Three.js simulation where an OpenAI model turns each plain-language instruction into a plan and Jev makes every reflex decision: two Score questions for move and turn plus one Noul for jump, with code acting only past a 0.33 dead zone and sending no Jev request while no step is active.
Loadable SQLite C extension and Python package that exposes Jev Noul, Choice, and Score judgments as SQL scalar functions and a jev_rows virtual table which batches up to 40 rows into one shared state and joins typed results back to the source by rowid.
Dependency-free Go 1.24+ client for Jev Noul, Choice, and Score questions: ChoiceOf[T] and ScoreOf[L] return the caller's own types and reject labels the question never offered, with validation before sending, retries that honor Retry-After, OpenRouter support, and eleven tested examples.
Pre-1.0 npm library that every Jev request in a loop goes through: it waits for room under 80% of the account's requests-per-minute and tokens-per-second limits, pauses all local callers on 429 for the server's Retry-After, and refuses requests above a per-key daily spend ceiling of USD 0.20 by default.
Open spike issue in the Orcha multi-agent orchestration platform: it proposes moving judgment out of expensive state-of-the-art models and onto Jev, which emits precise instructions for a cheaper executor to carry out, and requires findings, a cost model, and a recommendation as issue comments.
Go CLI for Jev with noul, choice, score, eval, rank, and models commands: rank sends one Score question per item per weighted YAML dimension in a single request and sums weight times score in code, while threshold flags turn a failed gate into exit code 10 for scripts and CI.
Permission gate extension for the pi coding agent: deterministic allow/deny rules and protected paths settle clear cases first, then gray-zone tool calls get one structured classify() verdict; Jev is an optional native classifier via TypeSafe direct, OpenRouter, or other gateways, failing closed on error.
Go linter whose rules are plain-English questions: AST matchers select nodes and Jev answers one Noul per fragment above the rule's threshold. It runs standalone, under go vet, and as a golangci-lint plugin, with cached answers so runs only ask about changed code.
Claude Code and ZCode skill that offloads classify, route, batch-screen, score and compliance-check judgments to Jev through OpenCode Zen's free tier, bundling a zero-dependency jev.py caller and a Taobao comment-triage pipeline that keeps raw items out of the agent context.
OpenCode plugin that replaces LLM compaction summaries with Jev keep/drop judgments: stale tool calls and outputs are pruned while user and assistant text is preserved verbatim, falling back to the built-in summary when Jev cannot help.
Open recipe for calibrated decision models: an LLM teacher writes training data from a one-sentence task description, a LoRA plus decision head on Qwen3-4B-Base answers Choice, Score and Noul questions with calibrated probabilities, evaluated next to Jev on identical items.
Novelty npm package whose crypto.randomUUID() look-alike sends 15 Noul questions plus one Choice to Jev and packs the returned probabilities into the bytes of a UUID v4-shaped string; explicitly not cryptographically random.
Data-cleaning framework that asks Jev Choice questions to classify each CSV column into a 13-code type vocabulary and each dataset into one of six scenes, then executes every write locally; an optional System 2 LLM handles the default path or escalations.
Privacy-first, self-hosted personal knowledge management system with block-level references and a local-first Markdown editor; the project integrates Jev's decision model for high-speed document classification, flashcard intent categorization and automated tag routing.
Proof of concept that flags suspicious purchase orders in SAP S/4HANA: ABAP computes risk indicators, a Fiori Elements app sends seven typed questions to Jev via OpenRouter, and ABAP applies thresholds to rate each order. Runs in shadow mode only.
Grounded information extraction library: deterministic code proposes candidate spans with exact offsets and Jev answers one Choice per span plus sentence-level Nouls, so every extraction is a substring of the source. Ships a bilingual benchmark and paper.
Reproducible billing and stability measurement of Jev's fan-out behavior across 2,976 requests to jev-1.13-20260917: asking N questions in one call bills the state once, with raw data, exact invoice checks and a batched-versus-single answer comparison.
Head-to-head benchmark of fast typed classifiers, TypeSafe Jev, OpenAI Decisions and Cloudflare Clef, against Claude models on a frozen, labeled corpus of 2,321 cases across 31 tasks, measuring accuracy, calibration, p95/p99 latency and registry-priced cost.
Browser Pac-Man race where a deterministic three-junction simulation filters fatal routes, then Jev makes one typed Choice among the surviving route IDs while the server rejects any answer outside the supplied set. Self-hosted Laya runs the same contract for comparison.
Dependency-free TypeScript SDK that turns Jev Noul, Choice and Score answers into named decisions: enter/exit thresholds with hysteresis, nested decision trees settled in one call, a JSONL journal, threshold replay without inference and Brier calibration.
Testable 48-requirement spec plus conformance suite, normalising proxy and fault-injecting mock for Jev-compatible System One API servers; against the eight most-starred open servers only two passed every MUST requirement.
Serves Shanghai AI Lab's Intern-Decision-0.8B vision decision model on Apple Silicon behind a /v1/systemone-shaped endpoint: screenshots plus typed questions in, calibrated Choice, Score and Noul probabilities out, for thresholding and escalation.
Multimodal finite-choice decision interface and MuJoCo robotic-arm workbench: dual-camera images and state go to a self-hosted Qwen3.5-9B with a corrective head, which returns one typed next-skill choice without generating text.
Volcengine's agent context database that unifies memory, knowledge RAG and skills under a viking:// virtual filesystem; it ships a Jev rerank client that scores candidate documents against api.typesafe.ai and treats the probability as relevance.
