Computer Science, 1987-2025
Permanent URI for this collectionhttps://theses-dissertations.princeton.edu/handle/88435/dsp01mp48sc83w
Permanent URI for this collectionhttps://theses-dissertations.princeton.edu/handle/88435/dsp01mp48sc83w
LLM Supervised ReAct Agents for Travel Planning
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.