Lorenze/ensure hooks work with lite agents flows (#3981)

* liteagent support hooks

* wip llm.call hooks work - needs tests for this

* fix tests

* fixed more

* more tool hooks test cassettes

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -246,6 +246,11 @@ class GeminiCompletion(BaseLLM):
                 messages
             )
 
+            messages_for_hooks = self._convert_contents_to_dict(formatted_content)
+
+            if not self._invoke_before_llm_call_hooks(messages_for_hooks, from_agent):
+                raise ValueError("LLM call blocked by before_llm_call hook")
+
             config = self._prepare_generation_config(
                 system_instruction, tools, response_model
             )
@@ -559,7 +564,9 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return content
+        return self._invoke_after_llm_call_hooks(
+            messages_for_event, content, from_agent
+        )
 
     def _handle_streaming_completion(
         self,
@@ -639,7 +646,9 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return full_response
+        return self._invoke_after_llm_call_hooks(
+            messages_for_event, full_response, from_agent
+        )
 
     async def _ahandle_completion(
         self,
@@ -787,7 +796,159 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return full_response
+        return self._invoke_after_llm_call_hooks(
+            messages_for_event, full_response, from_agent
+        )
+
+    async def _ahandle_completion(
+        self,
+        contents: list[types.Content],
+        system_instruction: str | None,
+        config: types.GenerateContentConfig,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
+    ) -> str | Any:
+        """Handle async non-streaming content generation."""
+        try:
+            # The API accepts list[Content] but mypy is overly strict about variance
+            contents_for_api: Any = contents
+            response = await self.client.aio.models.generate_content(
+                model=self.model,
+                contents=contents_for_api,
+                config=config,
+            )
+
+            usage = self._extract_token_usage(response)
+        except Exception as e:
+            if is_context_length_exceeded(e):
+                logging.error(f"Context window exceeded: {e}")
+                raise LLMContextLengthExceededError(str(e)) from e
+            raise e from e
+
+        self._track_token_usage_internal(usage)
+
+        if response.candidates and (self.tools or available_functions):
+            candidate = response.candidates[0]
+            if candidate.content and candidate.content.parts:
+                for part in candidate.content.parts:
+                    if hasattr(part, "function_call") and part.function_call:
+                        function_name = part.function_call.name
+                        if function_name is None:
+                            continue
+                        function_args = (
+                            dict(part.function_call.args)
+                            if part.function_call.args
+                            else {}
+                        )
+
+                        result = self._handle_tool_execution(
+                            function_name=function_name,
+                            function_args=function_args,
+                            available_functions=available_functions or {},
+                            from_task=from_task,
+                            from_agent=from_agent,
+                        )
+
+                        if result is not None:
+                            return result
+
+        content = response.text or ""
+        content = self._apply_stop_words(content)
+
+        messages_for_event = self._convert_contents_to_dict(contents)
+
+        self._emit_call_completed_event(
+            response=content,
+            call_type=LLMCallType.LLM_CALL,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=messages_for_event,
+        )
+
+        return content
+
+    async def _ahandle_streaming_completion(
+        self,
+        contents: list[types.Content],
+        config: types.GenerateContentConfig,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
+    ) -> str:
+        """Handle async streaming content generation."""
+        full_response = ""
+        function_calls: dict[str, dict[str, Any]] = {}
+
+        # The API accepts list[Content] but mypy is overly strict about variance
+        contents_for_api: Any = contents
+        stream = await self.client.aio.models.generate_content_stream(
+            model=self.model,
+            contents=contents_for_api,
+            config=config,
+        )
+        async for chunk in stream:
+            if chunk.text:
+                full_response += chunk.text
+                self._emit_stream_chunk_event(
+                    chunk=chunk.text,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+
+            if chunk.candidates:
+                candidate = chunk.candidates[0]
+                if candidate.content and candidate.content.parts:
+                    for part in candidate.content.parts:
+                        if hasattr(part, "function_call") and part.function_call:
+                            call_id = part.function_call.name or "default"
+                            if call_id not in function_calls:
+                                function_calls[call_id] = {
+                                    "name": part.function_call.name,
+                                    "args": dict(part.function_call.args)
+                                    if part.function_call.args
+                                    else {},
+                                }
+
+        if function_calls and available_functions:
+            for call_data in function_calls.values():
+                function_name = call_data["name"]
+                function_args = call_data["args"]
+
+                # Skip if function_name is None
+                if not isinstance(function_name, str):
+                    continue
+
+                # Ensure function_args is a dict
+                if not isinstance(function_args, dict):
+                    function_args = {}
+
+                result = self._handle_tool_execution(
+                    function_name=function_name,
+                    function_args=function_args,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+
+                if result is not None:
+                    return result
+
+        messages_for_event = self._convert_contents_to_dict(contents)
+
+        self._emit_call_completed_event(
+            response=full_response,
+            call_type=LLMCallType.LLM_CALL,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=messages_for_event,
+        )
+
+        return self._invoke_after_llm_call_hooks(
+            messages_for_event, full_response, from_agent
+        )
 
     def supports_function_calling(self) -> bool:
         """Check if the model supports function calling."""
@@ -851,7 +1012,7 @@ class GeminiCompletion(BaseLLM):
     def _convert_contents_to_dict(
         self,
         contents: list[types.Content],
-    ) -> list[dict[str, str]]:
+    ) -> list[LLMMessage]:
         """Convert contents to dict format."""
         result: list[dict[str, str]] = []
         for content_obj in contents: