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

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"""
Run a Strands Agents agent in an Airflow task, with Airflow's SQL toolset as its tools.

The agent is plain Strands: you build the model and the ``Agent`` yourself and call
it as you would anywhere else. Airflow supplies two things. The model's API key comes
from an Airflow connection, and the
:class:`~airflow.providers.common.ai.tools.strands.AirflowTools` plugin gives the agent a
``SQLToolset``'s tools, with read-only SQL validation and bounded results, and with
Airflow's secret masker applied to every tool result.

Before running:

1. Install Strands: ``pip install "strands-agents>=1.56.0"``. The Anthropic model
   below also needs ``anthropic``, which the ``anthropic`` extra of this provider
   installs.
2. Create a connection (``LLM_CONN_ID``, default ``anthropic_default``) whose
   password is an Anthropic API key. Set its host only to route through a gateway.
3. Create a database connection (``DB_CONN_ID``, default ``sql_default``) whose
   hook is a ``DbApiHook`` (e.g. SQLite, Postgres, MySQL).
"""

from __future__ import annotations

import os

from airflow.providers.common.compat.sdk import BaseHook, dag, task


[docs]
LLM_CONN_ID = os.environ.get("LLM_CONN_ID", "anthropic_default")


[docs]
LLM_MODEL = os.environ.get("LLM_MODEL", "claude-sonnet-5")


[docs]
DB_CONN_ID = os.environ.get("DB_CONN_ID", "sql_default")



[docs]
DEFAULT_QUESTION = "Which tables exist, and how many rows does each contain?"



# [START example_strands_agent]
@dag(tags=["example"])

[docs]
def example_strands_agent():
    """Answer a question about a database with a Strands agent."""

    @task
    def run_strands_agent(question: str = DEFAULT_QUESTION) -> str:
        from strands import Agent
        from strands.models.anthropic import AnthropicModel

        from airflow.providers.common.ai.tools.strands import AirflowTools
        from airflow.providers.common.ai.tools.tracing import agent_framework_tracing
        from airflow.providers.common.ai.toolsets.sql import SQLToolset

        llm = BaseHook.get_connection(LLM_CONN_ID)
        model = AnthropicModel(
            client_args={"api_key": llm.password, "base_url": llm.host or None},
            model_id=LLM_MODEL,
            max_tokens=2048,
        )
        # Spans carry the task's identity and no prompt text; see the tracing section of the guide.
        with agent_framework_tracing():
            agent = Agent(
                model=model,
                plugins=[AirflowTools(SQLToolset(db_conn_id=DB_CONN_ID))],
                system_prompt=(
                    "You are a SQL analyst. Use list_tables and get_schema to explore "
                    "the database, then run read-only queries to answer the question."
                ),
                # Strands streams the reply to stdout by default; the task returns it instead.
                callback_handler=None,
            )
            return str(agent(question))

    run_strands_agent()



# [END example_strands_agent]


example_strands_agent()