MultiAgents-with-Langgraph-TravelItineraryPlanner

Welcome to the AI Travel Itinerary Planner, a modular multi-agent system built with Streamlit, LangGraph, and Ollama. This application leverages multiple AI agents to generate personalized travel itineraries and provide additional travel-related insights based on user preferences. The system is designed for modularity, with agents split into individual scripts for maintainability and scalability.

• Repository: https://github.com/vikrambhat2/MultiAgents-with-Langgraph-TravelItineraryPlanner

Overview

The AI Travel Itinerary Planner uses a LangGraph workflow to manage a set of agents that collaboratively process user inputs (e.g., destination, month, duration) to produce a detailed itinerary, activity suggestions, weather forecasts, packing lists, food/culture recommendations, useful links, and a chat interface. The system integrates with Ollama (for the llama3.2 model) and the Google Serper API for web searches.

Features

• Generate a detailed travel itinerary with daily plans, dining options, and downtime.
• Suggest unique local activities based on the itinerary and preferences.
• Fetch the top 5 travel guide links for the destination and month.
• Provide weather forecasts, packing lists, and food/culture recommendations.
• Offer a conversational chat to answer itinerary-related questions.
• Export the itinerary as a PDF.

Directory Structure

MultiAgents-with-CrewAI-TravelItineraryPlanner/
│
├── agents/
│   ├── generate_itinerary.py
│   ├── recommend_activities.py
│   ├── fetch_useful_links.py
│   ├── weather_forecaster.py
│   ├── packing_list_generator.py
│   ├── food_culture_recommender.py
│   └── chat_agent.py
│
├── export_utils.py
├── travel_agent.py
├── requirements.txt
└── .env

• agents/: Contains individual Python scripts for each agent, modularizing the logic.
• export_utils.py: Houses shared utility functions (e.g., PDF export).
• travel_agent.py: The main Streamlit application file that orchestrates the workflow and UI.
• requirements.txt: Lists project dependencies.
• .env: Stores environment variables (e.g., SERPER_API_KEY).

Setup Instructions

Prerequisites

• Python 3.8 or higher.
• Ollama installed and running locally with the llama3.2 model (ollama pull llama3.2).
• A Google Serper API key.

Installation

1. Clone the repository:

   git clone https://github.com/vikrambhat2/MultiAgents-with-Langgraph-TravelItineraryPlanner.git
   cd MultiAgents-with-Langgraph-TravelItineraryPlanner
   

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:
   • Create a .env file in the root directory.
   • Add your SERPER_API_KEY:

     SERPER_API_KEY=your_api_key_here
     

4. Start Ollama locally (if not already running):

   ollama serve

Running the Application

1. Launch the Streamlit app:

   streamlit run travel_agent.py

2. Open your browser and navigate to the provided URL (e.g., http://localhost:8501).

Usage

• Enter your travel preferences (destination, month, duration, etc.) in the form.
• Click "Generate Itinerary" to create a base plan.
• Use the buttons to fetch additional details (e.g., activity suggestions, weather forecast).
• Interact with the chat to ask questions about your itinerary.
• Export the itinerary as a PDF using the "Export as PDF" button.

Contributing

Feel free to fork this repository, submit issues, or create pull requests to enhance the project. Contributions to improve agent logic, UI, or add new features are welcome!

License

This project is open-source. See the LICENSE file for details (if applicable).

Acknowledgements

• Built with Streamlit, LangGraph, LangChain, and Ollama.
• Thanks to the open-source community for tools and libraries!

Contact

For questions or support, reach out to me on LinkedIn: Vikram Bhat or open an issue in the repository.