Turn years of Apple Health data into usable CSV and JSON on your own computer. Keep raw values intact, handle large exports, and optionally chat with your data using local or cloud AI.

The new convert command runs without AI setup, API keys, uploads, telemetry, or third-party Python packages. From this source checkout:

./run convert ~/Downloads/export.zip --format both --out ./my-health-export

It also accepts export.xml or a directory containing it. With this checkout installed, the equivalent is:

healthai convert ~/Downloads/export.zip --format both --out ./my-health-export

New in v1.11.0. Install the v1.11.0 GitHub release or use ./run convert from source. PyPI installations may lag behind this release.

Choose --format csv (the default), json, or both. Use a new output directory for each run. Progress is shown locally; --quiet hides progress while retaining the completion summary and warnings.

CSV flattens metadata into metadata:<key> columns. Nested elements are preserved in a children column containing JSON, including repeated metadata entries. JSON keeps a convenient metadata object and structured children. Referenced GPX/ECG files inside ZIPs are not extracted. Top-level Me and ExportDate metadata are omitted and counted in the report; other unsupported top-level elements produce a warning and counts.

Raw conversion does not change units, combine sources, deduplicate measurements, or calculate daily totals. For source-aware summaries and unit conversion, use the interactive analysis commands below. This keeps the original measurements available for your own processing.

The converter streams records instead of retaining the whole XML tree. JSON takes one pass; CSV takes two so columns discovered near the end are included. Memory depends on the largest individual record and the number of distinct columns, rather than total record count. Outputs are staged locally and published together only after success; malformed XML, changed input between CSV passes, and write errors fail explicitly. Existing output directories are refused.

Privacy: conversion makes no network requests and does not read or save AI preferences. The report stays in your output folder; nothing is sent to us. Downloading/installing software still requires network access. Optional cloud AI chat sends data to the provider you choose; local conversion never invokes it.

Local synthetic CSV benchmark, November 2, 2026, on macOS arm64 / Python 3.14.6:

The measured benefit is predictable memory use: about 50× less peak memory at one million rows. Two-pass CSV was slightly slower at that size; this is not a universal speedup claim. Each measurement is one run in a fresh process with warm filesystem cache, including imports. The previous-algorithm baseline retains the XML tree, row dictionaries and a pandas DataFrame; it excludes AI startup and processed summaries. Fixtures contain flat synthetic step records, so ZIP decompression and nested metadata will have different costs. Output counts and raw values are checked independently after conversion. No personal health data or usage analytics were collected.

Reproduce locally (the comparison requires pandas; the streaming-only run does not):

PYTHONPATH=src .venv/bin/python benchmarks/export_benchmark.py --records 10000 100000 1000000
PYTHONPATH=src python3 benchmarks/export_benchmark.py --records 10000000 --streaming-only

The large run temporarily needs several GB of free disk space. Generated inputs and outputs are removed afterwards. Machine-readable results: comparison and large export. These are local benchmark results, not live tracking.

Install v1.11.0 directly from the GitHub release:

pipx install https://github.com/krumjahn/applehealth/releases/download/v1.11.0/healthai-1.11.0-py3-none-any.whl

The installer and PyPI commands below install the version currently available on PyPI, which may be older.

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. The saved export path is reused by slash commands and chat.

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

A local Apple Health data toolkit: convert an XML/ZIP export into usable files, then explore trends with charts or optional AI chat. Conversion works entirely offline. Choose a local model for private AI analysis or explicitly configure a cloud provider.

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

• 📤 Streaming raw export: XML/ZIP → CSV/JSON, progress, preserved nested workout details, and a local completeness report. No AI setup needed.
• 💬 Chat REPL: Ask questions in plain English. "What were my most active months last year?" — chat builds query-aware local summaries and includes recent complete rows.
• 🧠 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: Daily heart-rate means, source-reconciled sleep patterns, and workout heart-rate intensity.
• 💍 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.

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:

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.

Recommended (install once, run anywhere):

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

Apple Health exports can contain overlapping measurements from an iPhone, Apple Watch, and third-party devices. healthai now reconciles overlapping cumulative intervals instead of blindly adding every source. Its automatic order prefers manually entered Health data, then Apple Watch, then iPhone/iPad, then third-party sources. Because export.xml does not include the source order configured in the Health app, use /settings to set the exact priority for each metric when your preferred tracker differs.

All supported units are normalized before aggregation: distance to kilometers, body mass to kilograms, energy to kilocalories, and heart rate to BPM. Unsupported units are skipped and listed by /diagnose; they are never silently mislabeled.

Imperial units (miles / pounds): open /settings → Choose units and pick imperial. Charts, summaries, exported CSVs, and AI analyses then use miles and pounds instead of km and kg. You can also set APPLEHEALTH_UNITS=imperial in the environment.

Source filter (e.g. only Apple Watch): if a third-party app (Argus, Fitbit, etc.) pollutes your metrics, open /settings → Configure data sources and enter the sources to include — everything else is ignored entirely, no editing of export.xml required. Non-interactive equivalent: APPLEHEALTH_SOURCES="Keith's Apple Watch" (comma-separate multiple sources; names must match the sources listed in the settings screen).

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.

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

# 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 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 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

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 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)"

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.

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.

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.

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

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

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

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

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