Jev Ultrafast
A browser loop where Jev selects an operation and page element, with a text model handling field input.
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Public projects, checked against their original sources.
A browser loop where Jev selects an operation and page element, with a text model handling field input.
An Android agent using Jev decisions and Mobilerun, with a live studio, CLI, and execution traces.
Add sentence-by-sentence Jev judgment meters to a video.
Let Jev choose NES controller actions from structured emulator state.
Classify Git history by bug fixes, security changes, and change type.
Watch Jev choose a trading side from a live Monad order book.
A macOS control loop that combines OCR and accessibility data with Jev action selection.
Turn Jev answers about household state into Home Assistant sensors and automation responses.
Play a browser arena match against a Jev-driven opponent.
A staged Jev workflow for inspecting Git changes or source files, with a local review dashboard.
A Go CLI and MCP server that lets coding agents request typed Jev evaluations.
The official JavaScript and TypeScript client, with answer types inferred from your questions.
Use Jev through the AI SDK evaluation API, with Choice, Score, and Boolean questions in one request.
Official synchronous and asynchronous Python clients for Choice, Score, and Noul questions.
An official agent skill for designing TypeSafe workflows and finding the relevant API guidance.
A community Rust client with async and blocking APIs, typed answers, and configurable retries.
An independent Go SDK with question constructors, typed answer accessors, and context support.
A CLI and MCP browser tool pairing Jev decisions with agent-browser actions.
A Playwright browser harness splitting goal planning, typed action decisions, and text generation.
A Pi extension that adds Jev assessments before tool use and after file changes.
Explore a Neo4j graph by asking Jev which relationship to follow and whether the goal is reached.
A RubyLLM 2 provider that expresses Jev questions through RubyLLM's structured output interface.
A community n8n node for adding typed Jev questions to automation workflows.
An agent-loop experiment where Jev chooses actions and a LangChain model supplies text.
Small Pydantic AI examples that use Jev at prompt, tool, and application decision points.
Run System One-shaped evaluations through other model APIs to build comparisons with Jev.
A Discord bot that evaluates spam and scam signals with Jev.
Find relevant code paths with Jev-guided traversal of a repository.
Rank a collection of texts through Jev-judged pairwise tournaments.
Score OpenTelemetry records before routing them to deeper analysis.
Use semantic predicates, scores, and routes in shell pipelines.
Ask typed Jev questions directly from the terminal.
Ask a yes/no question across the functions in a codebase.
Label the tone of Bluesky posts and drafts as you browse.
Track time-stamped Jev forecasts against later public market observations.
Review JavaScript and TypeScript comments for accuracy and usefulness.
A configurable CLI for sending state and typed questions to Jev.
Review whether cited papers support the sentences in a manuscript.
Inspect typed quality signals while drafting an X post.
Explore emoji suggestions and tone signals for the text you type.
Replace flagged Spanish words with beeps using timestamped transcripts.
Sort readable documents into categories with a review queue.
Reorder search candidates with one Jev relevance judgment per document.
Inspect a CV with structured, traceable Jev assessments.
Evaluate application input, output, and retrieved text with Jev checks.
Route Persian or English support tickets with Jev and explicit business rules.
Refine a draft against your writing samples with a Jev feedback loop.
Assess npm lifecycle scripts before deciding how to handle an installation.
Find files, map a subject, and trace references using Jev judgments.
Inspect suspicious text messages using Jev signals and adjustable policy.
Lint source comments against five Jev evaluation dimensions.
Review a folder of CVs with an editable scoring policy.
Explore Jev workflows for tickets, operational alerts, and deployment risk.
Hide page clutter with reusable, Jev-classified browser rules.
Delegate a bounded browser step to Jev through MCP.
Control macOS apps through accessibility targets chosen by Jev.
Bring Jev’s indexed action selection to the ego lite browser.
A compact Jev browser loop paired with a timed task benchmark.
Add Jev judgment labels to X posts and Hacker News comments.
One Playwright core exposed as a persistent CLI, SDK, and MCP server.
Explore native desktop control with Jev and an optional planning model.
Search and follow web evidence with Jev-ranked pages and links.
