Conversational agents can be used to make diagnoses, classify mental states, promote health education, and provide emotional support. The benefits of adopting conversational agents include widespread access, increased treatment engagement, and improved patient relationships with the intervention. We propose here a framework to assist chat operators of mental healthcare services, instead of a fully automated conversational agent. This design eases to avoid the adverse effects of applying chatbots in mental healthcare. The proposed framework is capable of improving the quality and reducing the time of interactions via chat between a user and a chat operator. We also present a case study in the context of health promotion on reducing tobacco use. The proposed framework uses artificial intelligence, specifically natural language processing (NLP) techniques, to classify messages from chat users. A list of suggestions is offered to the chat operator, with topics to be discussed in the session. These suggestions were created based on service protocols and the classification of previous chat sessions. The operator can also edit the suggested messages. Data collected can be used in the future to improve the quality of the suggestions offered.