Jev Search

Jev is TypeSafe AI's System One model for making fast, structured decisions within software. It accepts unstructured data or program state and returns predefined, type-safe structured values with calibrated probabilities, confidence, and uncertainty rather than generated text. Jev uses a model architecture and parallel sampler that produces outputs in a single query, together with TypeSafe's Reinforcement Learning for Calibrated Decisions (RLCD) training method. It is intended for classification, routing, scoring, extraction, moderation, verification, guardrails, and other AI-powered workflow decisions. TypeSafe describes it as an early-access service for integrating probabilistic decision functions into software, with reported end-to-end response times in the tens to hundreds of milliseconds.

Jevry is an open-source, MIT-licensed desktop browser agent created by Michael Swissa. It is an Electron browser that combines a language model for planning and text or visual reasoning with TypeSafe's Jev model for selecting typed actions from controls observed on the current page. Chromium executes the selected operations through guarded native input rather than generated JavaScript, arbitrary selectors, or shell commands. Its loop observes page text and compatible controls, compiles supported operation-and-target choices such as clicking, typing, scrolling, or stopping, asks Jev to choose, validates the choice against the current document and target, executes it, records an input receipt, and checks the resulting evidence before continuing. It supports browser tasks such as forms, page-grounded questions, research and citation, documentation navigation, catalogs, and selected visual or numeric games. The project is an experimental developer preview for macOS and Windows; it requires a TypeSafe Jev connection plus a text-model connection, and some capabilities—including uploads, extensions, password-manager workflows, and certain cross-origin interactions—remain unsupported or unverified.

Jevgrep is a command-line tool for coding agents that finds relevant files and source context in unfamiliar repositories from natural-language questions. It uses Jev to judge relevance across folders, files, and declarations, explores qualifying branches of the repository hierarchy, selects files through content previews, identifies useful source units and surrounding context, and returns a summary followed by file locations, reading leads, and verbatim excerpts with line references. Python and TypeScript/JavaScript support declaration parsing, while other text uses a fallback. It also includes an agent skill that teaches supported coding agents when to invoke the CLI and how to use its results. The CLI is distributed through npm as @dzhng/jevgrep, requires Node.js 22 or later on macOS or Linux, and requires authentication with a supported AI provider. Searches send eligible source content to Jev through the selected provider; local filtering excludes ignored, hidden, dependency/build, binary, and obvious credential files but is not a guarantee that sensitive data is removed. The project is MIT-licensed.

LocalJev is a TypeScript service that provides a local Jev-compatible decision API for applications using TypeSafe's typed decision interface. It exposes a POST /v1/systemone endpoint and translates typed Jev questions and application state into classification prompts for an OpenAI-compatible DiffusionGemma inference server. The service validates and retries malformed JSON, normalizes probability vectors, calculates Jev-compatible choices, expected scores, and entropy-based confidence, and returns the standard Jev response shape. Its probabilities are model-generated or self-reported rather than read directly from model logits, so the repository advises evaluating calibration for the intended workload. It runs with Bun and can use oMLX as the local inference server; the default LocalJev address is http://127.0.0.1:8080.

Simple Jev is an open-model classifier and scoring server from Featherless AI. It accepts shared context or chat messages plus questions, then reads the model's next-token logits for predefined answer labels instead of generating a prose or JSON completion. The server constructs JSON responses for choice classification, ordered rubric scores, and truth or support judgments, including confidence and label distributions. Its Hugging Face Transformers implementation identifies the exact common token prefix across questions, evaluates that prefix once, reuses its KV cache for question-specific suffixes, and batches the suffixes before scoring the allowed answer tokens. The response-scoring code normalizes the selected logits; shared context is counted once for usage purposes, and no output tokens are generated. The API provides non-streaming /v1/classifier and /v1/systemone endpoints, with local execution supported through Python, PyTorch, and Transformers. A public demo API is available, while production deployments can use Featherless paid plans or run the server themselves. The repository also includes RFDT, which fine-tunes models directly on allowed answer-token logits using the same prompt structure.