fix: resolve forward refs for CrewAgentExecutor BaseModel

lib/crewai/src/crewai/__init__.py

@@ -6,6 +6,7 @@ import warnings
 
 from crewai.agent.core import Agent
 from crewai.agent.planning_config import PlanningConfig
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.flow.flow import Flow
@@ -19,6 +20,9 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
 
 
+CrewAgentExecutor.model_rebuild()
+
+
 def _suppress_pydantic_deprecation_warnings() -> None:
     """Suppress Pydantic deprecation warnings using targeted monkey patch."""
     original_warn = warnings.warn

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

@@ -14,8 +14,15 @@ import inspect
 import logging
 from typing import TYPE_CHECKING, Any, Literal, cast
 
-from pydantic import BaseModel, GetCoreSchemaHandler, ValidationError
-from pydantic_core import CoreSchema, core_schema
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    PrivateAttr,
+    ValidationError,
+    model_validator,
+)
+from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
@@ -23,6 +30,7 @@ from crewai.agents.parser import (
     AgentFinish,
     OutputParserError,
 )
+from crewai.agents.tools_handler import ToolsHandler
 from crewai.core.providers.human_input import ExecutorContext, get_provider
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.logging_events import (
@@ -38,6 +46,9 @@ from crewai.hooks.tool_hooks import (
     get_after_tool_call_hooks,
     get_before_tool_call_hooks,
 )
+from crewai.llms.base_llm import BaseLLM
+from crewai.tools.base_tool import BaseTool
+from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities.agent_utils import (
     aget_llm_response,
     convert_tools_to_openai_schema,
@@ -59,106 +70,65 @@ from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.file_store import aget_all_files, get_all_files
 from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
+from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult
 from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.tool_utils import (
     aexecute_tool_and_check_finality,
     execute_tool_and_check_finality,
 )
 from crewai.utilities.training_handler import CrewTrainingHandler
+from crewai.utilities.types import LLMMessage
 
 
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
-    from crewai.agents.tools_handler import ToolsHandler
     from crewai.crew import Crew
-    from crewai.llms.base_llm import BaseLLM
     from crewai.task import Task
-    from crewai.tools.base_tool import BaseTool
-    from crewai.tools.structured_tool import CrewStructuredTool
     from crewai.tools.tool_types import ToolResult
-    from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult
-    from crewai.utilities.types import LLMMessage
 
 
-class CrewAgentExecutor(CrewAgentExecutorMixin):
+class CrewAgentExecutor(BaseModel, CrewAgentExecutorMixin):
     """Executor for crew agents.
 
     Manages the execution lifecycle of an agent including prompt formatting,
     LLM interactions, tool execution, and feedback handling.
     """
 
-    def __init__(
-        self,
-        llm: BaseLLM,
-        task: Task,
-        crew: Crew,
-        agent: Agent,
-        prompt: SystemPromptResult | StandardPromptResult,
-        max_iter: int,
-        tools: list[CrewStructuredTool],
-        tools_names: str,
-        stop_words: list[str],
-        tools_description: str,
-        tools_handler: ToolsHandler,
-        step_callback: Any = None,
-        original_tools: list[BaseTool] | None = None,
-        function_calling_llm: BaseLLM | Any | None = None,
-        respect_context_window: bool = False,
-        request_within_rpm_limit: Callable[[], bool] | None = None,
-        callbacks: list[Any] | None = None,
-        response_model: type[BaseModel] | None = None,
-        i18n: I18N | None = None,
-    ) -> None:
-        """Initialize executor.
+    model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True)
 
-        Args:
-            llm: Language model instance.
-            task: Task to execute.
-            crew: Crew instance.
-            agent: Agent to execute.
-            prompt: Prompt templates.
-            max_iter: Maximum iterations.
-            tools: Available tools.
-            tools_names: Tool names string.
-            stop_words: Stop word list.
-            tools_description: Tool descriptions.
-            tools_handler: Tool handler instance.
-            step_callback: Optional step callback.
-            original_tools: Original tool list.
-            function_calling_llm: Optional function calling LLM.
-            respect_context_window: Respect context limits.
-            request_within_rpm_limit: RPM limit check function.
-            callbacks: Optional callbacks list.
-            response_model: Optional Pydantic model for structured outputs.
-        """
-        self._i18n: I18N = i18n or get_i18n()
-        self.llm = llm
-        self.task = task
-        self.agent = agent
-        self.crew = crew
-        self.prompt = prompt
-        self.tools = tools
-        self.tools_names = tools_names
-        self.stop = stop_words
-        self.max_iter = max_iter
-        self.callbacks = callbacks or []
-        self._printer: Printer = Printer()
-        self.tools_handler = tools_handler
-        self.original_tools = original_tools or []
-        self.step_callback = step_callback
-        self.tools_description = tools_description
-        self.function_calling_llm = function_calling_llm
-        self.respect_context_window = respect_context_window
-        self.request_within_rpm_limit = request_within_rpm_limit
-        self.response_model = response_model
-        self.ask_for_human_input = False
-        self.messages: list[LLMMessage] = []
-        self.iterations = 0
-        self.log_error_after = 3
-        self.before_llm_call_hooks: list[Callable[..., Any]] = []
-        self.after_llm_call_hooks: list[Callable[..., Any]] = []
+    llm: BaseLLM
+    task: Task | None = None
+    crew: Crew | None = None
+    agent: Agent
+    prompt: SystemPromptResult | StandardPromptResult
+    max_iter: int
+    tools: list[CrewStructuredTool]
+    tools_names: str
+    stop: list[str] = Field(alias="stop_words")
+    tools_description: str
+    tools_handler: ToolsHandler
+    step_callback: Any = None
+    original_tools: list[BaseTool] = Field(default_factory=list)
+    function_calling_llm: BaseLLM | Any | None = None
+    respect_context_window: bool = False
+    request_within_rpm_limit: Callable[[], bool] | None = None
+    callbacks: list[Any] = Field(default_factory=list)
+    response_model: type[BaseModel] | None = None
+    i18n: I18N | None = Field(default=None, exclude=True)
+    ask_for_human_input: bool = False
+    messages: list[LLMMessage] = Field(default_factory=list)
+    iterations: int = 0
+    log_error_after: int = 3
+    before_llm_call_hooks: list[Callable[..., Any]] = Field(default_factory=list)
+    after_llm_call_hooks: list[Callable[..., Any]] = Field(default_factory=list)
+    _i18n: I18N = PrivateAttr()
+    _printer: Printer = PrivateAttr(default_factory=Printer)
+
+    @model_validator(mode="after")
+    def _init_executor(self) -> Self:
+        self._i18n = self.i18n or get_i18n()
         self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
         if self.llm:
@@ -171,6 +141,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     else self.stop
                 )
             )
+        return self
 
     @property
     def use_stop_words(self) -> bool:
@@ -1687,14 +1658,3 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return format_message_for_llm(
             self._i18n.slice("feedback_instructions").format(feedback=feedback)
         )
-
-    @classmethod
-    def __get_pydantic_core_schema__(
-        cls, _source_type: Any, _handler: GetCoreSchemaHandler
-    ) -> CoreSchema:
-        """Generate Pydantic core schema for BaseClient Protocol.
-
-        This allows the Protocol to be used in Pydantic models without
-        requiring arbitrary_types_allowed=True.
-        """
-        return core_schema.any_schema()