Merge branch 'main' into lorenze/imp-pydantic

- Changed event type definitions across multiple event classes to use Literal for improved type safety and clarity.
- Updated the  definition in  to utilize Annotated for better schema representation.
- Ensured consistency in type definitions for various events, enhancing the robustness of event handling in the CrewAI framework.

lib/crewai-files/src/crewai_files/__init__.py

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.9.2"
+__version__ = "1.9.0"

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.9.2",
+    "crewai==1.9.0",
     "lancedb~=0.5.4",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -291,4 +291,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.9.2"
+__version__ = "1.9.0"

lib/crewai/pyproject.toml

@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.9.2",
+    "crewai-tools==1.9.0",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.9.2"
+__version__ = "1.9.0"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -37,8 +37,7 @@ class CrewAgentExecutorMixin:
             self.crew
             and self.agent
             and self.task
-            and f"Action: {sanitize_tool_name('Delegate work to coworker')}"
-            not in output.text
+            and f"Action: {sanitize_tool_name('Delegate work to coworker')}" not in output.text
         ):
             try:
                 if (
@@ -133,11 +132,10 @@ class CrewAgentExecutorMixin:
             and self.crew._long_term_memory
             and self.crew._entity_memory is None
         ):
-            if self.agent and self.agent.verbose:
-                self._printer.print(
-                    content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.",
-                    color="bold_yellow",
-                )
+            self._printer.print(
+                content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.",
+                color="bold_yellow",
+            )
 
     def _ask_human_input(self, final_answer: str) -> str:
         """Prompt human input with mode-appropriate messaging.

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -206,14 +206,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         try:
             formatted_answer = self._invoke_loop()
         except AssertionError:
-            if self.agent.verbose:
-                self._printer.print(
-                    content="Agent failed to reach a final answer. This is likely a bug - please report it.",
-                    color="red",
-                )
+            self._printer.print(
+                content="Agent failed to reach a final answer. This is likely a bug - please report it.",
+                color="red",
+            )
             raise
         except Exception as e:
-            handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+            handle_unknown_error(self._printer, e)
             raise
 
         if self.ask_for_human_input:
@@ -328,7 +327,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         messages=self.messages,
                         llm=self.llm,
                         callbacks=self.callbacks,
-                        verbose=self.agent.verbose,
                     )
                     break
 
@@ -343,41 +341,22 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     from_agent=self.agent,
                     response_model=self.response_model,
                     executor_context=self,
-                    verbose=self.agent.verbose,
                 )
                 # breakpoint()
                 if self.response_model is not None:
                     try:
-                        if isinstance(answer, BaseModel):
-                            output_json = answer.model_dump_json()
-                            formatted_answer = AgentFinish(
-                                thought="",
-                                output=answer,
-                                text=output_json,
-                            )
-                        else:
-                            self.response_model.model_validate_json(answer)
-                            formatted_answer = AgentFinish(
-                                thought="",
-                                output=answer,
-                                text=answer,
-                            )
-                    except ValidationError:
-                        # If validation fails, convert BaseModel to JSON string for parsing
-                        answer_str = (
-                            answer.model_dump_json()
-                            if isinstance(answer, BaseModel)
-                            else str(answer)
+                        self.response_model.model_validate_json(answer)
+                        formatted_answer = AgentFinish(
+                            thought="",
+                            output=answer,
+                            text=answer,
                         )
+                    except ValidationError:
                         formatted_answer = process_llm_response(
-                            answer_str, self.use_stop_words
+                            answer, self.use_stop_words
                         )  # type: ignore[assignment]
                 else:
-                    # When no response_model, answer should be a string
-                    answer_str = str(answer) if not isinstance(answer, str) else answer
-                    formatted_answer = process_llm_response(
-                        answer_str, self.use_stop_words
-                    )  # type: ignore[assignment]
+                    formatted_answer = process_llm_response(answer, self.use_stop_words)  # type: ignore[assignment]
 
                 if isinstance(formatted_answer, AgentAction):
                     # Extract agent fingerprint if available
@@ -420,7 +399,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     iterations=self.iterations,
                     log_error_after=self.log_error_after,
                     printer=self._printer,
-                    verbose=self.agent.verbose,
                 )
 
             except Exception as e:
@@ -435,10 +413,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                         i18n=self._i18n,
-                        verbose=self.agent.verbose,
                     )
                     continue
-                handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+                handle_unknown_error(self._printer, e)
                 raise e
             finally:
                 self.iterations += 1
@@ -484,7 +461,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         messages=self.messages,
                         llm=self.llm,
                         callbacks=self.callbacks,
-                        verbose=self.agent.verbose,
                     )
                     self._show_logs(formatted_answer)
                     return formatted_answer
@@ -506,7 +482,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     from_agent=self.agent,
                     response_model=self.response_model,
                     executor_context=self,
-                    verbose=self.agent.verbose,
                 )
 
                 # Check if the response is a list of tool calls
@@ -538,18 +513,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     self._show_logs(formatted_answer)
                     return formatted_answer
 
-                if isinstance(answer, BaseModel):
-                    output_json = answer.model_dump_json()
-                    formatted_answer = AgentFinish(
-                        thought="",
-                        output=answer,
-                        text=output_json,
-                    )
-                    self._invoke_step_callback(formatted_answer)
-                    self._append_message(output_json)
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
                 # Unexpected response type, treat as final answer
                 formatted_answer = AgentFinish(
                     thought="",
@@ -572,10 +535,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                         i18n=self._i18n,
-                        verbose=self.agent.verbose,
                     )
                     continue
-                handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+                handle_unknown_error(self._printer, e)
                 raise e
             finally:
                 self.iterations += 1
@@ -597,23 +559,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             from_agent=self.agent,
             response_model=self.response_model,
             executor_context=self,
-            verbose=self.agent.verbose,
         )
 
-        if isinstance(answer, BaseModel):
-            output_json = answer.model_dump_json()
-            formatted_answer = AgentFinish(
-                thought="",
-                output=answer,
-                text=output_json,
-            )
-        else:
-            answer_str = answer if isinstance(answer, str) else str(answer)
-            formatted_answer = AgentFinish(
-                thought="",
-                output=answer_str,
-                text=answer_str,
-            )
+        formatted_answer = AgentFinish(
+            thought="",
+            output=str(answer),
+            text=str(answer),
+        )
         self._show_logs(formatted_answer)
         return formatted_answer
 
@@ -803,10 +755,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         track_delegation_if_needed(func_name, args_dict, self.task)
 
         # Find the structured tool for hook context
-        structured_tool: CrewStructuredTool | None = None
-        for structured in self.tools or []:
-            if sanitize_tool_name(structured.name) == func_name:
-                structured_tool = structured
+        structured_tool = None
+        for tool in self.tools or []:
+            if sanitize_tool_name(tool.name) == func_name:
+                structured_tool = tool
                 break
 
         # Execute before_tool_call hooks
@@ -827,11 +779,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     hook_blocked = True
                     break
         except Exception as hook_error:
-            if self.agent.verbose:
-                self._printer.print(
-                    content=f"Error in before_tool_call hook: {hook_error}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"Error in before_tool_call hook: {hook_error}",
+                color="red",
+            )
 
         # If hook blocked execution, set result and skip tool execution
         if hook_blocked:
@@ -897,16 +848,15 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         after_hooks = get_after_tool_call_hooks()
         try:
             for after_hook in after_hooks:
-                after_hook_result = after_hook(after_hook_context)
-                if after_hook_result is not None:
-                    result = after_hook_result
+                hook_result = after_hook(after_hook_context)
+                if hook_result is not None:
+                    result = hook_result
                     after_hook_context.tool_result = result
         except Exception as hook_error:
-            if self.agent.verbose:
-                self._printer.print(
-                    content=f"Error in after_tool_call hook: {hook_error}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"Error in after_tool_call hook: {hook_error}",
+                color="red",
+            )
 
         # Emit tool usage finished event
         crewai_event_bus.emit(
@@ -992,14 +942,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         try:
             formatted_answer = await self._ainvoke_loop()
         except AssertionError:
-            if self.agent.verbose:
-                self._printer.print(
-                    content="Agent failed to reach a final answer. This is likely a bug - please report it.",
-                    color="red",
-                )
+            self._printer.print(
+                content="Agent failed to reach a final answer. This is likely a bug - please report it.",
+                color="red",
+            )
             raise
         except Exception as e:
-            handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+            handle_unknown_error(self._printer, e)
             raise
 
         if self.ask_for_human_input:
@@ -1050,7 +999,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         messages=self.messages,
                         llm=self.llm,
                         callbacks=self.callbacks,
-                        verbose=self.agent.verbose,
                     )
                     break
 
@@ -1065,41 +1013,22 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     from_agent=self.agent,
                     response_model=self.response_model,
                     executor_context=self,
-                    verbose=self.agent.verbose,
                 )
 
                 if self.response_model is not None:
                     try:
-                        if isinstance(answer, BaseModel):
-                            output_json = answer.model_dump_json()
-                            formatted_answer = AgentFinish(
-                                thought="",
-                                output=answer,
-                                text=output_json,
-                            )
-                        else:
-                            self.response_model.model_validate_json(answer)
-                            formatted_answer = AgentFinish(
-                                thought="",
-                                output=answer,
-                                text=answer,
-                            )
-                    except ValidationError:
-                        # If validation fails, convert BaseModel to JSON string for parsing
-                        answer_str = (
-                            answer.model_dump_json()
-                            if isinstance(answer, BaseModel)
-                            else str(answer)
+                        self.response_model.model_validate_json(answer)
+                        formatted_answer = AgentFinish(
+                            thought="",
+                            output=answer,
+                            text=answer,
                         )
+                    except ValidationError:
                         formatted_answer = process_llm_response(
-                            answer_str, self.use_stop_words
+                            answer, self.use_stop_words
                         )  # type: ignore[assignment]
                 else:
-                    # When no response_model, answer should be a string
-                    answer_str = str(answer) if not isinstance(answer, str) else answer
-                    formatted_answer = process_llm_response(
-                        answer_str, self.use_stop_words
-                    )  # type: ignore[assignment]
+                    formatted_answer = process_llm_response(answer, self.use_stop_words)  # type: ignore[assignment]
 
                 if isinstance(formatted_answer, AgentAction):
                     fingerprint_context = {}
@@ -1141,7 +1070,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     iterations=self.iterations,
                     log_error_after=self.log_error_after,
                     printer=self._printer,
-                    verbose=self.agent.verbose,
                 )
 
             except Exception as e:
@@ -1155,10 +1083,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                         i18n=self._i18n,
-                        verbose=self.agent.verbose,
                     )
                     continue
-                handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+                handle_unknown_error(self._printer, e)
                 raise e
             finally:
                 self.iterations += 1
@@ -1198,7 +1125,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         messages=self.messages,
                         llm=self.llm,
                         callbacks=self.callbacks,
-                        verbose=self.agent.verbose,
                     )
                     self._show_logs(formatted_answer)
                     return formatted_answer
@@ -1220,7 +1146,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     from_agent=self.agent,
                     response_model=self.response_model,
                     executor_context=self,
-                    verbose=self.agent.verbose,
                 )
                 # Check if the response is a list of tool calls
                 if (
@@ -1251,18 +1176,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     self._show_logs(formatted_answer)
                     return formatted_answer
 
-                if isinstance(answer, BaseModel):
-                    output_json = answer.model_dump_json()
-                    formatted_answer = AgentFinish(
-                        thought="",
-                        output=answer,
-                        text=output_json,
-                    )
-                    self._invoke_step_callback(formatted_answer)
-                    self._append_message(output_json)
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
                 # Unexpected response type, treat as final answer
                 formatted_answer = AgentFinish(
                     thought="",
@@ -1285,10 +1198,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                         i18n=self._i18n,
-                        verbose=self.agent.verbose,
                     )
                     continue
-                handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+                handle_unknown_error(self._printer, e)
                 raise e
             finally:
                 self.iterations += 1
@@ -1310,23 +1222,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             from_agent=self.agent,
             response_model=self.response_model,
             executor_context=self,
-            verbose=self.agent.verbose,
         )
 
-        if isinstance(answer, BaseModel):
-            output_json = answer.model_dump_json()
-            formatted_answer = AgentFinish(
-                thought="",
-                output=answer,
-                text=output_json,
-            )
-        else:
-            answer_str = answer if isinstance(answer, str) else str(answer)
-            formatted_answer = AgentFinish(
-                thought="",
-                output=answer_str,
-                text=answer_str,
-            )
+        formatted_answer = AgentFinish(
+            thought="",
+            output=str(answer),
+            text=str(answer),
+        )
         self._show_logs(formatted_answer)
         return formatted_answer
 
@@ -1437,11 +1339,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         )
 
         if train_iteration is None or not isinstance(train_iteration, int):
-            if self.agent.verbose:
-                self._printer.print(
-                    content="Invalid or missing train iteration. Cannot save training data.",
-                    color="red",
-                )
+            self._printer.print(
+                content="Invalid or missing train iteration. Cannot save training data.",
+                color="red",
+            )
             return
 
         training_handler = CrewTrainingHandler(TRAINING_DATA_FILE)
@@ -1461,14 +1362,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if train_iteration in agent_training_data:
                 agent_training_data[train_iteration]["improved_output"] = result.output
             else:
-                if self.agent.verbose:
-                    self._printer.print(
-                        content=(
-                            f"No existing training data for agent {agent_id} and iteration "
-                            f"{train_iteration}. Cannot save improved output."
-                        ),
-                        color="red",
-                    )
+                self._printer.print(
+                    content=(
+                        f"No existing training data for agent {agent_id} and iteration "
+                        f"{train_iteration}. Cannot save improved output."
+                    ),
+                    color="red",
+                )
                 return
 
         # Update the training data and save
@@ -1499,12 +1399,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             Final answer after feedback.
         """
-        output_str = (
-            formatted_answer.output
-            if isinstance(formatted_answer.output, str)
-            else formatted_answer.output.model_dump_json()
-        )
-        human_feedback = self._ask_human_input(output_str)
+        human_feedback = self._ask_human_input(formatted_answer.output)
 
         if self._is_training_mode():
             return self._handle_training_feedback(formatted_answer, human_feedback)
@@ -1563,12 +1458,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self.ask_for_human_input = False
             else:
                 answer = self._process_feedback_iteration(feedback)
-                output_str = (
-                    answer.output
-                    if isinstance(answer.output, str)
-                    else answer.output.model_dump_json()
-                )
-                feedback = self._ask_human_input(output_str)
+                feedback = self._ask_human_input(answer.output)
 
         return answer
 

lib/crewai/src/crewai/agents/parser.py

@@ -8,7 +8,6 @@ AgentAction or AgentFinish objects.
 from dataclasses import dataclass
 
 from json_repair import repair_json  # type: ignore[import-untyped]
-from pydantic import BaseModel
 
 from crewai.agents.constants import (
     ACTION_INPUT_ONLY_REGEX,
@@ -41,7 +40,7 @@ class AgentFinish:
     """Represents the final answer from an agent."""
 
     thought: str
-    output: str | BaseModel
+    output: str
     text: str
 
 

lib/crewai/src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.9.2"
+    "crewai[tools]==1.9.0"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.9.2"
+    "crewai[tools]==1.9.0"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/events/event_types.py

@@ -1,3 +1,7 @@
+from typing import Annotated
+
+from pydantic import Field
+
 from crewai.events.types.a2a_events import (
     A2AAgentCardFetchedEvent,
     A2AArtifactReceivedEvent,
@@ -102,7 +106,7 @@ from crewai.events.types.tool_usage_events import (
 )
 
