Llama 4 MCP AI Agents BUILD Travel Planner Agents in 5 mins!

Llama 4 MCP AI Agents BUILD Travel Planner Agents in 5 mins!

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AI Summary

Summary of Llama 4 MCP AI Agents Video

1. Introduction to Llama 4
   • New multimodal model for creating powerful AI agents.
   • Use of Grog for fast AI agent development.
2. User Interface Creation
   • Users input destination, travel dates, and preferences for itinerary generation.
   • AI agents work to finalize travel plans.
3. Agents Created:
   • Research Agent: Gathers information about travel destinations.
   • Flight Agent: Searches for and compares flights.
   • Hotel Agent: Finds accommodation options.
   • Planning Agent: Compiles final itinerary.
4. Development Steps:
   • Install required packages using: pip install praise ai agents with llm mcp gradio.
   • Set API keys for Grog and Brave.
   • Create app.py to define agents and their functionalities.
   • Example usage includes providing destination, dates, and budget to generate travel queries.
5. Running the Code:
   • Execute the code through terminal using python app.py.
   • Each agent performs its tasks sequentially to produce a summarized travel plan.
6. User Interface:
   • Modifications made to include Gradio for UI.
   • Functionality to input travel details and receive a generated plan.
   • Launch in terminal with python UI.py, followed by URL access for user interactions.
7. Conclusion:
   • Successfully developed AI agents using MCP for efficient itinerary planning.