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An LLM + Jev workflow that changes EVERYTHING. Boost your vertebrate brain with a spine inside.
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JevRev
A TypeScript layer beside an LLM: JevSift selects approaches, JevLoop audits progress with typed actions, and JevLong watches sessions for stalls without steering them.
- Stars
- 654
- Language
- TypeScript
- License
- MIT
- Reading time
- 3 min
- Published by
- Community
- Author
- Alex314618-create
- Last verified
- Sep 2026
About this project
An alloy spine for your LLM, built with Jev. Jump to: Examples · Sift / Loop / Long · Connect an agent · TUI · Providers · Docs. JevRev is an LLM + Jev system. Evolution gave animals a spine so they could make many decisions quickly and cheaply. JevRev applies the same split to a workflow: the LLM brings depth and range, while Jev brings very fast, low-cost decisions. Together they make the workflow more efficient. The tool has three parts: JevSift, JevLoop, and JevLong.
Quoted from the project's README. Read the full README.
Used inJev for Software & DevTools
Highlights
- JevSift removes weak, duplicate, and risky approaches, then emits bounded work orders for survivors.
- JevLoop returns typed actions: continue, fix_regression, verify, replan, waiting_human, or completed.
- JevLong reports stalls, repeated failures, drift, and budget risk, and never silently steers or kills the agent.
- The CSV parser demo rejects a regex shortcut on a correctness probe and keeps an indexed state machine.
- Providers cover hosted Jev, local SemIf through llama.cpp, or replay fixtures with no network or key.
Quickstart
bash
npm install
npm run build
npm run demo:workflowWatch out
Live runs need a JevRev API key or a local provider; replay fixtures work offline. Installing the skill does not put `jevrev` on PATH.
Questions about this entry
Short answers drawn from the linked project or page.
A layer beside it. The LLM proposes and executes the work; Jev makes decisions, filters paths and raises reminders, and the session stays with your host agent. Codex, Claude Code, OpenCode, CI and custom harnesses all use the same CLI and JSON or JSONL contracts, so nothing has to be rewritten.
Yes, through replay fixtures, which run offline and need no key. Live runs need a JevRev API key or a local provider such as SemIf through llama.cpp. Note that installing the skill does not put jevrev on your PATH, and there is a demo workflow command if you want to watch the whole loop first.
No, and that is its stated contract. It reads the JSONL events your agent writes and reports stalls, repeated failures, drift, tool problems and budget risk, but never silently steers or kills the agent. JevLoop is the part that returns typed next actions such as fix_regression, verify, replan or waiting_human.
One case is quantitative and one is not. The JSONL ingestion case shows six approaches scored, a correctness probe rejecting the regex shortcut for catching 65 of 184 malformed lines against 180,530 events per second, and three rounds ending completed in 838 ms and 1,628 replay-accounted tokens. The city-building case is before and after screenshots with no controlled numbers.
JevSift picks which approaches deserve budget, JevLoop audits one result and returns a typed next action, and JevLong watches long sessions for stalls, repeated failures, drift and budget risk.
It exposes a CLI and JSON or JSONL contracts; install the skill with node scripts/install-skill.mjs --target codex, claude, agents or dsh, or call the CLI directly from Codex, Claude Code, OpenCode, CI or a custom harness.
jevrev activation with an input JSON compares the expected cost of taking a wrong path against a few bounded probes and returns stable reason codes; it runs in shadow mode, does not call Jev, and does not start or skip Sift for you.
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