@agentic_function#

@agentic_function wraps a Python function whose body may run LLM calls through runtime.exec(). The wrapper records the function call in the session DAG unless expose="hidden" is used.

This page explains the usage patterns. Metadata rules live in function-metadata.md.

Basic pattern#

from openprogram import agentic_function

@agentic_function(input={
    "text": {"description": "Text to translate."},
})
def translate_to_chinese(text: str, runtime=None) -> str:
    """Translate text to Chinese."""
    return runtime.exec(content=[{"type": "text", "text": (
        "Translate the following text to Chinese. Return only the translation.\n\n"
        f"Text:\n{text}"
    )}])

Declare a runtime parameter and never pass it yourself: the framework injects it on direct Python calls and on tool dispatch alike (a missing or None value is filled before the signature is enforced), and filters it out of the tool schema so the model never sees it. runtime=None is the conventional spelling; a bare runtime without a default also works.

The docstring is the function-level description. The content block is the actual instruction and data for this LLM call.

Direct composition#

Use direct Python calls when the order is fixed.

@agentic_function(input={
    "task": {"description": "Research task."},
})
def research_pipeline(task: str, runtime) -> dict:
    """Run a fixed research pipeline."""
    survey = survey_topic(topic=task, runtime=runtime)
    gaps = identify_gaps(survey=survey, runtime=runtime)
    ideas = generate_ideas(gaps=gaps, runtime=runtime)
    return {"survey": survey, "gaps": gaps, "ideas": ideas}

LLM-selected tools#

Use runtime.exec(tools=[...]) when the model should choose from a specific menu of functions. Note that a bare runtime.exec(content=...) is not tool-free: with neither tools= nor toolset= passed, the call resolves the full registry toolset by default, so the model can already search, run code, and edit files. Pass toolset="none" (or tools=[]) for a pure reasoning call — see tool-calling.md.

@agentic_function(input={
    "task": {"description": "User task."},
})
def research_assistant(task: str, runtime) -> str:
    """Choose and run the appropriate research helper."""
    return runtime.exec(
        content=[{"type": "text", "text": (
            "Choose the appropriate helper for this task and complete the work.\n\n"
            f"Task:\n{task}"
        )}],
        tools=[survey_topic, identify_gaps, generate_ideas],
    )

@agentic_function provides .spec and .execute, so decorated functions can be passed directly into tools=[...].

Besides direct composition and tools, a third way to pick the next step is a decision menu via exec(choices=...) or decision.make — see next-step-decision.md.

Decorator fields#

The decorator fields (expose, render_range, input, system, workdir_mode, …) are documented in one place: function-metadata.md §3. The API reference at ../../../reference/api/agentic-function.md carries a condensed quick-reference table.

This page covers usage patterns; it intentionally does not duplicate the field-by-field reference. If you're looking for what a field does, go to function-metadata.md.