Jev for Home Assistant
Turn Jev answers about household state into Home Assistant sensors and automation responses.
Study a narrow Jev decision before adopting a full application: route a request, apply a rubric or gate an action. Read an example, check the source documentation and compare an integration that uses the same idea.
Guide: route support tickets · Guide: rerank search results
Public projects, checked against their original sources.
Turn Jev answers about household state into Home Assistant sensors and automation responses.
Use Jev through the AI SDK evaluation API, with Choice, Score, and Boolean questions in one request.
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.
Inspect typed quality signals while drafting an X post.
Replace flagged Spanish words with beeps using timestamped transcripts.
Explore Jev workflows for tickets, operational alerts, and deployment risk.
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.
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.
An official walkthrough of batching home-control questions and using only the relevant answers.
Start with the playground, make an HTTP request, and translate a first evaluation into Python.
Understand the three question types, the answers they return, and when to use each.
Explore fan-out, confidence-based routing, composite scoring, and intent routing.
The request and response contract for calling Jev directly over HTTP.
TypeSafe's model-specific notes on failure modes and how to structure questions around them.
TypeSafe's published comparison of structured workflows and single-prompt approaches.
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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: 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: Standalone TypeSafe Jev-based auto-routing for Agent Zero
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: 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: 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: 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: 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.