ax-python-flow

name	ax-python-flow
description	Use when writing Python code with `axllm` for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.
version	23.0.14

AxFlow 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

• Compose generators, agents, and nested flows into a workflow graph.
• Reason about flow state, node inputs, returns, caching, and errors.
• Use generated package examples for flow graphs and provider-backed flows.

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 ax, flow

draft = ax("topicText:string -> draftText:string")
wf = (
    flow({"id": "docs.coreFlow"})
    .execute("draft", draft, {"reads": ["topicText"], "writes": ["draftResult", "draftText"]})
    .returns({"draftText": "draftText"})
)

More Patterns

Typed programs

Build each flow node from its own input/output contract.

classifier = ax('requestText:string -> route:class "support, sales, engineering"')
responder = ax("requestText:string, route:string -> responseText:string")

Class decision

Declare reads and writes so the responder waits for the typed route.

branch_flow = (
    flow({"id": "docs.branchFlow"})
    .execute("classifier", classifier, {"reads": ["requestText"], "writes": ["classifierResult", "route"]})
    .execute("responder", responder, {"reads": ["requestText", "route"], "writes": ["responderResult", "responseText"]})
    .returns({"route": "route", "responseText": "responseText"})
)

Parallel fan-out and join

Independent reads let research and audience analysis share one planner group.

parallel_flow = (
    flow({"id": "docs.parallelFlow"})
    .execute("research", research, {"reads": ["topicText"], "writes": ["researchResult", "factList"]})
    .execute("audience", audience, {"reads": ["topicText"], "writes": ["audienceResult", "audienceAngle"]})
    .execute("join", join, {"reads": ["factList", "audienceAngle"], "writes": ["joinResult", "briefText"]})
    .returns({"briefText": "briefText"})
)

Draft, critique, revise

A linear refinement pipeline makes each dependency explicit.

refine_flow = (
    flow({"id": "docs.refineFlow"})
    .execute("draft", draft, {"reads": ["topicText"], "writes": ["draftResult", "draftText"]})
    .execute("critique", critique, {"reads": ["draftText"], "writes": ["critiqueResult", "critiqueText"]})
    .execute("revise", revise, {"reads": ["draftText", "critiqueText"], "writes": ["reviseResult", "revisedText"]})
    .returns({"revisedText": "revisedText"})
)

Run a flow

Forward accepts the provider client and the public flow inputs.

output = parallel_flow.forward(client, {"topicText": "Typed LLM workflows"})

Start from the complete programs under examples/, then browse the larger gallery at https://axllm.dev/python/subsystems/flow/.

Relevant API Surface

• Flow: flow, AxFlow

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.