OpenJev: An open-source, Jev-compatible System One decision engine

OpenJev, an independent open-source project from GPT-AGI, released a Jev-compatible System One decision engine that answers typed questions (Choice, Score, Noul) from open-weight models in a single forward pass with calibrated probabilities, without JSON parsing or token generation. The project reproduces the interface pattern of TypeSafe's Jev using open models and is not affiliated with or endorsed by TypeSafe. In measured experiments, OpenJev answered 1 question in 290 ms and 27 questions in 328 ms (output tokens 23 to 594), and completed a 14-move maze shortest path in 5.4 s with confidence dropping to 0.6 at corners and 0.99 in corridors.

An open-source, Jev-compatible System One decision engine with a Claude Code style REPL. Typed decisions Choice · Score · Noul from open models in one forward pass. No JSON parsing, no hallucinated shapes, every answer comes with a probability. English · 简体中文 https://github.com/GPT-AGI/OpenJev/blob/main/README.zh-CN.md openjev play maze · left: live state · right: streaming /v1/systemone response, option probabilities, rolling confidence & latency. Replays real Jev decisions 14 moves, shortest path, 0 tokens generated . Independent project. OpenJev reproduces the interface pattern of TypeSafe's Jev https://typesafe.ai/blog/introducing-system-one-models-and-jev with open-weight models. It does not reproduce Jev's undisclosed model or training, and it is not affiliated with or endorsed by TypeSafe. Jev and TypeSafe are trademarks of their respective owners. Most decisions inside an agent are small: route this, retry that, is this tool call dangerous, which option wins? A chat model can answer them, but it spends hundreds of tokens generating text that your code immediately parses back into an if . Jev showed that a System One model can answer typed questions in ~100 ms with calibrated probabilities. OpenJev brings that experience to open models, and adds the thing the ecosystem is missing: a terminal you can actually watch decisions happen in , the same way Clawd-Code https://github.com/GPT-AGI/Clawd-Code gives you a Claude Code style REPL in Python. <sub Same 27 questions, same state, started together. One forward pass per question vs. autoregressive JSON. Illustrative animation; measured numbers below. </sub The concept above is the Phase 1-2 target UI. The REPL that ships now already renders the same probability bars, confidence and latency for every decision: OpenJev ships a reference harness that runs real experiments against the official API, so every claim in this README can be re-measured with one command. Full write-up in docs/EXPERIMENTS.md https://github.com/GPT-AGI/OpenJev/blob/main/docs/EXPERIMENTS.md . | <sub The pelican test. Morph a pelican into a bicycle one sentence at a time. P bird and P vehicle cross exactly where Jev says the subject is both ; absurdity peaks while the pelican is riding.</sub | <sub Decision landscape. 64 tickets on a days-down × revenue-lost grid, one call each. P urgent rises monotonically from 0.11 to 0.85; priority flips P3 → P1 with a cliff around day 3-5.</sub | | <sub Adding questions is nearly free. Measured live: 290 ms for 1 question, 328 ms for 27 output tokens 23 → 594 . Dashed line is a modelled autoregressive baseline.</sub | <sub Jev plays a maze. One Choice per step, ASCII map as state. Shortest path 14 moves in 5.4 s. Confidence drops to 0.6 exactly at the corners and sits at 0.99 in corridors.</sub | echo 'TYPESAFE API KEY=...' .env uv pip install -e ". experiments " python scripts/experiments.py all pelican · surface · latency · maze, ~130 calls - Three primitives, Jev-compatible schema. Choice pick one of up to 255 options , Score probability-weighted grade on an ordered legend , Noul P true for a yes/no judgement . Same request/response shape as POST /v1/systemone . - Claude Code style REPL. Paste a state, add questions with slash commands, hit /ask , watch probability bars stream in with confidence and latency. - One forward pass. Logits are read at the answer position and softmaxed over your labels only. Nothing outside the option set can win. No decoding loop. - Pluggable backends. Hugging Face Transformers today CPU / CUDA / MPS . MLX, vLLM and a TypeSafe/OpenRouter reference mode on the roadmap. - Drop-in for the official SDKs Phase 1 . Point typesafe-sdk or @typesafe-ai/sdk at http://localhost:8000 and keep your code. git clone https://github.com/GPT-AGI/OpenJev.git && cd OpenJev uv venv --python 3.11 && source .venv/bin/activate uv pip install -e ". dev " core + REPL, runs anywhere with the mock backend uv pip install -e ". hf " + real models via Transformers openjev mock backend, instant, no download openjev --backend hf --model Qwen/Qwen2.5-0.5B-Instruct Inside the REPL: ❯ /state "Customer: our Stripe integration has been failing for 3 days, payments are down. Fix it today." ❯ /choice dept "Which department should handle this?" billing,technical,sales ❯ /score frustration "How frustrated is the customer?" 