Agent governance integration where Jev judges two Noul checks per tool call (beyond scope, destructive), an admission policy decides which judgments may become evidence, and admitted evidence degrades Trust, Delegation and Authorization in persistent R2R relation state.
Precision-first RAG orchestrator that wires Jev as a System 1 gate around an LLM: it routes chitchat, triages retrieved chunks with relevance and answer-evidence Nouls, then verifies each claim and withholds drafts whose claims are unsupported.
Claude Code plugin and CLI that runs Claude Code on Opus 5.5 and has Jev re-decide the reasoning effort when a prompt arrives and after every tool batch, sending each change as a per-message effort statement inside one unbroken prompt cache.
Node.js CLI that checks whether published Jev outputs were actually produced by Jev: it recomputes the Yurin confidence identity and the expected-score formula over a repository's artifacts and flags fixtures that violate them. No API calls, no dependencies, Node 18+.
Jev-compatible decision models with vision input: LoRA fine-tunes of Qwen3.5 at 4B and 9B serving Choice, Score and Noul on the /v1/systemone wire format, with screenshots and photos placed beside text in the state. Runs on your own NVIDIA GPU; weights are non-commercial.
Chrome MV3 extension that groups every tab in a window into named, colored groups from one parallel Jev call - one Choice question per tab against user-edited criteria, with a fixed fallback bucket, manual groups untouched, and the original tab order restored on ungroup.
macOS menu bar screen-time app that reads the screen as text through the Accessibility API and asks a System One model one Choice per user rule plus a Noul on whether the page is a payment, login or banking screen, stepping in only when a rule's probability clears its threshold.
Pi extension that has Jev score each user request on a continuous 0-2 scale and cuts that range into five bands choosing one of three model gears (light, standard, heavy) plus a thinking level. A plain "continue" can inherit the current gear without a new Jev call.
Zero-shot named entity recognition library on Jev: text is split at punctuation, candidate windows are offered as one Choice question per entity type, then verified and boundary-polished by further Choice calls. Types are defined in plain words; no training data or GPU is needed.
CLI and Python library that runs declarative YAML judgment tasks - classify, route, score, verify - natively on Jev Choice/Score/Noul, on any OpenAI-compatible backend, or on local keyword rules, with confidence gates, fallbacks and per-decision cost records.
Proof-of-concept multi-agent AI hedge fund that trades on paper: LLM investor agents analyse stocks, and a native JevLLM adapter lets an agent run on Jev's typed decisions through the same JSON contract the chat providers use. Educational only; no real trades.
Fixed-wing landing simulator where a human can compete against Jev: four Jev Choice decisions map to aileron, elevator, rudder and throttle commands in JSBSim, run in real time. The benchmark also compares Jev, classical controllers and Codex models on landing score and API cost.
Ruby gem that makes System One measurement a language primitive: psi prepares a state, judge/choose/score return calibrated distributions, and ? collapses them for control flow. Ships TypeSafe, protocol (/v1/systemone) and CUA providers behind one interface with its own spec suite.
Python agent framework whose TypeSafeModel class runs Jev as a decision model inside Pydantic AI agents: Pydantic output-type fields become typed Jev questions answered together in one request, each answer carrying its own confidence, and tools can be offered as Choice options.
Python library that records each Jev-class decision as the full distribution under a sha256 key of state, question, provider and model, then applies a separate policy. Thresholds, review bands, routing and budgets can change across the recorded history without new model calls.
MuJoCo simulation where a local decider-2b model picks the next skill for an AgileX PiPER arm through a Jev-style typed-choice interface while the red cube target is pushed to new positions mid-run. Deterministic checks may reject a chosen skill but never substitute another.
Graph RAG framework over Milvus vector search with no graph database required: Jev Noul judgments select graph relations and rerank full source passages alongside vector-search candidates, reaching 86.07% average Recall@5 on 1,000 MuSiQue and 1,000 2Wiki queries in the two-stage evaluation.
OpenAI-compatible /v1/chat/completions proxy that answers an agent host's approval-guardian call with APPROVE, DENY or ESCALATE. Jev answers one Choice question with a calibrated distribution; missing keys, timeouts and malformed answers all fail closed to ESCALATE.
Cisco's security scanner for agent skills added a System One analyzer: it sends the full skill text to POST /v1/systemone as one advisory Noul question and records the probability, while being deliberately unable to emit a finding or change a severity.
LangChain's LangSmith team benchmarks Jev as an agent evaluator against GPT-5.6 Luna, Terra, and Claude Sonnet 4.6 on five captured weather-agent runs. Jev matched the human oracle on all 500 binary pass/fail decisions with 92-913x lower quality-score variance and cost $0.00035 per call.
An index of 16 Jev design patterns, each framed as a problem you will run into, the way out, and the projects that took it. Patterns are grouped into four areas: what goes to the model, how one request is shaped, how a probability becomes an action, and how you know it holds.
A paper analyzing 2,170 public Jev projects on GitHub as of September 22, 2026. It maps how projects use Jev for attribute judgment, scoring, action selection, content filtering, and model and tool selection, and finds public attention concentrated in routing and interface projects rather than tracking project counts.
ggml's announcement that llama.cpp server supports decision models through a /v1/systemone endpoint: send a state and typed questions, get a probability per option from one forward pass. It lists six GGUF decision models from Julia-1 (144M) to OpenJev (27B) with single-question latencies.
SREGym benchmarks a Jev-only diagnosis pipeline with no LLM in the loop: it programmatically collects Kubernetes evidence, then Jev picks the root-cause component and supporting observations. Across 105 diagnoses on 21 faults it passed 80 (76.2%) at a 14.6 s median time and an estimated $0.15 total cost.
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.