@toon-format/toon

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 form for uniform arrays. Its sweet spot is uniform arrays of objects – multiple fields per row, same structure across items – reaching 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.

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
• CLI
• Using TOON with LLMs
• Ecosystem
• Documentation
• Media Type & File Extension
• 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:

{
  "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
    }
  ]
}

YAML already conveys the same information with fewer tokens.

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

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

Three things are happening at once. Two are forms – one rendering of a value, picked automatically from the data's shape – and the third is a header feature:

• alerts[2]: frost,wind is inline form: a primitive array on its header line.
• forecast[3]{day,…}: is tabular form: the field list is declared once in the header, then one row per element.
• temp{min,max} inside that header is a nested field group: the uniform nested temp objects fold into the header while rows stay flat.

The third form is keyed tabular, for objects whose values are uniform objects – config maps, feature flags, records by ID. The colon after the length ([2:]) marks it, and each row carries its own key:

JSON	TOON
{ "environments": { "production": { "region": "eu-central-1", "replicas": 6, "debug": false }, "staging": { "region": "eu-central-1", "replicas": 2, "debug": true } } }	environments[2:]{region,replicas,debug}: production: eu-central-1,6,false staging: eu-central-1,2,true

Anything that fits none of these – mixed types, non-uniform objects – falls back to the fourth form, list form: one - item per element, or a bare - for an empty object. Those four cover the shapes; the Format Overview covers the rest.

[!TIP] Try it on your own data – no install required:

cat data.json | npx @toon-format/cli --stats

It prints the TOON alongside what the conversion saved:

ℹ Token estimates: ~117 (JSON) → ~66 (TOON)
✔ Saved ~51 tokens (-43.6%)

Key Features

• 📊 Token-Efficient & Accurate: Matches JSON's retrieval accuracy while using 42.6% fewer tokens – see Benchmarks.
• 🔁 JSON Data Model: Encodes the same objects, arrays, and primitives as JSON with deterministic, lossless round-trips.
• 🛤️ LLM-Friendly Guardrails: Explicit [N] lengths and {fields} field lists give models a clear schema to follow, improving parsing reliability.
• 📐 Minimal Syntax: Uses indentation instead of braces and minimizes quoting, giving YAML-like readability with CSV-style compactness.
• 🧺 Tabular Forms: Uniform arrays of objects – and objects of uniform objects – collapse into tables that declare the field list once and stream row values line by line.
• 🌐 Multi-Language Ecosystem: Spec-driven implementations in TypeScript, Python, Go, Rust, .NET, and other languages.

When Not to Use TOON

TOON excels with uniform arrays of objects. Reach for something else when:

• Structures are deeply nested or non-uniform (tabular eligibility ≈ 0%) – compact JSON often wins outright.
• Arrays are semi-uniform (~40–60% eligibility) – savings shrink; stay on JSON if your pipeline already speaks it.
• Data is purely tabular – CSV is smaller. TOON's ~5–10% overhead buys declared lengths, field lists, and delimiter scoping, which is a reliability trade, not a size one.
• Latency dominates – some deployments (notably local or quantized models) process compact JSON faster despite the higher token count. Measure TTFT and total time on your own setup.

Benchmarks below quantify the token and accuracy trade-offs; latency is the one you have to measure yourself.

Benchmarks

Two tracks, so every comparison is like-for-like:

• Mixed-Structure Track: Nested and semi-uniform datasets (TOON vs JSON, YAML, XML). CSV is excluded – it cannot represent these structures without lossy flattening.
• Flat-Only Track: Flat, fully tabular-eligible datasets, where CSV is a fair competitor.

Retrieval Accuracy

Benchmarks test LLM comprehension across different input formats using 244 data retrieval questions on 4 models.

Show Dataset Catalog

Dataset Catalog

Dataset	Rows	Structure	CSV Support	Eligibility
Uniform employee records	100	uniform	✓	100%
E-commerce orders with nested structures	50	nested	✗	33%
Time-series analytics data	60	uniform	✓	100%
Top 100 GitHub repositories	100	uniform	✓	100%
Semi-uniform event logs	75	semi-uniform	✗	50%
Deeply nested configuration	1	deep	✗	0%
Valid complete dataset (control)	20	uniform	✓	100%
Array truncated: 3 rows removed from end	20	uniform	✓	100%
Extra rows added beyond declared length	20	uniform	✓	100%
Inconsistent field count (missing salary in row 10)	20	uniform	✓	100%
Missing required fields (no email in multiple rows)	20	uniform	✓	100%
Feature flags keyed by name	40	uniform	✗	100%
Contacts with nested address and plan groups	50	nested	✗	100%

