Walmart Reviews Scraper | Fast, Structured Product Reviews API

Extract Walmart product reviews at scale, straight into clean JSON. This Walmart Reviews Scraper pulls ratings, review text, verified purchase status, photos, seller data, and full product-level rating breakdowns for any product listed on Walmart.com, no browser rendering required.

⚡ Why this Walmart Reviews Scraper

• No browser session required. Most Walmart review scrapers spin up a headless browser just to load the product page before they can read anything. This one talks directly to the same backend Walmart's own app uses, so there's nothing to render and nothing to wait on.
• Full review content, not a snippet. Every review comes with the title, full text, star rating, submission date, verified purchase badge, reviewer nickname, and location, when available.
• Photos and videos included. Customer-uploaded media on each review is captured with full-size and thumbnail URLs.
• Product-level rating breakdown. Average rating, star-by-star distribution, recommended percentage, review aspects (build quality, fit, value for money, and more), and an AI-generated bullet summary are pulled once per product, alongside the individual reviews.
• Sort and filter built in. Sort by most recent, most helpful, or rating (high to low or low to high). Filter by verified purchases only or by star rating. Flag reviews containing your own keywords, in the title or the text.
• Cutoff date support. Stop collecting once you hit reviews older than a given date, ideal for daily or weekly monitoring runs.
• Batch scraping. Feed in multiple product URLs or item IDs and they run concurrently in the same job.
• Proxies included. Residential proxies are already wired in, no setup needed.
• Fast. Pure HTTP requests to Walmart's own API, no page load, no rendering delay.

🎯 Who uses this Walmart Reviews Scraper

• E-commerce sellers and brand managers monitoring customer sentiment, verified purchase ratios, and rating trends for their own listings.
• Market researchers and competitor benchmarking teams comparing rating breakdowns and review volume across Walmart products in the same category.
• Sentiment analysis and NLP teams feeding review text into models, pre-filtered by star rating or keyword.
• Reputation monitoring agencies tracking new reviews for a portfolio of Walmart product listings.
• Developers building e-commerce or BI dashboards who need structured Walmart review data without maintaining their own scraper or Walmart scraper API integration.

🧾 What you get, per review

• Review content: title, text, star rating, submission date
• Reviewer info: nickname, author id, location, badges (e.g. Verified Purchase)
• Media: customer photos and videos, full-size and thumbnail URLs
• Seller info: seller name, seller id, fulfillment type
• Item variant: the specific size/color variant the reviewer purchased, and its features
• Language: original locale and language, translated locale and language when applicable
• Feedback counts: helpful and unhelpful votes
• Keyword match: whether your search keywords were found, and in which field (title or text)

📊 What you get, per product (summary dataset)

• Average rating, rounded average, and rating range
• Total review count, filtered review count, and reviews-with-text count
• Full star-by-star rating breakdown, in both counts and percentages
• Recommended percentage
• Review aspects (e.g. Build Quality, Fit, Comfort, Value for Money) with snippet counts
• AI-generated bullet summary of common themes, when Walmart provides one
• Top positive and top negative review snapshots
• Top product media (customer photos/videos from the review section)

📋 Data Fields

📝 Review Details

Field	Description
product_id	ID of the product this review belongs to
review_id	Unique ID of the review
review_reference_id	Reference ID used to match media to their review
rating	Reviewer's star rating (1-5)
recommended	Whether the reviewer marked the product as recommended, if provided
title / text	Review title and body text
submission_date	Date the review was submitted
status	Moderation status of the review (e.g. Approved)

👤 Reviewer

Field	Description
author_nickname	Display name of the reviewer
author_id	Internal ID of the reviewer
user_location	Reviewer's location, if provided
badges	Badges shown on the review (e.g. Verified Purchase)
positive_feedback / negative_feedback	Helpful and unhelpful vote counts

🖼️ Media & Item Variant

Field	Description
media	Images/videos attached to the review, with full-size and thumbnail URLs
item_id / item_name / item_image_url	The specific size/color variant the reviewer purchased
features	Variant attributes tied to this review (e.g. size, color)

🏪 Seller & Language

Field	Description
seller_id / seller_name	Seller fulfilling the reviewed item
fulfilled_by	Fulfillment type (e.g. Walmart)
original_locale / original_language	Language the review was originally written in
translated_locale / translated_language	Language of the translated text, if translated
show_translation_cta	Whether Walmart shows a "translate" prompt for this review
external_source	Source system the review came from (e.g. Bazaarvoice)

⭐ Matching

Field	Description
keyword_match	True if any of your input search keywords were found in the title or text
matched_keywords	Which keywords matched and where (title, text, or both)

📦 Product Summary (per-product dataset)

