feat: cleanup Pydantic warning

A several warnings were addressed following by  https://docs.pydantic.dev/2.10/migration

src/crewai/crew.py

@@ -290,25 +290,18 @@ class Crew(BaseModel):
                 else EntityMemory(crew=self, embedder_config=self.embedder)
             )
             if (
-                self.memory_config and "user_memory" in self.memory_config and self.memory_config.get('provider') == 'mem0'
+                self.memory_config
+                and "user_memory" in self.memory_config
+                and self.memory_config.get("provider") == "mem0"
             ):  # Check for user_memory in config
                 user_memory_config = self.memory_config["user_memory"]
                 if isinstance(
                     user_memory_config, dict
                 ):  # Check if it's a configuration dict
-                    self._user_memory = UserMemory(
-                        crew=self
-                    )
+                    self._user_memory = UserMemory(crew=self)
                 else:
-                    raise TypeError(
-                        "user_memory must be a configuration dictionary"
-                    )
+                    raise TypeError("user_memory must be a configuration dictionary")
             else:
-                self._logger.log(
-                    "warning", 
-                    "User memory initialization failed. For setup instructions, please refer to the memory documentation: https://docs.crewai.com/concepts/memory#integrating-mem0-for-enhanced-user-memory", 
-                    color="yellow"
-                )
                 self._user_memory = None  # No user memory if not in config
         return self
 
@@ -1159,7 +1152,7 @@ class Crew(BaseModel):
     def copy(self):
         """
         Creates a deep copy of the Crew instance.
-        
+
         Returns:
             Crew: A new instance with copied components
         """
@@ -1181,7 +1174,6 @@ class Crew(BaseModel):
             "knowledge",
             "manager_agent",
             "manager_llm",
-
         }
 
         cloned_agents = [agent.copy() for agent in self.agents]

src/crewai/tools/base_tool.py

@@ -7,29 +7,27 @@ from pydantic import (
     BaseModel,
     ConfigDict,
     Field,
-    PydanticDeprecatedSince20,
     create_model,
-    validator,
+    field_validator,
 )
 from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
-# Ignore all "PydanticDeprecatedSince20" warnings globally
-warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
-
 
 class BaseTool(BaseModel, ABC):
     class _ArgsSchemaPlaceholder(PydanticBaseModel):
         pass
 
-    model_config = ConfigDict()
+    model_config = ConfigDict(arbitrary_types_allowed=True)
 
     name: str
     """The unique name of the tool that clearly communicates its purpose."""
     description: str
     """Used to tell the model how/when/why to use the tool."""
-    args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
+    args_schema: Type[PydanticBaseModel] = Field(
+        default_factory=_ArgsSchemaPlaceholder, validate_default=True
+    )
     """The schema for the arguments that the tool accepts."""
     description_updated: bool = False
     """Flag to check if the description has been updated."""
@@ -38,7 +36,8 @@ class BaseTool(BaseModel, ABC):
     result_as_answer: bool = False
     """Flag to check if the tool should be the final agent answer."""
 
-    @validator("args_schema", always=True, pre=True)
+    @field_validator("args_schema", mode="before")
+    @classmethod
     def _default_args_schema(
         cls, v: Type[PydanticBaseModel]
     ) -> Type[PydanticBaseModel]:

src/crewai/utilities/converter.py

@@ -287,8 +287,9 @@ def generate_model_description(model: Type[BaseModel]) -> str:
         else:
             return str(field_type)
 
-    fields = model.__annotations__
+    fields = model.model_fields
     field_descriptions = [
-        f'"{name}": {describe_field(type_)}' for name, type_ in fields.items()
+        f'"{name}": {describe_field(field.annotation)}'
+        for name, field in fields.items()
     ]
     return "{\n  " + ",\n  ".join(field_descriptions) + "\n}"