Structure classes:

• uniform: All objects have identical fields with primitive values
• semi-uniform: Mix of uniform and non-uniform structures
• nested: Objects with nested structures (nested objects or arrays)
• deep: Highly nested with minimal tabular eligibility

CSV Support: ✓ (supported), ✗ (not supported – would require lossy flattening)

Eligibility: Percentage of arrays that qualify for TOON's tabular form (uniform objects with primitive values)

Efficiency Ranking (Accuracy per 1K Tokens)

Each format ranked by efficiency (accuracy percentage per 1,000 tokens):

TOON           ████████████████████   29.2 acc%/1K tok  │  72.2%  ±2.8 acc  │  2,474 tokens
JSON compact   ████████████████░░░░   23.8 acc%/1K tok  │  69.0%  ±2.9 acc  │  2,892 tokens
YAML           ██████████████░░░░░░   20.1 acc%/1K tok  │  70.1%  ±2.9 acc  │  3,487 tokens
JSON           ███████████░░░░░░░░░   16.6 acc%/1K tok  │  71.4%  ±2.8 acc  │  4,308 tokens
XML            ██████████░░░░░░░░░░   14.4 acc%/1K tok  │  70.7%  ±2.9 acc  │  4,909 tokens

Efficiency score = (Accuracy % ÷ Tokens) × 1,000. Higher is better.

[!TIP] TOON achieves 72.2% accuracy (vs JSON's 71.4%) while using 42.6% fewer tokens.

[!NOTE] CSV is excluded from the ranking as it only supports 109 of 244 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 on Flat Datasets

Every format answers the same 109 flat-dataset questions per model, so CSV can be compared on equal footing here.

Format	Accuracy	Correct/Total	Avg Tokens
toon	63.1% ±4.5	275/436	1,994
csv	62.2% ±4.5	271/436	1,851
json-pretty	60.3% ±4.6	263/436	3,950
xml	60.1% ±4.6	262/436	4,516
yaml	59.9% ±4.6	261/436	3,270
json-compact	58.0% ±4.6	253/436	2,718

Per-Model Accuracy

Accuracy across 4 LLMs on 244 data retrieval questions:

claude-haiku-4-5-20251001
→ TOON           █████████████░░░░░░░    65.6% ±5.9 (160/244)
  JSON           █████████████░░░░░░░    63.5% ±6.0 (155/244)
  XML            ████████████░░░░░░░░    62.3% ±6.0 (152/244)
  YAML           ████████████░░░░░░░░    62.3% ±6.0 (152/244)
  JSON compact   ████████████░░░░░░░░    61.9% ±6.0 (151/244)
  CSV            ██████████░░░░░░░░░░    49.5% ±9.2 (54/109)

gemini-3.6-flash
→ TOON           ██████████████░░░░░░    69.3% ±5.8 (169/244)
  JSON           ██████████████░░░░░░    68.4% ±5.8 (167/244)
  YAML           ██████████████░░░░░░    67.6% ±5.8 (165/244)
  XML            █████████████░░░░░░░    65.2% ±5.9 (159/244)
  JSON compact   █████████████░░░░░░░    63.5% ±6.0 (155/244)
  CSV            ████████████░░░░░░░░    57.8% ±9.1 (63/109)

gpt-5.4-nano
  XML            ████████████░░░░░░░░    59.4% ±6.1 (145/244)
  JSON           ███████████░░░░░░░░░    57.4% ±6.2 (140/244)
→ TOON           ███████████░░░░░░░░░    57.0% ±6.2 (139/244)
  JSON compact   ███████████░░░░░░░░░    54.9% ±6.2 (134/244)
  YAML           ███████████░░░░░░░░░    54.5% ±6.2 (133/244)
  CSV            █████████░░░░░░░░░░░    46.8% ±9.2 (51/109)

grok-4.5
→ TOON           ███████████████████░    97.1% ±2.2 (237/244)
  JSON           ███████████████████░    96.3% ±2.5 (235/244)
  XML            ███████████████████░    95.9% ±2.6 (234/244)
  YAML           ███████████████████░    95.9% ±2.6 (234/244)
  JSON compact   ███████████████████░    95.5% ±2.7 (233/244)
  CSV            ███████████████████░    94.5% ±4.5 (103/109)

[!NOTE] Accuracy figures include Wilson 95% confidence intervals (±); when two formats' intervals overlap, the difference between them is not statistically meaningful. CSV answers only the 109 flat-dataset questions, so its per-model cells cover a smaller, easier population than the other formats.

