Token-Oriented Object Notation is a compact, human-readable format for serializing JSON data in LLM prompts. It represents the same objects, arrays, and primitives as JSON, but in a syntax that minimizes tokens and makes structure easy for models to follow.

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.

• Why TOON?
• Key Features
• When Not to Use TOON
• Benchmarks
• Playgrounds
• 📋 Full Specification
• Installation & Quick Start
• CLI
• Format Overview
• API
• Using TOON in LLM Prompts
• Notes and Limitations
• Syntax Cheatsheet
• Other Implementations

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:

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

YAML conveys the same infromation with fewer tokens:

users:
  - id: 1
    name: Alice
    role: admin
  - id: 2
    name: Bob
    role: user

TOON conveys the same information with even fewer tokens:

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

• 💸 Token-efficient: typically 30-60% fewer tokens on large uniform arrays vs formatted JSON¹
• 🤿 LLM-friendly guardrails: explicit lengths and fields enable validation
• 🍱 Minimal syntax: removes redundant punctuation (braces, brackets, most quotes)
• 📐 Indentation-based structure: like YAML, uses whitespace instead of braces
• 🧺 Tabular arrays: declare keys once, stream data as rows
• 🔗 Optional key folding: collapses single-key wrapper chains into dotted paths (e.g., data.metadata.items) to reduce indentation and tokens

TOON excels with uniform arrays of objects, but there are cases where other formats are better:

• 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 headers, delimiter scoping) that improves LLM reliability.
• Latency-critical applications: If end-to-end response time is your top priority, benchmark on your exact setup. Some deployments (especially local/quantized models like Ollama) may process compact JSON faster despite TOON's lower token count. Measure TTFT, tokens/sec, and total time for both formats and use whichever is faster.

See benchmarks for concrete comparisons across different data structures.

Benchmarks are organized into two tracks to ensure fair comparisons:

• Mixed-Structure Track: Datasets with nested or semi-uniform structures (TOON vs JSON, YAML, XML). CSV excluded as it cannot properly represent these structures.
• Flat-Only Track: Datasets with flat tabular structures where CSV is applicable (CSV vs TOON vs JSON, YAML, XML).

Benchmarks test LLM comprehension across different input formats using 209 data retrieval questions on 4 models.

Each format's overall performance, balancing accuracy against token cost:

TOON           ████████████████████   26.9  │  73.9% acc  │  2,744 tokens
JSON compact   █████████████████░░░   22.9  │  70.7% acc  │  3,081 tokens
YAML           ██████████████░░░░░░   18.6  │  69.0% acc  │  3,719 tokens
JSON           ███████████░░░░░░░░░   15.3  │  69.7% acc  │  4,545 tokens
XML            ██████████░░░░░░░░░░   13.0  │  67.1% acc  │  5,167 tokens

TOON achieves 73.9% accuracy (vs JSON's 69.7%) while using 39.6% fewer tokens.

Note on CSV: Excluded from ranking as it only supports 109 of 209 questions (flat tabular data only). While CSV is highly token-efficient for simple tabular data, it cannot represent nested structures that other formats handle.

Accuracy across 4 LLMs on 209 data retrieval questions:

claude-haiku-4-5-20251001
→ TOON           ████████████░░░░░░░░    59.8% (125/209)
  JSON           ███████████░░░░░░░░░    57.4% (120/209)
  YAML           ███████████░░░░░░░░░    56.0% (117/209)
  XML            ███████████░░░░░░░░░    55.5% (116/209)
  JSON compact   ███████████░░░░░░░░░    55.0% (115/209)
  CSV            ██████████░░░░░░░░░░    50.5% (55/109)

gemini-2.5-flash
→ TOON           ██████████████████░░    87.6% (183/209)
  CSV            █████████████████░░░    86.2% (94/109)
  JSON compact   ████████████████░░░░    82.3% (172/209)
  YAML           ████████████████░░░░    79.4% (166/209)
  XML            ████████████████░░░░    79.4% (166/209)
  JSON           ███████████████░░░░░    77.0% (161/209)

gpt-5-nano
→ TOON           ██████████████████░░    90.9% (190/209)
  JSON compact   ██████████████████░░    90.9% (190/209)
  JSON           ██████████████████░░    89.0% (186/209)
  CSV            ██████████████████░░    89.0% (97/109)
  YAML           █████████████████░░░    87.1% (182/209)
  XML            ████████████████░░░░    80.9% (169/209)

grok-4-fast-non-reasoning
→ TOON           ███████████░░░░░░░░░    57.4% (120/209)
  JSON           ███████████░░░░░░░░░    55.5% (116/209)
  JSON compact   ███████████░░░░░░░░░    54.5% (114/209)
  YAML           ███████████░░░░░░░░░    53.6% (112/209)
  XML            ███████████░░░░░░░░░    52.6% (110/209)
  CSV            ██████████░░░░░░░░░░    52.3% (57/109)

