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# to you under the Apache License, Version 2.0 (the
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#
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#
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"""
TaskFlow decorator for agentic LLM workflows.
The user writes a function that **returns the prompt string**. The decorator
handles hook creation, agent configuration with toolsets, multi-turn reasoning,
and output serialization.
"""
from __future__ import annotations
from collections.abc import Callable, Collection, Mapping, Sequence
from typing import TYPE_CHECKING, Any, ClassVar
from airflow.providers.common.ai.operators.agent import AgentOperator
from airflow.providers.common.compat.sdk import (
DecoratedOperator,
TaskDecorator,
context_merge,
determine_kwargs,
task_decorator_factory,
)
from airflow.sdk.definitions._internal.types import SET_DURING_EXECUTION
if TYPE_CHECKING:
from airflow.sdk import Context
class _AgentDecoratedOperator(DecoratedOperator, AgentOperator):
"""
Wraps a callable that returns a prompt for an agentic LLM workflow.
The user function is called at execution time to produce the prompt string.
All other parameters (``llm_conn_id``, ``toolsets``, ``system_prompt``, etc.)
are passed through to :class:`~airflow.providers.common.ai.operators.agent.AgentOperator`.
:param python_callable: A reference to a callable that returns the prompt string.
:param op_args: Positional arguments for the callable.
:param op_kwargs: Keyword arguments for the callable.
"""
template_fields: Sequence[str] = (
*DecoratedOperator.template_fields,
*AgentOperator.template_fields,
)
template_fields_renderers: ClassVar[dict[str, str]] = {
**DecoratedOperator.template_fields_renderers,
}
custom_operator_name: str = "@task.agent"
def __init__(
self,
*,
python_callable: Callable,
op_args: Collection[Any] | None = None,
op_kwargs: Mapping[str, Any] | None = None,
**kwargs,
) -> None:
super().__init__(
python_callable=python_callable,
op_args=op_args,
op_kwargs=op_kwargs,
prompt=SET_DURING_EXECUTION,
**kwargs,
)
def execute(self, context: Context) -> Any:
context_merge(context, self.op_kwargs)
kwargs = determine_kwargs(self.python_callable, self.op_args, context)
self.prompt = self.python_callable(*self.op_args, **kwargs)
if not isinstance(self.prompt, str) or not self.prompt.strip():
raise TypeError("The returned value from the @task.agent callable must be a non-empty string.")
self.render_template_fields(context)
return AgentOperator.execute(self, context)
[docs]
def agent_task(
python_callable: Callable | None = None,
**kwargs,
) -> TaskDecorator:
"""
Wrap a function that returns a prompt into an agentic LLM task.
The function body constructs the prompt (can use Airflow context, XCom, etc.).
The decorator handles hook creation, agent configuration with toolsets,
multi-turn reasoning, and output serialization.
Usage::
@task.agent(
llm_conn_id="pydanticai_default",
system_prompt="You are a data analyst.",
toolsets=[SQLToolset(db_conn_id="postgres_default")],
)
def analyze(question: str):
return f"Answer: {question}"
:param python_callable: Function to decorate.
"""
return task_decorator_factory(
python_callable=python_callable,
decorated_operator_class=_AgentDecoratedOperator,
**kwargs,
)