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Walmart Product Scraper | US CA MX Prices Reviews

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Walmart Product Scraper | US CA MX Prices Reviews

Walmart Product Scraper | US CA MX Prices Reviews

Scrape Walmart.com, Walmart.ca & Walmart.com.mx products — search, categories, catalogs & PDP. Extract prices, ratings, sellers, stock, variants & reviews. Export Excel/CSV/JSON.

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kane liu

kane liu

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Walmart Product Scraper — Prices, Search, Categories & Reviews (US / CA / MX)

Scrape Walmart product data from Walmart.com, Walmart.ca, and Walmart.com.mx — then download structured results as Excel, CSV, or JSON.

Use this Actor as a practical Walmart product data API alternative for price monitoring, catalog research, competitor tracking, and inventory checks. Enter a keyword, category id, or product URL / usItemId, pick a market, and get clean Dataset rows with prices, ratings, sellers, availability, and product links.

  • Scrape Walmart search results, category browse pages, department trees, category hubs, facet catalogs, and product detail pages
  • Cover United States (walmart.com), Canada (walmart.ca), and Mexico (walmart.com.mx)
  • Capture current price, ratings, review counts, seller, stock status, sponsored flags, grocery / SNAP-related fields, and optional PDP variants
  • Set ZIP / postal code for location-sensitive pricing and fulfillment signals
  • Export to Excel / CSV / JSON, or connect Make, n8n, Zapier, Python, or the Apify API
  • Pay per event — Actor start + each Dataset result; platform usage is included (you do not pay separate Apify compute on top)

What does the Walmart Product Scraper do?

Walmart Product Scraper lets you extract structured product and category data from Walmart storefronts without running your own browser farm or fighting Akamai / PerimeterX yourself. Give it a search keyword, browse category, or product id / URL, then start a run.

Under the hood, the Actor:

  1. Validates your input (market, mode, keyword / category / product ids, filters)
  2. Calls a managed Cloud Run worker that fetches Walmart pages with residential egress and unlock failover
  3. Parses guest-visible Next.js payload (#__NEXT_DATA__) into normalized product or category records
  4. Pushes Dataset rows ready for Excel / CSV / JSON export
  5. Writes RUN_SUMMARY (and ERROR_SUMMARY on failure) to the key-value store

You run it like any other Apify Actor — schedule it, call it from the API, or plug it into automations.


What Walmart data can I extract?

DataSearchBrowse / CatalogProduct detailDepartments / Hub
Product name / title
usItemId / offer id
Current price / currency
Rating / review count
Availability / seller
Sponsored flag
Grocery / SNAP / by-weight flags
Variants / secondary offers✅*
Reviews (first page)✅*
Category id / slug / browse URL
Direct Walmart product URL

*Enable enrichDetails, expandVariants, and/or maxReviews where needed. Review pagination beyond the first page is not claimed.


Why scrape Walmart?

Walmart is one of the largest retail catalogs in North America. Teams scrape Walmart to:

  • Monitor keyword and category prices across ZIP codes or markets
  • Build product catalogs from browse + facet crawl instead of a single SERP page
  • Track ratings, review volume, and seller mix for competitive research
  • Pull PDP details, variants, and grocery flags into pricing or assortment tools
  • Feed AI agents, BI dashboards, or enrichment pipelines with structured Walmart rows

Compared with single-mode Walmart Actors that only do search or only PDP / reviews, this Actor covers search + browse + catalog + departments + product across US / CA / MX.


More Actors like this

Need another marketplace or a different vertical? Use a dedicated Actor below for structured fields. Stay on Walmart Product Scraper for Walmart US / CA / MX search, browse, catalog, departments, and PDP.

Similar e-commerce scrapers

Prefer another specialized scraper?

If your target has a specialized Actor below, use that first — usually cheaper and more structured than a generic scrape.

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How to scrape Walmart products (no code required)

  1. Create a free Apify account
  2. Open Walmart Product Scraper
  3. Choose a mode: search, browse, catalog, departments, categoryHub, or product
  4. Choose a market: US, CA, or MX
  5. Enter a keyword (search), categoryId (browse / catalog), or usItemIds / productUrls (product)
  6. (Optional) Set postalCode (e.g. 10001), price filters, facetCrawl, or enrichDetails
  7. Click Start and download the Dataset as JSON, CSV, Excel, or JSONL

Start with maxResults: 5–10 to verify the query, then scale up.


How much does it cost to scrape Walmart?

This Actor uses pay-per-event pricing. You are charged for:

EventPrice
Actor Start (apify-actor-start)$0.005 per start
Result (apify-default-dataset-item)$3.00 / 1,000 results ($0.003 each)

Platform usage costs are included — you do not pay separate Apify compute/proxy usage for this Actor on top of the event prices above.

Example cost estimates

Results collectedApprox. event cost*
100~$0.31
1,000~$3.01
10,000~$30.01

*Assumes one Actor start at default memory billing for start events, plus $0.003 per Dataset item. Exact start billing can scale with allocated memory (one start event per GB, minimum one).

