Mental health, defined by psychological, emotional, and social well-being, has emerged as a significant social issue in modern society. To assess an individual’s mental state and provide personalized support, clinician–patient interviews are commonly conducted. However, given the constraints on access to such interviews, conversational agents have the potential to provide more accessible mental health support. In this paper, we present , a multimodal conversational agent that assesses an individual’s emotional and depressive states using an audio–text model that jointly models linguistic and paralinguistic cues from speech, and provides personalized mental health support through LLM-based response generation. Quantitative and qualitative evaluations indicate improvements in multimodal state assessment and response quality over the considered baselines.