Source code for airflow.providers.common.ai.example_dags.example_agent_durable

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"""Example Dags demonstrating durable execution with AgentOperator, @task.agent and a plain @task."""

from __future__ import annotations

from datetime import timedelta

from airflow.providers.common.ai.durable import AirflowDurability
from airflow.providers.common.ai.hooks.pydantic_ai import PydanticAIHook
from airflow.providers.common.ai.operators.agent import AgentOperator
from airflow.providers.common.compat.sdk import dag, task

try:
    from airflow.providers.common.ai.toolsets.sql import SQLToolset
except Exception:

[docs]
    SQLToolset = None  # type: ignore[assignment,misc]


# ---------------------------------------------------------------------------
# 1. Durable AgentOperator: a retry replays the steps the failed attempt completed
# ---------------------------------------------------------------------------


# [START howto_operator_agent_durable]
if SQLToolset is not None:

    @dag(default_args={"retries": 3, "retry_delay": timedelta(seconds=30)}, tags=["example"])

[docs]
    def example_agent_durable_operator():
        """Agent with durable execution: a retry replays the steps the failed attempt completed."""
        AgentOperator(
            task_id="durable_analyst",
            prompt="What are the top 5 customers by order count?",
            llm_conn_id="pydanticai_default",
            system_prompt=(
                "You are a SQL analyst. Use the available tools to explore "
                "the schema and answer the question with data."
            ),
            durable=True,
            toolsets=[
                SQLToolset(
                    db_conn_id="postgres_default",
                    allowed_tables=["customers", "orders"],
                    max_rows=20,
                )
            ],
        )


    # [END howto_operator_agent_durable]

    example_agent_durable_operator()


# ---------------------------------------------------------------------------
# 2. Durable @task.agent decorator
# ---------------------------------------------------------------------------


# [START howto_decorator_agent_durable]
if SQLToolset is not None:

    @dag(default_args={"retries": 3, "retry_delay": timedelta(seconds=30)}, tags=["example"])

[docs]
    def example_agent_durable_decorator():
        @task.agent(
            llm_conn_id="pydanticai_default",
            system_prompt="You are a data analyst. Use tools to answer questions.",
            durable=True,
            toolsets=[
                SQLToolset(
                    db_conn_id="postgres_default",
                    allowed_tables=["orders"],
                )
            ],
        )
        def analyze(question: str):
            return f"Answer this question about our orders data: {question}"

        analyze("What was our total revenue last month?")


    # [END howto_decorator_agent_durable]

    example_agent_durable_decorator()


# ---------------------------------------------------------------------------
# 3. A pydantic-ai agent in a plain @task
# ---------------------------------------------------------------------------


# [START howto_capability_airflow_durability]
if SQLToolset is not None:

    @dag(default_args={"retries": 3, "retry_delay": timedelta(seconds=30)}, tags=["example"])

[docs]
    def example_airflow_durability_capability():
        @task
        def analyze(question: str) -> str:
            agent = PydanticAIHook(llm_conn_id="pydanticai_default").create_agent(
                name="orders_analyst",
                instructions="You are a data analyst. Use tools to answer questions.",
                toolsets=[SQLToolset(db_conn_id="postgres_default", allowed_tables=["orders"])],
                capabilities=[AirflowDurability()],
            )
            return agent.run_sync(question).output

        analyze("What was our total revenue last month?")


    # [END howto_capability_airflow_durability]

    example_airflow_durability_capability()