These days, working in development and around AI, I’ve realized how crucial it is to structure and share data effectively. JSON has been the default choice for APIs and applications for a long time, but there’s a new format gaining traction in AI workflows: TOON.
In this article, we break down why TOON is necessary, compare both formats side-by-side, detail their unique advantages, and clarify when you should use each one in your next project.
🔹 What Is JSON?
JSON (JavaScript Object Notation) is the lightweight data-interchange format that powers the modern internet. It relies on a minimal, human-readable structure using key-value pairs, objects, and arrays.
- ✔ Universality: Full compatibility across all programming languages, APIs, and platforms.
- ✔ Simplicity: Extremely easy to read, edit, and parse.
- ✔ Data-Centric: Ideal for representing structured, hierarchical data (e.g., user profiles, product catalogs).
🔹 What Is TOON?
TOON (Token-Oriented Object Notation) is a newer format designed for AI workflows, especially those involving intent descriptions, tasks, operations, and structured actions Its core design principle is efficiency.
Reduce the number of tokens when exchanging structured data with language models.
TOON doesn’t aim to replace JSON entirely; instead, it provides a more semantic and action-oriented structure for agent systems and LLM-based automation.
- ✔ AI Optimization: Can reduce LLM token usage by 30-60%, lowering costs and improving inference speed.
- ✔ Operational Focus: Explicitly describes tasks, steps, and operational intent.
- ✔ Structured Messaging: Predefined, schema-driven structures for messages, tasks, and complex actions.
- ✔ Deterministic Behavior: Enables LLMs to generate reliable, executable output for tool-calling and orchestration.
⭐ Why Is TOON Important Now?
TOON steps in right where most AI systems start to struggle — turning raw LLM output into reliable, predictable behavior. As we move from simple prompts to multi-step, tool-driven agent workflows, we need a clear, machine-readable way to express intent and the sequence of actions an AI should take.
Key reasons TOON matters today
- 💰 Token Cost Efficiency: TOON is more compact, which means you spend less on passing detailed instructions to LLMs.
- 🔄 Handling complex AI workflows: Modern pipelines involve scraping, extracting, validating, calling tools, and combining results. TOON lays out each step clearly so systems can run them reliably.
- 🧩 Better coordination between agents and orchestrators: Frameworks and orchestration layers need a shared language for describing tasks. TOON gives them that common format so everything works together smoothly.
- 🛠️ Clear intent & accurate tool routing: TOON makes the purpose and required actions explicit, helping downstream systems pick the right tool and handle errors more safely.
- 📚 Easier debugging and observability: When a workflow is written out as structured steps, it’s much simpler to trace what happened, find failures, and reproduce results — especially in production.
- ⚖️ Safer and more controlled automation: By defining what actions are allowed and what output is expected, TOON reduces surprises and keeps agent behavior within safe bounds.
- 🚀 Faster adoption of advanced patterns: Teams can reuse TOON templates (like fetch → extract → match → assemble) across different projects, speeding up development of solid AI systems.
In short, TOON bridges the gap between raw model output and dependable, orchestrated AI behavior — becoming a key piece for the next wave of agentic systems and real-world AI workflows.
🆚 JSON vs TOON: Side-by-Side Comparison
| Feature | JSON | TOON |
|---|---|---|
| Simplicity | Very simple and universal | More structured and descriptive |
| Purpose | General data exchange | AI workflows, tasks, and operations |
| Support | Supported everywhere | Mainly used in modern AI ecosystems |
| Descriptive Power | Represents data | Represents intent, context, and steps |
| Human Readability | Very easy | More technical and semantically rich |
📌 Use Cases
When to Use JSON
- Public or Internal APIs (HTTP/REST responses).
- Storing simple, static configuration or structured data.
- Communication between traditional microservices or backend systems.
- Any scenario requiring absolute, universal compatibility.
When to Use TOON
- Agent-based systems and orchestration.
- Tool calling and function execution driven by LLMs.
- LLM workflows that describe step-by-step actions and decision paths.
- Projects where token cost optimization and semantic structure are paramount.
📘 Examples
To see the tangible difference in structure and verbosity, look at these comparison examples:
💻 How to Use TOON With JavaScript / TypeScript
TOON is designed to be language-agnostic and works seamlessly with JavaScript and TypeScript. You can use it to define tasks, steps, and workflows for your LLM or agent-based applications.
1. Installing a TOON Library
While TOON is mostly JSON-compatible, you can use helper libraries for validation and structure. For example, you can install a TOON helper library (if available) or just use standard JSON utilities:
# npm
npm install @toon-format/toon
# pnpm
pnpm add @toon-format/toon
# yarn
yarn add @toon-format/toon
2. Example in TypeScript
import { encode } from '@toon-format/toon'
const data = {
users: [
{ id: 1, name: 'Alice', role: 'admin' },
{ id: 2, name: 'Bob', role: 'user' }
]
}
console.log(encode(data))
// users[2]{id,name,role}:
// 1,Alice,admin
// 2,Bob,user🎯 Conclusion
JSON is still the go-to format for general data exchange. It’s simple, widely supported, and basically holds the internet together.
TOON, on the other hand, plays a different role. It’s built for AI workflows where you need to describe actions, intent, and step-by-step operations. In those scenarios, it brings more efficiency, consistency, and lower costs.
They’re not competing formats—they actually work well together. JSON is great for data, and TOON is great for giving clear, reliable instructions to your AI agents.
📚 Resources
For more details about the TOON specification, official examples, schemas, and tooling, you can visit the official GitHub repository:
- TOON Format – Official GitHub Repository
- TOON vs JSON: A Modern Data Format Showdown
- Online JSON to TOON Converter - Free JSON to TOON converter that reduces LLM token usage by 30-60% with TOON format
