chronometry-ai 1.0.6

Privacy-first activity tracker with AI-powered annotation and timeline generation

Chronometry

Privacy-first activity tracker with local AI-powered annotation.

Chronometry captures periodic screenshots of your desktop, annotates them with a local vision model (Ollama), and generates daily digests of your work activities — all running entirely on your machine.

Features

• Screenshot Capture — Periodic screenshots with configurable intervals, pre-capture notifications, and screen lock detection
• AI Annotation — Local vision models (Ollama / OpenAI-compatible) analyze screenshots and describe your activities
• Daily Digest — AI-generated summaries of your workday organized by category
• Timeline Visualization — Browse activities by date with expandable screenshot details
• Web Dashboard — Modern web UI with dark/light themes, analytics charts, and search
• macOS Menu Bar — Native menu bar app for quick access and manual capture (Cmd+Shift+6)
• Privacy First — Everything runs locally. Screenshots and annotations never leave your machine.
• Unified CLI — Single chrono command for all operations (services, annotation, search, config)

How It Works

┌────────────────────────────────────────────────────────────────┐
│                           Your Mac                             │
│                                                                │
│   ⏱️ Menu Bar App               📸 Capture Engine              │
│   ├─ Start/Pause Capture        ├─ Screenshots every 15 min    │
│   ├─ Manual Triggers            ├─ Screen lock detection       │
│   └─ Quick Actions              └─ Camera-in-use skip          │
│           │                                │                   │
│           ▼                                ▼                   │
│      ┌───────────────────────────────────────────────┐         │
│      │          ~/.chronometry/data/frames/          │         │
│      │          2026-02-28/20260228_143000.png       │         │
│      └───────────────────────┬───────────────────────┘         │
│                              │                                 │
│                              ▼                                 │
│      ┌───────────────────────────────────────────────┐         │
│      │          🤖 AI Annotation (Ollama)            │         │
│      │    Local vision model analyzes screenshots    │         │
│      │           → JSON summaries                    │         │
│      └───────────────────────┬───────────────────────┘         │
│                              │                                 │
│                  ┌───────────┴────────────┐                    │
│                  ▼                        ▼                    │
│      ┌─────────────────────┐  ┌──────────────────────┐         │
│      │   📊 Timeline       │  │ 📝 Daily Digest      │         │
│      │ Activity groups     │  │ AI summary by        │         │
│      │ + durations         │  │ category             │         │
│      └──────────┬──────────┘  └───────────┬──────────┘         │
│                 └────────────┬────────────┘                    │
│                              ▼                                 │
│      ┌───────────────────────────────────────────────┐         │
│      │      🌐 Web Dashboard (localhost:8051)        │         │
│      │      Timeline · Analytics · Search            │         │
│      └───────────────────────────────────────────────┘         │
│                                                                │
│      Everything runs locally. Nothing leaves your machine.     │
└────────────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

• macOS (menu bar app uses macOS-specific APIs)
• Python 3.10+ — check with python3 --version. If below 3.10, install it:

  brew install python@3.10
  

• Ollama — local LLM runtime

# Install Ollama
brew install ollama

# Start Ollama as a background service (auto-starts at login)
brew services start ollama

# Pull the vision model (used for screenshot annotation)
ollama pull qwen2.5vl:7b

Install

# From PyPI
pip3 install chronometry-ai

# Or with uv
uv pip install chronometry-ai

# Or in a dedicated virtual environment
mkdir -p ~/.chronometry
python3 -m venv ~/.chronometry/venv
source ~/.chronometry/venv/bin/activate
pip install chronometry-ai

Initialize

# Set up ~/.chronometry with default configuration
chrono init

This creates ~/.chronometry/ with config files, data directories, and log folders.