Inspect Jev browser decisions in a local visual control panel.
Let Jev drive browser steps and escalate uncertainty to the calling agent.
Run persistent Android tasks with Jev selecting valid device actions.
Add page checks and action judgment to an existing Playwright MCP setup.
Explore browser flows with Jev decisions and replayable test evidence.
Reorder Google results using Jev relevance and promotional-content judgments.
Optionally blur text flagged as advertising or AI-like prose.
Run browser goals through named actions and Jev selections.
Explore Jev tactical judgments inside a MuJoCo drone simulation.
Watch Jev pick from legal Snake moves with measured board features.
A structured-state controller for a StarCraft shareware mission.
Inspect the judgments behind guards in a stealth game.
Compare Jev-selected tactics inside two browser Doom instances.
Follow Jev decisions in an experimental Civilization II controller.
Use Jev to select individual Tetris actions from measured outcomes.
Race Jev at classifying messages as spam or not.
Switch a browser brick-breaker paddle between manual and Jev control.
Explore NPC decisions and shogi moves in one visual lab.
Inspect car-control judgments in a changing 3D road simulation.
Turn a robot-arm goal into a sequence of predefined motions.
Find a path between Wikipedia articles using Jev-ranked links.
A visual control lab for Jev across games and emulator backends.
Run Jev-versus-Jev gomoku with a MoonBit API client.
Let Jev choose serves and returns in a live browser table-tennis game.
Choose jump, duck, or run for each new dinosaur-game obstacle.
Ask Jev to choose a complete, collision-checked Tetris placement.
Compare parallel 2048 games while editing the state Jev receives.
Try to persuade a border inspector whose decisions come from Jev.
Drive a Pong paddle using symbolic state and Jev choices.
Watch Jev pilot an original river-shooter arcade game.
Experiment with rover missions, energy use, and inspectable decisions.
A social-deduction game concept with a Jev moderation prototype.
Inspect a customer-service agent’s action gate under adversarial testing.
Observe Jev controlling a small creature in a deterministic sandbox.
Watch two Jev players choose among legal chess moves.
Inspect Jev actions alongside a live Minecraft world view.
Use Jev for automatic model selection and typed evaluations in agents.
Send typed evaluation questions from shell scripts through AI Gateway.
Add a Jev decision companion to the Loki agent.
Display typed Jev answers and probability cards inside Agent Zero.
Route example email intents through an async LangGraph workflow.
Place Jev judgments in an HTTP inspection and forwarding service.
Integrate typed Jev requests into Laravel services and tests.
Ask Choice, Noul, and Score questions from PostgreSQL.
Add Jev configuration, usage records, and confidence policies to Rails.
Interpret Home Assistant voice commands with batched Jev questions.
Prototype Jev semantic checks for a Mellea generation loop.
Browse reusable React components for TypeSafe-style decision interfaces.
Use Jev to supervise evidence from a coding agent’s work.
Evaluate Pi edits and actions against project rules with Jev.
Archived Jev Codex Router: per-call model and effort selection for a local Codex setup.
Reduce large Claude Code tool results using Jev relevance judgments.
Expose typed Jev judgments as composable commands for coding agents.
Inspect a pull request’s risk, review depth, and test gaps.
Delegate file ranking, change checks, and triage to bounded Jev workflows.
Use a single model route with Jev selecting from a provider catalog.
Pair a Pi coding model with Jev judgments and explicit action policies.
Route Claude Code requests while accounting for prompt-cache tradeoffs.
Inspect MCP calls, results, and tool descriptions with Jev.
Review proposed database migrations with typed Jev judgments.
Add Jev option scores and optional skill routing to Claude Code.
Evaluate agent tool calls for risk, user intent, and untrusted influence.
Add an advisory Jev reviewer to an OMP coding session.
Run an issue-to-PR loop with tests, an independent reviewer, and Jev checks.
Combine repository policy with semantic change-risk signals.
Check a coding agent’s stopping point against plain-language rules.
Review whether a Pi session openly left required work unfinished.
Add semantic checks and traceable review signals to a single Pi agent.
Combine Jev safety signals and model selection in an LLM gateway.
