRecent",
  "ratingFilter": "all",
  "verifiedOnly": false,
  "includeProductSummary": true
}

Output

The dataset contains review rows and, when enabled, product summary rows. The output schema also exposes a run summary in Key-Value Store.

Field	Type	Description
_type	string or null	Record kind: review, product, or diagnostic.
reviewId	string or null	Stable Walmart review ID.
itemId	string or null	Walmart numeric item ID.
productName	string or null	Product title.
productUrl	string or null	Canonical Walmart product page URL.
rating	number or null	Star rating from 1 to 5.
title	string or null	Review headline.
text	string or null	Full review body text.
author	string or null	Reviewer display name.
verifiedPurchaser	boolean or null	Walmart verified purchaser flag.
helpfulVotes	number or null	Helpful vote count.
unhelpfulVotes	number or null	Not helpful vote count.
photos	array of strings	Review photo URLs.
pros	array of strings	Structured pros when present.
cons	array of strings	Structured cons when present.
syndicated	boolean or null	Syndication flag.
submittedAt	string or null	Walmart submission date as Walmart shows it (for example 10/9/2026).
reviewSource	string or null	scraped (live Walmart page) or managed-fallback (recovered through the managed fallback route when Walmart blocks the live scrape).
overallRating	number or null	Product summary average rating.
totalReviewCount	number or null	Total review count Walmart reports.
ratingBreakdown	object or null	1-5 star histogram.
brand	string or null	Product brand in summary rows.
scrapeSource	string or null	Summary source: reviews-page or managed-fallback.
status	string or null	Diagnostic status.
stopReason	string or null	Machine-readable diagnostic reason.
scrapedAt	string	ISO 8601 timestamp for the record.

Example rows (a subset of a real run, itemIds mode):

[
  {
    "_type": "product",
    "itemId": "17835006350",
    "productName": "Apple AirPods Pro 3",
    "overallRating": 4.4,
    "totalReviewCount": 18246,
    "ratingBreakdown": { "1": 1644, "2": 429, "3": 622, "4": 1407, "5": 14144 },
    "scrapeSource": "reviews-page"
  },
  {
    "_type": "review",
    "reviewId": "443574095",
    "itemId": "17835006350",
    "productName": "Apple AirPods Pro 3",
    "rating": 5,
    "text": "Love my new AirPods definitely happy about the upgrade.",
    "author": "RubysMan",
    "verifiedPurchaser": true,
    "helpfulVotes": 0,
    "unhelpfulVotes": 0,
    "photos": [],
    "pros": [],
    "cons": [],
    "syndicated": false,
    "reviewSource": "scraped"
  }
]

How it works

The Actor uses a Chromium browser crawler with Apify Residential proxies pinned to the US, with session rotation, fingerprinting and exponential backoff. If Walmart's anti-bot blocks every rotated session, a managed fallback route is tried for the targeted items before the run gives up.

Three input modes route into the same review extraction flow:

• search resolves top products from a keyword query and scrapes their reviews.
• productUrls opens the supplied Walmart product pages to resolve item IDs, then reads the review surface.
• itemIds goes directly to the Walmart review endpoint for the numeric item ID.

Reviews are deep-paginated in the selected sort order until the cap is reached or the product runs out of reviews. A per-product summary row is available when includeProductSummary is enabled.

Outcome codes

The OUTPUT and RUN_SUMMARY records carry an outcome field:

• COMPLETE: reviews were collected with no request errors.
• VALID_EMPTY: the products loaded but had no reviews matching your filters; no review events are charged.
• PARTIAL: some reviews were saved, but requests failed, the runtime deadline was reached, or reviews were recovered through the fallback route.
• INVALID_INPUT: no usable search query, URL or item ID was given.
• FREE_PLAN_LIMIT: the free-plan allowance for the managed fallback was used up; reviews already saved are kept.
• UPSTREAM_FAILED: every route was blocked and no review was saved; one diagnostic row is written and the run is marked failed.

Pricing

This Actor uses pay per event plus Apify platform usage. Review extraction is billed per review written to the dataset, and product summaries are billed per product when enabled. Apify platform usage, such as compute and proxy resources, is charged separately. Reviews removed by ratingFilter or verifiedOnly are never charged. See the Pricing tab for current event prices.

