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All Jev resources

This is the full index of everything Jev: 2231 entries for TypeSafe AI's flagship System One model, from official documentation, SDKs, and cookbooks to community-built clients, MCP servers, agent tools, and write-ups. Every entry is tagged by category, format, and topic, and sorted newest first.

Filter by category or tag, or search titles, descriptions, authors, and repos. Open any card for its detail page — most entries carry a verified deep dive with highlights, caveats, and a quickstart snippet.

Entries
2231
Categories
11
Official
93
Community
2138
Source sites
148
Deep dives
2231

Last updated . Entries are added as they appear and re-checked when they are touched.

Browse Jev by category

11 ways into the ecosystem. Each category page leads with its own shortlist of starting points.

All 2231 Jev resources

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Showing 49–93 of 93 resources

Rate content against 2 to 10 ordered levels. Returns a probability-weighted score that can land between levels, per-level probabilities, and confidence, with the arithmetic left to code.
Jev Practices & PatternsDocs#official#primitives#score
Official
The yes/no primitive: one probability from 0 to 1, where the value is the answer and no separate confidence is needed. True and false criteria pin down subtle boundaries.
Jev Practices & PatternsDocs#official#primitives#noul
Official
Instructions, option descriptions, score levels, and noul criteria all accept JSON, so you can label question parts, pass schemas and taxonomies, and keep candidate paths alive.
Jev Practices & PatternsDocs#official#primitives#architecture
Official
Break a judgment into atomic Score questions, normalize each by its top level, and combine them with weights you control in code, as in the resume example's four scored dimensions.
Jev Practices & PatternsDocs#official#patterns#score
Official
Use the answer to decide what to do and confidence to decide whether to act: the voice-banking example routes below 0.6 to a human and needs 0.85 or more to auto-approve a transfer.
Jev Practices & PatternsDocs#official#patterns#confidence
Official
Ask everything the system might need in one request, then let code throw away what it does not use. The ticket-triage example sends a category plus four speculative questions in parallel.
Jev Practices & PatternsDocs#official#patterns#batching
Official
Classify intent with a Choice and complexity with a Score, then send each branch to deterministic code, a specialist model, or a person, with a 0.5 intent-confidence floor.
Jev Practices & PatternsDocs#official#patterns#routing
Official
The official demo hub, currently home to the Smart Home Assistant, which uses speculative questions to pick a tool and falls back to a general model when nothing fits.
Jev Tools & CLIsDocs#official#demo#tool-calling
Official
The official SDK hub: Python and JavaScript/TypeScript clients with typed questions and answers plus automatic retries, and the HTTP API for every other language.
Jev Repos & SDKsDocs#official#docs#sdk
Official
Quickstart for @typesafe-ai/sdk on Node 20+: answer types inferred from your questions, a choice() helper, and one systemOne call to classify a support ticket.
Jev Repos & SDKsDocs#official#docs#sdk
Official
Usage guide for typesafe-sdk: sync and async clients, uv and pip installs, and one request mixing Noul, Choice, and Score questions with answers grouped by type.
Jev Repos & SDKsDocs#official#docs#sdk
Official
Semantic search over GitHub's Terms of Service: one request ranks all 218 lines with a Choice while a Noul checks whether the document contains an answer at all, including when it should say no.
Jev CookbooksDocs#official#cookbook#search
Official
A two-stage extraction cascade: a mini model extracts, a Noul battery verifies each field in one request, and a 0.7 gate escalates to a reasoning model, sitting on the cost/quality frontier.
Jev CookbooksDocs#official#cookbook#extraction
Official
Turn natural language into typed function calls: closed-set arguments become Choice questions, optional ones become Noul questions, and 54 questions ship in one request for a trading assistant.
Jev CookbooksDocs#official#cookbook#tool-calling
Official
Decide which of 450 candidate product pairs from two beer catalogues describe the same entity: one three-level Score per pair splits 40 merges, 50 curator reviews, and 360 non-matches.
Jev CookbooksDocs#official#cookbook#entity-matching
Official
Catch wrong and hallucinated citations against RFC 7519 with a string match plus one Choice question: four accurate citations verified at 0.93 or more and all four planted failures caught.
Jev CookbooksDocs#official#cookbook#citations
Official
A repeatability study: an 8-question moderation rubric run 15 times per condition shows a mean probability standard deviation of 0.0098, and a 0.60 uncertainty gate lifts agreement to 99.2%.
Jev CookbooksDocs#official#cookbook#consistency
Official
A 14-Noul claims-triage rubric over one insurance claim, repeated 15 times: mean probability standard deviation of 0.0102 at 111 ms per call, with a 0.30 to 0.70 band for human review.
Jev CookbooksDocs#official#cookbook#consistency
Official
Extract dates by asking seven Choice questions about a date's shape and parts, then resolving them in code: five of six examples auto-accepted and missing dates flagged at 0.46 confidence.
