BuyWhere: Real-Time Singapore Product Catalog API for AI Agents
If you're building AI agents that handle consumer queries or shopping research in Southeast Asia, you've probably hit the same wall: there's no clean, live data source for product prices.
Scrapers break. Static datasets go stale. Your agent answers questions about prices from three months ago.
That's why BuyWhere was built.
What Is BuyWhere?
BuyWhere is a real-time product catalog API built specifically for AI agents. One API call gives your agent live pricing data across:
- Harvey Norman
- Shopee
- Lazada
1,000+ products. Updated daily. No scraping required.
The Problem It Solves
Your user asks: "What's the cheapest 65-inch TV in Singapore right now?"
Without BuyWhere, your agent either:
- Hallucinates a price from training data
- Tells the user it can't access real-time info
- Tries to scrape a site and fails
With BuyWhere, your agent makes one API call and returns actual, current prices from real Singapore retailers.
Quick Example
import httpx
# Search for TVs under $2000 SGD
response = httpx.get(
"https://api.buywhere.ai/search",
params={"q": "65 inch TV", "max_price": 2000},
headers={"Authorization": "Bearer YOUR_API_KEY"}
)
products = response.json()["products"]
for p in products:
print(f"{p['name']} — ${p['price']} at {p['retailer']}")
MCP Support (Claude Agents)
BuyWhere ships with native MCP (Model Context Protocol) support. If you're building Claude-compatible agents, BuyWhere plugs in directly as an MCP tool — no custom glue code needed.
{
"mcpServers": {
"buywhere": {
"command": "npx",
"args": ["buywhere-mcp"],
"env": { "BUYWHERE_API_KEY": "your-key-here" }
}
}
}
Who Is This For?
- Agent developers building consumer/shopping tools for Singapore & SEA
- LangChain/CrewAI/LlamaIndex developers who want grounded product data
- MCP developers building Claude agents with real-world data access
- AI demo builders who need realistic, live data for showcasing agents
Get Started
Dev docs and API reference: buywhere.ai/developers
Building something cool with it? Drop a comment — happy to discuss agent architectures for shopping use cases.
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