Walmart Price Tracker & Data API — No API Key 🛒

🎉 Track Walmart prices, stock, and reviews on autopilot — the Walmart API alternative that needs no API key, no approval, and no developer account

Built for price-tracking SaaS founders, e-commerce ops teams, market researchers, and marketplace sellers who need clean Walmart.com data on a schedule

📋 Overview

Walmart price tracking made ridiculously simple. Point this actor at a product, a category, or a search term — click Run — and get clean, query-ready rows back. No login, no proxy setup, and no waiting on a Walmart API key or seller-account approval. It's the Walmart data API you can actually use today.

Most Walmart scrapers on the Store do one thing — search or product or reviews. This actor bundles five operations behind a single dropdown: keyword product search, full category listings, deep product details, every third-party seller offer (one row per offer), and paginated customer reviews. Pick one operation per run, hand it a single item or a whole list, and get one tidy dataset out. Perfect for building a Walmart price monitor, mapping competitor catalogs, mining voice-of-customer review data, and benchmarking your buy-box position against marketplace sellers.

What you get here that you don't get elsewhere:

• ⚡ 5 operations in one actor: search · products by category · product details · seller offers · customer reviews. Nearly every other Walmart actor on the Store does one of these — you'd need three or four subscriptions to cover the same ground.
• 🏷️ Per-offer seller rows — a SIÁN exclusive: the full third-party marketplace for any product (seller, price, shipping, WFS, returns), one row per offer. No other Walmart actor on the Store exposes seller offers as a first-class operation.
• 📎 Batch anything, pay one start fee: pass a list of product IDs, keywords, or category IDs and one run covers them all. Monitoring 100 products costs one actor start, not 100 — that's where the money goes on scheduled, repeated lookups.
• 💰 Built for price tracking: capture price, listPrice, savingsAmount, and stock status on a schedule, then diff the dataset to catch price moves and rollbacks
• 🎯 87 fields on a product row: curated camelCase aliases (productId, price, rating, seller, verifiedPurchase) plus raw upstream fields spread alongside — analyst-friendly and engineer-complete
• 💎 Pay only for successful rows: failed lookups land as status:"error" rows at zero cost — you're never billed for a hiccup
• ✅ Walmart API alternative, zero setup: no API key, no developer registration, no seller account — paste an input and run
• 🏬 Store-level results: pass storeId, state, or zip on search and category runs to see what a specific Walmart store shows, not just the national default

✨ Features

• 🔍 Product Search: keyword search across Walmart.com, paginated (~40 products/page) with sort and price filters — your Walmart search API alternative
• 🗂️ Products by Category: list an entire Walmart category by ID, paginated, for share-of-shelf and assortment analysis
• 📦 Product Details: deep single-product scrape — price, specs, variants, breadcrumbs, UPC, ratings, and full HTTPS image gallery
• 🏷️ Product Offers: every seller offer for a product, one row each — seller name, price, shipping, WFS, return policy, fulfillment type
• ⭐ Product Reviews: paginated customer reviews with text, rating, author, verified-purchase flag, and helpful-vote counts — returns all ratings by default
• 💲 Price & Stock Capture: price, listPrice, savingsAmount, availability, and outOfStock on every product row — ready for a price-tracking pipeline
• 🌍 US + Canada marketplaces: query walmart.com (us) or walmart.ca (ca) with one setting per run
• ↕️ Native Sort & Filter: best match, price low/high, best seller, top rated — plus min/max price, store ID, state, and ZIP localization
• 🖼️ HTTPS-Normalized URLs: every product image and link arrives ready to embed
• 📊 Single Clean Dataset Shape: one flat row per item, filterable by _operation and status — same export pipeline works across all five operations

🎬 Quick Start

So simple, no training needed! Pick an operation, fill the matching input, click Run.

# Or use the API — one line
curl -X POST https://api.apify.com/v2/acts/sian.agency~walmart-data-scraper/runs?token=YOUR_TOKEN \
-d '{"operation":"search","query":"coffee maker","sortBy":"best_match","maxPages":3}'

🚀 Getting Started (3 Simple Steps)

Step 1: Pick an Operation

Choose one of five operations from the dropdown: Product Search, Products by Category, Product Details, Product Offers, or Product Reviews. One run = one operation — but as many items as you like: use the batch fields (Product IDs, Search Queries, Category IDs) to cover a whole list in a single run and a single start fee.