0:calm,1:annoyed,2:furious ❯ /noul urgent "Does this need immediate escalation?" ❯ /ask Or use it as a library: python from openjev import Choice, Noul, Score, SystemOneRequest from openjev.backends import get backend backend = get backend "hf", model id="Qwen/Qwen2.5-0.5B-Instruct" resp = backend.decide SystemOneRequest state={"ticket": "Stripe integration failed for 3 days, payments down"}, questions={ "dept": Choice instructions="Which department?", options= "billing", "technical", "sales" , "urgent": Noul instructions="Does this need immediate escalation?" , "frustration": Score instructions="Customer frustration?", legend={"0": "calm", "1": "annoyed", "2": "furious"} , }, print resp.answers "dept" .choice, resp.answers "dept" .confidence technical 0.84 print resp.answers "urgent" .noul 0.96 print resp.answers "frustration" .score 1.23 print resp.latency ms Response shape identical to the Jev docs : { "model": "openjev-hf/Qwen2.5-0.5B-Instruct", "answers": { "dept": {"type": "choice", "choice": "technical", "probabilities": {"technical": 0.91, "billing": 0.07, "sales": 0.02}, "confidence": 0.84}, "urgent": {"type": "noul", "noul": 0.96}, "frustration": {"type": "score", "score": 1.23, "legend": {"0": "calm", "1": "annoyed", "2": "furious"}, "probabilities": {"0": 0.08, "1": 0.61, "2": 0.31}, "confidence": 0.42} }, "usage": {"input tokens": 312, "output tokens": 8} } php flowchart LR S state -- P prompt template Q question + labels -- P P -- M open causal LM<br/ one forward pass M -- logits at answer position -- R read only label tokens R -- SM softmax over labels SM -- C{Choice / Score / Noul} C -- A "typed answer<br/ + probabilities<br/ + confidence" confidence is 1 - normalized entropy of the label distribution TypeSafe does not publish its formula; this is a transparent approximation . Raw probabilities from zero-shot models are over-confident, so Phase 2 adds temperature calibration against your own labelled data. | Phase | Goal | Status | |---|---|---| | 0 | Core primitives, HF backend, Claude Code style REPL, demo GIFs | ✅ this release | | 1 | openjev serve : FastAPI POST /v1/systemone , official SDKs work by changing base url ; shared-state prefill with KV-cache reuse; session save/load | 🔜 | | 2 | Web Playground: side-by-side OpenJev vs Jev vs LLM-JSON with probability bars, latency and cost; openjev eval with accuracy + ECE on public sets; temperature calibration | ⏳ | | 3 | Backends: MLX Apple Silicon , vLLM prompt logprobs , TypeSafe / OpenRouter reference mode; agent middlewares tool-call guardrail, model router, RAG reranker ; SKILL.md for Claude Code / Clawd-Code | ⏳ | | 4 | Train a lightweight decision head NanoJev / jevlike style with calibration losses; publish weights; Docker + one-click deploy | ⏳ | Full design notes in docs/DESIGN.md https://github.com/GPT-AGI/OpenJev/blob/main/docs/DESIGN.md . The Jev launch triggered a wave of open reproductions. OpenJev builds on what they learned and focuses on the developer-facing experience. - TheoLeeCJ/SemIf https://github.com/TheoLeeCJ/SemIf formerly OpenJev — single-token logits, WebGPU browser demo - daseinlabs/open-jev https://github.com/daseinlabs/open-jev — multi-token option scoring with KV-cache reuse, MLX, Doom demo - ikermoel/open-alternative-jev https://github.com/ikermoel/open-alternative-jev — HF + vLLM, packed vs separate modes, honest benchmarks - TianyuCodings/NanoJev https://github.com/TianyuCodings/NanoJev — actually trains a decision head on Qwen3-0.6B - vinnylarouge/jevlike https://github.com/vinnylarouge/jevlike — option-attention scorer trained from scratch - vLLM PR 57250 https://github.com/vllm-project/vllm/pull/57250 — DiffusionGemma as a Jev-like via one denoising step PRs welcome. uv pip install -e ". dev " && pytest && ruff check src tests . See CONTRIBUTING.md https://github.com/GPT-AGI/OpenJev/blob/main/CONTRIBUTING.md . MIT. See LICENSE https://github.com/GPT-AGI/OpenJev/blob/main/LICENSE .