Examples and notebook¶

Runnable examples¶

Short scripts, one per use case, in examples/. Each runs on a laptop CPU with opendecider-nano (a 0.8 GB download on first use), and CI runs them against the released model whenever the package or the examples change (remote_backends.py, which needs a model server, is compile-checked).

script	what it shows
`quickstart.py`	three typed questions (`choice`, `score`, `noul`) about one ticket
`support_triage.py`	a queue of tickets in one call: route by team, flag churn risk, send unsure answers to a person
`agent_guardrail.py`	check an AI agent's JSON trace before its next step: continue, retry, ask the user or stop
`confident_automation.py`	on 2,000 labelled business decisions: how many you can automate at a given accuracy
`serve_client.py`	call `opendecider serve` over HTTP (Jev's `/v1/systemone` protocol), with retries
`remote_backends.py`	the same decision through LM Studio, Ollama or vLLM

git clone https://github.com/manjunathshiva/opendecider && cd opendecider
pip install opendecider pandas pyarrow
python examples/support_triage.py

Colab notebook¶

Open in Colab

On a free NVIDIA T4, about 15 minutes: nano on a support ticket and an agent trace, many states in one call, confident automation on the typed-decisions test split (with a chart), speed at 1–50 questions per call, opendecider serve, small and small-td, and rebuilding the benchmark tables. The notebook in the repository is saved with the outputs of a real T4 run, so you can read it without running it.

Live demo¶

Try it in your browser

opendecider-nano on a basic CPU Space: edit the state and the questions and see every option's probability.