Jev ecosystem projects — page 20

Teach your agent to work with evals: WHEN you actually need an eval or benchmark, HOW to build one that holds up, and how to read what it tells you. Deterministic-first, tool-agnostic.

Prompture is an API-first library for requesting structured JSON output from LLMs (or any structure), validating it against a schema, and running comparative tests between models.

Official Python SDK for VLM Run

Native DeepSeek Harness (DSH) plugin integrating TypeSafe Jev as a System One decision layer for agent selection, supervision, corrections, and approvals.

Classify text in PHP without an LLM bill: typed decisions in 100+ languages, self-hosted. Laravel-ready SDK for Laya, a Jev AI alternative.

Fast decision routing for agent harnesses — native MCP with Jev for tool selection and MCTS for multi-step decisions.

No project description available.

Typed-decision models (noul / choice / score) trained by a self-improving loop of AI agents — checkpoints, the code that produced them, and every version that failed.

Using Apple's Neural Engine as a fast and free local "System One" Decision Engine (macOS 27) - "Honey, we have Jev at home"

No project description available.

One local endpoint. The right model for every turn — enforced in code, not prompts.

Jev for PHP, TypeSafe AI PHP SDK

A curated list of projects, integrations, and resources for Jev, TypeSafe AI's System One model.

Semantic SQL for Postgres, powered by Jev

Pi extension: verbatim context compaction with TypeSafe Jev decisions

Small dependency-free CLI for TypeSafe Jev

Grill-me with Jev optional each turn

Open-source LLM router that uses TypeSafe's Jev to pick a model, on top of LiteLLM

Python prototype that plays NES Super Mario Bros. from structured RAM observations, with Jev answering focused movement and jump questions.

openvons (open-Jev): 有限選択肢に確率で答える判断層 — テキスト / 画像 / 日本語音声コマンド

Use Jev (TypeSafe's System One model) as a calibrated reranker: one call, up to 30 documents, a probability per document. Apache-2.0.

Interactive experiments with TypeSafe Jev, from support routing to 3D driving simulations with real AI decisions and visible sensor inputs.

Minimal harness-agnostic recursive language model layer — one binary, Python + llm()

AI summaries of Signal group content - text, images, links, voices. Runnable locally through ollama.