Hyper-Personalized Virtual Companion: Generative Tamagotchi to Cultivate Personal Goals

Abstract

LLM-powered virtual conversational companions tend to lose engagement once conversational novelty fades, as dialogue alone provides no persistent reason to return. Virtual pets address part of this problem through a care loop, but existing implementations tend to use generic characters that bear no relation to the user. In this work, we present PET (Personalized gEnerative Tamagotchi), a hyper-personalized virtual companion that combines generative character creation with the care-loop mechanic for personal goal-setting. PET uses a multi-agent AI pipeline – Profiler, Strategist, Designer, and CareTaker – to synthesize a unique virtual companion whose appearance, personality, and narrative are derived from each user’s goals and self-description. The companion’s vitality is coupled to daily behavior check-ins, while an LLM-driven agent maintains in-character interaction grounded in persistent memory. A 7-day field deployment revealed that the care loop was the primary driver of sustained engagement, with participants reporting real-world impact on their daily behaviors, motivated by their companion’s HP/XP state. Generative personalization contributed to emotional attachment and served as a mnemonic anchor for daily habits.