walmart-scraper

Scrape Walmart search results and product details as clean, structured JSON — name, price, currency, rating, reviews, brand, seller, availability, category and images — with multi-page collection, product enrichment and CSV export.

Ships both a command-line tool and a small Python library.

Powered by ScrapeUnblocker. Walmart is served behind an anti-bot layer, so this project routes requests through the ScrapeUnblocker getPageSource parsed_data API, which loads the page in a real browser and returns AI-parsed JSON instead of raw HTML. You get structured fields directly — no CSS selectors to maintain.

Features

• 🔎 Search — collect product tiles for any query, across multiple pages.
• 🏷️ Product details — price, currency, brand, rating count, dimensions, images.
• ➕ Enrichment — the search listing has no price; --enrich fetches each product page to fill it in.
• 🧾 Export — print JSON or write a CSV, from the CLI or the library.
• 🌍 Country targeting — route through a chosen proxy country (default us).
• 🔁 Resilient — automatic retry/backoff on transient bot walls and outages.
• 🧪 Tested — offline unit tests that mock the API (no credits spent in CI).

Install

pip install .
# or, for a plain runtime install:
pip install -r requirements.txt

Get an API key from scrapeunblocker.com and expose it (the SDK reads SCRAPEUNBLOCKER_KEY):

cp .env.example .env      # then edit it
export SCRAPEUNBLOCKER_KEY=your_key_here

CLI usage

# Search and print JSON
walmart-scraper search "coffee maker" --pages 2 --pretty

# Search, enrich the top 5 with prices, save a spreadsheet
walmart-scraper search "coffee maker" --enrich --limit 5 --csv coffee.csv

# One product, by URL or by numeric item id
walmart-scraper product 985675140
walmart-scraper product "https://www.walmart.com/ip/.../985675140"

# Route through another country
walmart-scraper --country us search "air fryer"

Library usage

from walmart_scraper import WalmartScraper

scraper = WalmartScraper()                     # reads SCRAPEUNBLOCKER_KEY

# Search result tiles (no price on the listing page)
items = scraper.search("coffee maker", pages=1)
for item in items[:5]:
    print(item.name, "-", item.average_rating, "stars")

# Full product detail (has price)
product = scraper.product(items[0].url)
print(product.title, product.price, product.currency)

# Or do both in one call, capped at N product fetches
priced = scraper.search_and_enrich("coffee maker", pages=1, limit=5)

Example output

A search item (walmart-scraper search "coffee maker"):

{
  "item_id": "985675140",
  "product_id": "2PPLV83Y1QHE",
  "name": "BLACK+DECKER Programmable 12-Cup Drip Coffee Maker",
  "url": "https://www.walmart.com/ip/BLACK-DECKER-Coffeemaker/985675140",
  "image_url": "https://i5.walmartimages.com/seo/...jpeg",
  "average_rating": 4.5,
  "number_of_reviews": 6253,
  "availability_status": "In stock",
  "seller_name": "Walmart.com",
  "is_sponsored": false,
  "badge_text": "100+ bought since yesterday",
  "category_path": "Home Page/Home/Appliances/Kitchen Appliances/Coffee Makers",
  "product_type": "Drip Coffee Makers",
  "out_of_stock": false
}

A product (walmart-scraper product 985675140):

{
  "url": "https://www.walmart.com/ip/985675140",
  "title": "BLACK+DECKER Programmable 12-Cup Drip Coffee Maker",
  "brand": "BLACK+DECKER",
  "price": 31.97,
  "currency": "USD",
  "rating_count": "6,253 ratings",
  "weight": "5.6 lb",
  "height": "12 in",
  "images": ["https://i5.walmartimages.com/seo/...jpeg"]
}

Project layout

src/walmart_scraper/
  __init__.py      package exports + version
  models.py        SearchItem / Product dataclasses + parsing helpers
  scraper.py       WalmartScraper: search, product, enrich, pagination, retry
  cli.py           argparse command-line interface
examples/          runnable scripts (JSON, CSV, single product)
tests/             offline unit tests (mock the API - no credits spent)

Development

make install       # pip install -e ".[dev]"
make lint          # ruff check .
make format        # ruff format + autofix
make test          # pytest -q

The test suite mocks the ScrapeUnblocker client, so it runs fully offline and spends no API credit. CI runs lint + tests on Python 3.9 and 3.12.

How it works

Each call goes to the ScrapeUnblocker get_parsed endpoint with a Walmart URL. The service renders the page and returns parsed JSON; this library normalises those fields into typed SearchItem / Product objects, handles multi-page collection, and retries transient failures. Because parsing happens server-side, the scraper does not break when Walmart tweaks its markup.

Links

• 🌐 ScrapeUnblocker
• 📚 API documentation
• 🐍 Python SDK

License

MIT © 2026 ScrapeUnblocker

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This is an example integration. Please scrape responsibly and in accordance with applicable laws and the target site's terms.