Token-Oriented Object Notation is an open specification and toolkit for a data serialization format called Token-Oriented Object Notation (TOON), designed specifically to optimize how structured data is passed to large language models. The format aims to reduce token overhead compared with traditional formats like JSON while remaining human-readable and structurally expressive. TOON represents the same data model as JSON but removes unnecessary syntax such as braces and quotes, relying instead on indentation and structured tokens to represent objects and arrays. This design allows prompts containing structured data to use significantly fewer tokens, which can reduce inference costs and improve efficiency in LLM applications. The project includes a formal specification, encoding rules, and reference implementations that developers can use to serialize and parse TOON data in their applications.
Features
- Token-efficient structured data format optimized for LLM prompts
- Human-readable syntax based on indentation rather than braces
- Lossless encoding compatible with the JSON data model
- Reference implementations and developer tooling
- Specification documentation and grammar definition
- Improved token efficiency for AI prompt serialization