Jev AgentA source-verified directory of 487 Jev projects — browser agents, model routers, SDKs and MCP servers — each one showing the exact decision it hands to Jev. Curated by the Awesome Jev community, mirrored here under MIT.
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.
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.
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.
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.
An optional Jev decision backend in the Atomic coding Agent for bounded structured choices such as routing.
Sends predefined questions to Jev and decodes answers for callers; regular models still generate code.
Separates structured decisions from text-generation interfaces.
An optional Jev routing path that matches VexJoy requests to specialist Agents, skills and workflows.
After deterministic routing guards, Jev judges the remaining candidates and required workflow components.
Keeps repeatable routing rules and model-selected candidates in separate stages.
Jev-powered model routing, memory, compaction, skill selection, computer and browser use for Hermes agents (also Claude Code and Codex)
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.
An optional Jev dispatch classifier for choosing among WrongStack specialist Agents.
Jev evaluates the task against eligible specialists; local dispatch rules use the result.
Makes specialist selection a configurable step in an existing coding Agent.
A Claude Code and CLI proxy that asks Jev to score task complexity and pick a model from the account’s available set.
Scores task, reasoning, and tool complexity, then chooses among the exact models the account can run; failures keep the current model.
Makes model choice an inspectable closed-set decision, and never blocks a prompt when Jev is down.
A self-hosted, versioned Agent skills library with optional Jev recommendations.
Jev scores task relevance among skills the client is allowed to access.
Suggests relevant skills while retaining the library’s client access scope.
Uses Jev to classify each Codex turn, then applies local rules to choose the model, reasoning effort, and speed mode.
Classifies task difficulty and reasoning needs before local policy selects a model configuration.
Keeps routing policy and decision logs local for inspection and tuning.
Routes among models, subagents, skills, MCP tools and CLIs using a shared candidate set.
Jev makes a Choice; the router separately checks availability, permissions, risk and confirmation policy.
Records model choices separately from execution policy.
Connect TypeSafe Jev to Grok Bot as a cheap decision layer - usage gates, skill template, examples
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.
An experimental semantic HTTP router for Hono using natural-language route descriptions.
Jev scores request-to-description matches; code selects the first route above its threshold.
Explores semantic routing; the author explicitly warns against using it for authentication or authorization.
Loki optionally adds Jev typed-judgment tools and routes a new session to a model within the selected gateway.
Assesses the first task’s capability needs; local policy chooses a same-gateway model and keeps the route sticky.
Makes model routing explicit while retaining the selected route for the session.
Automatic model router for Pi coding assistant: integrates Jev via Vercel AI Gateway to dispatch tasks efficiently.
Quickly estimates task complexity from code context and user query, routing between SLMs and frontier models.
Brings out-of-the-box intelligent cost-saving routing directly into the Pi terminal workflow.
An opt-in Hermes Agent plugin that asks Jev to suggest one relevant skill before a model call.
Compares the request with the skill roster; inserts a suggestion only when suitable and abstains on failure.
Adds a targeted skill hint that the Agent can still ignore.
The agent loop where decisions don't cost a large language model call. Zero deps, runs offline, no API key needed.
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.
Cost-aware LLM router that picks the cheapest model capable of handling a query, using TypeSafe's Jev for fast classification instead of an LLM call.
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.
An open-source LLM router built on LiteLLM and Jev: dynamically routes requests based on task complexity and context.
Evaluates reasoning requirements of input prompts via single forward pass, delegating dispatch to LiteLLM.
Achieves high-throughput prompt triage and cost optimization without brittle heuristic regexes.
Calibrated tool selection and schema pruning for AI agents. Dual-engine: zero-dependency offline TurboQuant or TypeSafe System One (Jev). Prunes candidate MCP tools and schemas down to the relevant set before calling LLMs to eliminate hallucinations and save tokens.
Jev makes atomic decisions on tool selection (`choice`) and whether user intent requires open-ended creative generation (`noul`), with calibrated probability distributions.
Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.
An OpenCode orchestrator that keeps a cheap sticky parent model and, when Jev flags a hard turn, escalates through a child subagent.
Scores task, reasoning, and tool complexity, then chooses fast / balanced / strong / long; local policy stays, escalates, or fans out.
Keeps a cheap sticky parent model and opens a stronger child only when Jev flags a hard turn.
Policy-driven model routing framework routing every LLM call to the cheapest capable tier in ~180 ms via TypeSafe Jev.
Uses declarative YAML policies evaluated by Jev discrete classification without requiring pre-collected training data.
Provides unified TypeScript and Python interfaces to minimize inference spend with minimal routing overhead.
A community-maintained directory and radar for Jev, highlighting open-source projects with verified code and clear decision architectures.
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 ↗We prioritize open-source projects with clear Jev implementation evidence. Repositories without verifiable source code are excluded from the main directory.
Directory data and design from Awesome Jev by logicrw, used under the MIT License.