Check source files against natural-language coding conventions.
Suggest a relevant installed capability for the current agent prompt.
Route an OpenAI-compatible model ID through a Jev decision.
Use Jev as a typed decision layer within Pi.
Give a Hermes agent a typed Jev tiebreaker.
Return structured code-quality judgments to a coding agent over MCP.
Combine Zod shape validation with Jev semantic checks.
Typed System One clients and Microsoft.Extensions.AI adapters for .NET.
Call TypeSafe with inferred answer types from Swift applications.
Use named tuples for typed Jev questions and answers in Scala.
Ask batched Choice, Noul, and Score questions from Elixir.
A strictly typed PHP client with configurable transport and retries.
Infer Jev answer types from the questions defined in Zig.
A Ruby client for direct access to the System One API.
A Go System One client with optional Langfuse instrumentation.
Build typed decision workflows with Jev or a custom provider.
Bind a validated state schema and Jev questions into one typed artifact.
Turn a natural-language request into choices from a fixed UI catalog.
Read writing diagnostics through a playful compiler-style interface.
Evaluate a paper’s presentation, clarity, and completeness.
Apply your own typed questions to selected CSV rows.
Inspect separate Jev checks for clarity, filler, tone, and actionability.
Bring a CSV and inspect Jev classifications in a distribution chart.
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Source description: ResolveIQ by Jev — An AI-powered customer support triage platform that intelligently classifies support tickets, prioritizes issues, and generates context-aware response suggestions using Jev, LangChain, Groq, and FastAPI.
Source description: Block, steer, and "fuzzy lint" with Jev to make agents follow your rules and conventions. CLI, github action, pi package, claude extension, and more. Built with Effect + TypeScript.
Source description: RuneBench harness for TypeSafe's Jev: bounded action catalog, tick-mode controller and a live dashboard
Source description: Pi coding-agent extension: TypeSafe Jev checks for tool calls, tool outputs and replies (prompt injection, approvals, secret scrubbing, task pinning)
Source description: Pass-through model router for Claude Code and Codex CLI that picks a model tier per human turn with Jev, TypeSafe AI's decision model
Source description: Automatic model routing for Pi using TypeSafe's Jev through Vercel AI Gateway
Source description: pre-commit hook: one Jev call judges whether your commit message matches the diff, plus debug leftovers, scope creep, and a secret belt
Source description: The complete cookbook for Jev by TypeSafe AI — 120+ use cases, 10 runnable examples, 4 composition patterns, and first-principles theory for the world's first System One AI model.
Source description: Polymarket trading bot driven by Jev (TypeSafe AI) via OpenRouter
Source description: Unofficial Java SDK for TypeSafe Jev and Vercel AI Gateway, with Spring Boot and WebClient support
Source description: A fuzzy linter for coding agents. It checks the code your agent writes against your team's best practices while the agent is still working, not at code review.
Source description: Agent Skill and Python CLI for local video remixing, Jev judgments, and originality reports.
Source description: Let TypeSafe Jev decide whether a message you send mid-turn should steer, queue, or interrupt your coding agent. Claude Code plugin; Codex CLI in testing.
Source description: Claude Opus 5.5 with the effort level re-decided every step by the TypeSafe Jev reflex — without breaking the prompt cache. CLI + Claude Code plugin.
Source description: Typed Scala client for Jev (TypeSafe AI): typed questions, typed answers, errors as values. Scala 3.9 and 2.13, Spark 4 ready.
Source description: Draw on a tldraw canvas with your voice and a pointing finger. Jev (TypeSafe System One) decides action, target and place in ~350 ms per spoken word.
Source description: A Kotlin Multiplatform DSL and client for TypeSafe's Jev model
Source description: Confidence-aware content moderation for Laravel: classify any Eloquent model with Jev and route each item to passed, flagged for human review, or blocked, using per-category thresholds.
Source description: Screens an AI agent's tool calls before they run and tool results before the agent reads them. An MCP proxy plus a hooks adapter for a client's built-in tools.
Source description: Command-line tool for TypeSafe's Jev AI model
Source description: Interactive TypeScript coding CLI powered by Jev typed decisions and constrained AST generation.
