TOON
TOON (Token-Oriented Object Notation) is an open-source, compact and human-readable encoding of the JSON data model designed for LLM prompts. It combines YAML-like indentation with CSV-style tabular forms to reduce token use while retaining explicit structure, declared lengths, field lists and lossless JSON round-trips.
At a Glance
- LLM application developers
- RAG and AI-search teams
- AI-agent and multi-agent framework developers
- Teams with high-volume OpenAI, Anthropic, Google, xAI or other LLM API traffic
- +2 more
AI Tools by TOON
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TOON
Token Efficient JSON Format for LLMs
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Latest News
TOON launched as a token-efficient data format for LLM prompts, created by Johann Schopplich.
The @toon-format/cli was reported as launched for terminal JSON↔TOON conversion and token-savings statistics.
freeCodeCamp published an explainer on TOON and its potential role in reducing token use for AI systems.
Medium coverage described TOON's launch, benchmark results, growing language ecosystem and token-cost rationale.
Products & Services
A compact, human-readable, lossless encoding of JSON data for LLM input, using indentation for nested objects and inline, list, tabular and keyed-tabular forms for arrays and uniform records.
Reference encoder/decoder library for converting between JSON data and TOON, with strict/non-strict decoding capabilities and a public npm package.
Command-line interface for JSON↔TOON conversion, available through npx or global npm/pnpm/yarn installation and able to report token statistics.
Language-agnostic specification, shared conformance tests, documentation, playground and official/community implementations across multiple programming languages.
Market Position
TOON positions itself as a specialized LLM-boundary representation rather than a general replacement for JSON. It competes with JSON and compact JSON for structured prompt data, with YAML for nested human-readable data, and with CSV for purely flat tables. Its differentiators are declared row counts and field lists, JSON-compatible structure, tabular compression for uniform objects, and a shared conformance specification; the documentation also cautions that compact JSON can win for deeply nested/non-uniform data and CSV can be smaller for purely tabular data.
Leadership
Founders
Johann Schopplich
Creator and primary maintainer of TOON; the reference TypeScript/JavaScript implementation and the toon-format GitHub organization identify him as the project author/maintainer.
Executive Team
Johann Schopplich
Creator and primary maintainer
Created TOON and maintains the reference TypeScript/JavaScript implementation, CLI, benchmarks and documentation repository.
Founding Story
TOON was started to address the token and cost overhead of sending verbose JSON to language models. The initial vision was a translation layer: applications continue to use JSON programmatically, then encode structured data as a more compact, readable and schema-aware representation at the LLM boundary.
Business Model
Revenue Model
TOON is distributed as open-source software under the MIT License, with public source code and npm packages; no paid subscription, usage fee or commercial pricing model is identified in the reviewed sources.
Pricing Tiers
The format, reference implementation and CLI are publicly available through GitHub, npm and npx.
Target Markets
- LLM application developers
- RAG and AI-search teams
- AI-agent and multi-agent framework developers
- Teams with high-volume OpenAI, Anthropic, Google, xAI or other LLM API traffic
- Data and analytics applications passing tabular results to language models
- Open-source developers building language-specific TOON implementations
- Reducing token and inference costs for LLM prompts
- Retrieval-augmented generation (RAG) context payloads
- AI-agent and multi-agent state or tool-data exchange
- Analytics and database result sets sent to LLMs
- Prompt examples and structured data used in prompt engineering
- Applications that need a compact text representation while retaining JSON structure