Field	Description
product_id	ID of the product this summary belongs to
average_overall_rating / rounded_average_overall_rating	Average star rating, raw and rounded
overall_rating_range	Rating scale range (typically 5)
total_review_count / filtered_reviews_count	Total reviews, and reviews matching applied filters
recommended_percentage	Percentage of reviewers who recommended the product
show_recommended_module	Whether Walmart displays the recommended-percentage module for this product
positive_count / negative_count	Positive vs negative review counts
rating_breakdown	Number of reviews per star rating (1-5)
rating_percentage_breakdown	Percentage of reviews per star rating (1-5)
reviews_with_text_count	Number of reviews that include written text
reviews_with_text_rating_breakdown	Reviews-with-text counts, broken down per star rating
aspects	Review aspects (e.g. Build Quality, Fit, Comfort) with snippet counts
review_features	Numeric aspect scores (e.g. Value for Money, Build Quality)
bullet_summary_aspects	AI-generated bullet points per aspect, with polarity and rank
bullet_summary_thumbs_up_count / bullet_summary_thumbs_down_count	Helpfulness votes on the AI-generated bullet summary
review_summary	AI-generated overall review summary, when Walmart provides one
top_positive_review / top_negative_review	Snapshot of the top-rated and lowest-rated review
top_product_media	Customer photos/videos surfaced from the review section
total_media_count	Total number of review photos/videos for the product
condition	Product condition filter applied, if any (e.g. new, refurbished)

🔧 Input options

Field	Description
productUrls	One or more Walmart product URLs, or plain numeric item IDs
maxResults	Max reviews to collect per product. Leave empty to collect all available
sortBy	Sort order: relevancy, most recent, most helpful, rating high to low, rating low to high
cutoffDate	Stop collecting once reviews older than this date are reached. Only applies with "most recent" sort
verifiedOnly	Only include reviews from verified purchasers
ratings	Only include reviews with these star ratings
searchKeywords	Flag reviews containing any of these keywords, in the title or text

📋 Example output - Review

[
    {
    "product_id": "15955722698",
    "review_id": "437411364",
    "review_reference_id": "1cea1eaa-a671-5b12-9bfa-5b2820faaed2",
    "rating": 5,
    "recommended": null,
    "title": "Bike works great, amazing customer service.",
    "text": "I have had this bike for around a month now, and it works great. I admit after less than two weeks of use, the innertube popped, but I contacted customer service, and in less than 15 minutes, they sent a free replacement, were kind the whole time, and got what I needed done. The bike rides well on dirt, roads, and flooded roads rain conditions up to half a foot with ease. On class 3 mode, the battery runs dry quite fast, but still lasts a good amount of miles. On class two mode it probably could last the whole 80 miles that is advertised. It's a great commuter bike, like how I used it and can be fun for other things as well. Overall, this is my first pick if you need a decent bike for a fair price that will last, from a company that Is actually customer oriented.",
    "keyword_match": false,
    "matched_keywords": [],
    "submission_date": "8/19/2026",
    "author_nickname": "Chrystal",
    "author_id": "486f2cd61eaffcfc201ad0280e24556b5ad30f83810410382116666dcb1aa710c7006468a0ba5a304b5b7995023a0604",
    "user_location": null,
    "badges": [
      "Verified Purchase",
      "Walmart Associate"
    ],
    "status": "APPROVED",
    "positive_feedback": 0,
    "negative_feedback": 0,
    "seller_id": "102632399",
    "seller_name": "TS TST GRP LLC",
    "fulfilled_by": "Walmart",
    "item_id": "15955722698",
    "item_name": "TST Electric Bike for Adults, 750W Peak 1500W Motor, 20\"x4\" Fat Tire, E Mountain Bike, 48V/15AH UL2849",
    "item_image_url": null,
    "original_locale": "en_US",
    "original_language": "English",
    "translated_locale": "en",
    "translated_language": "English",
    "show_translation_cta": false,
    "features": [
      {
        "name": "Color",
        "value": "R002-S-15AH-Black"
      }
    ],
    "media": [
      {
        "media_type": "IMAGE",
        "url": "https://i5.walmartimages.com/dfw/6e29e393-c735/k2-_9a2a86aa-7b47-4d70-8b73-0df9ca145b04.v1.jpg",
        "thumbnail_url": "https://i5.walmartimages.com/dfw/6e29e393-c735/k2-_9a2a86aa-7b47-4d70-8b73-0df9ca145b04.v1.jpg?odnWidth=150&odnHeight=150&odnBg=ffffff",
        "video_fallback_url": null
      }
    ],
    "external_source": "bazaarvoice"
  }
]