Source description: Jev decision layer for agents: MCP server, embeddable DecisionModel library, and an escalate-only Claude Code plugin (TypeSafe AI's Jev)
Source description: TypeSafe Jev as the reviewer for Hermes Agent smart command approvals. 8.7x faster, 4.4x fewer prompts, measured on 153 real commands. Approvals only.
Source description: MCP server for TypeSafe Jev: typed classify, score, check, match and screen for any agent, with confidence on every answer
Source description: Jev decision layer for Muse: a fast, cheap TypeSafe AI gate before expensive agent work — confidence policy, recipes, reference router, honest measurement.
Source description: Production TypeSafe Jev (System One) use-case harnesses with confidence-gated decision logic
Source description: One local endpoint. The right model for every turn — enforced in code, not prompts.
Source description: Jev-powered plugins for coding agents
Source description: Jev-powered decision support for OmO and senpi agents
Source description: Claude Code plugin: Jev for rule checks, verbatim compaction, and prompt routing
Source description: An independent, type-safe Swift SDK for TypeSafe Jev, with async/await, batching, retries, and SPM support.
Source description: A custom coding harness for TypeSafe AI's Jev: an LLM proposes, Jev answers narrow questions, code decides, every step leaves a receipt.
Source description: 用 TypeSafe Jev 推荐已安装 Skill / Bounded installed-skill recommendations with TypeSafe Jev. Python CLI, Codex skill, bilingual docs and live examples.
Source description: Jev (TypeSafe System One) backed auto mode for the Pi coding agent: semantically auto-approves bash, write, and edit tool calls and fails closed when a decision cannot be made.
Source description: ⚡ Millisecond System-1 judgement for every tool call in DeepSeek Harness — Jev-powered risk classification & evidence-gated auto-approval. Fail-closed by construction. dsh 生态第一个 System-1 决策插件
Source description: A Claude Code mod that uses TypeSafe's Jev to decide auto mode tool calls. 2x faster than the built-in classifier on the calls it decides.
Source description: Standalone TypeSafe Jev-based auto-routing for Agent Zero
Source description: Fast, cheap, typed decisions for Claude Code. Jev (TypeSafe's decision model on OpenRouter) sorts, checks, scores, gates and verifies at 0.3s and a fraction of a cent per item. Claude keeps the reading, writing and judgment.
Source description: Rank Pi Agent Skills for the current task with TypeSafe Jev
Source description: Jev-powered, rule-based grader for text files. Runs every rule against every line in parallel. No skimming, no missed lines.
Source description: A prose linter that sniffs out AI writing tells. Zero dependencies, countable rules plus one judgment model.
Source description: A Jev powered LinkedIn recruiting agent. Watch it browse relevant profiles, save links, and review evidence against your hiring brief.
Source description: A zero-embedding approach to video intelligence, using Jev: contact sheets + typed decisions, as MCP tools and a skill for any coding agent.
Source description: A claude code plugin for jev
Source description: A codebase scanner that helps you not run malicous code
Source description: Retrieve by relevance, not resemblance: filter an AI assistant's memories with TypeSafe's Jev
Source description: unofficial go sdk for typesafe ai. not affiliated with or endorsed by typesafe ai. a side project built to fill the missing go sdk gap, for the community to use.
Source description: MCP server that puts TypeSafe Jev on the coding loop in Cursor, Codex, and any MCP client
Source description: Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000 judgments, with typed escalation back to the LLM
Source description: Route HTTP requests by meaning. A semantic router for Hono powered by Jev.
Source description: Jev, TypeSafe's System One classifier, as a tool inside Claude Code, Codex, Pi, and OpenCode: typed classify, check, score, rank, and ask, plus one-command setup.
Source description: A linter to codify code taste using Jev
Source description: Stops AI coding agents from claiming work is done without evidence. Deterministic hooks decide, TypeSafe's Jev advises. Append-only ledger, zero runtime dependencies.
Source description: Bounded TypeSafe Jev workflows for coding agents.