Key tradeoff: TOON achieves 73.9% accuracy (vs JSON's 69.7%) while using 39.6% fewer tokens on these datasets.

Token counts are measured using the GPT-5 o200k_base tokenizer via gpt-tokenizer. Savings are calculated against formatted JSON (2-space indentation) as the primary baseline, with additional comparisons to compact JSON (minified), YAML, and XML. Actual savings vary by model and tokenizer.

The benchmarks test datasets across different structural patterns (uniform, semi-uniform, nested, deeply nested) to show where TOON excels and where other formats may be better.

Datasets with nested or semi-uniform structures. CSV excluded as it cannot properly represent these structures.

🛒 E-commerce orders with nested structures  ┊  Tabular: 33%
   │
   TOON                █████████████░░░░░░░    72,771 tokens
   ├─ vs JSON          (−33.1%)               108,806 tokens
   ├─ vs JSON compact  (+5.5%)                 68,975 tokens
   ├─ vs YAML          (−14.2%)                84,780 tokens
   └─ vs XML           (−40.5%)               122,406 tokens

🧾 Semi-uniform event logs  ┊  Tabular: 50%
   │
   TOON                █████████████████░░░   153,211 tokens
   ├─ vs JSON          (−15.0%)               180,176 tokens
   ├─ vs JSON compact  (+19.9%)               127,731 tokens
   ├─ vs YAML          (−0.8%)                154,505 tokens
   └─ vs XML           (−25.2%)               204,777 tokens

🧩 Deeply nested configuration  ┊  Tabular: 0%
   │
   TOON                ██████████████░░░░░░       631 tokens
   ├─ vs JSON          (−31.3%)                   919 tokens
   ├─ vs JSON compact  (+11.9%)                   564 tokens
   ├─ vs YAML          (−6.2%)                    673 tokens
   └─ vs XML           (−37.4%)                 1,008 tokens

──────────────────────────────────── Total ────────────────────────────────────
   TOON                ████████████████░░░░   226,613 tokens
   ├─ vs JSON          (−21.8%)               289,901 tokens
   ├─ vs JSON compact  (+14.9%)               197,270 tokens
   ├─ vs YAML          (−5.6%)                239,958 tokens
   └─ vs XML           (−31.0%)               328,191 tokens

Datasets with flat tabular structures where CSV is applicable.

👥 Uniform employee records  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░    46,954 tokens
   TOON                ████████████████████    49,831 tokens   (+6.1% vs CSV)
   ├─ vs JSON          (−60.7%)               126,860 tokens
   ├─ vs JSON compact  (−36.8%)                78,856 tokens
   ├─ vs YAML          (−50.0%)                99,706 tokens
   └─ vs XML           (−66.0%)               146,444 tokens

📈 Time-series analytics data  ┊  Tabular: 100%
   │
   CSV                 ██████████████████░░     8,388 tokens
   TOON                ████████████████████     9,120 tokens   (+8.7% vs CSV)
   ├─ vs JSON          (−59.0%)                22,250 tokens
   ├─ vs JSON compact  (−35.8%)                14,216 tokens
   ├─ vs YAML          (−48.9%)                17,863 tokens
   └─ vs XML           (−65.7%)                26,621 tokens

⭐ Top 100 GitHub repositories  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░     8,513 tokens
   TOON                ████████████████████     8,745 tokens   (+2.7% vs CSV)
   ├─ vs JSON          (−42.3%)                15,145 tokens
   ├─ vs JSON compact  (−23.7%)                11,455 tokens
   ├─ vs YAML          (−33.4%)                13,129 tokens
   └─ vs XML           (−48.8%)                17,095 tokens

