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🌍 WanderMind – AI-Powered Multi-Agent Travel Planner

WanderMind is an intelligent travel assistant that uses multiple AI agents to design smart, real-time itineraries through a conversational interface. It interprets user input, consults specialized agents, and presents a dynamic dashboard of trip suggestions.

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🧠 Inspiration

Most travel planning tools deliver static, overwhelming responses that lack personalization or live context. WanderMind was created to make trip planning feel like chatting with a personal assistant—interactive, informed, and intuitive.

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✨ What It Does

• Accepts natural language input (e.g., “Plan a trip from LA to Seattle this weekend”)

• Parses user intent and extracts relevant trip data

• Coordinates specialized agents to retrieve:

  • 🗺️ Routes & Transport
  • 🌦️ Weather Forecasts
  • ✈️ Flight Options
  • 📍 Attractions
  • 🍽️ Restaurants
  • 🎟️ Local Events

• Outputs a beautifully styled dashboard with visual cards and summaries

• Uses Gemini 2.0 Flash + LangChain for orchestration and planning

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🏗️ Architecture Overview

User Input → Supervisor Agent (Intent + Context)
           ↓
   Core Planner (LLM + LangChain)
           ↓
   Multiple AI Agents → External APIs
           ↓
     Dashboard → Visual UI Output

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⚙️ How It’s Built

• Frontend: React + Tailwind CSS

• Backend: FastAPI

• Orchestration: LangChain + Gemini 2.0 Flash

• Agents (Modular AI Units):

  • Weather
  • Flights
  • Events
  • Attractions
  • Restaurants
  • Routing

• APIs Integrated:

  • Google Maps, Google Places
  • Amadeus (flights)
  • Ticketmaster (events)
  • OpenWeatherMap

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🧩 Key Challenges

• Syncing multi-agent message flow
• Real-time data validation (e.g., invalid city pairs or date formats)
• Handling LLM reasoning across diverse data types
• Seamless frontend transition from chat → visual itinerary

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✅ Accomplishments

• Built and deployed 6+ independent AI agents
• Designed a UI that feels like a chat-to-dashboard transformation
• Modular code that allows future expansion (e.g., budgeting, hotels, co-planning)

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📚 What We Learned

• How to structure systems with autonomous AI agents
• Deepened skills in LLM orchestration using LangChain ReAct
• Best practices for secure and reliable multi-API integration
• Importance of UX in complex AI systems

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🚀 What’s Next

• ✨ Voice-based input
• 🏨 Hotel booking agent
• 💸 Budget estimation and tracker
• 👥 Shared planning with friends
• 📱 Mobile app version
• 🗂️ Itinerary history and login-based access

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🖼️ Screenshots

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📈 Tech Stack

• Frontend: React, TailwindCSS, TypeScript
• Backend: FastAPI (Python)
• AI/LLM: Gemini 2.0 Flash, LangChain (ReAct)
• Agents: Modular agent architecture
• APIs: Google Maps, OpenWeather, Amadeus, Ticketmaster
• Deployment: Vercel (frontend), Render (backend)

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📬 Contact

• GitHub: @SecretariatV
• Email: [email protected]
• Telegram: @ares_orb
• Twitter (X): @OVB_Coder