📄 中文摘要
管理代谢健康不应成为全职工作。随着 AI 健康助手和连续血糖监测（CGM）的兴起，能够实现从被动跟踪到主动干预的转变。设想一个能够实时监测 Dexcom 数据的助手，当检测到血糖控制不佳时，自动重写每周购物清单。本教程将探讨如何使用 LangGraph、OpenAI 函数调用和 Supabase 构建一个智能饮食助手。通过利用大语言模型编排和有状态工作流，连接生物信号与可行的饮食变化，最终使用户能够更好地管理饮食和健康。
构建 AI 健康助手：利用 LangGraph 和实时 CGM 数据自动化饮食管理
📄 English Summary
Building an AI Health Agent: Automating Your Diet with LangGraph and Real-Time CGM Data
Managing metabolic health should not feel like a full-time job. With the emergence of AI Health Agents and Continuous Glucose Monitoring (CGM), a shift from reactive tracking to proactive intervention is now possible. Imagine an agent that monitors your Dexcom data in real-time and automatically rewrites your weekly grocery list when it detects poor glycemic control. This tutorial explores how to build an Agentic Dietitian using LangGraph, OpenAI Function Calling, and Supabase. By leveraging LLM Orchestration and stateful workflows, the guide bridges the gap between biological signals and actionable dietary changes, enabling users to manage their diet and health more effectively.