fix: add 'task' (singular) to TRACE_EXCLUDE_FIELDS

agent_execution_error and task_failed events have a task: Any field
holding a full Task object. 'tasks' (plural) was excluded but 'task'
(singular) was not, so the full object leaked through serialization.

lib/crewai/src/crewai/__init__.py

@@ -6,7 +6,6 @@ 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
@@ -20,9 +19,6 @@ 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/agent/core.py

@@ -25,6 +25,7 @@ from pydantic import (
     BaseModel,
     ConfigDict,
     Field,
+    InstanceOf,
     PrivateAttr,
     model_validator,
 )
@@ -166,10 +167,10 @@ class Agent(BaseAgent):
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: str | BaseLLM | None = Field(
+    llm: str | InstanceOf[BaseLLM] | None = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: str | BaseLLM | None = Field(
+    function_calling_llm: str | InstanceOf[BaseLLM] | None = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: str | None = Field(

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

@@ -12,6 +12,7 @@ from pydantic import (
     UUID4,
     BaseModel,
     Field,
+    InstanceOf,
     PrivateAttr,
     field_validator,
     model_validator,
@@ -184,7 +185,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         default=None,
         description="Knowledge sources for the agent.",
     )
-    knowledge_storage: BaseKnowledgeStorage | None = Field(
+    knowledge_storage: InstanceOf[BaseKnowledgeStorage] | None = Field(
         default=None,
         description="Custom knowledge storage for the agent.",
     )

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

@@ -14,15 +14,8 @@ import inspect
 import logging
 from typing import TYPE_CHECKING, Any, Literal, cast
 
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    Field,
-    PrivateAttr,
-    ValidationError,
-    model_validator,
-)
-from typing_extensions import Self
+from pydantic import BaseModel, GetCoreSchemaHandler, ValidationError
+from pydantic_core import CoreSchema, core_schema
 
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
@@ -30,7 +23,6 @@ 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 (
@@ -46,9 +38,6 @@ 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,
@@ -70,65 +59,106 @@ 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(BaseModel, CrewAgentExecutorMixin):
+class CrewAgentExecutor(CrewAgentExecutorMixin):
     """Executor for crew agents.
 
     Manages the execution lifecycle of an agent including prompt formatting,
     LLM interactions, tool execution, and feedback handling.
     """
 
-    model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True)
+    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.
 
-    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()
+        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]] = []
         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:
@@ -141,7 +171,6 @@ class CrewAgentExecutor(BaseModel, CrewAgentExecutorMixin):
                     else self.stop
                 )
             )
-        return self
 
     @property
     def use_stop_words(self) -> bool:
@@ -1658,3 +1687,14 @@ class CrewAgentExecutor(BaseModel, 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()

lib/crewai/src/crewai/crew.py

@@ -22,6 +22,7 @@ from pydantic import (
     UUID4,
     BaseModel,
     Field,
+    InstanceOf,
     Json,
     PrivateAttr,
     field_validator,
@@ -175,7 +176,7 @@ class Crew(FlowTrackable, BaseModel):
     _rpm_controller: RPMController = PrivateAttr()
     _logger: Logger = PrivateAttr()
     _file_handler: FileHandler = PrivateAttr()
-    _cache_handler: CacheHandler = PrivateAttr(default_factory=CacheHandler)
+    _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default_factory=CacheHandler)
     _memory: Memory | MemoryScope | MemorySlice | None = PrivateAttr(default=None)
     _train: bool | None = PrivateAttr(default=False)
     _train_iteration: int | None = PrivateAttr()
@@ -209,13 +210,13 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
-    manager_llm: str | BaseLLM | None = Field(
+    manager_llm: str | InstanceOf[BaseLLM] | None = Field(
         description="Language model that will run the agent.", default=None
     )
     manager_agent: BaseAgent | None = Field(
         description="Custom agent that will be used as manager.", default=None
     )
-    function_calling_llm: str | LLM | None = Field(
+    function_calling_llm: str | InstanceOf[LLM] | None = Field(
         description="Language model that will run the agent.", default=None
     )
     config: Json[dict[str, Any]] | dict[str, Any] | None = Field(default=None)
@@ -266,7 +267,7 @@ class Crew(FlowTrackable, BaseModel):
         default=False,
         description="Plan the crew execution and add the plan to the crew.",
     )
-    planning_llm: str | BaseLLM | Any | None = Field(
+    planning_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
         default=None,
         description=(
             "Language model that will run the AgentPlanner if planning is True."
@@ -287,7 +288,7 @@ class Crew(FlowTrackable, BaseModel):
             "knowledge object."
         ),
     )
-    chat_llm: str | BaseLLM | Any | None = Field(
+    chat_llm: str | InstanceOf[BaseLLM] | Any | None = Field(
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
@@ -1799,7 +1800,7 @@ class Crew(FlowTrackable, BaseModel):
     def test(
         self,
         n_iterations: int,
-        eval_llm: str | BaseLLM,
+        eval_llm: str | InstanceOf[BaseLLM],
         inputs: dict[str, Any] | None = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations.

lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py

@@ -126,18 +126,59 @@ from crewai.events.types.tool_usage_events import (
 from crewai.events.utils.console_formatter import ConsoleFormatter
 
 
+# Fields to exclude from trace serialization to reduce redundant data.
+# These back-references and heavy objects create massive bloat when serialized
+# repeatedly across events (crew->agents->tasks->agent creates circular refs).
+TRACE_EXCLUDE_FIELDS = {
+    # Back-references that create redundant/circular data
+    "crew",
+    "agent",
+    "agents",
+    "task",
+    "tasks",
+    "context",
+    # Heavy fields not needed in individual trace events
+    # NOTE: "tools" intentionally NOT here - LLMCallStartedEvent.tools is lightweight
+    # (list of tool schemas). Agent.tools is excluded in _build_crew_started_data.
+    "llm",
+    "function_calling_llm",
+    "step_callback",
+    "task_callback",
+    "crew_callback",
+    "callbacks",
+    "_memory",
+    "_cache",
+    "_rpm_controller",
+    "_request_within_rpm_limit",
+    "_token_process",
+    "knowledge_sources",
+}
+
+
+def _serialize_for_trace(
+    event: Any, extra_exclude: set[str] | None = None
+) -> dict[str, Any]:
+    """Serialize an event for tracing, excluding redundant back-references.
+
+    Keeps all scalar fields (agent_role, task_name, etc.) that the AMP frontend uses.
+    Replaces heavy nested objects with lightweight ID references to reduce trace bloat.
+
+    Args:
+        event: The event object to serialize.
+        extra_exclude: Additional fields to exclude beyond TRACE_EXCLUDE_FIELDS.
+
+    Returns:
+        A dictionary with the serialized event data.
+    """
+    exclude = TRACE_EXCLUDE_FIELDS.copy()
+    if extra_exclude:
+        exclude.update(extra_exclude)
+    return safe_serialize_to_dict(event, exclude=exclude)
+
+
 class TraceCollectionListener(BaseEventListener):
     """Trace collection listener that orchestrates trace collection."""
 
-    complex_events: ClassVar[list[str]] = [
-        "task_started",
-        "task_completed",
-        "llm_call_started",
-        "llm_call_completed",
-        "agent_execution_started",
-        "agent_execution_completed",
-    ]
-
     _instance: Self | None = None
     _initialized: bool = False
     _listeners_setup: bool = False
@@ -810,9 +851,17 @@ class TraceCollectionListener(BaseEventListener):
     def _build_event_data(
         self, event_type: str, event: Any, source: Any
     ) -> dict[str, Any]:
-        """Build event data"""
-        if event_type not in self.complex_events:
-            return safe_serialize_to_dict(event)
+        """Build event data with optimized serialization to reduce trace bloat.
+
+        For most events, excludes heavy nested objects (crew, agents, tasks, tools)
+        that would create massive redundant data. Only crew_kickoff_started gets
+        the full crew structure as a one-time dump.
+        """
+        # crew_kickoff_started is special: include full crew structure ONCE
+        if event_type == "crew_kickoff_started":
+            return self._build_crew_started_data(event)
+
+        # Complex events have custom handling that already extracts only needed fields
         if event_type == "task_started":
             task_name = event.task.name or event.task.description
             task_display_name = (
@@ -853,19 +902,101 @@ class TraceCollectionListener(BaseEventListener):
                 "agent_backstory": event.agent.backstory,
             }
         if event_type == "llm_call_started":
-            event_data = safe_serialize_to_dict(event)
+            event_data = _serialize_for_trace(event)
             event_data["task_name"] = event.task_name or getattr(
                 event, "task_description", None
             )
             return event_data
         if event_type == "llm_call_completed":
-            return safe_serialize_to_dict(event)
+            return _serialize_for_trace(event)
 
-        return {
-            "event_type": event_type,
-            "event": safe_serialize_to_dict(event),
-            "source": source,
-        }
+        # Error events need agent/task identification extracted before generic
+        # serialization strips them (agent/task are in TRACE_EXCLUDE_FIELDS)
+        if event_type == "agent_execution_error":
+            event_data = _serialize_for_trace(event)
+            if event.agent:
+                event_data["agent_role"] = getattr(event.agent, "role", None)
+                event_data["agent_id"] = str(getattr(event.agent, "id", ""))
+            return event_data
+        if event_type == "task_failed":
+            event_data = _serialize_for_trace(event)
+            if event.task:
+                event_data["task_name"] = getattr(event.task, "name", None) or getattr(
+                    event.task, "description", None
+                )
+                event_data["task_id"] = str(getattr(event.task, "id", ""))
+            return event_data
+
+        # For all other events, use lightweight serialization
+        return _serialize_for_trace(event)
+
+    def _build_crew_started_data(self, event: Any) -> dict[str, Any]:
+        """Build comprehensive crew structure for crew_kickoff_started event.
+
+        This is the ONE place where we serialize the full crew structure.
+        Subsequent events use lightweight references to avoid redundancy.
+        """
+        event_data = _serialize_for_trace(event)
+
+        # Add full crew structure with optimized agent/task serialization
+        crew = getattr(event, "crew", None)
+        if crew is not None:
+            # Serialize agents with tools (first occurrence only)
+            agents_data = []
+            for agent in getattr(crew, "agents", []) or []:
+                agent_data = {
+                    "id": str(getattr(agent, "id", "")),
+                    "role": getattr(agent, "role", ""),
+                    "goal": getattr(agent, "goal", ""),
+                    "backstory": getattr(agent, "backstory", ""),
+                    "verbose": getattr(agent, "verbose", False),
+                    "allow_delegation": getattr(agent, "allow_delegation", False),
+                    "max_iter": getattr(agent, "max_iter", None),
+                    "max_rpm": getattr(agent, "max_rpm", None),
+                }
+                # Include tool names (not full tool objects)
+                tools = getattr(agent, "tools", None)
+                if tools:
+                    agent_data["tool_names"] = [
+                        getattr(t, "name", str(t)) for t in tools
+                    ]
+                agents_data.append(agent_data)
+
+            # Serialize tasks with lightweight agent references
+            tasks_data = []
+            for task in getattr(crew, "tasks", []) or []:
+                task_data = {
+                    "id": str(getattr(task, "id", "")),
+                    "name": getattr(task, "name", None),
+                    "description": getattr(task, "description", ""),
+                    "expected_output": getattr(task, "expected_output", ""),
+                    "async_execution": getattr(task, "async_execution", False),
+                    "human_input": getattr(task, "human_input", False),
+                }
+                # Replace full agent with lightweight reference
+                task_agent = getattr(task, "agent", None)
+                if task_agent:
+                    task_data["agent_ref"] = {
+                        "id": str(getattr(task_agent, "id", "")),
+                        "role": getattr(task_agent, "role", ""),
+                    }
+                # Replace context tasks with lightweight references
+                context_tasks = getattr(task, "context", None)
+                if context_tasks:
+                    task_data["context_task_ids"] = [
+                        str(getattr(ct, "id", "")) for ct in context_tasks
+                    ]
+                tasks_data.append(task_data)
+
+            event_data["crew_structure"] = {
+                "agents": agents_data,
+                "tasks": tasks_data,
+                "process": str(getattr(crew, "process", "")),
+                "verbose": getattr(crew, "verbose", False),
+                "memory": getattr(crew, "memory", False),
+            }
+
+        return event_data
 
     def _show_tracing_disabled_message(self) -> None:
         """Show a message when tracing is disabled."""

