Jev in Practice: A Composable Python Toolkit for TypeSafe's System One Decision Model

Jev is TypeSafe's flagship System One decision model: instead of generating text, it answers typed questions (Choice, Score, Noul) about a state with calibrated probabilities that software can branch on directly. This work presents daf-jev, an open, modular, composable Python toolkit for the Jev API, together with a live-API characterization of the model. The package layers ergonomic question builders, a decision-composition library (confidence gates, tiered routing, composite scoring), a concurrent corpus evaluator, a calibration module, a CLI, an agent skill, and a Model Context Protocol (MCP) server over a thin typed client. Live benchmarks show that batching questions into one call is faster and cheaper than sequential calls (up to ~18x speedup and ~4x fewer tokens), that decision pipelines complete within the model's millisecond envelope, and that reported confidence is self-consistent across repeated evaluations. All numbers in the accompanying manuscript are generated from benchmark artifacts; the full provenance chain (hashed documentation snapshot, figure registry, validation receipts) is machine-checked.