agentrun
★ 423▲ 13Parcha-ai/agentrun
A workflow DSL for agents with a Jev package for the steps that are decisions rather than generation.
Thirty worked uses arranged in ten levels, from one if statement to an agent that reaches for Jev itself, with a video walkthrough.
Checked, not fully reviewed
Thirty concrete uses of Jev, from an if-statement to an agent that reaches for it itself. Checked, not fully reviewed: its suite and its offline demo.
Good for
Watch out for
Tested Oct 4, 2026 at 777adaf47d37 · Node 24 in Docker; its 211 tests and the mock-backend demo
Checked, not fully reviewed. We ran its test suite — 201 pass, 10 live tests skipped without a key — and the whole demo against its mock backend. No Jev calls.
Thirty uses arranged in ten levels, three per level: single decisions as a smarter if, several
picks in one call, composite scores with the weights in code, confidence-gated routing, intent and
model routing, and on up to a coding agent that decides for itself when to ask. Each runs as a
function you can read in one screen, and JEV_BACKEND=mock runs the lot offline with deterministic
answers, which is how to read it the first time.
The repository is mostly a teaching artefact, and it is good at that: it puts the weights in code rather than the prompt, gates on confidence rather than trusting the top answer, and separates the decision from what the program does with it. Live mode talks to TypeSafe or OpenRouter at fixed addresses. There is a video walkthrough linked from the README.
MIT, 169 stars. Start here if the API reference reads as abstract.
See how it compares with other tools in Best Jev tools, tested hands-on.
Review updated Oct 4, 2026. Numbers quoted from the project are its author's own; we don't publish our own measurements of Jev.
Parcha-ai/agentrun
A workflow DSL for agents with a Jev package for the steps that are decisions rather than generation.
osolmaz/switch
TypeScript framework for decision-model harnesses: your program offers a short list of next steps and the model picks one at a time until it can answer, with chess, wikirace and rubric-judge examples run on Jev.
libingzheren/Jev-Mem
Memory layer for long-running agents that routes the frequent memory-management decisions to Jev and leaves the deeper reasoning to the LLM. Comes with a paper.
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