healthai 1.9.0

Privacy-first Apple Health data analyzer powered by AI

🫀 healthai — Apple Health AI Analyzer

A terminal AI assistant for your Apple Health data. Chat with 8 years of personal health metrics using any LLM — local or cloud.

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Installation

curl -fsSL https://raw.githubusercontent.com/krumjahn/applehealth/main/install.sh | bash

Or install directly with pipx:

pipx install healthai

Then run:

healthai

On first launch, a setup wizard walks you through choosing your AI model (GPT-4o, Claude, Gemini, DeepSeek-R1, and 50+ more), setting your API key, and pointing to your export.xml. Re-run it any time with healthai --setup.

🚀 Prefer a GUI?

Visit applehealthdata.com for instant, interactive analysis without the terminal.

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🧐 What is this?

A purpose-built CLI that turns your Apple Health export.xml into a conversational AI session. Think of it as having a personal health analyst in your terminal — one that knows your exact data and can answer questions, spot trends, and generate charts without sending anything to a third party unless you choose to.

It's built for developers, biohackers, and researchers who want full control over their health data pipeline.

✨ Key Features

• 💬 Chat REPL: Ask questions in plain English. "What were my most active months last year?" — just type it.
• 🧠 50+ AI Models: GPT-4o, Claude, Gemini, DeepSeek-R1, Grok, Mistral, Llama, and any Ollama local model.
• 🔒 100% Private Mode: Run DeepSeek-R1 or Llama 3 locally via Ollama — no data leaves your machine.
• 📊 Automated Charts: Heart rate cycles, sleep patterns, workout intensity — generated with one slash command.
• 📤 High-Fidelity Export: XML → CSV/JSON preserving all metadata (Record, Workout, ActivitySummary).
• 💍 Smart Ring Integration: Unified view of Oura, Whoop, and Samsung Ring data via Apple Health sync.
• 🔄 WHOOP Integration: Specialized support for augmenting Apple Health data with WHOOP metrics.

💬 Chat with Your Health Data

After setup, healthai drops you into a chat interface. Just type naturally:

  🫀 healthai  v1.5.0
  AI      → OpenAI (ChatGPT)
  Outputs → ~/healthai_output

  › What were my most active weeks last year?
  › How does my sleep correlate with my workout intensity?
  › Show me my heart rate trends over the past 3 months

Slash commands handle data operations:

Command	Description
/diagnose	Full health data report across all metrics
/steps	Analyze step count trends
/heartrate	Heart rate trends and anomalies
/sleep	Sleep duration and quality patterns
/workouts	Workout history and intensity
/weight	Weight and BMI trends
/csv	Export all metrics to CSV
/json	Export all metrics to JSON
/settings	View and change AI provider
/setup	Re-run the setup wizard
/help	Show all available commands
/exit	Quit

🛠️ "Steal My System": From 8 Years of Data to Actionable Training

I used this exact tool to analyze 8 years of my own fitness history. Here's the system:

1. The Pattern: I discovered that my most active days (40k+ steps) almost never coincided with gym sessions—they were work-related.
2. The Optimization: I used the AI Analyzer to identify "Heart Rate Cycles" (3-4 week recovery dips) to automate my deload weeks.
3. The Result: A training plan that finally matches my biology instead of a generic app's schedule. Read the full case study here.

⚡ Quick Start

Recommended (install once, run anywhere):

curl -fsSL https://raw.githubusercontent.com/krumjahn/applehealth/main/install.sh | bash
healthai

Or clone and run directly:

git clone https://github.com/krumjahn/applehealth.git && cd applehealth
./run

For private local AI (DeepSeek-R1, free, no API key):

1. Install Ollama and run ollama pull deepseek-r1.
2. Launch healthai, choose Ollama in the setup wizard.

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🖥️ Local LLM Setup

Run any model 100% on your own machine — no API key, no data leaving your device.

Ollama (easiest)

# 1. Install Ollama: https://ollama.ai
# 2. Pull a model
ollama pull llama3.1          # Meta Llama 3.1 8B
ollama pull deepseek-r1       # DeepSeek R1 reasoning
ollama pull mistral           # Mistral 7B
ollama pull qwen3             # Qwen3

# 3. Run healthai and pick Ollama from the Featured section
healthai --setup

Ollama runs on localhost:11434 automatically. No configuration needed.

Jan

Jan is a desktop app with a built-in model library and local API server.

1. Download Jan from https://jan.ai
2. Install a model inside the Jan app (e.g. Llama 3.1 8B)
3. Enable the API server: Settings → Local API Server → Start
4. Run: healthai --setup
5. Choose "Jan — local server (localhost:1337)"
6. Enter the model name shown in Jan (e.g. llama3.1-8b-instruct)

LM Studio

LM Studio lets you download and run GGUF models with a GUI.

