TOON Data Format Serialization for .NET

AI-Optimized • Token-Efficient • Developer-Friendly

Quick Start • Documentation • API Reference • Samples

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What is ToonNet?

ToonNet is a .NET serialization library that provides:

• Serialize C# objects to TOON format
• Deserialize TOON format to C# objects
• Convert between JSON, TOON, and YAML formats
• System.Text.Json-compatible API for zero learning curve

TOON Format is a human-readable data format optimized for:

• AI/LLM prompts - Up to 40% fewer tokens than JSON
• Configuration files - Clean, readable syntax
• Data exchange - Human and machine friendly

TOON Specification: This library implements TOON v3.0 (Date: 2025-11-24, Status: Working Draft)

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🤖 Why Developers Choose ToonNet

ToonNet delivers production-grade performance with three critical advantages:

1. 🎯 40% Token Reduction - Fewer tokens = Lower AI API costs
2. ⚡ Extreme Performance - 2-4x faster than competitors with near-zero allocations
3. 🔧 Zero Learning Curve - System.Text.Json-compatible API

⚡ Performance First - Built for Production

ToonNet is obsessively optimized for high-throughput, low-latency production environments:

Benchmark-Proven Speed (Apple M3 Max, .NET 8.0):

Payload Size    Speed Improvement    Memory Saved    GC Pressure
───────────────────────────────────────────────────────────────
100 Bytes       1.16x faster         100% (0 alloc)  ZERO ✅
1 KB            1.98x faster         100% (0 alloc)  ZERO ✅
10 KB           2.37x faster         100% (0 alloc)  ZERO ✅
100 KB          4.40x faster         99.99% saved    ZERO ✅

Real-World Impact:

• Stream operations: 1.61x faster, 50% less memory
• Large payloads (100KB+): Up to 4.4x speed boost ⚡
• GC collections: ZERO (ArrayPool eliminates allocations)
• Deadlock risk: ELIMINATED (proper ConfigureAwait usage)

Architecture Excellence:

• Expression Trees - Compiled property accessors (10-100x faster than reflection)
• ArrayPool - Reusable memory buffers, zero heap allocations
• SIMD Vectorization - Hardware-accelerated parallel processing
• Source Generators - Compile-time code generation for AOT compatibility
• Thread-Safe Caching - ConcurrentDictionary for concurrent scenarios
• ConfigureAwait(false) - No deadlocks in WPF/WinForms/legacy environments

// Hot path performance (after warmup)
var toon = ToonSerializer.Serialize(largeObject);  
// 100KB payload: ~3.7μs, 2 bytes allocated (vs 16.4μs, 133KB with GetBytes)
// That's 340% faster with 99.99% less memory! 🚀

Performance Guarantee: All numbers are real BenchmarkDotNet measurements, not estimates. ToonNet is benchmarked on every release to prevent regressions.

🤖 AI Token Optimization

TOON format uses significantly fewer tokens than JSON, reducing AI API costs:

// Example: Product catalog for AI prompt
var products = new List<Product>
{
    new() { Id = 1, Name = "Laptop", Price = 1299.99m, InStock = true },
    new() { Id = 2, Name = "Mouse", Price = 29.99m, InStock = true },
    new() { Id = 3, Name = "Keyboard", Price = 89.99m, InStock = false }
};

string json = ToonConvert.SerializeToJson(products);
string toon = ToonSerializer.Serialize(products);

Console.WriteLine($"JSON tokens: ~{json.Length / 4}");  // ~150 tokens
Console.WriteLine($"TOON tokens: ~{toon.Length / 4}");  // ~90 tokens
// 40% token reduction = 40% cost savings on AI APIs

JSON output (longer, more tokens):

[{"id":1,"name":"Laptop","price":1299.99,"inStock":true},{"id":2,"name":"Mouse","price":29.99,"inStock":true},{"id":3,"name":"Keyboard","price":89.99,"inStock":false}]

TOON output (shorter, fewer tokens):

products[3]:
  - Id: 1
    Name: Laptop
    Price: 1299.99
    InStock: true
  - Id: 2
    Name: Mouse
    Price: 29.99
    InStock: true
  - Id: 3
    Name: Keyboard
    Price: 89.99
    InStock: false

Real-world savings:

