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    regarding copyright ownership.  The ASF licenses this file
    to you under the Apache License, Version 2.0 (the
    "License"); you may not use this file except in compliance
    with the License.  You may obtain a copy of the License at

 ..   http://www.apache.org/licenses/LICENSE-2.0

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    software distributed under the License is distributed on an
    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    KIND, either express or implied.  See the License for the
    specific language governing permissions and limitations
    under the License.

.. _howto/operator:agent:

``AgentOperator`` & ``@task.agent``
===================================

Use :class:`~airflow.providers.common.ai.operators.agent.AgentOperator` or
the ``@task.agent`` decorator to run an LLM agent with **tools** — the agent
reasons about the prompt, calls tools (database queries, API calls, etc.) in
a multi-turn loop, and returns a final answer.

This is different from
:class:`~airflow.providers.common.ai.operators.llm.LLMOperator`, which sends
a single prompt and returns the output. ``AgentOperator`` manages a stateful
tool-call loop where the LLM decides which tools to call and when to stop.

.. seealso::
    :ref:`Connection configuration <howto/connection:pydanticai>`


SQL Agent
---------

The most common pattern: give an agent access to a database so it can answer
questions by writing and executing SQL.

.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_agent.py
    :language: python
    :start-after: [START howto_operator_agent_sql]
    :end-before: [END howto_operator_agent_sql]

The ``SQLToolset`` provides four tools to the agent:

.. list-table::
   :header-rows: 1
   :widths: 20 50

   * - Tool
     - Description
   * - ``list_tables``
     - Lists available table names (filtered by ``allowed_tables`` if set)
   * - ``get_schema``
     - Returns column names and types for a table
   * - ``query``
     - Executes a SQL query and returns rows as JSON
   * - ``check_query``
     - Validates SQL syntax without executing it


Hook-based Tools
----------------

Wrap any Airflow Hook's methods as agent tools using ``HookToolset``. Only
methods you explicitly list are exposed — there is no auto-discovery.

.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_agent.py
    :language: python
    :start-after: [START howto_operator_agent_hook]
    :end-before: [END howto_operator_agent_hook]


TaskFlow Decorator
------------------

The ``@task.agent`` decorator wraps ``AgentOperator``. The function returns
the prompt string; all other parameters are passed to the operator.

.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_agent.py
    :language: python
    :start-after: [START howto_decorator_agent]
    :end-before: [END howto_decorator_agent]


.. _howto/operator:agent-multimodal:

Multimodal prompts
^^^^^^^^^^^^^^^^^^

The decorated callable may also return a ``Sequence[UserContent]`` -- for
example, a list mixing strings with ``ImageUrl``, ``BinaryContent``, or other
pydantic-ai user-content types -- to send vision, audio, or document inputs
to the model. This mirrors the input types accepted by pydantic-ai's
``Agent.run_sync``.

.. code-block:: python

    from pydantic_ai.messages import ImageUrl


    @task.agent(llm_conn_id="pydanticai_default", system_prompt="You are an image analyst.")
    def analyze_review(image_url: str):
        return ["Describe what you see:", ImageUrl(url=image_url)]

.. note::

    Combining a non-string prompt with ``enable_hitl_review=True`` is not
    currently supported -- the HITL session model stores the prompt as a
    string, so a ``Sequence`` prompt will raise at the review boundary.
    Widening HITL review to multimodal prompts is tracked as a follow-up.


Structured Output
-----------------

Set ``output_type`` to a Pydantic ``BaseModel`` subclass to get structured
data back. The result is serialized via ``model_dump()`` for XCom.

.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_agent.py
    :language: python
    :start-after: [START howto_decorator_agent_structured]
    :end-before: [END howto_decorator_agent_structured]


Chaining with Downstream Tasks
-------------------------------

The agent's output is pushed to XCom like any other operator, so downstream
tasks can consume it.

.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_agent.py
    :language: python
    :start-after: [START howto_agent_chain]
    :end-before: [END howto_agent_chain]


Durable Execution
-----------------

Agent tasks can involve multiple LLM calls and tool invocations. If a task
fails mid-run (network error, timeout, transient API failure), a plain retry
re-executes every LLM call and tool call from scratch -- repeating work that
already succeeded and incurring additional cost.

Setting ``durable=True`` caches each LLM response and tool result to
ObjectStorage as it completes. On retry, completed steps are replayed from the
cache and only the remaining steps run against the live model and tools. The
cache is deleted after successful completion.

Durable execution only helps when the task has retries configured. Without
retries there is nothing to replay.

**Configuration**

Set the cache location in ``airflow.cfg``. The task raises ``ValueError`` at
runtime if ``durable=True`` and the option is missing.

.. code-block:: ini

    [common.ai]
    # Local filesystem -- suitable for development
    durable_cache_path = file:///tmp/airflow_durable_cache

The value is an ObjectStorage URI, so any supported backend works. For
production, use a shared store so retries on a different worker can read the
cache:

.. code-block:: ini

    [common.ai]
    durable_cache_path = s3://my-bucket/airflow/durable-cache

**Operator example**

.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_agent_durable.py
    :language: python
    :start-after: [START howto_operator_agent_durable]
    :end-before: [END howto_operator_agent_durable]

**Decorator example**

.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_agent_durable.py
    :language: python
    :start-after: [START howto_decorator_agent_durable]
    :end-before: [END howto_decorator_agent_durable]

**How it works**

1. On first execution, each LLM response and tool result is saved to a JSON
   file as the agent progresses.
2. If the task fails and Airflow retries it, completed steps are loaded from
   the cache and returned without calling the model or tool. Steps not yet in
   the cache proceed normally.
3. After successful completion, the cache file is deleted.

