# Walmart Reviews Scraper (`axlymxp/walmart-reviews-scraper`) Actor

Scrape Walmart.com customer reviews by product URL, item ID or keyword: rating, text, date, verified purchase, helpful votes, photos and seller replies. Filter by stars, verified buyers or date for monitoring. Pay only for results.

- **URL**: https://apify.com/axlymxp/walmart-reviews-scraper.md
- **Developed by:** [axly](https://apify.com/axlymxp) (community)
- **Categories:** E-commerce, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 dataset items

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Walmart Reviews Scraper

Collect **Walmart.com customer reviews** for any product: star rating, title, full text, date, reviewer nickname, verified-purchase flag, helpful votes, photos and the seller's or brand's reply. Give it product URLs, item ids, or just a keyword. It can pick the top products for that keyword and collect reviews for each one.

Filters let you keep only the reviews you care about: specific star ratings such as 1 and 2 stars for complaints, verified buyers only, or only reviews posted since a date. That last one turns a daily schedule into a new-reviews feed.

The actor reads Walmart's mobile-app data feed and fetches 50 reviews per request. It needs no browser, no proxy and no Walmart account.

**Who it's for:** brands and agencies monitoring product reputation, product teams researching competitors, and anyone building sentiment or AI pipelines on real customer feedback.

### What data you get

| Field | Example | Notes |
|---|---|---|
| `review_id` | `441841979` | |
| `rating` | `5` | 1–5 stars |
| `title` | `Definitely a great option` | often empty on Walmart |
| `text` | `I love my laptop it came with lots of perks…` | |
| `date` | `2026-09-25` | ISO date |
| `author` | `Jennifer` | reviewer nickname |
| `verified_purchase` | `true` | |
| `recommended` | `true` | when the reviewer answered |
| `helpful_votes`, `unhelpful_votes` | `12`, `1` | |
| `photos` | `["https://i5.walmartimages.com/…"]` | photos attached to the review |
| `seller_name`, `fulfilled_by` | `Walmart.com`, `Walmart` | who the reviewer bought from |
| `seller_response` | `Hello Linda, thank you for your feedback…` | brand or seller reply, when present |
| `syndication_source` | `influenster.com` | set when the review was syndicated from another site |
| `badges` | `["VerifiedPurchaser"]` | |
| `language` | `English` | |
| `us_item_id`, `product_name`, `product_url` | | the product reviewed |
| `product_average_rating`, `product_total_reviews` | `4.6`, `7095` | product-level context on every row |
| `scraped_at` | `2026-09-28T02:41:48Z` | |

### Use cases

- **Reputation monitoring.** Schedule a daily run with `sinceDate` set to yesterday and `ratings: ["1","2"]` to get every new negative review for your products.
- **Competitor research.** Use `searchQueries: ["air fryer"]` with `maxProductsPerQuery: 10` to compare what buyers praise and complain about across the top products in a category.
- **Seller and brand response tracking.** See which reviews got a reply in `seller_response`, and how fast.
- **Sentiment and AI analysis.** Feed dated, rated review text into a sentiment model, topic clustering or an LLM summary.
- **Product development.** Mine 1- and 2-star reviews for recurring defects and missing features.

### Input

| Parameter | Description |
|---|---|
| `productUrls` / `itemIds` | Product pages (`/ip/...` or `/reviews/product/...`) or Walmart item ids |
| `searchQueries` | Optional keywords; the top products with reviews are used |
| `maxProductsPerQuery` | Products to take per keyword (default 5) |
| `sort` | Newest first (default), oldest first, most relevant, most helpful, highest or lowest rating |
| `ratings` | Keep only these star ratings, e.g. `["1", "2"]` |
| `verifiedOnly` | Keep only verified purchases |
| `sinceDate` | `YYYY-MM-DD`; stop at the first older review (needs newest-first sort) |
| `maxReviewsPerProduct` | Cap per product (default 100) |
| `maxItems` | Total review rows (default 1000) |

#### Example input

```json
{
    "productUrls": [{ "url": "https://www.walmart.com/ip/5144605607" }],
    "searchQueries": ["coffee maker"],
    "maxProductsPerQuery": 3,
    "sort": "submission-desc",
    "ratings": ["1", "2"],
    "verifiedOnly": true,
    "maxReviewsPerProduct": 200,
    "maxItems": 1000
}
```

