Product research for purchasing decisions, catalog enrichment, or competitive analysis requires collecting data from multiple sources. A product research agent automates this by querying Google for reviews and expert opinions, Amazon for pricing and customer feedback, and Walmart for alternative pricing, then synthesizing everything into a structured report. This tutorial builds such an agent using the Scavio API's unified endpoint, without needing to manage multiple API keys or rate limits.
Prerequisites
- Python 3.10 or higher
- requests and concurrent.futures available
- A Scavio API key
- Basic understanding of data merging and formatting
Walkthrough
Step 1: Query all three platforms concurrently
Use ThreadPoolExecutor to query Google, Amazon, and Walmart simultaneously for a product name.
Python
from concurrent.futures import ThreadPoolExecutor
def fetch_all(product: str) -> dict:
queries = {
"google": lambda: search_google(product + " review"),
"amazon": lambda: search_amazon(product),
"walmart": lambda: search_walmart(product),
}
with ThreadPoolExecutor(max_workers=3) as ex:
futures = {name: ex.submit(fn) for name, fn in queries.items()}
return {name: fut.result() for name, fut in futures.items()}Step 2: Extract price range from Amazon and Walmart
Collect prices from both e-commerce platforms and compute the min and max.
Python
def price_range(data: dict) -> dict:
prices = []
for platform in ["amazon", "walmart"]:
for p in data[platform].get("products", [])[:5]:
ps = p.get("price", "")
if ps:
prices.append(float(ps.replace("$", "").replace(",", "")))
return {"min": min(prices) if prices else None, "max": max(prices) if prices else None}Step 3: Extract Google review sentiment
Collect organic result snippets from Google that contain review signals like ratings or recommendations.
Python
def extract_review_signals(google_data: dict) -> list[str]:
signals = []
for r in google_data.get("organic_results", [])[:5]:
snippet = r.get("snippet", "")
if any(word in snippet.lower() for word in ["recommend", "rating", "review", "best", "worth"]):
signals.append(snippet)
return signalsStep 4: Assemble and print the research report
Combine price range, review signals, and product count into a formatted research report.
Python
def research_product(product: str) -> str:
data = fetch_all(product)
pr = price_range(data)
signals = extract_review_signals(data["google"])
lines = [f"Product Research: {product}"]
lines.append(f"Price range: ${pr['min']} — ${pr['max']}")
lines.append("Review signals:")
for s in signals[:3]:
lines.append(f" - {s[:100]}")
return "\n".join(lines)Python Example
Python
import os
import requests
from concurrent.futures import ThreadPoolExecutor
API_KEY = os.environ.get("SCAVIO_API_KEY", "your_scavio_api_key")
ENDPOINT = "https://api.scavio.dev/api/v1/search"
def call(body: dict) -> dict:
r = requests.post(ENDPOINT, headers={"x-api-key": API_KEY}, json=body)
r.raise_for_status()
return r.json()
def research(product: str) -> dict:
with ThreadPoolExecutor(max_workers=3) as ex:
g = ex.submit(call, {"query": f"{product} review", "country_code": "us"})
a = ex.submit(call, {"platform": "amazon", "query": product, "marketplace": "US"})
w = ex.submit(call, {"platform": "walmart", "query": product})
return {"google": g.result(), "amazon": a.result(), "walmart": w.result()}
if __name__ == "__main__":
data = research("Sony WH-1000XM5")
amazon_products = data["amazon"].get("products", [])
print(f"Amazon listings: {len(amazon_products)}")
if amazon_products:
print(f"Top Amazon price: {amazon_products[0].get('price')}")
google_count = len(data["google"].get("organic_results", []))
print(f"Google results: {google_count}")JavaScript Example
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY || "your_scavio_api_key";
const ENDPOINT = "https://api.scavio.dev/api/v1/search";
async function call(body) {
const res = await fetch(ENDPOINT, {
method: "POST",
headers: { "x-api-key": API_KEY, "Content-Type": "application/json" },
body: JSON.stringify(body)
});
return res.json();
}
async function research(product) {
const [google, amazon, walmart] = await Promise.all([
call({ query: `${product} review`, country_code: "us" }),
call({ platform: "amazon", query: product, marketplace: "US" }),
call({ platform: "walmart", query: product })
]);
return { google, amazon, walmart };
}
async function main() {
const data = await research("Sony WH-1000XM5");
console.log(`Amazon listings: ${data.amazon.products?.length || 0}`);
console.log(`Google results: ${data.google.organic_results?.length || 0}`);
}
main().catch(console.error);Expected Output
JSON
Product Research: Sony WH-1000XM5
Price range: $249.99 — $349.00
Review signals:
- The WH-1000XM5 remains our top recommendation for noise-canceling headphones...
- Rated 4.8/5 stars across 50,000+ verified reviews on Amazon...
- Best premium wireless headphones in 2026 according to our testing...