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

@@ -1966,6 +1966,37 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
             "original_tool": original_tool,
         }
 
+    def _extract_tool_name(self, tool_call: Any) -> str:
+        """Extract tool name from various tool call formats."""
+        if hasattr(tool_call, "function"):
+            return sanitize_tool_name(tool_call.function.name)
+        if hasattr(tool_call, "function_call") and tool_call.function_call:
+            return sanitize_tool_name(tool_call.function_call.name)
+        if hasattr(tool_call, "name"):
+            return sanitize_tool_name(tool_call.name)
+        if isinstance(tool_call, dict):
+            func_info = tool_call.get("function", {})
+            return sanitize_tool_name(
+                func_info.get("name", "") or tool_call.get("name", "unknown")
+            )
+        return "unknown"
+
+    @router(execute_native_tool)
+    def check_native_todo_completion(
+        self,
+    ) -> Literal["todo_satisfied", "todo_not_satisfied"]:
+        """Check if the native tool execution satisfied the active todo.
+
+        Similar to check_todo_completion but for native tool execution path.
+        """
+        current_todo = self.state.todos.current_todo
+
+        if not current_todo:
+            return "todo_not_satisfied"
+
+        # For native tools, any tool execution satisfies the todo
+        return "todo_satisfied"
+
     @listen("initialized")
     def continue_iteration(self) -> Literal["check_iteration"]:
         """Bridge listener that connects iteration loop back to iteration check."""

lib/crewai/src/crewai/knowledge/storage/base_knowledge_storage.py

@@ -3,15 +3,12 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel, ConfigDict
-
 
 if TYPE_CHECKING:
     from crewai.rag.types import SearchResult
 
 
-class BaseKnowledgeStorage(BaseModel, ABC):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+class BaseKnowledgeStorage(ABC):
     """Abstract base class for knowledge storage implementations."""
 
     @abstractmethod

lib/crewai/src/crewai/knowledge/storage/knowledge_storage.py

@@ -3,9 +3,6 @@ import traceback
 from typing import Any, cast
 import warnings
 
-from pydantic import Field, PrivateAttr, model_validator
-from typing_extensions import Self
-
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 from crewai.rag.chromadb.config import ChromaDBConfig
 from crewai.rag.chromadb.types import ChromaEmbeddingFunctionWrapper
@@ -25,32 +22,31 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     search efficiency.
     """
 
-    collection_name: str | None = None
-    embedder: (
-        ProviderSpec
+    def __init__(
+        self,
+        embedder: ProviderSpec
         | BaseEmbeddingsProvider[Any]
         | type[BaseEmbeddingsProvider[Any]]
-        | None
-    ) = Field(default=None, exclude=True)
-    _client: BaseClient | None = PrivateAttr(default=None)
+        | None = None,
+        collection_name: str | None = None,
+    ) -> None:
+        self.collection_name = collection_name
+        self._client: BaseClient | None = None
 
-    @model_validator(mode="after")
-    def _init_client(self) -> Self:
         warnings.filterwarnings(
             "ignore",
             message=r".*'model_fields'.*is deprecated.*",
             module=r"^chromadb(\.|$)",
         )
 
-        if self.embedder:
-            embedding_function = build_embedder(self.embedder)  # type: ignore[arg-type]
+        if embedder:
+            embedding_function = build_embedder(embedder)  # type: ignore[arg-type]
             config = ChromaDBConfig(
                 embedding_function=cast(
                     ChromaEmbeddingFunctionWrapper, embedding_function
                 )
             )
             self._client = create_client(config)
-        return self
 
     def _get_client(self) -> BaseClient:
         """Get the appropriate client - instance-specific or global."""

lib/crewai/src/crewai/lite_agent.py

@@ -22,6 +22,7 @@ from pydantic import (
     UUID4,
     BaseModel,
     Field,
+    InstanceOf,
     PrivateAttr,
     field_validator,
     model_validator,
@@ -203,7 +204,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Goal of the agent")
     backstory: str = Field(description="Backstory of the agent")
-    llm: str | BaseLLM | Any | None = Field(
+    llm: str | InstanceOf[BaseLLM] | Any | None = Field(
         default=None, description="Language model that will run the agent"
     )
     tools: list[BaseTool] = Field(

lib/crewai/src/crewai/llm.py

@@ -20,7 +20,8 @@ from typing import (
 )
 
 from dotenv import load_dotenv
-from pydantic import BaseModel, Field, model_validator
+import httpx
+from pydantic import BaseModel, Field
 from typing_extensions import Self
 
 from crewai.events.event_bus import crewai_event_bus
@@ -36,12 +37,7 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from crewai.llms.base_llm import (
-    BaseLLM,
-    JsonResponseFormat,
-    get_current_call_id,
-    llm_call_context,
-)
+from crewai.llms.base_llm import BaseLLM, get_current_call_id, llm_call_context
 from crewai.llms.constants import (
     ANTHROPIC_MODELS,
     AZURE_MODELS,
@@ -67,6 +63,8 @@ except ImportError:
 
 if TYPE_CHECKING:
     from crewai.agent.core import Agent
+    from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.llms.providers.anthropic.completion import AnthropicThinkingConfig
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.utilities.types import LLMMessage
@@ -344,27 +342,6 @@ class AccumulatedToolArgs(BaseModel):
 
 class LLM(BaseLLM):
     completion_cost: float | None = None
-    timeout: float | int | None = None
-    top_p: float | None = None
-    n: int | None = None
-    max_completion_tokens: int | None = None
-    max_tokens: int | float | None = None
-    presence_penalty: float | None = None
-    frequency_penalty: float | None = None
-    logit_bias: dict[int, float] | None = None
-    response_format: JsonResponseFormat | type[BaseModel] | None = None
-    seed: int | None = None
-    logprobs: int | None = None
-    top_logprobs: int | None = None
-    api_base: str | None = None
-    api_version: str | None = None
-    callbacks: list[Any] | None = None
-    reasoning_effort: Literal["none", "low", "medium", "high"] | None = None
-    stream: bool = False
-    interceptor: Any = None
-    thinking: Any = None
-    context_window_size: int = 0
-    is_anthropic: bool = False
 
     def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
         """Factory method that routes to native SDK or falls back to LiteLLM.
@@ -459,7 +436,10 @@ class LLM(BaseLLM):
             logger.error(error_msg)
             raise ImportError(error_msg) from None
 
-        return object.__new__(cls)
+        instance = object.__new__(cls)
+        super(LLM, instance).__init__(model=model, is_litellm=True, **kwargs)
+        instance.is_litellm = True
+        return instance
 
     @classmethod
     def _matches_provider_pattern(cls, model: str, provider: str) -> bool:
@@ -644,23 +624,89 @@ class LLM(BaseLLM):
 
         return None
 
-    @model_validator(mode="before")
-    @classmethod
-    def _validate_llm_fields(cls, data: Any) -> Any:
-        if not isinstance(data, dict):
-            return data
-        model = data.get("model", "")
-        data["is_anthropic"] = cls._is_anthropic_model(model)
-        return data
+    def __init__(
+        self,
+        model: str,
+        timeout: float | int | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        n: int | None = None,
+        stop: str | list[str] | None = None,
+        max_completion_tokens: int | None = None,
+        max_tokens: int | float | None = None,
+        presence_penalty: float | None = None,
+        frequency_penalty: float | None = None,
+        logit_bias: dict[int, float] | None = None,
+        response_format: type[BaseModel] | None = None,
+        seed: int | None = None,
+        logprobs: int | None = None,
+        top_logprobs: int | None = None,
+        base_url: str | None = None,
+        api_base: str | None = None,
+        api_version: str | None = None,
+        api_key: str | None = None,
+        callbacks: list[Any] | None = None,
+        reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
+        stream: bool = False,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        thinking: AnthropicThinkingConfig | dict[str, Any] | None = None,
+        prefer_upload: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize LLM instance.
 
-    @model_validator(mode="after")
-    def _init_litellm(self) -> LLM:
-        self.is_litellm = True
-        if LITELLM_AVAILABLE:
-            litellm.drop_params = True
-            self.set_callbacks(self.callbacks or [])
-            self.set_env_callbacks()
-        return self
+        Note: This __init__ method is only called for fallback instances.
+        Native provider instances handle their own initialization in their respective classes.
+        """
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            base_url=base_url,
+            timeout=timeout,
+            **kwargs,
+        )
+        self.model = model
+        self.timeout = timeout
+        self.temperature = temperature
+        self.top_p = top_p
+        self.n = n
+        self.max_completion_tokens = max_completion_tokens
+        self.max_tokens = max_tokens
+        self.presence_penalty = presence_penalty
+        self.frequency_penalty = frequency_penalty
+        self.logit_bias = logit_bias
+        self.response_format = response_format
+        self.seed = seed
+        self.logprobs = logprobs
+        self.top_logprobs = top_logprobs
+        self.base_url = base_url
+        self.api_base = api_base
+        self.api_version = api_version
+        self.api_key = api_key
+        self.callbacks = callbacks
+        self.context_window_size = 0
+        self.reasoning_effort = reasoning_effort
+        self.prefer_upload = prefer_upload
+        self.additional_params = {
+            k: v for k, v in kwargs.items() if k not in ("is_litellm", "provider")
+        }
+        self.is_anthropic = self._is_anthropic_model(model)
+        self.stream = stream
+        self.interceptor = interceptor
+
+        litellm.drop_params = True
+
+        # Normalize self.stop to always be a list[str]
+        if stop is None:
+            self.stop: list[str] = []
+        elif isinstance(stop, str):
+            self.stop = [stop]
+        else:
+            self.stop = stop
+
+        self.set_callbacks(callbacks or [])
+        self.set_env_callbacks()
 
     @staticmethod
     def _is_anthropic_model(model: str) -> bool:
@@ -2396,7 +2442,7 @@ class LLM(BaseLLM):
             **filtered_params,
         )
 
-    def __deepcopy__(self, memo: dict[int, Any] | None = None) -> LLM:
+    def __deepcopy__(self, memo: dict[int, Any] | None) -> LLM:
         """Create a deep copy of the LLM instance."""
         import copy
 

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

@@ -14,18 +14,10 @@ from datetime import datetime
 import json
 import logging
 import re
-from typing import TYPE_CHECKING, Any, Final, Literal
+from typing import TYPE_CHECKING, Any, Final
 import uuid
 
-from pydantic import (
-    AliasChoices,
-    BaseModel,
-    ConfigDict,
-    Field,
-    PrivateAttr,
-    model_validator,
-)
-from typing_extensions import TypedDict
+from pydantic import BaseModel
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.llm_events import (
@@ -59,12 +51,6 @@ if TYPE_CHECKING:
     from crewai.utilities.types import LLMMessage
 
 
-class JsonResponseFormat(TypedDict):
-    """Response format requesting raw JSON output (e.g. ``{"type": "json_object"}``)."""
-
-    type: Literal["json_object"]
-
-
 DEFAULT_CONTEXT_WINDOW_SIZE: Final[int] = 4096
 DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True
 _JSON_EXTRACTION_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{.*}", re.DOTALL)
@@ -96,7 +82,7 @@ def get_current_call_id() -> str:
     return call_id
 
 
-class BaseLLM(BaseModel, ABC):
+class BaseLLM(ABC):
     """Abstract base class for LLM implementations.
 
     This class defines the interface that all LLM implementations must follow.
@@ -115,100 +101,56 @@ class BaseLLM(BaseModel, ABC):
         additional_params: Additional provider-specific parameters.
     """
 
-    model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True)
-
-    model: str
-    temperature: float | None = None
-    api_key: str | None = None
-    base_url: str | None = None
-    provider: str = Field(default="openai")
-    prefer_upload: bool = False
     is_litellm: bool = False
-    stop: list[str] = Field(
-        default_factory=list,
-        validation_alias=AliasChoices("stop", "stop_sequences"),
-    )
-    additional_params: dict[str, Any] = Field(default_factory=dict)
 
-    def __setattr__(self, name: str, value: Any) -> None:
-        if name in ("stop", "stop_sequences"):
-            if value is None:
-                value = []
-            elif isinstance(value, str):
-                value = [value]
-            elif not isinstance(value, list):
-                value = list(value)
-            name = "stop"
-        try:
-            super().__setattr__(name, value)
-        except ValueError:
-            if name in self.model_fields:
-                raise  # Re-raise validation errors on declared fields
-            # Fallback for attributes not declared as fields (e.g. mock patching)
-            object.__setattr__(self, name, value)
-        except AttributeError:
-            object.__setattr__(self, name, value)
+    def __init__(
+        self,
+        model: str,
+        temperature: float | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        provider: str | None = None,
+        prefer_upload: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize the BaseLLM with default attributes.
 