Source description: Connect TypeSafe Jev to Grok Bot as a cheap decision layer - usage gates, skill template, examples
Source description: TypeSafe Jev for DeepSeek Harness, the Model Context Protocol, and plain Node: typed judgments instead of prose, offline by default.
Source description: Jev DSH 决策引擎|面向 Agent Harness 的结构化决策插件。原生支持 DeepSeek Harness,通过 iPolloWork 支持 OpenCode、Codex Harness。
Source description: Native DeepSeek Harness (DSH) plugin integrating TypeSafe Jev or Decision api like luna as a System One decision layer for agent selection, supervision, corrections, and approvals.
Source description: Claude Code plugin: trim long Bash output with TypeSafe Jev before the model sees it
Source description: Coverage, security and code quality for coding agents
Source description: grep, but the pattern is a description. Filters lines by meaning with TypeSafe's Jev decision model: ~200 ms and a thousandth of a cent per line.
Source description: Fast, cheap, typed judgments from TypeSafe's Jev model, as MCP tools.
Source description: Search the web with TypeSafe's Jev: source selection, query understanding and relevance ranking. Built with Search1API.
Source description: A lightweight Jev-powered router for models, tools, and subagents
Source description: System One MCP connector to evaluate anything fast and cheap. Give your AI agent direct access to models like: Jev, D1, CLM and Laya
Source description: An easy way to use jev with your coding agent for tool calling reasoning
Source description: A native LangGraph conditional edge that replaces slow generative LLMs with TypeSafe AI's deterministic System One model (`Jev`). It enables ultra-fast (sub-500ms), schema-safe routing between specialized agents.
Source description: You say it. Your app does it. Voice and typed commands for Rails.
Source description: The three probability attributes occur only with scored and are in [0, 1]. They are independent judgments, not an overall confidence value. Questions are sent together using TypeSafe's noul primitive through the HTTP API.
Source description: Claude Code plugin that replaces the compaction summary with Jev decisions: every tool call and result is scored in one fast request, stale ones are dropped or truncated, everything kept stays verbatim.
Source description: Dependency-free Go client for TypeSafe AI's System One API and the Jev model
Source description: A policy engine for LLM decisions: declare routes, gates and actions once in TypeScript or Python, and every decision comes validated, explainable, replayable and audited.
Source description: Use Jev typed decisions from any ACP (Agent Client Protocol) client or IDE
Source description: Скилл для агентов Letta: суждения по критериям через TypeSafe System One (Jev)
Source description: No evidence, no answer. 🧢 System One evidence gate for RAG & AI agents: Jev / Laya / any LLM judges before your LLM speaks. MCP server, Python SDK, CI route tests.
Source description: .Net SDK for the TypeSafe Jev API
Source description: Home Assistant integration that uses TypeSafe AI's Jev to spot problems and suggest cleanups in your install, and asks before changing anything
Source description: Issue triage that stays quiet when it isn't sure. Calibrated labels, spam and duplicate detection — with abstention.
Source description: Démo : trier des demandes de support avec Jev (TypeSafe) et Symfony AI. Messenger, Live Components, Turbo, kit shadcn de UX Toolkit.
Source description: n8n community node for TypeSafe Jev structured AI decisions
Source description: Agent-facing command line for Jev, TypeSafe AI's System One model: ask typed questions, get calibrated probabilities. Unofficial, built on jev-sdk.
Source description: Type-safe inline judgments from TypeSafe Jev, for Python
Source description: Hands-on LangChain examples of Jev, TypeSafe AI's decision model: support ticket triage, model routing, reply guardrails, tool selection and finance-inbox fraud checks. An LLM writes; Jev decides.
Source description: Jev inference for metric assessment and retention in OpenTelemetry.
Source description: Filter Filament tables with a plain-English statement. Each row is scored by TypeSafe Jev.
Source description: A Home Assistant voice agent that decides with typed, calibrated judgements instead of an LLM. One ~300 ms call per command, and it asks when it isn't sure.
Source description: shadcn-style reusable components and blocks for using TypeSafe AI.
Source description: Per-turn skill selection for LangChain and deepagents agents: a fast judge picks the few skills a turn needs, so a catalog of hundreds stays out of the prompt.