Verify

# Check everything is set up correctly
chrono validate

# Confirm configuration is valid
chrono config --validate

# Check version
chrono version

Run

# Install as macOS services (auto-start at login)
chrono service install

# Or start manually
chrono service start

# Open the dashboard
chrono open

On first install, macOS will prompt "Chronometry" would like to control this computer using accessibility features. Click Open System Settings and toggle Chronometry on. The Cmd+Shift+6 hotkey for region capture will work immediately.

The dashboard is at http://localhost:8051.

CLI Reference

chrono init                       # Initialize ~/.chronometry
chrono status                     # Service status overview
chrono service start|stop|restart|install|uninstall [name]
chrono logs [-f] [-e] [name]      # View service logs
chrono annotate                   # Run annotation on pending frames
chrono timeline                   # Generate timeline
chrono digest [-d DATE] [-f]      # Show/generate daily digest
chrono stats                      # Overall statistics
chrono dates                      # List dates with data
chrono search <query>             # Search activities
chrono config [--validate]        # Show/validate configuration
chrono validate                   # Run system validation checks
chrono open                       # Open dashboard in browser
chrono version                    # Version info

Architecture

src/chronometry/
├── __init__.py       # Version, CHRONOMETRY_HOME constant
├── cli.py            # Unified CLI (Typer + Rich)
├── menubar_app.py    # macOS menu bar app (rumps)
├── web_server.py     # Flask web dashboard
├── capture.py        # Screenshot capture engine
├── annotate.py       # Vision model annotation
├── digest.py         # Daily digest generation
├── timeline.py       # Timeline visualization
├── llm_backends.py   # LLM provider abstraction (Ollama, OpenAI-compatible)
├── common.py         # Shared utilities, config loading, bootstrap
├── token_usage.py    # Token usage tracking
├── validate.py       # System validation checks
├── defaults/         # Default configs shipped with package
│   ├── system_config.yaml
│   ├── user_config.yaml
│   └── *.plist       # macOS launchd templates
└── templates/
    └── dashboard.html  # Web dashboard (Vue.js + Pico CSS)

Runtime Directory

All runtime data lives in ~/.chronometry/ (overridable via CHRONOMETRY_HOME env var):

~/.chronometry/
├── config/
│   ├── user_config.yaml     # User preferences (intervals, prompts)
│   ├── system_config.yaml   # System settings (ports, models, paths)
│   └── backup/              # Auto-backups before config changes
├── data/
│   ├── frames/              # Screenshots by date (YYYY-MM-DD/)
│   ├── digests/             # Cached daily digests
│   └── token_usage/         # LLM token tracking
├── logs/                    # Service logs
└── output/                  # Generated timeline HTML

Configuration

User Config (~/.chronometry/config/user_config.yaml)

capture:
  capture_interval_seconds: 900   # 15 minutes
  monitor_index: 1                # Which monitor (0 = all)
  retention_days: 1095            # ~3 years

annotation:
  annotation_mode: manual         # "manual" or "auto"
  screenshot_analysis_batch_size: 4
  screenshot_analysis_prompt: "What is shown in this screenshot?"

notifications:
  enabled: true
  notify_before_capture: true
  pre_capture_warning_seconds: 5

System Config (~/.chronometry/config/system_config.yaml)

Model settings, server port, logging, and category definitions. Edit directly or via the web dashboard.

LLM Backends

Chronometry supports two local backends:

Backend	Provider	Use Case
Ollama (default)	ollama	Easiest setup, auto-start, crash recovery
OpenAI-compatible	openai_compatible	vLLM, LM Studio, llama.cpp servers

Configure in system_config.yaml under annotation.local_model and digest.local_model.

Environment Variables

Variable	Default	Description
CHRONOMETRY_HOME	~/.chronometry	Override runtime directory location

Development

# Clone and install in dev mode
git clone https://github.com/pkasinathan/chronometry.git
cd chronometry
make dev

# Run linter
make lint

# Auto-format
make format

# Run tests
make test

# Run tests with coverage
make test-cov

# All quality checks
make check

License

Apache License 2.0 — see LICENSE.