A spatially-grounded conversational planner for personalized urban itineraries

We present a demo of RAGTrip, a modular conversational system that integrates Large Language Models (LLMs), spatial reasoning, and information retrieval to generate personalized walking itineraries in urban environments. Unlike traditional route planners or closed-book LLMs, RAGTrip interprets nuanced user preferences, avoids hallucinations, and grounds its suggestions in real-world geographic and factual data. The system features an interactive conversational interface that engages users in refining both the itinerary and the attractions to visit. Through dynamic map visualizations and contextual responses, users can explore and iteratively customize their routes. The demo includes a toggle to enable or disable Retrieval-Augmented Generation (RAG), allowing direct comparison between RAG-enhanced and closed-book LLM responses. This highlights the value of combining spatial and semantic grounding in conversational itinerary recommendation.