Token-Oriented Object Notation (TOON)

Token-Oriented Object Notation is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for LLM input as a drop-in, lossless representation of your existing JSON.

TOON combines YAML's indentation-based structure for nested objects with a CSV-style tabular layout for uniform arrays. TOON's sweet spot is uniform arrays of objects (multiple fields per row, same structure across items), achieving CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably. For deeply nested or non-uniform data, JSON may be more efficient.

The similarity to CSV is intentional: CSV is simple and ubiquitous, and TOON aims to keep that familiarity while remaining a lossless, drop-in representation of JSON for Large Language Models.

Think of it as a translation layer: use JSON programmatically, and encode it as TOON for LLM input.

[!TIP] The TOON format is stable, but also an idea in progress. Nothing's set in stone – help shape where it goes by contributing to the spec or sharing feedback.

Table of Contents

• Why TOON?
• Key Features
• When Not to Use TOON
• Benchmarks
• Installation & Quick Start
• Playgrounds
• Editor Support
• CLI
• Format Overview
• Using TOON with LLMs
• Documentation
• Other Implementations
• 📋 Full Specification

Why TOON?

AI is becoming cheaper and more accessible, but larger context windows allow for larger data inputs as well. LLM tokens still cost money – and standard JSON is verbose and token-expensive:

{
  "context": {
    "task": "Our favorite hikes together",
    "location": "Boulder",
    "season": "spring_2025"
  },
  "friends": ["ana", "luis", "sam"],
  "hikes": [
    {
      "id": 1,
      "name": "Blue Lake Trail",
      "distanceKm": 7.5,
      "elevationGain": 320,
      "companion": "ana",
      "wasSunny": true
    },
    {
      "id": 2,
      "name": "Ridge Overlook",
      "distanceKm": 9.2,
      "elevationGain": 540,
      "companion": "luis",
      "wasSunny": false
    },
    {
      "id": 3,
      "name": "Wildflower Loop",
      "distanceKm": 5.1,
      "elevationGain": 180,
      "companion": "sam",
      "wasSunny": true
    }
  ]
}

context:
  task: Our favorite hikes together
  location: Boulder
  season: spring_2025
friends:
  - ana
  - luis
  - sam
hikes:
  - id: 1
    name: Blue Lake Trail
    distanceKm: 7.5
    elevationGain: 320
    companion: ana
    wasSunny: true
  - id: 2
    name: Ridge Overlook
    distanceKm: 9.2
    elevationGain: 540
    companion: luis
    wasSunny: false
  - id: 3
    name: Wildflower Loop
    distanceKm: 5.1
    elevationGain: 180
    companion: sam
    wasSunny: true

TOON conveys the same information with even fewer tokens – combining YAML-like indentation with CSV-style tabular arrays:

context:
  task: Our favorite hikes together
  location: Boulder
  season: spring_2025
friends[3]: ana,luis,sam
hikes[3]{id,name,distanceKm,elevationGain,companion,wasSunny}:
  1,Blue Lake Trail,7.5,320,ana,true
  2,Ridge Overlook,9.2,540,luis,false
  3,Wildflower Loop,5.1,180,sam,true

Key Features

• 📊 Token-Efficient & Accurate: TOON reaches 76.4% accuracy (vs JSON's 75.0%) while using ~40% fewer tokens in mixed-structure benchmarks across 4 models.
• 🔁 JSON Data Model: Encodes the same objects, arrays, and primitives as JSON with deterministic, lossless round-trips.
• 🛤️ **LLM-Frie