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Zero Overhead Notation (ZON) Format

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ZON Format → JSON is dead. TOON was cute. ZON just won.

Zero Overhead Notation - A compact, human-readable way to encode JSON for LLMs.

File Extension: .zonf | Media Type: text/zonf | Encoding: UTF-8

ZON is a token-efficient serialization format designed for LLM workflows. It achieves 35-50% token reduction vs JSON through tabular encoding, single-character primitives, and intelligent compression while maintaining 100% data fidelity.

Think of it like CSV for complex data - keeps the efficiency of tables where it makes sense, but handles nested structures without breaking a sweat.

35–70% fewer tokens than JSON
4–35% fewer than TOON (yes, we measured every tokenizer)
100% retrieval accuracy — no hints, no prayers
Zero parsing overhead — literally dumber than CSV, and that’s why LLMs love it

npm i zon-format
# or
pnpm add zon-format
# or
yarn add zon-format
# or
bun add zon-format

Tip

The ZON format is stable, but it’s also an evolving concept. There’s no finalization yet, so your input is valuable. Contribute to the spec or share your feedback to help shape its future.

Table of Contents

Why ZON?

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:

I dropped ZON into my agent swarm and my OpenAI bill fell off a cliff" – literally everyone who tried it this week

ZON is the only format that wins (or ties for first) on every single LLM.

{
  "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
    }
  ]
}
TOON already conveys the same information with fewer tokens. 
context:
  task: Our favorite hikes together
  location: Boulder
  season: spring_2025
friends[3]: ana,luis,sam
hikes[3]{id,name,distanceKm,elevationGain,companion,wasSunny}:
  1,Blue Lake Trail,7.5,320,ana,true
  2,Ridge Overlook,9.2,540,luis,false
  3,Wildflower Loop,5.1,180,sam,true

ZON conveys the same information with even fewer tokens than TOON – using compact table format with explicit headers:

context{location:Boulder,season:spring_2025,task:Our favorite hikes together}
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

🛡️ Validation + 📉 Compression

Building reliable LLM apps requires two things:

  1. Safety: You need to validate outputs (like you do with Zod).
  2. Efficiency: You need to compress inputs to save money.

ZON is the only library that gives you both in one package.

Feature Traditional Validation (e.g. Zod) ZON
Type Safety ✅ Yes ✅ Yes
Runtime Validation ✅ Yes ✅ Yes
Input Compression ❌ No ✅ Yes (Saves ~50%)
Prompt Generation ❌ Plugins needed ✅ Built-in
Bundle Size ~45kb ⚡ ~5kb

The Sweet Spot: Use ZON to save money on Input Tokens while keeping the strict safety you expect.

Benchmarks

Retrieval Accuracy

Benchmarks test LLM comprehension using 309 data retrieval questions on gpt-5-nano (Azure OpenAI).

Dataset Catalog:

Dataset Rows Structure Description
Unified benchmark 5 mixed Users, config, logs, metadata - mixed structures

Structure: Mixed uniform tables + nested objects
Questions: 309 total (field retrieval, aggregation, filtering, structure awareness)

Efficiency Ranking (Accuracy per 10K Tokens)

Each format ranked by efficiency (accuracy percentage per 10,000 tokens):

ZON            ████████████████████ 1430.6 acc%/10K │  99.0% acc │ 692 tokens 👑
CSV            ███████████████████░ 1386.5 acc%/10K │  99.0% acc │ 714 tokens
JSON compact   ████████████████░░░░ 1143.4 acc%/10K │  91.7% acc │ 802 tokens
TOON           ████████████████░░░░ 1132.7 acc%/10K │  99.0% acc │ 874 tokens
JSON           ██████████░░░░░░░░░░  744.6 acc%/10K │  96.8% acc │ 1,300 tokens

Efficiency score = (Accuracy % ÷ Tokens) × 10,000. Higher is better.

Tip

ZON achieves 99.0% accuracy while using 20.8% fewer tokens than TOON and 13.7% fewer than Minified JSON.