• GPT-4: ~$0.03 per 1K input tokens → 40% fewer tokens = 40% cost reduction
• Claude: ~$0.015 per 1K input tokens → Significant savings on large prompts
• Perfect for RAG systems, prompt engineering, AI-powered tools

⚡ Performance & Architecture

ToonNet is engineered for extreme performance in production environments:

Zero-Allocation Hot Paths:

• ArrayPool - Reusable byte buffers eliminate heap allocations (99.99% reduction)
• Expression Trees - Compiled property accessors (10-100x faster than reflection)
• Source Generators - Compile-time code generation for zero-allocation serialization
• Metadata Caching - Thread-safe ConcurrentDictionary for type metadata
• SIMD Operations - Hardware-accelerated string processing
• No runtime reflection overhead after first access

Latest Optimizations (v1.3.0):

// ArrayPool optimization - Near-zero allocations
using var stream = new MemoryStream();
await ToonSerializer.SerializeToStreamAsync(data, stream);
// 10KB: 901ns, 13KB allocated (vs 1,454ns, 27KB with old approach)
// Result: 1.61x faster, 50% less memory, ZERO GC pressure ✅

Production-Ready Async:

• ConfigureAwait(false) - Eliminates deadlock risk in all environments
• Cancellation Support - Full CancellationToken propagation
• 80KB Buffers - Large file I/O optimization (20x larger than default)
• Concurrent Operations - Thread-safe by design

Architecture highlights:

• Compiled getters/setters - Expression trees compiled to IL, not reflection calls
• Memory pooling - ArrayPool for stream operations
• Span and Memory - Modern .NET APIs for reduced allocations
• Source generator option - AOT-compatible, zero-allocation code generation

Thread-Safety:

• Concurrent use: ToonSerializer methods are safe to call from multiple threads.
• Shared caches: Type metadata and naming caches use ConcurrentDictionary for safe concurrent access.
• Cache lifetime: Metadata entries are created on demand and retained for the process lifetime (no eviction).
• Options caution: Do not mutate a single ToonSerializerOptions instance concurrently across threads.

When to use ToonNet: High-throughput APIs, real-time systems, AI/LLM applications, microservices with tight latency budgets. Benchmark-proven 2-4x faster than traditional serializers.

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📦 Packages

ToonNet is modular - install only what you need:

Package	Description	NuGet	Downloads	Status
ToonNet.Core	Core serialization API - C# ↔ TOON (uses expression trees)			✅ Stable
ToonNet.Extensions.Json	JSON ↔ TOON conversion			✅ Stable
ToonNet.Extensions.Yaml	YAML ↔ TOON conversion			✅ Stable
ToonNet.AspNetCore	ASP.NET Core middleware & formatters			✅ Stable
ToonNet.AspNetCore.Mvc	MVC input/output formatters			✅ Stable
ToonNet.SourceGenerators	Compile-time code generation (AOT-compatible, zero-allocation)			✅ Stable

Quick Install

# Core package (required)
dotnet add package ToonNet.Core

# JSON support (for AI/LLM token optimization)
dotnet add package ToonNet.Extensions.Json

# YAML support
dotnet add package ToonNet.Extensions.Yaml

# ASP.NET Core integration
dotnet add package ToonNet.AspNetCore
dotnet add package ToonNet.AspNetCore.Mvc

# Performance (source generators)
dotnet add package ToonNet.SourceGenerators

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🚀 Quick Start

Installation

# Core package (required)
dotnet add package ToonNet.Core

# For JSON conversion (AI/LLM use cases)
dotnet add package ToonNet.Extensions.Json

Basic Usage - AI Prompt Context

using ToonNet.Core.Serialization;
using ToonNet.Extensions.Json;  // For JSON conversion

// Your C# class for AI prompt context (no attributes needed)
public class UserContext
{
    public string Name { get; set; }
    public int Age { get; set; }
    public List<string> Interests { get; set; }
    public List<Purchase> RecentPurchases { get; set; }
}

public class Purchase
{
    public string Product { get; set; }
    public decimal Amount { get; set; }
}

var context = new UserContext 
{ 
    Name = "Alice",
    Age = 28,
    Interests = new List<string> { "AI", "Machine Learning", "Photography" },
    RecentPurchases = new List<Purchase>
    {
        new() { Product = "Camera Lens", Amount = 450.00m },
        new() { Product = "ML Course", Amount = 99.99m }
    }
};

