ShopBuddy

Team: Muhammad Arham Hussain Khan, Partham Kumar (GrayCoders)

Problem statement

Online shopping decisions are often opaque: users do not know why certain products were selected, filtered out, or ranked higher than others. ShopBuddy solves this by providing transparent, step-by-step AI reasoning while comparing products across platforms and normalizing prices across currencies.

Why multi-agent?

This workflow has multiple specialized tasks that are easier, safer, and more reliable when split across cooperating agents: query understanding, marketplace collection, quality filtering, ranking, review intelligence, and explanation generation. A single agent can answer quickly, but a multi-agent pipeline gives clearer accountability, better explainability, and cleaner separation of concerns.

Agent architecture

Agent	Role
Input Safety Gate Agent	Blocks harmful, non-shopping, or prompt-injection style user input before orchestration starts.
Query Interpreter Agent (Supervisor)	Converts user intent into clean search terms, budget, and constraints; starts a search session.
Human Approval Checkpoint	Pauses for user confirmation of extracted keywords before scraping begins.
Marketplace Collector Agent (Scraper)	Fetches products from Daraz and Amazon, maps to a shared schema, and normalizes currency.
Quality Gate Agent (Filter)	Applies relevance, budget, reviews, and duplicate filters with explicit pass/fail reasons.
Value Ranker Agent (Analyzer)	Computes value scores and assigns recommendation badges to top candidates.
Review Analyst Agent (Reviewer)	Summarizes customer review sentiment, themes, and trust signals for top products.
Explainability Narrator Agent (Explainer)	Generates plain-English transparency reports and per-product recommendation reasoning.

State Graph Visualization:

How to run

1. Clone the repository.
2. Backend setup:
   • cd backend
   • Create .env from backend/.env.example
   • pip install -r requirements.txt
   • uvicorn main:app --reload
3. Frontend setup:
   • Open a new terminal and run cd frontend
   • Create .env from frontend/.env.example
   • npm install
   • npm run dev
4. Open the frontend URL shown by Vite (usually http://localhost:5173).

Demo

[Demo Video Link]

Tech stack

• Languages: Python, JavaScript (React)
• Backend: FastAPI, LangGraph, LangChain, SQLAlchemy
• Frontend: React, Vite, Tailwind CSS, shadcn/magicui components
• Models: Gemini 3 Flash, Llama 3.3 70B (Groq), Llama 3.1 8B (Groq)
• Data Sources: Daraz scraper, Amazon scraper
• APIs: Google GenAI API, Groq API, ExchangeRate-API
• Database: Supabase Postgres