-    def __delattr__(self, name: str) -> None:
-        try:
-            super().__delattr__(name)
-        except AttributeError:
-            object.__delattr__(self, name)
-
-    @property
-    def stop_sequences(self) -> list[str]:
-        """Alias for ``stop`` — kept for backward compatibility with provider APIs.
-
-        Writes are handled by ``__setattr__``, which normalizes and redirects
-        ``stop_sequences`` assignments to the ``stop`` field.
+        Args:
+            model: The model identifier/name.
+            temperature: Optional temperature setting for response generation.
+            stop: Optional list of stop sequences for generation.
+            prefer_upload: Whether to prefer file upload over inline base64.
+            **kwargs: Additional provider-specific parameters.
         """
-        return self.stop
+        if not model:
+            raise ValueError("Model name is required and cannot be empty")
 
-    _token_usage: dict[str, int] = PrivateAttr(
-        default_factory=lambda: {
+        self.model = model
+        self.temperature = temperature
+        self.api_key = api_key
+        self.base_url = base_url
+        self.prefer_upload = prefer_upload
+        # Store additional parameters for provider-specific use
+        self.additional_params = kwargs
+        self._provider = provider or "openai"
+
+        stop = kwargs.pop("stop", None)
+        if stop is None:
+            self.stop: list[str] = []
+        elif isinstance(stop, str):
+            self.stop = [stop]
+        elif isinstance(stop, list):
+            self.stop = stop
+        else:
+            self.stop = []
+
+        self._token_usage = {
             "total_tokens": 0,
             "prompt_tokens": 0,
             "completion_tokens": 0,
             "successful_requests": 0,
             "cached_prompt_tokens": 0,
         }
-    )
-
-    @model_validator(mode="before")
-    @classmethod
-    def _validate_init_fields(cls, data: Any) -> Any:
-        if not isinstance(data, dict):
-            return data
-
-        if not data.get("model"):
-            raise ValueError("Model name is required and cannot be empty")
-
-        # Normalize stop: accept str, list, or None; also accept stop_sequences alias
-        stop_seqs = data.pop("stop_sequences", None)
-        stop = stop_seqs if stop_seqs is not None else data.get("stop")
-        if stop is None:
-            data["stop"] = []
-        elif isinstance(stop, str):
-            data["stop"] = [stop]
-        elif isinstance(stop, list):
-            data["stop"] = stop
-        else:
-            data["stop"] = list(stop)
-
-        # Default provider
-        if not data.get("provider"):
-            data["provider"] = "openai"
-
-        # Collect unknown kwargs into additional_params
-        known_fields = set(cls.model_fields.keys())
-        extras = {k: v for k, v in data.items() if k not in known_fields}
-        for k in extras:
-            data.pop(k)
-        existing = data.get("additional_params") or {}
-        existing.update(extras)
-        data["additional_params"] = existing
-
-        return data
 
     def to_config_dict(self) -> dict[str, Any]:
         """Serialize this LLM to a dict that can reconstruct it via ``LLM(**config)``.
@@ -232,6 +174,16 @@ class BaseLLM(BaseModel, ABC):
 
         return config
 
+    @property
+    def provider(self) -> str:
+        """Get the provider of the LLM."""
+        return self._provider
+
+    @provider.setter
+    def provider(self, value: str) -> None:
+        """Set the provider of the LLM."""
+        self._provider = value
+
     @abstractmethod
     def call(
         self,

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

@@ -3,13 +3,12 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import Any, Final, Literal, TypeGuard, cast
+from typing import TYPE_CHECKING, Any, Final, Literal, TypeGuard, cast
 
-from pydantic import BaseModel, PrivateAttr, model_validator
+from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM, JsonResponseFormat, llm_call_context
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llms.base_llm import BaseLLM, llm_call_context
 from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -18,6 +17,9 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 from crewai.utilities.types import LLMMessage
 
 
+if TYPE_CHECKING:
+    from crewai.llms.hooks.base import BaseInterceptor
+
 try:
     from anthropic import Anthropic, AsyncAnthropic, transform_schema
     from anthropic.types import (
@@ -148,47 +150,60 @@ class AnthropicCompletion(BaseLLM):
     offering native tool use, streaming support, and proper message formatting.
     """
 
-    model: str = "claude-3-5-sonnet-20241022"
-    timeout: float | None = None
-    max_retries: int = 2
-    max_tokens: int = 4096
-    top_p: float | None = None
-    stream: bool = False
-    client_params: dict[str, Any] | None = None
-    interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None
-    thinking: AnthropicThinkingConfig | None = None
-    response_format: JsonResponseFormat | type[BaseModel] | None = None
-    tool_search: AnthropicToolSearchConfig | None = None
-    is_claude_3: bool = False
-    supports_tools: bool = True
+    def __init__(
+        self,
+        model: str = "claude-3-5-sonnet-20241022",
+        api_key: str | None = None,
+        base_url: str | None = None,
+        timeout: float | None = None,
+        max_retries: int = 2,
+        temperature: float | None = None,
+        max_tokens: int = 4096,  # Required for Anthropic
+        top_p: float | None = None,
+        stop_sequences: list[str] | None = None,
+        stream: bool = False,
+        client_params: dict[str, Any] | None = None,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        thinking: AnthropicThinkingConfig | None = None,
+        response_format: type[BaseModel] | None = None,
+        tool_search: AnthropicToolSearchConfig | bool | None = None,
+        **kwargs: Any,
+    ):
+        """Initialize Anthropic chat completion client.
 
-    _client: Any = PrivateAttr(default=None)
-    _async_client: Any = PrivateAttr(default=None)
-    _previous_thinking_blocks: list[Any] = PrivateAttr(default_factory=list)
+        Args:
+            model: Anthropic model name (e.g., 'claude-3-5-sonnet-20241022')
+            api_key: Anthropic API key (defaults to ANTHROPIC_API_KEY env var)
+            base_url: Custom base URL for Anthropic API
+            timeout: Request timeout in seconds
+            max_retries: Maximum number of retries
+            temperature: Sampling temperature (0-1)
+            max_tokens: Maximum tokens in response (required for Anthropic)
+            top_p: Nucleus sampling parameter
+            stop_sequences: Stop sequences (Anthropic uses stop_sequences, not stop)
+            stream: Enable streaming responses
+            client_params: Additional parameters for the Anthropic client
+            interceptor: HTTP interceptor for modifying requests/responses at transport level.
+            response_format: Pydantic model for structured output. When provided, responses
+                will be validated against this model schema.
+            tool_search: Enable Anthropic's server-side tool search. When True, uses "bm25"
+                variant by default. Pass an AnthropicToolSearchConfig to choose "regex" or
+                "bm25". When enabled, tools are automatically marked with defer_loading=True
+                and a tool search tool is injected into the tools list.
+            **kwargs: Additional parameters
+        """
+        super().__init__(
+            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
+        )
 
-    @model_validator(mode="before")
-    @classmethod
-    def _normalize_anthropic_fields(cls, data: Any) -> Any:
-        if not isinstance(data, dict):
-            return data
-        # Anthropic uses stop_sequences; normalize from stop kwarg
-        popped = data.pop("stop_sequences", None)
-        seqs = popped if popped is not None else (data.get("stop") or [])
-        if isinstance(seqs, str):
-            seqs = [seqs]
-        data["stop"] = seqs
-        data["is_claude_3"] = "claude-3" in data.get("model", "").lower()
-        # Normalize tool_search
-        ts = data.get("tool_search")
-        if ts is True:
-            data["tool_search"] = AnthropicToolSearchConfig()
-        elif ts is not None and not isinstance(ts, AnthropicToolSearchConfig):
-            data["tool_search"] = None
-        return data
+        # Client params
+        self.interceptor = interceptor
+        self.client_params = client_params
+        self.base_url = base_url
+        self.timeout = timeout
+        self.max_retries = max_retries
 
-    @model_validator(mode="after")
-    def _init_clients(self) -> AnthropicCompletion:
-        self._client = Anthropic(**self._get_client_params())
+        self.client = Anthropic(**self._get_client_params())
 
         async_client_params = self._get_client_params()
         if self.interceptor:
@@ -196,8 +211,51 @@ class AnthropicCompletion(BaseLLM):
             async_http_client = httpx.AsyncClient(transport=async_transport)
             async_client_params["http_client"] = async_http_client
 
-        self._async_client = AsyncAnthropic(**async_client_params)
-        return self
+        self.async_client = AsyncAnthropic(**async_client_params)
+
+        # Store completion parameters
+        self.max_tokens = max_tokens
+        self.top_p = top_p
+        self.stream = stream
+        self.stop_sequences = stop_sequences or []
+        self.thinking = thinking
+        self.previous_thinking_blocks: list[ThinkingBlock] = []
+        self.response_format = response_format
+        # Tool search config
+        self.tool_search: AnthropicToolSearchConfig | None
+        if tool_search is True:
+            self.tool_search = AnthropicToolSearchConfig()
+        elif isinstance(tool_search, AnthropicToolSearchConfig):
+            self.tool_search = tool_search
+        else:
+            self.tool_search = None
+        # Model-specific settings
+        self.is_claude_3 = "claude-3" in model.lower()
+        self.supports_tools = True
+
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return self.stop_sequences
+
+    @stop.setter
+    def stop(self, value: list[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Anthropic API.
+
+        Args:
+            value: Stop sequences as a list, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, list):
+            self.stop_sequences = value
+        else:
+            self.stop_sequences = []
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Anthropic-specific fields."""
@@ -693,11 +751,11 @@ class AnthropicCompletion(BaseLLM):
                         )
                 elif isinstance(content, list):
                     formatted_messages.append({"role": "assistant", "content": content})
-                elif self.thinking and self._previous_thinking_blocks:
+                elif self.thinking and self.previous_thinking_blocks:
                     structured_content = cast(
                         list[dict[str, Any]],
                         [
-                            *self._previous_thinking_blocks,
+                            *self.previous_thinking_blocks,
                             {"type": "text", "text": content if content else ""},
                         ],
                     )
@@ -751,7 +809,7 @@ class AnthropicCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: JsonResponseFormat | type[BaseModel] | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming message completion."""
         uses_file_api = _contains_file_id_reference(params.get("messages", []))
@@ -785,11 +843,11 @@ class AnthropicCompletion(BaseLLM):
         try:
             if betas:
                 params["betas"] = betas
-                response = self._client.beta.messages.create(
+                response = self.client.beta.messages.create(
                     **params, extra_body=extra_body
                 )
             else:
-                response = self._client.messages.create(**params)
+                response = self.client.messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -870,7 +928,7 @@ class AnthropicCompletion(BaseLLM):
                         thinking_blocks.append(cast(ThinkingBlock, thinking_block))
 
         if thinking_blocks:
-            self._previous_thinking_blocks = thinking_blocks
+            self.previous_thinking_blocks = thinking_blocks
 
         content = self._apply_stop_words(content)
         self._emit_call_completed_event(
@@ -894,7 +952,7 @@ class AnthropicCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: JsonResponseFormat | type[BaseModel] | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle streaming message completion."""
         betas: list[str] = []
@@ -933,9 +991,9 @@ class AnthropicCompletion(BaseLLM):
         current_tool_calls: dict[int, dict[str, Any]] = {}
 
         stream_context = (
-            self._client.beta.messages.stream(**stream_params, extra_body=extra_body)
+            self.client.beta.messages.stream(**stream_params, extra_body=extra_body)
             if betas
-            else self._client.messages.stream(**stream_params)
+            else self.client.messages.stream(**stream_params)
         )
         with stream_context as stream:
             response_id = None
@@ -1014,7 +1072,7 @@ class AnthropicCompletion(BaseLLM):
                     thinking_blocks.append(cast(ThinkingBlock, thinking_block))
 
         if thinking_blocks:
-            self._previous_thinking_blocks = thinking_blocks
+            self.previous_thinking_blocks = thinking_blocks
 
         usage = self._extract_anthropic_token_usage(final_message)
         self._track_token_usage_internal(usage)
@@ -1211,7 +1269,7 @@ class AnthropicCompletion(BaseLLM):
 
         try:
             # Send tool results back to Claude for final response
-            final_response: Message = self._client.messages.create(**follow_up_params)
+            final_response: Message = self.client.messages.create(**follow_up_params)
 
