A semantic cache for LLM queries, fully integrated with LangChain and llama_index. Zilliz's cache integrates TypeSafe Jev Noul checks to evaluate cache hit freshness and time-dependent query validity, cutting repeated requests and API cost.
A Python library that turns Jev into a bisection oracle: it repeatedly asks higher, lower or exactly about the midpoint of a numeric range and returns the answer, turn count and full decision history. Built on the official TypeSafe Python SDK and installable from PyPI.
Epistemic runtime for autonomous coding agents that uses Jev as a sub-100ms System 1 gate to shadow-evaluate capsule facts, propose evictions before bloat and prefetch memory between turns. Its fixture run reports 31.3% fewer turns and 52.6% fewer blind discovery operations, with a fail-open circuit breaker.
Agentic search evaluation inside Zilliz DeepSearcher: a standalone harness replaces the ChainOfRAG reflection call with one Jev Noul per round to decide whether to stop within a seven-round budget, then compares stopping behavior, evidence recall and decision cost on 100 sampled 2WikiMultiHopQA queries.
Open decision models from a self-improvement loop that publishes every experiment, failures included. They answer Noul, Choice and Score questions on the same POST /v1/systemone wire format as Jev, returning a calibrated probability per option in one forward pass; the 27B scores 65.64 on the Decision Index 0.3 set.
Calibre plugin that asks Jev typed questions about each book's genre and subjects, applies configurable per-category probability thresholds, preserves existing tags and leaves uncertain results for manual review. Supports single-book, selected-books and whole-library classification with caching, CSV export and restore.
On-call tooling where Jev answers four typed questions per alert - actionable, severity, owning team and duplicate-of - and plain code routes, pages or drops. Includes a root-cause benchmark where Jev and code investigate recorded incidents with no general-purpose LLM in the loop, enforcing budgets and verification.
Independent pre-registered audit of Jev on Spanish: 19,200 calls over 3,200 paired items on XNLI, PAWS-X, MASSIVE and Belebele. A Spanish state costs 3.0-6.4 accuracy points and roughly doubles ECE on the two hardest tasks, while Spanish instructions change nothing; a CLI reruns the same comparison on your own data.
Notebook benchmark that compares Jev with open models and specialised tools on five document-pipeline chores: language detection (Choice(12)), page orientation (Choice(4)), RVL-CDIP classification (Choice(16)), bundle splitting and parse-tier triage, with liteparse doing all PDF rendering and OCR work.
Vercel's official AI SDK for Python, in public beta: streaming models, agent tool loops, hooks for human approval and structured output, with provider IDs routing through AI Gateway by default. The SDK's evaluation operation and Gateway examples carry Jev as a decision and evaluation provider.
Self-hosted Python SDK and System One-compatible API for runtime-defined Choice, Boolean and Score decisions on pinned open models, across Torch, vLLM, MLX, llama.cpp and WebGPU. Each result records probabilities, token usage, timing, model revision and prompt version, with committed row-level benchmarks.
Custom ComfyUI node that routes a prompt to one of three workflow templates using a Jev decision instead of a keyword match. Supports noul, choice (up to 255 options) and score (2-10 levels) questions with parallel evaluation, exponential-backoff retries and full probability distributions as node outputs.
Local-first knowledge search with seven selectable pipelines: BM25 plus Jev reranking is the default vector-free path, plus agentic, embedding-hybrid, taxonomy, gate and line-search modes. Reranks up to 30 candidates in one batched set of Jev Noul relevance questions and streams cited answers from a configurable model.
Scanner pipeline that fingerprints each target with 30+ signal types, then asks Jev to score every Nuclei template 0-4 for relevance so high-value checks run first while all templates still run. A tag-based prefilter keeps about 8.6% of 13,764 templates with 100% recall on score >= 3, saving ~91% of scoring tokens.
Autonomous agent platform with 187,504 stars for building and running agents from plain English or a visual builder. Its platform features first-class TypeSafe Jev decision blocks for typed routing, filtering, scoring and confidence-gated next-action dispatching.
Open recipe for a local Jev-style decision model: a Qwen3.5-9B LoRA that scores one allowed answer token per Choice, boolean or rubric Score question, with data pipeline, training config and eval harness. Reports 90.1% on its 324-example holdout against Jev's 93.2%.
MIT-licensed Python tool router for coding agents that keeps obvious calls on a fast path, asks Jev (default typesafe/jev-1.13 via OpenRouter) for genuinely ambiguous choices, and can run bounded MCTS when multi-step consequences matter. Ships a CLI, MCP server and hook integrations.
Python sandbox and tutorial companion that turns Jev Choice and Noul judgments about issue category and reproduction into code-applied queue proposals, with confidence gates, a review fallback, synthetic offline fixtures and labeled replay. Live mode is opt-in.
Alpha playground that answers plain-English questions over SQLite by composing small Jev decisions -- table, column, comparison and operand selection -- into inspectable typed plans, deterministic SQL and previewed guarded writes. Bundles Cyberchase, DESE and Moneyball databases.
Independent evaluation running Jev jev-1.13.0 and GPT-4.1 as the same 300 Twin-2K-500 synthetic survey respondents across 24,596 paired cells, finding that asking a yes/no item as a Noul rather than a Choice moved results more than the model gap, at a thirty-fourth of the cost.
System One Mosaic Benchmark: a Python evaluator and Next.js viewer comparing Jev and open decision models across 137 English Choice, Noul and Score benchmarks from 106 dataset subsets, with recorded evaluation conditions and a public Hugging Face leaderboard.
