--- title: "toon" type: "tool" slug: "toon-format-toon" canonical_url: "https://www.graphcanon.com/tools/toon-format-toon" github_url: "https://github.com/toon-format/toon" homepage_url: "https://toonformat.dev" stars: 24792 forks: 1101 primary_language: "TypeScript" license: "MIT" categories: ["llm-frameworks"] tags: ["data-format", "llm", "serialization", "tokenization"] updated_at: "2026-07-07T18:24:34.125942+00:00" --- # toon > Token-Oriented Object Notation (TOON) - Compact, human-readable JSON for LLM prompts. A compact data format designed to minimize token usage in JSON for Large Language Models by combining YAML-like structure with CSV-style tabular layout. Intended as a drop-in replacement for standard JSON when submitting prompts and inputs to language models. ## Facts - Repository: https://github.com/toon-format/toon - Homepage: https://toonformat.dev - Stars: 24,792 · Forks: 1,101 · Open issues: 20 · Watchers: 117 - Primary language: TypeScript - License: MIT - Last pushed: 2026-06-12T16:01:19+00:00 ## Categories - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags data-format, llm, serialization, tokenization ## Related tools - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 144,575) - [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. (★ 116,702) - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711) - [TradingAgents](/tools/tauricresearch-tradingagents.md) - TradingAgents: Multi-Agents LLM Financial Trading Framework (★ 91,610) - [caveman](/tools/juliusbrussee-caveman.md) - Cuts 65% of tokens in AI coding agent responses. (★ 86,150) ## README (excerpt) ```text # Token-Oriented Object Notation (TOON) **Token-Oriented Object Notation** is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for *LLM input* as a drop-in, lossless representation of your existing JSON. TOON combines YAML's indentation-based structure for nested objects with a CSV-style tabular layout for uniform arrays. TOON's sweet spot is uniform arrays of objects (multiple fields per row, same structure across items), achieving CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably. For deeply nested or non-uniform data, JSON may be more efficient. The similarity to CSV is intentional: CSV is simple and ubiquitous, and TOON aims to keep that familiarity while remaining a lossless, drop-in representation of JSON for Large Language Models. Think of it as a translation layer: use JSON programmatically, and encode it as TOON for LLM input. > [!TIP] > The TOON format is stable, but also an idea in progress. Nothing's set in stone – help shape where it goes by contributing to the [spec](https://github.com/toon-format/spec) or sharing feedback. ## Table of Contents - [Why TOON?](#why-toon) - [Key Features](#key-features) - [When Not to Use TOON](#when-not-to-use-toon) - [Benchmarks](#benchmarks) - [Installation & Quick Start](#installation--quick-start) - [Playgrounds](#playgrounds) - [Editor Support](#editor-support) - [CLI](#cli) - [Format Overview](#format-overview) - [Using TOON with LLMs](#using-toon-with-llms) - [Documentation](#documentation) - [Other Implementations](#other-implementations) - [📋 Full Specification](https://github.com/toon-format/spec/blob/main/SPEC.md) ## Why TOON? 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: ```json { "context": { "task": "Our favorite hikes together", "location": "Boulder", "season": "spring_2025" }, "friends": ["ana", "luis", "sam"], "hikes": [ { "id": 1, "name": "Blue Lake Trail", "distanceKm": 7.5, "elevationGain": 320, "companion": "ana", "wasSunny": true }, { "id": 2, "name": "Ridge Overlook", "distanceKm": 9.2, "elevationGain": 540, "companion": "luis", "wasSunny": false }, { "id": 3, "name": "Wildflower Loop", "distanceKm": 5.1, "elevationGain": 180, "companion": "sam", "wasSunny": true } ] } ```
YAML already conveys the same information with fewer tokens. ```yaml context: task: Our favorite hikes together location: Boulder season: spring_2025 friends: - ana - luis - sam hikes: - id: 1 name: Blue Lake Trail distanceKm: 7.5 elevationGain: 320 companion: ana wasSunny: true - id: 2 name: Ridge Overlook distanceKm: 9.2 elevationGain: 540 companion: luis wasSunny: false - id: 3 name: Wildflower Loop distanceKm: 5.1 elevationGain: 180 companion: sam wasSunny: true ```
TOON conveys the same information with **even fewer tokens** – combining YAML-like indentation with CSV-style tabular arrays: ```yaml context: task: Our favorite hikes together location: Boulder season: spring_2025 friends[3]: ana,luis,sam hikes[3]{id,name,distanceKm,elevationGain,companion,wasSunny}: 1,Blue Lake Trail,7.5,320,ana,true 2,Ridge Overlook,9.2,540,luis,false 3,Wildflower Loop,5.1,180,sam,true ``` ## Key Features - 📊 **Token-Efficient & Accurate:** TOON reaches 76.4% accuracy (vs JSON's 75.0%) while using ~40% fewer tokens in mixed-structure benchmarks across 4 models. - 🔁 **JSON Data Model:** Encodes the same objects, arrays, and primitives as JSON with deterministic, lossless round-trips. - 🛤️ **LLM-Frie ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/toon-format-toon`](/api/graphcanon/tools/toon-format-toon) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_