Merge branch 'main' into feat/individual-react-agent

README.md

@@ -401,11 +401,16 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Using Crews and Flows Together
 
-CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. Here's how you can orchestrate multiple Crews within a Flow:
+CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines.
+CrewAI flows support logical operators like `or_` and `and_` to combine multiple conditions. This can be used with `@start`, `@listen`, or `@router` decorators to create complex triggering conditions.
+- `or_`: Triggers when any of the specified conditions are met. 
+- `and_`Triggers when all of the specified conditions are met.
+
+Here's how you can orchestrate multiple Crews within a Flow:
 
 ```python
-from crewai.flow.flow import Flow, listen, start, router
+from crewai.flow.flow import Flow, listen, start, router, or_
-from crewai import Crew, Agent, Task
+from crewai import Crew, Agent, Task, Process
 from pydantic import BaseModel
 
 # Define structured state for precise control
@@ -479,7 +484,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
         )
         return strategy_crew.kickoff()
 
-    @listen("medium_confidence", "low_confidence")
+    @listen(or_("medium_confidence", "low_confidence"))
     def request_additional_analysis(self):
         self.state.recommendations.append("Gather more data")
         return "Additional analysis required"

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}"