A sidecar plugin that puts fast typed-decision models such as TypeSafe Jev and Laya behind two tools over one wire format (POST /v1/systemone): judge asks typed questions about a state and returns probabilities, while semantic_find scores file chunks against a plain-language query.
A local experiment bench that uploads PDFs, extracts and chunks their text, then generates Full, Jev and RAG comparison records for consecutive questions, storing workspaces, chunk rules and answer history in a SQLite file at .runtime/jev_context.sqlite3.
A guardrail benchmark that ports the policy types of Amazon Bedrock Guardrails - six content filters, denied topics, word filters, sensitive information and contextual grounding - to TypeSafe's System One model Jev, then measures latency, cost and classification quality per case.
A Claude Code plugin that hands Claude Code's small, frequent decisions to Jev at six points: four event hooks and two routing decisions, each a roughly one-second call that reduces permission prompts, warns when fetched content addresses the agent, re-checks done, and routes effort and model.
A research workspace exploring visual System One models on 2D affordance tasks, predicting interaction regions for robot execution, with a five-document primer covering concepts, candidate models, prior work and first-round debugging of methods A and D.
A Hugo theme that abstracts the editorial look of typesafe.ai and the community OpenJev site into hand-written CSS with no build step, webfonts or runtime dependencies. It is an independent design homage named after System One and does not call Jev.
The web visualizer for Convex Evals hosted on Netlify: browse experiments, models and recent runs, with Coding and Decision sections. The page is client-rendered, so the captured HTML shows only the shell and a loading state.
A hosted decision API serving open-weights models behind a Jev-compatible request shape: one answer per option with probabilities for s1-fast, s1-pro and s1-vision, on a Peer-to-Peer tier or an EU tier built for GDPR. Not affiliated with TypeSafe.
The documentation site for OpenDecision, an open-source decision engine that runs locally and answers typed questions about text, JSON, documents and images, exposing Python, HTTP and TypeSafe-compatible interfaces with Choice, Noul, Score and Relation primitives.
Jourdan Labs' pre-registered, independently rescored audit of Jev's two launch claims: calibrated on CLINC150 (ECE 0.0204) but not on Banking77 (ECE 0.0936), and zero type errors across 8,576 responses. Protocol and receipts published in full.
Kyle Wild's October 13, 2026 post testing whether decision models inherit the primacy and recency effects of the LLMs underneath: Jev was not tricked by option ordering, while the other decision models he tested were. Reproduction code is linked on GitHub.
A 3D chess game played against TypeSafe Jev, described as realistic and mobile-friendly with Beginner, Pro and Legendary levels, free in the browser. Only the page's metadata could be recovered; the game itself is client-rendered.
An independent evaluation asking jev-1.13.0 and GPT-4.1 to stand in for the same 300 Twin-2K-500 respondents across 108 questions: asked as Noul, Jev led GPT-4.1's probabilities on all six measures at $4.02 versus about $136, with no arm predicting individuals.
Kanta Hayashi's calibration check using a hidden fair die: across 400 trials Jev picked '1' every time at an average 83% probability while being right 19% of the time, and handing forecast documents through Choice shrank a stated 30% risk to about 5%.
The PyPI page for jevclient 1.3.1, an independent async Python client for TypeSafe Jev: typed Noul, Choice, Score and Box questions batch into one request, with image assets, billing tables and error classes documented in the README.
A benchmark asking how well individual decisions add up to a completed task: decision and chat models steer an agent through 100 grid puzzles, where Jev chooses an optimal move in about 90% of fixed exam positions yet finishes only 57 of 100 games.
The Jev Day release post: Jev 1.14.0 under jev-preview plus three danger-zone features — composable images on both models, the box question type on 1.14 only, and opt-in 262k context.
The experimental image-input reference: PNG or JPEG files in a top-level assets map, referenced by string anywhere in state, instructions, or criteria, behind the TypeSafe-Danger-Zone-Images header.
The box question type: one bounding box per question, returned as four edges that are fractions of the image, plus confidence, with no criteria field and no not-found value.
A code-repair workflow that delegates bounded decisions to Jev as a low-cost probabilistic judge: across 400 SWE-bench tasks it cuts mean repair-model token use by 21.6-50.6% with higher observed repair rates on three models.
A 41,500-response bias audit of Jev across cognitive reflection, five bias paradigms, and a pricing experiment: selected answers look robust, but probabilities move with irrelevant numbers, option order, and framing.
A family of adapted Qwen3.5 models from 0.8B to 9B that scores agricultural candidates in one forward call: AgriJev-9B reaches 75.44% mean accuracy across 17 test views and narrows the gap to hosted Jev 1.13.0.
A jailbreak framework that uses multi-dimensional probabilistic judge feedback to guide prompt optimization: 89.8% average HarmBench attack success across ten open-source settings under a 10-query budget.
Cloudflare's 9B multimodal decision model on Ollama: text, JSON, or images in one non-autoregressive forward pass, Apache-2.0, and the lowest measured latency of any decision model in Cloudflare's Decision Index runs at 38.8 ms median.
Convai Innovations' 421M non-autoregressive decision model on Ollama: fine-tuned from ModernBERT-large, Apache-2.0, answering one question in under 10 ms on an M5 Max with 0.060 in-task calibration error.
Bespoke Labs' 9B decision model on Ollama, fine-tuned from Qwen3.5-9B: Apache-2.0, up to 64 typed questions per call, answers in under 100 ms on a MacBook Pro with an M5 Max, and 75.7% mean accuracy across 13 human-labelled datasets.
Together AI's experimental decision family on Ollama, fine-tuned from Qwen3.5 at 4B and 0.8B: 93K downloads, up to 64 questions per call, an 88.0% development accuracy on 1,000 decisions, and MIT-licensed dataset builders and training scripts.
