# Walmart Scraper & API: Products, Search, Reviews (`mvkdata/walmart-scraper-fast`) Actor

Walmart product scraper, reviews scraper and search scraper in one Walmart API: product details, search and category results and ALL customer reviews. Price, stock, seller, UPC, specs, variants, rating breakdown; past the 10-page limit; star filters. JSON, CSV, Excel.

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

## Pricing

from $0.90 / 1,000 products

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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 Scraper & API: Products, Search, Reviews

One actor for everything on **walmart.com**: full **product details**, **search and category results**, and **all customer reviews**. Paste any Walmart URL, type a keyword or give item IDs; mix them freely in one run.

It reads the data Walmart already embeds in its pages over plain HTTP instead of driving a browser, so it is **fast** (hundreds of rows per minute) and **cheap**. Pages Walmart blocks are retried on fresh IPs, then on US residential IPs (at our cost), and **you only pay for rows that actually arrive**.

### Why this one

- **All reviews, not just the first pages.** Products with 4,000+ written reviews come back complete. Filter by star rating (e.g. only 1 and 2 stars), sort by newest, most helpful, highest or lowest rating.
- **Past Walmart's page limit.** Walmart stops paging after 10 pages. When a search or category has more matches, the actor splits it into price ranges automatically and de-duplicates.
- **Full product record.** Price, was-price, savings, rollback/clearance, stock, seller (name, ID, rating, type, Walmart-fulfilled), other offers, UPC, model, specifications, description, highlights, every image, variants with their own price and stock, delivery date, pickup, return policy, rating breakdown 1-5 stars, recommended %, review aspects and Walmart's AI review summary.
- **One input, every page type.** Product (`/ip/...`), search (`/search?q=...`), category and browse (`/cp/...`, `/browse/...`), brand and seller pages, review pages (`/reviews/product/...`).
- **Pay only for results**, three clear prices: product, search row, review. Failed pages are never charged; they are listed in the key-value store record `FAILED`.

### Input

| Field | What it does |
|---|---|
| **Walmart URLs** | Any mix of product, search, category, brand, seller and review URLs |
| **Search terms** | Keywords, as typed in Walmart's search box |
| **Product IDs** | Item IDs, the number at the end of a product URL (`5113175776`) |
| **Max products per search / category** | Default 50, `0` = everything |
| **Open every search result for full details** | Off = fast summary rows; on = full product record per result |
| **Also scrape reviews** | Reviews for every product found |
| **Max reviews per product** | Default 100, `0` = all |
| **Review order / star filter** | Relevance, helpful, newest, oldest, highest, lowest; any set of 1-5 stars |
| **Search sort, min/max price** | Best match, price low/high, best sellers, rating, new |
| **Max results in total** | Hard cap on rows in the run, protects your budget |
| **Include raw Walmart data** | Adds the complete original JSON (hundreds of fields) under `raw` |

#### Examples

All 1- and 2-star reviews of one product, newest first:

```json
{
  "startUrls": [{ "url": "https://www.walmart.com/ip/5113175776" }],
  "includeReviews": true,
  "maxReviewsPerProduct": 0,
  "reviewsStarFilter": ["1", "2"],
  "reviewsSort": "submission-desc"
}
```

Price monitoring: full details for the top 200 results of two searches:

```json
{
  "searchTerms": ["air fryer", "robot vacuum"],
  "maxItemsPerSearch": 200,
  "scrapeProductDetails": true
}
```

A whole category (every TV), fast summary rows:

```json
{
  "startUrls": [{ "url": "https://www.walmart.com/browse/electronics/tvs/3944_1060825_447913" }],
  "maxItemsPerSearch": 0
}
```

### Output

Every row has a `type`: `product`, `searchResult` or `review`. The dataset has a table view for each.

