Merge branch 'main' into gl/fix/anon-plus-id

lib/crewai/src/crewai/agent/core.py

@@ -1156,11 +1156,15 @@ class Agent(BaseAgent):
         # Process platform apps and MCP tools
         if self.apps:
             platform_tools = self.get_platform_tools(self.apps)
-            if platform_tools and self.tools is not None:
+            if platform_tools:
+                if self.tools is None:
+                    self.tools = []
                 self.tools.extend(platform_tools)
         if self.mcps:
             mcps = self.get_mcp_tools(self.mcps)
-            if mcps and self.tools is not None:
+            if mcps:
+                if self.tools is None:
+                    self.tools = []
                 self.tools.extend(mcps)
 
         # Prepare tools

lib/crewai/src/crewai/events/types/llm_events.py

@@ -86,3 +86,11 @@ class LLMStreamChunkEvent(LLMEventBase):
     tool_call: ToolCall | None = None
     call_type: LLMCallType | None = None
     response_id: str | None = None
+
+
+class LLMThinkingChunkEvent(LLMEventBase):
+    """Event emitted when a thinking/reasoning chunk is received from a thinking model"""
+
+    type: str = "llm_thinking_chunk"
+    chunk: str
+    response_id: str | None = None

lib/crewai/src/crewai/llms/base_llm.py

@@ -26,6 +26,7 @@ from crewai.events.types.llm_events import (
     LLMCallStartedEvent,
     LLMCallType,
     LLMStreamChunkEvent,
+    LLMThinkingChunkEvent,
 )
 from crewai.events.types.tool_usage_events import (
     ToolUsageErrorEvent,
@@ -368,9 +369,6 @@ class BaseLLM(ABC):
         """Emit LLM call started event."""
         from crewai.utilities.serialization import to_serializable
 
-        if not hasattr(crewai_event_bus, "emit"):
-            raise ValueError("crewai_event_bus does not have an emit method") from None
-
         crewai_event_bus.emit(
             self,
             event=LLMCallStartedEvent(
@@ -416,9 +414,6 @@ class BaseLLM(ABC):
         from_agent: Agent | None = None,
     ) -> None:
         """Emit LLM call failed event."""
-        if not hasattr(crewai_event_bus, "emit"):
-            raise ValueError("crewai_event_bus does not have an emit method") from None
-
         crewai_event_bus.emit(
             self,
             event=LLMCallFailedEvent(
@@ -449,9 +444,6 @@ class BaseLLM(ABC):
             call_type: The type of LLM call (LLM_CALL or TOOL_CALL).
             response_id: Unique ID for a particular LLM response, chunks have same response_id.
         """
-        if not hasattr(crewai_event_bus, "emit"):
-            raise ValueError("crewai_event_bus does not have an emit method") from None
-
         crewai_event_bus.emit(
             self,
             event=LLMStreamChunkEvent(
@@ -465,6 +457,32 @@ class BaseLLM(ABC):
             ),
         )
 
+    def _emit_thinking_chunk_event(
+        self,
+        chunk: str,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
+        response_id: str | None = None,
+    ) -> None:
+        """Emit thinking/reasoning chunk event from a thinking model.
+
+        Args:
+            chunk: The thinking text content.
+            from_task: The task that initiated the call.
+            from_agent: The agent that initiated the call.
+            response_id: Unique ID for a particular LLM response.
+        """
+        crewai_event_bus.emit(
+            self,
+            event=LLMThinkingChunkEvent(
+                chunk=chunk,
+                from_task=from_task,
+                from_agent=from_agent,
+                response_id=response_id,
+                call_id=get_current_call_id(),
+            ),
+        )
+
     def _handle_tool_execution(
         self,
         function_name: str,

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

@@ -61,6 +61,7 @@ class GeminiCompletion(BaseLLM):
         interceptor: BaseInterceptor[Any, Any] | None = None,
         use_vertexai: bool | None = None,
         response_format: type[BaseModel] | None = None,
+        thinking_config: types.ThinkingConfig | None = None,
         **kwargs: Any,
     ):
         """Initialize Google Gemini chat completion client.
@@ -93,6 +94,10 @@ class GeminiCompletion(BaseLLM):
                          api_version="v1" is automatically configured.
             response_format: Pydantic model for structured output. Used as default when
                            response_model is not passed to call()/acall() methods.
+            thinking_config: ThinkingConfig for thinking models (gemini-2.5+, gemini-3+).
+                           Controls thought output via include_thoughts, thinking_budget,
+                           and thinking_level. When None, thinking models automatically
+                           get include_thoughts=True so thought content is surfaced.
             **kwargs: Additional parameters
         """
         if interceptor is not None:
@@ -139,6 +144,14 @@ class GeminiCompletion(BaseLLM):
             version_match and float(version_match.group(1)) >= 2.0
         )
 
+        self.thinking_config = thinking_config
+        if (
+            self.thinking_config is None
+            and version_match
+            and float(version_match.group(1)) >= 2.5
+        ):
+            self.thinking_config = types.ThinkingConfig(include_thoughts=True)
+
     @property
     def stop(self) -> list[str]:
         """Get stop sequences sent to the API."""
@@ -520,6 +533,9 @@ class GeminiCompletion(BaseLLM):
         if self.safety_settings:
             config_params["safety_settings"] = self.safety_settings
 
+        if self.thinking_config is not None:
+            config_params["thinking_config"] = self.thinking_config
+
         return types.GenerateContentConfig(**config_params)
 
     def _convert_tools_for_interference(  # type: ignore[override]
@@ -931,15 +947,6 @@ class GeminiCompletion(BaseLLM):
         if chunk.usage_metadata:
             usage_data = self._extract_token_usage(chunk)
 
-        if chunk.text:
-            full_response += chunk.text
-            self._emit_stream_chunk_event(
-                chunk=chunk.text,
-                from_task=from_task,
-                from_agent=from_agent,
-                response_id=response_id,
-            )
-
         if chunk.candidates:
             candidate = chunk.candidates[0]
             if candidate.content and candidate.content.parts:
@@ -976,6 +983,21 @@ class GeminiCompletion(BaseLLM):
                             call_type=LLMCallType.TOOL_CALL,
                             response_id=response_id,
                         )
+                    elif part.thought and part.text:
+                        self._emit_thinking_chunk_event(
+                            chunk=part.text,
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            response_id=response_id,
+                        )
+                    elif part.text:
+                        full_response += part.text
+                        self._emit_stream_chunk_event(
+                            chunk=part.text,
+                            from_task=from_task,
+                            from_agent=from_agent,
+                            response_id=response_id,
+                        )
 
         return full_response, function_calls, usage_data
 
@@ -1329,7 +1351,7 @@ class GeminiCompletion(BaseLLM):
         text_parts = [
             part.text
             for part in candidate.content.parts
-            if hasattr(part, "text") and part.text
+            if part.text and not part.thought
         ]
 
         return "".join(text_parts)