 
-EventTypes = (
+EventTypes = Annotated[
     A2AAgentCardFetchedEvent
     | A2AArtifactReceivedEvent
     | A2AAuthenticationFailedEvent
@@ -180,5 +184,6 @@ EventTypes = (
     | MCPConnectionFailedEvent
     | MCPToolExecutionStartedEvent
     | MCPToolExecutionCompletedEvent
-    | MCPToolExecutionFailedEvent
-)
+    | MCPToolExecutionFailedEvent,
+    Field(discriminator="type"),
+]

lib/crewai/src/crewai/events/types/a2a_events.py

@@ -73,7 +73,7 @@ class A2ADelegationStartedEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_delegation_started"
+    type: Literal["a2a_delegation_started"] = "a2a_delegation_started"
     endpoint: str
     task_description: str
     agent_id: str
@@ -106,7 +106,7 @@ class A2ADelegationCompletedEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_delegation_completed"
+    type: Literal["a2a_delegation_completed"] = "a2a_delegation_completed"
     status: str
     result: str | None = None
     error: str | None = None
@@ -140,7 +140,7 @@ class A2AConversationStartedEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_conversation_started"
+    type: Literal["a2a_conversation_started"] = "a2a_conversation_started"
     agent_id: str
     endpoint: str
     context_id: str | None = None
@@ -171,7 +171,7 @@ class A2AMessageSentEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_message_sent"
+    type: Literal["a2a_message_sent"] = "a2a_message_sent"
     message: str
     turn_number: int
     context_id: str | None = None
@@ -203,7 +203,7 @@ class A2AResponseReceivedEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_response_received"
+    type: Literal["a2a_response_received"] = "a2a_response_received"
     response: str
     turn_number: int
     context_id: str | None = None
@@ -237,7 +237,7 @@ class A2AConversationCompletedEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_conversation_completed"
+    type: Literal["a2a_conversation_completed"] = "a2a_conversation_completed"
     status: Literal["completed", "failed"]
     final_result: str | None = None
     error: str | None = None
@@ -263,7 +263,7 @@ class A2APollingStartedEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_polling_started"
+    type: Literal["a2a_polling_started"] = "a2a_polling_started"
     task_id: str
     context_id: str | None = None
     polling_interval: float
@@ -286,7 +286,7 @@ class A2APollingStatusEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_polling_status"
+    type: Literal["a2a_polling_status"] = "a2a_polling_status"
     task_id: str
     context_id: str | None = None
     state: str
@@ -309,7 +309,7 @@ class A2APushNotificationRegisteredEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_push_notification_registered"
+    type: Literal["a2a_push_notification_registered"] = "a2a_push_notification_registered"
     task_id: str
     context_id: str | None = None
     callback_url: str
@@ -334,7 +334,7 @@ class A2APushNotificationReceivedEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_push_notification_received"
+    type: Literal["a2a_push_notification_received"] = "a2a_push_notification_received"
     task_id: str
     context_id: str | None = None
     state: str
@@ -359,7 +359,7 @@ class A2APushNotificationSentEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_push_notification_sent"
+    type: Literal["a2a_push_notification_sent"] = "a2a_push_notification_sent"
     task_id: str
     context_id: str | None = None
     callback_url: str
@@ -381,7 +381,7 @@ class A2APushNotificationTimeoutEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_push_notification_timeout"
+    type: Literal["a2a_push_notification_timeout"] = "a2a_push_notification_timeout"
     task_id: str
     context_id: str | None = None
     timeout_seconds: float
@@ -405,7 +405,7 @@ class A2AStreamingStartedEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_streaming_started"
+    type: Literal["a2a_streaming_started"] = "a2a_streaming_started"
     task_id: str | None = None
     context_id: str | None = None
     endpoint: str
@@ -434,7 +434,7 @@ class A2AStreamingChunkEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_streaming_chunk"
+    type: Literal["a2a_streaming_chunk"] = "a2a_streaming_chunk"
     task_id: str | None = None
     context_id: str | None = None
     chunk: str
@@ -462,7 +462,7 @@ class A2AAgentCardFetchedEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_agent_card_fetched"
+    type: Literal["a2a_agent_card_fetched"] = "a2a_agent_card_fetched"
     endpoint: str
     a2a_agent_name: str | None = None
     agent_card: dict[str, Any] | None = None
@@ -486,7 +486,7 @@ class A2AAuthenticationFailedEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_authentication_failed"
+    type: Literal["a2a_authentication_failed"] = "a2a_authentication_failed"
     endpoint: str
     auth_type: str | None = None
     error: str
@@ -517,7 +517,7 @@ class A2AArtifactReceivedEvent(A2AEventBase):
         extensions: List of A2A extension URIs in use.
     """
 
-    type: str = "a2a_artifact_received"
+    type: Literal["a2a_artifact_received"] = "a2a_artifact_received"
     task_id: str
     artifact_id: str
     artifact_name: str | None = None
@@ -550,7 +550,7 @@ class A2AConnectionErrorEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_connection_error"
+    type: Literal["a2a_connection_error"] = "a2a_connection_error"
     endpoint: str
     error: str
     error_type: str | None = None
@@ -571,7 +571,7 @@ class A2AServerTaskStartedEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_server_task_started"
+    type: Literal["a2a_server_task_started"] = "a2a_server_task_started"
     task_id: str
     context_id: str
     metadata: dict[str, Any] | None = None
@@ -587,7 +587,7 @@ class A2AServerTaskCompletedEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_server_task_completed"
+    type: Literal["a2a_server_task_completed"] = "a2a_server_task_completed"
     task_id: str
     context_id: str
     result: str
@@ -603,7 +603,7 @@ class A2AServerTaskCanceledEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_server_task_canceled"
+    type: Literal["a2a_server_task_canceled"] = "a2a_server_task_canceled"
     task_id: str
     context_id: str
     metadata: dict[str, Any] | None = None
@@ -619,7 +619,7 @@ class A2AServerTaskFailedEvent(A2AEventBase):
         metadata: Custom A2A metadata key-value pairs.
     """
 
-    type: str = "a2a_server_task_failed"
+    type: Literal["a2a_server_task_failed"] = "a2a_server_task_failed"
     task_id: str
     context_id: str
     error: str
@@ -634,7 +634,7 @@ class A2AParallelDelegationStartedEvent(A2AEventBase):
         task_description: Description of the task being delegated.
     """
 
-    type: str = "a2a_parallel_delegation_started"
+    type: Literal["a2a_parallel_delegation_started"] = "a2a_parallel_delegation_started"
     endpoints: list[str]
     task_description: str
 
@@ -649,7 +649,7 @@ class A2AParallelDelegationCompletedEvent(A2AEventBase):
         results: Summary of results from each agent.
     """
 
-    type: str = "a2a_parallel_delegation_completed"
+    type: Literal["a2a_parallel_delegation_completed"] = "a2a_parallel_delegation_completed"
     endpoints: list[str]
     success_count: int
     failure_count: int

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

@@ -2,8 +2,7 @@
 
 from __future__ import annotations
 
-from collections.abc import Sequence
-from typing import Any
+from typing import Any, Literal
 
 from pydantic import ConfigDict, model_validator
 
@@ -18,9 +17,9 @@ class AgentExecutionStartedEvent(BaseEvent):
 
     agent: BaseAgent
     task: Any
-    tools: Sequence[BaseTool | CrewStructuredTool] | None
+    tools: list[BaseTool | CrewStructuredTool] | None
     task_prompt: str
-    type: str = "agent_execution_started"
+    type: Literal["agent_execution_started"] = "agent_execution_started"
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -44,7 +43,7 @@ class AgentExecutionCompletedEvent(BaseEvent):
     agent: BaseAgent
     task: Any
     output: str
-    type: str = "agent_execution_completed"
+    type: Literal["agent_execution_completed"] = "agent_execution_completed"
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -68,7 +67,7 @@ class AgentExecutionErrorEvent(BaseEvent):
     agent: BaseAgent
     task: Any
     error: str
-    type: str = "agent_execution_error"
+    type: Literal["agent_execution_error"] = "agent_execution_error"
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -91,9 +90,9 @@ class LiteAgentExecutionStartedEvent(BaseEvent):
     """Event emitted when a LiteAgent starts executing"""
 
     agent_info: dict[str, Any]
-    tools: Sequence[BaseTool | CrewStructuredTool] | None
+    tools: list[BaseTool | CrewStructuredTool] | None
     messages: str | list[dict[str, str]]
-    type: str = "lite_agent_execution_started"
+    type: Literal["lite_agent_execution_started"] = "lite_agent_execution_started"
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -103,7 +102,7 @@ class LiteAgentExecutionCompletedEvent(BaseEvent):
 
     agent_info: dict[str, Any]
     output: str
-    type: str = "lite_agent_execution_completed"
+    type: Literal["lite_agent_execution_completed"] = "lite_agent_execution_completed"
 
 
 class LiteAgentExecutionErrorEvent(BaseEvent):
@@ -111,7 +110,7 @@ class LiteAgentExecutionErrorEvent(BaseEvent):
 
     agent_info: dict[str, Any]
     error: str
-    type: str = "lite_agent_execution_error"
+    type: Literal["lite_agent_execution_error"] = "lite_agent_execution_error"
 
 
 # Agent Eval events
@@ -120,7 +119,7 @@ class AgentEvaluationStartedEvent(BaseEvent):
     agent_role: str
     task_id: str | None = None
     iteration: int
-    type: str = "agent_evaluation_started"
+    type: Literal["agent_evaluation_started"] = "agent_evaluation_started"
 
 
 class AgentEvaluationCompletedEvent(BaseEvent):
@@ -130,7 +129,7 @@ class AgentEvaluationCompletedEvent(BaseEvent):
     iteration: int
     metric_category: Any
     score: Any
-    type: str = "agent_evaluation_completed"
+    type: Literal["agent_evaluation_completed"] = "agent_evaluation_completed"
 
 
 class AgentEvaluationFailedEvent(BaseEvent):
@@ -139,4 +138,4 @@ class AgentEvaluationFailedEvent(BaseEvent):
     task_id: str | None = None
     iteration: int
     error: str
-    type: str = "agent_evaluation_failed"
+    type: Literal["agent_evaluation_failed"] = "agent_evaluation_failed"

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

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal
 
 from crewai.events.base_events import BaseEvent
 
@@ -40,14 +40,14 @@ class CrewKickoffStartedEvent(CrewBaseEvent):
     """Event emitted when a crew starts execution"""
 
     inputs: dict[str, Any] | None
-    type: str = "crew_kickoff_started"
+    type: Literal["crew_kickoff_started"] = "crew_kickoff_started"
 
 
 class CrewKickoffCompletedEvent(CrewBaseEvent):
     """Event emitted when a crew completes execution"""
 
     output: Any
-    type: str = "crew_kickoff_completed"
+    type: Literal["crew_kickoff_completed"] = "crew_kickoff_completed"
     total_tokens: int = 0
 
 
@@ -55,7 +55,7 @@ class CrewKickoffFailedEvent(CrewBaseEvent):
     """Event emitted when a crew fails to complete execution"""
 
     error: str
-    type: str = "crew_kickoff_failed"
+    type: Literal["crew_kickoff_failed"] = "crew_kickoff_failed"
 
 
 class CrewTrainStartedEvent(CrewBaseEvent):
@@ -64,7 +64,7 @@ class CrewTrainStartedEvent(CrewBaseEvent):
     n_iterations: int
     filename: str
     inputs: dict[str, Any] | None
-    type: str = "crew_train_started"
+    type: Literal["crew_train_started"] = "crew_train_started"
 
 
 class CrewTrainCompletedEvent(CrewBaseEvent):
@@ -72,14 +72,14 @@ class CrewTrainCompletedEvent(CrewBaseEvent):
 
     n_iterations: int
     filename: str
-    type: str = "crew_train_completed"
+    type: Literal["crew_train_completed"] = "crew_train_completed"
 
 
 class CrewTrainFailedEvent(CrewBaseEvent):
     """Event emitted when a crew fails to complete training"""
 
     error: str
-    type: str = "crew_train_failed"
+    type: Literal["crew_train_failed"] = "crew_train_failed"
 
 
 class CrewTestStartedEvent(CrewBaseEvent):
@@ -88,20 +88,20 @@ class CrewTestStartedEvent(CrewBaseEvent):
     n_iterations: int
     eval_llm: str | Any | None
     inputs: dict[str, Any] | None
-    type: str = "crew_test_started"
+    type: Literal["crew_test_started"] = "crew_test_started"
 
 
 class CrewTestCompletedEvent(CrewBaseEvent):
     """Event emitted when a crew completes testing"""
 
-    type: str = "crew_test_completed"
+    type: Literal["crew_test_completed"] = "crew_test_completed"
 
 
 class CrewTestFailedEvent(CrewBaseEvent):
     """Event emitted when a crew fails to complete testing"""
 
     error: str
-    type: str = "crew_test_failed"
+    type: Literal["crew_test_failed"] = "crew_test_failed"
 
 
 class CrewTestResultEvent(CrewBaseEvent):
@@ -110,4 +110,4 @@ class CrewTestResultEvent(CrewBaseEvent):
     quality: float
     execution_duration: float
     model: str
-    type: str = "crew_test_result"
+    type: Literal["crew_test_result"] = "crew_test_result"

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

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Literal
 
 from pydantic import BaseModel, ConfigDict
 
@@ -17,14 +17,14 @@ class FlowStartedEvent(FlowEvent):
 
     flow_name: str
     inputs: dict[str, Any] | None = None
-    type: str = "flow_started"
+    type: Literal["flow_started"] = "flow_started"
 
 
 class FlowCreatedEvent(FlowEvent):
     """Event emitted when a flow is created"""
 
     flow_name: str
-    type: str = "flow_created"
+    type: Literal["flow_created"] = "flow_created"
 
 
 class MethodExecutionStartedEvent(FlowEvent):
@@ -34,7 +34,7 @@ class MethodExecutionStartedEvent(FlowEvent):
     method_name: str
     state: dict[str, Any] | BaseModel
     params: dict[str, Any] | None = None
-    type: str = "method_execution_started"
+    type: Literal["method_execution_started"] = "method_execution_started"
 
 
 class MethodExecutionFinishedEvent(FlowEvent):
@@ -44,7 +44,7 @@ class MethodExecutionFinishedEvent(FlowEvent):
     method_name: str
     result: Any = None
     state: dict[str, Any] | BaseModel
-    type: str = "method_execution_finished"
+    type: Literal["method_execution_finished"] = "method_execution_finished"
 
 
 class MethodExecutionFailedEvent(FlowEvent):
@@ -53,7 +53,7 @@ class MethodExecutionFailedEvent(FlowEvent):
     flow_name: str
     method_name: str
     error: Exception
-    type: str = "method_execution_failed"
+    type: Literal["method_execution_failed"] = "method_execution_failed"
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -78,7 +78,7 @@ class MethodExecutionPausedEvent(FlowEvent):
     flow_id: str
     message: str
     emit: list[str] | None = None
-    type: str = "method_execution_paused"
+    type: Literal["method_execution_paused"] = "method_execution_paused"
 
 
 class FlowFinishedEvent(FlowEvent):
@@ -86,7 +86,7 @@ class FlowFinishedEvent(FlowEvent):
 
     flow_name: str
     result: Any | None = None
-    type: str = "flow_finished"
+    type: Literal["flow_finished"] = "flow_finished"
     state: dict[str, Any] | BaseModel
 
 
@@ -110,14 +110,14 @@ class FlowPausedEvent(FlowEvent):
     state: dict[str, Any] | BaseModel
     message: str
     emit: list[str] | None = None
-    type: str = "flow_paused"
+    type: Literal["flow_paused"] = "flow_paused"
 
 
 class FlowPlotEvent(FlowEvent):
     """Event emitted when a flow plot is created"""
 
     flow_name: str
-    type: str = "flow_plot"
+    type: Literal["flow_plot"] = "flow_plot"
 
 
 class HumanFeedbackRequestedEvent(FlowEvent):
@@ -138,7 +138,7 @@ class HumanFeedbackRequestedEvent(FlowEvent):
     output: Any
     message: str
     emit: list[str] | None = None
-    type: str = "human_feedback_requested"
+    type: Literal["human_feedback_requested"] = "human_feedback_requested"
 
 
 class HumanFeedbackReceivedEvent(FlowEvent):
@@ -157,4 +157,4 @@ class HumanFeedbackReceivedEvent(FlowEvent):
     method_name: str
     feedback: str
     outcome: str | None = None
-    type: str = "human_feedback_received"
+    type: Literal["human_feedback_received"] = "human_feedback_received"

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

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Literal
 
 from crewai.events.base_events import BaseEvent
 
@@ -20,14 +20,14 @@ class KnowledgeEventBase(BaseEvent):
 class KnowledgeRetrievalStartedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge retrieval is started."""
 
