~ / endpoints / Review API

Walmart Review Scraper API

Our Walmart review scraper takes any item URL or us_item_id and returns the overall rating, the 1-to-5 star distribution, and a page of full customer reviews: text, title, date, verified-purchase flag, and helpful-vote counts, all as JSON.

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1,000

free requests / mo

2.6s

median response

JSON

structured output

1-5

star distribution

why it blocks

Why Walmart Review data is locked behind a block

Walmart's review text loads inside the item page's __NEXT_DATA__ blob behind a residential-only wall, so a datacenter fetch returns a challenge instead of reviews. Scraping it by hand means parsing that shifting blob and rebuilding the star breakdown yourself.

run a request

Fire off the Walmart Review Scraper API

cURL

curl "https://api.walmartscraperapi.com/api/v1/walmart/reviews?url=https://www.walmart.com/ip/808715278&api_key=$API_KEY"

Python

import requests

BASE = "https://api.walmartscraperapi.com"
API_KEY = "YOUR_API_KEY"

# Pass a Walmart /ip/ URL, or use the id alias for a us_item_id.
data = requests.get(
    f"{BASE}/api/v1/walmart/reviews",
    params={
        "url": "https://www.walmart.com/ip/808715278",
        "api_key": API_KEY,
    },
    timeout=30,
).json()

print(data["overall_rating"], "from", data["total_reviews"], "reviews")
print("stars 5..1:", data["rating_distribution"])

for r in data["reviews"]:
    tag = "verified" if r["verified_purchase"] else "unverified"
    print(r["rating"], tag, r["reviewer_name"], "-", r["title"])

inputs

Parameters

Parameter	Required	Default	Notes
`url`	required	-	The Walmart item page URL, e.g. https://www.walmart.com/ip/808715278. This is the primary input. Required unless you pass id.
`id`	optional	-	Alias for the numeric Walmart us_item_id, e.g. 808715278. We build the /ip/{id} URL from it. One of url or id is required.
`country`	optional	-	Optional two-letter country code to fetch the item as seen from that region.
`api_key`	required	-	Your API key, passed as a query parameter. Get one free at signup.

the payload

The JSON the Walmart Review Scraper API hands back

200 OK

{
  "us_item_id": "808715278",
  "overall_rating": 4.5,
  "total_reviews": 4721,
  "rating_distribution": {
    "1": 244,
    "2": 112,
    "3": 216,
    "4": 596,
    "5": 3553
  },
  "reviews": [
    {
      "review_id": "393669526",
      "reviewer_name": "Laurie",
      "rating": 5,
      "title": "Great Cuisinart coffeemaker",
      "text": "Love the new sleek look of this Cuisinart 12 Cup Coffeemaker (DCC-1220 Series). I also like some of the new features ex, displaying how long the coffee has been brewed...",
      "review_date": "7/29/2025",
      "verified_purchase": true,
      "positive_feedback": 3,
      "negative_feedback": 0,
      "badges": ["Verified Purchase"]
    },
    {
      "review_id": "421664809",
      "reviewer_name": "Robert",
      "rating": 3,
      "title": "Multiple Problems",
      "text": "Not nearly as good as my previous Cuisinart. Coffeepot lid remains attached to coffee pot making it difficult to clean...",
      "review_date": "4/4/2026",
      "verified_purchase": true,
      "positive_feedback": 5,
      "negative_feedback": 1,
      "badges": ["Verified Purchase"]
    }
  ]
}

Field	Type	Description
`us_item_id`	string	The Walmart listing id these reviews belong to.
`overall_rating`	number	The item's average overall rating.
`total_reviews`	integer	Total number of reviews on the item.
`rating_distribution`	object	Count of reviews per star, keyed 1 through 5.
`reviews`	array	The reviews on this page, each an object with the fields below.
`reviews[].review_id`	string	Walmart's unique id for the review.
`reviews[].reviewer_name`	string	The reviewer's display nickname.
`reviews[].rating`	integer	The star rating this reviewer gave, 1 to 5.
`reviews[].title`	string	The review headline, or null when the reviewer left none.
`reviews[].text`	string	The full review body as plain text.
`reviews[].review_date`	string	The date the review was submitted.
`reviews[].verified_purchase`	boolean	True when Walmart marks the review as a verified purchase.
`reviews[].positive_feedback`	integer	Count of readers who marked the review helpful.
`reviews[].negative_feedback`	integer	Count of readers who marked the review unhelpful.
`reviews[].badges`	array	Badge labels on the review, e.g. Verified Purchase.

put it to work

Ways teams put Walmart data to work

Product sentiment analysis

Pull review text and ratings across a catalog to score customer sentiment and surface the phrases buyers repeat about each item.

