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?

How Walmart Product Reviews Scraper runs

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.

Agent Built prompt templates

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.

Agent Built exploration

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.

Agent Built run inputs

Agent Built run results

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

Automation and export flow

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

Walmart Product Reviews Scraper

History

FAQ About the template

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