Single-marketplace product research misses winners on the others. This tutorial builds a multi-platform agent that pulls Amazon + Walmart + Google Shopping + Reddit demand signal.
Prerequisites
- Python 3.10+
- Scavio API key
- An LLM API key
Walkthrough
Step 1: Define the seed query
Niche or product type.
Python
QUERY = 'home fitness equipment under $50'Step 2: Pull each marketplace
Three Scavio calls in parallel.
Python
import os, requests, asyncio
import aiohttp
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
async def fetch(url, body):
async with aiohttp.ClientSession() as s:
async with s.post(url, headers=H, json=body) as r:
return await r.json()
async def all_marketplaces(q):
return await asyncio.gather(
fetch('https://api.scavio.dev/api/v1/amazon/search', {'query': q}),
fetch('https://api.scavio.dev/api/v1/walmart/search', {'query': q}),
fetch('https://api.scavio.dev/api/v1/search', {'query': q, 'search_type': 'shopping'}),
)Step 3: Reddit demand signal
Optional cross-check.
Python
async def reddit(q):
return await fetch('https://api.scavio.dev/api/v1/reddit/search', {'query': q})Step 4: LLM ranking
Pass all candidates to an LLM with scoring rubric.
Text
# Rubric: cross-marketplace presence, price band, Reddit mention frequency.
# LLM returns ranked list of 10 candidates.Step 5: Output JSON to dashboard
Daily file or Sheets append.
Text
# Save as products_2026-04-29.json; surface to dashboard or email.Python Example
Python
# Per run: 4 calls = 4 credits = $0.017. Daily run for $0.50/mo.JavaScript Example
JavaScript
// Same architecture in TS using Promise.all.Expected Output
JSON
Daily ranked list of multi-marketplace winners. Beats single-marketplace tools by surfacing Walmart and Reddit-flagged candidates that Amazon-only tools miss.