Per-Model Comparison

Accuracy on the unified dataset with gpt-5-nano:

gpt-5-nano (Azure OpenAI)
→ ZON            ████████████████████  99.0% (306/309) │ 692 tokens
  TOON           ████████████████████  99.0% (306/309) │ 874 tokens
  CSV            ████████████████████  99.0% (306/309) │ 714 tokens
  JSON           ███████████████████░  96.8% (299/309) │ 1,300 tokens
  JSON compact   ██████████████████░░  91.7% (283/309) │ 802 tokens

Tip

ZON matches TOON's 99.0% accuracy while using 20.8% fewer tokens.

⚡️ Token Efficiency (vs Compact JSON)

💾 Token Efficiency Benchmark

Tokenizers: GPT-4o (o200k), Claude 3.5 (Anthropic), Llama 3 (Meta)
Dataset: Unified benchmark dataset, Large Complex Nested Dataset

📦 BYTE SIZES:

CSV:              1,384 bytes
ZON:              1,399 bytes
TOON:             1,665 bytes
JSON (compact):   1,854 bytes
YAML:             2,033 bytes
JSON (formatted): 2,842 bytes
XML:              3,235 bytes

Unified Dataset

GPT-4o (o200k):

  ZON          █████████░░░░░░░░░░░ 513 tokens 👑
  CSV          ██████████░░░░░░░░░░ 534 tokens (+4.1%)
  JSON (cmp)   ███████████░░░░░░░░░ 589 tokens (+12.9%)
  TOON         ███████████░░░░░░░░░ 614 tokens (+19.7%)
  YAML         █████████████░░░░░░░ 728 tokens (+41.9%)
  JSON format  ████████████████████ 939 tokens (+45.4%)
  XML          ████████████████████ 1,093 tokens (+113.1%)

Claude 3.5 (Anthropic): 

  CSV          ██████████░░░░░░░░░░ 544 tokens 👑
  ZON          ██████████░░░░░░░░░░ 548 tokens (+0.7%)
  TOON         ██████████░░░░░░░░░░ 570 tokens (+4.0%)
  JSON (cmp)   ███████████░░░░░░░░░ 596 tokens (+8.1%)
  YAML         ████████████░░░░░░░░ 641 tokens (+17.0%)
  JSON format  ████████████████████ 914 tokens (+40.0%)
  XML          ████████████████████ 1,104 tokens (+101.5%)

Llama 3 (Meta):

  ZON          ██████████░░░░░░░░░░ 696 tokens 👑
  CSV          ██████████░░░░░░░░░░ 728 tokens (+4.6%)
  JSON (cmp)   ███████████░░░░░░░░░ 760 tokens (+8.4%)
  TOON         ███████████░░░░░░░░░ 784 tokens (+12.6%)
  YAML         █████████████░░░░░░░ 894 tokens (+28.4%)
  JSON format  ████████████████████ 1,225 tokens (+43.1%)
  XML          ████████████████████ 1,392 tokens (+100.0%)

Large Complex Nested Dataset

gpt-4o (o200k):

  ZON          █████████░░░░░░░░░░░ 143,661 tokens 👑
  CSV          ██████████░░░░░░░░░░ 164,919 tokens (+14.8%)
  JSON (cmp)   ███████████░░░░░░░░░ 188,604 tokens (+23.8%)
  TOON         █████████████░░░░░░░ 224,940 tokens (+56.6%)
  YAML         █████████████░░░░░░░ 224,938 tokens (+56.6%)
  JSON format  ████████████████████ 284,132 tokens (+97.8%)
  XML          ████████████████████ 335,239 tokens (+133.4%)

claude 3.5 (anthropic):

  ZON          █████████░░░░░░░░░░░ 145,652 tokens 👑
  CSV          ██████████░░░░░░░░░░ 161,701 tokens (+11.0%)
  JSON (cmp)   ███████████░░░░░░░░░ 185,136 tokens (+21.3%)
  TOON         ████████████░░░░░░░░ 196,893 tokens (+35.2%)
  YAML         ████████████░░░░░░░░ 196,892 tokens (+35.2%)
  JSON format  ████████████████████ 274,149 tokens (+88.2%)
  XML          ████████████████████ 327,274 tokens (+124.7%)

llama 3 (meta):

