Nizzola.ToonConvert.NET 1.0.0

NIZZOLA.TOON.NET

Token-Oriented Object Notation (TOON) serializer for .NET

🚀 Overview

NIZZOLA.TOON.NET is a high-performance serialization library created for AI and LLM workloads. By using a compact, token-oriented format (TOON) it can reduce token usage by roughly 30–60% compared to JSON. This yields:

• ⚡ Lower API costs when calling LLMs
• 🏃 Faster end-to-end response times
• 📊 Better throughput for flat or tabular data
• 👁️ Human-readable output for debugging and prompts

📦 Installation

dotnet add package Nizzola.TOON.NET

Documentation

This README contains a concise implementation overview and quick reference. See the source code in src/NizzolaToon/ for API details and examples.

Implementation (summary)

• 🔧 ToonSerializer — Main static API for Serialize/Deserialize (familiar API surface like JsonSerializer).
• ✍️ ToonWriter — Formatting engine that writes TOON with optimized indentation and stream support.
• ⚙️ ToonSerializerOptions — Configuration (naming policies, indentation, tabular detection, async support).
• 🚫 ToonIgnoreAttribute — Attribute to exclude properties from serialization.

Quick feature overview

• 🧾 Automatic tabularization: detects homogeneous lists and emits a header + rows representation.
• 🔁 Token reduction: minimizes braces/quotes/commas and uses indentation and CSV-like compacting for simple arrays.
• ⚡ Performance: built on StringBuilder and optimized reflection paths.
• 🔌 Extensible: attributes and naming policies (CamelCase / snake_case) let you adapt the output to your LLM prompts.

Example usage

// Serialize
var toon = ToonSerializer.Serialize(obj, new ToonSerializerOptions { Indentation = 2, NamingPolicy = NamingPolicy.CamelCase });

// Deserialize
var model = ToonSerializer.Deserialize<MyModel>(toon);

// Streams / async
await ToonSerializer.SerializeAsync(stream, obj, options);
var result = await ToonSerializer.DeserializeAsync<MyModel>(stream);

Quick tips

• Use Indentation = 2 to balance readability and compactness.
• Enable tabular detection when serializing large homogeneous lists to maximize token savings.
• Align NamingPolicy with the conventions your LLM expects (camelCase, snake_case, etc.).

Short reference

Before and after comparison

Case 1 - with single data type

Case 2 - with mixed data types

The more rows an entity has, the greater the token savings with this model.

Key features

• Tabular data detection: automatically detects lists of objects with identical shape and serializes them as a table (header + rows).
• Token reduction strategies: removes unnecessary braces/quotes/commas, relies on indentation and compact CSV-like arrays for simple sequences.
• Flexible: serializes CLR objects, can convert existing JSON to TOON, and supports streaming/async scenarios.
• Naming policies: compatible with System.Text.Json-style policies (camelCase, snake_case).
• Performance: optimized for throughput using StringBuilder and targeted reflection caching.

✨ Benefits

• 🎯 Reduce token consumption by ~30–60% compared to JSON when used with LLMs.
• 👨‍💻 Familiar API for .NET developers, easing adoption.
• 🔒 Type-safe usage via generic Serialize / Deserialize overloads.
• 🔌 Extensible via attributes and options for customization.

This library is prepared for NuGet publication and production use with LLM-powered workflows.

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