-    type: str = "knowledge_search_query_started"
+    type: Literal["knowledge_search_query_started"] = "knowledge_search_query_started"
 
 
 class KnowledgeRetrievalCompletedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge retrieval is completed."""
 
     query: str
-    type: str = "knowledge_search_query_completed"
+    type: Literal["knowledge_search_query_completed"] = "knowledge_search_query_completed"
     retrieved_knowledge: str
 
 
@@ -35,13 +35,13 @@ class KnowledgeQueryStartedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query is started."""
 
     task_prompt: str
-    type: str = "knowledge_query_started"
+    type: Literal["knowledge_query_started"] = "knowledge_query_started"
 
 
 class KnowledgeQueryFailedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query fails."""
 
-    type: str = "knowledge_query_failed"
+    type: Literal["knowledge_query_failed"] = "knowledge_query_failed"
     error: str
 
 
@@ -49,12 +49,12 @@ class KnowledgeQueryCompletedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query is completed."""
 
     query: str
-    type: str = "knowledge_query_completed"
+    type: Literal["knowledge_query_completed"] = "knowledge_query_completed"
 
 
 class KnowledgeSearchQueryFailedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge search query fails."""
 
     query: str
-    type: str = "knowledge_search_query_failed"
+    type: Literal["knowledge_search_query_failed"] = "knowledge_search_query_failed"
     error: str

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

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any
+from typing import Any, Literal
 
 from pydantic import BaseModel
 
@@ -42,7 +42,7 @@ class LLMCallStartedEvent(LLMEventBase):
             multimodal content (text, images, etc.)
     """
 
-    type: str = "llm_call_started"
+    type: Literal["llm_call_started"] = "llm_call_started"
     messages: str | list[dict[str, Any]] | None = None
     tools: list[dict[str, Any]] | None = None
     callbacks: list[Any] | None = None
@@ -52,7 +52,7 @@ class LLMCallStartedEvent(LLMEventBase):
 class LLMCallCompletedEvent(LLMEventBase):
     """Event emitted when a LLM call completes"""
 
-    type: str = "llm_call_completed"
+    type: Literal["llm_call_completed"] = "llm_call_completed"
     messages: str | list[dict[str, Any]] | None = None
     response: Any
     call_type: LLMCallType
@@ -62,7 +62,7 @@ class LLMCallFailedEvent(LLMEventBase):
     """Event emitted when a LLM call fails"""
 
     error: str
-    type: str = "llm_call_failed"
+    type: Literal["llm_call_failed"] = "llm_call_failed"
 
 
 class FunctionCall(BaseModel):
@@ -80,7 +80,7 @@ class ToolCall(BaseModel):
 class LLMStreamChunkEvent(LLMEventBase):
     """Event emitted when a streaming chunk is received"""
 
-    type: str = "llm_stream_chunk"
+    type: Literal["llm_stream_chunk"] = "llm_stream_chunk"
     chunk: str
     tool_call: ToolCall | None = None
     call_type: LLMCallType | None = None

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

@@ -1,6 +1,6 @@
 from collections.abc import Callable
 from inspect import getsource
-from typing import Any
+from typing import Any, Literal
 
 from crewai.events.base_events import BaseEvent
 
@@ -27,7 +27,7 @@ class LLMGuardrailStartedEvent(LLMGuardrailBaseEvent):
         retry_count: The number of times the guardrail has been retried
     """
 
-    type: str = "llm_guardrail_started"
+    type: Literal["llm_guardrail_started"] = "llm_guardrail_started"
     guardrail: str | Callable
     retry_count: int
 
@@ -53,7 +53,7 @@ class LLMGuardrailCompletedEvent(LLMGuardrailBaseEvent):
         retry_count: The number of times the guardrail has been retried
     """
 
-    type: str = "llm_guardrail_completed"
+    type: Literal["llm_guardrail_completed"] = "llm_guardrail_completed"
     success: bool
     result: Any
     error: str | None = None
@@ -68,6 +68,6 @@ class LLMGuardrailFailedEvent(LLMGuardrailBaseEvent):
         retry_count: The number of times the guardrail has been retried
     """
 
-    type: str = "llm_guardrail_failed"
+    type: Literal["llm_guardrail_failed"] = "llm_guardrail_failed"
     error: str
     retry_count: int

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

@@ -1,6 +1,6 @@
 """Agent logging events that don't reference BaseAgent to avoid circular imports."""
 
-from typing import Any
+from typing import Any, Literal
 
 from pydantic import ConfigDict
 
@@ -13,7 +13,7 @@ class AgentLogsStartedEvent(BaseEvent):
     agent_role: str
     task_description: str | None = None
     verbose: bool = False
-    type: str = "agent_logs_started"
+    type: Literal["agent_logs_started"] = "agent_logs_started"
 
 
 class AgentLogsExecutionEvent(BaseEvent):
@@ -22,6 +22,6 @@ class AgentLogsExecutionEvent(BaseEvent):
     agent_role: str
     formatted_answer: Any
     verbose: bool = False
-    type: str = "agent_logs_execution"
+    type: Literal["agent_logs_execution"] = "agent_logs_execution"
 
     model_config = ConfigDict(arbitrary_types_allowed=True)

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

@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import Any
+from typing import Any, Literal
 
 from crewai.events.base_events import BaseEvent
 
@@ -24,7 +24,7 @@ class MCPEvent(BaseEvent):
 class MCPConnectionStartedEvent(MCPEvent):
     """Event emitted when starting to connect to an MCP server."""
 
-    type: str = "mcp_connection_started"
+    type: Literal["mcp_connection_started"] = "mcp_connection_started"
     connect_timeout: int | None = None
     is_reconnect: bool = (
         False  # True if this is a reconnection, False for first connection
@@ -34,7 +34,7 @@ class MCPConnectionStartedEvent(MCPEvent):
 class MCPConnectionCompletedEvent(MCPEvent):
     """Event emitted when successfully connected to an MCP server."""
 
-    type: str = "mcp_connection_completed"
+    type: Literal["mcp_connection_completed"] = "mcp_connection_completed"
     started_at: datetime | None = None
     completed_at: datetime | None = None
     connection_duration_ms: float | None = None
@@ -46,7 +46,7 @@ class MCPConnectionCompletedEvent(MCPEvent):
 class MCPConnectionFailedEvent(MCPEvent):
     """Event emitted when connection to an MCP server fails."""
 
-    type: str = "mcp_connection_failed"
+    type: Literal["mcp_connection_failed"] = "mcp_connection_failed"
     error: str
     error_type: str | None = None  # "timeout", "authentication", "network", etc.
     started_at: datetime | None = None
@@ -56,7 +56,7 @@ class MCPConnectionFailedEvent(MCPEvent):
 class MCPToolExecutionStartedEvent(MCPEvent):
     """Event emitted when starting to execute an MCP tool."""
 
-    type: str = "mcp_tool_execution_started"
+    type: Literal["mcp_tool_execution_started"] = "mcp_tool_execution_started"
     tool_name: str
     tool_args: dict[str, Any] | None = None
 
@@ -64,7 +64,7 @@ class MCPToolExecutionStartedEvent(MCPEvent):
 class MCPToolExecutionCompletedEvent(MCPEvent):
     """Event emitted when MCP tool execution completes."""
 
-    type: str = "mcp_tool_execution_completed"
+    type: Literal["mcp_tool_execution_completed"] = "mcp_tool_execution_completed"
     tool_name: str
     tool_args: dict[str, Any] | None = None
     result: Any | None = None
@@ -76,7 +76,7 @@ class MCPToolExecutionCompletedEvent(MCPEvent):
 class MCPToolExecutionFailedEvent(MCPEvent):
     """Event emitted when MCP tool execution fails."""
 
-    type: str = "mcp_tool_execution_failed"
+    type: Literal["mcp_tool_execution_failed"] = "mcp_tool_execution_failed"
     tool_name: str
     tool_args: dict[str, Any] | None = None
     error: str

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

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Literal
 
 from crewai.events.base_events import BaseEvent
 
@@ -23,7 +23,7 @@ class MemoryBaseEvent(BaseEvent):
 class MemoryQueryStartedEvent(MemoryBaseEvent):
     """Event emitted when a memory query is started"""
 
-    type: str = "memory_query_started"
+    type: Literal["memory_query_started"] = "memory_query_started"
     query: str
     limit: int
     score_threshold: float | None = None
@@ -32,7 +32,7 @@ class MemoryQueryStartedEvent(MemoryBaseEvent):
 class MemoryQueryCompletedEvent(MemoryBaseEvent):
     """Event emitted when a memory query is completed successfully"""
 
-    type: str = "memory_query_completed"
+    type: Literal["memory_query_completed"] = "memory_query_completed"
     query: str
     results: Any
     limit: int
@@ -43,7 +43,7 @@ class MemoryQueryCompletedEvent(MemoryBaseEvent):
 class MemoryQueryFailedEvent(MemoryBaseEvent):
     """Event emitted when a memory query fails"""
 
-    type: str = "memory_query_failed"
+    type: Literal["memory_query_failed"] = "memory_query_failed"
     query: str
     limit: int
     score_threshold: float | None = None
@@ -53,7 +53,7 @@ class MemoryQueryFailedEvent(MemoryBaseEvent):
 class MemorySaveStartedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation is started"""
 
-    type: str = "memory_save_started"
+    type: Literal["memory_save_started"] = "memory_save_started"
     value: str | None = None
     metadata: dict[str, Any] | None = None
     agent_role: str | None = None
@@ -62,7 +62,7 @@ class MemorySaveStartedEvent(MemoryBaseEvent):
 class MemorySaveCompletedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation is completed successfully"""
 
-    type: str = "memory_save_completed"
+    type: Literal["memory_save_completed"] = "memory_save_completed"
     value: str
     metadata: dict[str, Any] | None = None
     agent_role: str | None = None
@@ -72,7 +72,7 @@ class MemorySaveCompletedEvent(MemoryBaseEvent):
 class MemorySaveFailedEvent(MemoryBaseEvent):
     """Event emitted when a memory save operation fails"""
 
-    type: str = "memory_save_failed"
+    type: Literal["memory_save_failed"] = "memory_save_failed"
     value: str | None = None
     metadata: dict[str, Any] | None = None
     agent_role: str | None = None
@@ -82,14 +82,14 @@ class MemorySaveFailedEvent(MemoryBaseEvent):
 class MemoryRetrievalStartedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt starts"""
 
-    type: str = "memory_retrieval_started"
+    type: Literal["memory_retrieval_started"] = "memory_retrieval_started"
     task_id: str | None = None
 
 
 class MemoryRetrievalCompletedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt completes successfully"""
 
-    type: str = "memory_retrieval_completed"
+    type: Literal["memory_retrieval_completed"] = "memory_retrieval_completed"
     task_id: str | None = None
     memory_content: str
     retrieval_time_ms: float
@@ -98,6 +98,6 @@ class MemoryRetrievalCompletedEvent(MemoryBaseEvent):
 class MemoryRetrievalFailedEvent(MemoryBaseEvent):
     """Event emitted when memory retrieval for a task prompt fails."""
 
-    type: str = "memory_retrieval_failed"
+    type: Literal["memory_retrieval_failed"] = "memory_retrieval_failed"
     task_id: str | None = None
     error: str

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

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Literal
 
 from crewai.events.base_events import BaseEvent
 
@@ -24,7 +24,7 @@ class ReasoningEvent(BaseEvent):
 class AgentReasoningStartedEvent(ReasoningEvent):
     """Event emitted when an agent starts reasoning about a task."""
 
-    type: str = "agent_reasoning_started"
+    type: Literal["agent_reasoning_started"] = "agent_reasoning_started"
     agent_role: str
     task_id: str
 
@@ -32,7 +32,7 @@ class AgentReasoningStartedEvent(ReasoningEvent):
 class AgentReasoningCompletedEvent(ReasoningEvent):
     """Event emitted when an agent finishes its reasoning process."""
 
-    type: str = "agent_reasoning_completed"
+    type: Literal["agent_reasoning_completed"] = "agent_reasoning_completed"
     agent_role: str
     task_id: str
     plan: str
@@ -42,7 +42,7 @@ class AgentReasoningCompletedEvent(ReasoningEvent):
 class AgentReasoningFailedEvent(ReasoningEvent):
     """Event emitted when the reasoning process fails."""
 
-    type: str = "agent_reasoning_failed"
+    type: Literal["agent_reasoning_failed"] = "agent_reasoning_failed"
     agent_role: str
     task_id: str
     error: str

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

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Literal
 
 from crewai.events.base_events import BaseEvent
 from crewai.tasks.task_output import TaskOutput
@@ -7,7 +7,7 @@ from crewai.tasks.task_output import TaskOutput
 class TaskStartedEvent(BaseEvent):
     """Event emitted when a task starts"""
 
-    type: str = "task_started"
+    type: Literal["task_started"] = "task_started"
     context: str | None
     task: Any | None = None
 
@@ -28,7 +28,7 @@ class TaskCompletedEvent(BaseEvent):
     """Event emitted when a task completes"""
 
     output: TaskOutput
-    type: str = "task_completed"
+    type: Literal["task_completed"] = "task_completed"
     task: Any | None = None
 
     def __init__(self, **data):
@@ -48,7 +48,7 @@ class TaskFailedEvent(BaseEvent):
     """Event emitted when a task fails"""
 
     error: str
-    type: str = "task_failed"
+    type: Literal["task_failed"] = "task_failed"
     task: Any | None = None
 
     def __init__(self, **data):
@@ -67,7 +67,7 @@ class TaskFailedEvent(BaseEvent):
 class TaskEvaluationEvent(BaseEvent):
     """Event emitted when a task evaluation is completed"""
 
-    type: str = "task_evaluation"
+    type: Literal["task_evaluation"] = "task_evaluation"
     evaluation_type: str
     task: Any | None = None
 

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

@@ -1,6 +1,6 @@
 from collections.abc import Callable
 from datetime import datetime
-from typing import Any
+from typing import Any, Literal
 
 from pydantic import ConfigDict
 
@@ -55,7 +55,7 @@ class ToolUsageEvent(BaseEvent):
 class ToolUsageStartedEvent(ToolUsageEvent):
     """Event emitted when a tool execution is started"""
 
-    type: str = "tool_usage_started"
+    type: Literal["tool_usage_started"] = "tool_usage_started"
 
 
 class ToolUsageFinishedEvent(ToolUsageEvent):
@@ -65,35 +65,35 @@ class ToolUsageFinishedEvent(ToolUsageEvent):
     finished_at: datetime
     from_cache: bool = False
     output: Any
-    type: str = "tool_usage_finished"
+    type: Literal["tool_usage_finished"] = "tool_usage_finished"
 
 
 class ToolUsageErrorEvent(ToolUsageEvent):
     """Event emitted when a tool execution encounters an error"""
 
     error: Any
-    type: str = "tool_usage_error"
+    type: Literal["tool_usage_error"] = "tool_usage_error"
 
 
 class ToolValidateInputErrorEvent(ToolUsageEvent):
     """Event emitted when a tool input validation encounters an error"""
 
     error: Any
-    type: str = "tool_validate_input_error"
+    type: Literal["tool_validate_input_error"] = "tool_validate_input_error"
 
 
 class ToolSelectionErrorEvent(ToolUsageEvent):
     """Event emitted when a tool selection encounters an error"""
 
     error: Any
-    type: str = "tool_selection_error"
+    type: Literal["tool_selection_error"] = "tool_selection_error"
 
 
 class ToolExecutionErrorEvent(BaseEvent):
     """Event emitted when a tool execution encounters an error"""
 
     error: Any
-    type: str = "tool_execution_error"
+    type: Literal["tool_execution_error"] = "tool_execution_error"
     tool_name: str
     tool_args: dict[str, Any]
     tool_class: Callable

