This project implements an intelligent travel planning assistant powered by a multi-agent LangGraph framework integrated with large language models (LLMs). The system combines agentic AI architecture with dynamic tool execution to autonomously handle complex travel-related queries, including destination recommendations, travel itinerary planning, and package suggestions. By leveraging state management for contextual conversations and supporting multiple LLM backends (Groq and OpenAI), the assistant provides flexible, scalable, and intelligent travel assistance. Custom tools enable capabilities such as repository management and weather integration, while the modular design allows easy extension and configuration. This project demonstrates practical applications of multi-step reasoning, tool binding, and workflow orchestration in real-world travel planning scenarios.
The LangGraph-Based AI Travel Agent Assistant is a sophisticated, production-ready system that leverages advanced agentic AI patterns to automate and enhance travel planning processes. It combines multi-agent orchestration with large language models to provide personalized travel recommendations, itinerary planning, and destination insights. The system represents a complete implementation of multi-agent coordination principles using LangGraph's state management and workflow execution.
Travel planning often involves:
The Travel Agent solves these problems by automating research, integrating real-time data, and providing intelligent, personalized recommendations through a coordinated team of specialized AI agents.
Built on LangGraph, the system implements a multi-stage agent pipeline:
All agents operate on a shared state, ensuring consistency and enabling intelligent decision-making at each step.
Intelligent Travel Analysis
Real-Time Data Integration
Multi-Step Itinerary Planning
Flexible Tool Execution
| Component | Purpose | Technology |
|---|---|---|
| Agent Orchestration | Manage workflow and state | LangGraph |
| LLM Integration | Natural language processing | LangChain + Groq/OpenAI |
| Primary Language Model | Fast, cost-effective inference | Groq (llama-3.1-8b) |
| Alternative LLM | High-quality reasoning | OpenAI (gpt-4o-mini) |
| Configuration Management | Secure API key handling | python-dotenv |
| Type Safety | Validated state management | Python TypedDict |
| Core Language | Clean, maintainable code | Python 3.8+ |
This system embodies modern AI development principles:
From Structured Workflows to Intelligent Reasoning
Unlike static workflows, this system makes intelligent decisions at each step based on user input and context analysis.
Tool-Augmented Intelligence
Agents don't just generate text—they use tools to gather real data (weather, travel guides, availability) and make informed recommendations.
State-Driven Architecture
LangGraph's state management ensures that each agent builds on the work of previous agents, creating a coherent reasoning chain.
Production Readiness
Proper error handling, configuration management, secure credential storage, and clear logging throughout.
Modularity and Extensibility
Easy to add new tools (price APIs, booking integrations, review systems) without modifying core agent logic.
📦 Project Structure
travel-agent/
│── src/
│ ├── travel_agent.py # Main LangGraph agent implementation
│ ├── llm.py # LLM initialization and model selection
│ ├── custom_tools.py # Custom tool definitions
│ ├── paths.py # Path configuration
├── .env.example # Environment variables template
├── .eslintrc.json # ESLint configuration
├── requirements.txt # Python dependencies
├── README.md # Project documentation
└── .gitignore # Excludes .env, venv, pycache, etc.
git clone https://github.com/manosmj/travel-agent.git cd travel-agent
python -m venv venv venv\Scripts\activate # On Windows # Or source venv/bin/activate # On Mac/Linux
pip install -r requirements.txt
Copy .env.example to .env and add your API keys:
# For Groq API (Primary LLM) GROQ_API_KEY="your-groq-api-key" # For OpenAI API (Alternative LLM) OPENAI_API_KEY="your-openai-api-key"
python src/travel_agent.py
The agent will analyze weather condition, user inputs and travel purpose and return the best result.