Walmart Seller Product Research via API

Walmart seller product research via API returns structured product data -- pricing, ratings, review counts, seller info -- without scraping Walmart's JavaScript-heavy frontend. Each search is 1 credit and returns a full results page, so the $30/month plan's 7,000 credits cover 7,000 searches a month for 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 ID (Walmart.com for first-party listings)
Product image, brand and fulfillment options
Search position for keyword ranking analysis

Basic Walmart product search

Python

import os
import requests

API = "https://api.scavio.dev"
H = {"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}"}

def walmart_search(query: str, num_results: int = 10) -> list:
    """Search Walmart products via API."""
    resp = requests.post(
        f"{API}/api/v1/walmart/search",
        headers=H,
        json={"query": query},
        timeout=90,
    )
    resp.raise_for_status()
    return resp.json()["data"]["products"][:num_results]

# Research a product niche
products = walmart_search("air fryer large capacity")
for p in products:
    print(f"#{p['pos']} {p.get('title', 'N/A')[:60]}")
    print(f"  Price: ${p.get('price', 'N/A')}  Seller: {p.get('seller_name', 'N/A')}")
    print(f"  Rating: {p.get('rating', 'N/A')} ({p.get('rating_count', 0)} reviews)")
    print()

Cross-platform price comparison

Python

def compare_prices(query: str) -> dict:
    """Compare product prices across Amazon and Walmart."""
    paths = {
        "amazon": ("/api/v1/amazon/search", {"query": query, "country": "us"}),
        "walmart": ("/api/v1/walmart/search", {"query": query}),
    }
    comparison = {}

    for platform, (path, body) in paths.items():
        resp = requests.post(f"{API}{path}", headers=H, json=body, timeout=90)
        resp.raise_for_status()
        products = resp.json()["data"]["products"][:5]
        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']})")

Both endpoints return price as a number and rating as stars; Amazon's review count is reviews_count, Walmart's is rating_count.

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/walmart/search", {
      method: "POST",
      headers: {
        Authorization: `Bearer ${process.env.SCAVIO_API_KEY}`,
        "Content-Type": "application/json"
      },
      body: JSON.stringify({ query: kw })
    });

    const { data } = await resp.json();
    const products = (data.products || []).slice(0, 10);
    const priced = products.filter((p) => typeof p.price === "number");

    report.push({
      keyword: kw,
      lowestPrice: priced.length ? Math.min(...priced.map((p) => p.price)) : null,
      topSeller: products[0]?.title?.slice(0, 40) || "N/A",
      avgRating: products.reduce((sum, p) => sum + (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 searches/day of research = 1,500 credits/month. Daily competitor monitoring of 20 keywords = 600 credits/month. The 2,100-credit total fits inside the $30/month plan's 7,000 credits, so a typical Walmart seller research workflow costs $30/month or less.

Basic Walmart product search

Python

import os
import requests

API = "https://api.scavio.dev"
H = {"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}"}

def walmart_search(query: str, num_results: int = 10) -> list:
    """Search Walmart products via API."""
    resp = requests.post(
        f"{API}/api/v1/walmart/search",
        headers=H,
        json={"query": query},
        timeout=90,
    )
    resp.raise_for_status()
    return resp.json()["data"]["products"][:num_results]

# Research a product niche
products = walmart_search("air fryer large capacity")
for p in products:
    print(f"#{p['pos']} {p.get('title', 'N/A')[:60]}")
    print(f"  Price: ${p.get('price', 'N/A')}  Seller: {p.get('seller_name', 'N/A')}")
    print(f"  Rating: {p.get('rating', 'N/A')} ({p.get('rating_count', 0)} reviews)")
    print()

Cross-platform price comparison

Python

def compare_prices(query: str) -> dict:
    """Compare product prices across Amazon and Walmart."""
    paths = {
        "amazon": ("/api/v1/amazon/search", {"query": query, "country": "us"}),
        "walmart": ("/api/v1/walmart/search", {"query": query}),
    }
    comparison = {}

    for platform, (path, body) in paths.items():
        resp = requests.post(f"{API}{path}", headers=H, json=body, timeout=90)
        resp.raise_for_status()
        products = resp.json()["data"]["products"][:5]
        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']})")

Both endpoints return price as a number and rating as stars; Amazon's review count is reviews_count, Walmart's is rating_count.

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/walmart/search", {
      method: "POST",
      headers: {
        Authorization: `Bearer ${process.env.SCAVIO_API_KEY}`,
        "Content-Type": "application/json"
      },
      body: JSON.stringify({ query: kw })
    });

    const { data } = await resp.json();
    const products = (data.products || []).slice(0, 10);
    const priced = products.filter((p) => typeof p.price === "number");

    report.push({
      keyword: kw,
      lowestPrice: priced.length ? Math.min(...priced.map((p) => p.price)) : null,
      topSeller: products[0]?.title?.slice(0, 40) || "N/A",
      avgRating: products.reduce((sum, p) => sum + (p.rating || 0), 0) /
        (products.length || 1)
    });
  }

  return report;
}