Branching

Conditional Execution

The when parameter controls whether a step runs based on runtime conditions:

workflow = (
    Workflow(name="router")
    .step(classify, text="Urgent: server down")
    .step(handle_urgent,
        text="Server down",
        when=lambda: get_run_context().step_span("classify").output == "urgent")
    .step(handle_normal,
        text="Server down",
        when=lambda: get_run_context().step_span("classify").output == "normal")
)

Both handle_urgent and handle_normal wait for classify to complete. Only the one whose condition is met will execute.

Skipped Steps

If a step’s condition is not met, it is skipped along with all its dependents:

workflow = (
    Workflow(name="pipeline")
    .step(validate_input, data="...")
    .step(process,
        when=lambda: get_run_context().step_span("validate_input").output == "valid")
    .step(save_results,
        data=lambda: get_run_context().step_span("process").output)
)

If validate_input returns "invalid", both process and save_results are skipped. save_results depends on process, which never runs. Not all dependents of a skipped step are skipped. When a step uses depends_on, it only waits for the referenced steps to resolve (either complete or be skipped) — it doesn’t need their data. This is useful when you don’t know which branch will run:

workflow = (
    Workflow(name="pipeline")
    .step(classify, text="new customer signup")
    .step(handle_new,
        data="signup data",
        when=lambda: get_run_context().step_span("classify").output == "new")
    .step(handle_existing,
        data="update data",
        when=lambda: get_run_context().step_span("classify").output == "existing")
    .step(finalize, depends_on=["handle_new", "handle_existing"])
)

handle_new and handle_existing have inverse conditions, so only one runs. finalize depends on both via depends_on, so it waits for both to resolve (one completes, the other is skipped) and then executes regardless.

Conditional with Agents

Use an LLM to make routing decisions:

classifier = Agent(
    name="classifier",
    model="openai/gpt-5-mini",
    system_prompt="Classify the message as 'technical' or 'billing'. Respond with one word only.",
    max_tokens=64,
)

technical_agent = Agent(
    name="technical_agent",
    model="anthropic/claude-sonnet-4-6",
    system_prompt="You are a technical support specialist.",
    max_tokens=1024,
)

billing_agent = Agent(
    name="billing_agent",
    model="openai/gpt-5-mini",
    system_prompt="You are a billing support specialist.",
    max_tokens=1024,
)

workflow = (
    Workflow(name="support_router")
    .step(classifier)
    .step(technical_agent,
        when=lambda: "technical" in get_run_context().step_span("classifier").output.collect_text().lower())
    .step(billing_agent,
        when=lambda: "billing" in get_run_context().step_span("classifier").output.collect_text().lower())
)

result = await workflow(prompt="I can't access my account").collect()

The classifier agent decides which handler runs. Only the matching branch executes.

Getting started

Core Concepts

Agents

Workflows

Human in the Loop

Evals

Deployment

Conditional Execution

Skipped Steps

Conditional with Agents

​Conditional Execution

​Skipped Steps

​Conditional with Agents

Conditional Execution

Skipped Steps

Conditional with Agents