#### Example output

```json
{
    "review_id": "439905127",
    "rating": 1,
    "title": null,
    "text": "Very slow laptop. Very disappointing.",
    "date": "2026-09-09",
    "author": "E",
    "author_location": null,
    "verified_purchase": true,
    "recommended": null,
    "helpful_votes": 0,
    "unhelpful_votes": 0,
    "photos": [],
    "seller_name": "Best Electronics",
    "fulfilled_by": "Seller",
    "seller_response": null,
    "syndication_source": null,
    "badges": [
        "VerifiedPurchaser"
    ],
    "language": "English",
    "us_item_id": "443153637",
    "product_name": "HP 14 Touchscreen Laptop Intel Processor 4GB RAM 64GB eMMC WiFi 6 Bluetooth Windows 11 Home S 1 Year Office Pale Gold Type C Hub",
    "product_url": "https://www.walmart.com/ip/443153637",
    "product_average_rating": 4.1,
    "product_total_reviews": 20504,
    "scraped_at": "2026-09-28T02:45:10Z"
}
```

### Scheduling and integrations

- **New-review alerts.** Schedule a daily run with `sinceDate` and `sort: submission-desc`, and connect a webhook or Slack integration to post the results.
- **Exports.** Download results as JSON, CSV or Excel, or read them through the Apify API.
- **Integrations.** Works with Google Sheets, Make, Zapier, n8n and Airbyte.

### Use with AI assistants (MCP)

Add the actor through the [Apify MCP server](https://mcp.apify.com) in Claude, Cursor or another MCP client. Ask *"Summarize the most common complaints in 1-star Walmart reviews of the Ninja AF140 air fryer"*, and the assistant runs the actor and reads the reviews.

### FAQ

**Why do I get fewer reviews than the product's review count?** Walmart's review count includes star-only ratings with no text. Only reviews with text can be listed. For a popular laptop that's about 7,000 of 20,500 ratings, and some small products have ratings but no written reviews at all. `product_total_reviews` still shows the full count.

**How far back can I go?** Every review with text, back to the oldest one. The actor pages through them 50 at a time.

**How do the rating and verified filters affect cost?** You only pay for rows that pass the filters. The actor still reads the non-matching reviews, so rare filters on large products take longer.

**Do I need a proxy or an account?** No. The actor collects public reviews without logging in.

**Is this legal?** Reviews are public. Reviewer nicknames are included as Walmart shows them. Use the data in line with Walmart's terms and privacy laws that apply to you, such as GDPR and CCPA.

**Found a problem?** Open an issue on the actor's *Issues* tab and include the run link.

# Actor input Schema

## `productUrls` (type: `array`):

Walmart product pages (/ip/...) or review pages (/reviews/product/...).

## `itemIds` (type: `array`):

Walmart item ids (the number at the end of an /ip/ URL).

## `searchQueries` (type: `array`):

Optional: collect reviews for the top products of each keyword search (e.g. to compare competing products).

## `maxProductsPerQuery` (type: `integer`):

How many top search results (that have reviews) to take per keyword.

## `sort` (type: `string`):

Order in which reviews are collected. Use "Newest first" for monitoring and with "Only reviews since".

## `ratings` (type: `array`):

Keep only reviews with these ratings, e.g. \[1, 2] for negative reviews. Leave empty for all.

## `verifiedOnly` (type: `boolean`):

Keep only reviews marked 'Verified Purchase'.

## `sinceDate` (type: `string`):

YYYY-MM-DD. Stops at the first older review, so scheduled runs only collect new reviews. Requires 'Newest first' sorting.

## `maxReviewsPerProduct` (type: `integer`):

Walmart pages through reviews that have text (e.g. about 7,000 of 20,500 ratings for a popular laptop). Star-only ratings are counted in the product totals but have no review row.

## `maxItems` (type: `integer`):

Stop after this many review rows across all products.

## `proxyConfiguration` (type: `object`):

Optional. The actor works without a proxy; add one only if you see repeated blocks.

## Actor input object example

```json
{
  "productUrls": [
    {
      "url": "https://www.walmart.com/ip/5144605607"
    }
  ],
  "searchQueries": [
    "air fryer"
  ],
  "maxProductsPerQuery": 5,
  "sort": "submission-desc",
  "verifiedOnly": false,
  "maxReviewsPerProduct": 100,
  "maxItems": 1000,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `reviews` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "productUrls": [
        {
            "url": "https://www.walmart.com/ip/5144605607"
        }
    ],
    "proxyConfiguration": {
        "useApifyProxy": false
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("axlymxp/walmart-reviews-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "productUrls": [{ "url": "https://www.walmart.com/ip/5144605607" }],
    "proxyConfiguration": { "useApifyProxy": False },
}

# Run the Actor and wait for it to finish
run = client.actor("axlymxp/walmart-reviews-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "productUrls": [
    {
      "url": "https://www.walmart.com/ip/5144605607"
    }
  ],
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}' |
apify call axlymxp/walmart-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,axlymxp/walmart-reviews-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/6x709zW8CsqqhBcH9/builds/yl3IkWmwpI9v9PKG4/openapi.json