A simple example in words: if one execution extracts one hundred reviews and includes one product summary row, the event-based charges include one hundred review events plus one product-summary event, and Apify platform usage is added according to the live Pricing tab.

Free plan limit: Apify free-plan accounts can make 10 managed-recovery search requests per rolling 30 days across this developer's search-powered Actors. The live Walmart scrape is not limited; only the managed recovery source used when Walmart blocks the scrape counts, one request per review page. When the allowance is used up, the run keeps any reviews already saved and finishes with outcome: "FREE_PLAN_LIMIT". Paid Apify plans have no such limit.

Use with AI agents (MCP)

This Actor is available through Apify MCP as a structured Walmart review tool. The precise Actor identity is khadinakbar/walmart-reviews-scraper.

It returns normalized review records and optional product summary rows that an agent can sort, compare, summarize, or join with other product data. The dataset is the primary source of truth, and scrapedAt provides record-level provenance. When the reviews surface is paginated, the Actor keeps collecting until the configured cap is reached.

Scrape Walmart reviews for this product URL, return a bounded number of the newest reviews, include the product summary row, and give me the dataset items so I can summarize complaints and verified-purchase patterns.

Output interpretation guidance:

• review rows contain the per-review evidence.
• product rows contain the aggregate view for a product.
• reviewSource and scrapeSource show whether the record came from live scraping or the managed fallback path.
• submittedAt and scrapedAt help distinguish the Walmart submission time from the extraction time.
• Pagination continues until the review cap for each product is reached.
• Cost scales with the number of review events and optional product-summary events, plus Apify platform usage visible in the Pricing tab.

API example

JavaScript example using the Apify API, APIFY_TOKEN, and dataset readback:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
});

const run = await client.actor('khadinakbar/walmart-reviews-scraper').call({
  mode: 'itemIds',
  itemIds: ['1820546583'],
  maxReviewsPerProduct: 10,
  sortReviews: 'mostRecent',
  includeProductSummary: true,
});

const datasetId = run.defaultDatasetId;
const { items } = await client.dataset(datasetId).listItems();

console.log(items);

Best results and outcome guidance

Use the narrowest input that matches your task. If you already know the product, productUrls or itemIds creates a direct review path. If you want discovery from search terms, search resolves the top products first. When you are comparing sentiment changes, mostRecent is a natural default. When you are studying review themes or vote patterns, mostHelpful and the star-rating filter can focus the dataset.

For larger review corpora, raise maxReviewsPerProduct within the live schema bounds. For lighter checks, keep it smaller and read the summary row first. If you also need product metadata, pair this Actor with the linked Walmart product extractor workflow.

Design note

I designed the star and verified-purchase filters to apply client-side to every review, so they are exact even when Walmart ignores a filter parameter. A page whose reviews are all filtered out still counts as new content, so paging continues to the next page instead of stopping early; repeated pages that Walmart serves past the last page are caught by review-ID de-duplication.

FAQ

Can I start from a keyword instead of a product URL?

Yes. Use mode: "search" with a searchQuery, and the Actor will resolve top products before scraping their reviews.

Can I scrape by raw Walmart item ID?

Yes. Use mode: "itemIds" and pass the numeric item IDs from the end of Walmart product URLs.

Does the dataset include a product-level summary?

Yes. Keep includeProductSummary enabled to receive one summary row per product with overall rating, total review count, and a star histogram.

How do I connect this with a broader Walmart product workflow?

Start with Walmart Product, Price and Review Data Scraper for product-level attributes, then use this Actor for the review corpus.

Can I keep only verified purchases?

Yes. Set verifiedOnly: true. Non-verified reviews are skipped and never charged; combine it with ratingFilter to mine, for example, verified 1-star complaints.

How are review records paginated?

Reviews are collected in the selected sort order and deep-paginated until the configured cap is reached or the product runs out of reviews.

Responsible use

Use the data in line with Walmart's terms and applicable law. Review text can contain personal expression and context, so handle it carefully in analytics, storage, and redistribution workflows. Respect reviewer privacy, keep provenance intact, and apply appropriate safeguards when processing or publishing the data.