See how Jev fits into Agent architectures, directly in community source code.
Give Jev the current state and a few options. Get the Agent’s next step.
Core call · setup in the full example
const nextStep = choice("What next?", { login: null, askUser: null, retry: null });
const { answers } = await client.systemOne({ state: "Login form is ready", questions: { nextStep } });
console.log(answers.nextStep.choice);Node.js 20+ · Set TYPESAFE_API_KEY on your server. This page shows code and makes no model requests.
An optional Jev classifier integration for Python LangChain workflows.
Submits binary, categorical and ordered-score questions and returns typed answers with probabilities.
Adds a classification step that can participate in existing LangChain pipelines.
An educational AI hedge-fund prototype with an optional Jev adapter for structured judgments in fund decision workflows.
Converts strategy questions to System One requests and normalizes native answers to the project’s result format.
Allows Jev or other model backends to be used in the same research workflow.
LiteLLM can use Jev to classify requests for its complexity-based model router.
Maps requests to configured complexity classes that drive backend routing.
Makes the complexity judgment behind routing inspectable.
Oh My Pi includes an optional TypeSafe judgment provider for bounded decisions in coding-agent workflows.
Sends agent state and typed questions to Jev and parses structured answers.
Adds a replaceable judgment provider to existing agent workflows.
A community Claude Code Templates mod that uses Jev to suggest subagent models and reasoning levels.
Evaluates task tier, reasoning needs and production risk; local policy maps results to invocation settings.
Makes routing rules and per-request choices explicit and configurable.
An optional TypeSafe provider for Composio that uses Jev to choose among tools and bounded argument options.
Turns tool or action conditions into structured questions and passes Jev answers to local invocation logic.
Adds structured judgments to existing tool interfaces.
Cua’s preview jev-use example pairs Driver observation and execution with bounded Jev browser-action choices.
Reads DOM or supported visual-region descriptions and returns a supplied candidate action ID.
Provides Python and TypeScript loops with separate offline and live checks.
The TypeSafe provider in AI SDK lets TypeScript applications call Jev through the shared evaluate interface.
Maps choice, score, and yes/no questions to TypeSafe System One requests and parses typed results.
Reuses a common evaluation interface within AI SDK applications.
A browser Agent that uses Jev to choose actions and page controls, calling a text model only when input is needed.
Chooses an action and its matching DOM element in one request; a text model generates input text.
Separates UI choices, text generation, and browser execution so each step can be inspected.
An optional TypeSafe provider and Jev model integration for Pydantic AI.
Converts supported structured output fields into typed Jev questions and maps answers back to the output model.
Lets supported classification outputs use the existing Pydantic AI interface.
An optional TypeSafe HTTP adapter in Eliza’s source, not registered with the Agent runtime by default.
Only an explicit systemOne call sends state and questions, returning validated typed answers.
Provides a reusable server-side module; its presence is not evidence of deployed Jev adoption.
A Jev UI-composition experiment inside the json-render website that selects from predefined component and property options.
Evaluates component configurations through Vercel AI Gateway, then composeSpec assembles the UI specification.
Provides an inspectable composition path alongside token-by-token JSON generation.
An optional LangChain.js TypeSafeClassifier integration for sending state and predefined questions to Jev.
Uses invoke to call TypeSafe and parse choice, noul, score and probability fields.
Adds typed decisions to LangChain workflows without treating Jev as a chat generator.
OpenChamber’s optional automatic model router uses Jev to classify a message before selecting a configured model and reasoning level.
Jev selects a task category; local category mappings determine the model configuration.
Keeps routing categories and their model assignments explicit.
Tiny Jev-like decision head built atop Qwen2.5-0.5B that can be trained and run locally on Apple Silicon MacBooks.
Attaches a parallel decision head to an open 0.5B model to answer discrete questions directly from token activations.
Runs entirely offline on edge hardware with minimal memory footprint, offering an accessible base for custom judgment modeling.
An experimental TypeSafe crate in Rig for expressing Jev questions and answers with Rust types.
Sends application state and questions to Jev and parses Choice, Score or Noul answers.
Reuses field layouts between questions and answers and validates answers against questions.
Open Source Deep Research alternative with Jev search-stopping evaluation.
Jev returns a structured decision for the local program; consult the source for the exact decision policy.
Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.
Semantic cache with a Jev evaluator that uses Noul judgments to check whether a cached response can serve an incoming request.
Jev returns a structured decision for the local program; consult the source for the exact decision policy.
Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.
Firstmate can optionally use Jev to match task briefs to dispatch rules before local policy chooses an Agent configuration.
Sends the task brief and candidate rules to Jev, then resolves execution profiles with confidence and local conditions.
Separates semantic matching from the final dispatch policy.
Android chat copilot that reads conversations, uses Jev to assess intent, risk, and next actions, and drafts and ranks reply suggestions.
Android chat copilot that reads conversations, uses Jev to assess intent, risk, and next actions, and drafts and ranks reply suggestions.
References are pinned to a fixed revision; inclusion is not independent runtime or performance verification.
Prunes old tool calls and outputs for Claude Code while retaining surviving text verbatim.
Separately judges whether a tool call and its full output are still needed; code keeps, truncates or drops them.
Avoids rewriting paths, commands and errors into a new summary.
A Jev guardrail example in Agentgateway using a webhook to inspect model requests and responses.
Jev scores jailbreaks, harmful content and secret disclosure; thresholds or evaluation errors reject requests.
Demonstrates gateway integration, not guaranteed prevention of all attacks.
Latitude includes an optional Jev preclassifier for conversation checks and their selection records.
Judges which checks apply and can add checks when thresholds and rate limits permit.
Records models, thresholds, latency and selection reasons alongside the baseline.
A market-making experiment on Kuru’s MON-USDC book on Monad, with optional per-block Jev buy/sell decisions.
In Jev mode, order-book judgments feed code that simulates fills or submits configured post-only limit orders.
Defaults to a mock model and dry-runs without a private key; simulated results do not establish profitability.
A community catalog of Jev integrations, benchmarks, developer tools, research, and learning resources, with per-entry relationships, review basis, and license facts.
Jev is TypeSafe's decision model. This directory groups projects by how they use choices, scores and probability judgements, with implementation links to help you assess fit for your own task.
TypeSafe ↗Public repository material supports classification of the entry and its Jev relationship. Source, SQL, examples, and descriptive documents may all inform review; their basis and license facts are recorded separately. Stars indicate attention only.
