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zon-format
zon-format/dist/index.js

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Readme

ZON Format v1.0.1 - TypeScript/JavaScript

Zero Overhead Notation - A human-readable data serialization format optimized for LLM token efficiency, JSON for LLMs.

🚀 14% better compression than TOON | 📊 30-67% compression vs JSON | 🔍 100% Human Readable

🚀 What is ZON?

ZON is a smart compression format designed specifically for transmitting structured data to Large Language Models. Unlike traditional compression (which creates binary data), ZON remains 100% human-readable while dramatically reducing token usage.

Why ZON?

Problem	Solution
💸 High LLM costs from verbose JSON	ZON reduces tokens by 30-67%
🔍 Binary formats aren't debuggable	ZON is plain text - you can read it!
🎯 One-size-fits-all compression	ZON auto-selects optimal strategy per column

Key Features

✅ Entropy Tournament: Auto-selects best compression strategy per column
✅ 100% Safe: Guaranteed lossless reconstruction
✅ Zero Configuration: Works out of the box
✅ TypeScript Support: Full type definitions included

📊 Benchmarks

ZON vs TOON vs JSON (Token Efficiency)

We benchmarked ZON against TOON and JSON using the GPT-5 o200k_base tokenizer across 7 different dataset types.

🏆 ZON won on ALL 7 datasets.

Dataset Type	ZON Tokens	vs TOON	vs JSON (formatted)	vs JSON (compact)
Nested Structures	228 👑	-16.7%	-57.7%	-28.3%
Uniform Tabular	154 👑	-6.5%	-67.4%	-41.2%
Mixed Structures	125 👑	-8.8%	-62.8%	-38.7%
Deeply Nested	113 👑	-24.2%	-51.1%	-8.9%
Semi-uniform Logs	287 👑	-26.2%	-40.2%	-9.5%

Negative percentage means fewer tokens (better).

Summary:

ZON is 13.8% more efficient than TOON on average
ZON is 56.9% more efficient than JSON (formatted)
ZON is 27.5% more efficient than JSON (compact)

View full benchmark results

⚡ Quick Start

import { encode, decode } from 'zon-format';

// Your data
const users = {
  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
    }
  ]
};

// Encode (compress)
const compressed = encode(users);
console.log(compressed);

// Decode (decompress)
const original = decode(compressed);
console.log(original); // Exact match!

Output (ZON format - 96 tokens, 264 bytes):

context:"{task:Our favorite hikes together,location:Boulder,season:spring_2025}"
friends:"[ana,luis,sam]"

@hikes(3):companion,distanceKm,elevationGain,id,name,wasSunny
ana,7.5,320,1,Blue Lake Trail,T
luis,9.2,540,2,Ridge Overlook,F
sam,5.1,180,3,Wildflower Loop,T

vs JSON (compact - 139 tokens, 451 bytes)

📦 Installation

From npm

npm install zon-format

From yarn

yarn add zon-format

Verify Installation

import { encode, decode } from 'zon-format';

const data = { message: "Hello ZON!" };
const encoded = encode(data);
console.log(encoded); // message:Hello ZON!

const decoded = decode(encoded);
console.log(decoded); // { message: "Hello ZON!" }

📚 API Reference

`encode(data, anchorInterval?)`

Encodes a JavaScript object or array into a ZON-formatted string.

Parameters:

data (any): The input data to encode. Must be JSON-serializable (object, array, string, number, boolean, null).
anchorInterval (number, optional): Legacy parameter, defaults to 50. Not used in v1.0.1.

Returns:

string: The ZON-encoded string.

Example:

import { encode } from 'zon-format';

const data = { id: 1, name: "Alice" };
const zonStr = encode(data);
console.log(zonStr);
// Output: id:1
//         name:Alice

`decode(zonStr)`

Decodes a ZON-formatted string back into a JavaScript object or array.

Parameters:

zonStr (string): The ZON-encoded string to decode.

Returns:

any: The decoded JavaScript object or array.

