Purpose and audience

This experimental CLI develops binary, multiclass, multilabel and scoring functions through repeated evaluation, labeling and definition proposals.

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How Jev fits

Jev evaluates the data through TypeSafe, Vercel or Cloudflare; GEPA uses a separately configured reflection model.

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Using the project

Load CSV, Parquet or JSONL data, review ambiguous examples and audit samples, then accept, reject or revisit the proposed definition changes.

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Scope and limits

A higher training score never accepts a proposal automatically. Publishing to the public registry includes labeled inputs, labels and optional rationales.

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Repository description

Build calibrated AI Functions from human feedback using Jev and GEPA.

Project facts

Relationship to Jev
Jev application
Evidence
Documented
Language
Python
License
Apache-2.0
Origin
Original repository
Repository status
Not archived
Created
2026-09-19
Last code update
2026-09-20
GitHub stars
300
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: 3d997fc76593036655c28d4964de43b55f81fe2c

Discovered through: awesome-jev, typesafe-field-guide.

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