Beadle

Build an AI employee without building an agent platform.

Most of any job is noticing, not thinking — scanning, comparing against a baseline, concluding nothing changed. Noticing is constant and answerable in SQL. Judgment is rare, expensive, and the only part a model is good at. Most agent systems fuse the two and pay judgment prices for noticing. Beadle keeps them apart.

pip install beadle
beadle init my-employees
cd my-employees
./beadle doctor
./beadle run example-site-watch uptime-check

Every task is the same five steps:

GATHER   deterministic — SQL, an API call, a shell command. No model.
GATE     plain Python. Did anything happen? If not, stop here — silently.
JUDGE    one model call. Tools disabled, max-turns 1.
DELIVER  gated. Silence is the correct output most days.
RECORD   journal, archive.

There is no agent loop. On the production fleet this was extracted from — 21 live tasks — 10 never call a model at all, 18 can exit before the model is invoked, and the busiest task runs 144 times a day for zero tokens.

Beadle is a workspace you own and edit, not a library you import. beadle init fetches it and gets out of the way. Full docs, four working example employees, and a complete worked build (including the bug hit along the way) are in the workspace itself.

MIT · https://github.com/agentropicai/beadle