walmartecommerceapi
Walmart Seller Product Research via API
Walmart product data via API: pricing, ratings, review counts, seller info at $0.005/query. No scraping, no proxies. Cross-platform comparison with Amazon included.
8 min
Walmart seller product research via API returns structured product data -- pricing, ratings, review counts, seller info -- without scraping Walmart's JavaScript-heavy frontend. At $0.005/query, you can research 7,000 products per month on the $30 plan, covering niche research, competitor monitoring, and pricing analysis without proxy infrastructure.
What Walmart product data includes
- Product title, price, and availability
- Rating and review count
- Seller name and marketplace vs first-party flag
- Product images and category
- Search position for keyword ranking analysis
Basic Walmart product search
Python
import requests
def walmart_search(query: str, num_results: int = 10) -> list:
"""Search Walmart products via API."""
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": "YOUR_KEY"},
json={
"query": query,
"platform": "walmart",
"num_results": num_results
}
)
data = resp.json()
return data.get("product_results", [])
# Research a product niche
products = walmart_search("air fryer large capacity")
for p in products:
print(f"{p.get('title', 'N/A')[:60]}")
print(f" Price: {p.get('price', 'N/A')}")
print(f" Rating: {p.get('rating', 'N/A')} ({p.get('reviews_count', 0)} reviews)")
print()
Cross-platform price comparison
Python
import requests
def compare_prices(query: str) -> dict:
"""Compare product prices across Amazon and Walmart."""
comparison = {}
for platform in ["amazon", "walmart"]:
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": "YOUR_KEY"},
json={
"query": query,
"platform": platform,
"num_results": 5
}
)
data = resp.json()
products = data.get("product_results", [])
comparison[platform] = [
{
"title": p.get("title", "")[:50],
"price": p.get("price", "N/A"),
"rating": p.get("rating", "N/A")
}
for p in products
]
return comparison
result = compare_prices("robot vacuum self emptying")
for platform, products in result.items():
print(f"\n{platform.upper()}:")
for p in products:
print(f" {p['title']} - {p['price']} ({p['rating']})")
Competitor monitoring for Walmart sellers
JavaScript
// Daily competitor price tracking
async function monitorCompetitors(keywords) {
const report = [];
for (const kw of keywords) {
const resp = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: {
"x-api-key": process.env.SCAVIO_KEY,
"Content-Type": "application/json"
},
body: JSON.stringify({
query: kw,
platform: "walmart",
num_results: 10
})
});
const data = await resp.json();
const products = data.product_results || [];
report.push({
keyword: kw,
lowestPrice: products.reduce((min, p) => {
const price = parseFloat(p.price?.replace("$", "") || "999");
return price < min ? price : min;
}, 999),
topSeller: products[0]?.title?.slice(0, 40) || "N/A",
avgRating: products.reduce((sum, p) =>
sum + (parseFloat(p.rating) || 0), 0
) / (products.length || 1)
});
}
return report;
}
Use cases for Walmart sellers
- Niche research: find product categories with demand but low competition
- Price optimization: track competitor pricing daily
- Listing quality audit: compare your titles/ratings vs top sellers
- Cross-platform arbitrage: find price gaps between Amazon and Walmart
Cost for Walmart research
50 products/day research = 1,500 queries/month = $7.50 on Scavio. Daily competitor monitoring of 20 keywords = 600/month = $3.00. Total for a typical Walmart seller research workflow: under $15/month.