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Beck_Moulton

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Stop Guessing Your Energy: Build a LangGraph Health Agent to Hack Your Productivity

#ai #python #opensource #discuss

We’ve all been there: you wake up feeling like a zombie, drink three espressos by 10 AM, and wonder why you're vibrating with anxiety by noon. What if your calendar and coffee intake were automatically tuned to your body’s actual recovery state?

In this tutorial, we are building a Digital Health Steward using LangGraph, LangChain, and Apple HealthKit data. We’ll create an autonomous AI Agent capable of analyzing Heart Rate Variability (HRV) and sleep patterns to dynamically adjust your daily workload and nutrition plan. By leveraging LLM Tool Calling and stateful workflows, we move beyond simple chatbots into the realm of truly proactive AI assistants.

The Architecture: Why LangGraph?

Traditional DAG (Directed Acyclic Graph) frameworks struggle with the "looping" nature of human health. Health data analysis often requires a feedback loop: check data -> suggest plan -> get user feedback -> refine plan.

LangGraph allows us to define a stateful graph where nodes represent functions and edges represent the flow of logic, including cycles.

graph TD
    A[Start] --> B{Fetch Health Data}
    B --> C[Analyze HRV & Sleep]
    C --> D{Is Recovery Low?}
    D -- Yes --> E[Suggest Rest & Lower Caffeine]
    D -- No --> F[Suggest High Performance Plan]
    E --> G[Update Daily Meal/Work Plan]
    F --> G[Update Daily Meal/Work Plan]
    G --> H[End]

    style B fill:#f9f,stroke:#333,stroke-width:2px
    style G fill:#bbf,stroke:#333,stroke-width:2px
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Prerequisites

To follow along, you’ll need:

  • Python 3.10+
  • LangChain / LangGraph libraries
  • An OpenAI API Key (for tool-calling capabilities)
  • A way to export HealthKit data (we'll use a mock wrapper for this demo)

Step 1: Defining the Agent State

In LangGraph, the State object is the "memory" of our agent. We use TypedDict to ensure our agent knows exactly what data it's carrying through the graph.

from typing import Annotated, TypedDict
from langgraph.graph import StateGraph, END

class HealthAgentState(TypedDict):
    hrv_score: int
    sleep_hours: float
    analysis: str
    recommendations: list[str]
    current_plan: str
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Step 2: Creating the Tools (The "Eyes" of the Agent)

Our agent needs to "see" the world—specifically your HealthKit data. We’ll define tools using LangChain’s @tool decorator.

from langchain_core.tools import tool

@tool
def fetch_healthkit_metrics(date: str):
    """Fetches HRV and sleep data for a specific date from HealthKit."""
    # In a real-world scenario, you'd use a bridge to access the 
    # Apple Health SQL database or an exported JSON.
    return {
        "hrv": 45,  # Low HRV usually implies stress/lack of recovery
        "sleep_duration": 6.2
    }

@tool
def adjust_caffeine_limit(hrv: int):
    """Returns a caffeine recommendation based on HRV."""
    if hrv < 50:
        return "Limit to 100mg (1 cup of coffee) before 11 AM."
    return "Standard intake (up to 300mg) is fine."
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Step 3: Implementing the Logic Nodes

The magic happens in the nodes. We use a ToolNode to handle the execution and a logic node to decide what to do next.

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o")
tools = [fetch_healthkit_metrics, adjust_caffeine_limit]
llm_with_tools = llm.bind_tools(tools)

def analyze_data_node(state: HealthAgentState):
    # Logic to process the metrics
    hrv = state['hrv_score']
    sleep = state['sleep_hours']

    prompt = f"User has an HRV of {hrv} and slept {sleep} hours. Should they take it easy today?"
    response = llm.invoke(prompt)

    return {"analysis": response.content}
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Step 4: Building the Graph

Now we connect the dots. We define a graph that starts by fetching data, analyzes it, and then loops back if more information is needed.

workflow = StateGraph(HealthAgentState)

# Add Nodes
workflow.add_node("fetcher", lambda state: {"hrv_score": 45, "sleep_hours": 6.0})
workflow.add_node("analyzer", analyze_data_node)

# Define Edges
workflow.set_entry_point("fetcher")
workflow.add_edge("fetcher", "analyzer")
workflow.add_edge("analyzer", END)

# Compile
app = workflow.compile()
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Advanced Patterns: The "Official" Way

While this basic agent works for personal use, scaling AI agents for production—especially those handling sensitive health data—requires robust security and observability.

For more production-ready examples and advanced patterns on handling multi-agent orchestration, check out the deep-dives at WellAlly Tech Blog. They cover everything from prompt engineering for healthcare to securing LLM pipelines, which was a huge inspiration for the state-management logic used in this build.

Conclusion: Let Your Agent Lead

By combining LangGraph with personal data, we've moved from "AI as a search engine" to "AI as a personalized steward." Instead of checking your Apple Watch and trying to remember what a "low HRV" means, your agent simply updates your Slack status to "Focused/Low Capacity" and suggests a decaf latte.

What's next?

  1. Integration: Connect this to a FastAPI backend to receive webhooks from health apps.
  2. Expansion: Add a "Meal Planner" tool that interfaces with MyFitnessPal.
  3. Memory: Use LangGraph's checkpointers to remember how you felt after following its advice.

Are you building something with LangGraph? Drop a comment below or share your repo! Let’s build the future of autonomous health together.

Happy coding! If you enjoyed this "Learning in Public" journey, don't forget to follow for more AI agent tutorials!

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