cadenza-cli

A command-line platform for physical-AI developers: scaffold a robot project, describe a multi-phase mission as JSON, run it in simulation, and turn the rollouts into fine-tuning data for a vision-language-action policy.

Run cadenza with no arguments for an interactive shell, or pass a command to run it once (cadenza env run my-project --headless).

Commands

Use cadenza as an API (megan-tk SDK)

Your Cadenza sign-in token doubles as an API key — pip install cadenzalabs, drop the SDK into any project, and drive the hosted megan-tk governance directly. No Supabase credentials; the server meters every call against your account so usage (CLI) / megan.usage() (SDK) can report it. Three surfaces:

• tk.governor() — the progress-guided governor: feed it live scalar progress each control step and apply the corrective action-frame rotation phi it returns (the whole bandit + gating runs server-side as the real megantk).
• tk.session(...) — the milestone / perception governance token (continue vs. adapt).
• tk.anticipator(...) — learn a periodic disturbance and pick a protective action.

from cadenzalabs import megan

tk = megan(api_key="<your-token>")            # or megan() to auto-resolve it
# (MeganTK is the same class, kept as a back-compat alias.)

# progress-guided governor — recover an execution shift on-device, no retraining
g = tk.governor(n_candidates=9, patience=6, eval_window=6)
for episode in episodes:
    g.episode_start()                          # imposes the learned correction
    for step in episode:
        phi = g.step(progress_now)             # radians; apply to your action's (x, y)
        env.step(rotate_xy(action, phi))
g.close()

# milestone path — act where the token is focused
with tk.session("pick up the red cube", ["reach", "grasp", "lift"]) as s:
    d = s.step(reached=[0], obstacles=[{"id": "wall", "milestone": 1}])
    if d.adapt:
        handle(d.frontier)

# disturbance anticipator — learn a rhythm, pick a protective action
a = tk.anticipator(actions=["brace", "dodge"])
a.disturbance(t=1.2); a.outcome("brace", saved=True)
p = a.protect(t=3.4)                          # p.should_protect, p.best_action

print(tk.usage())                             # your metered consumption

The key is resolved from the api_key argument, CADENZA_API_KEY / CADENZA_TOKEN, or the signed-in CLI session. See examples/megantk_quickstart.py. Run apikey in the CLI for a ready-to-paste snippet.