Meet TOON: A Token-First Data Format Built for AI

Source: Dev.to

TL;DR

• AI models think in tokens, not characters.
• JSON wastes tokens on syntax.
• TOON removes that noise, delivering lower cost, better outputs, and more usable context.
• JSON remains useful for APIs; TOON lives inside AI systems.

What Is TOON?

TOON (Token-Oriented Object Notation) is a lightweight, instruction‑style data format designed specifically for token‑based AI systems.
Instead of heavy syntax ({}, :, ,, " "), TOON focuses on:

• Meaningful tokens
• Clear hierarchy using whitespace
• Human‑readable, AI‑friendly structure

Same Data, Two Formats

JSON

{
  "task": "analyzeResume",
  "input": {
    "experience": "2 years",
    "skills": ["React", "JavaScript", "Tailwind"]
  }
}

TOON

task analyzeResume  
input  
  experience 2years  
  skills React JavaScript Tailwind  

Less noise. Same meaning. Better for AI.

Why Do We Need TOON?

Because LLMs don’t care about punctuation — they care about tokens.

Problems with JSON in AI Systems

• Wasted tokens on syntax
• Higher API cost
• Frequent output breakage
• Poor streaming support
• Smaller usable context window

How TOON Solves This

• Removes syntax junk
• Aligns with LLM tokenization
• Works safely with streaming
• Improves output reliability

How Effective Is TOON?

Token Reduction (Realistic)

That’s a 35–45% reduction in tokens for the same data.

Cost Savings at Scale

Example

• 1,000 AI requests per day
• ~1,200 tokens per request

~14 million tokens saved every month → ~40% reduction in AI API cost.

How TOON Improves AI Training & LLM Usage

Training Data

Cleaner samples mean better embeddings and cheaper fine‑tuning.

intent createUser  
input  
  name Kiran  
  role frontendDeveloper  
output  
  status success  

Fewer tokens, less noise, better learning signals.

Prompt Engineering

LLMs follow instruction‑style formats more reliably than strict JSON.

task analyzeFrontendProject  
constraints  
  maxWords 100  
input  
  stack React Tailwind  
  experience 2years  
output  
  summary  
  improvements  

Model Outputs

JSON often breaks due to missing commas or braces.
TOON degrades gracefully and remains usable even when partial.

score 82
feedback Clean architecture and reusable components

JSON ↔ TOON Conversion Strategy

TOON is not a replacement for JSON everywhere.

Best‑practice architecture

User → TOON → LLM → TOON → Parser → JSON (for APIs / storage)
Internal AI communication → TOON
External contracts & APIs → JSON

This gives cost efficiency without losing compatibility.

Industry Impact & Economic Growth

Where TOON Makes a Difference

• AI agents and autonomous workflows
• LLM‑powered SaaS platforms
• Fine‑tuning pipelines
• Real‑time chat systems
• Edge and embedded AI

Business & Economic Benefits

• Lower infrastructure cost
• Faster responses
• More context per request
• More reliable AI behavior
• Better scalability and margins

At scale, token efficiency directly translates into profitability and growth.

Key Benefits

• 35–45% fewer tokens
• 30–40% lower API costs
• ~40% more usable context
• Fewer output failures
• Streaming‑friendly
• Easy to parse
• Better instruction adherence

Demo Usage

Tool invocation

tool sendEmail  
params  
  to user@gmail.com  
  subject Interview Update  
  urgent true  

Agent memory

memory  
  user Kiran  
  skill React  
  lastAction buildResume  

Implementation Basics

TOON Parsing Rules

• New line = new statement
• First token = key
• Indentation = nesting
• Remaining tokens = values

Simple Pseudo Logic

Read line
Split by space
First token → key
Remaining tokens → value
Indentation → hierarchy

No heavy parsers. No strict failures. Easy recovery.

When Should You Use TOON?

Use TOON for

• LLM‑powered applications
• AI agents and workflows
• Prompt‑heavy systems
• Streaming AI outputs
• Token‑sensitive pipelines

Avoid TOON for

• Public REST APIs
• Browser‑native data exchange
• Standards‑heavy integrations

Final Thought

JSON was built for humans.

TOON is built for AI.

If AI thinks in tokens, our internal data formats should too.

Let’s Talk

• Would you try TOON in your AI system?
• Should AI‑native data formats exist officially?
• Want an open‑source TOON parser?

Let’s build smarter, cheaper, better AI together 🚀