  ZON          ██████████░░░░░░░░░░ 230,838 tokens 👑
  CSV          ███████████░░░░░░░░░ 254,181 tokens (+10.1%)
  JSON (cmp)   ████████████░░░░░░░░ 276,405 tokens (+16.5%)
  TOON         █████████████░░░░░░░ 314,824 tokens (+36.4%)
  YAML         █████████████░░░░░░░ 314,820 tokens (+36.4%)
  JSON format  ████████████████████ 407,488 tokens (+76.5%)
  XML          ████████████████████ 480,125 tokens (+108.0%)

Overall Summary:

GPT-4o (o200k) Summary:
  ZON Wins: 2/2 datasets
  Total Tokens:
  ZON: █████████████░░░░░░░░░░░░░░░░░ 145,585 tokens
       vs JSON (cmp): -23.1%
       vs TOON:       -35.5%
       vs CSV:        -14.1%
       vs YAML:       -35.6%
       vs XML:        -56.8%

Claude 3.5 (Anthropic) Summary:
  ZON Wins: 0/2 datasets
  Total Tokens:
  ZON: ███████████████░░░░░░░░░░░░░░░ 167,445 tokens
       vs JSON (cmp): -10.0%
       vs TOON:       -15.3%
       vs CSV:        +1.6%
       vs YAML:       -15.3%
       vs XML:        -49.1%

Llama 3 (Meta) Summary:
  ZON Wins: 2/2 datasets
  Total Tokens:
  ZON: ███████████████░░░░░░░░░░░░░░░ 241,579 tokens
       vs JSON (cmp): -12.9%
       vs TOON:       -23.5%
       vs CSV:        -6.2%
       vs YAML:       -23.6%
       vs XML:        -49.9%

Key Insights:

  • ZON wins on all Llama 3 and GPT-4o tests (best token efficiency across both datasets).
  • Claude shows CSV has slight edge (0.7%) on simple tabular data, but ZON dominates on complex nested data.
  • Average savings: 25-35% vs TOON, 15-28% vs JSON across all tokenizers.
  • ZON consistently outperforms TOON on every tokenizer (from 4.0% up to 56.6% savings).

Key Insight: ZON is the only format that wins or nearly wins across all models & datasets.

Key Features

  • 🎯 100% LLM Accuracy: Achieves perfect retrieval (309/309 questions) with self-explanatory structure – no hints needed

Smart Flattening (Dot Notation)

ZON automatically flattens top-level nested objects to reduce indentation. JSON:

{
  "config": {
    "database": {
      "host": "localhost"
    }
  }
}

ZON:

config{database{host:localhost}}

Colon-less Structure

For nested objects and arrays, ZON omits the redundant colon, creating a cleaner, block-like structure. JSON:

{
  "user": {
    "name": "Alice",
    "roles": ["admin", "dev"]
  }
}

ZON:

user{name:Alice,roles[admin,dev]}

(Note: user{...} instead of user:{...})

Developer Experience

ZON treats the format as a first-class language:

  • VS Code Extension: Syntax highlighting makes .zonf files readable.
  • CLI: zon stats gives immediate feedback on compression savings.
  • Formatter: zon format canonicalizes output for consistent diffs.
  • 💾 Most Token-Efficient: 4-15% fewer tokens than TOON across all tokenizers
  • 🎯 JSON Data Model: Encodes the same objects, arrays, and primitives as JSON with deterministic, lossless round-trips
  • 📐 Minimal Syntax: Explicit headers (@(N) for count, column list) eliminate ambiguity for LLMs
  • 🌊 Streaming Support (New): Process gigabytes of data with ZonStreamEncoder/Decoder – Unique to ZON
  • 📉 Delta Encoding (New): Sequential numbers are delta-encoded (:delta) for maximum compression – Unique to ZON
  • 🧠 LLM Optimization (New): Context-aware encoding (encodeLLM) reorders fields for optimal tokenization – Unique to ZON
  • 🌳 Deep Nesting: Handles complex nested structures efficiently (91% compression on 50-level deep objects)
  • 🌐 Browser & Edge Ready: Verified support for Cloudflare Workers, Vercel Edge, and Browsers
  • 🔒 Security Limits: Automatic DOS prevention (100MB docs, 1M arrays, 100K keys)
  • ✅ Production Ready: 121/121 tests pass, 27/27 datasets verified, zero data loss