ActBro's 2B calibrated System 1 decision model on Ollama, built on OpenBMB's MiniCPM5-2B: 78.79% on JevBench among sub-2B open-weight models, 86.41% on the 10-source decision-v7 set, and one output token per decision via /v1/systemone.
Open Spark Jev's Qwen3.5-4B decision model on Ollama: one forward pass returns a calibrated probability per option for allow, ask, or deny tool gating, with 4b-v6 and 4b-v8 tags carrying fitted temperatures of 1.481 and 2.5 under Apache-2.0.
AutoTrust's JEV-35B page on ModelScope: metadata records a fine-tune of Qwen/Qwen3.5-35B-A3B under the Qwen3_5MoeForConditionalGeneration architecture, apache-2.0, 521 downloads, English only, with safetensors and PyTorch files.
AutoTrust's JEV-9B on ModelScope: a Blocks of Experts System 1 + System 2 model fine-tuned from Qwen3.5-9B, Apache-2.0, about 8.95 GB of BF16 safetensors in five shards, taking images since 3 October 2026, with a GuruSearch live demo.
AutoTrust's vision-language decision model on ModelScope: an adapter on Qwen3.8-27B, Apache-2.0, 1,735 downloads, with lora, safetensors, and pytorch assets, trained on the SargeDev/jev-distill-corpus-v3 corpus and English metadata.
The Laya family mirror on ModelScope: a 421M non-autoregressive System 1 decision model, Apache-2.0, with a pip-installable package, more than 100 languages claimed at about 33 ms per pass, and 16,713 downloads.
The typed-decisions checkpoint of the Laya family on ModelScope: a 421M ModernBERT-large fine-tune reporting 0.766 accuracy over 2,000 decisions, above TypeSafe Jev 1.13.0's published 0.727 and the 0.735 teacher ceiling, Apache-2.0.
A community re-upload on ModelScope of NanoJev, the 0.6B Qwen3-based parallel decision model: full probability distributions over 2-255 dynamic choices in one batched forward pass with zero output-token decoding, approval-gated and license-free.
barakplasma's Replicate deployment of Jev-Omni: ask a typed yes/no or multiple-choice question about an image and get a calibrated probability per option from one forward pass, $0.00098 per run on L40S, Apache-2.0, and #1 on Image JevBench v0.1.
karthiksync's Replicate model for structured choices: supply a case record as state, then ask one question with two to twenty named choices, or batch up to eight questions against the same state and get decisions with probabilities.
run-saina's 0.8B decision model for workflow automation on Replicate: context, question, and answers become structured probabilities for yes/no, single-choice, multi-choice, and rating questions, with distribution or thresholded decision modes.
psyb0t's Docker image that serves local Laya, Von, a CLM projection head, or hosted Jev through one TypeSafe-compatible HTTP and MCP endpoint, returning typed choice, score, and noul answers with model probabilities.
Docker's verified ai/openjev image: an 18.9 GB artifact converting the OpenJev weights from huggingface.co/openjev/openjev, served with llama serve through the /v1/systemone API and released under CC BY-NC 4.0 for non-commercial use only.
Nace.AI's 34.6B decision model on Fireworks AI: state plus typed yes/no, multiple-choice, or score questions return a probability for every option in one forward pass, deployable on demand with a 131K-token context window.
The official JavaScript and TypeScript SDK for the TypeSafe API, at version 0.7.0: typed Choice, Score, Noul, and box questions, experimental image inputs, and zstd request compression on Node.js 22.
DevMortimer's Pi extension hands batched Choice, Score, and Noul judgments to Jev: a typesafe_evaluate agent tool, a terminal playground, and a typed API other extensions reuse, with daily request, token, and dollar caps.
A Pi extension that reviews shell commands before execution in three tiers — auto-permitted, auto-blocked, and model-reviewed — with Jev, OpenRouter, or a pi model returning ALLOW or BLOCK for risky commands.
Opt-in task routing for Pi: Jev classifies each request, then a deterministic policy sends it to one of three configured routes, with TypeSafe, Cloudflare, Vercel, and OpenRouter backends and a doctor command for checks.
A typed classification layer for the Pi coding agent: five independently switchable Jev modules — router, gate, shield, prune, and watchdog — that ship in shadow mode, one request per hook, with measured fixture numbers.
Pi extension that registers MiniMax, StepFun, and Command Code as custom providers and adds a typesafe_decide tool answering typed Noul, Choice, and Score questions through TypeSafe Jev, with a Command Code fallback.
Amidabuddha's local stdio MCP server wraps TypeSafe's Jev in a single jev_decide tool: batched choice, score, and noul judgments with probabilities and token usage, leaving thresholds and actions to the caller.
Jev Agents & MCP#community#mcp#typescript
Community
Start here with the Jev AI model
Hand-picked entries the maintainers recommend first, from official primitives to community tools.
The Jev Day release post: Jev 1.14.0 under jev-preview plus three danger-zone features — composable images on both models, the box question type on 1.14 only, and opt-in 262k context.
The box question type: one bounding box per question, returned as four edges that are fractions of the image, plus confidence, with no criteria field and no not-found value.
LiteLLM's Day 0 post for TypeSafe Jev 1.14: the proxy adds box questions, image input and long-context mode on both POST /v1/systemone and POST /v1/decisions, forwarding TypeSafe-Danger-Zone headers only to TypeSafe deployments.
Every entry is reviewed 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 the Jev AI model?
Jev is TypeSafe AI's first System One model — an AI model that 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. 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 reviewed, 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 3919 entries from 293 source sites, last updated Nov 6, 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. 3919 of the 3919 entries carry one today.