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -341,7 +341,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             messages=list(self.state.messages),
             llm=self.llm,
             callbacks=self.callbacks,
-            verbose=self.agent.verbose,
         )
 
         self.state.current_answer = formatted_answer
@@ -367,7 +366,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 from_agent=self.agent,
                 response_model=None,
                 executor_context=self,
-                verbose=self.agent.verbose,
             )
 
             # Parse the LLM response
@@ -403,7 +401,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 return "context_error"
             if e.__class__.__module__.startswith("litellm"):
                 raise e
-            handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+            handle_unknown_error(self._printer, e)
             raise
 
     @listen("continue_reasoning_native")
@@ -438,7 +436,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 from_agent=self.agent,
                 response_model=None,
                 executor_context=self,
-                verbose=self.agent.verbose,
             )
 
             # Check if the response is a list of tool calls
@@ -477,7 +474,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 return "context_error"
             if e.__class__.__module__.startswith("litellm"):
                 raise e
-            handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+            handle_unknown_error(self._printer, e)
             raise
 
     @router(call_llm_and_parse)
@@ -673,10 +670,10 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
 
             track_delegation_if_needed(func_name, args_dict, self.task)
 
-            structured_tool: CrewStructuredTool | None = None
-            for structured in self.tools or []:
-                if sanitize_tool_name(structured.name) == func_name:
-                    structured_tool = structured
+            structured_tool = None
+            for tool in self.tools or []:
+                if sanitize_tool_name(tool.name) == func_name:
+                    structured_tool = tool
                     break
 
             hook_blocked = False
@@ -696,11 +693,10 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                         hook_blocked = True
                         break
             except Exception as hook_error:
-                if self.agent.verbose:
-                    self._printer.print(
-                        content=f"Error in before_tool_call hook: {hook_error}",
-                        color="red",
-                    )
+                self._printer.print(
+                    content=f"Error in before_tool_call hook: {hook_error}",
+                    color="red",
+                )
 
             if hook_blocked:
                 result = f"Tool execution blocked by hook. Tool: {func_name}"
@@ -762,16 +758,15 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             after_hooks = get_after_tool_call_hooks()
             try:
                 for after_hook in after_hooks:
-                    after_hook_result = after_hook(after_hook_context)
-                    if after_hook_result is not None:
-                        result = after_hook_result
+                    hook_result = after_hook(after_hook_context)
+                    if hook_result is not None:
+                        result = hook_result
                         after_hook_context.tool_result = result
             except Exception as hook_error:
-                if self.agent.verbose:
-                    self._printer.print(
-                        content=f"Error in after_tool_call hook: {hook_error}",
-                        color="red",
-                    )
+                self._printer.print(
+                    content=f"Error in after_tool_call hook: {hook_error}",
+                    color="red",
+                )
 
             # Emit tool usage finished event
             crewai_event_bus.emit(
@@ -819,6 +814,15 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 self.state.is_finished = True
                 return "tool_result_is_final"
 
+        # Add reflection prompt once after all tools in the batch
+        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
+
+        reasoning_message: LLMMessage = {
+            "role": "user",
+            "content": reasoning_prompt,
+        }
+        self.state.messages.append(reasoning_message)
+
         return "native_tool_completed"
 
     def _extract_tool_name(self, tool_call: Any) -> str:
@@ -907,7 +911,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             iterations=self.state.iterations,
             log_error_after=self.log_error_after,
             printer=self._printer,
-            verbose=self.agent.verbose,
         )
 
         if formatted_answer:
@@ -927,7 +930,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             llm=self.llm,
             callbacks=self.callbacks,
             i18n=self._i18n,
-            verbose=self.agent.verbose,
         )
 
         self.state.iterations += 1
@@ -1019,7 +1021,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             self._console.print(fail_text)
             raise
         except Exception as e:
-            handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+            handle_unknown_error(self._printer, e)
             raise
         finally:
             self._is_executing = False
@@ -1104,7 +1106,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             self._console.print(fail_text)
             raise
         except Exception as e:
-            handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
+            handle_unknown_error(self._printer, e)
             raise
         finally:
             self._is_executing = False

lib/crewai/src/crewai/flow/flow.py

@@ -7,7 +7,7 @@ for building event-driven workflows with conditional execution and routing.
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable, Sequence
+from collections.abc import Callable
 from concurrent.futures import Future
 import copy
 import inspect
@@ -2382,7 +2382,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         message: str,
         output: Any,
         metadata: dict[str, Any] | None = None,
-        emit: Sequence[str] | None = None,
+        emit: list[str] | None = None,
     ) -> str:
         """Request feedback from a human.
         Args:
@@ -2453,7 +2453,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
     def _collapse_to_outcome(
         self,
         feedback: str,
-        outcomes: Sequence[str],
+        outcomes: list[str],
         llm: str | BaseLLM,
     ) -> str:
         """Collapse free-form feedback to a predefined outcome using LLM.

lib/crewai/src/crewai/flow/human_feedback.py

@@ -53,7 +53,7 @@ Example (asynchronous with custom provider):
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable, Sequence
+from collections.abc import Callable
 from dataclasses import dataclass, field
 from datetime import datetime
 from functools import wraps
@@ -128,7 +128,7 @@ class HumanFeedbackConfig:
     """
 
     message: str
-    emit: Sequence[str] | None = None
+    emit: list[str] | None = None
     llm: str | BaseLLM | None = None
     default_outcome: str | None = None
     metadata: dict[str, Any] | None = None
@@ -154,7 +154,7 @@ class HumanFeedbackMethod(FlowMethod[Any, Any]):
 
 def human_feedback(
     message: str,
-    emit: Sequence[str] | None = None,
+    emit: list[str] | None = None,
     llm: str | BaseLLM | None = None,
     default_outcome: str | None = None,
     metadata: dict[str, Any] | None = None,

lib/crewai/src/crewai/flow/persistence/decorators.py

@@ -118,20 +118,17 @@ class PersistenceDecorator:
                 )
             except Exception as e:
                 error_msg = LOG_MESSAGES["save_error"].format(method_name, str(e))
-                if verbose:
-                    cls._printer.print(error_msg, color="red")
+                cls._printer.print(error_msg, color="red")
                 logger.error(error_msg)
                 raise RuntimeError(f"State persistence failed: {e!s}") from e
         except AttributeError as e:
             error_msg = LOG_MESSAGES["state_missing"]
-            if verbose:
-                cls._printer.print(error_msg, color="red")
+            cls._printer.print(error_msg, color="red")
             logger.error(error_msg)
             raise ValueError(error_msg) from e
         except (TypeError, ValueError) as e:
             error_msg = LOG_MESSAGES["id_missing"]
-            if verbose:
-                cls._printer.print(error_msg, color="red")
+            cls._printer.print(error_msg, color="red")
             logger.error(error_msg)
             raise ValueError(error_msg) from e
 

lib/crewai/src/crewai/flow/utils.py

@@ -151,9 +151,7 @@ def _unwrap_function(function: Any) -> Any:
     return function
 
 
-def get_possible_return_constants(
-    function: Any, verbose: bool = True
-) -> list[str] | None:
+def get_possible_return_constants(function: Any) -> list[str] | None:
     """Extract possible string return values from a function using AST parsing.
 
     This function analyzes the source code of a router method to identify
@@ -180,11 +178,10 @@ def get_possible_return_constants(
         # Can't get source code
         return None
     except Exception as e:
-        if verbose:
-            _printer.print(
-                f"Error retrieving source code for function {function.__name__}: {e}",
-                color="red",
-            )
+        _printer.print(
+            f"Error retrieving source code for function {function.__name__}: {e}",
+            color="red",
+        )
         return None
 
     try:
@@ -193,28 +190,25 @@ def get_possible_return_constants(
         # Parse the source code into an AST
         code_ast = ast.parse(source)
     except IndentationError as e:
-        if verbose:
-            _printer.print(
-                f"IndentationError while parsing source code of {function.__name__}: {e}",
-                color="red",
-            )
-            _printer.print(f"Source code:\n{source}", color="yellow")
+        _printer.print(
+            f"IndentationError while parsing source code of {function.__name__}: {e}",
+            color="red",
+        )
+        _printer.print(f"Source code:\n{source}", color="yellow")
         return None
     except SyntaxError as e:
-        if verbose:
-            _printer.print(
-                f"SyntaxError while parsing source code of {function.__name__}: {e}",
-                color="red",
-            )
-            _printer.print(f"Source code:\n{source}", color="yellow")
+        _printer.print(
+            f"SyntaxError while parsing source code of {function.__name__}: {e}",
+            color="red",
+        )
+        _printer.print(f"Source code:\n{source}", color="yellow")
         return None
     except Exception as e:
-        if verbose:
-            _printer.print(
-                f"Unexpected error while parsing source code of {function.__name__}: {e}",
-                color="red",
-            )
-            _printer.print(f"Source code:\n{source}", color="yellow")
+        _printer.print(
+            f"Unexpected error while parsing source code of {function.__name__}: {e}",
+            color="red",
+        )
+        _printer.print(f"Source code:\n{source}", color="yellow")
         return None
 
     return_values: set[str] = set()
@@ -394,17 +388,15 @@ def get_possible_return_constants(
 
                 StateAttributeVisitor().visit(class_ast)
             except Exception as e:
-                if verbose:
-                    _printer.print(
-                        f"Could not analyze class context for {function.__name__}: {e}",
-                        color="yellow",
-                    )
+                _printer.print(
+                    f"Could not analyze class context for {function.__name__}: {e}",
+                    color="yellow",
+                )
     except Exception as e:
-        if verbose:
-            _printer.print(
-                f"Could not introspect class for {function.__name__}: {e}",
-                color="yellow",
-            )
+        _printer.print(
+            f"Could not introspect class for {function.__name__}: {e}",
+            color="yellow",
+        )
 
     VariableAssignmentVisitor().visit(code_ast)
     ReturnVisitor().visit(code_ast)

lib/crewai/src/crewai/lite_agent.py

@@ -72,13 +72,13 @@ from crewai.utilities.agent_utils import (
 from crewai.utilities.converter import (
     Converter,
     ConverterError,
+    generate_model_description,
 )
 from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
 from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import Printer
-from crewai.utilities.pydantic_schema_utils import generate_model_description
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
 from crewai.utilities.types import LLMMessage
@@ -344,12 +344,11 @@ class LiteAgent(FlowTrackable, BaseModel):
             )
 
         except Exception as e:
-            if self.verbose:
-                self._printer.print(
-                    content="Agent failed to reach a final answer. This is likely a bug - please report it.",
-                    color="red",
-                )
-            handle_unknown_error(self._printer, e, verbose=self.verbose)
+            self._printer.print(
+                content="Agent failed to reach a final answer. This is likely a bug - please report it.",
+                color="red",
+            )
+            handle_unknown_error(self._printer, e)
             # Emit error event
             crewai_event_bus.emit(
                 self,
@@ -397,11 +396,10 @@ class LiteAgent(FlowTrackable, BaseModel):
                 if isinstance(result, BaseModel):
                     formatted_result = result
             except ConverterError as e:
-                if self.verbose:
-                    self._printer.print(
-                        content=f"Failed to parse output into response format after retries: {e.message}",
-                        color="yellow",
-                    )
+                self._printer.print(
+                    content=f"Failed to parse output into response format after retries: {e.message}",
+                    color="yellow",
+                )
 
         # Calculate token usage metrics
         if isinstance(self.llm, BaseLLM):
@@ -607,7 +605,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                         messages=self._messages,
                         llm=cast(LLM, self.llm),
                         callbacks=self._callbacks,
-                        verbose=self.verbose,
                     )
 
                 enforce_rpm_limit(self.request_within_rpm_limit)
@@ -620,7 +617,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                         printer=self._printer,
                         from_agent=self,
                         executor_context=self,
-                        verbose=self.verbose,
                     )
 
                 except Exception as e:
@@ -650,18 +646,16 @@ class LiteAgent(FlowTrackable, BaseModel):
 
                 self._append_message(formatted_answer.text, role="assistant")
             except OutputParserError as e:  # noqa: PERF203
-                if self.verbose:
-                    self._printer.print(
-                        content="Failed to parse LLM output. Retrying...",
-                        color="yellow",
-                    )
+                self._printer.print(
+                    content="Failed to parse LLM output. Retrying...",
+                    color="yellow",
+                )
                 formatted_answer = handle_output_parser_exception(
                     e=e,
                     messages=self._messages,
                     iterations=self._iterations,
                     log_error_after=3,
                     printer=self._printer,
-                    verbose=self.verbose,
                 )
 
             except Exception as e:
@@ -676,10 +670,9 @@ class LiteAgent(FlowTrackable, BaseModel):
                         llm=cast(LLM, self.llm),
                         callbacks=self._callbacks,
                         i18n=self.i18n,
-                        verbose=self.verbose,
                     )
                     continue
-                handle_unknown_error(self._printer, e, verbose=self.verbose)
+                handle_unknown_error(self._printer, e)
                 raise e
 
             finally:

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

@@ -497,7 +497,7 @@ class BaseLLM(ABC):
                 from_agent=from_agent,
             )
 
-            return str(result) if not isinstance(result, str) else result
+            return result
 
         except Exception as e:
             error_msg = f"Error executing function '{function_name}': {e!s}"
@@ -620,13 +620,11 @@ class BaseLLM(ABC):
         try:
             # Try to parse as JSON first
             if response.strip().startswith("{") or response.strip().startswith("["):
-                data = json.loads(response)
-                return response_format.model_validate(data)
+                return response_format.model_validate_json(response)
 
             json_match = _JSON_EXTRACTION_PATTERN.search(response)
             if json_match:
-                data = json.loads(json_match.group())
-                return response_format.model_validate(data)
+                return response_format.model_validate_json(json_match.group())
 
             raise ValueError("No JSON found in response")
 
@@ -737,25 +735,22 @@ class BaseLLM(ABC):
             task=None,
             crew=None,
         )
-        verbose = getattr(from_agent, "verbose", True) if from_agent else True
         printer = Printer()
 
         try:
             for hook in before_hooks:
                 result = hook(hook_context)
                 if result is False:
-                    if verbose:
-                        printer.print(
-                            content="LLM call blocked by before_llm_call hook",
-                            color="yellow",
-                        )
+                    printer.print(
+                        content="LLM call blocked by before_llm_call hook",
+                        color="yellow",
+                    )
                     return False
         except Exception as e:
-            if verbose:
-                printer.print(
-                    content=f"Error in before_llm_call hook: {e}",
-                    color="yellow",
-                )
+            printer.print(
+                content=f"Error in before_llm_call hook: {e}",
+                color="yellow",
+            )
 
         return True
 
@@ -808,7 +803,6 @@ class BaseLLM(ABC):
             crew=None,
             response=response,
         )
-        verbose = getattr(from_agent, "verbose", True) if from_agent else True
         printer = Printer()
         modified_response = response
 
@@ -819,10 +813,9 @@ class BaseLLM(ABC):
                     modified_response = result
                     hook_context.response = modified_response
         except Exception as e:
-            if verbose:
-                printer.print(
-                    content=f"Error in after_llm_call hook: {e}",
-                    color="yellow",
-                )
+            printer.print(
+                content=f"Error in after_llm_call hook: {e}",
+                color="yellow",
+            )
 
         return modified_response

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

@@ -23,7 +23,7 @@ if TYPE_CHECKING:
 try:
     from anthropic import Anthropic, AsyncAnthropic, transform_schema
     from anthropic.types import Message, TextBlock, ThinkingBlock, ToolUseBlock
-    from anthropic.types.beta import BetaMessage, BetaTextBlock
+    from anthropic.types.beta import BetaMessage
     import httpx
 except ImportError:
     raise ImportError(
@@ -337,7 +337,6 @@ class AnthropicCompletion(BaseLLM):
             available_functions: Available functions for tool calling
             from_task: Task that initiated the call
             from_agent: Agent that initiated the call
-            response_model: Optional response model.
 
