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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.
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.
Get the toon source
Clone the repository and explore it locally.
git clone https://github.com/toon-format/toon.gitcd toon# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
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 |
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.
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.
Talk to DEV.coRelated open-source tools
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toon FAQ
How much can I save on tokens with TOON?
Do I need to train my LLM on TOON?
Is TOON stable? Can I use it in production?
Which languages are supported?
Custom software development, end to end
DEV.co has shipped ai frameworks software across regulated and high-growth industries. Our software development services and AI development services cover the work that follows a decision to adopt toon.
Ready to reduce LLM token costs?
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.