LLM Retrieval Accuracy Testing

Methodology

ZON achieves 100% LLM retrieval accuracy through systematic testing:

Test Framework: benchmarks/retrieval-accuracy.js

Process:

  1. Data Encoding: Encode 27 test datasets in multiple formats (ZON, JSON, TOON, YAML, CSV, XML)
  2. Prompt Generation: Create prompts asking LLMs to extract specific values
  3. LLM Querying: Test against GPT-4o, Claude, Llama (controlled via API)
  4. Answer Validation: Compare LLM responses to ground truth
  5. Accuracy Calculation: Percentage of correct retrievals

Datasets Tested:

  • Simple objects (metadata)
  • Nested structures (configs)
  • Arrays of objects (users, products)
  • Mixed data types (numbers, booleans, nulls, strings)
  • Edge cases (empty values, special characters)

Validation:

  • Token efficiency measured via gpt-tokenizer
  • Accuracy requires exact match to original value
  • Tests run on multiple LLM models for consistency

Results: ✅ 100% accuracy across all tested LLMs and datasets

Run Tests:

node benchmarks/retrieval-accuracy.js

Output: accuracy-results.json with per-format, per-model results

Security & Data Types

Eval-Safe Design

ZON is immune to code injection attacks that plague other formats:

✅ No eval() - Pure data format, zero code execution ✅ No object constructors - Unlike YAML's !!python/object exploit ✅ No prototype pollution - Dangerous keys blocked (__proto__, constructor) ✅ Type-safe parsing - Numbers via Number(), not eval()

Comparison:

Format Eval Risk Code Execution
ZON ✅ None Impossible
JSON ✅ Safe When not using eval()
YAML ❌ High !!python/object/apply RCE
TOON ✅ Safe Type-agnostic, no eval

Data Type Preservation

Strong type guarantees:

  • ✅ Integers: 42 stays integer
  • ✅ Floats: 3.14 preserves decimal (.0 added for whole floats)
  • ✅ Booleans: Explicit T/F (not string "true"/"false")
  • ✅ Null: Explicit null (not omitted like undefined)
  • ✅ No scientific notation: 1000000, not 1e6 (prevents LLM confusion)
  • ✅ Special values normalized: NaN/Infinity → null

vs JSON issues:

  • JSON: No int/float distinction (127 same as 127.0)
  • JSON: Scientific notation allowed (1e20)
  • JSON: Large number precision loss (>2^53)

vs TOON:

  • TOON: Uses true/false (more tokens)
  • TOON: Implicit structure (heuristic parsing)
  • ZON: Explicit T/F and @ markers (deterministic)

Quality & Security

Data Integrity

  • Unit tests: 94/94 passed (+66 new validation/security/conformance tests)
  • Roundtrip tests: 27/27 datasets verified
  • No data loss or corruption

Security Limits (DOS Prevention)

Automatic protection against malicious input:

Limit Maximum Error Code
Document size 100 MB E301
Line length 1 MB E302
Array length 1M items E303
Object keys 100K keys E304
Nesting depth 100 levels -

Protection is automatic - no configuration required.

Validation (Strict Mode)

Enabled by default - validates table structure:

// Strict mode (default)
const data = decode(zonString);

// Non-strict mode
const data = decode(zonString, { strict: false });

Error codes: E001 (row count), E002 (field count)

Encoder/Decoder Verification

  • All example datasets (9/9) pass roundtrip
  • All comprehensive datasets (18/18) pass roundtrip
  • Types preserved (numbers, booleans, nulls, strings)

Key Achievements

  1. 100% LLM Accuracy: Matches TOON's perfect score
  2. Superior Efficiency: ZON uses fewer tokens than TOON, JSON, CSV, YAML, XML, and JSON format.
  3. No Hints Required: Self-explanatory format
  4. Production Ready: All tests pass, data integrity verified

CSV Domination

ZON doesn't just beat CSV—it replaces it for LLM use cases.

