AxFlow For C++

Use when writing C++ code with axllm for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.

Install

Install only this skill for C++:

Shell

npx skills add https://ax-llm.github.io/ax/cpp/ --skill 'ax-cpp-flow'

Published skill file: ax-cpp-flow/SKILL.md.

Source

Source: packages/cpp/skills/ax-cpp-flow/SKILL.md
Version: 23.0.5

Skill Instructions

This skill helps an agent write C++ 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: C++.
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

C++

auto draft = axllm::ax("topicText:string -> draftText:string");
auto wf = axllm::flow(axllm::object({{"id", "docs.coreFlow"}}))
    .execute("draft", draft, axllm::object({
      {"reads", axllm::array({"topicText"})},
      {"writes", axllm::array({"draftResult", "draftText"})}
    }))
    .returns(axllm::object({{"draftText", "draftText"}}));

More Patterns

Typed programs

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

C++

auto classifier = axllm::ax("requestText:string -> route:class \"support, sales, engineering\"");
auto responder = axllm::ax("requestText:string, route:string -> responseText:string");

Class decision

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

C++

auto branch_flow = axllm::flow(axllm::object({{"id", "docs.branchFlow"}}))
    .execute("classifier", classifier, axllm::object({{"reads", axllm::array({"requestText"})}, {"writes", axllm::array({"classifierResult", "route"})}}))
    .execute("responder", responder, axllm::object({{"reads", axllm::array({"requestText", "route"})}, {"writes", axllm::array({"responderResult", "responseText"})}}))
    .returns(axllm::object({{"route", "route"}, {"responseText", "responseText"}}));

Parallel fan-out and join

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

C++

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

Draft, critique, revise

A linear refinement pipeline makes each dependency explicit.

C++

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

Run a flow

Forward accepts the provider client and public inputs.

C++

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

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

Relevant API Surface

Flow: axllm::flow, axllm::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.