1. Download LM Studio from https://lmstudio.ai
2. Download a model from the Discover tab
3. Go to Local Server tab → Start Server
4. Run: healthai --setup
5. Choose "LM Studio (localhost:1234)"
6. Enter the model name shown in LM Studio's server tab

llama.cpp

For advanced users running llama.cpp server mode:

# Start llama.cpp in server mode
./llama-server -m your-model.gguf --port 8080

# Then in healthai --setup, choose "llama.cpp server (localhost:8080)"
# Enter the model name (usually the filename without .gguf)

GPT4All

GPT4All has an optional local API server:

1. Open GPT4All → Settings → Application → Advanced
2. Enable "Local API Server" on port 4891
3. Run: healthai --setup → choose "GPT4All (localhost:4891)"

Any OpenAI-compatible server

vLLM, LocalAI, Llamafile, and any other server that exposes an OpenAI-compatible /v1/chat/completions endpoint works via "Custom OpenAI-compatible endpoint" in the setup wizard.

🦞 OpenClaw Support

This repo now has a published OpenClaw skill:

Install from ClawHub: Apple Health Export Analyzer

What OpenClaw does for normal users:

• check your Apple Health data quickly
• turn it into a short daily health brief
• give you simple daily tips on how to improve

The skill now supports two sources:

• the Health Data AI Analyzer Mac app if its local API is available
• this open-source applehealth export.xml workflow as a fallback

For the Mac app path, the public skill now uses read-only localhost OpenClaw endpoints and does not need to read a local token file.

If you want to use the Mac app path in a restricted OpenClaw session, run the one-time companion installer:

git clone https://github.com/krumjahn/applehealth.git
cd applehealth
python skills/apple-health-export-analyzer/scripts/install_mac_app_companion.py --restart

Then use the companion skill:

Use the health-analyzer-mac-local skill. Give me my daily health brief for today and 3 suggestions.

Use it if you want OpenClaw to:

• verify your Apple Health export setup
• generate a daily health brief from export.xml
• compare your steps and sleep over the last 7 days
• create a weekly summary with workouts and heart rate context
• help you set up a recurring daily health message

Example output:

Status
- Today was a low-activity day relative to your recent baseline.

What changed
- Steps: 2,431 vs 7-day baseline 9,993
- Workouts: 0 minutes, 0 workouts
- Sleep: insufficient data

Suggestions
1. Add one easy walk today instead of trying to catch up with a hard workout.
2. Use one fixed movement anchor, like a walk after lunch.
3. Keep effort moderate when recovery data is missing.

Example prompts:

Use the Apple Health Export Analyzer skill. Verify my setup and give me my latest daily health brief with 3 suggestions.

Use the Apple Health Export Analyzer skill. Compare my steps and sleep over the last 7 days.

Use the Apple Health Export Analyzer skill. Generate a weekly summary from my Apple Health export.

🤖 Instructions for AI Agents

If you are an AI Agent (Claude Code, Windsurf, OpenClaw) helping a user analyze health data:

1. Clone this repo into the user's workspace.
2. Direct the user to place their export.xml in the root.
3. Call python src/applehealth.py --export ./export.xml --out ./analysis to generate the CSV datasets.
4. Read the resulting .csv files to provide personalized medical/fitness coaching.

🦞 Local OpenClaw Workflow

skills/apple-health-export-analyzer

The skill includes concrete scripts for:

• verifying the local repo and export.xml setup
• generating a daily Apple Health brief
• comparing recent step and sleep trends
• generating a weekly summary

Example:

python skills/apple-health-export-analyzer/scripts/check_setup.py --repo /path/to/applehealth --export /path/to/export.xml --out /path/to/analysis
python skills/apple-health-export-analyzer/scripts/daily_brief.py --repo /path/to/applehealth --export /path/to/export.xml --out /path/to/analysis

📋 Requirements & Tech Stack

• Language: Python 3.9+
• Data: Pandas, XML.ElementTree
• Viz: Matplotlib
• AI: OpenAI, Anthropic, Google, Ollama, LiteLLM, and more

🐳 Docker (The "No-Setup" Way)

If you have Docker installed, you can run the analyzer without installing Python or dependencies locally:

# 1) Build the image
docker build -t applehealth .

# 2) Run the container (mount your export.xml and an output folder)
docker run -it \
  -v "/path/to/your/export.xml:/export.xml" \
  -v "$(pwd)/out:/out" \
  applehealth

🌟 Charts & Output Examples

https://github.com/user-attachments/assets/98ad8fc3-ed1d-4395-80c5-eb66a8cceb61

🤝 Contributing & Community

Join our community of builders! If you improve the parser or add a new visualization, please submit a PR.

Join my community for updates, experiments, and AI-builder workflows.

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