Performance by dataset and question type

Performance by Question Type

Question Type	TOON	JSON	XML	YAML	JSON compact	CSV
Field Retrieval	97.8%	99.2%	99.2%	99.7%	98.9%	100.0%
Aggregation	48.4%	48.4%	46.0%	46.0%	45.2%	32.8%
Filtering	38.0%	41.1%	37.5%	40.1%	38.0%	33.3%
Structure Awareness	90.3%	84.0%	84.0%	79.2%	78.5%	82.8%
Structural Validation	100.0%	50.0%	80.0%	50.0%	45.0%	80.0%

Performance by Dataset

Uniform employee records

Format	Accuracy	Tokens	Correct/Total
csv	64.6%	2,336	106/164
toon	62.8%	2,537	103/164
json-compact	62.2%	3,919	102/164
yaml	64.0%	4,982	105/164
json-pretty	62.2%	6,326	102/164
xml	61.0%	7,286	100/164

E-commerce orders with nested structures

Format	Accuracy	Tokens	Correct/Total
json-compact	70.7%	6,875	116/164
toon	71.3%	7,344	117/164
yaml	72.0%	8,456	118/164
json-pretty	71.3%	10,842	117/164
xml	74.4%	12,180	122/164

Time-series analytics data

Format	Accuracy	Tokens	Correct/Total
csv	64.2%	1,408	77/120
toon	63.3%	1,595	76/120
json-compact	59.2%	2,351	71/120
yaml	62.5%	2,951	75/120
json-pretty	65.0%	3,678	78/120
xml	62.5%	4,386	75/120

Top 100 GitHub repositories

Format	Accuracy	Tokens	Correct/Total
toon	57.6%	9,017	76/132
csv	54.5%	8,726	72/132
json-compact	53.8%	11,650	71/132
yaml	53.8%	13,350	71/132
json-pretty	55.3%	15,350	73/132
xml	53.8%	17,304	71/132

Semi-uniform event logs

Format	Accuracy	Tokens	Correct/Total
json-compact	56.7%	4,793	68/120
toon	60.8%	5,814	73/120
json-pretty	60.0%	6,759	72/120
yaml	55.0%	5,798	66/120
xml	50.8%	7,668	61/120

Deeply nested configuration

Format	Accuracy	Tokens	Correct/Total
json-compact	91.4%	562	106/116
yaml	93.1%	675	108/116
toon	91.4%	669	106/116
json-pretty	94.8%	918	110/116
xml	94.0%	1,007	109/116

Valid complete dataset (control)

Format	Accuracy	Tokens	Correct/Total
toon	100.0%	566	4/4
json-compact	100.0%	772	4/4
yaml	100.0%	984	4/4
json-pretty	100.0%	1,259	4/4
xml	0.0%	1,441	0/4
csv	0.0%	473	0/4

Array truncated: 3 rows removed from end

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	408	4/4
toon	100.0%	498	4/4
xml	100.0%	1,229	4/4
json-pretty	0.0%	1,075	0/4
yaml	0.0%	841	0/4
json-compact	0.0%	660	0/4

Extra rows added beyond declared length

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	547	4/4
toon	100.0%	644	4/4
xml	100.0%	1,663	4/4
json-pretty	0.0%	1,452	0/4
yaml	0.0%	1,135	0/4
json-compact	0.0%	893	0/4

Inconsistent field count (missing salary in row 10)

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	470	4/4
toon	100.0%	563	4/4
json-compact	75.0%	767	3/4
xml	100.0%	1,432	4/4
yaml	75.0%	977	3/4
json-pretty	75.0%	1,251	3/4

Missing required fields (no email in multiple rows)

Format	Accuracy	Tokens	Correct/Total
csv	100.0%	442	4/4
toon	100.0%	535	4/4
xml	100.0%	1,386	4/4
yaml	75.0%	941	3/4
json-pretty	75.0%	1,207	3/4
json-compact	50.0%	732	2/4

Feature flags keyed by name

Format	Accuracy	Tokens	Correct/Total
toon	97.1%	931	66/68
json-compact	94.1%	1,264	64/68
yaml	92.6%	1,443	63/68
json-pretty	95.6%	1,873	65/68
xml	95.6%	2,306	65/68

