feat: add new callbacks to handle tool and model errors

This CL add new callbacks in plugin system:
- `on_tool_error_callback`
- `on_model_error_callback`

This allow the user to create plugins that can handle errors.

PiperOrigin-RevId: 783052800

Author: pwwpche

src/google/adk/flows/llm_flows/base_llm_flow.py

@@ -521,7 +521,13 @@ async def _call_llm_async(
     with tracer.start_as_current_span('call_llm'):
       if invocation_context.run_config.support_cfc:
         invocation_context.live_request_queue = LiveRequestQueue()
-        async for llm_response in self.run_live(invocation_context):
+        responses_generator = self.run_live(invocation_context)
+        async for llm_response in self._run_and_handle_error(
+            responses_generator,
+            invocation_context,
+            llm_request,
+            model_response_event,
+        ):
           # Runs after_model_callback if it exists.
           if altered_llm_response := await self._handle_after_model_callback(
               invocation_context, llm_response, model_response_event
@@ -540,10 +546,16 @@ async def _call_llm_async(
         # the counter beyond the max set value, then the execution is stopped
         # right here, and exception is thrown.
         invocation_context.increment_llm_call_count()
-        async for llm_response in llm.generate_content_async(
+        responses_generator = llm.generate_content_async(
             llm_request,
             stream=invocation_context.run_config.streaming_mode
             == StreamingMode.SSE,
+        )
+        async for llm_response in self._run_and_handle_error(
+            responses_generator,
+            invocation_context,
+            llm_request,
+            model_response_event,
         ):
           trace_call_llm(
               invocation_context,
@@ -660,6 +672,43 @@ def _finalize_model_response_event(
 
     return model_response_event
 
+  async def _run_and_handle_error(
+      self,
+      response_generator: AsyncGenerator[LlmResponse, None],
+      invocation_context: InvocationContext,
+      llm_request: LlmRequest,
+      model_response_event: Event,
+  ) -> AsyncGenerator[LlmResponse, None]:
+    """Runs the response generator and processes the error with plugins.
+
+    Args:
+      response_generator: The response generator to run.
+      invocation_context: The invocation context.
+      llm_request: The LLM request.
+      model_response_event: The model response event.
+
+    Yields:
+      A generator of LlmResponse.
+    """
+    try:
+      async for response in response_generator:
+        yield response
+    except Exception as model_error:
+      callback_context = CallbackContext(
+          invocation_context, event_actions=model_response_event.actions
+      )
+      error_response = (
+          await invocation_context.plugin_manager.run_on_model_error_callback(
+              callback_context=callback_context,
+              llm_request=llm_request,
+              error=model_error,
+          )
+      )
+      if error_response is not None:
+        yield error_response
+      else:
+        raise model_error
+
   def __get_llm(self, invocation_context: InvocationContext) -> BaseLlm:
     from ...agents.llm_agent import LlmAgent
 

src/google/adk/flows/llm_flows/functions.py

@@ -176,9 +176,21 @@ async def handle_function_calls_async(
 
       # Step 3: Otherwise, proceed calling the tool normally.
       if function_response is None:
-        function_response = await __call_tool_async(
-            tool, args=function_args, tool_context=tool_context
-        )
+        try:
+          function_response = await __call_tool_async(
+              tool, args=function_args, tool_context=tool_context
+          )
+        except Exception as tool_error:
+          error_response = await invocation_context.plugin_manager.run_on_tool_error_callback(
+              tool=tool,
+              tool_args=function_args,
+              tool_context=tool_context,
+              error=tool_error,
+          )
+          if error_response is not None:
+            function_response = error_response
+          else:
+            raise tool_error
 
       # Step 4: Check if plugin after_tool_callback overrides the function
       # response.

src/google/adk/plugins/base_plugin.py

@@ -265,6 +265,31 @@ async def after_model_callback(
     """
     pass
 
+  async def on_model_error_callback(
+      self,
+      *,
+      callback_context: CallbackContext,
+      llm_request: LlmRequest,
+      error: Exception,
+  ) -> Optional[LlmResponse]:
+    """Callback executed when a model call encounters an error.
+
+    This callback provides an opportunity to handle model errors gracefully,
+    potentially providing alternative responses or recovery mechanisms.
+
+    Args:
+      callback_context: The context for the current agent call.
+      llm_request: The request that was sent to the model when the error
+        occurred.
+      error: The exception that was raised during model execution.
+
+    Returns:
+      An optional LlmResponse. If an LlmResponse is returned, it will be used
+      instead of propagating the error. Returning `None` allows the original
+      error to be raised.
+    """
+    pass
+
   async def before_tool_callback(
       self,
       *,
@@ -315,3 +340,29 @@ async def after_tool_callback(
       result.
     """
     pass
+
+  async def on_tool_error_callback(
+      self,
+      *,
+      tool: BaseTool,
+      tool_args: dict[str, Any],
+      tool_context: ToolContext,
+      error: Exception,
+  ) -> Optional[dict]:
+    """Callback executed when a tool call encounters an error.
+
+    This callback provides an opportunity to handle tool errors gracefully,
+    potentially providing alternative responses or recovery mechanisms.
+
+    Args:
+      tool: The tool instance that encountered an error.
+      tool_args: The arguments that were passed to the tool.
+      tool_context: The context specific to the tool execution.
+      error: The exception that was raised during tool execution.
+
+    Returns:
+      An optional dictionary. If a dictionary is returned, it will be used as
+      the tool response instead of propagating the error. Returning `None`
+      allows the original error to be raised.
+    """
+    pass

src/google/adk/plugins/plugin_manager.py

@@ -48,6 +48,8 @@
     "after_tool_callback",
     "before_model_callback",
     "after_model_callback",
+    "on_tool_error_callback",
+    "on_model_error_callback",
 ]
 
 logger = logging.getLogger("google_adk." + __name__)
@@ -195,6 +197,21 @@ async def run_after_tool_callback(
         result=result,
     )
 
