Collect structured public customer-review records for a Walmart item. Configure a product URL or item ID and result count to return review text, ratings, authors, dates, votes, variants, badges, seller context, and summary ratings.
What Does BrowserAct Walmart Product Reviews Scraper Do?
This BrowserAct template runs a validated, reusable extraction path against publicly accessible Walmart pages. Configure the inputs, run the Bot, and receive structured records without writing selectors or browser scripts.
Key Features
- Accept either a Walmart product URL or item ID.
- Return distinct public review rows up to Count.
- Capture review text, rating, author, date, votes, and verification.
- Include product-level rating summary and review context on every row.
What Data Can I Extract from Walmart?
| Field | Description |
|---|---|
product_item_id |
Structured product item id value returned by the Bot. |
overall_average_rating |
Public overall average rating metric. |
total_review_count |
Visible total review count value. |
rating_breakdown |
Rating distribution by star level. |
review_id |
Structured review id value returned by the Bot. |
star_rating |
Public star rating metric. |
review_title |
Public review title. |
review_text |
Public review text text. |
author_nickname |
Public author nickname. |
submitted_date |
Visible submitted date. |
verified_purchase |
Structured verified purchase value returned by the Bot. |
helpful_votes |
Visible helpful votes value. |
not_helpful_votes |
Visible not helpful votes value. |
selected_variant |
Structured selected variant value returned by the Bot. |
badges |
Structured badges value returned by the Bot. |
fulfillment_provider |
Structured fulfillment provider value returned by the Bot. |
seller_name |
Public seller name. |
media_count |
Visible media count value. |
source_review_url |
Public source review url for the record. |
How to Use Walmart Product Reviews Scraper
- Click Run task.
- Enter the configurable values shown below.
- Run the Bot and review the structured records.
- Export the result or connect it to an API, automation platform, or agent workflow.
Input Parameters
| Parameter | Required | Default | Description |
|---|---|---|---|
Product URL or Item ID |
Yes | 18656507313 |
Public Walmart product URL or numeric item ID. |
Count |
Yes | 10 |
Maximum distinct review records to return. |
Example Output
{
"results": [
{
"product_item_id": "18656507313",
"overall_average_rating": 4.4,
"total_review_count": 325,
"review_id": "review-123",
"star_rating": 5,
"review_title": "Great value",
"review_text": "The product works well for everyday use.",
"author_nickname": "VerifiedCustomer",
"submitted_date": "2026-08-01",
"verified_purchase": true,
"helpful_votes": 8,
"media_count": 1
}
]
}
How Does it Work?
BrowserAct resolves the configured Walmart item, opens its public review data, and reads the structured review payload exposed by the rendered product experience.
The Bot deduplicates reviews, paginates or reads additional review data when needed, and returns exactly up to Count review records with product-level rating context.
How to Build a New Scraper Bot with Agent Built
Step 1: Describe the Data You Need
Open BrowserAct and start Agent Built from the Home prompt box or from Create -> Build with Agent. Paste a clear request that includes the website, the records to collect, the filters or search conditions, the fields to return, the result limit, and the inputs you want to reuse later.
Step 2: Let BrowserAct Build and Test the Bot
BrowserAct explores the live website, works out the navigation and extraction path, and validates the result. If BrowserAct asks for clarification, reply in the same build conversation with the missing URL, condition, field, example, or expected output.
Step 3: Run the Bot and Review the Result
When the build is complete, run the Bot with the default inputs first. Review the structured records, source URLs, missing fields, and row count before reusing the Bot with new inputs or connecting it to another workflow.
Why Use Walmart Product Reviews Scraper?
Analyze customer feedback, compare product satisfaction, monitor quality signals, and route review data into reporting or sentiment workflows.
Who Can Use This Template?
- Voice-of-customer teams.
- Product and quality analysts.
- Retail researchers.
- Automation builders monitoring reviews.
How Many Results Can You Scrape?
Count controls the maximum distinct reviews. The Bot stops earlier when the public product has fewer available reviews.
Automation and Export
BrowserAct returns structured data first. You can then use the completed output through API JSON responses, n8n workflows, Make scenarios, Zapier automations, MCP-based agent workflows, or manual CSV and spreadsheet review.
Need Help?
Contact us at
Discord: Discord Community
E-mail: service@browseract.com