Star-distribution monitoring

Store rating_distribution over time so a drift toward 1 and 2 star reviews shows up before the average rating visibly drops.

Voice-of-customer feeds

Route new reviews into a dashboard or Slack channel so product and support teams read fresh feedback the day it lands.

Competitor review mining

Read reviews on rival listings to find recurring complaints and feature requests you can answer in your own product.

Verified-purchase filtering

Use the verified_purchase flag to weight or filter reviews so your analysis leans on confirmed buyers.

Review-quality signals

Rank reviews by positive_feedback to pull the most helpful feedback first for summaries and highlight reels.

under the hood

What powers our Walmart Review Scraper API

Pass an item URL or a us_item_id and we return the rating summary, the star breakdown, and a page of full reviews, no residential proxy pool or __NEXT_DATA__ parser to run yourself. Every request routes through residential IPs with anti-bot handling and retries, returning validated JSON in about 2.6 seconds.

URL or id input

Pass a full /ip/ URL as url or the numeric us_item_id as id and we resolve the item for you.

Rating summary and breakdown

Every call returns overall_rating, total_reviews, and the 1-to-5 rating_distribution alongside the review list.

Verified-purchase flags

Each review carries a verified_purchase boolean and its badge labels, so you can filter to confirmed buyers.

Residential egress and retries

Walmart blocks datacenter traffic, so requests route through residential IPs and retry on a challenge before returning.

Clean review text

Review bodies come back as plain text with title, date, and helpful-vote counts, ready for analysis or an LLM.

Validated JSON schema

Parity-checked, regression-tested fields return in the same shape on every call.

weigh the routes

The Walmart Review Scraper API against every other route

	Our API	DIY (requests / headless)	Walmart.io affiliate API
Full review text	Yes, plain text per review	Parse the review blob yourself	Not exposed as public reviews
Star distribution	1-to-5 counts in every call	Rebuild it from raw fields	Not applicable
Reaches the page	Residential egress built in	Datacenter IPs get blocked	Affiliate feed, not reviews
Verified-purchase flag	Per review boolean	Parse badges manually	Not applicable
Setup	API key only	Proxies, headless browser, parsers	Impact Radius approval and keys
Access	Open signup, free tier	You build and maintain it	Affiliate program gated

what it costs

Pay for the requests that land

Plan	Price	Best for
Free	1,000 requests	Testing and small jobs
Pro	$0.60 / 1k	Production workloads
Pay-as-you-go	$0.90 / 1k	Spiky or one-off volume

Median response 2.6s. You only pay for successful requests.

FAQ

What is a Walmart review scraper?

A Walmart review scraper is a tool that reads a Walmart item's public reviews and returns them in a structured format. Our Walmart review scraper API takes a product URL or us_item_id and returns the overall rating, the 1-to-5 star distribution, and a page of full reviews with text, title, date, verified-purchase flag, and helpful-vote counts as JSON.

How many reviews does one call return?

One call returns the first page of reviews for the item, along with the overall_rating, total_reviews, and the full rating_distribution. The reviews array holds the individual reviews from that page. The summary fields describe every review on the listing, so you always see the complete rating picture even from a single request.

Can I tell which reviews are from verified purchases?

Yes. Each review includes a verified_purchase boolean and a badges array carrying labels such as Verified Purchase. You can filter or weight your analysis to confirmed buyers by checking that flag, which is useful when you want to discount reviews that Walmart has not tied to a purchase.

Why does scraping Walmart reviews directly fail?

Walmart serves reviews inside the item page's __NEXT_DATA__ JSON and blocks datacenter IP ranges, so a direct fetch from a cloud server usually returns a challenge screen rather than the reviews. Our API routes through residential IPs, handles the anti-bot layer, and parses that JSON, which is why it returns structured review data where a plain request does not.

Do I get the star rating breakdown?

Yes. Every response includes rating_distribution, an object keyed 1 through 5 with the number of reviews at each star level, plus the overall_rating and total_reviews. That lets you chart how ratings are spread and track shifts in the distribution over time rather than watching only the single average number.

How fast is the Walmart review scraper API?

Median end-to-end response is about 2.6 seconds, which includes residential proxy routing, anti-bot handling, retries, and parsing. The rating summary, star distribution, and first page of reviews all come back in that one call.

Pull review api as clean JSON

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