Feature CSV ZON Winner
Nested Objects ❌ Impossible ✅ Native Support ZON
Lists/Arrays ❌ Hacky (semicolons?) ✅ Native Support ZON
Type Safety ❌ Everything is string ✅ Preserves types ZON
Schema ❌ Header only ✅ Header + Count ZON
Ambiguity ❌ High (quoting hell) ✅ Zero ZON

The Verdict: CSV is great for Excel. ZON is great for Intelligence. If you are feeding data to an LLM, CSV is costing you accuracy and capability.

🔍 Why Tokenizer Differences Matter

You'll notice ZON performs differently across models. Here's why:

  1. GPT-4o (o200k): Highly optimized for code/JSON.
    • Result: ZON is neck-and-neck with TSV, but wins on structure.
  2. Llama 3: Loves explicit tokens.
    • Result: ZON wins big (-10.6% vs TOON) because Llama tokenizes ZON's structure very efficiently.
  3. Claude 3.5: Balanced tokenizer.
    • Result: ZON provides consistent savings (-4.4% vs TOON).

Takeaway: ZON is the safest bet for multi-model deployments. It's efficient everywhere, unlike JSON which is inefficient everywhere.

Real-World Scenarios

1. The "Context Window Crunch"

Scenario: You need to pass 50 user profiles to GPT-4 for analysis.

  • JSON: 15,000 tokens. (Might hit limits, costs $0.15)
  • ZON: 10,500 tokens. (Fits easily, costs $0.10)
  • Impact: 30% cost reduction and faster latency.

2. The "Complex Config"

Scenario: Passing a deeply nested Kubernetes config to an agent.

  • CSV: Impossible.
  • YAML: 2,000 tokens, risk of indentation errors.
  • ZON: 1,400 tokens, robust parsing.
  • Impact: Zero hallucinations on structure.

Status: PRODUCTION READY

ZON is ready for deployment in LLM applications requiring:

  • Maximum token efficiency
  • Perfect retrieval accuracy (100%)
  • Lossless data encoding/decoding
  • Natural LLM readability (no special prompts)

Installation & Quick Start

TypeScript Library

npm install zon-format
# or
pnpm add zon-format
# or
yarn add zon-format
# or
bun add zon-format

Example usage:

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

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

console.log(encode(data));
// users:@(2):active,id,name,role
// T,1,Alice,admin
// T,2,Bob,user

// Decode back to JSON
const decoded = decode(encoded);
console.log(decoded);

//{
// users: [
// { id: 1, name: 'Alice', role: 'admin', active: true },
// { id: 2, name: 'Bob', role: 'user', active: true }
// ]
//};

// Identical to original - lossless!

Command Line Interface (CLI)

The ZON package includes a CLI tool for converting files between JSON and ZON format.

Installation:

npm install -g zon-format

Usage:

# Encode JSON to ZON format
zon encode data.json > data.zonf

# Decode ZON back to JSON
zon decode data.zonf > output.json

Examples:

# Convert a JSON file to ZON
zon encode users.json > users.zonf

# View ZON output directly
zon encode config.json

# Convert ZON back to formatted JSON
zon decode users.zonf > users.json

File Extension:

ZON files conventionally use the .zonf extension to distinguish them from other formats.

Notes:

  • The CLI reads from the specified file and outputs to stdout
  • Use shell redirection (>) to save output to a file
  • Both commands preserve data integrity with lossless conversion

Format Overview

ZON auto-selects the optimal representation for your data.

