🎧 djkr8 - Playlist Optimizer for rekordbox

Optimize rekordbox playlists for harmonic mixing using Google OR-Tools constraint programming.

Features

• ✨ Longest Path Optimization: Finds the maximum number of tracks that can be mixed together
• ⚡ Energy Flow Management: Enforces non-decreasing energy progression with max +1 increase per transition (1-5 range)
• 🎵 Harmonic Mixing: Uses the Camelot Wheel system for key compatibility
• 🚀 Energy Boost Transitions: Strategic +2 hour jumps (5A→7A) for crowd excitement, used sparingly (max 3 per set)
• 📊 Transition Quality Scoring: Intelligently weights transitions by harmonic quality (1.0 = perfect, 0.6 = energy boost)
• 🎧 Rekordbox Integration: Read playlists directly from your local Rekordbox 6/7 database (tested with v7.2.8)
• 🔊 BPM Matching: Supports direct, halftime, and doubletime BPM compatibility
• ⚙️ Configurable Strictness: STRICT, MODERATE, or RELAXED harmonic compatibility levels
• 📤 Rekordbox Export: Export results to Rekordbox XML or write directly to the Rekordbox database
• 🚀 Fast: Powered by Google OR-Tools CP-SAT solver (award-winning constraint solver)
• 📦 SDK + CLI: Use as a Python library or command-line tool

Installation

uv add djkr8

Or with pip:

pip install djkr8

Quick Start

SDK Usage

from djkr8 import PlaylistOptimizer, Track, HarmonicLevel

tracks = [
    Track(id="track_001", key="8A", bpm=128),
    Track(id="track_002", key="8B", bpm=130),
    Track(id="track_003", key="9A", bpm=125),
]

optimizer = PlaylistOptimizer(
    bpm_tolerance=10,
    allow_halftime_bpm=True,
    max_violation_pct=0.10,
    harmonic_level=HarmonicLevel.STRICT,
)

result = optimizer.optimize(tracks)

for i, track in enumerate(result.playlist, 1):
    print(f"{i}. {track.id} ({track.key}, {track.bpm} BPM)")

CLI Usage

# Basic usage
djkr8 tracks.json

# With custom settings
djkr8 tracks.json --bpm-tolerance 8 --harmonic-level moderate

# Energy flow management
djkr8 tracks.json --energy-weight 5.0      # Prioritize higher energy tracks
djkr8 tracks.json --allow-energy-drops    # Disable strict non-decreasing energy constraint

# Save results to JSON
djkr8 tracks.json --output result.json

# Use with Rekordbox (v6/v7)
krate --rekordbox                                      # List playlists
krate --rekordbox --playlist "Techno"                  # Optimize specific playlist
krate --rekordbox --playlist "Techno" --output r.xml   # Export to Rekordbox XML
krate --rekordbox --playlist "Techno" --write-to-db    # Write directly to Rekordbox DB

# Enable verbose logging
djkr8 tracks.json -v          # INFO level
djkr8 tracks.json -vv         # DEBUG level

Rekordbox Integration

The tool provides two ways to save your optimized playlists back to Rekordbox:

1. XML Export (Recommended)

Export the results to an XML file that can be imported into Rekordbox:

krate --rekordbox --playlist "My Playlist" --output optimized.xml

In Rekordbox:

1. Go to File > Import > Import Playlist
2. Select optimized.xml
3. The playlist will appear in the ROOT folder (e.g., My Playlist_20260115_120000)

2. Direct Database Write (Advanced)

Write the optimized playlist directly to your Rekordbox 6 database:

krate --rekordbox --playlist "My Playlist" --write-to-db

⚠️ WARNING:

• Close Rekordbox before running this command.
• This modifies your master.db file directly.
• Backup your database before using this feature.

Input Format

JSON file with tracks containing id, key (Camelot notation), and bpm:

{
  "tracks": [
    {"id": "track_001", "key": "8A", "bpm": 128},
    {"id": "track_002", "key": "8B", "bpm": 130},
    {"id": "track_003", "key": "9A", "bpm": 125}
  ]
}

How It Works

1. BPM Compatibility

Adjacent tracks must have compatible BPMs within tolerance:

2. Harmonic Mixing (Camelot Wheel)

Harmonic compatibility levels:

STRICT (default):

• Same key (8A → 8A)
• ±1 hour same letter (8A → 7A, 9A)
• Same hour different letter (8A → 8B)

MODERATE:

• Above + ±1 hour different letter (8A → 9B, 7B)

RELAXED:

• Above + ±3 hours (8A → 5A, 11A)

3. Optimization Goal

Maximize playlist length while optimizing transition quality and respecting harmonic constraints.

Multi-Objective Optimization:

• Primary: Maximize playlist length (more tracks = better)
• Secondary: Maximize transition quality scores (smoother mixes = better)
• Constraints: Energy flow, harmonic violations, energy boosts

Configuration Options

Advanced Features

Energy Boost Mixing

Based on Mixed In Key's Energy Boost technique, you can use strategic harmonic jumps to add excitement:

+2 Hours (Energy Boost): Jump forward 2 hours on the Camelot wheel

# Example: 5A → 7A
# Gives a quick burst of energy to wake up the dancefloor
optimizer = PlaylistOptimizer(max_energy_boosts=3)  # Allow up to 3 boosts

-5 Hours (Armin Variation): Jump backward 5 hours

# Example: 12A → 7A
# Used by Armin Van Buuren and Skrillex for dramatic energy spikes

Best Practices (from professional DJ analysis):

• Use energy boosts sparingly (every 20-30 minutes, not constantly)
• Top DJs average 2-3 energy boosts per set
• Combine with energy level increases for maximum impact
• Default limit: max_energy_boosts=3 per playlist

Transition Quality Scoring

The optimizer now scores each transition by harmonic quality:

Quality scores are weighted in the objective function, so the solver prefers smoother transitions when possible.

Examples

See examples/ directory:

• example_tracks.json - Sample input data
• sdk_usage.py - SDK usage demonstration
• logging_example.py - Logging configuration example

Development

# Clone repository
git clone https://github.com/yourusername/djkr8
cd krate

# Install with dev dependencies
uv sync --dev

# Install pre-commit hooks
uv run pre-commit install
uv run pre-commit install --hook-type commit-msg

# Run tests
uv run pytest

# Lint and format
uv run ruff check          # Check for issues
uv run ruff check --fix    # Auto-fix issues
uv run ruff format         # Format code

# Run pre-commit on all files
uv run pre-commit run --all-files

# Run example
uv run python examples/sdk_usage.py

How the Solver Works

The optimizer uses Google OR-Tools CP-SAT solver with:

1. Binary Variables: included[i] = track i is in playlist
2. Edge Variables: edge[i,j] = track j follows track i
3. Circuit Constraint: AddCircuit ensures valid track ordering
4. BPM Constraints: Only create edges between BPM-compatible tracks
5. Harmonic Soft Constraints: Penalize non-harmonic transitions
6. Objective: Maximize sum(included)

License

MIT

Credits

Built with:

• Google OR-Tools - Constraint programming solver
• pyrekordbox - Rekordbox database access
• Camelot Wheel system by Mark Davis (Mixed In Key)