We run 14 local-first agent harnesses with all working memory in TOON instead of Markdown — measured benchmarks (including the number that doesn't favor us) and the upstream bug report we got wrong

We moved our agent fleet's working memory off Markdown and onto TOON (Token-Oriented Object Notation) in December 2025 and just wrote up what 14 harnesses taught us.

The honest numbers (tiktoken o200k, 100 uniform CRM records):

- TOON 2,068 tokens vs row-object JSON 3,074 (-33%) vs pretty-printed 4,973 (-58%)

- A tight Markdown *table* is nearly equal to TOON (-4%) — the win is vs JSON, not vs a hand-optimized table

- Columnar JSON beats TOON by ~4% — we publish that too. TOON's edge over columnar is readability + declared `[N]`/field counts a validator can check, not tokens

- At a 2,500-token budget: TOON fits all 100 rows → correct answer; row-object JSON is cut at 81 rows → confidently wrong. Same data, same question.

The part r/LocalLLaMA might enjoy most: we filed a bug against the TOON CLI, the maintainer closed it not-a-bug, and he was right — our dialect had drifted off spec and our own machine memory had recorded the correct answer months earlier. Nobody pulled the thread until a human editor read the draft.

Article: https://netstatz.com/toon-structured-machine-memory/

Repro repo (scripts + every artifact, MIT): https://github.com/ianbmacdonald/article-toon-benchmarks

Everything runs local — the harnesses drive local models for bulk pipeline steps (Qwen3.6 on a lemonade fleet) with frontier models only where reasoning depth pays. Happy to answer questions; the benchmarks take ~30s to re-run with uv.