         Returns:
             Chat completion response or tool call result
@@ -678,31 +677,31 @@ class AnthropicCompletion(BaseLLM):
         if _is_pydantic_model_class(response_model) and response.content:
             if use_native_structured_output:
                 for block in response.content:
-                    if isinstance(block, (TextBlock, BetaTextBlock)):
-                        structured_data = response_model.model_validate_json(block.text)
+                    if isinstance(block, TextBlock):
+                        structured_json = block.text
                         self._emit_call_completed_event(
-                            response=structured_data.model_dump_json(),
+                            response=structured_json,
                             call_type=LLMCallType.LLM_CALL,
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
                         )
-                        return structured_data
+                        return structured_json
             else:
                 for block in response.content:
                     if (
                         isinstance(block, ToolUseBlock)
                         and block.name == "structured_output"
                     ):
-                        structured_data = response_model.model_validate(block.input)
+                        structured_json = json.dumps(block.input)
                         self._emit_call_completed_event(
-                            response=structured_data.model_dump_json(),
+                            response=structured_json,
                             call_type=LLMCallType.LLM_CALL,
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
                         )
-                        return structured_data
+                        return structured_json
 
         # Check if Claude wants to use tools
         if response.content:
@@ -898,29 +897,28 @@ class AnthropicCompletion(BaseLLM):
 
         if _is_pydantic_model_class(response_model):
             if use_native_structured_output:
-                structured_data = response_model.model_validate_json(full_response)
                 self._emit_call_completed_event(
-                    response=structured_data.model_dump_json(),
+                    response=full_response,
                     call_type=LLMCallType.LLM_CALL,
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
                 )
-                return structured_data
+                return full_response
             for block in final_message.content:
                 if (
                     isinstance(block, ToolUseBlock)
                     and block.name == "structured_output"
                 ):
-                    structured_data = response_model.model_validate(block.input)
+                    structured_json = json.dumps(block.input)
                     self._emit_call_completed_event(
-                        response=structured_data.model_dump_json(),
+                        response=structured_json,
                         call_type=LLMCallType.LLM_CALL,
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
                     )
-                    return structured_data
+                    return structured_json
 
         if final_message.content:
             tool_uses = [
@@ -1168,31 +1166,31 @@ class AnthropicCompletion(BaseLLM):
         if _is_pydantic_model_class(response_model) and response.content:
             if use_native_structured_output:
                 for block in response.content:
-                    if isinstance(block, (TextBlock, BetaTextBlock)):
-                        structured_data = response_model.model_validate_json(block.text)
+                    if isinstance(block, TextBlock):
+                        structured_json = block.text
                         self._emit_call_completed_event(
-                            response=structured_data.model_dump_json(),
+                            response=structured_json,
                             call_type=LLMCallType.LLM_CALL,
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
                         )
-                        return structured_data
+                        return structured_json
             else:
                 for block in response.content:
                     if (
                         isinstance(block, ToolUseBlock)
                         and block.name == "structured_output"
                     ):
-                        structured_data = response_model.model_validate(block.input)
+                        structured_json = json.dumps(block.input)
                         self._emit_call_completed_event(
-                            response=structured_data.model_dump_json(),
+                            response=structured_json,
                             call_type=LLMCallType.LLM_CALL,
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
                         )
-                        return structured_data
+                        return structured_json
 
         if response.content:
             tool_uses = [
@@ -1364,29 +1362,28 @@ class AnthropicCompletion(BaseLLM):
 
         if _is_pydantic_model_class(response_model):
             if use_native_structured_output:
-                structured_data = response_model.model_validate_json(full_response)
                 self._emit_call_completed_event(
-                    response=structured_data.model_dump_json(),
+                    response=full_response,
                     call_type=LLMCallType.LLM_CALL,
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
                 )
-                return structured_data
+                return full_response
             for block in final_message.content:
                 if (
                     isinstance(block, ToolUseBlock)
                     and block.name == "structured_output"
                 ):
-                    structured_data = response_model.model_validate(block.input)
+                    structured_json = json.dumps(block.input)
                     self._emit_call_completed_event(
-                        response=structured_data.model_dump_json(),
+                        response=structured_json,
                         call_type=LLMCallType.LLM_CALL,
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
                     )
-                    return structured_data
+                    return structured_json
 
         if final_message.content:
             tool_uses = [

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

@@ -557,7 +557,7 @@ class AzureCompletion(BaseLLM):
         params: AzureCompletionParams,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-    ) -> BaseModel:
+    ) -> str:
         """Validate content against response model and emit completion event.
 
         Args:
@@ -568,23 +568,24 @@ class AzureCompletion(BaseLLM):
             from_agent: Agent that initiated the call
 
         Returns:
-            Validated Pydantic model instance
+            Validated and serialized JSON string
 
         Raises:
             ValueError: If validation fails
         """
         try:
             structured_data = response_model.model_validate_json(content)
+            structured_json = structured_data.model_dump_json()
 
             self._emit_call_completed_event(
-                response=structured_data.model_dump_json(),
+                response=structured_json,
                 call_type=LLMCallType.LLM_CALL,
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
             )
 
-            return structured_data
+            return structured_json
         except Exception as e:
             error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
             logging.error(error_msg)

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

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from collections.abc import Mapping, Sequence
+from collections.abc import Sequence
 from contextlib import AsyncExitStack
 import json
 import logging
@@ -538,7 +538,7 @@ class BedrockCompletion(BaseLLM):
         self,
         messages: list[LLMMessage],
         body: BedrockConverseRequestBody,
-        available_functions: Mapping[str, Any] | None = None,
+        available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
@@ -1009,7 +1009,7 @@ class BedrockCompletion(BaseLLM):
         self,
         messages: list[LLMMessage],
         body: BedrockConverseRequestBody,
-        available_functions: Mapping[str, Any] | None = None,
+        available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,

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

@@ -132,9 +132,6 @@ class GeminiCompletion(BaseLLM):
         self.supports_tools = bool(
             version_match and float(version_match.group(1)) >= 1.5
         )
-        self.is_gemini_2_0 = bool(
-            version_match and float(version_match.group(1)) >= 2.0
-        )
 
     @property
     def stop(self) -> list[str]:
@@ -442,11 +439,6 @@ class GeminiCompletion(BaseLLM):
 
         Returns:
             GenerateContentConfig object for Gemini API
-
-        Note:
-            Structured output support varies by model version:
-            - Gemini 1.5 and earlier: Uses response_schema (Pydantic model)
-            - Gemini 2.0+: Uses response_json_schema (JSON Schema) with propertyOrdering
         """
         self.tools = tools
         config_params: dict[str, Any] = {}
@@ -474,13 +466,9 @@ class GeminiCompletion(BaseLLM):
         if response_model:
             config_params["response_mime_type"] = "application/json"
             schema_output = generate_model_description(response_model)
-            schema = schema_output.get("json_schema", {}).get("schema", {})
-
-            if self.is_gemini_2_0:
-                schema = self._add_property_ordering(schema)
-                config_params["response_json_schema"] = schema
-            else:
-                config_params["response_schema"] = response_model
+            config_params["response_schema"] = schema_output.get("json_schema", {}).get(
+                "schema", {}
+            )
 
         # Handle tools for supported models
         if tools and self.supports_tools:
@@ -644,7 +632,7 @@ class GeminiCompletion(BaseLLM):
         messages_for_event: list[LLMMessage],
         from_task: Any | None = None,
         from_agent: Any | None = None,
-    ) -> BaseModel:
+    ) -> str:
         """Validate content against response model and emit completion event.
 
         Args:
@@ -655,23 +643,24 @@ class GeminiCompletion(BaseLLM):
             from_agent: Agent that initiated the call
 
         Returns:
-            Validated Pydantic model instance
+            Validated and serialized JSON string
 
         Raises:
             ValueError: If validation fails
         """
         try:
             structured_data = response_model.model_validate_json(content)
+            structured_json = structured_data.model_dump_json()
 
             self._emit_call_completed_event(
-                response=structured_data.model_dump_json(),
+                response=structured_json,
                 call_type=LLMCallType.LLM_CALL,
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=messages_for_event,
             )
 
-            return structured_data
+            return structured_json
         except Exception as e:
             error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
             logging.error(error_msg)
@@ -684,7 +673,7 @@ class GeminiCompletion(BaseLLM):
         response_model: type[BaseModel] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-    ) -> str | BaseModel:
+    ) -> str:
         """Finalize completion response with validation and event emission.
 
         Args:
@@ -695,7 +684,7 @@ class GeminiCompletion(BaseLLM):
             from_agent: Agent that initiated the call
 
         Returns:
-            Final response content after processing (str or Pydantic model if response_model provided)
+            Final response content after processing
         """
         messages_for_event = self._convert_contents_to_dict(contents)
 
@@ -881,7 +870,7 @@ class GeminiCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | BaseModel | list[dict[str, Any]]:
+    ) -> str | list[dict[str, Any]]:
         """Finalize streaming response with usage tracking, function execution, and events.
 
         Args:
@@ -1001,7 +990,7 @@ class GeminiCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | BaseModel | list[dict[str, Any]] | Any:
+    ) -> str | Any:
         """Handle streaming content generation."""
         full_response = ""
         function_calls: dict[int, dict[str, Any]] = {}
@@ -1201,36 +1190,6 @@ class GeminiCompletion(BaseLLM):
 
         return "".join(text_parts)
 
-    @staticmethod
-    def _add_property_ordering(schema: dict[str, Any]) -> dict[str, Any]:
-        """Add propertyOrdering to JSON schema for Gemini 2.0 compatibility.
-
-        Gemini 2.0 models require an explicit propertyOrdering list to define
-        the preferred structure of JSON objects. This recursively adds
-        propertyOrdering to all objects in the schema.
-
-        Args:
-            schema: JSON schema dictionary.
-
-        Returns:
-            Modified schema with propertyOrdering added to all objects.
-        """
-        if isinstance(schema, dict):
-            if schema.get("type") == "object" and "properties" in schema:
-                properties = schema["properties"]
-                if properties and "propertyOrdering" not in schema:
-                    schema["propertyOrdering"] = list(properties.keys())
-
-            for value in schema.values():
-                if isinstance(value, dict):
-                    GeminiCompletion._add_property_ordering(value)
-                elif isinstance(value, list):
-                    for item in value:
-                        if isinstance(item, dict):
-                            GeminiCompletion._add_property_ordering(item)
-
-        return schema
-
     @staticmethod
     def _convert_contents_to_dict(
         contents: list[types.Content],

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

@@ -1570,14 +1570,15 @@ class OpenAICompletion(BaseLLM):
 
                 parsed_object = parsed_response.choices[0].message.parsed
                 if parsed_object:
+                    structured_json = parsed_object.model_dump_json()
                     self._emit_call_completed_event(
-                        response=parsed_object.model_dump_json(),
+                        response=structured_json,
                         call_type=LLMCallType.LLM_CALL,
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
                     )
-                    return parsed_object
+                    return structured_json
 
             response: ChatCompletion = self.client.chat.completions.create(**params)
 
@@ -1691,7 +1692,7 @@ class OpenAICompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | BaseModel:
+    ) -> str:
         """Handle streaming chat completion."""
         full_response = ""
         tool_calls: dict[int, dict[str, Any]] = {}
@@ -1727,14 +1728,15 @@ class OpenAICompletion(BaseLLM):
                     if final_completion.choices:
                         parsed_result = final_completion.choices[0].message.parsed
                         if parsed_result:
+                            structured_json = parsed_result.model_dump_json()
                             self._emit_call_completed_event(
-                                response=parsed_result.model_dump_json(),
+                                response=structured_json,
                                 call_type=LLMCallType.LLM_CALL,
                                 from_task=from_task,
                                 from_agent=from_agent,
                                 messages=params["messages"],
                             )
-                            return parsed_result
+                            return structured_json
 
             logging.error("Failed to get parsed result from stream")
             return ""
@@ -1885,14 +1887,15 @@ class OpenAICompletion(BaseLLM):
 
                 parsed_object = parsed_response.choices[0].message.parsed
                 if parsed_object:
+                    structured_json = parsed_object.model_dump_json()
                     self._emit_call_completed_event(
-                        response=parsed_object.model_dump_json(),
+                        response=structured_json,
                         call_type=LLMCallType.LLM_CALL,
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
                     )
-                    return parsed_object
+                    return structured_json
 
             response: ChatCompletion = await self.async_client.chat.completions.create(
                 **params
@@ -2003,7 +2006,7 @@ class OpenAICompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | BaseModel:
+    ) -> str:
         """Handle async streaming chat completion."""
         full_response = ""
         tool_calls: dict[int, dict[str, Any]] = {}
@@ -2041,16 +2044,17 @@ class OpenAICompletion(BaseLLM):
 
             try:
                 parsed_object = response_model.model_validate_json(accumulated_content)
+                structured_json = parsed_object.model_dump_json()
 
                 self._emit_call_completed_event(
-                    response=parsed_object.model_dump_json(),
+                    response=structured_json,
                     call_type=LLMCallType.LLM_CALL,
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
                 )
 
-                return parsed_object
+                return structured_json
             except Exception as e:
                 logging.error(f"Failed to parse structured output from stream: {e}")
                 self._emit_call_completed_event(

lib/crewai/src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -12,17 +12,15 @@ from crewai.utilities.paths import db_storage_path
 class LTMSQLiteStorage:
     """SQLite storage class for long-term memory data."""
 
-    def __init__(self, db_path: str | None = None, verbose: bool = True) -> None:
+    def __init__(self, db_path: str | None = None) -> None:
         """Initialize the SQLite storage.
 
         Args:
             db_path: Optional path to the database file.
-            verbose: Whether to print error messages.
         """
         if db_path is None:
             db_path = str(Path(db_storage_path()) / "long_term_memory_storage.db")
         self.db_path = db_path
-        self._verbose = verbose
         self._printer: Printer = Printer()
         Path(self.db_path).parent.mkdir(parents=True, exist_ok=True)
         self._initialize_db()
@@ -46,11 +44,10 @@ class LTMSQLiteStorage:
 
                 conn.commit()
         except sqlite3.Error as e:
-            if self._verbose:
-                self._printer.print(
-                    content=f"MEMORY ERROR: An error occurred during database initialization: {e}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred during database initialization: {e}",
+                color="red",
+            )
 
     def save(
         self,
@@ -72,11 +69,10 @@ class LTMSQLiteStorage:
                 )
                 conn.commit()
         except sqlite3.Error as e:
-            if self._verbose:
-                self._printer.print(
-                    content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
+                color="red",
+            )
 
     def load(self, task_description: str, latest_n: int) -> list[dict[str, Any]] | None:
         """Queries the LTM table by task description with error handling."""
@@ -105,11 +101,10 @@ class LTMSQLiteStorage:
                     ]
 
         except sqlite3.Error as e:
-            if self._verbose:
-                self._printer.print(
-                    content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
+                color="red",
+            )
         return None
 
     def reset(self) -> None:
@@ -121,11 +116,10 @@ class LTMSQLiteStorage:
                 conn.commit()
 
         except sqlite3.Error as e:
-            if self._verbose:
-                self._printer.print(
-                    content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
+                color="red",
+            )
 
     async def asave(
         self,
@@ -153,11 +147,10 @@ class LTMSQLiteStorage:
                 )
                 await conn.commit()
         except aiosqlite.Error as e:
-            if self._verbose:
-                self._printer.print(
-                    content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
+                color="red",
+            )
 
     async def aload(
         self, task_description: str, latest_n: int
@@ -194,11 +187,10 @@ class LTMSQLiteStorage:
                         for row in rows
                     ]
         except aiosqlite.Error as e:
-            if self._verbose:
-                self._printer.print(
-                    content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
+                color="red",
+            )
         return None
 
     async def areset(self) -> None:
@@ -208,8 +200,7 @@ class LTMSQLiteStorage:
                 await conn.execute("DELETE FROM long_term_memories")
                 await conn.commit()
         except aiosqlite.Error as e:
-            if self._verbose:
-                self._printer.print(
-                    content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
-                    color="red",
-                )
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
+                color="red",
+            )

lib/crewai/src/crewai/rag/chromadb/types.py

@@ -1,6 +1,5 @@
 """Type definitions specific to ChromaDB implementation."""
 
