Headline: styxx v3. 9. 0 ships @trust — the one-decorator, one-line hallucination prevention layer for any LLM call. from styxx import trust followed by @trust on any LLM-calling function is the entire API. Every output is cognometrically verified (via styxx. guardrail. check (), AUC 0. 9012 on HaluEval-QA — see v17) before it reaches the caller. Risk above threshold is intercepted via fallback / retry / raise / annotate. Design principles: Zero config out of the box; ships with HaluEval-calibrated LR weights. Shape-preserving: handles OpenAI (choices0. message. content), Anthropic (content0. text), LangChain, dict, str — automatically. Prompt auto-detection from kwargs (prompt, question, query, messages) or positional args. Sync and async both supported (coroutine auto-detected). Four halt policies: fallback (safe text), retry (up to N), raise (TrustViolation), annotate (TrustResult). No residual-stream access required. No API keys. No external services. Tests: 31 new tests in tests/testₜrust. py. Full suite 562 pass, 1 skip, 0 fail. Installation: pip install styxx==3. 9. 0 The bet: this is the product that tries to change the LLM space forever. TLS for LLM cognition. Nothing crosses unseen. Repository: github. com/fathom-lab/styxx (tag v3. 9. 0). Package: pypi. org/project/styxx/3. 9. 0. Predecessor: v17 (10. 5281/zenodo. 19701750) introduced the four-signal guardrail at AUC 0. 9012. v18 packages it behind a single decorator so every LLM developer can install and use it in one line. License: CC-BY-4. 0 (this deposit, paper, demo) / MIT (styxx code).
Alexander Rodabaugh (Wed,) studied this question.