  Conversational Health Agents (CHAs) are interactive systems that provide
healthcare services, such as assistance and diagnosis. Current CHAs, especially
those utilizing Large Language Models (LLMs), primarily focus on conversation
aspects. However, they offer limited agent capabilities, specifically lacking
multi-step problem-solving, personalized conversations, and multimodal data
analysis. Our aim is to overcome these limitations. We propose openCHA, an
open-source LLM-powered framework, to empower conversational agents to generate
a personalized response for users' healthcare queries. This framework enables
developers to integrate external sources including data sources, knowledge
bases, and analysis models, into their LLM-based solutions. openCHA includes an
orchestrator to plan and execute actions for gathering information from
external sources, essential for formulating responses to user inquiries. It
facilitates knowledge acquisition, problem-solving capabilities, multilingual
and multimodal conversations, and fosters interaction with various AI
platforms. We illustrate the framework's proficiency in handling complex
healthcare tasks via three demonstrations. Moreover, we release openCHA as open
source available to the community via GitHub.
