Independent project. OpenJev reproduces the interface pattern of TypeSafe's 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.

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Why

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 gives you a Claude Code style REPL in Python.

What you can run today

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:

Live experiments against the real Jev

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.

echo 'TYPESAFE_API_KEY=...' > .env
uv pip install -e ".[experiments]"
python scripts/experiments.py all      # pelican · surface · latency · maze, ~130 calls

What you get

• 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.

Quick start

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:

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}
}

How it works

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.

Roadmap

Full design notes in docs/DESIGN.md.

Related work

The Jev launch triggered a wave of open reproductions. OpenJev builds on what they learned and focuses on the developer-facing experience.

• TheoLeeCJ/SemIf (formerly OpenJev) — single-token logits, WebGPU browser demo
• daseinlabs/open-jev — multi-token option scoring with KV-cache reuse, MLX, Doom demo
• ikermoel/open-alternative-jev — HF + vLLM, packed vs separate modes, honest benchmarks
• TianyuCodings/NanoJev — actually trains a decision head on Qwen3-0.6B
• vinnylarouge/jevlike — option-attention scorer trained from scratch
• vLLM PR #57250 — DiffusionGemma as a Jev-like via one denoising step

Contributing

PRs welcome. uv pip install -e ".[dev]" && pytest && ruff check src tests. See CONTRIBUTING.md.

License

MIT. See LICENSE.