Jev CookbooksDocs#official#cookbook#extraction
Official
Score retrieved passages with four Noul questions each, then route in code: prompt injections dropped, false premises kept as conflicts, and evidence assembled separately for the generator.
Jev CookbooksDocs#official#cookbook#search
Official
Rank 182 agent skills in one request and re-read the top three in a second: over 488 requests, wrong skill loads fell from 16.8% to 7.3% and needless loads from 9.8% to 4.0%.
Jev CookbooksDocs#official#cookbook#agents
Official
Reconstruct Markdown from unformatted text in two requests: 16 Noul line-pair questions stitch split sentences, then 62 questions classify each block's type and companions.
Jev CookbooksDocs#official#cookbook#formatting
Official
Turn wine tasting notes into 67 numeric columns for a CatBoost regressor: five rounds of reading its own errors cut held-out RMSE from 3.09 to 1.77 across 800 unseen reviews.
Jev CookbooksDocs#official#cookbook#research
Official
Regex finds candidate emails, phone numbers, and amounts; Jev picks the requested span so code copies a verbatim value that cannot be invented, with no free-text extraction required.
Jev CookbooksDocs#official#cookbook#extraction
Official
TypeSafe AI's official launch post: the company is out of stealth and developers can get access to Jev from the website, alongside the founder's launch thread.
Jev Guides & ArticlesPost#official#x#launch
Official
TypeSafe's announcement that Jev is available on the Vercel AI Gateway, linking the model page with the Jev API, pricing, and playground.
Jev Tools & CLIsPost#official#x#vercel
Official
The launch story: why TypeSafe built decision models, the side-by-side demo, the Doom and Wikiracing bots, and the published workflow evals.
Jev Guides & ArticlesArticle#official#announcement#benchmarks
Official
The canonical introduction: Jev is TypeSafe's flagship model and the first System One model. Send state and typed questions, get structured answers your code can use directly.
Jev Guides & ArticlesDocs#docs#official#primitives
Official
Everything you need to make your first request: install an SDK, get an API key, and send state plus typed questions.
Jev Guides & ArticlesDocs#docs#official#quickstart
Official
The three TypeSafe question types, the typed answers they return, how to choose between them, and how to ask several in a single call.
Jev Practices & PatternsDocs#official#primitives#choice
Official
How TypeSafe reports certainty, how it differs from probability, and how to use it architecturally to gate and route decisions.
Jev Practices & PatternsDocs#official#confidence#routing
Official
Architectural patterns for System One systems: speculative fan-out, confidence-gated routing, composite scoring, and intent routing.
Jev Practices & PatternsDocs#official#patterns#architecture
Official
Design AI-powered software by keeping code in control and giving System One narrow, structured decisions to make.
Jev Practices & PatternsDocs#official#patterns#architecture
Official
Explore TypeSafe use cases by industry and turn promising ideas into concrete software workflows.
Jev Practices & PatternsDocs#official#use-cases
Official
A 13-question regulatory briefing that shows batching every question into one call is 12.2x cheaper and 10x faster with no change in answers.
Jev CookbooksDocs#official#cookbook#batching
Official
One question per query-candidate pair raises top-1 accuracy from 5% to 18% and top-10 accuracy from 38% to 62% on 40 CLERC legal queries.
Jev CookbooksDocs#official#cookbook#reranking
Official
Screen every message going into and out of an LLM app: a Noul describes possible hazards, a Score rates severity, and code thresholds the probabilities.
Jev CookbooksDocs#official#cookbook#guardrails
Official
Classify documents through deep patent, retail product, biomedical, and source-code hierarchies using parallel beam search over Choice probabilities.
Jev CookbooksDocs#official#cookbook#classification
Official
Classify SEC annual reports into 75 industry groups with one Choice each, then read confidence to decide when to fall back to a broader division.
Jev CookbooksDocs#official#cookbook#classification
Official
A smart home assistant that uses TypeSafe to evaluate user requests, pick a tool, and fill its arguments from plain language.
Jev CookbooksDocs#official#demo#tool-calling
Official
2.7kGitHub stars
Drop-in skill for Claude Code, Codex, and other agent environments that teaches the right way to structure states and questions.
Jev Tools & CLIsDocs#official#agent#skill
Official TypeScript/JavaScript client with inferred answer types, retries, and a question DSL. `npm install @typesafe-ai/sdk`.
Jev Repos & SDKs#official#sdk#typescript
281GitHub stars
Official sync and async Python client for the TypeSafe API with typed questions, retry policies, and inferred answers. `pip install typesafe-sdk`.
Jev Repos & SDKs#official#sdk#python
Official drop-in TypeSafeClient replacement backed by OpenAI, Anthropic, and compatible LLM APIs, for comparing Jev against chat models on the same questions.
Jev Repos & SDKs#official#adapter#python
Get an API key, manage models, and try the playground: three walkthrough lessons plus realistic use cases like resume screening and ticket routing.
Jev Tools & CLIsPlayground#official#playground#api-keys
Official

Start here with Jev

Hand-picked entries the maintainers recommend first, from official primitives to community tools.