+  async def run_on_model_error_callback(
+      self,
+      *,
+      callback_context: CallbackContext,
+      llm_request: LlmRequest,
+      error: Exception,
+  ) -> Optional[LlmResponse]:
+    """Runs the `on_model_error_callback` for all plugins."""
+    return await self._run_callbacks(
+        "on_model_error_callback",
+        callback_context=callback_context,
+        llm_request=llm_request,
+        error=error,
+    )
+
   async def run_before_model_callback(
       self, *, callback_context: CallbackContext, llm_request: LlmRequest
   ) -> Optional[LlmResponse]:
@@ -215,6 +232,23 @@ async def run_after_model_callback(
         llm_response=llm_response,
     )
 
+  async def run_on_tool_error_callback(
+      self,
+      *,
+      tool: BaseTool,
+      tool_args: dict[str, Any],
+      tool_context: ToolContext,
+      error: Exception,
+  ) -> Optional[dict]:
+    """Runs the `on_tool_error_callback` for all plugins."""
+    return await self._run_callbacks(
+        "on_tool_error_callback",
+        tool=tool,
+        tool_args=tool_args,
+        tool_context=tool_context,
+        error=error,
+    )
+
   async def _run_callbacks(
       self, callback_name: PluginCallbackName, **kwargs: Any
   ) -> Optional[Any]:

tests/unittests/flows/llm_flows/test_plugin_model_callbacks.py

@@ -20,19 +20,33 @@
 from google.adk.models import LlmResponse
 from google.adk.plugins.base_plugin import BasePlugin
 from google.genai import types
+from google.genai.errors import ClientError
 import pytest
 
 from ... import testing_utils
 
+mock_error = ClientError(
+    code=429,
+    response_json={
+        'error': {
+            'code': 429,
+            'message': 'Quota exceeded.',
+            'status': 'RESOURCE_EXHAUSTED',
+        }
+    },
+)
+
 
 class MockPlugin(BasePlugin):
   before_model_text = 'before_model_text from MockPlugin'
   after_model_text = 'after_model_text from MockPlugin'
+  on_model_error_text = 'on_model_error_text from MockPlugin'
 
   def __init__(self, name='mock_plugin'):
     self.name = name
     self.enable_before_model_callback = False
     self.enable_after_model_callback = False
+    self.enable_on_model_error_callback = False
     self.before_model_response = LlmResponse(
         content=testing_utils.ModelContent(
             [types.Part.from_text(text=self.before_model_text)]
@@ -43,6 +57,11 @@ def __init__(self, name='mock_plugin'):
             [types.Part.from_text(text=self.after_model_text)]
         )
     )
+    self.on_model_error_response = LlmResponse(
+        content=testing_utils.ModelContent(
+            [types.Part.from_text(text=self.on_model_error_text)]
+        )
+    )
 
   async def before_model_callback(
       self, *, callback_context: CallbackContext, llm_request: LlmRequest
@@ -58,6 +77,17 @@ async def after_model_callback(
       return None
     return self.after_model_response
 
+  async def on_model_error_callback(
+      self,
+      *,
+      callback_context: CallbackContext,
+      llm_request: LlmRequest,
+      error: Exception,
+  ) -> Optional[LlmResponse]:
+    if not self.enable_on_model_error_callback:
+      return None
+    return self.on_model_error_response
+
 
 CANONICAL_MODEL_CALLBACK_CONTENT = 'canonical_model_callback_content'
 
@@ -124,5 +154,36 @@ def test_before_model_callback_fallback_model(mock_plugin):
   ]
 
 
+def test_on_model_error_callback_with_plugin(mock_plugin):
+  """Tests that the model error is handled by the plugin."""
+  mock_model = testing_utils.MockModel.create(error=mock_error, responses=[])
+  mock_plugin.enable_on_model_error_callback = True
+  agent = Agent(
+      name='root_agent',
+      model=mock_model,
+  )
+
+  runner = testing_utils.InMemoryRunner(agent, plugins=[mock_plugin])
+
+  assert testing_utils.simplify_events(runner.run('test')) == [
+      ('root_agent', mock_plugin.on_model_error_text),
+  ]
+
+
+def test_on_model_error_callback_fallback_to_runner(mock_plugin):
+  """Tests that the model error is not handled and falls back to raise from runner."""
+  mock_model = testing_utils.MockModel.create(error=mock_error, responses=[])
+  mock_plugin.enable_on_model_error_callback = False
+  agent = Agent(
+      name='root_agent',
+      model=mock_model,
+  )
+
+  try:
+    testing_utils.InMemoryRunner(agent, plugins=[mock_plugin])
+  except Exception as e:
+    assert e == mock_error
+
+
 if __name__ == '__main__':
   pytest.main([__file__])