gopidesupavan opened a new pull request, #67373:
URL: https://github.com/apache/airflow/pull/67373
Introduces `BaseAIHook`, an abstract hook that defines the contract every
agent-framework backend must implement. `AgentOperator` (and the
`@task.agent`
decorator) now selects the backend at runtime from the Airflow connection's
`conn_type` — no new operator class is needed when adding another
framework.
## What changed
### New: `BaseAIHook` contract (`hooks/base_ai.py`)
- `BaseAIHook(BaseHook)` — abstract base with three abstract methods:
- `get_conn()` — return the backend model/client
- `create_agent(output_type, instructions, **kwargs)` — build an agent
- `run_agent(agent, *, prompt, usage_limits, message_history) →
AgentRunResult`
- `AgentRunResult` dataclass — backend-neutral result:
`output`, `message_history`, `model_name`, `usage`, `tool_names`
- `AgentUsage` dataclass — normalized token/request counters:
`requests`, `tool_calls`, `input_tokens`, `output_tokens`, `total_tokens`
- Class-level capability flags: `supports_toolsets`, `supports_durable`,
`supports_usage_limits`
- `BaseAIHook.get_agent_hook(conn_id)` — resolves the registered hook from
`conn_type` and raises `TypeError` if it is not a `BaseAIHook`
### Refactor: `PydanticAIHook(BaseAIHook)`
`PydanticAIHook` (and its subclasses `PydanticAIAzureHook`,
`PydanticAIBedrockHook`, `PydanticAIVertexHook`) now subclass `BaseAIHook`.
`run_agent()` wraps the pydantic-ai `RunResult` into `AgentRunResult`, so
all
callers receive a backend-neutral object.
### `AgentOperator` dispatch via `get_agent_hook()`
`AgentOperator.execute()` calls `BaseAIHook.get_agent_hook(conn_id)`
instead of
importing `PydanticAIHook` directly. This is the main extensibility seam:
a future hook registered under a new `conn_type` is picked up with no
operator
changes.
### `LLMOperator` / `LLMFileAnalysisOperator` aligned
Both operators previously called `agent.run_sync()` directly, bypassing
the hook
abstraction and receiving a pydantic-ai `RunResult` rather than an
`AgentRunResult`. They now call `self.llm_hook.run_agent(agent,
prompt=…)`,
which means:
- `log_run_summary()` receives a consistent `AgentRunResult` from all
operators
- The operators are backend-agnostic alongside `AgentOperator`
### `logging.py` backend-agnostic
`log_run_summary()` now reads `result.model_name`, `result.usage.*`, and
`result.tool_names` from `AgentRunResult` directly — no more pydantic-ai
`result.response.model_name` or `result.usage()` callable.
### Tests
- New `tests/unit/common/ai/hooks/test_base_ai.py` covering the contract,
`get_agent_hook()` dispatch, and `TypeError` on non-agent hook
- Updated `test_pydantic_ai.py`, `test_agent.py`, `test_llm.py`,
`test_llm_file_analysis.py` to mock `hook.run_agent()` returning
`AgentRunResult` instead of a raw pydantic-ai `RunResult`
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