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

Token-Oriented Object Notation is a compact, human-readable format designed for passing structured data to Large Language Models with significantly reduced token usage. It's intended for LLM input, not output.

TOON's sweet spot is uniform arrays of objects – multiple fields per row, same structure across items. It borrows YAML's indentation-based structure for nested objects and CSV's tabular format for uniform data rows, then optimizes both for token efficiency in LLM contexts. For deeply nested or non-uniform data, JSON may be more efficient.

var serializer = new ToonSerializer();

var data = new {
  user = new {
    id = 123,
    name = "Ada",
    tags = new[] { "reading", "gaming" },
    active = true,
    preferences = new[] { }
  }
}

var dataString = serializer.Serialize(data);

Console.WriteLine(dataString);

//// example output:
// user:
//   id: 123
//   name: Ada
//   tags[2]: reading,gaming
//   active: true
//   preferences[0]:

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:

{
  "users": [
    { "id": 1, "name": "Alice", "role": "admin" },
    { "id": 2, "name": "Bob", "role": "user" }
  ]
}

TOON conveys the same information with fewer tokens:

users[2]{id,name,role}:
  1,Alice,admin
  2,Bob,user

• 💸 Token-efficient: typically 30–60% fewer tokens than JSON
• 🤿 LLM-friendly guardrails: explicit lengths and field lists help models validate output
• 🍱 Minimal syntax: removes redundant punctuation (braces, brackets, most quotes)
• 📐 Indentation-based structure: replaces braces with whitespace for better readability
• 🧺 Tabular arrays: declare keys once, then stream rows without repetition

This is a port of https://github.com/johannschopplich/toon to .NET