AI & Agents
ratel
Last updated 2026-07-22 · benchmark measured 2026-07-22 — deterministic & reproducible
Context engineering library for AI agents that reduces token usage by ~80%.
Overall production-readiness score — reserved
Legit.Show benchmarked ratel across all seven frames. The single overall score is shown to the verified maker; the frame-by-frame breakdown is public below.
Is ratel production-ready?
Legit.Show ran its deterministic 7-Frame production-readiness benchmark on ratel (github assessment), measured from the public surface with no LLM in the scoring path. Its strongest frame is Security; its weakest is Maintenance. The per-frame breakdown is public on the Legit.Show listing; the single overall production-readiness score is reserved for the verified maker.
The 7 Frames
- Security — 100/100
- Standards — 100/100
- Discoverability — 100/100
- Maintenance — 100/100
What we measured
- No Content-Security-Policy and no HSTS.
- No privacy policy found.
- Sets cookies / loads scripts with no consent prompt.
Who it's for
AI agent developers · LLM application builders · Teams optimizing token costs · Open-source model users · Frontier model users