Agentic Stage-Based LLM Framework for Multi-Turn Mental Health Support Conversations in Thai

Abstract

While mental health support needs are growing in Thailand, access to professional support remains limited. LLMbased chatbots offer a scalable solution, yet most existing systems are restricted to single-turn, solution-oriented responses. We propose an Agentic Stage-Based LLM Framework for Multi- Turn Mental Health Support Conversations in Thai, structuring conversations around five core counseling stages: rapport building, problem identification, goal setting, working, and termination. Drawing from Person-Centered Therapy (PCT) and Acceptance and Commitment Therapy (ACT), our framework incorporates two key components: an approach-selection agent that selects appropriate counseling approaches and a monitoring agent that manages stage transitions in multi-turn conversations. We evaluated the proposed framework against three single-agent baselines using LLM-simulated users and compared positive user reaction rates as judged by LLMs. Our framework achieved a 79.01% positive user reaction rate, outperforming single agents with standard, AugESC, and COOPER-CoT prompts by 8.91%, 11.68%, and 17.02%, respectively. Ablation studies validated the necessity of each agentic module in the proposed framework, with results surpassing single-agent baselines without stage-based architecture by 96.15% and 88.46% in Process and Working evaluations, respectively. A/B testing with real users and evaluation by 3 counseling practitioners demonstrated significant improvements in seven of eight mental health support evaluation metrics, highlighting the potential to deliver LLM-based mental health support in Thai.