model-selection

Model selection

Build a terminal model picker from the account's live model catalog, then apply the selection through a dynamic model-policy callback.

catalog client -> get_available_models() -> model/context/reasoning picker
                                                    |
execution client <- resolve_model(policy) -----------+

Complete the repository setup, then:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python main.py

The picker displays enabled models, configurable context-window sizes, and reasoning-effort levels from runtime metadata. Press Enter to accept each default, or enter a displayed number or value.

The catalog and execution sessions are separate so the interactive selection happens outside resolve_model. The callback remains fast and simply returns the cached policy for each LLM request.