Migrating from JSON to TOON: A Practical Guide

So you've decided to slash your OpenAI bills and reclaim 40% of your context window by switching from JSON to TOON. Congratulations! But moving production data between formats is rarely as simple as a find-and-replace. This guide covers the practical steps, hidden pitfalls, and validation strategies for a smooth migration.

Why the Move? The Token Tax

In the world of LLMs, JSON is expensive. Every "key": and { } is a set of tokens that adds zero semantic value to the model but costs you real money. TOON (Token-Oriented Object Notation) was designed to strip away this redundancy while remaining human- (and AI-) readable.

Common Pitfalls (and How to Avoid Them)

1. The "Excessive Nesting" Trap

JSON encourages deep hierarchies. While TOON supports nesting, it rewards flattening. If your JSON looks like user.profile.settings.theme.color, consider if the LLM actually needs that structure.

Pro-tip: When migrating, use the opportunity to flatten your data objects. Every level of indentation in JSON is a token penalty that translates to TOON syntax too (though less severe).

2. String Escaping vs. Raw Text

JSON is notoriously picky about double quotes and backslashes. TOON handles raw text much more gracefully, but if you are migrating programmatically, ensure your migration script doesn't double-escape characters that are now "safe" in TOON.

Transformation Strategies

Method A: The Regex Approach (Quick & Dirty)

For simple, flat JSON objects used in prompts, a series of regex replacements can work. This is often faster for one-off scripts.

// Example: Convert simple JSON to TOON
const json = '{"name": "Alice", "role": "admin"}';
const toon = json
  .replace(/"([^"]+)":/g, '$1:')  // Remove quotes from keys
  .replace(/{/g, '(')              // Replace braces with parens
  .replace(/}/g, ')'); 

⚠️ Warning: Regex is fragile. It fails on nested objects, escaped quotes within strings, or multiline values.

Method B: The Official TypeScript Library (Recommended)

The @toon-format/toon library provides a robust, native way to transform your data. It handles edge cases, type coercion, and follows the official TOON specification.

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

const data = await db.users.findMany();
// Encode to a compact TOON string
const toonData = encode(data); 

Pro-tip: Use encodeLines for memory-efficient streaming of large datasets, especially useful when feeding data directly into LLM responses.

Validation Strategies

How do you know your converted data is valid? We recommend a Three-Stage Validation process:

1. Syntactic Validation: Use the toon-lint tool to ensure the output is valid TOON.
2. Semantic Parity: Convert TOON back to JSON and compare the resulting object with the original using a deep-equal check. They should be identical (excluding key order).
3. Shadow Prompting: Run your LLM calls with both JSON and TOON in parallel (shadow mode) and compare the output quality. You'll likely find that TOON not only saves tokens but often improves model focus.