Where does the model stop and my business logic start?

The model answers how risky or urgent the input looks as probability, score, or choice, while application code decides what numbers mean through threshold rules. Model output never encodes business policy and policy never reinterprets prose.

How is this different from a thin Jev client wrapper?

Core holds decisions, thresholds, workflows, and records with no vendor code, while @jevflow/provider-jev holds the TypeSafe integration. Any other model or rules engine can plug in through one small provider interface.

How do I debug why a workflow picked an action?

Use probability, value, and confidence selectors with strict boundary operators, then inspect matchedRules for field, operator, threshold, and actual value. Greater-than excludes the boundary; use greaterThanOrEqual for inclusive checks.

Can I test workflows without calling the API?

Yes. MockProvider takes canned per-decision results and can simulate failures, and unmocked decisions fall back to per-kind defaults. The payments example runs deterministically with no key; basic, GitHub, and support examples need a real key.

How do I install it and which packages do I need?

Run pnpm add @jevflow/core @jevflow/provider-jev. Core is provider-agnostic and holds decisions, thresholds, workflows, and records; the provider package holds the TypeSafe integration. Set TYPESAFE_API_KEY, or pass the key to JevProvider directly.

What does a noul's confidence actually mean?

A noul has no model-reported confidence, so JevFlow derives one from binary entropy: how far the probability sits from 0.5. It is labeled as derived rather than model-reported, unlike choice and score confidence, which come from the model.

Are the evaluation records safe to log?

Yes. Every evaluation produces an EvaluationRecord with the decision name, provider, version, calibrated result, latency, attempt count, request id, and timestamp, and input is never included. An optional listener receives lifecycle events such as decision.completed and provider.error.

CommunityJev Tools & CLIs

Jevflow

TypeScript library that keeps probabilistic Jev judgments separate from deterministic workflow policy with explicit thresholds and inspectable outcomes.

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Stars: 11
Language: TypeScript
Published by: Community
Author: Mawfyy
Last verified: Oct 2026

Mawfyy/jevflow

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Probabilistic AI decisions as composable backend primitives — typed judgments (noul/score/choice), deterministic thresholds, and explainable workflows. Powered by TypeSafe's Jev, provider-agnostic.

Forks: 1
Last pushed: Updated Sep 2026

Repo stats from the GitHub API, cached Oct 2026.

About this project

JevFlow lets backend developers define typed probabilistic judgments (yes/no, scoring, classification) and use them inside normal workflows — with explicit thresholds, deterministic policy, and full explainability. It defaults to TypeSafe's Jev/System One model as the AI provider, but the provider is a replaceable seam, not a hard dependency. Two things are often conflated in "add AI to your backend" tools: Probabilistic intelligence — the model answers a question about some state.

Quoted from the project's README. Read the full README.

Tagscommunity awesomejev catalog-import

Works forJev for Classification Jev for Ranking

Used inJev for Software & DevTools

Languages: TypeScript

Highlights

Noul, score, and choice decisions declared once and evaluated many times as pure data.
Workflow thresholds over probability, value, and confidence return explainable action sets.
Jev provider batches independent decisions into one systemOne request with timeout control.
Records omit input for safe logging plus listener events for metrics integration.

Watch out

Workflows only return ordered action sets for the app to execute, and noul confidence is derived from binary entropy rather than reported by the model. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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