Step 2: Fill the Matching Input

• Search → a query (e.g. coffee maker) — or queries for a whole list
• Products by Category → a categoryId (e.g. 3944 = Electronics) — or categoryIds
• Product Details / Offers / Reviews → a productId — or productIds for a batch

The batch fields take priority when filled, and duplicates and blanks are dropped for you. Up to 500 items per run; note that maxPages applies per item, so the two multiply.

Optionally set domain (us / ca), sortBy, price filters, and maxPages.

Step 3: Click Run

One click and we scrape, flatten, normalize, and push clean rows to your dataset. An HTML run report lands in the key-value store. Export to JSON, CSV, or Excel from the Apify console — or pull via API.

That's it! In seconds, you'll have:

• Clean flat rows from any of five Walmart endpoints — same shape, ready to export
• 87 fields on a product-details row: curated camelCase aliases plus raw upstream data spread alongside
• Price, list price, savings, rating, and stock status on every product row
• Per-offer seller rows ready to filter on wfsEnabled, freeShipping, or seller
• HTTPS image and product URLs ready to embed
• Error rows for failed inputs — never billed

📥 Input Configuration

One operation per run. Each operation has its own required field (validated before charging). The optional domain and pagination/filter fields apply where relevant.

Field	Type	Required	Description
operation	enum	Yes	One of: search, productsByCategory, productDetails, productOffers, productReviews
query	string	search	Keyword to search Walmart (e.g. coffee maker)
queries	array	search	Batch — many keywords in one run. Wins over query.
categoryId	string	productsByCategory	Walmart category ID (e.g. 3944 = Electronics)
categoryIds	array	productsByCategory	Batch — many categories in one run. Wins over categoryId.
productId	string	productDetails, productOffers, productReviews	Numeric us_item_id or alphanumeric product_id
productIds	array	productDetails, productOffers, productReviews	Batch — many products in one run, one start fee. Wins over productId.
domain	enum	No	us (walmart.com, default) or ca (walmart.ca)
sortBy	enum	No (search / category)	best_match, price_low, price_high, best_seller, top_rated
reviewSort	enum	No (reviews)	relevancy, recent, rating_high_low, rating_low_high
reviewRating	integer	No (reviews)	Return only reviews with this star rating (1–5); blank = all ratings
reviewLimit	integer	No (reviews)	Reviews per page (1–50, default 10)
minPrice / maxPrice	integer	No (search / category)	Price filters
storeId / state / zip	string	No (search / category)	Localize availability and shipping
maxPages	integer	No	1–50, controls paginated runs (default 5)

Example — Product Search:

{
  "operation": "search",
  "query": "coffee maker",
  "sortBy": "best_match",
  "maxPages": 3
}

Example — Products by Category:

{
  "operation": "productsByCategory",
  "categoryId": "3944",
  "sortBy": "best_seller",
  "maxPages": 2
}

Example — Product Details (single product):

{
  "operation": "productDetails",
  "productId": "609040889"
}

Example — Product Offers (every seller, one row each):

{
  "operation": "productOffers",
  "productId": "609040889"
}

Example — Product Reviews (all ratings, newest first):

{
  "operation": "productReviews",
  "productId": "609040889",
  "reviewSort": "recent",
  "maxPages": 5
}

💡 Workflow tip: Run Search or Products by Category to discover product IDs, then loop Details / Offers / Reviews per product.

📤 Output

One flat row per item, saved to the Apify dataset with up to 87 fields. Search and category rows carry product fields; offers carry seller fields; reviews carry review fields. Curated camelCase aliases land on every row alongside the raw upstream data. Filter by _operation to split modes, or by status to separate success from error rows.

Field	Type	Description
productId / usItemId	string	Walmart product IDs (alphanumeric + numeric)
productTitle	string	Product display title
price / listPrice	number	Current price and list price
savingsAmount	number	Rollback / markdown savings
currency / currencySymbol	string	USD, $
rating / reviewCount	number / integer	Average rating and review count
ratingBreakdown	object	Star distribution (five/four/three/two/one)
brand / model / upc	string	Catalog identifiers
availability / outOfStock	string / boolean	Stock status
seller / sellerType	string	Seller name and type
wfsEnabled / freeShipping / twoDayShipping	boolean	Fulfillment flags
returnReturnable / returnFree / returnWindowDays	boolean / integer	Return policy
categories / specifications / variants	array	Breadcrumbs, specs, variant options
image / images / productUrl	string / array	HTTPS image gallery and product URL
offerId / sellerName / shippingPrice	string	Per-offer seller fields (Product Offers)
reviewId / reviewBody / reviewRating / verifiedPurchase	string / integer / boolean	Review fields (Product Reviews)
_operation / _fetchedAt / _page / status	metadata	Always-present row metadata