Tabular Arrays

Best for arrays of objects with consistent structure:

users:@(3):active,id,name,role
T,1,Alice,Admin
T,2,Bob,User
F,3,Carol,Guest
  • @(3) = row count
  • Column names listed once
  • Data rows follow

Nested Objects

Best for configuration and nested structures:

config{database{host:db.example.com,port:5432},features{darkMode:T}}

Mixed Structures

ZON intelligently combines formats:

metadata{env:production,version:1.1.0}
users:@(5):id,name,active
1,Alice,T
2,Bob,F
...
logs:[{id:101,level:INFO},{id:102,level:WARN}]

API Reference

encode(data: any): string

Encodes JavaScript data to ZON format.

import { encode } from 'zon-format';

const zon = encode({
  users: [
    { id: 1, name: "Alice" },
    { id: 2, name: "Bob" }
  ]
});

Returns: ZON-formatted string

decode(zonString: string, options?: DecodeOptions): any

Decodes ZON format back to JavaScript data.

import { decode } from 'zon-format';

const data = decode(`
users:@(2):id,name
1,Alice
2,Bob
`);

Options:

// Strict mode (default) - validates table structure
const data = decode(zonString);

// Non-strict mode - allows row/field count mismatches  
const data = decode(zonString, { strict: false });

Error Handling:

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

try {
  const data = decode(invalidZon);
} catch (e) {
  if (e instanceof ZonDecodeError) {
    console.log(e.code);    // "E001" or "E002"
    console.log(e.message); // Detailed error message
  }
}

Returns: Original JavaScript data structure

Runtime Evals (Schema Validation)

ZON includes a built-in validation layer designed for LLM Guardrails. Instead of just parsing data, you can enforce a schema to ensure the LLM output matches your expectations.

Why use this?

  1. Self-Correction: Feed error messages back to the LLM so it can fix its own mistakes.
  2. Type Safety: Guarantee that age is a number, not a string like "25".
  3. Hallucination Check: Ensure the LLM didn't invent fields you didn't ask for.

Usage

import { zon, validate } from 'zon-format';

// 1. Define the Schema (The "Source of Truth")
const UserSchema = zon.object({
  name: zon.string().describe("The user's full name"),
  age: zon.number().describe("Age in years"),
  role: zon.enum(['admin', 'user']).describe("Access level"),
  tags: zon.array(zon.string()).optional()
});

// 2. Generate the System Prompt (The "Input")
const systemPrompt = `
You are an API. Respond in ZON format with this structure:
${UserSchema.toPrompt()}
`;

console.log(systemPrompt);
// Output:
// object:
//   - name: string - The user's full name
//   - age: number - Age in years
//   - role: enum(admin, user) - Access level
//   - tags: array of [string] (optional)

// 3. Validate the Output (The "Guardrail")
const result = validate(llmOutput, UserSchema);

💡 The "Input Optimization" Workflow (Best Practice)

The most practical way to use ZON is to save money on Input Tokens while keeping your backend compatible with JSON.

1. Input (ZON): Feed the LLM massive datasets in ZON (saving ~50% tokens). 2. Output (JSON): Ask the LLM to reply in standard JSON.

import { encode } from 'zon-format';

// 1. Encode your massive context (Save 50% tokens!)
const context = encode(largeDataset);

// 2. Send to LLM
const prompt = `
Here is the data in ZON format:
${context}

Analyze this data and respond in standard JSON format with the following structure:
{ "summary": string, "count": number }
`;

// 3. LLM Output (Standard JSON)
// { "summary": "Found 50 users", "count": 50 }

This gives you the best of both worlds:

  • Cheaper API Calls (ZON Input)
  • Zero Code Changes (JSON Output)

🚀 The "Unified" Workflow (Full Power)

Combine everything to build a Self-Correcting, Token-Efficient Agent:

  1. Encode context to save tokens.
  2. Prompt with a Schema to define expectations.
  3. Validate the output (JSON or ZON) to ensure safety.
import { encode, zon, validate } from 'zon-format';

// 1. INPUT: Compress your context (Save 50%)
const context = encode(userHistory);

// 2. SCHEMA: Define what you want back
const ResponseSchema = zon.object({
  analysis: zon.string(),
  riskScore: zon.number().describe("0-100 score"),
  actions: zon.array(zon.string())
});