Source description: Helper n8n community node for Jev by TypeSafe. Classify, route, and score text with questions you define, and get a probability for every answer so unsure items can go to review.
Source description: n8n community node for Jev by TypeSafe AI: classify, score and check text with calibrated probabilities. Parallel requests and multi-item batching.
Source description: Semantic decisions as ordinary Ruby #feels?, #decide, #score, Rails validations and pattern matching powered by Jev
Source description: Craft Type-Safe Uncertainty-aware semantic pattern matching, control flow, and smart procedures for Effect DecisionModel and Jev
Source description: Semantic SQL for Postgres, powered by Jev
Source description: Compile agent policy prose into deterministic verdict programs: narrow evidence questions for the model, the verdict computed in code. Install: npm i -g jev-compiler
Source description: LlamaIndex reranker + router powered by TypeSafe Jev - typed scores/choices, cheaper than LLM-as-judge.
Source description: Unofficial Go SDK for the TypeSafe AI System One API — 1:1 parity with the official JS and Python SDKs. Not affiliated with TypeSafe AI.
Source description: First-class, NativeAOT-ready .NET SDK for TypeSafe AI System One, generated with AutoSDK.
Source description: Ask your Postgres tables questions in plain language. A PostgreSQL extension powered by TypeSafe's Jev.
Source description: Fish-style zsh history autosuggestions ranked by Jev (TypeSafe)
Source description: Open-source AI email triage for Gmail. Sorts your inbox into Needs reply, Updates, Promos, Sales and Spam with Jev, TypeSafe AI's decision model, via Vercel AI Gateway. Read-only, runs locally, 1,000 emails in about a minute for 3 cents.
Source description: Async Rust SDK for decision models served through the System One API, with an adapter for OpenAI, Anthropic and Gemini models. Ports of typesafe-sdk-python and system-one-adapter-python.
Source description: Typed AI decisions for .NET: ask Jev (TypeSafe AI System One) yes/no, choice and score questions and get a C# enum with calibrated probabilities back.
Source description: Go SDK for TypeSafe AI API https://docs.typesafe.ai/api
Source description: Turn raw app and game state into state Jev reads well. Benchmark: 74% → 100% accuracy, 43% fewer tokens.
Source description: Typed Elixir client for TypeSafe AI and its Jev System One model, with offline test stubs, concurrent fan-out, and atom-keyed answers.
Source description: An unofficial Zig client for the TypeSafe System One API (Jev)
Source description: Grounded information extraction that cannot hallucinate: code proposes spans, Jev decides. An open-source alternative to LangExtract, with a bilingual benchmark and paper.
Source description: Rust client for the TypeSafe System One API (Jev)
Source description: A Go SDK for the TypeSafe API, enabling quick integration with Jev.
Source description: Unofficial Ruby SDK for the TypeSafe AI API (Jev model) - typed questions, retries, and typed errors. Community port of typesafe-sdk-js.
Source description: Demo app: Jev (TypeSafe System One) use cases with the AI SDK and shadcn/ui
Source description: Generative UI two ways: json-render with an LLM vs Jev (TypeSafe System One), via the AI SDK and OpenRouter
Source description: Unofficial .NET client for the TypeSafe AI System One API
Source description: Provider-independent, strongly typed decision and evaluation model for .NET. TypeSafe JEV is the first provider.
Source description: A .NET client for the System One API used by Jev and compatible Laya servers
Source description: An unofficial, provider-neutral Unix client for Jev from TypeSafe AI. Written in Go.
Source description: Unofficial Go and Rust clients for the TypeSafe AI System One API (Jev)
Source description: Ask Jev over columns of data: closed-set questions, cached and joined back.
Source description: Unofficial Elixir client for the TypeSafe AI API — typed structs, Req-based HTTP with retries, and ergonomic noul/choice/score questions. Not affiliated with TypeSafe AI.
Source description: Terminal Gmail client for Omarchy. Jev (TypeSafe AI) files your inbox into Gmail labels and checks your replies as you write
Source description: A tinystruct-based TypeSafe SDK with JEV model.