             # Track token usage for follow-up call
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
@@ -1230,7 +1288,7 @@ class AnthropicCompletion(BaseLLM):
                             thinking_blocks.append(cast(ThinkingBlock, thinking_block))
 
             if thinking_blocks:
-                self._previous_thinking_blocks = thinking_blocks
+                self.previous_thinking_blocks = thinking_blocks
 
             final_content = self._apply_stop_words(final_content)
 
@@ -1272,7 +1330,7 @@ class AnthropicCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: JsonResponseFormat | type[BaseModel] | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming async message completion."""
         uses_file_api = _contains_file_id_reference(params.get("messages", []))
@@ -1306,11 +1364,11 @@ class AnthropicCompletion(BaseLLM):
         try:
             if betas:
                 params["betas"] = betas
-                response = await self._async_client.beta.messages.create(
+                response = await self.async_client.beta.messages.create(
                     **params, extra_body=extra_body
                 )
             else:
-                response = await self._async_client.messages.create(**params)
+                response = await self.async_client.messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -1403,7 +1461,7 @@ class AnthropicCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: JsonResponseFormat | type[BaseModel] | None = None,
+        response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle async streaming message completion."""
         betas: list[str] = []
@@ -1440,11 +1498,11 @@ class AnthropicCompletion(BaseLLM):
         current_tool_calls: dict[int, dict[str, Any]] = {}
 
         stream_context = (
-            self._async_client.beta.messages.stream(
+            self.async_client.beta.messages.stream(
                 **stream_params, extra_body=extra_body
             )
             if betas
-            else self._async_client.messages.stream(**stream_params)
+            else self.async_client.messages.stream(**stream_params)
         )
         async with stream_context as stream:
             response_id = None
@@ -1606,7 +1664,7 @@ class AnthropicCompletion(BaseLLM):
         ]
 
         try:
-            final_response: Message = await self._async_client.messages.create(
+            final_response: Message = await self.async_client.messages.create(
                 **follow_up_params
             )
 
@@ -1728,8 +1786,8 @@ class AnthropicCompletion(BaseLLM):
             from crewai_files.uploaders.anthropic import AnthropicFileUploader
 
             return AnthropicFileUploader(
-                client=self._client,
-                async_client=self._async_client,
+                client=self.client,
+                async_client=self.async_client,
             )
         except ImportError:
             return None

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

@@ -3,13 +3,11 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import Any, TypedDict
-from urllib.parse import urlparse
+from typing import TYPE_CHECKING, Any, TypedDict
 
-from pydantic import BaseModel, PrivateAttr, model_validator
+from pydantic import BaseModel
 from typing_extensions import Self
 
-from crewai.llms.hooks.base import BaseInterceptor
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -18,6 +16,10 @@ from crewai.utilities.pydantic_schema_utils import generate_model_description
 from crewai.utilities.types import LLMMessage
 
 
+if TYPE_CHECKING:
+    from crewai.llms.hooks.base import BaseInterceptor
+
+
 try:
     from azure.ai.inference import (
         ChatCompletionsClient,
@@ -74,84 +76,109 @@ class AzureCompletion(BaseLLM):
     offering native function calling, streaming support, and proper Azure authentication.
     """
 
-    endpoint: str | None = None
-    api_version: str | None = None
-    timeout: float | None = None
-    max_retries: int = 2
-    top_p: float | None = None
-    frequency_penalty: float | None = None
-    presence_penalty: float | None = None
-    max_tokens: int | None = None
-    stream: bool = False
-    interceptor: BaseInterceptor[Any, Any] | None = None
-    response_format: type[BaseModel] | None = None
-    is_openai_model: bool = False
-    is_azure_openai_endpoint: bool = False
+    def __init__(
+        self,
+        model: str,
+        api_key: str | None = None,
+        endpoint: str | None = None,
+        api_version: str | None = None,
+        timeout: float | None = None,
+        max_retries: int = 2,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        frequency_penalty: float | None = None,
+        presence_penalty: float | None = None,
+        max_tokens: int | None = None,
+        stop: list[str] | None = None,
+        stream: bool = False,
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        response_format: type[BaseModel] | None = None,
+        **kwargs: Any,
+    ):
+        """Initialize Azure AI Inference chat completion client.
 
-    _client: Any = PrivateAttr(default=None)
-    _async_client: Any = PrivateAttr(default=None)
-
-    @model_validator(mode="before")
-    @classmethod
-    def _normalize_azure_fields(cls, data: Any) -> Any:
-        if not isinstance(data, dict):
-            return data
-
-        if data.get("interceptor") is not None:
+        Args:
+            model: Azure deployment name or model name
+            api_key: Azure API key (defaults to AZURE_API_KEY env var)
+            endpoint: Azure endpoint URL (defaults to AZURE_ENDPOINT env var)
+            api_version: Azure API version (defaults to AZURE_API_VERSION env var)
+            timeout: Request timeout in seconds
+            max_retries: Maximum number of retries
+            temperature: Sampling temperature (0-2)
+            top_p: Nucleus sampling parameter
+            frequency_penalty: Frequency penalty (-2 to 2)
+            presence_penalty: Presence penalty (-2 to 2)
+            max_tokens: Maximum tokens in response
+            stop: Stop sequences
+            stream: Enable streaming responses
+            interceptor: HTTP interceptor (not yet supported for Azure).
+            response_format: Pydantic model for structured output. Used as default when
+                           response_model is not passed to call()/acall() methods.
+                           Only works with OpenAI models deployed on Azure.
+            **kwargs: Additional parameters
+        """
+        if interceptor is not None:
             raise NotImplementedError(
                 "HTTP interceptors are not yet supported for Azure AI Inference provider. "
                 "Interceptors are currently supported for OpenAI and Anthropic providers only."
             )
 
-        # Resolve env vars
-        data["api_key"] = data.get("api_key") or os.getenv("AZURE_API_KEY")
-        data["endpoint"] = (
-            data.get("endpoint")
+        super().__init__(
+            model=model, temperature=temperature, stop=stop or [], **kwargs
+        )
+
+        self.api_key = api_key or os.getenv("AZURE_API_KEY")
+        self.endpoint = (
+            endpoint
             or os.getenv("AZURE_ENDPOINT")
             or os.getenv("AZURE_OPENAI_ENDPOINT")
             or os.getenv("AZURE_API_BASE")
         )
-        data["api_version"] = (
-            data.get("api_version") or os.getenv("AZURE_API_VERSION") or "2024-06-01"
-        )
+        self.api_version = api_version or os.getenv("AZURE_API_VERSION") or "2024-06-01"
+        self.timeout = timeout
+        self.max_retries = max_retries
 
-        if not data["api_key"]:
+        if not self.api_key:
             raise ValueError(
                 "Azure API key is required. Set AZURE_API_KEY environment variable or pass api_key parameter."
             )
-        if not data["endpoint"]:
+        if not self.endpoint:
             raise ValueError(
                 "Azure endpoint is required. Set AZURE_ENDPOINT environment variable or pass endpoint parameter."
             )
 
-        model = data.get("model", "")
-        data["endpoint"] = AzureCompletion._validate_and_fix_endpoint(
-            data["endpoint"], model
-        )
-        data["is_openai_model"] = any(
-            prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
-        )
-        parsed = urlparse(data["endpoint"])
-        hostname = parsed.hostname or ""
-        data["is_azure_openai_endpoint"] = (
-            hostname == "openai.azure.com" or hostname.endswith(".openai.azure.com")
-        ) and "/openai/deployments/" in data["endpoint"]
-        return data
+        # Validate and potentially fix Azure OpenAI endpoint URL
+        self.endpoint = self._validate_and_fix_endpoint(self.endpoint, model)
 
-    @model_validator(mode="after")
-    def _init_clients(self) -> AzureCompletion:
-        if not self.api_key:
-            raise ValueError("Azure API key is required.")
-        client_kwargs: dict[str, Any] = {
+        # Build client kwargs
+        client_kwargs = {
             "endpoint": self.endpoint,
             "credential": AzureKeyCredential(self.api_key),
         }
+
+        # Add api_version if specified (primarily for Azure OpenAI endpoints)
         if self.api_version:
             client_kwargs["api_version"] = self.api_version
 
-        self._client = ChatCompletionsClient(**client_kwargs)
-        self._async_client = AsyncChatCompletionsClient(**client_kwargs)
-        return self
+        self.client = ChatCompletionsClient(**client_kwargs)  # type: ignore[arg-type]
+
+        self.async_client = AsyncChatCompletionsClient(**client_kwargs)  # type: ignore[arg-type]
+
+        self.top_p = top_p
+        self.frequency_penalty = frequency_penalty
+        self.presence_penalty = presence_penalty
+        self.max_tokens = max_tokens
+        self.stream = stream
+        self.response_format = response_format
+
+        self.is_openai_model = any(
+            prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
+        )
+
+        self.is_azure_openai_endpoint = (
+            "openai.azure.com" in self.endpoint
+            and "/openai/deployments/" in self.endpoint
+        )
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Azure-specific fields."""
@@ -188,11 +215,7 @@ class AzureCompletion(BaseLLM):
         Returns:
             Validated and potentially corrected endpoint URL
         """
-        ep_host = urlparse(endpoint).hostname or ""
-        is_azure_openai = ep_host == "openai.azure.com" or ep_host.endswith(
-            ".openai.azure.com"
-        )
-        if is_azure_openai and "/openai/deployments/" not in endpoint:
+        if "openai.azure.com" in endpoint and "/openai/deployments/" not in endpoint:
             endpoint = endpoint.rstrip("/")
 
             if not endpoint.endswith("/openai/deployments"):
@@ -708,7 +731,7 @@ class AzureCompletion(BaseLLM):
         """Handle non-streaming chat completion."""
         try:
             # Cast params to Any to avoid type checking issues with TypedDict unpacking
-            response: ChatCompletions = self._client.complete(**params)
+            response: ChatCompletions = self.client.complete(**params)  # type: ignore[assignment,arg-type]
             return self._process_completion_response(
                 response=response,
                 params=params,
@@ -903,7 +926,7 @@ class AzureCompletion(BaseLLM):
         tool_calls: dict[int, dict[str, Any]] = {}
 
         usage_data = {"total_tokens": 0}
-        for update in self._client.complete(**params):
+        for update in self.client.complete(**params):  # type: ignore[arg-type]
             if isinstance(update, StreamingChatCompletionsUpdate):
                 if update.usage:
                     usage = update.usage
@@ -944,7 +967,7 @@ class AzureCompletion(BaseLLM):
         """Handle non-streaming chat completion asynchronously."""
         try:
             # Cast params to Any to avoid type checking issues with TypedDict unpacking
-            response: ChatCompletions = await self._async_client.complete(**params)
+            response: ChatCompletions = await self.async_client.complete(**params)  # type: ignore[assignment,arg-type]
             return self._process_completion_response(
                 response=response,
                 params=params,
@@ -970,8 +993,8 @@ class AzureCompletion(BaseLLM):
 
         usage_data = {"total_tokens": 0}
 
-        stream = await self._async_client.complete(**params)
-        async for update in stream:
+        stream = await self.async_client.complete(**params)  # type: ignore[arg-type]
+        async for update in stream:  # type: ignore[union-attr]
             if isinstance(update, StreamingChatCompletionsUpdate):
                 if hasattr(update, "usage") and update.usage:
                     usage = update.usage
@@ -1087,8 +1110,8 @@ class AzureCompletion(BaseLLM):
         This ensures proper cleanup of the underlying aiohttp session
         to avoid unclosed connector warnings.
         """
-        if hasattr(self._async_client, "close"):
-            await self._async_client.close()
+        if hasattr(self.async_client, "close"):
+            await self.async_client.close()
 
     async def __aenter__(self) -> Self:
         """Async context manager entry."""