All four examples below are real captured output (June 2026; trimmed to the most useful fields — actual product rows carry up to 87):

Example — Product Details (Restored MacBook Air):

{
  "_operation": "productDetails",
  "productId": "4SZSM8SXAAJT",
  "usItemId": "609040889",
  "productTitle": "Restored MacBook Air 13.3\" Laptop - Apple M1 chip - 8GB Memory - 256GB SSD - Space Gray",
  "brand": "Apple",
  "model": "MGN63LL/A",
  "upc": "194252048955",
  "productType": "Laptop Computers",
  "price": 399,
  "listPrice": null,
  "currency": "USD",
  "priceDisplay": "$399.00",
  "rating": 4.2,
  "reviewCount": 6406,
  "ratingBreakdown": { "five": 4671, "four": 424, "three": 193, "two": 137, "one": 936 },
  "recommendedPercentage": 78,
  "availability": "In stock",
  "outOfStock": false,
  "condition": "New",
  "seller": "Nebsys Corp",
  "sellerType": "EXTERNAL",
  "fulfillmentType": "MARKETPLACE",
  "returnReturnable": true,
  "returnFree": true,
  "returnWindowDays": 30,
  "categories": [
    { "name": "Electronics", "url": "https://www.walmart.com/cp/electronics/3944" },
    { "name": "Laptops", "url": "https://www.walmart.com/cp/laptops/3951" }
  ],
  "specifications": [
    { "name": "Processor", "value": "Apple M1" },
    { "name": "RAM memory", "value": "8 GB" },
    { "name": "Data storage", "value": "256 GB" }
  ],
  "image": "https://i5.walmartimages.com/asr/806127b8-4e03-497a-a1d0-eb6b395f6d17.4870b2f40b01e60ec3286e524eca6686.jpeg",
  "productUrl": "https://www.walmart.com/ip/Apple-MacBook-Air-13-3-inch-Laptop-Space-Gray-M1-Chip-8GB-RAM-256GB-storage/609040889",
  "status": "success",
  "_fetchedAt": "2026-06-05T10:14:22.913Z"
}

Example — Product Search row:

{
  "_operation": "search",
  "productId": "4JWKYM3HL0LE",
  "usItemId": "5254334127",
  "productTitle": "Keurig K-Express Essentials Plus, Iced and Hot Single-Serve K-Cup Pod Coffee Maker, Black",
  "price": 69,
  "listPrice": 79,
  "currency": "USD",
  "rating": 4.3,
  "reviewCount": 25489,
  "seller": "Walmart.com",
  "freeShipping": true,
  "productUrl": "https://www.walmart.com/ip/Keurig-K-Iced-Essentials-Iced-and-Hot-Single-Serve-K-Cup-Pod-Coffee-Maker-Black/5254334127",
  "status": "success"
}

Example — Product Offer row (one per seller):

{
  "_operation": "productOffers",
  "productId": "4SZSM8SXAAJT",
  "offerId": "EEA013B1F09D347B9C6BCB34246F7113",
  "sellerName": "Coretek Enterprises, LLC",
  "sellerType": "EXTERNAL",
  "storefrontUrl": "https://www.walmart.com/seller/7A9124E7AF31494EA9EC37A862AC98ED",
  "price": 469,
  "priceDisplay": "$469.00",
  "currency": "USD",
  "condition": "New",
  "wfsEnabled": false,
  "returnReturnable": true,
  "returnWindowDays": 15,
  "inStock": true,
  "status": "success"
}

Example — Product Review row:

{
  "_operation": "productReviews",
  "productId": "609040889",
  "reviewId": "410161011",
  "reviewTitle": "It's basically a $600 paperweight",
  "reviewRating": 1,
  "reviewBody": "I've bought two different ones and neither battery will charge beyond 1%...",
  "reviewDate": "12/25/2025",
  "reviewAuthor": "anonymous",
  "positiveFeedback": 4,
  "verifiedPurchase": true,
  "status": "success"
}

💼 Use Cases & Examples

1. Price-Tracking SaaS — Build a Walmart Price Monitor

For SaaS founders building a Walmart price tracker without waiting on official API access.