──────────────────────────────────── Total ────────────────────────────────────
   CSV                 ███████████████████░    63,855 tokens
   TOON                ████████████████████    67,696 tokens   (+6.0% vs CSV)
   ├─ vs JSON          (−58.8%)               164,255 tokens
   ├─ vs JSON compact  (−35.2%)               104,527 tokens
   ├─ vs YAML          (−48.2%)               130,698 tokens
   └─ vs XML           (−64.4%)               190,160 tokens

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  "metrics": [
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  2025-01-04,2927,77,11,1211.69,0.62
  2025-01-05,3530,82,8,462.77,0.56

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Try TOON instantly with npx:

# Convert JSON to TOON
npx @toon-format/cli input.json -o output.toon

# Pipe from stdin
echo '{"name": "Ada", "role": "dev"}' | npx @toon-format/cli

See CLI section for all options and examples.

# npm
npm install @toon-format/toon

# pnpm
pnpm add @toon-format/toon

# yarn
yarn add @toon-format/toon

Example usage:

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

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

console.log(encode(data))
// users[2]{id,name,role}:
//   1,Alice,admin
//   2,Bob,user

Experiment with TOON format interactively using these community-built tools for token comparison, format conversion, and validation:

• Format Tokenization Playground
• TOON Tools

Command-line tool for converting between JSON and TOON formats.

npx @toon-format/cli [options] [input]

Standard input: Omit the input argument or use - to read from stdin. This enables piping data directly from other commands.

Auto-detection: The CLI automatically detects the operation based on file extension (.json → encode, .toon → decode). When reading from stdin, use --encode or --decode flags to specify the operation (defaults to encode).

# Encode JSON to TOON (auto-detected)
npx @toon-format/cli input.json -o output.toon

# Decode TOON to JSON (auto-detected)
npx @toon-format/cli data.toon -o output.json

# Output to stdout
npx @toon-format/cli input.json

# Pipe from stdin (no argument needed)
cat data.json | npx @toon-format/cli
echo '{"name": "Ada"}' | npx @toon-format/cli

# Explicit stdin with hyphen (equivalent to above)
cat data.json | npx @toon-format/cli -

# Decode from stdin
cat data.toon | npx @toon-format/cli --decode

# Show token savings when encoding
npx @toon-format/cli data.json --stats -o output.toon

# Tab-separated output (often more token-efficient)
npx @toon-format/cli data.json --delimiter "\t" -o output.toon

# Pipe-separated output
npx @toon-format/cli data.json --delimiter "|" -o output.toon

# Lenient decoding (skip validation)
npx @toon-format/cli data.toon --no-strict -o output.json

# Key folding for nested data
npx @toon-format/cli data.json --key-folding safe -o output.toon

# Stdin workflows
echo '{"name": "Ada", "age": 30}' | npx @toon-format/cli --stats
cat large-dataset.json | npx @toon-format/cli --delimiter "\t" > output.toon

Simple objects with primitive values:

encode({
  id: 123,
  name: 'Ada',
  active: true
})

id: 123
name: Ada
active: true

Nested objects:

encode({
  user: {
    id: 123,
    name: 'Ada'
  }
})

user:
  id: 123
  name: Ada

New in spec v1.5: Optionally collapse single-key wrapper chains into dotted paths to reduce tokens. Enable with keyFolding: 'safe'.

Standard nesting:

data:
  metadata:
    items[2]: a,b

With key folding:

data.metadata.items[2]: a,b

Round-trip with path expansion:

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

const original = { data: { metadata: { items: ['a', 'b'] } } }

const toon = encode(original, { keyFolding: 'safe' })
// → "data.metadata.items[2]: a,b"

const restored = decode(toon, { expandPaths: 'safe' })
// → Matches original structure

See §13.4 in the specification for folding rules and safety guarantees.

encode({
  tags: ['admin', 'ops', 'dev']
})

tags[3]: admin,ops,dev

When all objects share the same primitive fields, TOON uses an efficient tabular format:

encode({
  items: [
    { sku: 'A1', qty: 2, price: 9.99 },
    { sku: 'B2', qty: 1, price: 14.5 }
  ]
})

items[2]{sku,qty,price}:
  A1,2,9.99
  B2,1,14.5

Tabular formatting applies recursively: nested arrays of objects (whether as object properties or inside list items) also use tabular format if they meet the same requirements.

