Multi-Platform Search for E-commerce Agents
E-commerce agents need Amazon, Walmart, and Google Shopping data in one pipeline. One API covering all three platforms at $0.005/query vs separate integrations.
E-commerce agents that monitor prices, track inventory, and detect product trends need data from Amazon, Walmart, and Google Shopping simultaneously. Using separate APIs for each platform means managing three authentication flows, three response schemas, and three billing relationships. A single multi-platform search API eliminates this integration tax.
The Multi-Vendor Problem
A typical product intelligence agent queries Amazon for pricing and reviews, Walmart for availability and price comparison, and Google Shopping for merchant-level price data across retailers. With separate tools, the agent needs: Keepa ($19/mo) for Amazon price history, a Walmart scraper ($50+/mo), and a Google Shopping API. That is three vendors, three schemas, and three failure modes.
The single-API alternative: query all three platforms through one endpoint, one auth token, one response format. The tradeoff is less depth per platform (Keepa's Amazon price history is unmatched), but the integration simplicity is significant for agents that need breadth over depth.
Cross-Platform Price Comparison
import os, requests
API_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": API_KEY, "Content-Type": "application/json"}
def search_platform(query, platform):
res = requests.post("https://api.scavio.dev/api/v1/search",
headers=H, json={"query": query, "platform": platform})
return res.json()
def compare_prices(product_name):
platforms = ["amazon", "walmart", "google-shopping"]
results = {}
for p in platforms:
data = search_platform(product_name, p)
items = data.get("organic_results", data.get("shopping_results", []))
prices = []
for item in items[:5]:
price = item.get("price") or item.get("extracted_price")
if price:
prices.append({"title": item.get("title", "")[:60],
"price": price, "platform": p})
results[p] = prices
return results
comparison = compare_prices("Sony WH-1000XM5 headphones")
for platform, items in comparison.items():
print(f"\n{platform.upper()}:")
for item in items:
print(f" {item['price']:>8} | {item['title']}")Building a Product Intelligence Agent
The agent workflow: accept a product name or ASIN, query all platforms, compare prices, check for promotions, and alert if a price drops below a threshold. Run daily for a watchlist of products.
import json, os, requests
from datetime import date
API_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": API_KEY, "Content-Type": "application/json"}
def get_best_price(query, platforms=None):
if platforms is None:
platforms = ["amazon", "walmart"]
best = {"price": float("inf"), "platform": None, "title": None, "link": None}
for p in platforms:
res = requests.post("https://api.scavio.dev/api/v1/search",
headers=H, json={"query": query, "platform": p})
for item in res.json().get("organic_results", [])[:5]:
price = item.get("extracted_price")
if price and price < best["price"]:
best = {"price": price, "platform": p,
"title": item.get("title", "")[:80],
"link": item.get("link", "")}
return best
watchlist = [
{"name": "AirPods Pro 2", "threshold": 200},
{"name": "Kindle Paperwhite 2024", "threshold": 120},
{"name": "Dyson V15 Detect", "threshold": 550},
]
alerts = []
for item in watchlist:
best = get_best_price(item["name"])
if best["price"] < item["threshold"]:
alerts.append({**item, **best})
print(f"ALERT: {item['name']} at {best['price']} on {best['platform']}")
else:
print(f"OK: {item['name']} best price {best['price']} "
f"(threshold: {item['threshold']})")
print(f"\nTotal cost: {len(watchlist) * 2 * 0.005:.3f} "
f"({len(watchlist)} products x 2 platforms)")When Single-API Falls Short
Keepa ($19/mo) remains unbeatable for Amazon-specific price history, Buy Box tracking, and sales rank data. If your use case is purely Amazon (FBA sellers, Amazon affiliate sites), Keepa is the right choice. Multi-platform APIs return current search results, not historical price curves.
For Google Shopping specifically, DataForSEO offers a dedicated endpoint at $0.003/query with more granular merchant data than SERP-based approaches. If your product intelligence agent needs merchant-level pricing across 20+ retailers, DataForSEO's Shopping endpoint provides deeper data.
Scavio's advantage is the breadth: one $0.005 credit queries any of six platforms. For agents that need "good enough" pricing across Amazon, Walmart, and Google Shopping without managing three vendor relationships, it reduces both cost and complexity.