# Walmart Scraper — Products, Prices, Ratings & Stock (`nexgendata/walmart-scraper`) Actor - **URL**: https://apify.com/nexgendata/walmart-scraper.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** E-commerce, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 product extracteds 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Walmart Scraper — Products, Prices, Ratings & Stock **The fast, reliable Walmart.com product scraper for Apify.** Feed it search keywords or product URLs and get back clean, structured JSON — one record per product — with title, price, sale (was) price, rating, review count, brand, seller, availability, image and the Walmart item id. Ready to drop straight into a pricing dashboard, repricer, BI warehouse or quant model. Walmart is the largest retailer in the United States — **~$640B annual revenue, 4,600+ US stores, and walmart.com is the #2 US e-commerce site after Amazon.** It is also one of the richest public price-and-availability surfaces on the web: first-party Walmart inventory *and* a sprawling third-party marketplace, side by side. This actor turns that surface into a programmatic feed. --- ### What you get per product | Field | Type | Description | |---|---|---| | `title` | string | Product name / title | | `price` | number | Current selling price | | `was_price` | number | Original / strikethrough price (only present when the item is on sale) | | `currency` | string | Currency code (e.g. `USD`) | | `rating` | number | Average star rating (0–5) | | `reviews_count` | int | Number of customer reviews | | `walmart_id` | string | Walmart US item id (`usItemId`) — the stable join key | | `brand` | string | Brand name | | `seller` | string | Seller — `Walmart.com` for first-party, or the marketplace seller name | | `availability` | string | Stock status (`IN_STOCK` / `OUT_OF_STOCK` / display string) | | `url` | string | Canonical Walmart product URL | | `image_url` | string | Primary product image URL | | `category` | string | Product category / type (when exposed) | | `query` | string | The keyword or URL that produced this record | | `mode` | string | `keyword_search` or `product_url` | | `data_source` | string | Provenance (`httpx` vs `playwright`) | | `as_of_timestamp` | string | UTC ISO-8601 capture time | Alongside the product rows, the actor emits a **per-query summary** record (`record_type: "query_summary"`) for each keyword with the product `count` and `avg_price` — handy for at-a-glance category price benchmarking without re-aggregating downstream. --- ### Two input modes #### Search keywords Provide a `keywords` array (e.g. `["coffee maker", "air fryer", "running shoes"]`). Each keyword runs a Walmart search across multiple result pages and returns one record per product, up to `maxItems`. Use this for category sweeps, competitor catalogue discovery, or price benchmarking. #### Product URLs Provide a `productUrls` array of exact Walmart product pages (e.g. `https://www.walmart.com/ip/.../121002347`). Each URL returns a single, detailed product record. Use this for watchlists, repricing, and tracking specific SKUs over time. You can supply both in one run. --- ### How the actor handles Walmart's anti-bot wall Walmart defends walmart.com with PerimeterX / bot-detection on datacenter IPs. The actor uses a resilient waterfall: 1. **`httpx` + realistic browser headers (warmed session)** — pulls the homepage first to pick up cookies, then requests the search / product page and parses the embedded **`__NEXT_DATA__`** JSON blob (the same hydration state Walmart's own front-end uses). This is the reliable, structured path — not brittle DOM scraping. 2. **Playwright headless Chromium fallback** — with stealth countermeasures (`navigator.webdriver` patch, realistic locale / timezone / viewport, plugin spoofing) for when the static HTML is blocked or JS-gated. 3. **Graceful no-result handling** — if every strategy is blocked, the actor pushes a single `status` row explaining the situation **(no charge applied)** rather than crashing your pipeline. All paths run behind Apify's **RESIDENTIAL proxy** pool by default, which is strongly recommended for Walmart. --- ### How buyers actually use this actor - **Repricing / price intelligence** — daily `productUrls` pull of a SKU watchlist to feed a dynamic-pricing engine; `was_price` exposes live markdown depth. - **Brand & MAP monitoring** — `keyword_search` across a brand portfolio to catch unauthorized marketplace sellers and minimum-advertised-price violations. - **Category research** — weekly keyword sweeps with the `query_summary` rows to track average price and assortment depth per category over time. - **Quant / consumer-discretionary desks** — Walmart price & availability as an alt-data signal joinable by `walmart_id`. - **Affiliate & comparison sites** — keep a Walmart price/stock column fresh next to other retailers. --- ### Related NexGenData e-commerce actors Pair the Walmart Scraper with the rest of the NexGenData retail-intelligence fleet: - 🔗 **[Amazon Product Scraper](https://apify.com/nexgendata/amazon-product-scraper?fpr=2ayu9b)** — the #1 US e-commerce surface; price, BSR, ratings & buy-box. - 🔗 **[Google Shopping Scraper](https://apify.com/nexgendata/google-shopping-scraper?fpr=2ayu9b)** — cross-retailer price comparison from a single query. - 🔗 **[eBay Sold Comps](https://apify.com/nexgendata/ebay-sold-comps?fpr=2ayu9b)** — real sold-listing comps for resale & valuation. - 🏢 **[About NexGenData](https://apify.com/nexgendata?fpr=2ayu9b)** — the full fleet of production e-commerce, finance and social-data scrapers. Run **Walmart + Amazon + Google Shopping** on the same SKU list to build a complete cross-retailer price-and-stock matrix. --- ### Get 20% off your first 3 months on Apify New to Apify? Use [this referral link](https://www.apify.com/?fpr=2ayu9b) to sign up and get **20% off for 3 months, then 30% ongoing.