AgentTravel: Knowledge-Augmented LLM Agent Framework for Urban Travel Planning

Large language models are opening new opportunities for intelligent decision support, with urban travel planning as a challenging and high-impact use case. Effective planning requires integrating real-time, multi-source data—such as points of interest, transportation, and user preferences—while reasoning spatially to generate feasible itineraries. This paper proposes AgentTravel, a unified framework that combines knowledge-grounded modeling, agentic reasoning, and multi-perspective evaluation. It includes: 1) TravelLLM, a domain-adapted model enriched with urban and spatial knowledge; 2) TravelAgent, an agentic planner with structured itinerary memory and real-time data retrieval; and 3) TravelBench, a benchmark assessing both knowledge grounding and plan quality. Experiments on five Chinese cities show that AgentTravel surpasses strong baselines in factual reasoning and itinerary feasibility, offering a promising step toward grounded and adaptive LLMs for urban intelligence. Source code and datasets are available at \url{https://github.com/csjiezhao/AgentTravel}.