Agentic Travel Assistant

An intelligent AI-powered travel planning assistant built with LangChain and Groq. This application helps users plan their trips by providing flight recommendations, hotel suggestions, destination information, weather forecasts, and budget estimates.

Features

• ✈️ Flight Search: Find and compare flight options for your travel dates
• 🏨 Hotel Recommendations: Get personalized hotel suggestions based on preferences
• 🌍 Destination Planning: Discover attractions and places to visit
• 🌤️ Weather Forecasts: Check weather conditions for your destination
• 💰 Budget Estimation: Calculate estimated costs for your entire trip
• 🤖 AI-Powered Agent: Intelligent agent that understands natural language queries and leverages multiple tools

Tech Stack

• Backend: Flask (Python)
• AI/LLM: LangChain + Groq API
• Frontend: HTML, CSS, JavaScript
• Dependencies: See requirements.txt

Project Structure

agentic-travel-assistant/
├── agent/                 # AI Agent logic
│   ├── travel_agent.py   # Main agent creation and setup
│   ├── prompts.py        # System prompts and instructions
│   └── __init__.py
├── app/                   # Application configuration
│   ├── config.py
│   └── data/             # Sample data files
│       ├── flights.json
│       ├── hotels.json
│       └── places.json
├── tools/                 # Tool implementations
│   ├── flight_tool.py
│   ├── hotel_tool.py
│   ├── places_tool.py
│   ├── weather_tool.py
│   └── budget_tool.py
├── web/                   # Frontend assets
│   ├── templates/
│   │   └── index.html
│   └── static/
│       ├── script.js
│       └── style.css
├── main.py               # Flask application entry point
├── requirements.txt      # Python dependencies
└── render.yaml          # Deployment configuration

Installation

Prerequisites

• Python 3.8+
• Groq API Key (get one at console.groq.com)

Setup

1. Clone the repository

   git clone https://github.com/SA9572/AI_Agent_travel-Assistant.git
   cd agentic-travel-assistant

2. Create a virtual environment

   python -m venv .venv
   # On Windows
   .venv\Scripts\activate
   # On macOS/Linux
   source .venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment variables

   # Create a .env file in the root directory
   echo GROQ_API_KEY=your_api_key_here > .env

Usage

Run the Application

python main.py

The application will start on http://localhost:5000

API Endpoints

GET /

• Description: Serves the web interface
• Response: HTML page

POST /plan

• Description: Submit a travel planning query
• Request Body:

  {
    "query": "I need to plan a trip to Paris for 5 days with a budget of $2000"
  }

• Response:

  {
    "result": "AI-generated travel plan..."
  }

Example Queries

• "Find flights from New York to London for next week"
• "Recommend hotels in Tokyo within $150 per night"
• "What are the best attractions in Barcelona?"
• "What's the weather like in Sydney?"
• "Plan a 7-day trip to Italy with a $3000 budget"

Tools

Flight Tool

Searches for available flights based on origin, destination, and dates.

Hotel Tool

Recommends hotels based on location, price range, and preferences.

Places Tool

Provides information about attractions and places to visit in destinations.

Weather Tool

Fetches weather forecasts for specified locations.

Budget Tool

Calculates estimated trip costs including flights, accommodation, and activities.

Environment Variables

Create a .env file in the root directory:

GROQ_API_KEY=your_groq_api_key_here

Note: The .env file is ignored in git for security purposes. See .gitignore for details.

Deployment

The project includes a render.yaml configuration file for easy deployment to Render:

1. Push your code to GitHub
2. Connect your repository to Render
3. The app will automatically deploy using the configuration in render.yaml

Development

Running in Debug Mode

The application runs in debug mode by default, which enables:

• Auto-reloading on code changes
• Detailed error messages
• Interactive debugger

Project Architecture

The agent uses a ReAct (Reasoning + Acting) pattern:

1. User submits a natural language query
2. LLM analyzes the query and decides which tools to use
3. Tools execute and return results
4. LLM synthesizes the results into a comprehensive response

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is open source and available under the MIT License.

Support

For issues and questions, please open an issue on the GitHub repository.

Author

SA9572