| name | ax-python-agent-optimize |
| description | Use when writing Python code with `axllm` for agent optimization, verified agent-playbook evolution, evaluators, judges, optimizer artifacts, BootstrapFewShot, and GEPA. |
| version | 23.0.15 |
AxAgent Optimize 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
- Optimize an AxAgent or reusable program component.
- Mine grounded weaknesses from failed agent tasks and keep only playbook proposals that pass the verification gate.
- Create evaluator callbacks and persist optimizer artifacts.
- Keep optimization runs bounded by explicit budgets and dataset rows.
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
from axllm import AxGEPA
engine = AxGEPA(reflection_client)
result = engine.optimize(request, evaluator)
Relevant API Surface
- Agents And RLM:
agent, AxAgent
- Optimizers:
optimize, playbook, AxPlaybook, AxBootstrapFewShot, AxGEPA, OptimizerEngine, OptimizerEvaluator
Guardrails
- Start from package examples for exact native syntax before inventing a new call shape.
- Use
provider-api examples only when the user explicitly has provider credentials available.
- Use
no-key examples for deterministic local checks and provider request mapping.
- Treat AxIR as the source of generated package truth: if package docs disagree with source code, update the compiler and regenerate packages.
- Do not copy repo-maintainer skills from
tools/*/skills/ into user packages.
