AI Frameworks · toon-format

toon

TOON is a compact, human-readable data format optimized for LLM input that encodes JSON data using CSV-style tables for arrays and YAML-like indentation for objects. It claims ~40% token reduction versus JSON on mixed structures while maintaining lossless round-trip conversion, backed by benchmarks showing 76.4% vs 75.0% accuracy on retrieval tasks.

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Source: GitHub — github.com/toon-format/toon

24.8k

GitHub stars

1.1k

Forks

TypeScript

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	toon-format/toon
Owner	toon-format
Primary language	TypeScript
License	MIT — OSI-approved
Stars	24.8k
Forks	1.1k
Open issues	20
Latest release	v2.3.0 (2026-05-20)
Last updated	2026-06-12
Source	https://github.com/toon-format/toon

What toon is

TOON combines indentation-based nesting (YAML-style) with tabular array representation (CSV-style), using explicit length declarations [N] and field headers {fields} to signal schema. The format is deterministic, lossless, and spec-driven (v3.3); TypeScript SDK available via npm with CI/CD validation. Best suited for uniform arrays of objects with primitive fields; degrades for deeply nested or non-uniform structures.

Quickstart

Get the toon source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/toon-format/toon.git2cd toon3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

LLM prompt engineering with structured data

Feed uniform arrays (customers, transactions, logs, records) to LLMs with 40% fewer tokens, improving cost per request and latency while maintaining accuracy on retrieval and reasoning tasks.

Cost optimization for high-volume API-to-LLM pipelines

When prompting hundreds or thousands of requests daily, token savings compound significantly. Measure TTFT and cost reduction empirically on your model and data shape.

Improving LLM parsing reliability on tabular data

Explicit field headers and array length declarations provide guardrails that help models extract and validate data accurately, reducing parsing errors on structured retrieval tasks.

Implementation considerations

Benchmark your exact data shape and LLM (gpt-4, Claude, Llama, etc.) empirically; token savings vary by structure (0–60% depending on tabular eligibility) and model tokenizer.
TypeScript SDK available via npm (@toon-format/toon); verify Go, Python, Rust implementations in ecosystem match v3.3 spec and are actively maintained if multi-language support required.
Encode JSON to TOON before sending to LLM; decode LLM responses back to JSON if your system expects JSON downstream. Benchmark end-to-end latency, not just token count.
TOON format is 'stable but an idea in progress' (README); spec may evolve. Pin your SDK version and test upgrades; community-driven (GitHub spec repo), not maintained by a commercial vendor.
For semi-uniform or non-uniform arrays (40–60% tabular eligibility), token savings diminish; evaluate cost/benefit carefully and consider fallback to JSON if conversion overhead exceeds savings.

When to avoid it — and what to weigh

Deeply nested or highly non-uniform JSON structures — TOON's tabular compaction applies only to uniform arrays. Complex configuration objects or variable-schema data may see negligible or negative token savings; JSON remains more efficient.
Pure flat tabular data (no nesting required) — CSV is more compact than TOON for simple tables. TOON adds 5–10% overhead (length declarations, field headers) that benefit LLMs but waste tokens for dumb parsers or pure tabular contexts.
Latency-critical real-time applications — If end-to-end inference time is the bottleneck, benchmark empirically. Some quantized or local models (Ollama) may parse compact JSON faster than TOON despite lower token count; measure TTFT on your exact setup.
Non-LLM-based systems expecting JSON — TOON requires round-trip conversion (JSON ↔ TOON). If your downstream system is not LLM-based or does not benefit from token savings, added encoding/decoding overhead may outweigh gains.

License & commercial use

MIT License. Permissive; allows commercial use, modification, and distribution with attribution. No restrictions on proprietary or closed-source applications. Review the LICENSE file in the repo to confirm no additional terms.

MIT license explicitly permits commercial use without licensing fees or restrictions. No vendor lock-in or commercial support obligations; community-driven. For production SLAs or vendor support, consider contacting maintainers or forking for internal maintenance.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

TOON is a data format with no cryptographic, authentication, or network security built in. Use TLS/HTTPS to transmit TOON-encoded data. No buffer overflow or parsing vulnerabilities disclosed. Encoding/decoding is deterministic and lossless. Potential consideration: ensure SDK dependencies (TypeScript) are scanned for CVEs in CI/CD. No formal security audit mentioned in documentation.

Alternatives to consider

JSON (compact variant)

Ubiquitous, native LLM training, no encoding overhead. Suitable if token savings <30% on your data or if integration simplicity outweighs cost. Baseline for benchmarking.

YAML

Human-readable, indentation-based, ~20% fewer tokens than JSON. Lacks CSV-style tabular compaction and explicit field headers for LLMs, but simpler syntax and wider tool support.

CSV + metadata sidecar

Flat tabular data only. For pure tables (no nesting), CSV is most compact. Add a JSON schema or YAML metadata file to describe structure; requires custom parsing logic and loses TOON's schema-in-data approach.

Software development agency

Build on toon with DEV.co software developers

Start with a pilot: integrate TOON encoding into your LLM request pipeline and benchmark token savings on your exact data. Use the TypeScript SDK (npm) or multi-language ecosystem to experiment with no vendor lock-in.

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toon FAQ

How much can I save on tokens with TOON?

Data-dependent: 0–60% depending on structure. Uniform arrays of objects see 40% savings in benchmarks; deeply nested or non-uniform data may see no gain or slight overhead. Benchmark your exact data shape and model.

Do I need to train my LLM on TOON?

No. LLMs are trained on text (including YAML-like and CSV formats). TOON is readable text; models can parse it out of the box. Explicit field headers and length declarations improve comprehension, not parse accuracy from training.

Is TOON stable? Can I use it in production?

Format is described as 'stable but an idea in progress.' Spec v3.3 is current; unlikely to break, but monitor GitHub spec repo for changes. Community-driven; no SLA or commercial support. Suitable for cost-optimization pilots; pin SDK versions and test upgrades.

Which languages are supported?

TypeScript (npm), Python, Go, Rust, .NET mentioned in README. Verify the language binding you need is current and tested against v3.3 spec before committing to production.

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