AI-Powered Multi-Agent Personalized Trip Planning Platform Using LLMs, RAG, and Cloud Services

Abstract

Travel planning is a complex, multi-dimensional challenge requiring travelers to manually synthesize information across fragmented platforms—flight aggregators, hotel portals, weather services, and restaurant guides. The process is time-consuming, error-prone, and poorly adaptive to real-time disruptions. This paper presents an AI-Powered Personalized Trip Planner—a full-stack intelligent system that dynamically generates end-to-end itineraries tailored to individual preferences, budget constraints, and live environmental conditions. The architecture integrates a multi-agent orchestration layer, Large Language Model (LLM) inference augmented by Retrieval-Augmented Generation (RAG), a hybrid collaborative and content-based recommendation engine, and asynchronous real-time APIs for weather, geospatial Points of Interest (POI), transport scheduling, and local events. A Genetic Algorithm solves the multi-constraint Constraint Satisfaction Problem (CSP) to optimize daily schedules. The system generates complete, bookable itineraries in under 30 seconds. Evaluation over 50 diverse trip scenarios demonstrates 91.4% recommendation precision, 96.2% budget adherence, a Mean Opinion Score of 4.6/5, and 88.7% real-time re-planning success rate—substantially advancing the state of the art in intelligent travel planning. Index Terms — Artificial Intelligence, Trip Planner, Large Language Models, Retrieval-Augmented Generation, Multi-Agent Systems, Recommendation Engine, Constraint Satisfaction, Real-Time APIs, Streamlit, NLP.