Input: Schedule productDetails (or search) across your watched product IDs on a daily cron Output: price, listPrice, savingsAmount, priceDisplay, and availability per product, per run Use: Diff consecutive datasets to detect price drops, rollbacks, and restocks — power "price dropped" alerts and historical price charts for your subscribers.

2. E-commerce Ops — Competitor Catalog & Assortment Mapping

For e-commerce ops teams tracking competitor SKUs and pricing on Walmart.com.

Input: Run productsByCategory on your category IDs weekly with sortBy: "best_seller" Output: Full category listings with prices, ratings, badges, and seller info (~40/page) Use: Map competitor assortment, catch new SKU launches, and benchmark price points across your segment.

3. Marketplace Seller Intelligence — Buy-Box & Offer Mapping

For Walmart Marketplace sellers benchmarking their position against third-party offers.

Input: Run productOffers on products you compete on Output: Every seller offer as its own row — seller name, price, shipping, WFS status, return policy Use: See exactly who's undercutting you, which offers are WFS-fulfilled, and where you can win the buy box. No other Walmart actor exposes per-offer rows this cleanly.

4. Market Researcher — Category Share-of-Shelf

For market researchers and consultancies sizing Walmart categories for client briefs.

Input: productsByCategory paginated to maxPages: 10, plus search on the category keyword Output: Hundreds of category + search rows with prices, ratings, sponsored flags, and badge distribution Use: Calculate brand share-of-shelf, average price points, sponsored-placement density, and ratings distribution for client-ready reports.

5. Voice-of-Customer — Review Mining & Sentiment Analysis

For brand teams and product researchers mining Walmart review sentiment at scale.

Input: productReviews paginated across competitor product IDs, all ratings by default Output: Reviews with full text, star rating, verifiedPurchase flag, author, and helpful-vote counts, plus product-level ratingBreakdown Use: Feed into sentiment models to surface complaints, feature requests, and competitor weaknesses — segment by rating with reviewRating.

6. Catalog Enrichment — Specs, Variants & Images

For teams building or enriching a product database from Walmart data.

Input: productDetails on a list of product IDs (numeric or alphanumeric) Output: Specs, variants, breadcrumbs, UPC, brand, model, and full HTTPS image gallery Use: Enrich your own catalog with structured, query-ready rows, or build a competitive product database with images ready to embed.

🔌 Integration Examples

JavaScript/Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });

// Discover product IDs, then track their prices
const search = await client.actor('sian.agency/walmart-data-scraper').call({
  operation: 'search',
  query: 'coffee maker',
  maxPages: 2
});

const { items } = await client.dataset(search.defaultDatasetId).listItems();
const ids = items.filter(i => i.status === 'success').map(i => i.productId);
console.log(`${ids.length} product IDs to track`);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')

# Daily price snapshot for a watched product
run = client.actor('sian.agency/walmart-data-scraper').call(
    run_input={'operation': 'productDetails', 'productId': '609040889'}
)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    if item.get('status') == 'success':
        print(f"{item['productTitle']} — {item['priceDisplay']} ({item['availability']})")

cURL

curl -X POST 'https://api.apify.com/v2/acts/sian.agency~walmart-data-scraper/runs?token=YOUR_TOKEN' \
-H 'Content-Type: application/json' \
-d '{"operation":"productOffers","productId":"609040889"}'

Automation Workflows (N8N / Zapier / Make)

1. Trigger: Schedule (daily price refresh) or webhook (new SKU added to your tracker)
2. HTTP Request: Call the actor API with operation and the per-op input fields
3. Process: Diff against yesterday's price, filter offers on wfsEnabled, or score review sentiment
4. Action: Push price-drop alerts to Slack, sync rows to Google Sheets, or update your price-tracking database

📈 Performance & Pricing

Transparent pay-per-event — you're charged only for successful rows, plus a near-zero start fee per run. Failed lookups land as status:"error" rows and cost $0. Higher Apify plans get automatic volume discounts on every event.