**product** (shortened):

```json
{
  "type": "product",
  "usItemId": "5113175776",
  "name": "Acer Chromebook 315 15.6 inch Laptop Intel Processor N4500 4GB RAM 64GB eMMC Moonstone Purple",
  "brand": "Acer",
  "upc": "195133229036",
  "model": "CB315-4H-C0VN",
  "price": 239.54,
  "wasPrice": null,
  "availability": "IN_STOCK",
  "rating": 4.3,
  "reviewCount": 20702,
  "ratingBreakdown": { "5": 15130, "4": 2129, "3": 983, "2": 557, "1": 1903 },
  "recommendedPercentage": 83,
  "reviewSummaryAI": "Customers are pleased with the laptop's affordable price...",
  "seller": { "name": "VeloCore Tech US", "id": 103122918, "type": "EXTERNAL", "rating": 4.7, "reviewCount": 34, "fulfilledByWalmart": true },
  "otherOffersCount": 7,
  "deliveryDate": "2026-11-07T22:59:00.000Z",
  "returnPolicy": { "returnable": true, "freeReturns": true, "windowDays": 30 },
  "categories": ["Electronics", "Computers, Laptops and Tablets", "Laptops"],
  "images": ["https://i5.walmartimages.com/..."],
  "specifications": { "Processor": "Intel Celeron", "RAM memory": "4 GB", "Screen size": "15.6 in" },
  "variants": [{ "usItemId": "5113175776", "attributes": ["hard_drive_capacity-64gb", "actual_color-moonstonepurple"], "price": 239.54, "availability": "IN_STOCK" }]
}
```

**searchResult**: `usItemId`, `name`, `price`, `wasPrice`, `rating`, `reviewCount`, `availability`, `isSponsored`, `sellerName`, `badges`, `fulfillmentType`, `deliveryOptions`, `image`, `url`, `position`, `source`.

**review**: `reviewId`, `rating`, `title`, `text`, `date` (YYYY-MM-DD), `userNickname`, `verifiedPurchase`, `incentivized`, `helpfulVotes`, `unhelpfulVotes`, `variant` (e.g. color/size bought), `photos`, `videos`, `sellerName`, `fulfilledBy`, `syndicationSource`, `sellerResponses`, plus `usItemId` and `productName`.

### Use it from code (API)

Call it as a Walmart API from any language. One HTTP request returns the rows as JSON:

```bash
curl -s -X POST \
  "https://api.apify.com/v2/acts/mvkdata~walmart-scraper-fast/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"searchTerms": ["air fryer"], "maxItemsPerSearch": 100}'
```

Ready-to-run Python, JavaScript and curl examples (reviews to CSV, daily price monitor):
[github.com/mvkdata/walmart-scraper](https://github.com/mvkdata/walmart-scraper). Step-by-step guide:
[How to scrape every Walmart review](https://mvkdata.github.io/how-to-scrape-walmart-reviews/).

### Pricing

Pay per result, cheaper with higher Apify plans. No start fee. You pay only for rows in your dataset; blocked or failed pages cost nothing.

### FAQ

**Why does `reviewCount` differ from the number of reviews I get?** `reviewCount` counts star ratings; many shoppers rate without writing text. Walmart only lists written reviews, and the actor returns all of them.

**Can I scrape walmart.ca or Walmart Mexico?** Not yet: walmart.com (US) only.

**Is scraping Walmart legal?** The actor collects publicly visible data only, without logging in. Reviews contain public nicknames; check your own use case and local rules (e.g. GDPR) before storing personal data.

# Actor input Schema

## `startUrls` (type: `array`):

Any mix of walmart.com product pages (/ip/...), search pages (/search?q=...), category or browse pages (/cp/..., /browse/...), seller or brand pages, and review pages (/reviews/product/...). Each is detected automatically.

## `searchTerms` (type: `array`):

Keywords to search on walmart.com, like typing them in the search box.

## `productIds` (type: `array`):

Walmart item IDs (the number at the end of a product URL, e.g. 5113175776).