Contacts with nested address and plan groups

Format	Accuracy	Tokens	Correct/Total
toon	94.4%	1,444	68/72
json-compact	91.7%	2,357	66/72
yaml	94.4%	2,797	68/72
json-pretty	97.2%	4,014	70/72
xml	98.6%	4,534	71/72

Run Configuration

• Models tested: claude-haiku-4-5-20251001, gemini-3.6-flash, gpt-5.4-nano, grok-4.5
• Formats compared: TOON, JSON, XML, YAML, JSON compact, CSV
• Token counting: Using gpt-tokenizer with o200k_base encoding (GPT-5 tokenizer). Other providers tokenize differently, so absolute counts are tokenizer-specific; relative differences between formats hold directionally.
• Reasoning: Disabled via the AI SDK's universal reasoning: 'none' (Gemini 3 floors at minimal thinking, grok-4.5 at low)
• Temperature: Not set (models use their defaults)
• Total evaluations: 244 questions × 6 formats × 4 models = 5,856 LLM calls

What the datasets contain, how the questions are generated, and how answers are validated is documented in the benchmark README.

Token Efficiency

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.

Mixed-Structure Track

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                █████████████░░░░░░░    72,832 tokens
   ├─ vs JSON          (−32.9%)               108,611 tokens
   ├─ vs JSON compact  (+5.6%)                 68,944 tokens
   ├─ vs YAML          (−14.0%)                84,701 tokens
   └─ vs XML           (−40.4%)               122,119 tokens

🧾 Semi-uniform event logs  ┊  Tabular: 50%
   │
   TOON                █████████████████░░░   154,084 tokens
   ├─ vs JSON          (−15.0%)               181,201 tokens
   ├─ vs JSON compact  (+19.9%)               128,529 tokens
   ├─ vs YAML          (−0.8%)                155,397 tokens
   └─ vs XML           (−25.2%)               205,859 tokens

🧩 Deeply nested configuration  ┊  Tabular: 0%
   │
   TOON                █████████████░░░░░░░       589 tokens
   ├─ vs JSON          (−34.9%)                   905 tokens
   ├─ vs JSON compact  (+6.7%)                    552 tokens
   ├─ vs YAML          (−11.0%)                   662 tokens
   └─ vs XML           (−40.9%)                   997 tokens

📊 Feature flags keyed by name  ┊  Tabular: 100%
   │
   TOON                █████████░░░░░░░░░░░    10,503 tokens
   ├─ vs JSON          (−54.6%)                23,141 tokens
   ├─ vs JSON compact  (−32.8%)                15,635 tokens
   ├─ vs YAML          (−41.3%)                17,905 tokens
   └─ vs XML           (−63.3%)                28,655 tokens

📊 Contacts with nested address and plan groups  ┊  Tabular: 100%
   │
   TOON                ███████░░░░░░░░░░░░░    26,726 tokens
   ├─ vs JSON          (−66.5%)                79,779 tokens
   ├─ vs JSON compact  (−42.9%)                46,791 tokens
   ├─ vs YAML          (−51.8%)                55,475 tokens
   └─ vs XML           (−70.4%)                90,306 tokens

──────────────────────────────────── Total ────────────────────────────────────
   TOON                █████████████░░░░░░░   264,734 tokens
   ├─ vs JSON          (−32.7%)               393,637 tokens
   ├─ vs JSON compact  (+1.6%)                260,451 tokens
   ├─ vs YAML          (−15.7%)               314,140 tokens
   └─ vs XML           (−40.9%)               447,936 tokens

Flat-Only Track

Datasets with flat, fully tabular-eligible data where CSV is applicable.

👥 Uniform employee records  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░    47,153 tokens
   TOON                ████████████████████    49,978 tokens   (+6.0% vs CSV)
   ├─ vs JSON          (−60.7%)               127,061 tokens
   ├─ vs JSON compact  (−36.8%)                79,057 tokens
   ├─ vs YAML          (−50.0%)               100,054 tokens
   └─ vs XML           (−65.9%)               146,605 tokens

📈 Time-series analytics data  ┊  Tabular: 100%
   │
   CSV                 ██████████████████░░     8,383 tokens
   TOON                ████████████████████     9,115 tokens   (+8.7% vs CSV)
   ├─ vs JSON          (−59.0%)                22,245 tokens
   ├─ vs JSON compact  (−35.9%)                14,211 tokens
   ├─ vs YAML          (−49.0%)                17,858 tokens
   └─ vs XML           (−65.8%)                26,616 tokens

