Getting Started

What Is 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.

TOON combines YAML's indentation-based structure for nested objects with CSV-style tabular forms for uniform data. Its sweet spot is uniform objects – same fields across items, whether in an array or keyed by ID – reaching CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably.

Think of it as a translation layer: use JSON programmatically, and encode it as TOON for LLM input – a drop-in, lossless representation of the JSON you already have.

Why TOON?

LLM tokens cost money – and standard JSON is verbose. A weather forecast in TOON:

location:
  city: Berlin
  country: DE
  units: metric
alerts[2]: frost,wind
forecast[3]{day,temp{min,max},condition,rainChance}:
  Mon,-2,4,snow,80
  Tue,1,7,cloudy,20
  Wed,3,11,sunny,5

The same data as JSON – ~117 tokens against TOON's ~66:

{
  "location": {
    "city": "Berlin",
    "country": "DE",
    "units": "metric"
  },
  "alerts": [
    "frost",
    "wind"
  ],
  "forecast": [
    {
      "day": "Mon",
      "temp": {
        "min": -2,
        "max": 4
      },
      "condition": "snow",
      "rainChance": 80
    },
    {
      "day": "Tue",
      "temp": {
        "min": 1,
        "max": 7
      },
      "condition": "cloudy",
      "rainChance": 20
    },
    {
      "day": "Wed",
      "temp": {
        "min": 3,
        "max": 11
      },
      "condition": "sunny",
      "rainChance": 5
    }
  ]
}

TOON combines YAML's indentation for the location object, inline form for the primitive alerts array, and tabular form for the forecast array: [3] declares the array length (letting LLMs answer dataset-size questions and detect truncation), {day,…} declares the field names once, and each row streams comma-separated values. The uniform nested temp objects fold into the header as a nested field group (temp{min,max}) while rows stay flat. Each form is chosen automatically from the data's shape.

The pattern is the same throughout TOON: declare structure once, stream data compactly – landing close to CSV density with explicit structure preserved.

Maps of uniform objects collapse as well: the keyed tabular form turns them into tables whose rows carry their own keys.

Design Goals

TOON is optimized for specific use cases. It aims to:

• Make uniform arrays of objects as compact as possible by declaring structure once and streaming data.
• Stay fully lossless and deterministic – round-trips preserve all data and structure.
• Keep parsing simple and robust for both LLMs and humans through explicit structure markers.
• Provide validation guardrails (array lengths, field counts) that help detect truncation and malformed output.

When to Use TOON

TOON excels with uniform arrays of objects – data with the same structure across items. For LLM prompts, the format produces deterministic, minimally quoted text with built-in validation. Explicit array lengths ([N]) and field lists ({fields}) help detect truncation and malformed data, while tabular form declares the field list once rather than repeating it in every row.

::: 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. :::

When Not to Use TOON

TOON is not always the best choice. Consider alternatives when:

• Deeply nested or non-uniform structures (tabular eligibility ≈ 0%): JSON-compact often uses fewer tokens. Example: complex configuration objects with many nested levels.
• Semi-uniform arrays (~40–60% tabular eligibility): Token savings diminish. Prefer JSON if your pipelines already rely on it.
• Pure tabular data: CSV is smaller than TOON for flat tables. TOON adds minimal overhead (~5–10%) to provide structure (array length declarations, field lists, delimiter scoping) that improves LLM reliability.
• Latency-critical applications: Benchmark on your exact setup. Some deployments (especially local/quantized models) may process compact JSON faster despite TOON's lower token count.

::: info For data-driven comparisons across different structures, see Benchmarks. When optimizing for latency, measure TTFT, tokens/sec, and total time for both TOON and JSON-compact, and use whichever is faster in your specific environment. :::

Installation

TypeScript Library

Install the library via your preferred package manager:

::: code-group

npm install @toon-format/toon

pnpm add @toon-format/toon

yarn add @toon-format/toon

:::

CLI

The CLI can be used without installation via npx, or installed globally:

::: code-group

npx @toon-format/cli input.json -o output.toon

npm install -g @toon-format/cli

pnpm add -g @toon-format/cli

yarn global add @toon-format/cli

:::

For full CLI documentation, see the CLI reference.

Media Type & File Extension

TOON files conventionally use the .toon extension. For HTTP transmission, the provisional media type is text/toon, always with UTF-8 encoding. While you may specify charset=utf-8 explicitly, it's optional – UTF-8 is the default assumption. This follows the registration process outlined in spec §17.

Your First Example

The examples below use the TypeScript library for demonstration, but the same operations work in any language with a TOON implementation.

Let's encode a simple dataset with the TypeScript library:

import { encode } from '@toon-format/toon'

const data = {
  users: [
    { id: 1, name: 'Ada', role: 'admin' },
    { id: 2, name: 'Bob', role: 'user' }
  ]
}

console.log(encode(data))

Output:

users[2]{id,name,role}:
  1,Ada,admin
  2,Bob,user

Decoding Back to JSON

Decoding is just as simple:

import { decode } from '@toon-format/toon'

const toon = `
users[2]{id,name,role}:
  1,Ada,admin
  2,Bob,user
`

const data = decode(toon)
console.log(JSON.stringify(data, null, 2))

Output:

{
  "users": [
    { "id": 1, "name": "Ada", "role": "admin" },
    { "id": 2, "name": "Bob", "role": "user" }
  ]
}

Round-tripping is lossless: decode(encode(x)) always equals x (after normalization of non-JSON types like Date, NaN, etc.).

Where to Go Next

Now that you've seen your first TOON document, read the Format Overview for complete syntax details (objects, arrays, tabular forms, quoting rules), then explore Using TOON with LLMs to see how to use it effectively in prompts. For implementation details, check the API Reference (TypeScript) or the Specification (language-agnostic normative rules).