## `maxItemsPerSearch` (type: `integer`):

How many products to take from each search term or listing URL. 0 = everything. Walmart shows at most ~1,000 per query; with 'Go past Walmart's page limit' on, the actor splits the query by price to get more.

## `scrapeProductDetails` (type: `boolean`):

Off: search/category results come as fast summary rows (name, price, rating, seller, stock). On: each result is opened for the full product record (description, specs, UPC, variants, all images, delivery, return policy, rating breakdown) and charged as a product.

## `includeReviews` (type: `boolean`):

Also collect customer reviews for every product found (product URLs, IDs and search results).

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

0 = all reviews (Walmart serves every written review, also on products with tens of thousands).

## `reviewsSort` (type: `string`):

Order in which reviews are collected.

## `reviewsStarFilter` (type: `array`):

Collect only reviews with these star ratings, e.g. 1 and 2 for complaint analysis. Empty = all.

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

Sort order for search and category results.

## `minPrice` (type: `integer`):

Only search/category results at or above this price.

## `maxPrice` (type: `integer`):

Only search/category results at or below this price.

## `deepSearch` (type: `boolean`):

Walmart stops paging after ~25 pages. When a query has more matches than that and you asked for more, the actor splits it into price ranges and collects each range, de-duplicated.

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

Hard cap on all dataset rows (products + search rows + reviews) in this run. 0 = no cap. Protects your budget.

## `includeRawData` (type: `boolean`):

Adds the complete original Walmart product JSON under 'raw' (hundreds of fields) to every product record.

## `proxy` (type: `object`):

Default Apify Proxy works. Pages Walmart blocks are retried on fresh IPs and, if needed, on US residential IPs.

## `residentialFallback` (type: `boolean`):

With the default proxy, pages blocked several times move to US residential IPs (paid by us, not extra for you).

## `maxConcurrency` (type: `integer`):

Pages fetched at the same time.

## `maxRequestRetries` (type: `integer`):

Each retry uses a new IP.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://www.walmart.com/ip/5113175776"
    },
    {
      "url": "https://www.walmart.com/search?q=airpods"
    }
  ],
  "maxItemsPerSearch": 20,
  "scrapeProductDetails": false,
  "includeReviews": false,
  "maxReviewsPerProduct": 100,
  "reviewsSort": "relevancy",
  "sort": "best_match",
  "deepSearch": true,
  "maxItems": 0,
  "includeRawData": false,
  "proxy": {
    "useApifyProxy": true
  },
  "residentialFallback": true,
  "maxConcurrency": 20,
  "maxRequestRetries": 10
}
```

# Actor output Schema

## `products` (type: `string`):

No description

## `search` (type: `string`):

No description

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

No description

## `results` (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 = {
    "startUrls": [
        {
            "url": "https://www.walmart.com/ip/5113175776"
        },
        {
            "url": "https://www.walmart.com/search?q=airpods"
        }
    ],
    "maxItemsPerSearch": 20,
    "proxy": {
        "useApifyProxy": true
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("mvkdata/walmart-scraper-fast").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 = {
    "startUrls": [
        { "url": "https://www.walmart.com/ip/5113175776" },
        { "url": "https://www.walmart.com/search?q=airpods" },
    ],
    "maxItemsPerSearch": 20,
    "proxy": { "useApifyProxy": True },
}

# Run the Actor and wait for it to finish
run = client.actor("mvkdata/walmart-scraper-fast").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 '{
  "startUrls": [
    {
      "url": "https://www.walmart.com/ip/5113175776"
    },
    {
      "url": "https://www.walmart.com/search?q=airpods"
    }
  ],
  "maxItemsPerSearch": 20,
  "proxy": {
    "useApifyProxy": true
  }
}' |
apify call mvkdata/walmart-scraper-fast --silent --output-dataset

```

## MCP server setup

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

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/pgOjvFx64hlty1ID7/builds/0u8D5o8Zq8WxeET7i/openapi.json