Trip Assistant

AI-powered travel planner that suggests destinations and creates itineraries using LLM agents and LangGraph.

How It Works

1. User describes the ideal trip
2. AI suggests 3-5 destinations matching the description
3. User picks a destination from the list
4. AI generates a detailed travel itinerary

The workflow is orchestrated as a LangGraph state graph with an interrupt mechanism for user interaction.

Tech Stack

• Python >= 3.10
• LangGraph — agent workflow orchestration
• LangChain + Google Gemini — LLM integration
• Pydantic — structured output validation

Project Structure

trip_assistant/
├── main.py                          # Entry point
├── config/                          # Environment & app configuration
├── agents/
│   ├── base_agent.py                # Abstract base agent
│   ├── choose_destination_place_agent/
│   │   └── agent.py                 # Suggests destinations
│   └── create_itinerary_agent/
│       └── agent.py                 # Generates itinerary
├── graph/
│   ├── compile_graph.py             # Graph assembly & compilation
│   ├── nodes/                       # Graph node implementations
│   └── states/                      # State definitions
├── types/                           # Pydantic/TypedDict models
└── utils/
    └── logger.py                    # Logging setup

Setup

# Clone and enter the project
git clone <repo-url>
cd trip-assistant

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Add your GOOGLE_API_KEY in .env

Environment Variables

Variable	Required	Default	Description
GOOGLE_API_KEY	Yes	—	Google Gemini API key
GOOGLE_MODEL_ID	No	gemini-2.5-flash	Gemini model to use
LOG_LEVEL	No	INFO	Logging level

Usage

python -m trip_assistant.main

The app will ask you to describe your ideal trip, propose destinations, and generate an itinerary based on your choice.

Graph Flow

START → choose_destination_place → (user input) → create_itinerary → END