ZEON is a whitespace-sensitive data serialization language that completely reimagines how structured data is presented to Large Language Models (LLMs).

Heavily inspired by Python's clean visual structure and YAML's lightness, ZEON eliminates the most expensive parts of JSON: redundant keys, brackets, and quotes. It introduces a unique "Suffix-driven Tabular Grammar" that achieves extreme token density without sacrificing human readability or parser reliability.

Table of Contents

• The Token Problem
• The ZEON Solution
• Syntax Guide
• Performance Benchmarks
• CLI Usage
• Installation

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The Token Problem

In modern AI development, feeding context to LLMs via JSON payloads is incredibly expensive. JSON was built for machines, not for LLM tokenizers.

When dealing with arrays of objects (like a list of 1,000 products), JSON forces you to repeat the keys "id", "name", "price" 1,000 times. Every repeated key, colon, and comma consumes precious tokens, slowing down inference and increasing API costs.

The ZEON Solution

ZEON solves this through Tabular Indentation. By using special suffixes ([] and [][]) directly on the keys, you tell the parser exactly how to read the indented block below it. The header is declared only once, and the data flows cleanly underneath.

Example comparison

JSON Payload:

"items": [
  {"id": "SKU-100", "qty": 1, "price": 150.0},
  {"id": "SKU-205", "qty": 2, "price": 45.5}
]

ZEON Equivalent:

items[]
  id qty price
  SKU-100 1 150.0
  SKU-205 2 45.5

By declaring items[], ZEON understands the first indented line is the header, and maps all subsequent lines to those keys.

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Syntax Guide

ZEON uses pure spaces for separation and Python-like primitive types (True, False, None).

1. Primitive Key-Value Pairs

Standard assignments use =. Unquoted strings are natively supported for ISO dates and identifiers.

order_id=ORD-99321
is_active=True
deleted_at=None
created_at=2026-08-16T14:30:00Z

2. Flat Objects

Objects use indentation. No {} required.

customer
  id name email
  8472 "Maria Silva" maria@example.com

3. Arrays of Objects (Tabular Format)

Use the [] suffix. The first line is the header.

users[]
  id role
  1 admin
  2 guest

4. Nested Tuples

If an object contains a sub-object uniformly, you can declare it in the header using key(sub1 sub2) and map values using (val1 val2).

products[]
  id dimensions(weight unit)
  1 (1.2 kg)

5. 2D Matrices

Use the [][] suffix for pure multidimensional arrays with no headers (e.g., coordinates, tensor data).

shipping_route[][]
  -23.5505 -46.6333
  -23.5501 -46.6341

6. Visual Annotations (Ignored by Parser)

ZEON allows human-friendly annotations to enhance readability without affecting the actual parsed JSON data.

• Suffixes () and [] on columns: Mark complex columns explicitly.
• Inline Annotations [text]: Describe what a key belongs to.

users[]
  id name preferences(theme) nicknames() aliases[]
  1 Maria (light) nicknames[Maria]=(1=M 2=Mah) [mary mah]

The parser completely ignores (), [] and [Maria], resulting in pristine JSON keys. The ZEON stringifier automatically generates () and [] for deep objects and arrays.

7. Multiline Objects and Arrays

Unlike JSON which requires commas, ZEON allows beautiful multiline formatting inside (...) (dictionaries) and [...] (arrays) while natively ignoring indentation and line breaks, exactly like Python.

config
  db_pool (
    host=localhost
    port=5432
    options=(
      ssl=True
      timeout=30
    )
  )
  nodes [
    192.168.0.1
    192.168.0.2
  ]

8. Mixed Data Fallback

If an array contains irregular or heavily nested mixed types, a tabular header won't fit. In these cases, ZEON gracefully falls back to an "inline" format.

JSON:

"mixed_data": [
  1,
  "hello",
  {"flag": true},
  [2, 3]
]

ZEON Equivalent:

mixed_data=[1 "hello" (flag=True) [2 3]]

Notice how [] brackets are used for arrays and () for nested objects (flag=True). This allows you to represent any deeply nested chaos securely, retaining the exact structure of JSON while stripping away commas and quotes.

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Performance Benchmarks

ZEON excels in large, list-heavy data structures. We ran our official Python implementation against complex E-commerce API responses.

Results (Using OpenAI TikToken on 6 Structural Shapes):

Dataset (Large Scale)	JSON Compact	YAML	ZEON	Reduction (vs JSON)
Uniform Flat	12,246	15,742	9,493	-22.5%
Uniform Nested Uniform	13,002	17,500	11,002	-15.4%
Uniform Nested Non-uniform	10,752	13,250	9,502	-11.6%
Non-uniform Nested Uniform	4,033	6,034	3,036	-24.7%
Non-uniform Flat	1,555	1,600	1,466	-5.7%
Non-uniform Nested Non-uniform	1,615	2,371	1,564	-3.2%

In highly uniform, list-heavy structures, ZEON achieves up to ~25% token reduction against purely minified JSON, and over ~40% against YAML.

This translates to nearly double the context window capacity for your LLM applications.

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CLI Usage

ZEON ships with a powerful bidirectional CLI tool to convert your existing datasets between ZEON, JSON and YAML formats.

# Convert JSON or YAML to ZEON
zeon convert data.json -o data.zeon
zeon convert config.yaml -o config.zeon

# Convert ZEON back to JSON or YAML
zeon convert data.zeon -o data.json
zeon convert config.zeon -o config.yaml

# Print to stdout
zeon convert data.yaml --print

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Installation

ZEON is officially available on PyPI and can be installed via pip:

pip install zeon-format

To use it in your code:

import zeon

# Decode ZEON string to Python Dictionary
data = zeon.loads(text)

# Encode Python Dictionary to ZEON format
zeon_text = zeon.dumps(data)

# Direct string conversion
json_text = zeon.convert(zeon_text).to_json()
yaml_text = zeon.convert(zeon_text).to_yaml()
zeon_text = zeon.convert(yaml_text).to_zeon()

# Direct file conversion
# 1. Provide only the filename to save it automatically in the same directory as the original
zeon.convert("path/to/data.zeon").to_json("data.json")
zeon.convert("path/to/data.zeon").to_yaml("data.yaml")

# 2. Or provide a custom path to save it elsewhere
zeon.convert("path/to/data.zeon").to_json("exports/my_data.json")
zeon.convert("path/to/data.json").to_zeon("exports/my_data.zeon")

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ZEON - The future of AI data serialization.