Zero-dependency Python CLI that routes small semantic judgments to Jev for agents and CLI pipelines: choose (Choice), check (Noul), score (ordered ratings), batch (1-32 questions in one request) and purify (verbatim passage extraction). GPL-3.0 licensed.
Open System One decision models (27B, 9B and 4B on Qwen chat bases, in bf16, GGUF and MLX) that answer Choice, Noul and Score questions with a probability for every option from one forward pass. The repo carries the trainer, data builders, evaluation harnesses and a record of every run.
Chinese-language research project for multimodal social-risk moderation: a local Qwen3-VL-32B with language-side LoRA emits structured evidence JSON, and a fixed Jev policy turns that evidence into risk bands, with frozen test leaderboards and pure-Jev ablations.
Small test harness and benchmark for Jev through OpenRouter's Decisions API. It ships a thin decide wrapper, 17 documentation-derived cases with 23 checks covering Noul, Choice and Score, and a script comparing Jev against GPT-5.6 Terra and Claude Opus 5.
Python CLI that runs declarative YAML, JSON or JSONL test suites natively on Jev Noul, Choice and Score decisions for RAG faithfulness, hallucination detection and agent safety, with watch mode, parallel batching, a pytest plugin and GitHub Actions PR reporting.
Supervisor for unattended coding agents: it tracks cross-turn state and asks one batched Jev Choice whether a trajectory should continue, pause or escalate, while deterministic code owns dangerous-command blocking, budget caps and Ed25519-signed receipts.
A local Jev-compatible server that reads Choice, Score and Noul answers from the logits of any MLX checkpoint or OpenAI-compatible endpoint with no training, serving POST /v1/systemone as a drop-in for the official TypeSafe SDK.
An open recipe for a 27B multimodal decision model trained with full-weight SFT on 73,000 examples over one H200, serving choice, noul and score on /v1/systemone with per-checkpoint provenance and released calibration plots.
A Jev-style one-pass skill router for OmO agents: a frozen Qwen2.5-0.5B with a tiny option-attention head scores the whole skill catalog per prompt, and an extension shrinks the system prompt to the top-K skills, failing open.
Alibaba's multi-agent platform, which implements native TypeSafe Jev classification through JevClassifierModel for binary, choice and score routing across agent pipelines. Python 3.11+, Apache-2.0, with a FastAPI agent service and web UI.
A CLI that checks new papers, repositories and feeds each morning against your open research questions, asking Jev Noul gates and Score dimensions through Pydantic AI's typesafe model; an LLM writes only the prose into Obsidian notes.
An open benchmark runtime for document-grounded decision models: it scores Jev Noul, Choice and Score answers on pinned tasks and counts malformed probability distributions as misses so model comparisons stay reproducible.
A benchmark that steers decision models through 100 key-and-door grid puzzles with one Jev Choice per move, scored against an exact BFS solver; it found Jev picks an optimal move about 90% of the time yet finishes only 57 of 100 games.
A Craftax (Crafter) survival harness where deterministic code lists every feasible action with its facts and Jev picks one Choice per step, optionally guided by an LLM-written objective and standing rules; three-seed ablations are included.
A Jev harness for PostgreSQL: 41 semantic operators plus a native Rust extension evaluate row-level predicates with Noul questions and keep the returned probabilities for explicit decision thresholds, leaving joins and aggregation to SQL.
A training-free runtime that turns dense Qwen3 or Qwen3.5 checkpoints into a Jev-style Noul, Choice and Score service, sharing one state prefill across isolated questions and reading each answer from candidate-token logits.
A Claude Code PreToolUse hook that blocks known key formats locally and sends only masked unknown high-entropy strings to Jev for a Noul judgment, blocking at 0.80 and asking the human from 0.30 or whenever Jev is unavailable.
Edge deployment demo that exports a jevlike one-pass text scorer as ESP-IDF firmware; packed TinyScorer weights run on the ESP32 in C with no tensor library and classify ASCII text as command, weather, or complex, alongside a Python trainer and host-side check.
Independent training toolkit and open weights for System One decision models in the same category as Jev: 17M, 68M, and 400M models answering Choice, Noul, Score, and reranking questions, with Python and ONNX inference and a documented generalization gap to Jev 1.13 on the project's S1MB benchmark.
Jag, a Jev-inspired visual grounding model: a Qwen3.5-0.8B backbone plus a lightweight MLP predicts normalized cxcywh bounding boxes for an image and referring expression in a single forward pass, removing autoregressive coordinate generation from the base model.
Two runnable demo apps that send identical typed questions to OpenAI Decisions (gpt-6-luna) and TypeSafe Jev (jev-latest) in parallel: a support ticket router over 20 labelled tickets and a cloud alert triage over 15 labelled alerts, with thresholds in plain Python making the final calls.
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.
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.
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.
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.
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.
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.
Every decision path has a failure posture, whether you chose one or not. How to put retries in the client, bound the whole call, and make the fallback an explicit, per-action policy.
Support is a long chain of small judgments over text. Triage category and urgency separately, route with a confidence gate, decompose hard tickets, and decide the action in code.
A test loop is a long series of small judgments. What the Playwright, mobile QA, and CI experiments report: two calls per probe, 131 ms medians, and near coin-flip failures to design around.
Jev Practices & Patterns#testing#qa#confidence
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Watch: Jev in action
Video walkthroughs and demos from the community.
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.
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.