Building an AI-Powered Smart Travel Planner with Multi-Agent AI and LangGraph

Author(s): Vikram Bhat

Originally published on Towards AI.

Image generated using napkin.ai

In today’s rapidly evolving AI landscape, the ability to synthesize diverse information into personalized, actionable travel plans is more valuable than ever. Whether it’s a spontaneous weekend escape or a meticulously curated vacation, travelers increasingly seek intelligent systems that can fetch real-time data from the web and convert it into meaningful itineraries, activity suggestions, and travel insights.

This blog introduces a Multi-Agent Travel Itinerary Planner built using LangGraph and powered by the LLaMA 3.x model. The system brings together a collaborative team of agents, each with a specific responsibility:

Generate Itinerary Agent — crafts day-by-day travel plans based on user preferencesRecommend Activities Agent — suggests unique local experiencesFetch Useful Links Agent — retrieves relevant travel guides using the Google Serper APIWeather Forecaster Agent — provides weather expectations for the tripPacking List Generator Agent — curates a personalized packing checklistFood & Culture Recommender Agent — offers insights into local cuisine and etiquetteChat Agent — answers follow-up questions conversationally

Built with a user-friendly Streamlit interface , this system showcases how modern LLMs and agent-based workflows can come together to deliver a seamless travel planning experience.

You can find the complete code on GitHub: 👉 MultiAgents-with-Langgraph-TravelItineraryPlanner

Follow along to learn how to build this project… Read the full blog for free on Medium.

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Published via Towards AI