kaxil · Oct 1, 2026 · Sep 30, 2026 · Sep 30, 2026 · Oct 1, 2026
@@ -862,7 +862,7 @@ def get_airflow_extras():
     {
         "python-version": "3.10",
         "airflow-version": "2.11.1",
-        "remove-providers": "anthropic common.messaging common.dataquality edge3 fab git keycloak informatica common.ai modal opensearch",
+        "remove-providers": "anthropic common.messaging common.dataquality edge3 fab git keycloak informatica modal opensearch",
         "run-unit-tests": "true",
     },
     {

@@ -53,10 +53,11 @@ Requirements
 ==========================================  ==================
 PIP package                                 Version required
 ==========================================  ==================
-``apache-airflow``                          ``>=3.0.0``
+``apache-airflow``                          ``>=2.11.0``
 ``apache-airflow-providers-common-compat``  ``>=1.15.0``
 ``apache-airflow-providers-standard``       ``>=1.20.0``
 ``pydantic-ai-slim``                        ``>=2.33.0``
+``structlog``                               ``>=24.2.0``
 ==========================================  ==================
 
 Optional cross provider package dependencies

@@ -172,15 +172,16 @@ For the minimum Airflow version supported, see ``Requirements`` below.
 Requirements
 ------------
 
-The minimum Apache Airflow version supported by this provider distribution is ``3.0.0``.
+The minimum Apache Airflow version supported by this provider distribution is ``2.11.0``.
 
 ==========================================  ==================
 PIP package                                 Version required
 ==========================================  ==================
-``apache-airflow``                          ``>=3.0.0``
+``apache-airflow``                          ``>=2.11.0``
 ``apache-airflow-providers-common-compat``  ``>=1.15.0``
 ``apache-airflow-providers-standard``       ``>=1.20.0``
 ``pydantic-ai-slim``                        ``>=2.33.0``
+``structlog``                               ``>=24.2.0``
 ==========================================  ==================
 
 Optional cross provider package dependencies

@@ -20,7 +20,7 @@
 Installation
 ============
 
-The provider needs Airflow 3.0 or later. Install it with the extra that matches the
+The provider needs Airflow 2.11 or later. Install it with the extra that matches the
 model vendor your connection will point at:
 
 .. code-block:: bash
@@ -62,21 +62,50 @@ package each extra installs.
 Features gated on the Airflow version
 -------------------------------------
 
-The provider runs on Airflow 3.0, but some features need a newer core:
+The provider runs on Airflow 2.11, but some features need a newer core:
 
 .. list-table::
    :header-rows: 1
    :widths: 60 40
 
    * - Feature
      - Needs
+   * - The ``skills`` and ``git`` extras (``apache-airflow-providers-git`` needs Airflow 3)
+     - Airflow 3.0
    * - :doc:`Approval gates <approval_gates>` and :doc:`HITL review <hitl_review>`
      - Airflow 3.1
+   * - The **Model** field in the connection form; on older cores put the model in
+       **Extra**, for example ``{"model": "openai:gpt-5"}``
+     - Airflow 3.2
    * - :doc:`Retry policies <retry_policies>`
      - Airflow 3.3
    * - :doc:`Durable execution <durable_execution>` without configuring
        ``[common.ai] durable_cache_path`` (the task state store)
      - Airflow 3.3
+   * - :doc:`Tool approval <tool_approval>` that pauses the task; on older cores a tool
+       marked for approval fails the task
+     - Airflow 3.3
+   * - A :doc:`structured output <structured_output>` reaching downstream tasks as the
+       Pydantic model; on older cores it arrives as a ``dict``
+     - Airflow 3.3
+
+Airflow 2.11
+------------
+
+On Airflow 2.11 the operators, decorators, hooks and toolsets run as they do on Airflow
+3.0, apart from the table above. Three things differ from an Airflow 3 install:
+
+* The examples in these docs import ``dag``, ``task`` and ``Param`` from ``airflow.sdk``.
+  On Airflow 2 import ``dag`` and ``task`` from ``airflow.decorators`` and ``Param`` from
+  ``airflow.models.param``; the provider's own imports stay the same.
+* Install Airflow with its constraints file as usual, then add the provider without it. The
+  Airflow 2.11 constraints pin ``apache-airflow-providers-common-compat`` and
+  ``apache-airflow-providers-common-sql`` to releases older than this provider needs.
+  Installing Airflow 2.11.0 without its constraints can also pull in a ``universal-pathlib``
+  0.3 release, which Airflow 2's ``ObjectStoragePath`` rejects; 2.11.1 and later cap it.
+  Leave out the ``skills`` and ``git`` extras: they need Airflow 3, and without
+  constraints ``pip`` upgrades Airflow to satisfy them.
+* Python 3.10 to 3.12: the provider needs 3.10 or later, and Airflow 2.11 supports up to 3.12.
 
