Vaiage: A Multi-Agent Solution to Personalized Travel Planning

TL;DR

Vaiage introduces a multi-agent framework utilizing large language models to create adaptive, personalized travel itineraries that respond to real-time context and user preferences, improving over static planning tools.

Contribution

This paper presents a novel multi-agent system leveraging LLMs for dynamic, personalized travel planning with real-time interaction and contextual adaptation.

Findings

Achieved an average system score of 8.5/10 in human-in-the-loop evaluations.

Outperformed baseline variants lacking strategies or external APIs.

Agent coordination improved itinerary quality and feasibility.

Abstract

Planning trips is a cognitively intensive task involving conflicting user preferences, dynamic external information, and multi-step temporal-spatial optimization. Traditional platforms often fall short - they provide static results, lack contextual adaptation, and fail to support real-time interaction or intent refinement. Our approach, Vaiage, addresses these challenges through a graph-structured multi-agent framework built around large language models (LLMs) that serve as both goal-conditioned recommenders and sequential planners. LLMs infer user intent, suggest personalized destinations and activities, and synthesize itineraries that align with contextual constraints such as budget, timing, group size, and weather. Through natural language interaction, structured tool use, and map-based feedback loops, Vaiage enables adaptive, explainable, and end-to-end travel planning grounded in both symbolic reasoning and conversational understanding. To evaluate Vaiage, we conducted human-in-the-loop experiments using rubric-based GPT-4 assessments and qualitative feedback. The full system achieved an average score of 8.5 out of 10, outperforming the no-strategy (7.2) and no-external-API (6.8) variants, particularly in feasibility. Qualitative analysis indicated that agent coordination - especially the Strategy and Information Agents - significantly improved itinerary quality by optimizing time use and integrating real-time context. These results demonstrate the effectiveness of combining LLM reasoning with symbolic agent coordination in open-ended, real-world planning tasks.