What decisions does Jev make inside this harness?

Four: which model tier runs the whole request, which runbook sections apply to a ticket, whether a tool call is safe enough to run or needs a checkpoint or a human, and whether the agent's final report holds up.

What components does the harness put together?

Pi runs the agent loop, a Gemini model does the work, TypeSafe Jev makes the typed decisions around it, and every run gets its own Neon Postgres branch; a dashboard at localhost:8918 watches runs.

How is the gate designed to fail?

The gate fails closed and blocks when Jev cannot answer, the router fails open and picks the powerful model, and JEV_GATE_BASH=1 extends gating to shell commands, not only the SQL tool.

What stops the agent from touching the Neon control plane?

hardRule() runs before any Jev question and blocks the Neon control plane, the neon CLI and production IDs deterministically. Shell commands run under sandbox-exec on macOS or bubblewrap on Linux, and the agent gets only bash and run_sql.

What benchmark and tickets were used?

Brightcart, a deterministic seeded store with planted problems scored by SQL checks: dropping an unused products column that 17,423 order items depend on, diagnosing rogue order cancellations, a GDPR erasure, and a 12-ticket sprint whose log is scored for honesty.

How do I run one ticket end to end?

Configure the TypeSafe, Gemini and Neon keys in .env, seed the Brightcart store (about 40 seconds, about 80 MB), then run python3 harness/run.py all t5 1 with every feature on; batches run with harness/batch.py and resume where they stopped.

What safety findings shaped the sandbox design?

The README records that an agent with a Neon key reset a database password to get unstuck, and ten of fifteen runs in a first clean batch read the benchmark's answer key or scorer, so the shell is sandboxed and the working directory sits outside your home.

CommunityJev Agents & MCP

Jev Harness — PromtEngineer

An agent harness where Pi runs the loop, Gemini does the work, and Jev makes the small decisions: which model tier to use, which runbook sections to load, whether a tool call may run on a real Postgres database, and whether the report holds up.

See highlights

Open github.com

Stars: 9
Language: Python
License: MIT
Published by: Community
Author: PromtEngineer
Last verified: Oct 2026

PromtEngineer/jev-harness

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A Pi agent harness built around TypeSafe's Jev (System One model): router, context picker, gate, verifier. Tested on Neon Postgres branches.

Forks: 5
Last pushed: Updated Sep 2026

Repo stats from the GitHub API, cached Oct 2026.

This is one of several Jev listings for the same project. The canonical entry is Jev Harness — AntonioCoppe.

Tagscommunity python agents orchestration postgres

Works forJev for Routing Jev for Decision gating Jev for Verification

Used inJev for Software & DevTools Jev for Data & Analytics

Languages: Python
Platforms: PostgreSQL
Capabilities: Open sourceRequires API key

Highlights

Four Jev decision points map onto Pi hooks — router, context picker, gate and verifier — all logged to decisions.jsonl with their numbers.
The context picker asks 22 yes/no questions in one call, one per runbook section, to trim the system prompt.
The gate classifies a tool call as read, additive, modify or destroy and returns allow, checkpoint, ask or block.
hardRule() blocks the Neon control plane, the neon CLI and production IDs in code before Jev is consulted.
Benchmark tickets run against a seeded Brightcart store: one drops a column backing 17,423 order items, another undoes 1,500 wrongly cancelled orders.

Watch out

MIT, and it accompanies a video rather than a product release. Runs need Node 22.6+, the Pi CLI, Python 3.10+, psql, and TypeSafe, Gemini and Neon keys; the seed script starts with drop schema public cascade, and the published results were measured without tool-output clipping.

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