encode({
  items: [
    {
      users: [
        { id: 1, name: 'Ada' },
        { id: 2, name: 'Bob' }
      ],
      status: 'active'
    }
  ]
})

items[1]:
  - users[2]{id,name}:
    1,Ada
    2,Bob
    status: active

Arrays that don't meet the tabular requirements use list format:

items[3]:
  - 1
  - a: 1
  - text

When objects appear in list format, the first field is placed on the hyphen line:

items[2]:
  - id: 1
    name: First
  - id: 2
    name: Second
    extra: true

When you have arrays containing primitive inner arrays:

encode({
  pairs: [
    [1, 2],
    [3, 4]
  ]
})

pairs[2]:
  - [2]: 1,2
  - [2]: 3,4

Empty containers have special representations:

encode({ items: [] }) // items[0]:
encode([]) // [0]:
encode({}) // (empty output)
encode({ config: {} }) // config:

TOON quotes strings only when necessary to maximize token efficiency:

• Inner spaces are allowed; leading or trailing spaces force quotes.
• Unicode and emoji are safe unquoted.
• Quotes and control characters are escaped with backslash.

Keys are unquoted if they match the identifier pattern: start with a letter or underscore, followed by letters, digits, underscores, or dots (e.g., id, userName, user_name, user.name, _private). All other keys must be quoted (e.g., "user name", "order-id", "123", "order:id", "").

String values are quoted when any of the following is true:

Examples of unquoted strings: Unicode and emoji are safe (hello 👋 world), as are strings with inner spaces (hello world).

Some non-JSON types are automatically normalized for LLM-safe output:

Converts any JSON-serializable value to TOON format.

Parameters:

• value – Any JSON-serializable value (object, array, primitive, or nested structure). Non-JSON-serializable values (functions, symbols, undefined, non-finite numbers) are converted to null. Dates are converted to ISO strings, and BigInts are emitted as decimal integers (no quotes).
• options – Optional encoding options:
  • indent?: number – Number of spaces per indentation level (default: 2)
  • delimiter?: ',' | '\t' | '|' – Delimiter for array values and tabular rows (default: ',')
  • keyFolding?: 'off' | 'safe' – Enable key folding to collapse single-key wrapper chains into dotted paths (default: 'off'). When 'safe', only valid identifier segments are folded
  • flattenDepth?: number – Maximum number of segments to fold when keyFolding is enabled (default: Infinity). Values 0-1 have no practical effect

Returns:

A TOON-formatted string with no trailing newline or spaces.

Example:

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

const items = [
  { sku: 'A1', qty: 2, price: 9.99 },
  { sku: 'B2', qty: 1, price: 14.5 }
]

encode({ items })

Output:

items[2]{sku,qty,price}:
  A1,2,9.99
  B2,1,14.5

The delimiter option allows you to choose between comma (default), tab, or pipe delimiters for array values and tabular rows. Alternative delimiters can provide additional token savings in specific contexts.

Using tab delimiters instead of commas can reduce token count further, especially for tabular data:

const data = {
  items: [
    { sku: 'A1', name: 'Widget', qty: 2, price: 9.99 },
    { sku: 'B2', name: 'Gadget', qty: 1, price: 14.5 }
  ]
}

encode(data, { delimiter: '\t' })

Output:

items[2	]{sku	name	qty	price}:
  A1	Widget	2	9.99
  B2	Gadget	1	14.5

Benefits:

• Tabs are single characters and often tokenize more efficiently than commas.
• Tabs rarely appear in natural text, reducing the need for quote-escaping.
• The delimiter is explicitly encoded in the array header, making it self-descriptive.

Considerations:

• Some terminals and editors may collapse or expand tabs visually.
• String values containing tabs will still require quoting.

Pipe delimiters offer a middle ground between commas and tabs:

encode(data, { delimiter: '|' })

Output:

items[2|]{sku|name|qty|price}:
  A1|Widget|2|9.99
  B2|Gadget|1|14.5

Converts a TOON-formatted string back to JavaScript values.

Parameters:

• input – A TOON-formatted string to parse
• options – Optional decoding options:
  • indent?: number – Expected number of spaces per indentation level (default: 2)
  • strict?: boolean – Enable strict validation (default: true)
  • expandPaths?: 'off' | 'safe' – Enable path expansion to reconstruct dotted keys into nested objects (default: 'off'). Pairs with keyFolding: 'safe' for lossless round-trips

Returns:

A JavaScript value (object, array, or primitive) representing the parsed TOON data.