-from collections.abc import Mapping
 from typing import Any, NamedTuple
 
 from chromadb.api import AsyncClientAPI, ClientAPI
@@ -49,7 +48,7 @@ class PreparedDocuments(NamedTuple):
 
     ids: list[str]
     texts: list[str]
-    metadatas: list[Mapping[str, str | int | float | bool]]
+    metadatas: list[dict[str, str | int | float | bool]]
 
 
 class ExtractedSearchParams(NamedTuple):

lib/crewai/src/crewai/rag/chromadb/utils.py

@@ -1,6 +1,5 @@
 """Utility functions for ChromaDB client implementation."""
 
-from collections.abc import Mapping
 import hashlib
 import json
 from typing import Literal, TypeGuard, cast
@@ -66,7 +65,7 @@ def _prepare_documents_for_chromadb(
     """
     ids: list[str] = []
     texts: list[str] = []
-    metadatas: list[Mapping[str, str | int | float | bool]] = []
+    metadatas: list[dict[str, str | int | float | bool]] = []
     seen_ids: dict[str, int] = {}
 
     try:
@@ -111,7 +110,7 @@ def _prepare_documents_for_chromadb(
 
 def _create_batch_slice(
     prepared: PreparedDocuments, start_index: int, batch_size: int
-) -> tuple[list[str], list[str], list[Mapping[str, str | int | float | bool]] | None]:
+) -> tuple[list[str], list[str], list[dict[str, str | int | float | bool]] | None]:
     """Create a batch slice from prepared documents.
 
     Args:

lib/crewai/src/crewai/rag/embeddings/providers/ibm/embedding_callable.py

@@ -1,6 +1,6 @@
 """IBM WatsonX embedding function implementation."""
 
-from typing import Any, cast
+from typing import cast
 
 from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
 from typing_extensions import Unpack
@@ -15,18 +15,14 @@ _printer = Printer()
 class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
     """Embedding function for IBM WatsonX models."""
 
-    def __init__(
-        self, *, verbose: bool = True, **kwargs: Unpack[WatsonXProviderConfig]
-    ) -> None:
+    def __init__(self, **kwargs: Unpack[WatsonXProviderConfig]) -> None:
         """Initialize WatsonX embedding function.
 
         Args:
-            verbose: Whether to print error messages.
             **kwargs: Configuration parameters for WatsonX Embeddings and Credentials.
         """
         super().__init__(**kwargs)
         self._config = kwargs
-        self._verbose = verbose
 
     @staticmethod
     def name() -> str:
@@ -60,7 +56,7 @@ class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
         if isinstance(input, str):
             input = [input]
 
-        embeddings_config: dict[str, Any] = {
+        embeddings_config: dict = {
             "model_id": self._config["model_id"],
         }
         if "params" in self._config and self._config["params"] is not None:
@@ -94,7 +90,7 @@ class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
         if "credentials" in self._config and self._config["credentials"] is not None:
             embeddings_config["credentials"] = self._config["credentials"]
         else:
-            cred_config: dict[str, Any] = {}
+            cred_config: dict = {}
             if "url" in self._config and self._config["url"] is not None:
                 cred_config["url"] = self._config["url"]
             if "api_key" in self._config and self._config["api_key"] is not None:
@@ -163,6 +159,5 @@ class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]):
             embeddings = embedding.embed_documents(input)
             return cast(Embeddings, embeddings)
         except Exception as e:
-            if self._verbose:
-                _printer.print(f"Error during WatsonX embedding: {e}", color="red")
+            _printer.print(f"Error during WatsonX embedding: {e}", color="red")
             raise

lib/crewai/src/crewai/rag/embeddings/types.py

@@ -1,6 +1,8 @@
 """Type definitions for the embeddings module."""
 
-from typing import Any, Literal, TypeAlias
+from typing import Annotated, Any, Literal, TypeAlias
+
+from pydantic import Field
 
 from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
 from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
@@ -29,7 +31,7 @@ from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec
 from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec
 
 
-ProviderSpec: TypeAlias = (
+ProviderSpec: TypeAlias = Annotated[
     AzureProviderSpec
     | BedrockProviderSpec
     | CohereProviderSpec
@@ -47,8 +49,9 @@ ProviderSpec: TypeAlias = (
     | Text2VecProviderSpec
     | VertexAIProviderSpec
     | VoyageAIProviderSpec
-    | WatsonXProviderSpec
-)
+    | WatsonXProviderSpec,
+    Field(discriminator="provider"),
+]
 
 AllowedEmbeddingProviders = Literal[
     "azure",

lib/crewai/src/crewai/rag/types.py

@@ -1,6 +1,6 @@
 """Type definitions for RAG (Retrieval-Augmented Generation) systems."""
 
-from collections.abc import Callable, Mapping
+from collections.abc import Callable
 from typing import Any, TypeAlias
 
 from typing_extensions import Required, TypedDict
@@ -19,8 +19,8 @@ class BaseRecord(TypedDict, total=False):
     doc_id: str
     content: Required[str]
     metadata: (
-        Mapping[str, str | int | float | bool]
-        | list[Mapping[str, str | int | float | bool]]
+        dict[str, str | int | float | bool]
+        | list[dict[str, str | int | float | bool]]
     )
 
 

lib/crewai/src/crewai/task.py

@@ -767,11 +767,10 @@ class Task(BaseModel):
             if files:
                 supported_types: list[str] = []
                 if self.agent.llm and self.agent.llm.supports_multimodal():
-                    provider: str = str(
-                        getattr(self.agent.llm, "provider", None)
-                        or getattr(self.agent.llm, "model", "openai")
+                    provider = getattr(self.agent.llm, "provider", None) or getattr(
+                        self.agent.llm, "model", "openai"
                     )
-                    api: str | None = getattr(self.agent.llm, "api", None)
+                    api = getattr(self.agent.llm, "api", None)
                     supported_types = get_supported_content_types(provider, api)
 
                 def is_auto_injected(content_type: str) -> bool:
@@ -888,11 +887,10 @@ Follow these guidelines:
             try:
                 crew_chat_messages = json.loads(crew_chat_messages_json)
             except json.JSONDecodeError as e:
-                if self.agent and self.agent.verbose:
-                    _printer.print(
-                        f"An error occurred while parsing crew chat messages: {e}",
-                        color="red",
-                    )
+                _printer.print(
+                    f"An error occurred while parsing crew chat messages: {e}",
+                    color="red",
+                )
                 raise
 
             conversation_history = "\n".join(
@@ -1134,12 +1132,11 @@ Follow these guidelines:
                 guardrail_result_error=guardrail_result.error,
                 task_output=task_output.raw,
             )
-            if agent and agent.verbose:
-                printer = Printer()
-                printer.print(
-                    content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
-                    color="yellow",
-                )
+            printer = Printer()
+            printer.print(
+                content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
+                color="yellow",
+            )
 
             # Regenerate output from agent
             result = agent.execute_task(
@@ -1232,12 +1229,11 @@ Follow these guidelines:
                 guardrail_result_error=guardrail_result.error,
                 task_output=task_output.raw,
             )
-            if agent and agent.verbose:
-                printer = Printer()
-                printer.print(
-                    content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
-                    color="yellow",
-                )
+            printer = Printer()
+            printer.print(
+                content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
+                color="yellow",
+            )
 
             result = await agent.aexecute_task(
                 task=self,

lib/crewai/src/crewai/tools/structured_tool.py

@@ -200,9 +200,12 @@ class CrewStructuredTool:
         """
         if isinstance(raw_args, str):
             try:
-                raw_args = json.loads(raw_args)
+                validated_args = self.args_schema.model_validate_json(raw_args)
+                return validated_args.model_dump()
             except json.JSONDecodeError as e:
                 raise ValueError(f"Failed to parse arguments as JSON: {e}") from e
+            except Exception as e:
+                raise ValueError(f"Arguments validation failed: {e}") from e
 
         try:
             validated_args = self.args_schema.model_validate(raw_args)

lib/crewai/src/crewai/tools/tool_usage.py

@@ -384,8 +384,6 @@ class ToolUsage:
                         if (
                             hasattr(available_tool, "max_usage_count")
                             and available_tool.max_usage_count is not None
-                            and self.agent
-                            and self.agent.verbose
                         ):
                             self._printer.print(
                                 content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
@@ -398,8 +396,6 @@ class ToolUsage:
                         if (
                             hasattr(available_tool, "max_usage_count")
                             and available_tool.max_usage_count is not None
-                            and self.agent
-                            and self.agent.verbose
                         ):
                             self._printer.print(
                                 content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
@@ -614,8 +610,6 @@ class ToolUsage:
                         if (
                             hasattr(available_tool, "max_usage_count")
                             and available_tool.max_usage_count is not None
-                            and self.agent
-                            and self.agent.verbose
                         ):
                             self._printer.print(
                                 content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
@@ -628,8 +622,6 @@ class ToolUsage:
                         if (
                             hasattr(available_tool, "max_usage_count")
                             and available_tool.max_usage_count is not None
-                            and self.agent
-                            and self.agent.verbose
                         ):
                             self._printer.print(
                                 content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
@@ -892,17 +884,15 @@ class ToolUsage:
         # Attempt 4: Repair JSON
         try:
             repaired_input = str(repair_json(tool_input, skip_json_loads=True))
-            if self.agent and self.agent.verbose:
-                self._printer.print(
-                    content=f"Repaired JSON: {repaired_input}", color="blue"
-                )
+            self._printer.print(
+                content=f"Repaired JSON: {repaired_input}", color="blue"
+            )
             arguments = json.loads(repaired_input)
             if isinstance(arguments, dict):
                 return arguments
         except Exception as e:
             error = f"Failed to repair JSON: {e}"
-            if self.agent and self.agent.verbose:
-                self._printer.print(content=error, color="red")
+            self._printer.print(content=error, color="red")
 
         error_message = (
             "Tool input must be a valid dictionary in JSON or Python literal format"

lib/crewai/src/crewai/translations/en.json

@@ -10,10 +10,9 @@
     "memory": "\n\n# Useful context: \n{memory}",
     "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
     "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
-    "no_tools": "",
-    "task_no_tools": "\nCurrent Task: {input}\n\nProvide your complete response:",
-    "native_tools": "",
-    "native_task": "\nCurrent Task: {input}",
+    "no_tools": "\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
+    "native_tools": "\nUse available tools to gather information and complete your task.",
+    "native_task": "\nCurrent Task: {input}\n\nThis is VERY important to you, your job depends on it!",
     "post_tool_reasoning": "Analyze the tool result. If requirements are met, provide the Final Answer. Otherwise, call the next tool. Deliver only the answer without meta-commentary.",
     "format": "Decide if you need a tool or can provide the final answer. Use one at a time.\nTo use a tool, use:\nThought: [reasoning]\nAction: [name from {tool_names}]\nAction Input: [JSON object]\n\nTo provide the final answer, use:\nThought: [reasoning]\nFinal Answer: [complete response]",
     "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable, Sequence
+from collections.abc import Callable
 import json
 import re
 from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict
@@ -98,7 +98,7 @@ def parse_tools(tools: list[BaseTool]) -> list[CrewStructuredTool]:
     return tools_list
 
 
-def get_tool_names(tools: Sequence[CrewStructuredTool | BaseTool]) -> str:
+def get_tool_names(tools: list[CrewStructuredTool | BaseTool]) -> str:
     """Get the sanitized names of the tools.
 
     Args:
@@ -111,7 +111,7 @@ def get_tool_names(tools: Sequence[CrewStructuredTool | BaseTool]) -> str:
 
 
 def render_text_description_and_args(
-    tools: Sequence[CrewStructuredTool | BaseTool],
+    tools: list[CrewStructuredTool | BaseTool],
 ) -> str:
     """Render the tool name, description, and args in plain text.
 
@@ -130,7 +130,7 @@ def render_text_description_and_args(
 
 
 def convert_tools_to_openai_schema(
-    tools: Sequence[BaseTool | CrewStructuredTool],
+    tools: list[BaseTool | CrewStructuredTool],
 ) -> tuple[list[dict[str, Any]], dict[str, Callable[..., Any]]]:
     """Convert CrewAI tools to OpenAI function calling format.
 
@@ -210,7 +210,6 @@ def handle_max_iterations_exceeded(
     messages: list[LLMMessage],
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
-    verbose: bool = True,
 ) -> AgentFinish:
     """Handles the case when the maximum number of iterations is exceeded. Performs one more LLM call to get the final answer.
 
@@ -221,16 +220,14 @@ def handle_max_iterations_exceeded(
         messages: List of messages to send to the LLM.
         llm: The LLM instance to call.
         callbacks: List of callbacks for the LLM call.
-        verbose: Whether to print output.
 
     Returns:
         AgentFinish with the final answer after exceeding max iterations.
     """
-    if verbose:
-        printer.print(
-            content="Maximum iterations reached. Requesting final answer.",
-            color="yellow",
-        )
+    printer.print(
+        content="Maximum iterations reached. Requesting final answer.",
+        color="yellow",
+    )
 
     if formatted_answer and hasattr(formatted_answer, "text"):
         assistant_message = (
@@ -248,11 +245,10 @@ def handle_max_iterations_exceeded(
     )
 
     if answer is None or answer == "":
-        if verbose:
-            printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
+        printer.print(
+            content="Received None or empty response from LLM call.",
+            color="red",
+        )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
     formatted = format_answer(answer=answer)
@@ -326,8 +322,7 @@ def get_llm_response(
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
     executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None = None,
-    verbose: bool = True,
-) -> str | BaseModel | Any:
+) -> str | Any:
     """Call the LLM and return the response, handling any invalid responses.
 
     Args:
@@ -341,11 +336,10 @@ def get_llm_response(
         from_agent: Optional agent context for the LLM call.
         response_model: Optional Pydantic model for structured outputs.
         executor_context: Optional executor context for hook invocation.
-        verbose: Whether to print output.
 
     Returns:
-        The response from the LLM as a string, Pydantic model (when response_model is provided),
-        or tool call results if native function calling is used.
+        The response from the LLM as a string, or tool call results if
+        native function calling is used.
 
     Raises:
         Exception: If an error occurs.
@@ -353,7 +347,7 @@ def get_llm_response(
     """
 
     if executor_context is not None:
-        if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
+        if not _setup_before_llm_call_hooks(executor_context, printer):
             raise ValueError("LLM call blocked by before_llm_call hook")
         messages = executor_context.messages
 
@@ -370,16 +364,13 @@ def get_llm_response(
     except Exception as e:
         raise e
     if not answer:
-        if verbose:
-            printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
+        printer.print(
+            content="Received None or empty response from LLM call.",
+            color="red",
+        )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
-    return _setup_after_llm_call_hooks(
-        executor_context, answer, printer, verbose=verbose
-    )
+    return _setup_after_llm_call_hooks(executor_context, answer, printer)
 
 
 async def aget_llm_response(
@@ -393,8 +384,7 @@ async def aget_llm_response(
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
     executor_context: CrewAgentExecutor | AgentExecutor | None = None,
-    verbose: bool = True,
-) -> str | BaseModel | Any:
+) -> str | Any:
     """Call the LLM asynchronously and return the response.
 
     Args:
@@ -410,15 +400,15 @@ async def aget_llm_response(
         executor_context: Optional executor context for hook invocation.
 
     Returns:
-        The response from the LLM as a string, Pydantic model (when response_model is provided),
-        or tool call results if native function calling is used.
+        The response from the LLM as a string, or tool call results if
+        native function calling is used.
 
