ax-python-ai

AxAI Providers For Python

This skill helps an agent write Python code with the generated Ax package axllm. Use the generated package API, examples, and manifests; do not import TypeScript-only APIs unless you are editing the TypeScript package.

When To Use

• Create provider clients or normalize provider options.
• Choose a named deployment profile separately from the model ID served by that deployment.
• Attach renewable per-request credentials for expiring cloud tokens.
• Resolve structured-output modes from the selected profile and model.
• Choose between model-list routing, ordered failover, and adaptive operational routing.
• Route multimodal requests without flattening native images or files when the selected provider supports them.
• Use scripted transports for deterministic no-key examples.
• Use provider-api examples only when explicit provider credentials are available.

Package Facts

• Language: Python.
• Package: axllm.
• Package API docs: API.md and axir-api.json.
• Capability manifest: axir-capabilities.json.
• Runnable examples: examples/.
• Real network support: yes.
• Scripted no-key transport support: yes.
• Runtime profiles: javascript-quickjs, python-pyodide.

Core Pattern

import os
from axllm import ai

llm = ai("openai", api_key=os.environ["OPENAI_API_KEY"])

Decision evidence

Add (evidence) to boolean/class/score/box signature outputs to retain probabilities, values, provenance, and native class/score confidence through AxGen. Boolean threshold 0.7 is local to that field; scalar fields still use provider trueThreshold. A plain score returns a continuous zero-based rubric index.

Native selected classes and continuous scores are preserved, even when they differ from argmax or a weighted mean. Generative providers elicit probabilities, derive values locally, omit confidence, and use provenance.source=elicited. Ax attaches actual provider/model provenance. The result shape does not make confidence or calibration comparable across models.