Example:

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

const toon = `
items[2]{sku,qty,price}:
  A1,2,9.99
  B2,1,14.5
`

const data = decode(toon)
// {
//   items: [
//     { sku: 'A1', qty: 2, price: 9.99 },
//     { sku: 'B2', qty: 1, price: 14.5 }
//   ]
// }

Strict Mode:

By default, the decoder validates input strictly:

• Invalid escape sequences: Throws on "\x", unterminated strings.
• Syntax errors: Throws on missing colons, malformed headers.
• Array length mismatches: Throws when declared length doesn't match actual count.
• Delimiter mismatches: Throws when row delimiters don't match header.

• Format familiarity and structure matter as much as token count. TOON's tabular format requires arrays of objects with identical keys and primitive values only. When this doesn't hold (due to mixed types, non-uniform objects, or nested structures), TOON switches to list format where JSON can be more efficient at scale.
  • TOON excels at: Uniform arrays of objects (same fields, primitive values), especially large datasets with consistent structure.
  • JSON is better for: Non-uniform data, deeply nested structures, and objects with varying field sets.
  • CSV is more compact for: Flat, uniform tables without nesting. TOON adds structure ([N] array lengths, delimiter scoping, deterministic quoting) that improves LLM reliability with minimal token overhead.
• Token counts vary by tokenizer and model. Benchmarks use a GPT-style tokenizer (cl100k/o200k); actual savings will differ with other models (e.g., SentencePiece).
• TOON is designed for LLM input where human readability and token efficiency matter. It's not a drop-in replacement for JSON in APIs or storage.

TOON works best when you show the format instead of describing it. The structure is self-documenting – models parse it naturally once they see the pattern.

Wrap your encoded data in a fenced code block (label it ```toon for clarity). The indentation and headers are usually enough – models treat it like familiar YAML or CSV. The explicit array lengths ([N]) and field headers ({field1,field2}) help the model track structure, especially for large tables.

For output, be more explicit. When you want the model to generate TOON:

• Show the expected header (users[N]{id,name,role}:). The model fills rows instead of repeating keys, reducing generation errors.
• State the rules: 2-space indent, no trailing spaces, [N] matches row count.

Here's a prompt that works for both reading and generating:

Data is in TOON format (2-space indent, arrays show length and fields).

```toon
users[3]{id,name,role,lastLogin}:
  1,Alice,admin,2025-01-15T10:30:00Z
  2,Bob,user,2025-01-14T15:22:00Z
  3,Charlie,user,2025-01-13T09:45:00Z
```

Task: Return only users with role "user" as TOON. Use the same header. Set [N] to match the row count. Output only the code block.

// Object
{ id: 1, name: 'Ada' }          → id: 1
                                  name: Ada

// Nested object
{ user: { id: 1 } }             → user:
                                    id: 1

// Primitive array (inline)
{ tags: ['foo', 'bar'] }        → tags[2]: foo,bar

// Tabular array (uniform objects)
{ items: [                      → items[2]{id,qty}:
  { id: 1, qty: 5 },                1,5
  { id: 2, qty: 3 }                 2,3
]}

// Mixed / non-uniform (list)
{ items: [1, { a: 1 }, 'x'] }   → items[3]:
                                    - 1
                                    - a: 1
                                    - x

// Array of arrays
{ pairs: [[1, 2], [3, 4]] }     → pairs[2]:
                                    - [2]: 1,2
                                    - [2]: 3,4

// Root array
['x', 'y']                      → [2]: x,y

// Empty containers
{}                              → (empty output)
{ items: [] }                   → items[0]:

// Special quoting
{ note: 'hello, world' }        → note: "hello, world"
{ items: ['true', true] }       → items[2]: "true",true

• .NET: toon_format (in development)
• Dart: toon (in development)
• Go: gotoon (in development)
• Python: toon_format (in development)
• Rust: toon_format (in development)

• C++: ctoon
• Clojure: toon
• Crystal: toon-crystal
• Elixir: toon_ex
• Gleam: toon_codec
• Go: gotoon
• Java: JToon
• Scala: toon4s
• Lua/Neovim: toon.nvim
• OCaml: ocaml-toon
• PHP: toon-php
• R: toon
• Ruby: toon-ruby
• Swift: TOONEncoder
• Kotlin: Kotlin-Toon Encoder/Decoder

• Logo design by 鈴木ックス(SZKX)

MIT License © 2025-PRESENT Johann Schopplich