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

@@ -7,7 +7,7 @@ import logging
 import os
 from typing import TYPE_CHECKING, Any, TypedDict, cast
 
-from pydantic import BaseModel, PrivateAttr, model_validator
+from pydantic import BaseModel
 from typing_extensions import Required
 
 from crewai.events.types.llm_events import LLMCallType
@@ -33,7 +33,7 @@ if TYPE_CHECKING:
         ToolTypeDef,
     )
 
-from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.llms.hooks.base import BaseInterceptor
 
 
 try:
@@ -228,97 +228,129 @@ class BedrockCompletion(BaseLLM):
     - Model-specific conversation format handling (e.g., Cohere requirements)
     """
 
-    model: str = "anthropic.claude-3-5-sonnet-20241022-v2:0"
-    aws_access_key_id: str | None = None
-    aws_secret_access_key: str | None = None
-    aws_session_token: str | None = None
-    region_name: str | None = None
-    max_tokens: int | None = None
-    top_p: float | None = None
-    top_k: int | None = None
-    stream: bool = False
-    guardrail_config: dict[str, Any] | None = None
-    additional_model_request_fields: dict[str, Any] | None = None
-    additional_model_response_field_paths: list[str] | None = None
-    interceptor: BaseInterceptor[Any, Any] | None = None
-    response_format: type[BaseModel] | None = None
-    is_claude_model: bool = False
-    supports_tools: bool = True
-    supports_streaming: bool = True
-    model_id: str = ""
+    def __init__(
+        self,
+        model: str = "anthropic.claude-3-5-sonnet-20241022-v2:0",
+        aws_access_key_id: str | None = None,
+        aws_secret_access_key: str | None = None,
+        aws_session_token: str | None = None,
+        region_name: str | None = None,
+        temperature: float | None = None,
+        max_tokens: int | None = None,
+        top_p: float | None = None,
+        top_k: int | None = None,
+        stop_sequences: Sequence[str] | None = None,
+        stream: bool = False,
+        guardrail_config: dict[str, Any] | None = None,
+        additional_model_request_fields: dict[str, Any] | None = None,
+        additional_model_response_field_paths: list[str] | None = None,
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        response_format: type[BaseModel] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize AWS Bedrock completion client.
 
-    _client: Any = PrivateAttr(default=None)
-    _async_exit_stack: Any = PrivateAttr(default=None)
-    _async_client_initialized: bool = PrivateAttr(default=False)
-    _async_client: Any = PrivateAttr(default=None)
-
-    @model_validator(mode="before")
-    @classmethod
-    def _normalize_bedrock_fields(cls, data: Any) -> Any:
-        if not isinstance(data, dict):
-            return data
-
-        if data.get("interceptor") is not None:
+        Args:
+            model: The Bedrock model ID to use
+            aws_access_key_id: AWS access key (defaults to environment variable)
+            aws_secret_access_key: AWS secret key (defaults to environment variable)
+            aws_session_token: AWS session token for temporary credentials
+            region_name: AWS region name
+            temperature: Sampling temperature for response generation
+            max_tokens: Maximum tokens to generate
+            top_p: Nucleus sampling parameter
+            top_k: Top-k sampling parameter (Claude models only)
+            stop_sequences: List of sequences that stop generation
+            stream: Whether to use streaming responses
+            guardrail_config: Guardrail configuration for content filtering
+            additional_model_request_fields: Model-specific request parameters
+            additional_model_response_field_paths: Custom response field paths
+            interceptor: HTTP interceptor (not yet supported for Bedrock).
+            response_format: Pydantic model for structured output. Used as default when
+                           response_model is not passed to call()/acall() methods.
+            **kwargs: Additional parameters
+        """
+        if interceptor is not None:
             raise NotImplementedError(
                 "HTTP interceptors are not yet supported for AWS Bedrock provider. "
                 "Interceptors are currently supported for OpenAI and Anthropic providers only."
             )
 
-        # Force provider to bedrock
-        data.pop("provider", None)
-        data["provider"] = "bedrock"
+        # Extract provider from kwargs to avoid duplicate argument
+        kwargs.pop("provider", None)
 
-        # Normalize stop_sequences from stop kwarg
-        popped = data.pop("stop_sequences", None)
-        seqs = popped if popped is not None else (data.get("stop") or [])
-        if isinstance(seqs, str):
-            seqs = [seqs]
-        elif isinstance(seqs, Sequence) and not isinstance(seqs, list):
-            seqs = list(seqs)
-        data["stop"] = seqs
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            stop=stop_sequences or [],
+            provider="bedrock",
+            **kwargs,
+        )
 
-        # Resolve env vars
-        data["aws_access_key_id"] = data.get("aws_access_key_id") or os.getenv(
-            "AWS_ACCESS_KEY_ID"
+        # Configure client with timeouts and retries following AWS best practices
+        config = Config(
+            read_timeout=300,
+            retries={
+                "max_attempts": 3,
+                "mode": "adaptive",
+            },
+            tcp_keepalive=True,
         )
-        data["aws_secret_access_key"] = data.get("aws_secret_access_key") or os.getenv(
-            "AWS_SECRET_ACCESS_KEY"
-        )
-        data["aws_session_token"] = data.get("aws_session_token") or os.getenv(
-            "AWS_SESSION_TOKEN"
-        )
-        data["region_name"] = (
-            data.get("region_name")
+
+        self.region_name = (
+            region_name
             or os.getenv("AWS_DEFAULT_REGION")
             or os.getenv("AWS_REGION_NAME")
             or "us-east-1"
         )
 
-        model = data.get("model", "anthropic.claude-3-5-sonnet-20241022-v2:0")
-        data["is_claude_model"] = "claude" in model.lower()
-        data["model_id"] = model
-        return data
-
-    @model_validator(mode="after")
-    def _init_clients(self) -> BedrockCompletion:
-        config = Config(
-            read_timeout=300,
-            retries={"max_attempts": 3, "mode": "adaptive"},
-            tcp_keepalive=True,
+        self.aws_access_key_id = aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID")
+        self.aws_secret_access_key = aws_secret_access_key or os.getenv(
+            "AWS_SECRET_ACCESS_KEY"
         )
+        self.aws_session_token = aws_session_token or os.getenv("AWS_SESSION_TOKEN")
+
+        # Initialize Bedrock client with proper configuration
         session = Session(
             aws_access_key_id=self.aws_access_key_id,
             aws_secret_access_key=self.aws_secret_access_key,
             aws_session_token=self.aws_session_token,
             region_name=self.region_name,
         )
-        self._client = session.client("bedrock-runtime", config=config)
+
+        self.client = session.client("bedrock-runtime", config=config)
+
         self._async_exit_stack = AsyncExitStack() if AIOBOTOCORE_AVAILABLE else None
-        return self
+        self._async_client_initialized = False
+
+        # Store completion parameters
+        self.max_tokens = max_tokens
+        self.top_p = top_p
+        self.top_k = top_k
+        self.stream = stream
+        self.stop_sequences = stop_sequences
+        self.response_format = response_format
+
+        # Store advanced features (optional)
+        self.guardrail_config = guardrail_config
+        self.additional_model_request_fields = additional_model_request_fields
+        self.additional_model_response_field_paths = (
+            additional_model_response_field_paths
+        )
+
+        # Model-specific settings
+        self.is_claude_model = "claude" in model.lower()
+        self.supports_tools = True  # Converse API supports tools for most models
+        self.supports_streaming = True
+
+        # Handle inference profiles for newer models
+        self.model_id = model
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Bedrock-specific fields."""
         config = super().to_config_dict()
+        # NOTE: AWS credentials (access_key, secret_key, session_token) are
+        # intentionally excluded — they must come from env on resume.
         if self.region_name and self.region_name != "us-east-1":
             config["region_name"] = self.region_name
         if self.max_tokens is not None:
@@ -331,6 +363,30 @@ class BedrockCompletion(BaseLLM):
             config["guardrail_config"] = self.guardrail_config
         return config
 
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return [] if self.stop_sequences is None else list(self.stop_sequences)
+
+    @stop.setter
+    def stop(self, value: Sequence[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Bedrock API.
+
+        Args:
+            value: Stop sequences as a Sequence, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, Sequence):
+            self.stop_sequences = list(value)
+        else:
+            self.stop_sequences = []
+
     def call(
         self,
         messages: str | list[LLMMessage],
@@ -654,7 +710,7 @@ class BedrockCompletion(BaseLLM):
                     raise ValueError(f"Invalid message format at index {i}")
 
             # Call Bedrock Converse API with proper error handling
-            response = self._client.converse(
+            response = self.client.converse(
                 modelId=self.model_id,
                 messages=cast(
                     "Sequence[MessageTypeDef | MessageOutputTypeDef]",
@@ -938,13 +994,13 @@ class BedrockCompletion(BaseLLM):
         accumulated_tool_input = ""
 
         try:
-            response = self._client.converse_stream(
+            response = self.client.converse_stream(
                 modelId=self.model_id,
                 messages=cast(
                     "Sequence[MessageTypeDef | MessageOutputTypeDef]",
                     cast(object, messages),
                 ),
-                **body,
+                **body,  # type: ignore[arg-type]
             )
 
             stream = response.get("stream")

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

@@ -5,13 +5,12 @@ import json
 import logging
 import os
 import re
-from typing import Any, Literal, cast
+from typing import TYPE_CHECKING, Any, Literal, cast
 
-from pydantic import BaseModel, Field, PrivateAttr, model_validator
+from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM, llm_call_context
-from crewai.llms.hooks.base import BaseInterceptor
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -20,6 +19,10 @@ from crewai.utilities.pydantic_schema_utils import generate_model_description
 from crewai.utilities.types import LLMMessage
 
 
+if TYPE_CHECKING:
+    from crewai.llms.hooks.base import BaseInterceptor
+
+
 try:
     from google import genai
     from google.genai import types
@@ -41,84 +44,137 @@ class GeminiCompletion(BaseLLM):
     offering native function calling, streaming support, and proper Gemini formatting.
     """
 
-    model: str = "gemini-2.0-flash-001"
-    project: str | None = None
-    location: str | None = None
-    top_p: float | None = None
-    top_k: int | None = None
-    max_output_tokens: int | None = None
-    stream: bool = False
-    safety_settings: dict[str, Any] = Field(default_factory=dict)
-    client_params: dict[str, Any] = Field(default_factory=dict)
-    interceptor: BaseInterceptor[Any, Any] | None = None
-    use_vertexai: bool = False
-    response_format: type[BaseModel] | None = None
-    thinking_config: Any = None
-    tools: list[dict[str, Any]] | None = None
-    supports_tools: bool = False
-    is_gemini_2_0: bool = False
+    def __init__(
+        self,
+        model: str = "gemini-2.0-flash-001",
+        api_key: str | None = None,
+        project: str | None = None,
+        location: str | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        top_k: int | None = None,
+        max_output_tokens: int | None = None,
+        stop_sequences: list[str] | None = None,
+        stream: bool = False,
+        safety_settings: dict[str, Any] | None = None,
+        client_params: dict[str, Any] | None = None,
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        use_vertexai: bool | None = None,
+        response_format: type[BaseModel] | None = None,
+        thinking_config: types.ThinkingConfig | None = None,
+        **kwargs: Any,
+    ):
+        """Initialize Google Gemini chat completion client.
 
