Build AI Travel Agents with Llama 4 & MCP: Complete Guide
https://mer.vin/2025/04/llama-4-agents/
🌟 Learn how to create powerful AI travel planning agents using Llama 4, Model Context Protocol (MCP), and Groq for lightning-fast performance!
In this tutorial, I show you how to build a complete AI travel planning system with multiple specialized agents working together: Research Agent, Flight Agent, Hotel Agent, and Planning Agent - all powered by internet search capabilities.
🛠️ What You'll Build
Multi-agent AI system that plans complete travel itineraries
User-friendly interface where you input destination, dates, budget & preferences
Agents that work together to research destinations, find flights, book hotels, and create day-by-day itineraries
📋 Step-by-Step Tutorial Contents:
1. Setting up required packages (praise, AI agents, gradio)
2. Configuring API keys (Groq and Brave Search)
3. Building specialized AI agents with MCP and internet search
4. Creating a user interface with gradio
5. Testing the complete system with a real travel plan
🔑 API Requirements:
Groq API key (free tier available)
Brave Search API key (free queries available)
💻 Technologies Used:
Llama 4 (multimodal AI model)
Model Context Protocol (MCP)
Groq (for fast AI agent processing)
Brave Search API (for internet search capability)
Gradio (for user interface)
⚡ Why This Approach Works:
Multiple specialized agents working together creates comprehensive travel plans
Internet search enables up-to-date information
Llama 4's powerful capabilities deliver high-quality results
Groq provides exceptional speed for real-time planning
📱 Connect With Me:
Like this video if you found it helpful!
Comment below with your thoughts or questions
Check out my other tutorial on WhatsApp MCP agents
🔗 Resources:
All code and commands available in my GitHub repository: [LINK]
Free tiers available for all APIs mentioned
#AIAgents #Llama4 #TravelPlanning #MCP #PythonTutorial #AITutorial #GradioUI #AITravel
Timestamp:
0:00 - Introduction to Llama 4 MCP AI Agents
1:18 - Setting Up Environment and Dependencies
1:50 - Creating the Python Application
2:16 - Building the Research Agent
2:58 - Creating Multiple Specialized Agents
3:20 - Configuring Example Travel Query
3:59 - Running the Application and Testing Agents
4:43 - Building the User Interface with Gradio
6:03 - Live Demo of Travel Planner UI
6:38 - Conclusion