Free Apify trial credit can cover a small test run — try maxResults: 5 first.

Free-tier caps (Actor developer limits)

CapValue
Free Apify-plan runs of this Actor10 total
Free results / run200
Paying Apify usersUnlimited runs (schema max still applies)

These limits are enforced in Actor code (set by the Actor developer, not Apify).


Input

Open the Input tab for the full form. Common fields:

FieldDescription
marketUS / CA / MX
modesearch / browse / departments / product / catalog / categoryHub
keywords / keywordSearch keywords
categoryId / categorySlugBrowse / catalog category (e.g. 3944 + electronics)
facetFacet expression such as brand:Apple
sorte.g. best_match, price_low, best_seller
postalCode / storeIdLocation-sensitive price / fulfillment
minPrice / maxPriceOptional listing price filter
excludeSponsoredDrop sponsored cards from listing output
facetCrawl / facetCrawlMaxFacetsSlice browse facets for deeper catalog coverage
productUrls / usItemIdsProduct detail mode targets
enrichDetails / enrichLimitEnrich listing rows with PDP fields (default limit 5, max 50)
expandVariantsEmit variant SKU rows from PDP
maxReviewsCap reviews returned per enriched product (first page)
maxResultsCap Dataset rows (default 40; free plan capped at 200)

Output

Each product row typically includes:

FieldMeaning
usItemIdWalmart item id
nameProduct title
price / currencyParsed list / current price
averageRating / numberOfReviewsRating signals
availabilityStatuse.g. IN_STOCK
sellerNameSeller display name
sponsoredSponsored listing flag when present
snapEligible / finalCostByWeightGrocery-related flags when present
productUrlCanonical Walmart URL
postalCode / marketLocation / market context
imageUrlPrimary image when available

Coverage matrix

DimensionCoverage
Markets✅ US www.walmart.com · ✅ CA www.walmart.ca · ✅ MX www.walmart.com.mx
Categories✅ Department tree · ✅ Category hubs · ✅ Browse categoryId · ✅ Catalog / facet crawl
Modes✅ search · ✅ browse · ✅ departments · ✅ product · ✅ catalog · ✅ categoryHub
LocationpostalCode / storeIdlocationResolved diagnostics
Listing filters✅ facet · ✅ sort · ✅ min/max price · ✅ excludeSponsored
Product enrichment✅ details (enrichLimit ≤ 50) · ✅ variants · ✅ reviews (first page)

Use cases

1. Keyword price monitoring

In plain English: Track Walmart search prices for a product query in a specific ZIP.

You give:

FieldWhat to enter
modesearch
keyworde.g. wireless earbuds
postalCodee.g. 10001
maxResults40

You get back: Product titles, prices, ratings, sellers, stock flags, and Walmart URLs — exportable as Excel / CSV / JSON.

2. Category / catalog harvest

In plain English: Crawl an Electronics (or Grocery) browse tree with facet slices for broader unique usItemId coverage.

You give: mode=catalog, categoryId, optional facetCrawl=true.

3. Product detail enrichment

In plain English: Pull PDP fields, optional variants, and first-page reviews for known item ids or URLs.

You give: mode=product, usItemIds or productUrls, optional expandVariants / maxReviews.

4. Cross-border assortment check

In plain English: Run the same keyword on US, CA, and MX markets to compare assortment and URLs.

5. Department discovery

In plain English: Pull the live department / category hub tree to seed downstream browse jobs.


Tips for better Walmart scrapes

  • Always set a realistic postalCode when price or fulfillment matters — location can change displayed prices
  • Prefer catalog + facetCrawl for deep category coverage; a single browse list is capped by Walmart pagination
  • Use excludeSponsored when you want organic listing rows only
  • Keep enrichLimit modest unless you need PDP fields on every row
  • Start small (maxResults: 5–10) before large catalog runs

FAQ

Is this a Walmart API?
No. It collects guest-visible storefront data and returns structured Dataset rows. It is useful when you need a Walmart product-data workflow without maintaining unlock infrastructure.

Do you support Walmart.ca and Walmart.com.mx?
Yes — set market to CA or MX. Antibot pressure varies by market; diagnostics report provider / failover when used.

Can I scrape customer reviews?
Product mode / enrichment can return the first page of reviews when present. Full review pagination is not claimed until a stable page channel is confirmed.

Do I pay Apify platform usage separately?
No. With this Actor’s pay-per-event setup, platform usage costs are included in the event prices (Actor start + results).

Is login / order history supported?
No. This Actor does not access account, cart, or order surfaces.


Architecture

Actor (this) --HTTPS + WORKER_AUTH--> walmart-com Cloud Run --> Walmart storefronts

Env: WORKER_BASE_URL, WORKER_AUTH / WORKER_API_KEY (secret), WORKER_PROVIDES_PROXY=1.

Standby

usesStandbyMode + .actor/openapi.json for Live View convenience searches (capped).

Only scrape data you have a lawful right to access. Respect Walmart’s terms and applicable laws. Do not use this Actor for account takeover, credential stuffing, or personal-data harvesting beyond publicly shown product content.


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