-    _client: Any = PrivateAttr(default=None)
-
-    @model_validator(mode="before")
-    @classmethod
-    def _normalize_gemini_fields(cls, data: Any) -> Any:
-        if not isinstance(data, dict):
-            return data
-
-        if data.get("interceptor") is not None:
+        Args:
+            model: Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')
+            api_key: Google API key for Gemini API authentication.
+                    Defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var.
+                    NOTE: Cannot be used with Vertex AI (project parameter). Use Gemini API instead.
+            project: Google Cloud project ID for Vertex AI with ADC authentication.
+                    Requires Application Default Credentials (gcloud auth application-default login).
+                    NOTE: Vertex AI does NOT support API keys, only OAuth2/ADC.
+                    If both api_key and project are set, api_key takes precedence.
+            location: Google Cloud location (for Vertex AI with ADC, defaults to 'us-central1')
+            temperature: Sampling temperature (0-2)
+            top_p: Nucleus sampling parameter
+            top_k: Top-k sampling parameter
+            max_output_tokens: Maximum tokens in response
+            stop_sequences: Stop sequences
+            stream: Enable streaming responses
+            safety_settings: Safety filter settings
+            client_params: Additional parameters to pass to the Google Gen AI Client constructor.
+                          Supports parameters like http_options, credentials, debug_config, etc.
+            interceptor: HTTP interceptor (not yet supported for Gemini).
+            use_vertexai: Whether to use Vertex AI instead of Gemini API.
+                         - True: Use Vertex AI (with ADC or Express mode with API key)
+                         - False: Use Gemini API (explicitly override env var)
+                         - None (default): Check GOOGLE_GENAI_USE_VERTEXAI env var
+                         When using Vertex AI with API key (Express mode), http_options with
+                         api_version="v1" is automatically configured.
+            response_format: Pydantic model for structured output. Used as default when
+                           response_model is not passed to call()/acall() methods.
+            thinking_config: ThinkingConfig for thinking models (gemini-2.5+, gemini-3+).
+                           Controls thought output via include_thoughts, thinking_budget,
+                           and thinking_level. When None, thinking models automatically
+                           get include_thoughts=True so thought content is surfaced.
+            **kwargs: Additional parameters
+        """
+        if interceptor is not None:
             raise NotImplementedError(
                 "HTTP interceptors are not yet supported for Google Gemini provider. "
                 "Interceptors are currently supported for OpenAI and Anthropic providers only."
             )
 
-        # Normalize stop_sequences from stop kwarg
-        popped = data.pop("stop_sequences", None)
-        seqs = popped if popped is not None else (data.get("stop") or [])
-        if isinstance(seqs, str):
-            seqs = [seqs]
-        data["stop"] = seqs
-
-        # Resolve env vars
-        data["api_key"] = (
-            data.get("api_key")
-            or os.getenv("GOOGLE_API_KEY")
-            or os.getenv("GEMINI_API_KEY")
-        )
-        data["project"] = data.get("project") or os.getenv("GOOGLE_CLOUD_PROJECT")
-        data["location"] = (
-            data.get("location") or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
+        super().__init__(
+            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
         )
 
-        use_vx = data.get("use_vertexai")
-        if use_vx is None:
-            use_vx = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
-        data["use_vertexai"] = use_vx
+        # Store client params for later use
+        self.client_params = client_params or {}
+
+        # Get API configuration with environment variable fallbacks
+        self.api_key = (
+            api_key or os.getenv("GOOGLE_API_KEY") or os.getenv("GEMINI_API_KEY")
+        )
+        self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
+        self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
+
+        if use_vertexai is None:
+            use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
+
+        self.client = self._initialize_client(use_vertexai)
+
+        # Store completion parameters
+        self.top_p = top_p
+        self.top_k = top_k
+        self.max_output_tokens = max_output_tokens
+        self.stream = stream
+        self.safety_settings = safety_settings or {}
+        self.stop_sequences = stop_sequences or []
+        self.tools: list[dict[str, Any]] | None = None
+        self.response_format = response_format
 
         # Model-specific settings
-        model = data.get("model", "gemini-2.0-flash-001")
         version_match = re.search(r"gemini-(\d+(?:\.\d+)?)", model.lower())
-        data["supports_tools"] = bool(
+        self.supports_tools = bool(
             version_match and float(version_match.group(1)) >= 1.5
         )
-        data["is_gemini_2_0"] = bool(
+        self.is_gemini_2_0 = bool(
             version_match and float(version_match.group(1)) >= 2.0
         )
 
-        # Auto-enable thinking for gemini-2.5+
+        self.thinking_config = thinking_config
         if (
-            data.get("thinking_config") is None
+            self.thinking_config is None
             and version_match
             and float(version_match.group(1)) >= 2.5
         ):
-            data["thinking_config"] = types.ThinkingConfig(include_thoughts=True)
+            self.thinking_config = types.ThinkingConfig(include_thoughts=True)
 
-        return data
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return self.stop_sequences
 
-    @model_validator(mode="after")
-    def _init_client(self) -> GeminiCompletion:
-        self._client = self._initialize_client(self.use_vertexai)
-        return self
+    @stop.setter
+    def stop(self, value: list[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Gemini API.
+
+        Args:
+            value: Stop sequences as a list, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, list):
+            self.stop_sequences = value
+        else:
+            self.stop_sequences = []
 
     def to_config_dict(self) -> dict[str, Any]:
         """Extend base config with Gemini/Vertex-specific fields."""
@@ -227,8 +283,8 @@ class GeminiCompletion(BaseLLM):
 
         if (
             hasattr(self, "client")
-            and hasattr(self._client, "vertexai")
-            and self._client.vertexai
+            and hasattr(self.client, "vertexai")
+            and self.client.vertexai
         ):
             # Vertex AI configuration
             params.update(
@@ -1096,7 +1152,7 @@ class GeminiCompletion(BaseLLM):
         try:
             # The API accepts list[Content] but mypy is overly strict about variance
             contents_for_api: Any = contents
-            response = self._client.models.generate_content(
+            response = self.client.models.generate_content(
                 model=self.model,
                 contents=contents_for_api,
                 config=config,
@@ -1136,7 +1192,7 @@ class GeminiCompletion(BaseLLM):
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents
-        for chunk in self._client.models.generate_content_stream(
+        for chunk in self.client.models.generate_content_stream(
             model=self.model,
             contents=contents_for_api,
             config=config,
@@ -1174,7 +1230,7 @@ class GeminiCompletion(BaseLLM):
         try:
             # The API accepts list[Content] but mypy is overly strict about variance
             contents_for_api: Any = contents
-            response = await self._client.aio.models.generate_content(
+            response = await self.client.aio.models.generate_content(
                 model=self.model,
                 contents=contents_for_api,
                 config=config,
@@ -1214,7 +1270,7 @@ class GeminiCompletion(BaseLLM):
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents
-        stream = await self._client.aio.models.generate_content_stream(
+        stream = await self.client.aio.models.generate_content_stream(
             model=self.model,
             contents=contents_for_api,
             config=config,
@@ -1418,6 +1474,6 @@ class GeminiCompletion(BaseLLM):
         try:
             from crewai_files.uploaders.gemini import GeminiFileUploader
 
-            return GeminiFileUploader(client=self._client)
+            return GeminiFileUploader(client=self.client)
         except ImportError:
             return None

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

@@ -14,11 +14,10 @@ from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion import Choice
 from openai.types.chat.chat_completion_chunk import ChoiceDelta
 from openai.types.responses import Response
-from pydantic import BaseModel, PrivateAttr, model_validator
+from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM, JsonResponseFormat, llm_call_context
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llms.base_llm import BaseLLM, llm_call_context
 from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -30,6 +29,7 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent.core import Agent
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
 
@@ -183,69 +183,77 @@ class OpenAICompletion(BaseLLM):
         "computer_use": "computer_use_preview",
     }
 
-    model: str = "gpt-4o"
-    organization: str | None = None
-    project: str | None = None
-    timeout: float | None = None
-    max_retries: int = 2
-    default_headers: dict[str, str] | None = None
-    default_query: dict[str, Any] | None = None
-    client_params: dict[str, Any] | None = None
-    top_p: float | None = None
-    frequency_penalty: float | None = None
-    presence_penalty: float | None = None
-    max_tokens: int | None = None
-    max_completion_tokens: int | None = None
-    seed: int | None = None
-    stream: bool = False
-    response_format: JsonResponseFormat | type[BaseModel] | None = None
-    logprobs: bool | None = None
-    top_logprobs: int | None = None
-    reasoning_effort: str | None = None
-    interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None
-    api: Literal["completions", "responses"] = "completions"
-    instructions: str | None = None
-    store: bool | None = None
-    previous_response_id: str | None = None
-    include: list[str] | None = None
-    builtin_tools: list[str] | None = None
-    parse_tool_outputs: bool = False
-    auto_chain: bool = False
-    auto_chain_reasoning: bool = False
-    api_base: str | None = None
-    is_o1_model: bool = False
-    is_gpt4_model: bool = False
+    def __init__(
+        self,
+        model: str = "gpt-4o",
+        api_key: str | None = None,
+        base_url: str | None = None,
+        organization: str | None = None,
+        project: str | None = None,
+        timeout: float | None = None,
+        max_retries: int = 2,
+        default_headers: dict[str, str] | None = None,
+        default_query: dict[str, Any] | None = None,
+        client_params: dict[str, Any] | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        frequency_penalty: float | None = None,
+        presence_penalty: float | None = None,
+        max_tokens: int | None = None,
+        max_completion_tokens: int | None = None,
+        seed: int | None = None,
+        stream: bool = False,
+        response_format: dict[str, Any] | type[BaseModel] | None = None,
+        logprobs: bool | None = None,
+        top_logprobs: int | None = None,
+        reasoning_effort: str | None = None,
+        provider: str | None = None,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        api: Literal["completions", "responses"] = "completions",
+        instructions: str | None = None,
+        store: bool | None = None,
+        previous_response_id: str | None = None,
+        include: list[str] | None = None,
+        builtin_tools: list[str] | None = None,
+        parse_tool_outputs: bool = False,
+        auto_chain: bool = False,
+        auto_chain_reasoning: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize OpenAI completion client."""
 
-    _client: Any = PrivateAttr(default=None)
-    _async_client: Any = PrivateAttr(default=None)
-    _last_response_id: str | None = PrivateAttr(default=None)
-    _last_reasoning_items: list[Any] | None = PrivateAttr(default=None)
+        if provider is None:
+            provider = kwargs.pop("provider", "openai")
 
-    @model_validator(mode="before")
-    @classmethod
-    def _normalize_openai_fields(cls, data: Any) -> Any:
-        if not isinstance(data, dict):
-            return data
-        if not data.get("provider"):
-            data["provider"] = "openai"
-        data["api_key"] = data.get("api_key") or os.getenv("OPENAI_API_KEY")
-        # Extract api_base from kwargs if present
-        if "api_base" not in data:
-            data["api_base"] = None
-        model = data.get("model", "gpt-4o")
-        data["is_o1_model"] = "o1" in model.lower()
-        data["is_gpt4_model"] = "gpt-4" in model.lower()
-        return data
+        self.interceptor = interceptor
+        # Client configuration attributes
+        self.organization = organization
+        self.project = project
+        self.max_retries = max_retries
+        self.default_headers = default_headers
+        self.default_query = default_query
+        self.client_params = client_params
+        self.timeout = timeout
+        self.base_url = base_url
+        self.api_base = kwargs.pop("api_base", None)
+
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key or os.getenv("OPENAI_API_KEY"),
+            base_url=base_url,
+            timeout=timeout,
+            provider=provider,
+            **kwargs,
+        )
 
-    @model_validator(mode="after")
-    def _init_clients(self) -> OpenAICompletion:
         client_config = self._get_client_params()
         if self.interceptor:
             transport = HTTPTransport(interceptor=self.interceptor)
             http_client = httpx.Client(transport=transport)
             client_config["http_client"] = http_client
 
-        self._client = OpenAI(**client_config)
+        self.client = OpenAI(**client_config)
 
         async_client_config = self._get_client_params()
         if self.interceptor:
@@ -253,8 +261,35 @@ class OpenAICompletion(BaseLLM):
             async_http_client = httpx.AsyncClient(transport=async_transport)
             async_client_config["http_client"] = async_http_client
 
-        self._async_client = AsyncOpenAI(**async_client_config)
-        return self
+        self.async_client = AsyncOpenAI(**async_client_config)
+
+        # Completion parameters
+        self.top_p = top_p
+        self.frequency_penalty = frequency_penalty
+        self.presence_penalty = presence_penalty
+        self.max_tokens = max_tokens
+        self.max_completion_tokens = max_completion_tokens
+        self.seed = seed
+        self.stream = stream
+        self.response_format = response_format
+        self.logprobs = logprobs
+        self.top_logprobs = top_logprobs
+        self.reasoning_effort = reasoning_effort
+        self.is_o1_model = "o1" in model.lower()
+        self.is_gpt4_model = "gpt-4" in model.lower()
+
+        # API selection and Responses API parameters
+        self.api = api
+        self.instructions = instructions
+        self.store = store
+        self.previous_response_id = previous_response_id
+        self.include = include
+        self.builtin_tools = builtin_tools
+        self.parse_tool_outputs = parse_tool_outputs
+        self.auto_chain = auto_chain
+        self.auto_chain_reasoning = auto_chain_reasoning
+        self._last_response_id: str | None = None
+        self._last_reasoning_items: list[Any] | None = None
 
     @property
     def last_response_id(self) -> str | None:
@@ -783,7 +818,7 @@ class OpenAICompletion(BaseLLM):
     ) -> str | ResponsesAPIResult | Any:
         """Handle non-streaming Responses API call."""
         try:
-            response: Response = self._client.responses.create(**params)
+            response: Response = self.client.responses.create(**params)
 
