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.

翻译：对话式健康代理（CHAs）是提供辅助诊断等医疗服务的交互系统。现有CHAs（尤其是基于大语言模型（LLMs）的系统）主要聚焦于对话功能，但在多步骤问题解决、个性化对话和多模态数据分析等智能体能力方面存在局限。为突破这些限制，我们提出开源框架openCHA，该框架基于LLMs为对话代理赋能，使其能够针对用户的健康咨询生成个性化响应。该框架支持开发者将外部数据源、知识库和分析模型等资源集成到基于LLM的解决方案中。openCHA包含协调器模块，能够规划并执行从外部资源获取信息的操作——这些信息对响应用户查询至关重要。该框架支持知识获取、问题求解、多语言多模态对话，并能与多种AI平台交互。通过三个案例演示，我们展示了框架处理复杂医疗任务的能力。此外，openCHA已作为开源项目发布至GitHub社区。