📋 Example output - Summary

[
  {
    "product_id": "15955722698",
    "average_overall_rating": 4,
    "rounded_average_overall_rating": 4,
    "total_review_count": 1213,
    "recommended_percentage": 72,
    "rating_breakdown": {
      "one_star": 192,
      "two_star": 44,
      "three_star": 68,
      "four_star": 155,
      "five_star": 754
    },
    "reviews_with_text_count": 388,
    "aspects": [
      {
        "id": "252",
        "name": "Ease Of Assembly",
        "score": null,
        "snippet_count": 46
      },
      {
        "id": "6049",
        "name": "Battery Life",
        "score": null,
        "snippet_count": 30
      },
      {
        "id": "48",
        "name": "Value For Money",
        "score": null,
        "snippet_count": 27
      },
      {
        "id": "184",
        "name": "Speed",
        "score": null,
        "snippet_count": 22
      },
      {
        "id": "5925",
        "name": "Motor Power",
        "score": null,
        "snippet_count": 19
      },
      {
        "id": "13948",
        "name": "Seat Comfort",
        "score": null,
        "snippet_count": 19
      },
      {
        "id": "29637",
        "name": "Tires",
        "score": null,
        "snippet_count": 18
      },
      {
        "id": "155",
        "name": "Build Quality",
        "score": null,
        "snippet_count": 12
      },
      {
        "id": "6051",
        "name": "Charging Time",
        "score": null,
        "snippet_count": 11
      },
      {
        "id": "124041",
        "name": "Suspension System",
        "score": null,
        "snippet_count": 11
      },
      {
        "id": "7420",
        "name": "Brakes",
        "score": null,
        "snippet_count": 11
      },
      {
        "id": "582",
        "name": "Appearance",
        "score": null,
        "snippet_count": 10
      },
      {
        "id": "4466",
        "name": "Ride Comfort",
        "score": null,
        "snippet_count": 10
      },
      {
        "id": "63908",
        "name": "Battery Range",
        "score": null,
        "snippet_count": 10
      },
      {
        "id": "246",
        "name": "User Manual",
        "score": null,
        "snippet_count": 10
      },
      {
        "id": "70390",
        "name": "Hill Climbing",
        "score": null,
        "snippet_count": 9
      },
      {
        "id": "205627",
        "name": "Throttle Functionality",
        "score": null,
        "snippet_count": 8
      },
      {
        "id": "22688",
        "name": "Motor Torque",
        "score": null,
        "snippet_count": 8
      },
      {
        "id": "798",
        "name": "Handling",
        "score": null,
        "snippet_count": 8
      },
      {
        "id": "160",
        "name": "Lighting",
        "score": null,
        "snippet_count": 8
      },
      {
        "id": "5205",
        "name": "Screw Assembly",
        "score": null,
        "snippet_count": 1
      }
    ],
    "review_features": [
      {
        "id": "582",
        "name": "Appearance",
        "actual_value": 5,
        "type": "NUMERIC",
        "aspect_polarity": "Positive"
      },
      {
        "id": "184",
        "name": "Speed",
        "actual_value": 4.5,
        "type": "NUMERIC",
        "aspect_polarity": "Positive"
      },
      {
        "id": "48",
        "name": "Value for money",
        "actual_value": 4.4,
        "type": "NUMERIC",
        "aspect_polarity": "Positive"
      },
      {
        "id": "155",
        "name": "Build quality",
        "actual_value": 4.2,
        "type": "NUMERIC",
        "aspect_polarity": "Positive"
      },
      {
        "id": "5925",
        "name": "Motor power",
        "actual_value": 3.7,
        "type": "NUMERIC",
        "aspect_polarity": "Positive"
      },
      {
        "id": "13948",
        "name": "Seat comfort",
        "actual_value": 2.4,
        "type": "NUMERIC",
        "aspect_polarity": "Neutral"
      },
      {
        "id": "6049",
        "name": "Battery life",
        "actual_value": 2.8,
        "type": "NUMERIC",
        "aspect_polarity": "Neutral"
      },
      {
        "id": "6051",
        "name": "Charging time",
        "actual_value": 2.7,
        "type": "NUMERIC",
        "aspect_polarity": "Neutral"
      },
      {
        "id": "252",
        "name": "Ease of assembly",
        "actual_value": 3.2,
        "type": "NUMERIC",
        "aspect_polarity": "Neutral"
      }
    ],
    "bullet_summary_aspects": [
      {
        "aspect_id": "5925",
        "aspect_name": "motor power",
        "pill_text": "Sufficient power",
        "polarity": "Positive",
        "summary": "Has enough power for various riding conditions.",
        "rank": 5
      },
      {
        "aspect_id": "155",
        "aspect_name": "build quality",
        "pill_text": "Sturdy frame build quality",
        "polarity": "Positive",
        "summary": "Frame is solidly constructed with smooth finish.",
        "rank": 4
      },
      {
        "aspect_id": "48",
        "aspect_name": "value for money",
        "pill_text": "Competitive price",
        "polarity": "Positive",
        "summary": "Offers high quality at a lower cost.",
        "rank": 3
      },
      {
        "aspect_id": "184",
        "aspect_name": "speed",
        "pill_text": "Fast top speed",
        "polarity": "Positive",
        "summary": "Reaches speeds up to 30mph.",
        "rank": 2
      },
      {
        "aspect_id": "582",
        "aspect_name": "appearance",
        "pill_text": "Sleek design",
        "polarity": "Positive",
        "summary": "Has a modern and visually appealing look.",
        "rank": 1
      },
      {
        "aspect_id": "252",
        "aspect_name": "ease of assembly",
        "pill_text": "Assembly ease",
        "polarity": "Neutral",
        "summary": "Can be straightforward, but also confusing for some users.",
        "rank": 2
      }
    ],
    "total_media_count": 31
  }
]

