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ax-python-flow

Build Python Ax flow code for typed workflow graphs in axllm.

2.9k|186|Updated Feb 23, 2023
Search Tags:#caching#python#agents#flows#optimizers#axllm#workflow-graphs
One-click install
npx skills add https://github.com/ax-llm/ax --skill ax-python-flow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ax-python-flow
Source: https://github.com/ax-llm/ax/tree/main/website/static/python/.well-known/agent-skills/ax-python-flow
Command: npx skills add https://github.com/ax-llm/ax --skill ax-python-flow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you write correct Python code for Ax flows without guessing the generated API, making it easier to compose typed workflows, nested programs, and provider-backed pipelines.

Core Features & Use Cases

  • Flow graph composition with AxFlow, flow, nodes, and returns projection.
  • Guidance for state, caching, dynamic options, runtime errors, and nested programs.
  • Useful for building generator pipelines, agent workflows, and optimizer-driven evaluation loops from the generated Python package.

Quick Start

Ask for a Python Ax flow example that uses axllm to build a typed workflow graph with caching and nested programs, following the package examples for exact syntax.

Frequently Asked Questions about ax-python-flow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build typed workflow graphs in Python for agent pipelines?▼

Typed workflow graphs in Python are built using the AxFlow package to compose generators, agents, and nested programs into structured pipelines. This Skill generates package-accurate Python syntax for these exact workflow compositions.

Can I add caching and dynamic options to a Python agent workflow?▼

Yes, you can add caching and dynamic options to a Python agent workflow by applying AxFlow state and node wiring techniques. This Skill provides the exact syntax needed to configure runtime caching and dynamic option selection.

What's the best way to handle runtime errors in Python flow graphs?▼

The best way to handle runtime errors in Python flow graphs is through error-aware node wiring within the AxFlow framework. This Skill guides you in writing the correct package-accurate code to manage these execution edge cases.

How do I compose nested programs and optimizer-driven loops in axllm?▼

You compose nested programs and optimizer-driven evaluation loops in axllm by structuring your Python code with AxFlow nodes and flow projection. This Skill helps generate the exact example-driven syntax required for these complex loops.

Do I need to learn a specific syntax to write Ax flows in Python?▼

Yes, writing Ax flows requires package-accurate Python syntax aligned with the AxIR documentation and generated examples. This Skill helps you write the code correctly without guessing the generated API or flow usage.

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