LLM Supervised ReAct Agents for Travel Planning

Abstract

Constraint driven travel planning, easy for humans, remains difficult for AI systems. We examine the TravelPlanner benchmark provided by Xie et al, and provide the new ManagerAgent architecture for travel planning. We find a preliminary 5% success rate across “Easy” queries, an improvement from 0% success rates reported from other models and frameworks. This thesis details the ManagerAgent for travel planning, providing an in-depth implementation description. Code is available at github.com/ctarnold/TravelPlanner.