Jevbox

An open-source long-document search tool that finds pages answering a question by treating page selection as a multiple-choice decision for Jev, without a vector database or embeddings. For shorter documents, it presents each page as an option in one Choice; for longer documents, PageIndex builds a hierarchical tree of document sections and pages, and Jev selects among a small number of nodes at each level. The repository also demonstrates beam search over multiple candidate paths and Noul-based yes/no checking of candidate pages. It accepts PDF documents and questions, uses TypeSafe and PageIndex APIs, and is released under the Apache 2.0 license.

DocJev is an open-source Python library, CLI, and local app for classifying and splitting PDF, DOCX, and PPTX files with user-written natural-language rules. LiteParse extracts page text locally, then the hosted Jev decision engine assigns document categories or identifies boundaries between component documents; optional LlamaParse tiers provide cloud OCR for difficult inputs. Classification returns a category, probabilities, and review flags. Splitting returns ordered categories and contiguous page ranges, with optional export of each segment as a PDF and review reasons for uncertain boundaries. DOCX and PPTX processing requires LibreOffice, and inference is not offline because Jev is a hosted service. The project is distributed under the Apache-2.0 license.

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.

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.

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.