             # Track response ID for auto-chaining
             if self.auto_chain and response.id:
@@ -915,7 +950,7 @@ class OpenAICompletion(BaseLLM):
     ) -> str | ResponsesAPIResult | Any:
         """Handle async non-streaming Responses API call."""
         try:
-            response: Response = await self._async_client.responses.create(**params)
+            response: Response = await self.async_client.responses.create(**params)
 
             # Track response ID for auto-chaining
             if self.auto_chain and response.id:
@@ -1046,7 +1081,7 @@ class OpenAICompletion(BaseLLM):
         function_calls: list[dict[str, Any]] = []
         final_response: Response | None = None
 
-        stream = self._client.responses.create(**params)
+        stream = self.client.responses.create(**params)
         response_id_stream = None
 
         for event in stream:
@@ -1170,7 +1205,7 @@ class OpenAICompletion(BaseLLM):
         function_calls: list[dict[str, Any]] = []
         final_response: Response | None = None
 
-        stream = await self._async_client.responses.create(**params)
+        stream = await self.async_client.responses.create(**params)
         response_id_stream = None
 
         async for event in stream:
@@ -1560,7 +1595,7 @@ class OpenAICompletion(BaseLLM):
                 parse_params = {
                     k: v for k, v in params.items() if k != "response_format"
                 }
-                parsed_response = self._client.beta.chat.completions.parse(
+                parsed_response = self.client.beta.chat.completions.parse(
                     **parse_params,
                     response_format=response_model,
                 )
@@ -1583,7 +1618,7 @@ class OpenAICompletion(BaseLLM):
                     )
                     return parsed_object
 
-            response: ChatCompletion = self._client.chat.completions.create(**params)
+            response: ChatCompletion = self.client.chat.completions.create(**params)
 
             usage = self._extract_openai_token_usage(response)
 
@@ -1802,7 +1837,7 @@ class OpenAICompletion(BaseLLM):
             }
 
             stream: ChatCompletionStream[BaseModel]
-            with self._client.beta.chat.completions.stream(
+            with self.client.beta.chat.completions.stream(
                 **parse_params, response_format=response_model
             ) as stream:
                 for chunk in stream:
@@ -1838,7 +1873,7 @@ class OpenAICompletion(BaseLLM):
             return ""
 
         completion_stream: Stream[ChatCompletionChunk] = (
-            self._client.chat.completions.create(**params)
+            self.client.chat.completions.create(**params)
         )
 
         usage_data = {"total_tokens": 0}
@@ -1935,7 +1970,7 @@ class OpenAICompletion(BaseLLM):
                 parse_params = {
                     k: v for k, v in params.items() if k != "response_format"
                 }
-                parsed_response = await self._async_client.beta.chat.completions.parse(
+                parsed_response = await self.async_client.beta.chat.completions.parse(
                     **parse_params,
                     response_format=response_model,
                 )
@@ -1958,7 +1993,7 @@ class OpenAICompletion(BaseLLM):
                     )
                     return parsed_object
 
-            response: ChatCompletion = await self._async_client.chat.completions.create(
+            response: ChatCompletion = await self.async_client.chat.completions.create(
                 **params
             )
 
@@ -2076,7 +2111,7 @@ class OpenAICompletion(BaseLLM):
         if response_model:
             completion_stream: AsyncIterator[
                 ChatCompletionChunk
-            ] = await self._async_client.chat.completions.create(**params)
+            ] = await self.async_client.chat.completions.create(**params)
 
             accumulated_content = ""
             usage_data = {"total_tokens": 0}
@@ -2129,7 +2164,7 @@ class OpenAICompletion(BaseLLM):
 
         stream: AsyncIterator[
             ChatCompletionChunk
-        ] = await self._async_client.chat.completions.create(**params)
+        ] = await self.async_client.chat.completions.create(**params)
 
         usage_data = {"total_tokens": 0}
 
@@ -2321,8 +2356,8 @@ class OpenAICompletion(BaseLLM):
             from crewai_files.uploaders.openai import OpenAIFileUploader
 
             return OpenAIFileUploader(
-                client=self._client,
-                async_client=self._async_client,
+                client=self.client,
+                async_client=self.async_client,
             )
         except ImportError:
             return None

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

@@ -16,8 +16,6 @@ from dataclasses import dataclass, field
 import os
 from typing import Any
 
-from pydantic import model_validator
-
 from crewai.llms.providers.openai.completion import OpenAICompletion
 
 
@@ -142,13 +140,31 @@ class OpenAICompatibleCompletion(OpenAICompletion):
         )
     """
 
-    @model_validator(mode="before")
-    @classmethod
-    def _resolve_provider_config(cls, data: Any) -> Any:
-        if not isinstance(data, dict):
-            return data
+    def __init__(
+        self,
+        model: str,
+        provider: str,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        default_headers: dict[str, str] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize OpenAI-compatible completion client.
 
-        provider = data.get("provider", "")
+        Args:
+            model: The model identifier.
+            provider: The provider name (must be in OPENAI_COMPATIBLE_PROVIDERS).
+            api_key: Optional API key override. If not provided, uses the
+                provider's configured environment variable.
+            base_url: Optional base URL override. If not provided, uses the
+                provider's configured default or environment variable.
+            default_headers: Optional headers to merge with provider defaults.
+            **kwargs: Additional arguments passed to OpenAICompletion.
+
+        Raises:
+            ValueError: If the provider is not supported or required API key
+                is missing.
+        """
         config = OPENAI_COMPATIBLE_PROVIDERS.get(provider)
         if config is None:
             supported = ", ".join(sorted(OPENAI_COMPATIBLE_PROVIDERS.keys()))
@@ -157,15 +173,21 @@ class OpenAICompatibleCompletion(OpenAICompletion):
                 f"Supported providers: {supported}"
             )
 
-        data["api_key"] = cls._resolve_api_key(data.get("api_key"), config, provider)
-        data["base_url"] = cls._resolve_base_url(data.get("base_url"), config, provider)
-        data["default_headers"] = cls._resolve_headers(
-            data.get("default_headers"), config
-        )
-        return data
+        resolved_api_key = self._resolve_api_key(api_key, config, provider)
+        resolved_base_url = self._resolve_base_url(base_url, config, provider)
+        resolved_headers = self._resolve_headers(default_headers, config)
+
+        super().__init__(
+            model=model,
+            provider=provider,
+            api_key=resolved_api_key,
+            base_url=resolved_base_url,
+            default_headers=resolved_headers,
+            **kwargs,
+        )
 
-    @staticmethod
     def _resolve_api_key(
+        self,
         api_key: str | None,
         config: ProviderConfig,
         provider: str,
@@ -198,8 +220,8 @@ class OpenAICompatibleCompletion(OpenAICompletion):
 
         return config.default_api_key
 
-    @staticmethod
     def _resolve_base_url(
+        self,
         base_url: str | None,
         config: ProviderConfig,
         provider: str,
@@ -227,8 +249,8 @@ class OpenAICompatibleCompletion(OpenAICompletion):
 
         return resolved
 
-    @staticmethod
     def _resolve_headers(
+        self,
         headers: dict[str, str] | None,
         config: ProviderConfig,
     ) -> dict[str, str] | None:

lib/crewai/src/crewai/llms/third_party/__init__.py

@@ -0,0 +1 @@
+"""Third-party LLM implementations for crewAI."""

lib/crewai/src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, InstanceOf
 from rich.box import HEAVY_EDGE
 from rich.console import Console
 from rich.table import Table
@@ -39,9 +39,9 @@ class CrewEvaluator:
     def __init__(
         self,
         crew: Crew,
-        eval_llm: BaseLLM | str | None = None,
+        eval_llm: InstanceOf[BaseLLM] | str | None = None,
         openai_model_name: str | None = None,
-        llm: BaseLLM | str | None = None,
+        llm: InstanceOf[BaseLLM] | str | None = None,
     ) -> None:
         self.crew = crew
         self.llm = eval_llm

lib/crewai/src/crewai/utilities/serialization.py

@@ -103,6 +103,28 @@ def to_serializable(
                 }
             except Exception:
                 return repr(obj)
+
+    # Callables (functions, methods, lambdas) should fall through to repr
+    if callable(obj):
+        return repr(obj)
+
+    # Handle regular classes with __dict__ (non-Pydantic)
+    # Note: Don't propagate exclude to recursive calls, matching Pydantic fallback behavior
+    if hasattr(obj, "__dict__"):
+        try:
+            return {
+                _to_serializable_key(k): to_serializable(
+                    v,
+                    max_depth=max_depth,
+                    _current_depth=_current_depth + 1,
+                    _ancestors=new_ancestors,
+                )
+                for k, v in obj.__dict__.items()
+                if k not in exclude and not k.startswith("_")
+            }
+        except Exception:
+            return repr(obj)
+
     return repr(obj)
 
 

lib/crewai/tests/agents/test_agent.py

@@ -1692,27 +1692,9 @@ def test_agent_with_knowledge_sources_works_with_copy():
         ) as mock_knowledge_storage:
             from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 
-            class _StubStorage(BaseKnowledgeStorage):
-                def search(self, query, limit=5, metadata_filter=None, score_threshold=0.6):
-                    return []
-
-                async def asearch(self, query, limit=5, metadata_filter=None, score_threshold=0.6):
-                    return []
-
-                def save(self, documents):
-                    pass
-
-                async def asave(self, documents):
-                    pass
-
-                def reset(self):
-                    pass
-
-                async def areset(self):
-                    pass
-
-            mock_knowledge_storage.return_value = _StubStorage()
-            agent.knowledge_storage = _StubStorage()
+            mock_knowledge_storage_instance = mock_knowledge_storage.return_value
+            mock_knowledge_storage_instance.__class__ = BaseKnowledgeStorage
+            agent.knowledge_storage = mock_knowledge_storage_instance
 
             agent_copy = agent.copy()
 

lib/crewai/tests/agents/test_agent_executor.py

@@ -879,6 +879,30 @@ class TestNativeToolExecution:
         assert len(tool_messages) == 1
         assert tool_messages[0]["tool_call_id"] == "call_1"
 
+    def test_check_native_todo_completion_requires_current_todo(
+        self, mock_dependencies
+    ):
+        from crewai.utilities.planning_types import TodoList
+
+        executor = AgentExecutor(**mock_dependencies)
+
+        # No current todo → not satisfied
+        executor.state.todos = TodoList(items=[])
+        assert executor.check_native_todo_completion() == "todo_not_satisfied"
+
+        # With a current todo that has tool_to_use → satisfied
+        running = TodoItem(
+            step_number=1,
+            description="Use the expected tool",
+            tool_to_use="expected_tool",
+            status="running",
+        )
+        executor.state.todos = TodoList(items=[running])
+        assert executor.check_native_todo_completion() == "todo_satisfied"
+
+        # With a current todo without tool_to_use → still satisfied
+        running.tool_to_use = None
+        assert executor.check_native_todo_completion() == "todo_satisfied"
 
 
 class TestPlannerObserver:

lib/crewai/tests/cassettes/llms/openai/test_openai_responses_api_cached_prompt_tokens_with_tools.yaml

