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Walmart Product Scraper | US CA MX Prices Reviews

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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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from $2.20 / 1,000 results

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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:

Validates your input (market, mode, keyword / category / product ids, filters)
Calls a managed Cloud Run worker that fetches Walmart pages with residential egress and unlock failover
Parses guest-visible Next.js payload (#__NEXT_DATA__) into normalized product or category records
Pushes Dataset rows ready for Excel / CSV / JSON export
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?

Data	Search	Browse / Catalog	Product detail	Departments / 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)	—	—	✅*	—
Seller contact / lead fields (US seller storefront)	—	—	✅*	—
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)

Create a free Apify account
Open Walmart Product Scraper
Choose a mode: search, browse, catalog, departments, categoryHub, or product
Choose a market: US, CA, or MX
Enter a keyword (search), categoryId (browse / catalog), or usItemIds / productUrls (product)
(Optional) Set postalCode (e.g. 10001), price filters, facetCrawl, or enrichDetails
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:

Event	Price
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 collected	Approx. 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)

Cap	Value
Free Apify-plan runs of this Actor	10 total
Free results / run	200
Paying Apify users	Unlimited 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:

Field	Description
`market`	`US` / `CA` / `MX`
`mode`	`search` / `browse` / `departments` / `product` / `catalog` / `categoryHub`
`keywords` / `keyword`	Search keywords
`categoryId` / `categorySlug`	Browse / catalog category (e.g. `3944` + `electronics`)
`facet`	Facet expression such as `brand:Apple`
`sort`	e.g. `best_match`, `price_low`, `best_seller`
`postalCode` / `storeId`	Location-sensitive price / fulfillment
`minPrice` / `maxPrice`	Optional listing price filter
`excludeSponsored`	Drop sponsored cards from listing output
`facetCrawl` / `facetCrawlMaxFacets`	Slice browse facets for deeper catalog coverage
`productUrls` / `usItemIds`	Product detail mode targets
`enrichDetails` / `enrichLimit`	Enrich listing rows with PDP fields (default off; limit 5, max 50). Turn on for US seller phone/email/门牌
`expandVariants`	Emit variant SKU rows from PDP
`maxReviews`	Cap reviews returned per enriched product (first page)
`maxResults`	Cap Dataset rows (default 1; free plan capped at 200)

Output

Each product row typically includes:

Field	Meaning
`usItemId`	Walmart item id
`name`	Product title
`price` / `currency`	Parsed list / current price
`averageRating` / `numberOfReviews`	Rating signals
`availabilityStatus`	e.g. `IN_STOCK`
`sellerName`	Seller display name
`sponsored`	Sponsored listing flag when present
`snapEligible` / `finalCostByWeight`	Grocery-related flags when present
`productUrl`	Canonical Walmart URL
`postalCode` / `market`	Location / market context
`imageUrl`	Primary image when available

Seller & lead fields

Every Dataset row carries the standard lead/clue keys alongside the product data: phones, emails, address, geo, person, org, web, leadSource, leadReveal, leadAbsences (plus the PDP pointer sellerStoreFrontURL). They are always present as keys — an empty value is always paired with a machine-readable reason in leadAbsences.

Field	Type	Meaning
`phones`	`[{value, type, e164?}]`	Seller phone(s) from US seller storefront hydration (`initialData.seller` + DOM `tel:` href), e.g. `8447252333` (`+18447252333`). `[]` on CA/MX (see limitations)
`emails`	`[{value, type}]`	Seller email in plain text from the same hydration, e.g. `walmart@eanxa.com` (`type: "seller"`). `[]` on CA/MX
`address`	`{street, unit, locality, region, postalCode, country, full}`	Seller address. `unit` is the street/suite number (site field `address2`), e.g. `"Room 2212, Huatong Building, No. 8 Ganli 2nd Road"`; `street` is `address1`, `full` is the assembled one-line address
`geo`	`{lat, lng}`	Always `{lat: null, lng: null}` — Walmart product/seller pages carry no coordinates
`person`	`{name, role?}` or `null`	Always `null` — the site has no individual contact names, only selling organizations
`org`	`{name, id?}`	Seller legal entity: `name` = `sellerName`, `id` = `sellerId` GUID (consistent across search, PDP, and seller pages)
`web`	`{website, socials[]}`	`website` = the US seller storefront URL (`/global/seller/{id}`); `socials` is always `[]` — no per-seller social links exist on the site
`leadSource`	enum	How this row's lead data was obtained: `search` (listing card), `detail` (PDP), `entity` (seller storefront hydration)
`leadReveal`	enum	`visible` for US seller hydration (data is already-visible); `absent` on rows without lead data
`leadAbsences`	object	Per-subfield absence reasons: `本站无` (site has none) / `闸门锁` (gated) / `未验证` (not verified)
`sellerStoreFrontURL`	string \| null	PDP lead exit pointer, e.g. `https://www.walmart.com/global/seller/101077439`. `null` on CA/MX. Location note: Walmart has shifted this field from `primaryOffer.sellerStoreFrontURL` to the product root; this Actor reads it from both positions for compatibility

Reveal cost

There are no click-to-reveal controls anywhere on Walmart (no "show phone/email" buttons, revealControls=[]). All seller contact data is already-visible once the US seller storefront page hydrates. The cost is therefore just the enrich chain per seller:

PDP → read sellerStoreFrontURL
/global/seller/{id} → 302 → /seller/{numericId} → hydrate initialData.seller

That is 1–2 extra requests per seller (the redirect included). No login, no clicks, no XHR-after-click.

Honest limitations

US only for seller contacts. Walmart CA (/en/seller/{GUID} → 404) and Walmart MX (/seller/{GUID} → blank page) have no public seller pages, so phones / emails / address stay [] on those markets and leadAbsences records 闸门锁; buyers contact sellers through platform messaging only.
person: 本站无 — Walmart shows only organizational sellers (sellerName), never an individual contact person.
geo: 本站无 — no coordinates are published for products or sellers.
web.socials: 本站无 — the seller object has no social/website fields; the storefront URL is the only web exit.
Seller-set exemptions. When a seller hides contact info (isContactVisibilityExempted / isAddressVisibilityExempted), the affected subfields are recorded as 闸门锁 in leadAbsences rather than left silently empty.
Platform-level contact is not per-seller. Walmart's corporate contact channels (e.g. 1-800-WALMART, ir@walmart.com) are not emitted into dataset rows.
Listing (search/browse/catalog) rows carry only org (seller name + id) — full seller contact data appears on product-detail rows via the seller hydration described above.

Coverage matrix

Dimension	Coverage
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
Location	✅ `postalCode` / `storeId` → `locationResolved` 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:

Field	What to enter
`mode`	`search`
`keyword`	e.g. `wireless earbuds`
`postalCode`	e.g. `10001`
`maxResults`	`40`

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).

Legal / responsible use

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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