 Next steps
 ----------

@@ -81,6 +81,9 @@ How it works
   tool approval (see :doc:`tool_approval`) continues as
   ``<task-instance id>-resumed``, which is the ``run_id`` the operator pushes;
   ``usage`` covers both parts.
+  Airflow 2 has no task-instance id, so there the key is
+  ``<dag_id>/<run_id>/<task_id>/<map_index>/<try_number>``, and spans carry the five
+  identity keys without ``airflow.task_instance.id``.
 * **Scope.** The ``run_id`` / ``usage`` XComs come only from ``AgentOperator`` and
   ``@task.agent``, and so do the ``airflow.*`` identity attributes, apart from a Strands or
   ADK agent run inside ``agent_framework_tracing`` (see below). The other LLM

@@ -282,8 +282,8 @@ provider's own batch listing first.
 ``cancel_on_kill`` cancels the batch if the task is killed. In deferrable mode this runs from the
 trigger's ``on_kill``, which only **Airflow 3.3+** calls; on those versions clearing, marking
 success or marking failed on a deferred task from the UI counts as a kill, so the batch is
-cancelled and the next attempt submits a fresh one rather than re-attaching. On Airflow 3.0 to
-3.2 a killed deferred task's batch keeps running and a clear re-attaches to it. Set
+cancelled and the next attempt submits a fresh one rather than re-attaching. Before
+Airflow 3.3 a killed deferred task's batch keeps running and a clear re-attaches to it. Set
 ``cancel_on_kill=False`` if you want clear-to-re-attach on 3.3+ as well.
 
 ``cancel_on_timeout=False`` lets a batch keep running (and billing) past this task's own

@@ -25,7 +25,8 @@ which one task asks a model to summarize release notes and a second task uses th
 At the end you know where the model's output lands and what a successful run looks like.
 
 You need a working :doc:`Airflow installation <apache-airflow:installation/index>` on
-Airflow 3.0 or later and an API key for the model vendor you plan to use. Step 4 makes one
+Airflow 2.11 or later and an API key for the model vendor you plan to use. On Airflow 2,
+see :ref:`howto/installation` for what differs. Step 4 makes one
 real, billed API call.
 
 1. Install the provider

@@ -32,7 +32,8 @@ Before you start
 ------------------
 
 This guide assumes a working :doc:`apache-airflow:installation/index`
-(Airflow 3.0+) already exists. Its job stops at wiring Airflow to a server
+(Airflow 2.11+; on Airflow 2 see :ref:`howto/installation` for
+what differs) already exists. Its job stops at wiring Airflow to a server
 that's already running -- it doesn't cover installing or operating the
 model-serving stack itself.
 

@@ -67,14 +67,17 @@ requires-python = ">=3.10"
 # Make sure to run ``prek update-providers-dependencies --all-files``
 # After you modify the dependencies, and rebuild your Breeze CI image with ``breeze ci-image build``
 dependencies = [
-    "apache-airflow>=3.0.0",
-    "apache-airflow-providers-common-compat>=1.15.0",
+    "apache-airflow>=2.11.0",
+    "apache-airflow-providers-common-compat>=1.15.0",  # use next version
     "apache-airflow-providers-standard>=1.20.0",
     # 2.33.0 is the first release that works with anthropic>=1: it moved to httpx2 alongside
     # the SDK and stopped passing temperature/top_p/top_k as messages.create() kwargs, both
     # of which raise TypeError on 2.31 and earlier. The cost API this provider relies on
     # (RunUsage.cost, UsageLimits.cost_limit) landed earlier, in 2.23.0.
     "pydantic-ai-slim>=2.33.0",
+    # Airflow 3 brings structlog in through the Task SDK; Airflow 2 does not. 24.2.0 is the first
+    # release whose render_to_log_kwargs hands ``stacklevel`` to stdlib logging under that name.
+    "structlog>=24.2.0",
 ]
 
 # The optional dependencies should be modified in place in the generated file

@@ -32,8 +32,8 @@
 __version__ = "0.10.0"
 
 if packaging.version.parse(packaging.version.parse(airflow_version).base_version) < packaging.version.parse(
-    "3.0.0"
+    "2.11.0"
 ):
     raise RuntimeError(
-        f"The package `apache-airflow-providers-common-ai:{__version__}` needs Apache Airflow 3.0.0+"
+        f"The package `apache-airflow-providers-common-ai:{__version__}` needs Apache Airflow 2.11.0+"
     )
@@ -32,8 +32,6 @@
 from collections.abc import Iterator
 from typing import TYPE_CHECKING, Any
 
