Are you fascinated by the intersection of advanced AI, travel planning, and user-friendly interfaces? In just a short time, you can create an AI travel planning system featuring multiple specialized agents like Research Agent, Flight Agent, Hotel Agent, and Planning Agent. These agents work in harmony to provide you with a detailed, day-by-day travel itinerary—all powered by state-of-the-art technology like Llama 4, MCP (Model Context Protocol), and Groq. Let’s break it all down step by step.
🌟 1. Why Use Llama 4 and MCP for Travel Planning?
If you’ve ever wished for an efficient, personalized travel planning assistant, this approach is your answer. Here’s why:
👉 Llama 4, a multimodal AI model, intelligently processes diverse tasks. It uses text, images, and more to generate high-quality outputs.
👉 MCP (Model Context Protocol) integrates search tools to fetch real-time, internet-based data for up-to-date planning.
👉 Groq, an ultrafast processing platform, speeds up operations, making it possible to handle complex searches in real-time.
This combination ensures that your travel plans are not only reliable but are also tailored to your preferences and budget.
🔑 2. Building AI Agents: The Core Pillars of the Travel Planner
Creating a travel planner involves forming several AI agents that specialize in different tasks. Let’s explore these agents and why they’re vital for an efficient system.
🕵️ Key AI Agents
- Research Agent
-
Purpose: Looks up travel destinations, top attractions, and local information.
-
Example: Searching for the best attractions in London for a trip between August 15–22.
Tip: Want quick results? Start by testing just the Research Agent before moving onto others.
- Flight Agent
- Purpose: Scans for flights, compares pricing, and chooses the best options based on budget.
- Hotel Agent
- Purpose: Performs a similar operation as the Flight Agent but for accommodations.
- Planning Agent
-
Purpose: Compiles outputs from other agents into a cohesive day-by-day itinerary.
🛠️ Practical Setup:
Each agent is created with Llama 4 and integrated with MCP for internet search using APIs like Brave Search.
💡 Pro Tip: Combine all agents into a pipeline. This ensures smooth communication, where one agent passes its results to the next.
📋 3. Step-By-Step Process to Create the System
Even if you’re new to AI, following these actionable steps will get your travel planner up and running. Let’s dive in!
3.1 Setting Up the Environment
- Open your terminal and begin installing key packages:
pip install praise ai_agents llm mcp gradio
- Add API keys (they’re free for initial queries!):
export GROQ_API_KEY="YourKeyHere"
export BRAVE_API_KEY="YourKeyHere"
🌐 Why These APIs?
- Groq API: Offers blazing speed for handling requests.
- Brave Search API: Provides trustworthy, real-time search results from the web.
3.2 Code for Creating Agents
In your code editor, make a file called app.py. Below is the main structure of how the agents are organized:
from praise_agents import Agent, MCP
import os
# Define API keys
brave_api_key = os.getenv('BRAVE_API_KEY')
# Creating a Research Agent
research_agent = Agent(task="Research attractions in destination", model="Llama4", search_tool="BraveSearch")
flight_agent = Agent(task="Look for flights", model="Llama4", search_tool="BraveSearch")
hotel_agent = Agent(task="Search for hotels", model="Llama4", search_tool="BraveSearch")
planning_agent = Agent(task="Compile day-by-day plan", model="Llama4")
Tip: Start with one agent and run basic queries before building all four agents.
3.3 Testing Output from Agents
To test if the agents are working as expected, simply run a query:
result = research_agent.start(query="Best attractions in London for a trip from Aug 15 to Aug 22")
print(result)
⚡ Cool Example:
Running the above could return: “Tower of London, British Museum, and Hyde Park are must-visit attractions.”
3.4 Bringing It All Together into a System
Once you’ve verified individual agents, connect them into a pipeline. Here’s how:
# Combine all agents
agents = [research_agent, flight_agent, hotel_agent, planning_agent]
# Define a travel query
travel_query = {
"destination": "London",
"dates": "Aug 15-22",
"budget": "1000 USD",
"preferences": "Cultural attractions, local food"
}
# Execute the system and print results
final_output = planning_agent.start(travel_query)
print(final_output)
🖥️ 4. Adding a User-Friendly Interface with Gradio
A great tool is one that everyone can use easily! Here’s how to build a sleek interface for your travel planner.
Gradio’s Role
Gradio offers an intuitive, web-based platform where users can input their travel preferences without touching code.
Code Example
Add the following to your existing app.py:
import gradio as gr
# Define input and output fields
def generate_travel_plan(destination, dates, budget, preferences):
query = {"destination": destination, "dates": dates, "budget": budget, "preferences": preferences}
return planning_agent.start(query)
# Interface components
interface = gr.Interface(
generate_travel_plan,
inputs=["text", "text", "text", "text"],
outputs="text",
title="AI Travel Planner",
description="Plan your perfect trip using AI agents!"
)
interface.launch()
🏗️ Steps:
- Launch your terminal:
python app.py. - Open the auto-generated URL in your browser.
- Test it with any destination, budget, and preferences.
Tip: Watch the agents work seamlessly together as the app generates a full, tailored itinerary!
🚀 5. Real-Time Results: What to Expect
When testing with London as the destination, here’s an example of the output you might receive:
Example Day-by-Day Plan:
- Day 1: Explore the Tower of London; dine at local street-food markets.
- Day 2: Visit the British Museum and enjoy a theater performance in the evening.
- Day 3: Take a day trip to Windsor Castle.
- Budget Overview: Approx. $900 (flights, hotels, attractions).
🎉 How fast? In under 30 seconds! Thanks to Groq’s high-speed computation.
🧰 Toolbox: Tools & Resources
Here are tools you’ll need to replicate or extend this project:
- ✅ Groq API – For lightning-fast agent task execution.
- ✅ Brave Search API – Integrates real-time web searches for current travel data.
- ✅ Gradio Documentation – Learn how to build interactive UIs.
- ✅ GitHub Code Repository – Get all the code and commands.
- ✅ Llama 4 Details – Learn more about the model behind the magic.
💡 Bringing It All Together
By combining multiple AI agents and integrating cutting-edge tools like MCP, Brave Search, Gradio, and Groq, you can create a fully functioning travel planner in minutes. Whether you’re planning your next vacation or experimenting with AI applications, this project is a great example of how powerful and fast these systems can be.
Ready to give it a shot? 🤩 Try building your version today! And if you’ve ever dreamed of extending this concept—say, for restaurant curation, event planning, or more—this is your perfect starting point.