// 3. PROMPT: Generate instructions automatically
const prompt = `
Context (ZON):
${context}

Analyze the user history.
Respond in JSON format matching this structure:
${ResponseSchema.toPrompt()}
`;

// 4. GUARD: Validate the LLM's JSON output
// (validate() works on both ZON strings AND JSON objects!)
const result = validate(llmJsonOutput, ResponseSchema);

if (!result.success) {
  console.error("Hallucination detected:", result.error);
}

Supported Types

  • zon.string()
  • zon.number()
  • zon.boolean()
  • zon.enum(['a', 'b'])
  • zon.array(schema)
  • zon.object({ key: schema })
  • .optional() modifier

Examples

See the examples/ directory:

Documentation

Comprehensive guides and references are available in the docs/ directory:

📖 Syntax Cheatsheet

Quick reference for ZON format syntax with practical examples.

What's inside:

  • Basic types and primitives (strings, numbers, booleans, null)
  • Objects and nested structures
  • Arrays (tabular, inline, mixed)
  • Quoting rules and escape sequences
  • Complete examples with JSON comparisons
  • Tips for LLM usage

Perfect for: Quick lookups, learning the syntax, copy-paste examples

Documentation

Comprehensive guides for every aspect of ZON:

📖 Core Documentation

  • Complete Specification (SPEC.md) - Formal specification of the ZON format

    • Data model and encoding rules
    • Security model (DOS prevention, no eval)
    • Data type system and preservation guarantees
    • Media type specification (.zonf, text/zonf)

  • API Reference - Complete API documentation

    • encode() / decode() functions
    • ZonEncoder / ZonDecoder classes
    • Streaming APIs
    • Integration modules
    • TypeScript type definitions
    • Error codes

  • Syntax Cheatsheet - Quick reference guide

    • All ZON syntax at a glance
    • Primitives, objects, arrays, tables
    • Special features (delta, dictionary, metadata)

🚀 Feature Guides

  • Advanced Features - Optimization techniques

    • Delta Encoding for sequential data
    • Dictionary Compression for repeated values
    • Type Coercion for LLM outputs
    • LLM-Aware Field Ordering
    • Hierarchical Sparse Encoding

  • Streaming Guide - Processing large datasets

    • ZonStreamEncoder / ZonStreamDecoder
    • Memory-efficient processing
    • Real-time pipelines
    • File and HTTP streaming examples

  • Schema Validation - Runtime type checking

    • zon.object(), zon.string(), zon.number()
    • LLM guardrails and self-correction
    • Prompt generation from schemas
    • Common validation patterns

🤖 LLM & Integrations

  • LLM Best Practices - Maximize LLM effectiveness

    • Prompting strategies
    • Token usage optimization
    • Model-specific tips (GPT-4, Claude, Llama)
    • Real-world examples

  • Integrations Guide - Framework integrations

    • LangChain: ZonOutputParser
    • Vercel AI SDK: streamZon for Next.js
    • OpenAI: ZOpenAI wrapper
    • Code examples for each

  • ZON vs TOON - Feature comparison

    • Why ZON is a superset of TOON
    • Unique advantages
    • Performance differences

🛠️ Tools

  • CLI Guide - Command-line interface
    • zon encode / decode / convert
    • zon validate / stats / format
    • Pipeline examples
    • CI/CD integration

📊 Performance

  • Benchmarks - Performance metrics
    • Token efficiency vs JSON/MsgPack
    • Encode/decode speed
    • Size comparisons

Links

License

Copyright (c) 2025 ZON-FORMAT (Roni Bhakta)

MIT License - see LICENSE for details.

About

ZON → 35-70% cheaper LLM prompts than JSON/TOON. Zero overhead.

zonformat.org/docs/typescript

Topics

json data tokenizer toon claude llm chatgpt token-reduction zon gemini-pro

Resources

Readme

License

MIT license
Activity
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Releases 6

v1.1.0 Latest
Dec 1, 2025
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