What you can build with it

Purpose and audience

A candidate-scoring head returns distributions for gates, routes and scores without decoding prose or JSON answers.

Evidence 1 ↗ · Evidence 2 ↗

How Jev fits

The README compares a benchmark-trained AgentJev specialist with Jev 1.13.0 evaluated zero-shot; these use different training conditions.

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Using the project

Download the weights, wrap the safetensors file for the torch server and use the local workbench or Python client.

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Scope and limits

Inputs beyond 2,048 tokens are rejected. The included Claude Code PreToolUse hook allows the tool to proceed when its server is unavailable.

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Repository description

AgentJev-0.6B - a fast 'System One' decision model for AI Agents: feed it any unstructured state (diffs, traces, logs) and structured questions, get calibrated probability distributions back in one ~50ms forward pass. Zero output-token decoding.

Project facts

Relationship to Jev
Research
Evidence
Documented
Language
Python
License
Apache-2.0
Origin
Original repository
Repository status
Not archived
Created
2026-09-21
Last code update
2026-09-23
GitHub stars
322
Evidence checked
Sep 30, 2026
Metadata checked
Sep 29, 2026
Check status
Up to date

Stars measure the whole repository, including work unrelated to Jev.

Evidence and scope

Documented records the linked documentation or source. JevHunt has not independently run or benchmarked this project.

Evidence commit: a965ca8ff06ccabc0c796dca5447b55cc2069cee

Discovered through: awesome-jev.

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