Event	Price	Applies to
apify-actor-start	$0.0005	One-time per run — and a batch run of 500 items still pays it once
🔍 search-result	$0.0015 / row	Product Search
🗂️ category-result	$0.0015 / row	Products by Category
📦 product-detail-result	$0.002 / product	Product Details
🏷️ product-offer-result	$0.008 / offer	Product Offers (one per seller)
⭐ product-review-result	$0.002 / review	Product Reviews

Cost examples:

• 1,000 search results → $1.50 + $0.0005 start = ~$1.50
• 500 category products → $0.75 + $0.0005 = ~$0.75
• A full product detail lookup → $0.002 + $0.0005 = a quarter of a cent
• All seller offers for a product (e.g. 5 offers) → $0.04 + $0.0005 = ~$0.04
• 1,000 customer reviews → $2.00 + $0.0005 = ~$2.00
• 100 products in one batch run → $0.20 + $0.0005 = ~$0.20 — the same 100 products as 100 separate runs on a per-run-fee actor would cost several times that

💰 The start fee is where repeated small lookups get expensive. Most Walmart actors charge one per run, so a 100-product price monitor pays it 100 times a day. Ours is $0.0005 and a batch pays it once — which is the whole point of the batch fields.

🔗 View current pricing

❓ Frequently Asked Questions

Q: Does Walmart have an API? A: Walmart's official APIs (Marketplace, Affiliate, Content) are gated behind seller or partner approval and don't cover open product search, full category listings, per-seller offers, or customer reviews. This actor is the practical Walmart API alternative — it returns clean JSON for all of those, with no approval process.

Q: How do I get a Walmart API key? A: You don't need one. This actor requires no Walmart API key, no developer registration, and no seller account — just an Apify token. Paste your input, click Run, get structured data back.

Q: Where is the Walmart API documentation I should follow? A: There's no Walmart developer documentation to wade through here. Your "documentation" is the Input tab on the actor page: pick an operation, fill the matching field, and read the output schema below. That's the whole integration.

Q: Can I use this as a Walmart price tracker? A: Yes — that's a core use case. Schedule productDetails or search on a cron and diff the dataset run-to-run to capture price drops, rollbacks (savingsAmount), and restocks. Every product row carries price, listPrice, and availability.

Q: What does Product Offers return that other scrapers don't? A: One row per third-party seller offer — seller name, price, shipping, WFS status, and return policy. It maps the full marketplace behind any product so you can benchmark your buy-box position. No other Walmart actor on the Store exposes per-offer rows this cleanly.

Q: Which marketplaces are supported? A: US (walmart.com, domain: "us") and Canada (walmart.ca, domain: "ca"). One setting per run, applied to every row.

Q: How does review filtering work? A: Product Reviews returns all ratings by default. Set reviewRating (1–5) to return only one star level, reviewSort to control order, and reviewLimit for page size. Pagination is controlled by maxPages and stops automatically at the last page.

Q: What output formats are available? A: JSON, CSV, and Excel — export directly from the Apify dataset console, or pull via API.

Q: How am I billed, and what about failed lookups? A: Pay-per-event — only successful rows are charged, plus a one-time start fee. Failed lookups land as status:"error" rows at $0.

🐛 Troubleshooting

A run returns status:"error" with "temporarily unavailable"

• The data source hit a transient hiccup. The actor retries automatically with backoff. Re-run after a moment — error rows are never charged.

Paginated runs stop early before maxPages

• The actor stops pagination when the data source reports the last page (total_pages). This is correct behavior, not a bug — you're not billed for empty pages.

Product Details / Offers / Reviews returns "not found"

• Confirm the productId. Either the numeric us_item_id (e.g. 609040889) or the alphanumeric product_id (e.g. 4SZSM8SXAAJT) works — both appear in any Search or Category row and in product URLs.

Search or Category returns fewer rows than expected

• Thin coverage for a query returns fewer than ~40 rows/page. Broaden the query, relax minPrice/maxPrice, or paginate deeper with maxPages.

Category ID isn't returning the right products

• Category IDs appear in the categories[].url of any Product Details result (e.g. .../cp/electronics/3944 → 3944) and in Walmart's site navigation URLs.

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• Browse all SIÁN actors →

🤝 Support

Join our active support community

• For issues or feature requests, open an issue in the actor's repository or use the Issues tab on the actor page
• Check SIÁN Agency Store for more automation tools
• 📧 apify@sian-agency.online
• ⭐ If this saves you time, a 5-star review helps us ship more features.

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⚠️ Trademark Disclaimer

This actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Walmart Inc. "Walmart" and related marks are trademarks of their respective owners and are used here only to describe the data this tool helps you collect. Use this actor responsibly and in compliance with applicable laws, Walmart's terms of service, and data-protection regulations (including GDPR and CCPA where applicable). You are responsible for how you use the data you extract.

Legal

Scrape only publicly available data and use it lawfully. For guidance on ethical and compliant web scraping, see Apify's guide to web scraping legality. Do not collect personal data in violation of GDPR, CCPA, or other applicable privacy laws.

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