🚀 Running the actor

Via Apify Console

Open the actor page, fill in the input form, and click Start.

Via Apify API: Python

from apify_client import ApifyClient

client = ApifyClient("your_apify_token")

run_input = {
    "productUrls": [
        {"url": "https://www.walmart.com/ip/Kushyshoo-Kids-Sneakers/10002316828"}
    ],
    "maxResults": 100,
    "sortBy": "most_recent",
    "cutoffDate": "",
    "verifiedOnly": False,
    "ratings": [],
    "searchKeywords": []
}

run = client.actor("mof1re/walmart-reviews-scraper").call(run_input=run_input)
reviews = client.dataset(run["defaultDatasetId"]).list_items().items

print(f"Fetched {len(reviews)} reviews")

Via Apify API: Node.js

import { ApifyClient } from 'apify-client';

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

const input = {
    productUrls: [
        { url: 'https://www.walmart.com/ip/Kushyshoo-Kids-Sneakers/10002316828' }
    ],
    maxResults: 100,
    sortBy: 'most_recent',
    cutoffDate: '',
    verifiedOnly: false,
    ratings: [],
    searchKeywords: [],
};

const run = await client.actor('mof1re/walmart-reviews-scraper').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log(`Fetched ${items.length} reviews`);

Example input JSON

{
  "productUrls": [
    { "url": "https://www.walmart.com/ip/Kushyshoo-Kids-Sneakers/10002316828" },
    { "url": "934937982" }
  ],
  "maxResults": 100,
  "sortBy": "rating_high",
  "verifiedOnly": true,
  "ratings": ["4", "5"],
  "searchKeywords": ["quality", "comfortable"]
}

🤖 Using this actor with AI agents and MCP

This actor works as a tool call for AI agents that need structured Walmart review data on demand, no scraping code to write or maintain. Point an agent at the Walmart Reviews Scraper when it needs to:

• Pull the latest customer reviews for a Walmart product, given a product URL or item ID
• Retrieve a product's aggregate rating breakdown and sentiment aspects for a summary or report
• Check whether recent reviews mention a specific concern, feature, or competitor comparison
• Filter reviews down to verified purchases or a specific star-rating range before summarizing them
• Monitor a product's reviews on a schedule and stop at a cutoff date, so only new reviews come back each run

Input and output are both plain JSON, so an agent can call this actor, wait for the dataset, and read the results directly, no HTML parsing, no pagination logic, no proxy setup on the agent's side.

❓ FAQ

How is this different from other Walmart review scrapers? Most run a full browser to load the product page before pulling reviews. This one calls Walmart's own backend directly and returns both individual reviews and a full product-level rating summary, in one run.

Does this scraper need an account or API key? No. It extracts publicly available review data.

Can I pass a product ID instead of a URL? Yes. Drop the numeric Walmart item ID straight into productUrls and it works the same as a full URL, including URLs with extra query parameters.

Can I filter reviews by star rating or verified purchase? Yes. Set ratings to one or more star values, and verifiedOnly to true to only include verified purchasers.

Does the keyword search filter reviews out? No. searchKeywords flags matching reviews with keyword_match: true and shows which keyword matched, and where, without removing any review from the results.

Do I need my own proxies? No. Proxies are built into the actor.

Is this legal? Can I scrape publicly available reviews? This tool extracts publicly available review data. You're responsible for complying with the target site's terms of service and applicable data protection laws for your use case.

💳 Pricing

Pay-per-result. You're charged for reviews actually delivered, not for pages fetched or requests made.

⚠️ Notes

• Free-tier accounts are capped at a small sample size per run, and to a single product per run. Upgrade to a paid Apify plan for full volume and batch scraping.
• Data reflects what's publicly listed on walmart.com at the time of the run; the source updates continuously.

🔗 See also

Need reviews from another platform? Check out:

• Expedia Reviews Scraper
• Tripadvisor Reviews Scraper
• Booking.com Reviews Scraper
• AirBnb Reviews Scraper
• ULTA Reviews Scraper