Vetted

Inspiration

Online shopping is highly suboptimal. As a consumer, you might spend hours bouncing between platforms like Amazon, eBay, Walmart, or Best Buy comparing prices. Or read hundreds of reviews without knowing which ones are fake and miss coupon codes buried across webpages. And when you find something on Facebook Marketplace, you have no idea if the seller may be legitimate. We realized that every pain point in online shopping--whether it be discovery, trust, pricing, or savings--is fundamentally a research problem. Since research is exactly what AI agents are built to do, we were inspired to build a shopping experience where every product is vetted before you ever see it.

What it does

Vetted is an agentic shopping platform where you describe what you want in natural language, and a specialized multi-agent team handles the rest.

• The Search Agent scours sites like Amazon, Walmart, Facebook Marketplace, and Craigslist simultaneously.
• The Trust Agent verifies every seller, detects fake review patterns, and flags potential scams.
• The Price Agent researches price history and finds competitor prices.
• The Savings agent surfaces active coupon codes/cash-back opportunities for retail platforms and negotiation strategies for marketplace listings.

How we built it

Frontend UI

• Built with TypeScript, Next.js, and Tailwind CSS
• Designed to feel like a familiar shopping platform by encapsulating search functionalities and product listings
• Features data gathered on trust, pricing, and savings for each product
• Real-time pipeline tracker shower agent progress

Backend API

• Built with Python, FastAPI, and Pydantic
• Error handling on external API calls with third-party tools
• RESTful endpoints for session management and search

Multi-Agent Orchestration

• Built with LangGraph StateGraph for pipeline orchestration with parallel execution and state checkpointing
• Bright Data for Search Agent
• Perplexity Sonar for Trust Agent and Price Agent
• OpenAI API for Savings Agent

Challenges we ran into

• LangGraph orchestration: getting the multi-agent orchestration correct with conditional edges, parallel execution, and human-in-the-loop required careful state management
• Rate limiting: running trust and price analysis in parallel for 15+ products meant sending concurrent requests to the Perplexity Sonar API in rapid succession

Accomplishments that we're proud of

• Establishing an end-to-end workflow across all agents for any particular product search
• Developing a frontend that emulates a real shopping platform
• Building as a solo hacker!

What we learned

• Multi-agent systems require careful thought into what should be automated vs. user-triggered (certain tradeoffs exist between speed and relevance)
• The right tool for each job matters much more than applying one tool everywhere (Bright Data for structured scraping, Perplexity Sonar for research, OpenAI for persuasive writing)

What's next for Vetted

• Expanding negotiation capabilities to voiced-based and automated workflows (one that can act autonomously on your behalf)
• Introducing a social layer where users can share a community-driven shopping intelligence network

Built With

• brightdata
• claude
• fastapi
• gpt
• langgraph
• next.js
• perplexitysonar
• pydantic
• python
• tailwind-css
• typescript