Source description: Typesafe AI SDK in Elixir using Req
Source description: Unofficial typed async Rust client for the TypeSafe System One API
Source description: Latency-first Rust SDK for TypeSafe System One.
Source description: PHP & Laravel SDK for TypeSafe AI's JEV Model series
Source description: A Go client for the TypeSafe AI's System One API and its model, Jev.
Source description: Unofficial Go SDK for TypeSafe AI's Jev API
Source description: TypeSafe.ai Jev Go SDK
Source description: Per-turn router for agent harnesses: one 350ms Jev call picks the model tier, effort, tools and skill, behind a hard deadline with a regex fallback. Claude Agent SDK adapter included.
Source description: Typed decisions from a System One model, where uncertainty is something you have to handle.
Source description: A lightweight .NET client for the TypeSafe AI API (System One / Jev). One dependency; a faithful port of the official Python SDK.
Source description: PHP SDK for TypeSafe's Jev: send text and typed questions, get typed answers with calibrated confidence. PHP 8.1+, works with any PSR-18 client, Laravel 8–13.
Source description: Type-safe Java 21 client for the TypeSafe AI Jev (System One) decision API
Source description: Async Python client for TypeSafe Jev. Typed questions in, probabilities and choices out, no prose to parse.
Source description: Idiomatic Go SDK for the TypeSafe AI API
Source description: .NET client for TypeSafe System One (Jev) — typed questions in, typed answers with probabilities and confidence out. No prompt engineering, no output parsing.
Source description: Use the TypeSafe Jev decision model (System One) anywhere: zero-dependency OpenAI-compatible bridge for 9Router, Claude Code, Cursor, Cline & any OpenAI SDK. Typed yes/no, choice & score judgments via CLI or HTTP.
Source description: Unofficial .NET port of the TypeSafe AI client SDK (typed questions & answers)
Source description: Rust SDK for the TypeSafe AI API
Source description: Community .NET 10 SDK for official TypeSafe AI Jev typed decisions and Microsoft.Extensions.AI integrations.
Source description: A Kotlin Android SDK for UI automation powered by TypeSafe Jev, with an accessibility runtime and sample app.
Source description: Go client for TypeSafe's System One API and its model Jev: typed judgments and calibrated probabilities instead of generated text
Source description: System One TypeSafe AI Rust Client (unofficial)
Source description: Community-maintained Java SDK for the TypeSafe AI System One (Jev) API. Not an official TypeSafe product.
Source description: Ruby client for the typesafe AI Jev model
Source description: Community .NET SDK for the TypeSafe AI System One API — typed noul, choice, and score questions with structured, confidence-scored answers. Not affiliated with TypeSafe AI.
Source description: Agent-first Haskell DSL for TypeSafe's Jev judgment model: typed packets, inferred types, answers under the same labels
Source description: Unofficial Go client for the TypeSafe AI System One API (Jev) — typed questions in, calibrated answers out.
Source description: Scala SDK for Jev. No effect system bundled.
Source description: Community Go SDK for TypeSafe AI Jev / System One
Source description: Go SDK and CLI for TypeSafe Jev: typed decisions (yes/no, choice, score) from a model
Source description: Software, by reflex. Say it, and the right small program runs: chosen by a calibrated classifier, run only when it is sure enough, and it asks before anything that cannot be undone. A CLI, a package manager and a TypeScript SDK: the first i
Source description: Jev-shaped (TypeSafe System One) classification wrapper over OpenAI-like clients: probabilities and confidence instead of prose
Source description: A Swift client for TypeSafe AI's Jev — typed judgements, not text
Source description: Go SDK for the TypeSafe AI API — typed questions in, probability distributions out.
Source description: A small, type-safe client for asking AI questions about your data, powered by TypeSafe Jev.
Source description: Ruby client for decision models such as Typesafe Jev
Source description: TypeSafe Jev for OTP: reply to Jev from a GenServer and pattern match on its answer
Source description: A Java SDK for the TypeSafe AI JEV API, & Spring AI TypeSafe integrations.
Source description: The decision layer for your Rails app. A Rails-native wrapper around TypeSafe's Jev System One API: typed, calibrated decisions in your control flow.