⭐ Top 100 GitHub repositories  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░     8,711 tokens
   TOON                ████████████████████     8,937 tokens   (+2.6% vs CSV)
   ├─ vs JSON          (−41.7%)                15,337 tokens
   ├─ vs JSON compact  (−23.2%)                11,640 tokens
   ├─ vs YAML          (−33.0%)                13,337 tokens
   └─ vs XML           (−48.3%)                17,294 tokens

──────────────────────────────────── Total ────────────────────────────────────
   CSV                 ███████████████████░    64,247 tokens
   TOON                ████████████████████    68,030 tokens   (+5.9% vs CSV)
   ├─ vs JSON          (−58.7%)               164,643 tokens
   ├─ vs JSON compact  (−35.2%)               104,908 tokens
   ├─ vs YAML          (−48.2%)               131,249 tokens
   └─ vs XML           (−64.3%)               190,515 tokens

Token counts use gpt-tokenizer with o200k_base encoding (GPT-5 tokenizer). Other providers tokenize differently, so absolute counts are tokenizer-specific; relative differences between formats hold directionally.

Show detailed examples

📈 Time-series analytics data

Savings: 13,130 tokens (59.0% reduction vs JSON)

JSON (22,245 tokens):

{
  "metrics": [
    {
      "date": "2025-01-01",
      "views": 6138,
      "clicks": 174,
      "conversions": 12,
      "revenue": 2712.49,
      "bounceRate": 0.35
    },
    {
      "date": "2025-01-02",
      "views": 4616,
      "clicks": 274,
      "conversions": 34,
      "revenue": 9156.29,
      "bounceRate": 0.56
    },
    {
      "date": "2025-01-03",
      "views": 4460,
      "clicks": 143,
      "conversions": 8,
      "revenue": 1317.98,
      "bounceRate": 0.59
    },
    {
      "date": "2025-01-04",
      "views": 4740,
      "clicks": 125,
      "conversions": 13,
      "revenue": 2934.77,
      "bounceRate": 0.37
    },
    {
      "date": "2025-01-05",
      "views": 6428,
      "clicks": 369,
      "conversions": 19,
      "revenue": 1317.24,
      "bounceRate": 0.3
    }
  ]
}

TOON (9,115 tokens):

metrics[5]{date,views,clicks,conversions,revenue,bounceRate}:
  2025-01-01,6138,174,12,2712.49,0.35
  2025-01-02,4616,274,34,9156.29,0.56
  2025-01-03,4460,143,8,1317.98,0.59
  2025-01-04,4740,125,13,2934.77,0.37
  2025-01-05,6428,369,19,1317.24,0.3

⭐ Top 100 GitHub repositories

Savings: 6,400 tokens (41.7% reduction vs JSON)

JSON (15,337 tokens):

{
  "repositories": [
    {
      "id": 132750724,
      "name": "build-your-own-x",
      "repo": "codecrafters-io/build-your-own-x",
      "description": "Master programming by recreating your favorite technologies from scratch.",
      "createdAt": "2018-05-09T12:03:18Z",
      "updatedAt": "2026-07-23T18:57:15Z",
      "pushedAt": "2026-07-14T19:25:58Z",
      "stars": 530712,
      "watchers": 6778,
      "forks": 50205,
      "defaultBranch": "master"
    },
    {
      "id": 21737465,
      "name": "awesome",
      "repo": "sindresorhus/awesome",
      "description": "😎 Awesome lists about all kinds of interesting topics",
      "createdAt": "2014-07-11T13:42:37Z",
      "updatedAt": "2026-07-23T18:57:24Z",
      "pushedAt": "2026-06-30T18:21:16Z",
      "stars": 488074,
      "watchers": 8292,
      "forks": 36010,
      "defaultBranch": "main"
    },
    {
      "id": 28457823,
      "name": "freeCodeCamp",
      "repo": "freeCodeCamp/freeCodeCamp",
      "description": "freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…",
      "createdAt": "2014-12-24T17:49:19Z",
      "updatedAt": "2026-07-22T07:01:33Z",
      "pushedAt": "2026-07-21T18:00:51Z",
      "stars": 452380,
      "watchers": 8590,
      "forks": 45624,
      "defaultBranch": "main"
    }
  ]
}