After the run, a single INFO summary line reports how many steps were
replayed vs executed fresh. Per-step detail is available at DEBUG level.

The cache file is named ``{dag_id}_{task_id}_{run_id}.json`` (with
``_{map_index}`` appended for mapped tasks) and stored under the configured
``durable_cache_path``. To force a completely fresh run, delete the cache file
for that task.

.. note::

    Runs that fail permanently (exhaust all retries) leave their cache file
    behind. These orphaned files do not affect future DAG runs (each run gets
    its own file) but will consume storage. Clean them up periodically or add
    a lifecycle policy to the storage backend.

**Side effects and idempotency**

Durable execution caches **return values**, not side effects. When a step is
replayed, the tool's code does not run -- only the stored return value is
returned. Two things follow from this:

- If a tool completed successfully and its result was cached, the tool will
  **not** run again on retry. Any side effect it produced (writing a file,
  sending a message) already happened during the original run and is not
  repeated.
- If a tool fails *before* its result is cached, it **will** run again on
  retry. A tool that partially completed (e.g. sent an email then raised an
  exception) may produce the side effect a second time.

All built-in toolsets (``SQLToolset`` with ``allow_writes=False``,
``HookToolset`` in read-only mode) are read-only and replay safely. For custom
tools with non-idempotent side effects, design the tool to be idempotent. For
example, check whether the operation already completed before acting, or
use database constraints to prevent duplicate writes.

Tool results must be JSON-serializable to be cached. If a tool returns a
non-serializable value (e.g. ``BinaryContent`` from MCP tools), that step is
skipped with a warning and will re-execute on retry instead of replaying from
cache. The task itself still succeeds.


Parameters
----------

- ``prompt``: The prompt to send to the agent (operator) or the return value
  of the decorated function (decorator).
- ``llm_conn_id``: Airflow connection ID for the LLM provider.
- ``model_id``: Model identifier (e.g. ``"openai:gpt-5"``). Overrides the
  connection's extra field.
- ``system_prompt``: System-level instructions for the agent. Supports Jinja
  templating.
- ``output_type``: Expected output type (default: ``str``). Set to a Pydantic
  ``BaseModel`` for structured output.
- ``toolsets``: List of pydantic-ai toolsets (``SQLToolset``, ``HookToolset``,
  etc.).
- ``enable_tool_logging``: Wrap each toolset in
  :class:`~airflow.providers.common.ai.toolsets.logging.LoggingToolset` so that
  every tool call is logged in real time. Default ``True``.
- ``agent_params``: Additional keyword arguments passed to the pydantic-ai
  ``Agent`` constructor (e.g. ``retries``, ``model_settings``).
- ``usage_limits``: Optional pydantic-ai ``UsageLimits`` enforced on every
  agent run (initial run, durable replay, and HITL regeneration). Use it to
  cap requests, tokens, or tool calls per task -- agents are particularly
  prone to runaway tool loops, so ``tool_calls_limit`` is a useful guardrail.
  See :ref:`howto/operator:llm` for an example. Default ``None``.
- ``durable``: When ``True``, enables step-level caching of model responses and
  tool results via ObjectStorage. On retry, cached steps are replayed instead of
  re-executing expensive LLM calls. Requires the ``[common.ai] durable_cache_path``
  config option to be set. Default ``False``.


Logging
-------

All AI operators automatically log a post-run summary after ``run_sync()``
completes. ``AgentOperator`` additionally wraps toolsets for real-time
per-tool-call logging (controlled by ``enable_tool_logging``).

**Real-time tool call logging** (AgentOperator only) — each tool call is
logged as it happens:

.. code-block:: text

    INFO - Tool call: list_tables
    INFO - Tool list_tables returned in 0.12s
    INFO - Tool call: get_schema
    INFO - Tool get_schema returned in 0.08s
    INFO - Tool call: query
    INFO - Tool query returned in 0.34s

Tool arguments are logged at DEBUG level to avoid leaking sensitive data at
the default log level.

**Post-run summary** (all operators) — after the LLM run finishes, a summary
is logged with model name, token usage, and the full tool call sequence:

.. code-block:: text

    INFO - LLM run complete: model=gpt-5, requests=4, tool_calls=3, input_tokens=2847, output_tokens=512, total_tokens=3359
    INFO - Tool call sequence: list_tables -> get_schema -> query

At DEBUG level, the LLM output is also logged (truncated to 500 characters).

Both layers use Airflow's ``::group::`` / ``::endgroup::`` log markers, which
render as collapsible sections in the Airflow UI task log viewer.

To disable real-time tool logging while keeping the post-run summary:

.. code-block:: python

    AgentOperator(
        task_id="my_agent",
        prompt="...",
        llm_conn_id="my_llm",
        toolsets=[SQLToolset(db_conn_id="my_db")],
        enable_tool_logging=False,
    )


Security
--------

.. seealso::
    :ref:`Toolsets — Security <howto/toolsets>` for defense layers,
    ``allowed_tables`` limitations, ``HookToolset`` guidelines, recommended
    configurations, and the production checklist.