     Raises:
         Exception: If an error occurs.
         ValueError: If the response is None or empty.
     """
     if executor_context is not None:
-        if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
+        if not _setup_before_llm_call_hooks(executor_context, printer):
             raise ValueError("LLM call blocked by before_llm_call hook")
         messages = executor_context.messages
 
@@ -435,16 +425,13 @@ async def aget_llm_response(
     except Exception as e:
         raise e
     if not answer:
-        if verbose:
-            printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
+        printer.print(
+            content="Received None or empty response from LLM call.",
+            color="red",
+        )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
-    return _setup_after_llm_call_hooks(
-        executor_context, answer, printer, verbose=verbose
-    )
+    return _setup_after_llm_call_hooks(executor_context, answer, printer)
 
 
 def process_llm_response(
@@ -511,19 +498,13 @@ def handle_agent_action_core(
     return formatted_answer
 
 
-def handle_unknown_error(
-    printer: Printer, exception: Exception, verbose: bool = True
-) -> None:
+def handle_unknown_error(printer: Printer, exception: Exception) -> None:
     """Handle unknown errors by informing the user.
 
     Args:
         printer: Printer instance for output
         exception: The exception that occurred
-        verbose: Whether to print output.
     """
-    if not verbose:
-        return
-
     error_message = str(exception)
 
     if "litellm" in error_message:
@@ -545,7 +526,6 @@ def handle_output_parser_exception(
     iterations: int,
     log_error_after: int = 3,
     printer: Printer | None = None,
-    verbose: bool = True,
 ) -> AgentAction:
     """Handle OutputParserError by updating messages and formatted_answer.
 
@@ -568,7 +548,7 @@ def handle_output_parser_exception(
         thought="",
     )
 
-    if verbose and iterations > log_error_after and printer:
+    if iterations > log_error_after and printer:
         printer.print(
             content=f"Error parsing LLM output, agent will retry: {e.error}",
             color="red",
@@ -598,7 +578,6 @@ def handle_context_length(
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
     i18n: I18N,
-    verbose: bool = True,
 ) -> None:
     """Handle context length exceeded by either summarizing or raising an error.
 
@@ -614,20 +593,16 @@ def handle_context_length(
         SystemExit: If context length is exceeded and user opts not to summarize
     """
     if respect_context_window:
-        if verbose:
-            printer.print(
-                content="Context length exceeded. Summarizing content to fit the model context window. Might take a while...",
-                color="yellow",
-            )
-        summarize_messages(
-            messages=messages, llm=llm, callbacks=callbacks, i18n=i18n, verbose=verbose
+        printer.print(
+            content="Context length exceeded. Summarizing content to fit the model context window. Might take a while...",
+            color="yellow",
         )
+        summarize_messages(messages=messages, llm=llm, callbacks=callbacks, i18n=i18n)
     else:
-        if verbose:
-            printer.print(
-                content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
-                color="red",
-            )
+        printer.print(
+            content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
+            color="red",
+        )
         raise SystemExit(
             "Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
         )
@@ -638,7 +613,6 @@ def summarize_messages(
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
     i18n: I18N,
-    verbose: bool = True,
 ) -> None:
     """Summarize messages to fit within context window.
 
@@ -670,11 +644,10 @@ def summarize_messages(
 
     total_groups = len(messages_groups)
     for idx, group in enumerate(messages_groups, 1):
-        if verbose:
-            Printer().print(
-                content=f"Summarizing {idx}/{total_groups}...",
-                color="yellow",
-            )
+        Printer().print(
+            content=f"Summarizing {idx}/{total_groups}...",
+            color="yellow",
+        )
 
         summarization_messages = [
             format_message_for_llm(
@@ -932,14 +905,12 @@ def extract_tool_call_info(
 def _setup_before_llm_call_hooks(
     executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
     printer: Printer,
-    verbose: bool = True,
 ) -> bool:
     """Setup and invoke before_llm_call hooks for the executor context.
 
     Args:
         executor_context: The executor context to setup the hooks for.
         printer: Printer instance for error logging.
-        verbose: Whether to print output.
 
     Returns:
         True if LLM execution should proceed, False if blocked by a hook.
@@ -954,29 +925,26 @@ def _setup_before_llm_call_hooks(
             for hook in executor_context.before_llm_call_hooks:
                 result = hook(hook_context)
                 if result is False:
-                    if verbose:
-                        printer.print(
-                            content="LLM call blocked by before_llm_call hook",
-                            color="yellow",
-                        )
+                    printer.print(
+                        content="LLM call blocked by before_llm_call hook",
+                        color="yellow",
+                    )
                     return False
         except Exception as e:
-            if verbose:
-                printer.print(
-                    content=f"Error in before_llm_call hook: {e}",
-                    color="yellow",
-                )
+            printer.print(
+                content=f"Error in before_llm_call hook: {e}",
+                color="yellow",
+            )
 
         if not isinstance(executor_context.messages, list):
-            if verbose:
-                printer.print(
-                    content=(
-                        "Warning: before_llm_call hook replaced messages with non-list. "
-                        "Restoring original messages list. Hooks should modify messages in-place, "
-                        "not replace the list (e.g., use context.messages.append() not context.messages = [])."
-                    ),
-                    color="yellow",
-                )
+            printer.print(
+                content=(
+                    "Warning: before_llm_call hook replaced messages with non-list. "
+                    "Restoring original messages list. Hooks should modify messages in-place, "
+                    "not replace the list (e.g., use context.messages.append() not context.messages = [])."
+                ),
+                color="yellow",
+            )
             if isinstance(original_messages, list):
                 executor_context.messages = original_messages
             else:
@@ -987,79 +955,49 @@ def _setup_before_llm_call_hooks(
 
 def _setup_after_llm_call_hooks(
     executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
-    answer: str | BaseModel,
+    answer: str,
     printer: Printer,
-    verbose: bool = True,
-) -> str | BaseModel:
+) -> str:
     """Setup and invoke after_llm_call hooks for the executor context.
 
     Args:
         executor_context: The executor context to setup the hooks for.
-        answer: The LLM response (string or Pydantic model).
+        answer: The LLM response string.
         printer: Printer instance for error logging.
-        verbose: Whether to print output.
 
     Returns:
-        The potentially modified response (string or Pydantic model).
+        The potentially modified response string.
     """
     if executor_context and executor_context.after_llm_call_hooks:
         from crewai.hooks.llm_hooks import LLMCallHookContext
 
         original_messages = executor_context.messages
 
-        # For Pydantic models, serialize to JSON for hooks
-        if isinstance(answer, BaseModel):
-            pydantic_answer = answer
-            hook_response: str = pydantic_answer.model_dump_json()
-            original_json: str = hook_response
-        else:
-            pydantic_answer = None
-            hook_response = str(answer)
-
-        hook_context = LLMCallHookContext(executor_context, response=hook_response)
+        hook_context = LLMCallHookContext(executor_context, response=answer)
         try:
             for hook in executor_context.after_llm_call_hooks:
                 modified_response = hook(hook_context)
                 if modified_response is not None and isinstance(modified_response, str):
-                    hook_response = modified_response
+                    answer = modified_response
 
         except Exception as e:
-            if verbose:
-                printer.print(
-                    content=f"Error in after_llm_call hook: {e}",
-                    color="yellow",
-                )
+            printer.print(
+                content=f"Error in after_llm_call hook: {e}",
+                color="yellow",
+            )
 
         if not isinstance(executor_context.messages, list):
-            if verbose:
-                printer.print(
-                    content=(
-                        "Warning: after_llm_call hook replaced messages with non-list. "
-                        "Restoring original messages list. Hooks should modify messages in-place, "
-                        "not replace the list (e.g., use context.messages.append() not context.messages = [])."
-                    ),
-                    color="yellow",
-                )
+            printer.print(
+                content=(
+                    "Warning: after_llm_call hook replaced messages with non-list. "
+                    "Restoring original messages list. Hooks should modify messages in-place, "
+                    "not replace the list (e.g., use context.messages.append() not context.messages = [])."
+                ),
+                color="yellow",
+            )
             if isinstance(original_messages, list):
                 executor_context.messages = original_messages
             else:
                 executor_context.messages = []
 
-        # If hooks modified the response, update answer accordingly
-        if pydantic_answer is not None:
-            # For Pydantic models, reparse the JSON if it was modified
-            if hook_response != original_json:
-                try:
-                    model_class: type[BaseModel] = type(pydantic_answer)
-                    answer = model_class.model_validate_json(hook_response)
-                except Exception as e:
-                    if verbose:
-                        printer.print(
-                            content=f"Warning: Hook modified response but failed to reparse as {type(pydantic_answer).__name__}: {e}. Using original model.",
-                            color="yellow",
-                        )
-        else:
-            # For string responses, use the hook-modified response
-            answer = hook_response
-
     return answer

lib/crewai/src/crewai/utilities/converter.py

@@ -62,10 +62,7 @@ class Converter(OutputConverter):
                     ],
                     response_model=self.model,
                 )
-                if isinstance(response, BaseModel):
-                    result = response
-                else:
-                    result = self.model.model_validate_json(response)
+                result = self.model.model_validate_json(response)
             else:
                 response = self.llm.call(
                     [
@@ -208,11 +205,10 @@ def convert_to_model(
         )
 
     except Exception as e:
-        if agent and getattr(agent, "verbose", True):
-            Printer().print(
-                content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
-                color="red",
-            )
+        Printer().print(
+            content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
+            color="red",
+        )
         return result
 
 
@@ -266,11 +262,10 @@ def handle_partial_json(
         except ValidationError:
             raise
         except Exception as e:
-            if agent and getattr(agent, "verbose", True):
-                Printer().print(
-                    content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
-                    color="red",
-                )
+            Printer().print(
+                content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
+                color="red",
+            )
 
     return convert_with_instructions(
         result=result,
@@ -328,11 +323,10 @@ def convert_with_instructions(
     )
 
     if isinstance(exported_result, ConverterError):
-        if agent and getattr(agent, "verbose", True):
-            Printer().print(
-                content=f"Failed to convert result to model: {exported_result}",
-                color="red",
-            )
+        Printer().print(
+            content=f"Failed to convert result to model: {exported_result}",
+            color="red",
+        )
         return result
 
     return exported_result

lib/crewai/src/crewai/utilities/prompts.py

@@ -23,13 +23,7 @@ class SystemPromptResult(StandardPromptResult):
 
 
 COMPONENTS = Literal[
-    "role_playing",
-    "tools",
-    "no_tools",
-    "native_tools",
-    "task",
-    "native_task",
-    "task_no_tools",
+    "role_playing", "tools", "no_tools", "native_tools", "task", "native_task"
 ]
 
 
@@ -80,14 +74,11 @@ class Prompts(BaseModel):
             slices.append("no_tools")
         system: str = self._build_prompt(slices)
 
-        # Determine which task slice to use:
-        task_slice: COMPONENTS
-        if self.use_native_tool_calling:
-            task_slice = "native_task"
-        elif self.has_tools:
-            task_slice = "task"
-        else:
-            task_slice = "task_no_tools"
+        # Use native_task for native tool calling (no "Thought:" prompt)
+        # Use task for ReAct pattern (includes "Thought:" prompt)
+        task_slice: COMPONENTS = (
+            "native_task" if self.use_native_tool_calling else "task"
+        )
         slices.append(task_slice)
 
         if (

lib/crewai/tests/agents/test_lite_agent.py

@@ -1004,53 +1004,3 @@ def test_prepare_kickoff_param_files_override_message_files():
 
     assert "files" in inputs
     assert inputs["files"]["same.png"] is param_file  # param takes precedence
-
-
-def test_lite_agent_verbose_false_suppresses_printer_output():
-    """Test that setting verbose=False suppresses all printer output."""
-    from crewai.agents.parser import AgentFinish
-    from crewai.types.usage_metrics import UsageMetrics
-
-    mock_llm = Mock(spec=LLM)
-    mock_llm.call.return_value = "Final Answer: Hello!"
-    mock_llm.stop = []
-    mock_llm.supports_stop_words.return_value = False
-    mock_llm.get_token_usage_summary.return_value = UsageMetrics(
-        total_tokens=100,
-        prompt_tokens=50,
-        completion_tokens=50,
-        cached_prompt_tokens=0,
-        successful_requests=1,
-    )
-
-    with pytest.warns(DeprecationWarning):
-        agent = LiteAgent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            llm=mock_llm,
-            verbose=False,
-        )
-
-    result = agent.kickoff("Say hello")
-
-    assert result is not None
-    assert isinstance(result, LiteAgentOutput)
-    # Verify the printer was never called
-    agent._printer.print = Mock()
-    # For a clean verification, patch printer before execution
-    with pytest.warns(DeprecationWarning):
-        agent2 = LiteAgent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            llm=mock_llm,
-            verbose=False,
-        )
-
-    mock_printer = Mock()
-    agent2._printer = mock_printer
-
-    agent2.kickoff("Say hello")
-
-    mock_printer.print.assert_not_called()

lib/crewai/tests/cassettes/utilities/TestRealLLMNoThoughtLeakage.test_agent_without_tools_no_thought_in_output.yaml

@@ -1,112 +0,0 @@
-interactions:
-- request:
-    body: '{"messages":[{"role":"system","content":"You are Language Detector. You
-      are an expert linguist who can identify languages.\nYour personal goal is: Detect
-      the language of text"},{"role":"user","content":"\nCurrent Task: What language
-      is this text written in: ''Hello, how are you?''\n\nThis is the expected criteria
-      for your final answer: The detected language (e.g., English, Spanish, etc.)\nyou
-      MUST return the actual complete content as the final answer, not a summary.\n\nProvide
-      your complete response:"}],"model":"gpt-4o-mini"}'
-    headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
-      accept:
-      - application/json
-      accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      authorization:
-      - AUTHORIZATION-XXX
-      connection:
-      - keep-alive
-      content-length:
-      - '530'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      x-stainless-arch:
-      - X-STAINLESS-ARCH-XXX
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - X-STAINLESS-OS-XXX
-      x-stainless-package-version:
-      - 1.83.0
-      x-stainless-read-timeout:
-      - X-STAINLESS-READ-TIMEOUT-XXX
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.13.3
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: "{\n  \"id\": \"chatcmpl-D39bkotgEapBcz1sSIXvhPhK9G7FD\",\n  \"object\":
-        \"chat.completion\",\n  \"created\": 1769644288,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-        \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-        \"assistant\",\n        \"content\": \"English\",\n        \"refusal\": null,\n
-        \       \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
-        \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 101,\n    \"completion_tokens\":
-        1,\n    \"total_tokens\": 102,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-        0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
-        {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-        0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-        \"default\",\n  \"system_fingerprint\": \"fp_3683ee3deb\"\n}\n"
-    headers:
-      CF-RAY:
-      - CF-RAY-XXX
-      Connection:
-      - keep-alive
-      Content-Type:
-      - application/json
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-      - Wed, 28 Jan 2026 23:51:28 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - SET-COOKIE-XXX
-      Strict-Transport-Security:
-      - STS-XXX
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - X-CONTENT-TYPE-XXX
-      access-control-expose-headers:
-      - ACCESS-CONTROL-XXX
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - OPENAI-ORG-XXX
-      openai-processing-ms:
-      - '279'
-      openai-project:
-      - OPENAI-PROJECT-XXX
-      openai-version:
-      - '2020-10-01'
-      x-openai-proxy-wasm:
-      - v0.1
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-      x-ratelimit-limit-tokens:
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-      x-ratelimit-remaining-requests:
-      - X-RATELIMIT-REMAINING-REQUESTS-XXX
-      x-ratelimit-remaining-tokens:
-      - X-RATELIMIT-REMAINING-TOKENS-XXX
-      x-ratelimit-reset-requests:
-      - X-RATELIMIT-RESET-REQUESTS-XXX
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-      - X-RATELIMIT-RESET-TOKENS-XXX
-      x-request-id:
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-      message: OK
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lib/crewai/tests/cassettes/utilities/TestRealLLMNoThoughtLeakage.test_simple_task_clean_output.yaml

