Comprehensive guides, tutorials, and technical articles about Token-Oriented Object Notation, LLM optimization, and the future of AI data formats.
TOON (Token-Oriented Object Notation) is a serialization format designed specifically for Large Language Models. Unlike JSON, TOON minimizes token count while maintaining readability and schema awareness.
Benchmarks show TOON reduces token usage by 30-60% compared to JSON. For tabular data with repeated structures, TOON's header-based format eliminates redundant key names.
TOON excels with structured data: database exports, API responses, CSV conversions, and analytics datasets. Perfect for RAG systems, prompt engineering, and data-heavy LLM applications.
Learn how to integrate TOON encoding/decoding in Python, JavaScript, and TypeScript. Compatible with OpenAI API, Anthropic Claude, and any LLM that accepts text input.
Real-world analysis: A 1000-row dataset in JSON uses ~15,000 tokens, while TOON uses ~6,000 tokens. For high-volume applications, TOON can save thousands in API costs monthly.
Complete technical specification: header format, delimiter options, indentation rules, length markers, and escape sequences. Understand the TOON grammar for custom parsers.
How TOON will maximize efficiency with GPT-5's advanced context windows. Explore multimodal data serialization, native TOON support predictions, and next-generation prompt engineering strategies.
Complete guide to integrating TOON with MCP agents. Learn how TOON enhances agent communication, existing solutions, libraries, and practical implementations for autonomous AI systems.