Toon
🎒 Token-Oriented Object Notation (TOON) – Compact, human-readable, schema-aware JSON for LLM prompts. Spec, benchmarks, TypeScript SDK.
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What is Toon?
TOON (Token-Oriented Object Notation) is a compact, human-readable data format designed specifically for Large Language Model prompts. It encodes the same objects, arrays, and primitives as JSON with deterministic, lossless round-trips while using approximately 40% fewer tokens. TOON reaches 76.4% accuracy versus JSON's 75.0% in mixed-structure benchmarks across 4 models, improving LLM parsing reliability.
Key features include explicit [N] array lengths and {fields} headers that give models a clear schema to follow, indentation-based syntax instead of braces for YAML-like readability, and CSV-style compactness that minimizes quoting. Uniform arrays of objects collapse into tables that declare fields once and stream row values line by line, making it ideal for repeated structures. The format supports all JSON data types (objects, arrays, strings, numbers, booleans, null) and includes a TypeScript library, CLI tool, and online playground for conversion.
TOON is designed for developers, AI engineers, and teams working with LLMs who need to optimize token costs and maximize context window usage. It's perfect for RAG pipelines, e-commerce data, analytics reports, chat histories, API responses embedded in prompts, and cost-sensitive AI workflows. The format is production-ready with spec v3.3, 15.3K GitHub stars, and 21+ community implementations across Python, Go, Rust, .NET, Ruby, PHP, and more.
Toon pricing
Pricing model: Freemium
TOON is 100% free to use. The online JSON to TOON converter requires no signup, no API key, and no tracking. All tools run client-side in your browser for maximum privacy and speed. The TypeScript library (@toon-format/toon) is free via npm/pnpm/yarn. The CLI tool (@toon-format/cli) is free via npx or npm install -g. No paid tiers or subscription plans exist.
Toon pros
- Uses 30-60% fewer tokens than JSON
- Higher LLM accuracy (73.9% vs JSON's 69.7%)
- Lossless round-trip conversion with JSON
- Human-readable with YAML-like indentation
- Explicit [N] array lengths for schema clarity
- {fields} headers help models parse reliably
- Uniform arrays collapse into compact tables
- Minimizes quoting for cleaner output
- No signup required for online converter
- All tools run client-side in browser
- Free TypeScript library available
- CLI tool for quick conversions
- 21+ community implementations across languages
- Catches truncated/corrupted data with metadata
- Perfect for RAG pipelines and context windows
- Production-ready with spec v3.3
- 15.3K GitHub stars confirming adoption
- Works with OpenAI, Anthropic, and all LLM APIs
Toon cons
- Not ideal for deeply nested structures
- Not suitable for semi-uniform arrays
- Not for pure CSV-like flat tables (CSV is smaller)
- Not for latency-critical quantized models
- Not directly readable by JSON parsers
- Requires custom TOON parser implementation
- Not for public REST APIs
- Not for system-to-system integrations
- Spec still in working draft (not finalized)
- Python/Go/Rust libraries still in development
Frequently asked questions about Toon
What is TOON format?
TOON (Token-Oriented Object Notation) is a compact, human-readable data encoding format designed specifically for LLM input. It uses approximately 40% fewer tokens than JSON while maintaining the same data model, combining YAML's indentation with CSV-style tabular layout for uniform arrays of objects.
How much can I save with TOON?
TOON reduces token usage by 30-60% compared to JSON. Benchmarks show 39.6% fewer tokens on average (2,744 tokens vs JSON's 4,545), with up to 60.7% reduction for flat data. In real-world examples, processing 10,000 analytics queries/month drops from $242/month to $99/month in token costs.
Is TOON compatible with all LLMs?
Yes! TOON works great with OpenAI, Anthropic, Claude, Gemini, and all other LLM APIs. Simply convert your data to TOON before sending it and convert back when receiving responses. Benchmarks tested across 4 different AI models showing improved accuracy.
Can I convert TOON back to JSON?
Yes! TOON is fully reversible with lossless round-trips. Both encode() and decode() functions are available in the TypeScript library. You can convert JSON→TOON and TOON→JSON without any data loss, supporting all JSON data types (objects, arrays, strings, numbers, booleans, null).
What's the difference between TOON and JSON?
JSON has universal compatibility and native browser support but is verbose with higher token count and quote-heavy syntax. TOON has 30-60% fewer tokens, higher LLM accuracy (73.9% vs 69.7%), cleaner quote-free syntax, and lossless JSON conversion. Use JSON for web APIs and cross-platform exchange; use TOON for LLM prompts and token cost optimization.
What types of data work best with TOON?
TOON is best for uniform arrays of objects (same fields across rows), large datasets with consistent structure, e-commerce orders, analytics reports, RAG pipelines with LLM context windows, and cost-sensitive AI workflows. Avoid for deeply nested structures, semi-uniform data, pure CSV tables, or latency-critical applications.
Is my data safe when using the TOON converter?
Yes! Everything runs client-side in your browser. No signup, no data sent anywhere, no tracking, no API key required, and no server uploads. All tools run entirely in your browser for maximum privacy and speed, with local caching for fast reuse.
Is TOON free to use?
Yes! TOON is 100% free with no signup required. The online converter, TypeScript library, CLI tool, and all community implementations are free. There are no paid tiers or subscription plans. All tools run client-side for privacy and speed.
How do I install TOON?
For TypeScript: run 'npm install @toon-format/toon', 'pnpm add @toon-format/toon', or 'yarn add @toon-format/toon'. For CLI: run 'npx @toon-format/cli input.json -o output.toon' or 'npm install -g @toon-format/toon'. Then use 'toon encode input.json -o output.toon' and 'toon decode input.toon -o output.json'.
Does TOON work with RAG systems?
Definitely! TOON is excellent for RAG (Retrieval-Augmented Generation) since it lets you fit more context in the same token budget, improving your LLM's knowledge base efficiency. The 30-60% token savings allows more retrieved documents in context windows while maintaining structured data clarity.