---
name: ax-llm/ax-python-flow
source: https://app.decimal.ai/s/ax-llm-ax-python-flow@1/SKILL.md
source_sha256: 5872c7eeacc5
---

# 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

```python
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.

```python
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.

```python
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.

```python
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.

```python
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.

```python
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.