-import structlog
-
 from airflow.providers.common.ai.batch.base import (
     BatchAdapter,
     BatchState,
@@ -43,9 +41,10 @@
     SubmitResult,
 )
 from airflow.providers.common.ai.exceptions import LLMBatchLimitExceededError
+from airflow.providers.common.ai.utils.task_logger import get_task_logger
 from airflow.providers.common.compat.sdk import AirflowOptionalProviderFeatureException
 
-log = structlog.get_logger(logger_name="task")
+log = get_task_logger()
 
 if TYPE_CHECKING:
     from airflow.providers.common.ai.batch.base import BatchRequest

@@ -32,8 +32,6 @@
 from datetime import datetime
 from typing import TYPE_CHECKING, Any
 
-import structlog
-
 from airflow.providers.common.ai.batch.base import (
     BatchAdapter,
     BatchState,
@@ -43,9 +41,10 @@
     SubmitResult,
 )
 from airflow.providers.common.ai.exceptions import LLMBatchLimitExceededError, LLMBatchModelMismatchError
+from airflow.providers.common.ai.utils.task_logger import get_task_logger
 from airflow.providers.common.compat.sdk import AirflowOptionalProviderFeatureException
 
-log = structlog.get_logger(logger_name="task")
+log = get_task_logger()
 
 if TYPE_CHECKING:
     from airflow.providers.common.ai.batch.base import BatchRequest

@@ -34,17 +34,16 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any
 
-import structlog
-
 from airflow.providers.common.ai.batch.base import evaluate_batch_counts
 from airflow.providers.common.ai.batch.output_schema import validate_extracted_output
+from airflow.providers.common.ai.utils.task_logger import get_task_logger
 
 if TYPE_CHECKING:
     from airflow.providers.common.ai.batch.base import BatchAdapter, RawResultItem
     from airflow.providers.common.ai.batch.output_schema import OutputSpec
     from airflow.sdk import ObjectStoragePath
 
-log = structlog.get_logger(logger_name="task")
+log = get_task_logger()
 
 STATUS_SUCCESS = "success"
 STATUS_ERROR = "error"

@@ -33,13 +33,13 @@
     validate_prompt,
 )
 from airflow.providers.common.compat.sdk import (
+    SET_DURING_EXECUTION,
     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

@@ -35,13 +35,13 @@
     validate_prompt,
 )
 from airflow.providers.common.compat.sdk import (
+    SET_DURING_EXECUTION,
     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

@@ -29,13 +29,13 @@
 
 from airflow.providers.common.ai.operators.llm_batch import LLMBatchOperator
 from airflow.providers.common.compat.sdk import (
+    SET_DURING_EXECUTION,
     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

@@ -33,13 +33,13 @@
     validate_prompt,
 )
 from airflow.providers.common.compat.sdk import (
+    SET_DURING_EXECUTION,
     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

@@ -23,13 +23,13 @@
 
 from airflow.providers.common.ai.operators.llm_file_analysis import LLMFileAnalysisOperator
 from airflow.providers.common.compat.sdk import (
+    SET_DURING_EXECUTION,
     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

@@ -33,13 +33,13 @@
     validate_prompt,
 )
 from airflow.providers.common.compat.sdk import (
+    SET_DURING_EXECUTION,
     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

@@ -33,13 +33,13 @@
     validate_prompt,
 )
 from airflow.providers.common.compat.sdk import (
+    SET_DURING_EXECUTION,
     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

@@ -21,14 +21,14 @@
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any
 
-import structlog
 from pydantic_ai.messages import ModelResponse, ToolCallPart
 from pydantic_ai.models.wrapper import WrapperModel
 
 from airflow.providers.common.ai.durable.base import build_model_step_key, build_tool_step_key
 from airflow.providers.common.ai.durable.fingerprint import fingerprint_model_request
+from airflow.providers.common.ai.utils.task_logger import get_task_logger
 
-log = structlog.get_logger(logger_name="task")
+log = get_task_logger()
 
 if TYPE_CHECKING:
     from pydantic_ai.messages import ModelMessage

@@ -21,11 +21,11 @@
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any
 
-import structlog
 from pydantic_ai.toolsets.wrapper import WrapperToolset
 
 from airflow.providers.common.ai.durable.base import build_tool_step_key
 from airflow.providers.common.ai.durable.fingerprint import fingerprint_tool_call
+from airflow.providers.common.ai.utils.task_logger import get_task_logger
 from airflow.providers.common.ai.utils.tool_metrics import record_tool_call
 from airflow.providers.common.ai.utils.toolset_base import AirflowToolset
 
@@ -36,7 +36,7 @@
     from airflow.providers.common.ai.durable.replay_usage import ReplayUsageLedger
     from airflow.providers.common.ai.durable.step_counter import DurableStepCounter
 
-log = structlog.get_logger(logger_name="task")
+log = get_task_logger()
 
 
 @dataclass