Garmer - Garmin Data Extraction Tool

A Python library for extracting health and fitness data from Garmin Connect, designed for integration with OpenClaw and other health insight applications.

Features

• Comprehensive Data Extraction: Access activities, sleep, heart rate, stress, steps, body composition, hydration, and more
• Easy Authentication: OAuth-based authentication with token persistence
• OpenClaw Integration Ready: Designed for seamless integration with AI health assistants
• CLI Tool: Command-line interface for quick data access
• Type-Safe Models: Pydantic-based data models with full type hints
• Flexible Export: Export data in JSON format for analysis

Installation

# Install from source
pip install -e .

# Or with development dependencies
pip install -e ".[dev]"

Quick Start

Command Line

# Login to Garmin Connect
garmer login

# Check authentication status
garmer status

# Get today's summary
garmer summary

# Get sleep data
garmer sleep

# Get recent activities
garmer activities

# Get full health snapshot
garmer snapshot --json

# Export data
garmer export --days 7 -o my_data.json

Python API

from garmer import GarminClient

# Login with credentials (tokens are saved automatically)
client = GarminClient.from_credentials(
    email="your-email@example.com",
    password="your-password",
)

# Or use saved tokens
client = GarminClient.from_saved_tokens()

# Get today's summary
summary = client.get_daily_summary()
print(f"Steps: {summary.total_steps}")

# Get sleep data
sleep = client.get_sleep()
print(f"Sleep: {sleep.total_sleep_hours:.1f} hours")

# Get recent activities
activities = client.get_recent_activities(limit=5)
for activity in activities:
    print(f"{activity.activity_name}: {activity.distance_km:.1f} km")

# Get comprehensive health snapshot
snapshot = client.get_health_snapshot()

# Get weekly report
report = client.get_weekly_health_report()

Available Data Types

Activities

• Running, cycling, swimming, and 20+ activity types
• Duration, distance, pace, heart rate zones
• Laps, splits, GPS data (via detailed endpoints)
• Training effect metrics

Sleep

• Total sleep duration and phases (deep, light, REM)
• Sleep score and quality metrics
• Heart rate, HRV, respiration during sleep
• Sleep phases timeline

Heart Rate

• Resting heart rate
• Heart rate samples throughout the day
• Heart rate zones
• 7-day averages

Stress

• Overall stress level
• Stress samples throughout the day
• Rest vs. stress duration
• Body Battery correlation

Steps & Activity

• Total steps and goal tracking
• Distance traveled
• Floors climbed
• Intensity minutes (moderate/vigorous)
• Sedentary time

Body Composition

• Weight tracking
• Body fat percentage
• Muscle mass, bone mass
• BMI, metabolic age

Hydration

• Water intake tracking
• Daily goals
• Sweat loss correlation

Respiration

• Breathing rate (waking and sleeping)
• Respiratory trends

OpenClaw Integration

Garmer is designed to work seamlessly with OpenClaw for health insights:

from garmer.examples.openclaw_integration import GarminIntegration

integration = GarminIntegration()

# Get health summary for AI analysis
summary = integration.get_health_summary()
# Returns structured data with metrics and insights

# Get activity analysis
activities = integration.get_activity_insights(days=7)

# Get sleep trend analysis
sleep_trends = integration.get_sleep_trends(days=7)

# Generate daily briefing
briefing = integration.get_daily_briefing()

Configuration

Garmer stores configuration and tokens in ~/.garmer/:

~/.garmer/
├── garmin_tokens    # OAuth tokens (auto-created after login)
├── config.json      # Optional configuration file
└── exports/         # Default export directory

Environment Variables

• GARMER_TOKEN_DIR: Directory for token storage
• GARMER_LOG_LEVEL: Logging level (DEBUG, INFO, WARNING, ERROR)
• GARMER_CACHE_ENABLED: Enable/disable caching (true/false)

Data Models

All data is returned as Pydantic models with type hints:

from garmer.models import (
    Activity,
    SleepData,
    HeartRateData,
    StressData,
    StepsData,
    DailySummary,
    UserProfile,
)

# Models can be converted to dictionaries
activity_dict = activity.to_dict()

# Or accessed with full type support
print(activity.distance_km)  # float
print(activity.avg_heart_rate)  # int | None

Error Handling

from garmer.auth import AuthenticationError, SessionExpiredError

try:
    client = GarminClient.from_saved_tokens()
except AuthenticationError:
    print("Please login first: garmer login")

try:
    data = client.get_daily_summary()
except SessionExpiredError:
    # Token expired, need to re-authenticate
    client.login(email, password)

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Type checking
mypy src/garmer

# Linting
ruff check src/garmer

License

MIT License

Acknowledgments

• Uses the garth library for Garmin Connect authentication
• Inspired by the need for comprehensive health data integration with AI assistants