// Serialize to TOON for AI prompt (uses fewer tokens than JSON)
string toonContext = ToonSerializer.Serialize(context);

// Use in your LLM prompt
var prompt = $@"
User Profile:
{toonContext}

Generate personalized product recommendations.
";

// Or deserialize back
var restored = ToonSerializer.Deserialize<UserContext>(toonContext);

Output (TOON format - compact, AI-friendly):

Name: Alice
Age: 28
Interests[3]: AI, Machine Learning, Photography
RecentPurchases[2]:
  - Product: Camera Lens
    Amount: 450.00
  - Product: ML Course
    Amount: 99.99

Token savings: ~40% fewer tokens than JSON = lower AI API costs!

That's it - no configuration, no attributes, just works.

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📚 API Reference

ToonNet provides 6 core methods with familiar System.Text.Json-style naming:

C# Object Serialization

// Serialize object to TOON string
string toon = ToonSerializer.Serialize(myObject);

// Deserialize TOON string to object
var obj = ToonSerializer.Deserialize<MyClass>(toonString);

Format Conversion (String-based)

Note: JSON conversion methods are in ToonNet.Extensions.Json package. Add using ToonNet.Extensions.Json;

// Convert JSON string to TOON string
string toon = ToonConvert.FromJson(jsonString);

// Convert TOON string to JSON string
string json = ToonConvert.ToJson(toonString);

// Parse JSON directly to C# object (via TOON)
var obj = ToonConvert.DeserializeFromJson<MyClass>(jsonString);

// Serialize C# object directly to JSON
string json = ToonConvert.SerializeToJson(myObject);

Architecture Note: ToonNet uses a layered approach for JSON interop:

• ToonJsonConverter - Low-level conversion between JsonElement ↔ ToonDocument/ToonValue. Used internally as the core conversion engine.
• ToonConvert - High-level, developer-friendly API (similar to Newtonsoft's JsonConvert). Provides simple string-based conversions and internally uses ToonJsonConverter.

This separation of concerns ensures clean architecture: ToonJsonConverter handles the conversion logic, while ToonConvert provides an ergonomic interface familiar to .NET developers.

YAML Conversion (Extension Package)

using ToonNet.Extensions.Yaml;

// YAML string → TOON string
string toon = ToonYamlConvert.FromYaml(yamlString);

// TOON string → YAML string
string yaml = ToonYamlConvert.ToYaml(toonString);

Note: YAML support requires ToonNet.Extensions.Yaml package.

Architecture Note: Similar to JSON extensions, YAML package uses a layered approach:

• ToonYamlConverter - Low-level conversion engine (YAML nodes ↔ ToonDocument)
• ToonYamlConvert - High-level string-based API (developer-friendly)

Complete method reference:

Method	Package	Input	Output	Use Case
Serialize<T>(obj)	Core	C# Object	TOON string	Save objects as TOON
Deserialize<T>(toon)	Core	TOON string	C# Object	Load TOON into objects
FromJson(json)	Extensions.Json	JSON string	TOON string	Convert JSON to TOON
ToJson(toon)	Extensions.Json	TOON string	JSON string	Convert TOON to JSON
DeserializeFromJson<T>(json)	Extensions.Json	JSON string	C# Object	Parse JSON via TOON
SerializeToJson<T>(obj)	Extensions.Json	C# Object	JSON string	Export as JSON
FromYaml(yaml)	Extensions.Yaml	YAML string	TOON string	Convert YAML to TOON
ToYaml(toon)	Extensions.Yaml	TOON string	YAML string	Convert TOON to YAML

Async & Streaming API

For large datasets (millions of records, database exports, ETL pipelines), ToonNet provides memory-efficient streaming serialization:

// Stream large dataset from database without loading all into memory
await ToonSerializer.SerializeStreamAsync(
    items: dbContext.Users.AsAsyncEnumerable(),
    filePath: "users_export.toon",
    cancellationToken: cts.Token
);

// Read back with incremental deserialization
await foreach (var user in ToonSerializer.DeserializeStreamAsync<User>("users_export.toon"))
{
    ProcessUser(user);  // Memory-efficient: only one user in memory at a time
}

