AI-Based Mental Health Chatbot using LangChain and LangGraph

Abstract: Mental health disorders such as depression, anxiety, and stress are increasing at an alarming rate worldwide, while access to professional mental healthcare remains limited due to cost, stigma, and resource constraints. Artificial Intelligence (AI)-based mental health chatbots have emerged as scalable and accessible solutions to address this gap. This paper presents a comprehensive study and proposed architecture for an AI-based mental health chatbot using LangChain and LangGraph frameworks. LangChain enables modular, context-aware conversational capabilities with memory and knowledge integration, while LangGraph introduces multi-agent workflows, state management, and human-in-the-loop control for improved safety and decision-making. The proposed system integrates Natural Language Processing (NLP), Cognitive Behavioral Therapy (CBT), sentiment analysis, and Retrieval-Augmented Generation (RAG) to deliver personalized and context-aware responses. The paper also reviews existing literature, discusses system architecture, methodology, evaluation techniques, and highlights challenges such as ethical concerns and safety risks. The results from existing studies demonstrate that AI chatbots significantly improve mental well-being, although further clinical validation is required.