Example:

import { decode } from 'zon-format';

const zonStr = `id:1
name:Alice`;

const data = decode(zonStr);
console.log(data); // { id: 1, name: "Alice" }

🤖 Examples

Basic Usage

import { encode, decode } from 'zon-format';

// Simple object
const user = { name: "Alice", age: 30, active: true };
const encoded = encode(user);
const decoded = decode(encoded);
// decoded === { name: "Alice", age: 30, active: true }

Array of Objects (Table Format)

import { encode, decode } from 'zon-format';

const products = [
  { id: 1, name: "Laptop", price: 999, inStock: true },
  { id: 2, name: "Mouse", price: 29, inStock: true },
  { id: 3, name: "Keyboard", price: 79, inStock: false }
];

const encoded = encode(products);
console.log(encoded);
// Output:
// @data(3):id,inStock,name,price
// 1,T,Laptop,999
// 2,T,Mouse,29
// 3,F,Keyboard,79

const decoded = decode(encoded);
// decoded === products (exact match)

Mixed Metadata and Table

import { encode, decode } from 'zon-format';

const report = {
  title: "Q1 Sales Report",
  year: 2024,
  quarter: 1,
  sales: [
    { month: "Jan", revenue: 50000, expenses: 30000 },
    { month: "Feb", revenue: 55000, expenses: 32000 },
    { month: "Mar", revenue: 60000, expenses: 35000 }
  ]
};

const encoded = encode(report);
const decoded = decode(encoded);
// Perfect reconstruction

Nested Objects

import { encode, decode } from 'zon-format';

const config = {
  app: {
    name: "MyApp",
    version: "1.0.0",
    features: {
      authentication: true,
      logging: true,
      caching: false
    }
  }
};

const encoded = encode(config);
const decoded = decode(encoded);
// Nested structure preserved

LLM Integration Example

import { encode } from 'zon-format';
import OpenAI from 'openai';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

// Prepare data
const users = [
  { id: 1, name: "Alice", purchases: 15, total: 1500 },
  { id: 2, name: "Bob", purchases: 8, total: 800 },
  // ... 100 more users
];

// Compress with ZON (saves tokens = saves money!)
const zonData = encode(users);

// Use in prompt
const response = await openai.chat.completions.create({
  model: "gpt-4",
  messages: [
    {
      role: "system",
      content: "You will receive data in ZON format. Decode mentally and analyze."
    },
    {
      role: "user",
      content: `Analyze this user data:\n\n${zonData}\n\nWhat's the average purchase total?`
    }
  ]
});

console.log(response.choices[0].message.content);
// Cost Savings: ~30-40% fewer tokens vs JSON!

📖 Format Reference

Metadata (YAML-like)

key:value
nested.key:value
list:[item1,item2,item3]

No spaces after : for compactness
Dot notation for nested objects
Minimal quoting (only when necessary)

Tables (@table syntax)

@tablename(count):col1,col2,col3
val1,val2,val3
val1,val2,val3

@ marks table start
(count) shows row count
Columns separated by commas (no spaces)

Compression Tokens

Token	Meaning	Example
`T`	Boolean true	`T` instead of `true`
`F`	Boolean false	`F` instead of `false`

Type Handling

Booleans: Encoded as T or F
Null: Encoded as null
Numbers: Preserved with exact representation
Floats: Always include decimal point (e.g., 42.0)
Strings: Minimal quoting, quoted only when needed
Objects/Arrays: Inline ZON format {key:val} or [val1,val2]

🧪 Testing

Run tests:

npm test

Build:

npm run build

🤝 Contributing

This is a port of the original Python implementation. Contributions are welcome!

Fork the repository
Create a feature branch
Add tests for new features
Submit a pull request

📄 License

MIT License

See LICENSE for full terms.

🙏 Acknowledgments

Original Python implementation: ZON-Format/ZON
Inspired by TOON format for LLM token efficiency
TypeScript port maintains 100% compatibility with Python version

✉️ Support

Issues: GitHub Issues
Original Docs: Python ZON

Made with ❤️ for the LLM community

ZON v1.0.1 - Compression that scales with complexity