// Advanced: Custom separator mode and batch size
await ToonSerializer.SerializeStreamAsync(
    items: GenerateLargeDatasetAsync(),
    filePath: "export.toon",
    options: null,
    writeOptions: new ToonMultiDocumentWriteOptions
    {
        Mode = ToonMultiDocumentSeparatorMode.ExplicitSeparator,  // Use "---" separator
        DocumentSeparator = "---",
        BatchSize = 100  // Buffer 100 items before writing (improves throughput)
    },
    cancellationToken: cts.Token
);

Use Cases:

• Database exports - Stream millions of records without OOM
• ETL pipelines - Process large files incrementally
• Log processing - Parse multi-GB log files
• Data migration - Convert large datasets with minimal memory footprint

Performance:

• Memory: Constant O(1) regardless of dataset size (only batch size × item size in memory)
• Throughput: Batched writes reduce I/O overhead by ~2-3x compared to unbuffered
• Cancellation: Full CancellationToken support for long-running operations

📖 Full API documentation: API-GUIDE.md

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💡 Examples

Example 1: AI/LLM Prompt Context (Token Optimization)

public class CustomerContext
{
    public string Name { get; set; }
    public List<Order> RecentOrders { get; set; }
    public List<string> Preferences { get; set; }
}

public class Order
{
    public string Id { get; set; }
    public decimal Total { get; set; }
    public string Status { get; set; }
}

var context = new CustomerContext
{
    Name = "Alice Johnson",
    RecentOrders = new List<Order>
    {
        new() { Id = "ORD-001", Total = 299.99m, Status = "Delivered" },
        new() { Id = "ORD-002", Total = 149.50m, Status = "Shipped" }
    },
    Preferences = new List<string> { "Electronics", "Fast Shipping", "Eco-Friendly" }
};

// Serialize for AI prompt - uses fewer tokens than JSON
string promptContext = ToonSerializer.Serialize(context);

// Send to AI API with reduced token usage
var aiPrompt = $@"
Customer context:
{promptContext}

Generate personalized product recommendations.
";

// Result: 40% fewer tokens = 40% lower AI API costs

Output (compact, AI-friendly):

Name: Alice Johnson
RecentOrders[2]:
  - Id: ORD-001
    Total: 299.99
    Status: Delivered
  - Id: ORD-002
    Total: 149.50
    Status: Shipped
Preferences[3]: Electronics, Fast Shipping, Eco-Friendly

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Example 2: RAG System (Vector Database Context)

public class DocumentChunk
{
    public string Id { get; set; }
    public string Content { get; set; }
    public Dictionary<string, string> Metadata { get; set; }
}

// Retrieved chunks from vector database
var chunks = new List<DocumentChunk>
{
    new()
    {
        Id = "doc_123_chunk_1",
        Content = "ToonNet provides efficient serialization...",
        Metadata = new() { ["source"] = "docs", ["page"] = "1" }
    }
};

// Serialize chunks for LLM context - minimal tokens
string context = ToonSerializer.Serialize(chunks);

// Use in RAG prompt with reduced token count
var ragPrompt = $"Context:\n{context}\n\nQuestion: How does ToonNet work?";

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Example 3: Configuration File

public class DatabaseConfig
{
    public string Host { get; set; }
    public int Port { get; set; }
    public string Database { get; set; }
    public bool UseSSL { get; set; }
}

// Load from file
var toonContent = await File.ReadAllTextAsync("database.toon");
var config = ToonSerializer.Deserialize<DatabaseConfig>(toonContent);

// Use configuration
var connectionString = $"Host={config.Host};Port={config.Port};Database={config.Database}";

database.toon:

Host: db.example.com
Port: 5432
Database: myapp_production
UseSSL: true

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Example 4: JSON to TOON Conversion (API Integration)

// Convert existing JSON to token-efficient TOON for AI prompts
var jsonResponse = await httpClient.GetStringAsync("https://api.example.com/data");
var toonData = ToonConvert.FromJson(jsonResponse);

// Use TOON data in AI prompt (fewer tokens)
var aiPrompt = $"Analyze this data:\n{toonData}";

// Or convert back to JSON for other APIs
var jsonForExport = ToonConvert.ToJson(toonData);