@@ -1,111 +0,0 @@
-interactions:
-- request:
-    body: '{"messages":[{"role":"system","content":"You are Classifier. You classify
-      text sentiment accurately.\nYour personal goal is: Classify text sentiment"},{"role":"user","content":"\nCurrent
-      Task: Classify the sentiment of: ''I love this product!''\n\nThis is the expected
-      criteria for your final answer: One word: positive, negative, or neutral\nyou
-      MUST return the actual complete content as the final answer, not a summary.\n\nProvide
-      your complete response:"}],"model":"gpt-4o-mini"}'
-    headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
-      accept:
-      - application/json
-      accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      authorization:
-      - AUTHORIZATION-XXX
-      connection:
-      - keep-alive
-      content-length:
-      - '481'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      x-stainless-arch:
-      - X-STAINLESS-ARCH-XXX
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - X-STAINLESS-OS-XXX
-      x-stainless-package-version:
-      - 1.83.0
-      x-stainless-read-timeout:
-      - X-STAINLESS-READ-TIMEOUT-XXX
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.13.3
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: "{\n  \"id\": \"chatcmpl-D39bkVPelOZanWIMBoIyzsuj072sM\",\n  \"object\":
-        \"chat.completion\",\n  \"created\": 1769644288,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-        \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-        \"assistant\",\n        \"content\": \"positive\",\n        \"refusal\": null,\n
-        \       \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\":
-        \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 89,\n    \"completion_tokens\":
-        1,\n    \"total_tokens\": 90,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-        0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
-        {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-        0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-        \"default\",\n  \"system_fingerprint\": \"fp_3683ee3deb\"\n}\n"
-    headers:
-      CF-RAY:
-      - CF-RAY-XXX
-      Connection:
-      - keep-alive
-      Content-Type:
-      - application/json
-      Date:
-      - Wed, 28 Jan 2026 23:51:29 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - SET-COOKIE-XXX
-      Strict-Transport-Security:
-      - STS-XXX
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
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-      access-control-expose-headers:
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-      cf-cache-status:
-      - DYNAMIC
-      openai-organization:
-      - OPENAI-ORG-XXX
-      openai-processing-ms:
-      - '323'
-      openai-project:
-      - OPENAI-PROJECT-XXX
-      openai-version:
-      - '2020-10-01'
-      x-openai-proxy-wasm:
-      - v0.1
-      x-ratelimit-limit-requests:
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-      x-ratelimit-limit-tokens:
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-      x-ratelimit-remaining-requests:
-      - X-RATELIMIT-REMAINING-REQUESTS-XXX
-      x-ratelimit-remaining-tokens:
-      - X-RATELIMIT-REMAINING-TOKENS-XXX
-      x-ratelimit-reset-requests:
-      - X-RATELIMIT-RESET-REQUESTS-XXX
-      x-ratelimit-reset-tokens:
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-      x-request-id:
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-      message: OK
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lib/crewai/tests/llms/anthropic/test_anthropic_async.py

@@ -157,10 +157,10 @@ async def test_anthropic_async_with_response_model():
         "Say hello in French",
         response_model=GreetingResponse
     )
-    # When response_model is provided, the result is already a parsed Pydantic model instance
-    assert isinstance(result, GreetingResponse)
-    assert isinstance(result.greeting, str)
-    assert isinstance(result.language, str)
+    model = GreetingResponse.model_validate_json(result)
+    assert isinstance(model, GreetingResponse)
+    assert isinstance(model.greeting, str)
+    assert isinstance(model.language, str)
 
 
 @pytest.mark.vcr()

lib/crewai/tests/llms/google/test_google.py

@@ -799,131 +799,3 @@ def test_google_express_mode_works() -> None:
         assert result.token_usage.prompt_tokens > 0
         assert result.token_usage.completion_tokens > 0
         assert result.token_usage.successful_requests >= 1
-
-
-def test_gemini_2_0_model_detection():
-    """Test that Gemini 2.0 models are properly detected."""
-    # Test Gemini 2.0 models
-    llm_2_0 = LLM(model="google/gemini-2.0-flash-001")
-    from crewai.llms.providers.gemini.completion import GeminiCompletion
-    assert isinstance(llm_2_0, GeminiCompletion)
-    assert llm_2_0.is_gemini_2_0 is True
-
-    llm_2_5 = LLM(model="google/gemini-2.5-flash")
-    assert isinstance(llm_2_5, GeminiCompletion)
-    assert llm_2_5.is_gemini_2_0 is True
-
-    # Test non-2.0 models
-    llm_1_5 = LLM(model="google/gemini-1.5-pro")
-    assert isinstance(llm_1_5, GeminiCompletion)
-    assert llm_1_5.is_gemini_2_0 is False
-
-
-def test_add_property_ordering_to_schema():
-    """Test that _add_property_ordering correctly adds propertyOrdering to schemas."""
-    from crewai.llms.providers.gemini.completion import GeminiCompletion
-
-    # Test simple object schema
-    simple_schema = {
-        "type": "object",
-        "properties": {
-            "name": {"type": "string"},
-            "age": {"type": "integer"},
-            "email": {"type": "string"}
-        }
-    }
-
-    result = GeminiCompletion._add_property_ordering(simple_schema)
-
-    assert "propertyOrdering" in result
-    assert result["propertyOrdering"] == ["name", "age", "email"]
-
-    # Test nested object schema
-    nested_schema = {
-        "type": "object",
-        "properties": {
-            "user": {
-                "type": "object",
-                "properties": {
-                    "name": {"type": "string"},
-                    "contact": {
-                        "type": "object",
-                        "properties": {
-                            "email": {"type": "string"},
-                            "phone": {"type": "string"}
-                        }
-                    }
-                }
-            },
-            "id": {"type": "integer"}
-        }
-    }
-
-    result = GeminiCompletion._add_property_ordering(nested_schema)
-
-    assert "propertyOrdering" in result
-    assert result["propertyOrdering"] == ["user", "id"]
-    assert "propertyOrdering" in result["properties"]["user"]
-    assert result["properties"]["user"]["propertyOrdering"] == ["name", "contact"]
-    assert "propertyOrdering" in result["properties"]["user"]["properties"]["contact"]
-    assert result["properties"]["user"]["properties"]["contact"]["propertyOrdering"] == ["email", "phone"]
-
-
-def test_gemini_2_0_response_model_with_property_ordering():
-    """Test that Gemini 2.0 models include propertyOrdering in response schemas."""
-    from pydantic import BaseModel, Field
-
-    class TestResponse(BaseModel):
-        """Test response model."""
-        name: str = Field(..., description="The name")
-        age: int = Field(..., description="The age")
-        email: str = Field(..., description="The email")
-
-    llm = LLM(model="google/gemini-2.0-flash-001")
-
-    # Prepare generation config with response model
-    config = llm._prepare_generation_config(response_model=TestResponse)
-
-    # Verify that the config has response_json_schema
-    assert hasattr(config, 'response_json_schema') or 'response_json_schema' in config.__dict__
-
-    # Get the schema
-    if hasattr(config, 'response_json_schema'):
-        schema = config.response_json_schema
-    else:
-        schema = config.__dict__.get('response_json_schema', {})
-
-    # Verify propertyOrdering is present for Gemini 2.0
-    assert "propertyOrdering" in schema
-    assert "name" in schema["propertyOrdering"]
-    assert "age" in schema["propertyOrdering"]
-    assert "email" in schema["propertyOrdering"]
-
-
-def test_gemini_1_5_response_model_uses_response_schema():
-    """Test that Gemini 1.5 models use response_schema parameter (not response_json_schema)."""
-    from pydantic import BaseModel, Field
-
-    class TestResponse(BaseModel):
-        """Test response model."""
-        name: str = Field(..., description="The name")
-        age: int = Field(..., description="The age")
-
-    llm = LLM(model="google/gemini-1.5-pro")
-
-    # Prepare generation config with response model
-    config = llm._prepare_generation_config(response_model=TestResponse)
-
-    # Verify that the config uses response_schema (not response_json_schema)
-    assert hasattr(config, 'response_schema') or 'response_schema' in config.__dict__
-    assert not (hasattr(config, 'response_json_schema') and config.response_json_schema is not None)
-
-    # Get the schema
-    if hasattr(config, 'response_schema'):
-        schema = config.response_schema
-    else:
-        schema = config.__dict__.get('response_schema')
-
-    # For Gemini 1.5, response_schema should be the Pydantic model itself
-    # The SDK handles conversion internally
-    assert schema is TestResponse or isinstance(schema, type)

lib/crewai/tests/llms/openai/test_openai.py

@@ -540,9 +540,7 @@ def test_openai_streaming_with_response_model():
         result = llm.call("Test question", response_model=TestResponse)
 
         assert result is not None
-        assert isinstance(result, TestResponse)
-        assert result.answer == "test"
-        assert result.confidence == 0.95
+        assert isinstance(result, str)
 
         assert mock_stream.called
         call_kwargs = mock_stream.call_args[1]

lib/crewai/tests/test_crew.py

@@ -2585,7 +2585,6 @@ def test_warning_long_term_memory_without_entity_memory():
         goal="You research about math.",
         backstory="You're an expert in research and you love to learn new things.",
         allow_delegation=False,
-        verbose=True,
     )
 
     task1 = Task(

lib/crewai/tests/utilities/test_prompts_no_thought_leakage.py

@@ -1,234 +0,0 @@
-"""Tests for prompt generation to prevent thought leakage.
-
-These tests verify that:
-1. Agents without tools don't get ReAct format instructions
-2. The generated prompts don't encourage "Thought:" prefixes that leak into output
-3. Real LLM calls produce clean output without internal reasoning
-"""
-
-from __future__ import annotations
-
-from unittest.mock import MagicMock
-
-import pytest
-
-from crewai import Agent, Crew, Task
-from crewai.llm import LLM
-from crewai.utilities.prompts import Prompts
-
-
-class TestNoToolsPromptGeneration:
-    """Tests for prompt generation when agent has no tools."""
-
-    def test_no_tools_uses_task_no_tools_slice(self) -> None:
-        """Test that agents without tools use task_no_tools slice instead of task."""
-        mock_agent = MagicMock()
-        mock_agent.role = "Test Agent"
-        mock_agent.goal = "Test goal"
-        mock_agent.backstory = "Test backstory"
-
-        prompts = Prompts(
-            has_tools=False,
-            use_native_tool_calling=False,
-            use_system_prompt=True,
-            agent=mock_agent,
-        )
-
-        result = prompts.task_execution()
-
-        # Verify it's a SystemPromptResult with system and user keys
-        assert "system" in result
-        assert "user" in result
-        assert "prompt" in result
-
-        # The user prompt should NOT contain "Thought:" (ReAct format)
-        assert "Thought:" not in result["user"]
-
-        # The user prompt should NOT mention tools
-        assert "use the tools available" not in result["user"]
-        assert "tools available" not in result["user"].lower()
-
-        # The system prompt should NOT contain ReAct format instructions
-        assert "Thought:" not in result["system"]
-        assert "Final Answer:" not in result["system"]
-
-    def test_no_tools_prompt_is_simple(self) -> None:
-        """Test that no-tools prompt is simple and direct."""
-        mock_agent = MagicMock()
-        mock_agent.role = "Language Detector"
-        mock_agent.goal = "Detect language"
-        mock_agent.backstory = "Expert linguist"
-
-        prompts = Prompts(
-            has_tools=False,
-            use_native_tool_calling=False,
-            use_system_prompt=True,
-            agent=mock_agent,
-        )
-
-        result = prompts.task_execution()
-
-        # Should contain the role playing info
-        assert "Language Detector" in result["system"]
-
-        # User prompt should be simple with just the task
-        assert "Current Task:" in result["user"]
-        assert "Provide your complete response:" in result["user"]
-
-    def test_with_tools_uses_task_slice_with_react(self) -> None:
-        """Test that agents WITH tools use the task slice (ReAct format)."""
-        mock_agent = MagicMock()
-        mock_agent.role = "Test Agent"
-        mock_agent.goal = "Test goal"
-        mock_agent.backstory = "Test backstory"
-
-        prompts = Prompts(
-            has_tools=True,
-            use_native_tool_calling=False,
-            use_system_prompt=True,
-            agent=mock_agent,
-        )
-
-        result = prompts.task_execution()
-
-        # With tools and ReAct, the prompt SHOULD contain Thought:
-        assert "Thought:" in result["user"]
-
-    def test_native_tools_uses_native_task_slice(self) -> None:
-        """Test that native tool calling uses native_task slice."""
-        mock_agent = MagicMock()
-        mock_agent.role = "Test Agent"
-        mock_agent.goal = "Test goal"
-        mock_agent.backstory = "Test backstory"
-
-        prompts = Prompts(
-            has_tools=True,
-            use_native_tool_calling=True,
-            use_system_prompt=True,
-            agent=mock_agent,
-        )
-
-        result = prompts.task_execution()
-
-        # Native tool calling should NOT have Thought: in user prompt
-        assert "Thought:" not in result["user"]
-
-        # Should NOT have emotional manipulation
-        assert "your job depends on it" not in result["user"]
-
-
-class TestNoThoughtLeakagePatterns:
-    """Tests to verify prompts don't encourage thought leakage."""
-
-    def test_no_job_depends_on_it_in_no_tools(self) -> None:
-        """Test that 'your job depends on it' is not in no-tools prompts."""
-        mock_agent = MagicMock()
-        mock_agent.role = "Test"
-        mock_agent.goal = "Test"
-        mock_agent.backstory = "Test"
-
-        prompts = Prompts(
-            has_tools=False,
-            use_native_tool_calling=False,
-            use_system_prompt=True,
-            agent=mock_agent,
-        )
-
-        result = prompts.task_execution()
-
-        full_prompt = result["prompt"]
-        assert "your job depends on it" not in full_prompt.lower()
-        assert "i must use these formats" not in full_prompt.lower()
-
-    def test_no_job_depends_on_it_in_native_task(self) -> None:
-        """Test that 'your job depends on it' is not in native task prompts."""
-        mock_agent = MagicMock()
-        mock_agent.role = "Test"
-        mock_agent.goal = "Test"
-        mock_agent.backstory = "Test"
-
-        prompts = Prompts(
-            has_tools=True,
-            use_native_tool_calling=True,
-            use_system_prompt=True,
-            agent=mock_agent,
-        )
-
-        result = prompts.task_execution()
-
-        full_prompt = result["prompt"]
-        assert "your job depends on it" not in full_prompt.lower()
-
-
-class TestRealLLMNoThoughtLeakage:
-    """Integration tests with real LLM calls to verify no thought leakage."""
-
-    @pytest.mark.vcr()
-    def test_agent_without_tools_no_thought_in_output(self) -> None:
-        """Test that agent without tools produces clean output without 'Thought:' prefix."""
-        agent = Agent(
-            role="Language Detector",
-            goal="Detect the language of text",
-            backstory="You are an expert linguist who can identify languages.",
-            tools=[],  # No tools
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-        )
-
-        task = Task(
-            description="What language is this text written in: 'Hello, how are you?'",
-            expected_output="The detected language (e.g., English, Spanish, etc.)",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-
-        # The output should NOT start with "Thought:" or contain ReAct artifacts
-        output = str(result.raw)
-        assert not output.strip().startswith("Thought:")
-        assert "Final Answer:" not in output
-        assert "I now can give a great answer" not in output
-
-        # Should contain an actual answer about the language
-        assert any(
-            lang in output.lower()
-            for lang in ["english", "en", "language"]
-        )
-
-    @pytest.mark.vcr()
-    def test_simple_task_clean_output(self) -> None:
-        """Test that a simple task produces clean output without internal reasoning."""
-        agent = Agent(
-            role="Classifier",
-            goal="Classify text sentiment",
-            backstory="You classify text sentiment accurately.",
-            tools=[],
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-        )
-
-        task = Task(
-            description="Classify the sentiment of: 'I love this product!'",
-            expected_output="One word: positive, negative, or neutral",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        output = str(result.raw).strip().lower()
-
-        # Output should be clean - just the classification
-        assert not output.startswith("thought:")
-        assert "final answer:" not in output
-
-        # Should contain the actual classification
-        assert any(
-            sentiment in output
-            for sentiment in ["positive", "negative", "neutral"]
-        )

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.9.2"
+__version__ = "1.9.0"