Find JEV AI projects you can verify.

TradingAgents combines fundamental, sentiment, news and technical analysts with debating researchers, a trader and risk management roles. With TYPESAFE_API_KEY, Jev removes unrelated StockTwits and Reddit posts and classifies stance; without the key, posts pass through unscreened.

AgentScope builds agents around model reasoning and tool use, with context management, event streaming, memory and permission controls. JevClassifierModel integrates the Jev classifier through the TypeSafe SDK within this broader agent framework.

Planning and execution agents gather, summarize and track sources, then combine findings into a cited research report. When TYPESAFE_API_KEY is present, Jev scores passage usefulness for the research question before the LLM reads the context.

Laya uses a non-autoregressive model to answer choice, score and noul questions, with a router selecting checkpoints. Its self-hosted server exposes POST /v1/systemone for existing Jev clients; the underlying model is Laya rather than hosted Jev.

This browser agent turns visible page controls into an indexed action space for a natural-language goal. Jev chooses the operation and compatible element in one request; a separate small LLM generates text for TYPE_TEXT.

Developers define allowed components and actions so generated interfaces follow their own catalog and schema. The playground's experimental composer uses typesafe-ai/jev to evaluate candidate components while building a UI specification.

This self-hosted trading stack covers Python strategies, backtests, paper trading, live execution and monitoring. Jev evaluates order, strategy, exposure, position and budget context, returning typed choices with probabilities for the entry filter.

The self-hosted platform organizes agents, isolated workspaces and task boards across your own computers. The README lists TypeSafe's Jev as an available model that returns typed decisions with confidence rather than generated text.

Kev provides pretrained decision models and fine-tuning for yes/no, multiple-choice and rating questions over shared text. Its API follows TypeSafe System One, allowing the TypeSafe Python SDK to target a local Kev server.

This npm library and Claude Code plugin compact history by pruning tool calls and results instead of rewriting conversation text. Jev answers separate noul questions about retaining each call and result; thresholds determine whether to keep, shorten or remove the pair.

The Android assistant reads visible conversations through accessibility and local OCR, displaying analysis and reply suggestions in an overlay. Jev judges intent and response choices; a generative model drafts three candidates and Jev ranks them.

Instructions, tools, skills, channels and schedules live in conventional directories, making an agent project inspectable and extensible. The evaluate helper sends typed questions about supplied state through eve's model authentication, defaulting to Jev.

SemIf handles small semantic decisions such as routing, retries and evidence checks by reading option probabilities without generating an answer sentence. It reproduces Jev's interface pattern with open models, not Jev's undisclosed model or training, and has no TypeSafe affiliation.

The native CLI supports an interactive shell, scripted requests and embedding, with skills, MCP servers and subagents. Jev receives review policy, context and the pending action, returning a clear or caution decision for the permission pipeline.

Searchable memory helps recover past decisions, debugging context and feature history across supported coding agents; Markdown remains the source of truth. Optional Jev reranking uses the remote TypeSafe API to reorder memory search results without downloading a local reranker model.

The pipeline develops a learning brief, mathematics, visual plan and Manim scene, then reviews the rendered result. Jev evaluates candidate artifacts and Astra's text audits through typed Score, Noul and Choice answers; it does not receive the images.

The bot replaces post-only limit quotes on Monad blocks and streams book, order and fill events to a dashboard. With MODEL=jev and TYPESAFE_AI_API_KEY, Jev chooses buy or sell; the default mock model is a momentum heuristic.

The model answers choice, Boolean and ordered-score questions from states and candidates without decoding answer tokens. NanoJev is a separate local model inspired by Jev; recorded comparisons include Jev and untuned Qwen on the same game interfaces.

This official resource helps agents design TypeSafe workflows, find documentation and compose typed judgments in code. The skill describes Jev as a System One model returning typed answers and probabilities for application-controlled workflows.

PyGPT is a multi-provider desktop assistant for Linux, Windows and macOS, with chat, agents, RAG and tool integrations. The Jev plugin exposes jev_evaluate(state, questions), returning Choice, Score or Noul results to the current model.