Published evaluations of four automation workflows, security incidents, agent trace observability, invoice processing, and customer service, comparing Jev and frontier LLMs as structured workflows versus single prompts.
Jev Practices & PatternsDocs#official#benchmarks#evaluation
Official
6GitHub stars
.NET SDK for the System One API with typed question sets, HttpClientFactory and dependency-injection wiring, plus Microsoft.Extensions.AI guardrail, routing, tool, and evaluator adapters.
Jev Repos & SDKs#community#sdk#csharp
A NativeAOT-ready .NET SDK generated from TypeSafe's OpenAPI definition, with typed question sets, batching, dependency injection, and Microsoft.Extensions.AI integrations.
Jev Repos & SDKs#community#sdk#csharp

Watch: Jev in action

Walkthroughs, tests, and demos from the community.

YouTube: KodeKloud's three-minute primer: what a System One model decides, and why it answers in milliseconds.

YouTube: Caleb Writes Code explains what Jev is and how to call it, in seven minutes.

YouTube: Greg Isenberg and Ryan Vogel on the businesses Jev unlocks, and a path in through the Vercel AI Gateway.

YouTube: CJ from Syntax demos browser use, classification, code review, a model router, and a chat bot with no LLM.

YouTube: Ryan Vogel runs Jev on 100 and then 1,000 emails: category, priority, spam, and reply predictions.

YouTube: AICodeKing tests support routing, refund detection, prompt-injection resistance, and browser automation.

Discussions about Jev

Threads from Reddit and Hacker News about building with Jev.

Hacker News: Show HN: Ad Spend Guard gates budget before the auction, not the creative after.

Show HN: Gating ad budget with a decision modelRead the thread on Hacker News

Reddit: Reranking rebooking options with a decision model, ranked with reasons.

Hacker News: Pick Verify: a single decision at the warehouse pick station catches confusable picks.

Show HN: Verifying robot picks with a decision modelRead the thread on Hacker News

Hacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.

ag_212 pointsShow HN: Jev as the Bletchley Park Analyst: A Software Bombe on EnigmaRead the thread on Hacker News

Hacker News: Show HN for the decide CLI: brew install, file context, and multi-question shell scripts.

vsekhar2 pointsShow HN: Decide – Jev decisions in the shell, scripts, and agent skillsRead the thread on Hacker News

How entries are chosen

Every entry is hand-picked against the same bar, whether it comes from TypeSafe or the community.

  • Public and working — no paywalls, sign-up walls, or dead links.
  • Concrete — it has to be about Jev and the System One API, not generic LLM content.
  • Useful today — something a developer can learn from or use right now.
  • Distinct — if a similar entry exists, the description has to say what makes this one different: a different runtime, a cheaper path, a measured result, or a narrower focus.
  • Documented — repos state their license, and docs are in English or carry an English summary.

Spam, affiliate links, and thin AI-generated content are rejected. New entries are expected to ship with a deep dive: two to five highlights, caveats, a quickstart snippet where one helps, and a verification date. Star counts are refreshed when an entry is touched.

Common questions

Short answers about Jev and how this directory works.

What is Jev?

Jev is TypeSafe AI's first System One model. It takes a state plus typed questions and returns one structured answer per question — a choice, an ordered score, or the probability of yes — with calibrated confidence on Choice and Score answers, so code can branch on the result without parsing generated text.

What is the difference between Choice, Score, and Noul?

They are the three question primitives. Choice picks one of up to 255 options, Score rates on 2 to 10 ordered levels, and Noul answers yes or no. Choice and Score answers carry a confidence between 0 and 1; a Noul answer is the probability of yes.

Which Jev SDKs and client libraries are available?

TypeSafe maintains official JavaScript/TypeScript and Python SDKs plus a Python adapter. The community has built clients for Go, .NET, PHP, Laravel, Rust, Elixir, Ruby, and Scala, along with framework integrations such as DSPy, LlamaIndex, and Home Assistant. All of them are listed in the Repos & SDKs category.

Is the directory free, and is it affiliated with TypeSafe AI?

The directory is free to browse: every entry is hand-picked, and spam, affiliate links, and sign-up walls are rejected. JevDirectory.org is community-run and not affiliated with TypeSafe AI — Jev and System One are TypeSafe AI trademarks.

How often is the directory updated?

Entries are added as new resources appear and re-checked when they are touched. The directory currently holds 2231 entries from 148 source sites, last updated Oct 12, 2026.

What is a verified deep dive?

Most entries include a deep dive: two to five highlight bullets someone would only know from using the resource, a caveats note about limits and requirements, an optional quickstart snippet, and the date the link was last checked. 2231 of the 2231 entries carry one today.

For the canonical details, read the official TypeSafe docs and the API reference.

From the Jev community

Posts from builders shipping with Jev right now.

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