THE JEV ECOSYSTEM, INDEXED
Jev is TypeSafe's model for fast, structured decisions. Explore what people built with it, with sources and evidence labels attached.
OpenHarness ships a Jev Sheets harness for trying typed questions on table rows. Its Question Lab compares two formulations on a frozen sample and records answers, provider usage and review notes.
GoModel includes a Jev provider that forwards native System One decision requests. Its passthrough path preserves request and answer structures through the gateway.
Pi MCP Adapter can rank MCP tools by meaning using a System One evaluator. Project policy specifies which servers may share data for semantic search, alongside the existing word and regex search modes.
Paca adds per-project Jev decisions for agent selection, task assignment and filling supported empty task fields. Automation conditions can branch on typed answers while keeping human-set values intact.
PyGPT offers an inline Jev plugin for structured semantic decisions inside its assistant workflows. It supports classification, selection, verification and scoring over supplied state.
XERJ can send retrieved documents to Jev for query-relevance judgments and reorder the result list. Its search implementation keeps the decision step separate from ordinary retrieval scores.
A mathematics-to-animation pipeline uses Jev to review checkpoints while a separate authoring model develops explanations and Manim scenes. Typed questions assess evidence, blocking defects and the proposed repair target.
Pro Workflow can add Jev judgments to coding-agent correction hints and risk suggestions. Its optional classifier also supports a compatible local Laya server.
Agent Squad includes JevClassifier implementations in TypeScript and Python. A typed Choice question selects among configured agents using the request and conversation context.
A desktop office suite indexes local document text in SQLite and can use Jev to reorder search results. The optional reranker evaluates candidate files against the search question before showing the ranked list.
Ripple detects statements affected by an edit inside a Google Doc. Jev identifies likely inconsistencies, while Gemini drafts suggestions that the writer can review and apply.
Needle uses Jev to rank passages and select the strongest source sentence for a natural-language query. Its Chrome extension highlights those sentences on the original webpage, and a React interface also accepts pasted text.
A Pi extension collection includes TypeSafe decision tools, Jev-assisted context compaction and searchable local conversation history. The compaction evaluator scores bounded batches of history units.
OpenMuse represents Jev decisions as interactive controls inside its assistant interface. Server adapters and shared schemas connect typed answers with selectable actions and human review.
OpenMausBot can use Jev to select an agent for a collaborative room. The decider validates offered choices and probability values before forwarding a decision to the surrounding routing system.
A local SQLite memory system exposes retrieval through MCP and can rerank a candidate pool with Jev. Its implementation uses Noul relevance judgments before returning selected memories.
JevHarness lets a language model author task-specific code that builds Jev questions and combines typed answers into actions. A frozen harness can execute without calling the authoring model for every decision.
An independent model inspired by Jev evaluates text, images and video against supplied candidate decisions. A Qwen backbone and decision head produce candidate probabilities without generating answer text.
ThoughtDAG searches local agent sessions and visual conversation canvases. Its optional Jev layer helps select relevant excerpts and topics before the chosen language model develops an answer.
An experimental Photoshop chat router uses Jev to select supported direct editing commands and short recipes. Complex requests, visual checks and ambiguous instructions are routed to the configured language model.
Surf provides optional semantic commands that ask Jev to choose from allowed browser actions and check progress toward a goal. The controller validates each selected action against the current page.
The macOS Agent! application can ask Jev for advisory judgments about its planned actions. A Swift middleware layer defines the questions and converts returned verdicts into application decisions.
ReqLLM adds a TypeSafe provider for structured evaluation requests in Elixir. Its evaluate operation handles state and questions separately from the library’s text-generation operations.
LLPhant exposes Jev through a PHP classifier and typed request objects. Applications can send a state with classification questions through the native TypeSafe endpoint.
AgentConnect provides a TypeSafe decision service inside its local agent daemon. It validates Noul, Choice and Score responses before using them in agent coordination.
MemSearch includes an optional Jev reranker for project-memory passages. Candidate excerpts are scored against the query before the application chooses which memories to return.
QuantDinger can apply Jev checks before live entry orders, combining strategy context with account exposure and risk information. The application records individual choices, confidence and timing in a decision timeline.
The investment-agent framework includes a native TypeSafe adapter for its structured result contract. Jev answers are converted into the compatibility JSON expected by the surrounding agent pipeline.
TradingAgents optionally screens social posts with Jev before a sentiment analyst reads them. It removes unrelated company mentions and groups remaining posts by their stated stance.
OpenCodex adds Jev decision services to its provider and combo-routing system. Optional automatic routing chooses a configured model and reasoning effort before the main coding request.
The source repository contains an experimental composer that uses Jev to select elements from a prepared UI grammar. A bounded composition loop builds a JSON render specification from those decisions.
Opik instruments synchronous and asynchronous TypeSafe SDK clients. Evaluation states, questions, returned answers and usage can be captured in application traces.
DeepEval offers Jev-based evaluation metrics and a TypeSafe model configuration. Custom evaluation criteria are converted into structured judgments for individual responses and conversations.
GPT Researcher can use Jev to score scraped passages for usefulness to a research question. Its compressor selects context from the same chunk pool and output budget used by the alternative retrieval pipeline.
Composio includes a TypeSafe provider for selecting tools and binding arguments with explicit choices. Its implementation separates closed argument sets from values that require an open-ended companion model.
AgentScope exposes Jev through its classifier interface, translating binary, choice and score questions into TypeSafe primitives. The adapter converts structured answers back into framework classifier results.
Claude Code orchestration includes optional Jev judgment points alongside existing heuristics. The registry defines question schemas and distinguishes blocking decisions from background observations.
A Rust agent harness uses Jev to rank deferred tools against the current intent. Its TinyHumans-connected evaluator combines a tool Choice with a Noul question about whether a tool is needed.
A self-hosted API gateway forwards Jev requests through the native System One endpoint and manages TypeSafe API-key accounts. Choice, Noul and Score answers keep their structured response format.
A native Mac switcher asks Jev which running app the user is likely to open next. A Rust core supplies recent activation and dwell-time history, while a Swift menu-bar app executes the selected app activation and exposes the request and response.
A browser-native experiment compares Jev control with a labelled local policy using isolated Chocolate Doom WebAssembly engines. Jev chooses movement, view, firing and interaction controls from structured spatial state, and local code executes those controls.
A 3D rover sandbox compares a local baseline with a Jev controller under energy, storm and sample-delivery constraints. A decision timeline exposes observations, available actions, model choices, execution and recorded usage.
A classic-novel reader adds Jev's whole-sentence emotion associations alongside a deterministic character map. Readers can adjust which emotion scores are highlighted and explore nearby text without changing the original novel.
A ManiSkill Franka simulator turns a spoken or typed English goal into a chain of hardcoded robot primitives. Jev chooses the next primitive and target, while local PD control executes the motion.
A Chinese and English caption interface combines streaming speech recognition with Jev judgments for important statements, emotion and communicative intent. Partial captions receive provisional annotations, followed by another judgment when the sentence closes.
A command-line tool fetches an artist's discography and lyrics, asks Jev for theme, mood and lyrical-complexity judgments, and displays the results in a terminal dashboard. Saved classification files can be rendered again without new model calls.
An independent rebuild of a TypeSafe community teleprompter scores unfinished talking points against the speech transcript. Code checks off a point when its score crosses the selected threshold, so the speaker can cover topics in any order.
A local laboratory routes Doom observations and control commands through F prime flight software, CCSDS, Yamcs and Open MCT. Jev selects bounded game actions on the ground, with alternative rule and compatible-model providers for comparison.
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