TOON (8,937 tokens):

repositories[3]{id,name,repo,description,createdAt,updatedAt,pushedAt,stars,watchers,forks,defaultBranch}:
  132750724,build-your-own-x,codecrafters-io/build-your-own-x,Master programming by recreating your favorite technologies from scratch.,"2018-05-09T12:03:18Z","2026-07-23T18:57:15Z","2026-07-14T19:25:58Z",530712,6778,50205,master
  21737465,awesome,sindresorhus/awesome,😎 Awesome lists about all kinds of interesting topics,"2014-07-11T13:42:37Z","2026-07-23T18:57:24Z","2026-06-30T18:21:16Z",488074,8292,36010,main
  28457823,freeCodeCamp,freeCodeCamp/freeCodeCamp,"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…","2014-12-24T17:49:19Z","2026-07-22T07:01:33Z","2026-07-21T18:00:51Z",452380,8590,45624,main

Installation & Quick Start

# npm
npm install @toon-format/toon

# pnpm
pnpm add @toon-format/toon

# yarn
yarn add @toon-format/toon

To keep the CLI around instead of invoking it through npx, install it globally:

npm install -g @toon-format/cli

Example usage:

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

Streaming large datasets:

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

const largeData = await fetchThousandsOfRecords()

// Memory-efficient streaming for large data
for (const line of encodeLines(largeData)) {
  process.stdout.write(`${line}\n`)
}

[!TIP] For streaming decode APIs, see decodeFromLines() and decodeStream().

Transforming values with replacer:

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

// Remove sensitive fields
const user = { name: 'Ada', password: 'secret', email: '[e-mail]' }
const safe = encode(user, {
  replacer: (key, value) => key === 'password' ? undefined : value
})
// name: Ada
// email: [e-mail]

// Transform values
const data = { status: 'active', count: 5 }
const transformed = encode(data, {
  replacer: (key, value) =>
    typeof value === 'string' ? value.toUpperCase() : value
})
// status: ACTIVE
// count: 5

[!TIP] The replacer function provides fine-grained control over encoding, similar to JSON.stringify's replacer but with path tracking. See the API Reference for more examples, including verbatim output with rawString.

CLI

Command-line tool for quick JSON↔TOON conversions, token analysis, and pipeline integration. Auto-detects format from file extension, supports stdin/stdout workflows, and offers delimiter options (comma, tab, pipe) that trade readability for fewer tokens.

# 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

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

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

# Show token savings
npx @toon-format/cli data.json --stats

[!TIP] See the full CLI documentation for all options, examples, and advanced usage.

Using TOON with LLMs

TOON works best when you show the format instead of describing it. Once a model sees one tabular example, the header – [N] length plus {fields} field list – tells it how to read the rest. Wrap data in ```toon code blocks for input, and show the expected header template when asking models to generate TOON. Tab delimiters buy further token savings. Full-line # comments are stripped on decode, so hand-annotated prompt data – and model output with explainer lines – still decodes cleanly.

Follow the detailed LLM integration guide for strategies, examples, and validation techniques.

Ecosystem

Playgrounds – the official playground converts JSON or YAML to TOON in real time, compares token counts, and shares experiments by URL. Community alternatives: Format Tokenization Playground, TOON Tools.

Editors – TOON Language Support for VS Code (code --install-extension vishalraut.vscode-toon) adds highlighting, validation, and token analysis. tree-sitter-toon covers Neovim, Helix, Emacs, and Zed; toon.nvim is a Lua-native alternative. Elsewhere, YAML highlighting is a close approximation.

Tooling – Tooner is an MCP proxy that converts JSON tool responses to TOON.

Documentation

Comprehensive guides, references, and resources to help you get the most out of the TOON format and tools.

Getting Started

• Introduction & Installation – What TOON is, when to use it, first steps
• Format Overview – Complete syntax with examples
• Benchmarks – Accuracy & token efficiency results

Tools & Integration

• CLI – Command-line tool for JSON↔TOON conversions
• Playgrounds – Interactive tools
• Using TOON with LLMs – Prompting strategies & validation

References

• API Reference – TypeScript/JavaScript encode/decode API
• Syntax Cheatsheet – Quick format lookup
• Specification – Normative rules for implementers
• Glossary – One name per concept, for contributors and tooling

Media Type & File Extension

TOON files use the .toon extension and the provisional media type text/toon. Documents are always UTF-8; the charset=utf-8 parameter may be given but is assumed when absent. See SPEC.md §17 for normative details.

Other Implementations

TOON has official and community implementations across multiple languages including Python, Rust, Go, Java, Swift, .NET, and many more.

See the full list of implementations in the documentation.

Credits

• Logo design by 鈴木ックス(SZKX)

License

MIT License © 2025-PRESENT Johann Schopplich