** Pairs cleanly with any of the e-commerce actors above. --- ### Input parameters - `keywords` — array of Walmart search terms (`keyword_search` mode). - `productUrls` — array of exact Walmart product page URLs (`product_url` mode). - `maxItems` — max product records per run (1–1000; default 50). - `proxyConfiguration` — Apify proxy (defaults to RESIDENTIAL — strongly recommended). --- ### Disclaimer This actor collects only publicly visible Walmart.com product data using the same page endpoints any logged-out walmart.com visitor reaches. It does not bypass logins, paywalls or private data. Buyers are responsible for compliance with Walmart's Terms of Service and applicable data regulations in their jurisdiction. # Actor input Schema ## `keywords` (type: `array`): List of search terms to scrape from Walmart.com (e.g. ['coffee maker', 'air fryer', 'running shoes']). Each keyword runs a Walmart search and returns one record per product. Leave empty if you are scraping specific product URLs instead. ## `productUrls` (type: `array`): List of specific Walmart product page URLs to scrape (e.g. ['https://www.walmart.com/ip/Keurig-K-Classic/121002347']). Each URL returns a single detailed product record. Use this instead of (or in addition to) keywords when you already know exact products. ## `maxItems` (type: `integer`): Maximum number of product records to return per run (across all keywords / URLs). 20-50 is appropriate for monitoring; 100+ for research sweeps. ## `proxyConfiguration` (type: `object`): Apify proxy configuration. RESIDENTIAL is strongly recommended — Walmart applies aggressive anti-bot detection on datacenter IPs. The actor warms a session and falls back from httpx to a Playwright headless browser when the static HTML is blocked. ## Actor input object example ```json { "keywords": [ "coffee maker" ], "productUrls": [], "maxItems": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ```` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "coffee maker" ], "productUrls": [], "maxItems": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/walmart-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "keywords": ["coffee maker"], "productUrls": [], "maxItems": 50, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/walmart-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "coffee maker" ], "productUrls": [], "maxItems": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call nexgendata/walmart-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/walmart-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Walmart Scraper — Products, Prices, Ratings & Stock", "version": "0.0", "x-build-id": "tUVIYbSraJlEcSiO0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~walmart-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-walmart-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~walmart-scraper/runs": { "post": { "operationId": "runs-sync-nexgendata-walmart-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~walmart-scraper/run-sync": { "post": { "operationId": "run-sync-nexgendata-walmart-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "keywords": { "title": "Search keywords", "type": "array", "description": "List of search terms to scrape from Walmart.com (e.g. ['coffee maker', 'air fryer', 'running shoes']). Each keyword runs a Walmart search and returns one record per product. Leave empty if you are scraping specific product URLs instead.", "default": [ "coffee maker" ], "items": { "type": "string" } }, "productUrls": { "title": "Product URLs", "type": "array", "description": "List of specific Walmart product page URLs to scrape (e.g. ['https://www.walmart.com/ip/Keurig-K-Classic/121002347']). Each URL returns a single detailed product record. Use this instead of (or in addition to) keywords when you already know exact products.", "default": [], "items": { "type": "string" } }, "maxItems": { "title": "Max products", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of product records to return per run (across all keywords / URLs). 20-50 is appropriate for monitoring; 100+ for research sweeps.", "default": 50 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify proxy configuration. RESIDENTIAL is strongly recommended — Walmart applies aggressive anti-bot detection on datacenter IPs. The actor warms a session and falls back from httpx to a Playwright headless browser when the static HTML is blocked.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```