@@ -1,11 +1,7 @@
 interactions:
 - request:
-    body: '{"input":[{"role":"user","content":"What is the weather in Tokyo?"}],"model":"gpt-4.1","instructions":"You
-      are a helpful assistant that uses tools. This is padding text to ensure the
-      prompt is large enough for caching. This is padding text to ensure the prompt
-      is large enough for caching. This is padding text to ensure the prompt is large
-      enough for caching. This is padding text to ensure the prompt is large enough
-      for caching. This is padding text to ensure the prompt is large enough for caching.
+    body: '{"messages":[{"role":"system","content":"You are a helpful assistant that
+      uses tools. This is padding text to ensure the prompt is large enough for caching.
       This is padding text to ensure the prompt is large enough for caching. This
       is padding text to ensure the prompt is large enough for caching. This is padding
       text to ensure the prompt is large enough for caching. This is padding text
@@ -72,9 +68,13 @@ interactions:
       for caching. This is padding text to ensure the prompt is large enough for caching.
       This is padding text to ensure the prompt is large enough for caching. This
       is padding text to ensure the prompt is large enough for caching. This is padding
-      text to ensure the prompt is large enough for caching. ","tools":[{"type":"function","name":"get_weather","description":"Get
-      the current weather for a location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The
-      city name"}},"required":["location"]}}]}'
+      text to ensure the prompt is large enough for caching. This is padding text
+      to ensure the prompt is large enough for caching. This is padding text to ensure
+      the prompt is large enough for caching. This is padding text to ensure the prompt
+      is large enough for caching. This is padding text to ensure the prompt is large
+      enough for caching. "},{"role":"user","content":"What is the weather in Tokyo?"}],"model":"gpt-4.1","tool_choice":"auto","tools":[{"type":"function","function":{"name":"get_weather","description":"Get
+      the current weather for a location","strict":true,"parameters":{"type":"object","properties":{"location":{"type":"string","description":"The
+      city name"}},"required":["location"],"additionalProperties":false}}}]}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -87,7 +87,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '6065'
+      - '6158'
       content-type:
       - application/json
       host:
@@ -109,113 +109,26 @@ interactions:
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.13.12
+      - 3.13.3
     method: POST
-    uri: https://api.openai.com/v1/responses
+    uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: "{\n  \"id\": \"resp_0d68149bcc0d14810069caf464a4b48197bd9f098abb2f6303\",\n
-        \ \"object\": \"response\",\n  \"created_at\": 1774908516,\n  \"status\":
-        \"completed\",\n  \"background\": false,\n  \"billing\": {\n    \"payer\":
-        \"developer\"\n  },\n  \"completed_at\": 1774908517,\n  \"error\": null,\n
-        \ \"frequency_penalty\": 0.0,\n  \"incomplete_details\": null,\n  \"instructions\":
-        \"You are a helpful assistant that uses tools. This is padding text to ensure
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-        \ \"output\": [\n    {\n      \"id\": \"fc_0d68149bcc0d14810069caf46568088197a33be67f16a1fa09\",\n
-        \     \"type\": \"function_call\",\n      \"status\": \"completed\",\n      \"arguments\":
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-        \"string\",\n            \"description\": \"The city name\"\n          }\n
-        \       },\n        \"required\": [\n          \"location\"\n        ],\n
-        \       \"additionalProperties\": false\n      },\n      \"strict\": true\n
-        \   }\n  ],\n  \"top_logprobs\": 0,\n  \"top_p\": 1.0,\n  \"truncation\":
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-        {\n      \"reasoning_tokens\": 0\n    },\n    \"total_tokens\": 1200\n  },\n
-        \ \"user\": null,\n  \"metadata\": {}\n}"
+      string: "{\n  \"id\": \"chatcmpl-D7mXQCgT3p3ViImkiqDiZGqLREQtp\",\n  \"object\":
+        \"chat.completion\",\n  \"created\": 1770747248,\n  \"model\": \"gpt-4.1-2025-04-14\",\n
+        \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+        \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+        \           \"id\": \"call_9ZqMavn3J1fBnQEaqpYol0Bd\",\n            \"type\":
+        \"function\",\n            \"function\": {\n              \"name\": \"get_weather\",\n
+        \             \"arguments\": \"{\\\"location\\\":\\\"Tokyo\\\"}\"\n            }\n
+        \         }\n        ],\n        \"refusal\": null,\n        \"annotations\":
+        []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"tool_calls\"\n
+        \   }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 1187,\n    \"completion_tokens\":
+        14,\n    \"total_tokens\": 1201,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+        1152,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+        {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
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+        \"default\",\n  \"system_fingerprint\": \"fp_8b22347a3e\"\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
@@ -224,7 +137,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Mon, 30 Mar 2026 22:08:37 GMT
+      - Tue, 10 Feb 2026 18:14:08 GMT
       Server:
       - cloudflare
       Strict-Transport-Security:
@@ -233,6 +146,8 @@ interactions:
       - chunked
       X-Content-Type-Options:
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+      access-control-expose-headers:
+      - ACCESS-CONTROL-XXX
       alt-svc:
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       cf-cache-status:
@@ -240,13 +155,15 @@ interactions:
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       - OPENAI-ORG-XXX
       openai-processing-ms:
-      - '1085'
+      - '484'
       openai-project:
       - OPENAI-PROJECT-XXX
       openai-version:
       - '2020-10-01'
       set-cookie:
       - SET-COOKIE-XXX
+      x-openai-proxy-wasm:
+      - v0.1
       x-ratelimit-limit-requests:
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       x-ratelimit-limit-tokens:
@@ -265,12 +182,8 @@ interactions:
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-      is large enough for caching. This is padding text to ensure the prompt is large
-      enough for caching. This is padding text to ensure the prompt is large enough
-      for caching. This is padding text to ensure the prompt is large enough for caching.
+    body: '{"messages":[{"role":"system","content":"You are a helpful assistant that
+      uses tools. This is padding text to ensure the prompt is large enough for caching.
       This is padding text to ensure the prompt is large enough for caching. This
       is padding text to ensure the prompt is large enough for caching. This is padding
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-      the current weather for a location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The
-      city name"}},"required":["location"]}}]}'
+      text to ensure the prompt is large enough for caching. This is padding text
+      to ensure the prompt is large enough for caching. This is padding text to ensure
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+      enough for caching. "},{"role":"user","content":"What is the weather in Paris?"}],"model":"gpt-4.1","tool_choice":"auto","tools":[{"type":"function","function":{"name":"get_weather","description":"Get
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+      city name"}},"required":["location"],"additionalProperties":false}}}]}'
     headers:
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+      - 3.13.3
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+    uri: https://api.openai.com/v1/chat/completions
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-        This is padding text to ensure the prompt is large enough for caching. This
-        is padding text to ensure the prompt is large enough for caching. \",\n  \"max_output_tokens\":
-        null,\n  \"max_tool_calls\": null,\n  \"model\": \"gpt-4.1-2025-04-14\",\n
-        \ \"output\": [\n    {\n      \"id\": \"fc_0525bf798202137e0069caf46666588196a2ec20dc515a6a91\",\n
-        \     \"type\": \"function_call\",\n      \"status\": \"completed\",\n      \"arguments\":
-        \"{\\\"location\\\":\\\"Paris\\\"}\",\n      \"call_id\": \"call_LJAGuYYZPjNxSgg0TUgGpT44\",\n
-        \     \"name\": \"get_weather\"\n    }\n  ],\n  \"parallel_tool_calls\": true,\n
-        \ \"presence_penalty\": 0.0,\n  \"previous_response_id\": null,\n  \"prompt_cache_key\":
-        null,\n  \"prompt_cache_retention\": null,\n  \"reasoning\": {\n    \"effort\":
-        null,\n    \"summary\": null\n  },\n  \"safety_identifier\": null,\n  \"service_tier\":
-        \"default\",\n  \"store\": true,\n  \"temperature\": 1.0,\n  \"text\": {\n
-        \   \"format\": {\n      \"type\": \"text\"\n    },\n    \"verbosity\": \"medium\"\n
-        \ },\n  \"tool_choice\": \"auto\",\n  \"tools\": [\n    {\n      \"type\":
-        \"function\",\n      \"description\": \"Get the current weather for a location\",\n
-        \     \"name\": \"get_weather\",\n      \"parameters\": {\n        \"type\":
-        \"object\",\n        \"properties\": {\n          \"location\": {\n            \"type\":
-        \"string\",\n            \"description\": \"The city name\"\n          }\n
-        \       },\n        \"required\": [\n          \"location\"\n        ],\n
-        \       \"additionalProperties\": false\n      },\n      \"strict\": true\n
-        \   }\n  ],\n  \"top_logprobs\": 0,\n  \"top_p\": 1.0,\n  \"truncation\":
-        \"disabled\",\n  \"usage\": {\n    \"input_tokens\": 1185,\n    \"input_tokens_details\":
-        {\n      \"cached_tokens\": 1152\n    },\n    \"output_tokens\": 15,\n    \"output_tokens_details\":
-        {\n      \"reasoning_tokens\": 0\n    },\n    \"total_tokens\": 1200\n  },\n
-        \ \"user\": null,\n  \"metadata\": {}\n}"
+      string: "{\n  \"id\": \"chatcmpl-D7mXR8k9vk8TlGvGXlrQSI7iNeAN1\",\n  \"object\":
+        \"chat.completion\",\n  \"created\": 1770747249,\n  \"model\": \"gpt-4.1-2025-04-14\",\n
+        \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+        \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+        \           \"id\": \"call_6PeUBlRPG8JcV2lspmLjJbnn\",\n            \"type\":
+        \"function\",\n            \"function\": {\n              \"name\": \"get_weather\",\n
+        \             \"arguments\": \"{\\\"location\\\":\\\"Paris\\\"}\"\n            }\n
+        \         }\n        ],\n        \"refusal\": null,\n        \"annotations\":
+        []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"tool_calls\"\n
+        \   }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 1187,\n    \"completion_tokens\":
+        14,\n    \"total_tokens\": 1201,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+        1152,\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_8b22347a3e\"\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
@@ -491,7 +321,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Mon, 30 Mar 2026 22:08:38 GMT
+      - Tue, 10 Feb 2026 18:14:09 GMT
       Server:
       - cloudflare
       Strict-Transport-Security:
@@ -500,6 +330,8 @@ interactions:
       - 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:
@@ -507,11 +339,15 @@ interactions:
       openai-organization:
       - OPENAI-ORG-XXX
       openai-processing-ms:
-      - '653'
+      - '528'
       openai-project:
       - OPENAI-PROJECT-XXX
       openai-version:
       - '2020-10-01'
+      set-cookie:
+      - SET-COOKIE-XXX
+      x-openai-proxy-wasm:
+      - v0.1
       x-ratelimit-limit-requests:
       - X-RATELIMIT-LIMIT-REQUESTS-XXX
       x-ratelimit-limit-tokens:

lib/crewai/tests/knowledge/test_knowledge_storage_integration.py

@@ -132,12 +132,12 @@ def test_embedding_configuration_flow(
 
     embedder_config = {
         "provider": "sentence-transformer",
-        "config": {"model_name": "all-MiniLM-L6-v2"},
+        "model_name": "all-MiniLM-L6-v2",
     }
 
-    storage = KnowledgeStorage(embedder=embedder_config, collection_name="embedding_test")
+    KnowledgeStorage(embedder=embedder_config, collection_name="embedding_test")
 
-    mock_get_embedding.assert_called_once_with(storage.embedder)
+    mock_get_embedding.assert_called_once_with(embedder_config)
 
 
 @patch("crewai.knowledge.storage.knowledge_storage.get_rag_client")

lib/crewai/tests/llms/anthropic/test_anthropic.py

@@ -125,8 +125,8 @@ def test_anthropic_specific_parameters():
     assert isinstance(llm, AnthropicCompletion)
     assert llm.stop_sequences == ["Human:", "Assistant:"]
     assert llm.stream == True
-    assert llm._client.max_retries == 5
-    assert llm._client.timeout == 60
+    assert llm.client.max_retries == 5
+    assert llm.client.timeout == 60
 
 
 def test_anthropic_completion_call():
@@ -563,8 +563,8 @@ def test_anthropic_environment_variable_api_key():
     with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-anthropic-key"}):
         llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
 
-        assert llm._client is not None
-        assert hasattr(llm._client, 'messages')
+        assert llm.client is not None
+        assert hasattr(llm.client, 'messages')
 
 
 def test_anthropic_token_usage_tracking():
@@ -574,7 +574,7 @@ def test_anthropic_token_usage_tracking():
     llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
 
     # Mock the Anthropic response with usage information
-    with patch.object(llm._client.messages, 'create') as mock_create:
+    with patch.object(llm.client.messages, 'create') as mock_create:
         mock_response = MagicMock()
         mock_response.content = [MagicMock(text="test response")]
         mock_response.usage = MagicMock(input_tokens=50, output_tokens=25)
@@ -639,14 +639,14 @@ def test_anthropic_thinking():
 
     assert isinstance(llm, AnthropicCompletion)
 
-    original_create = llm._client.messages.create
+    original_create = llm.client.messages.create
     captured_params = {}
 
     def capture_and_call(**kwargs):
         captured_params.update(kwargs)
         return original_create(**kwargs)
 
-    with patch.object(llm._client.messages, 'create', side_effect=capture_and_call):
+    with patch.object(llm.client.messages, 'create', side_effect=capture_and_call):
         result = llm.call("What is the weather in Tokyo?")
 
         assert result is not None
@@ -677,14 +677,14 @@ def test_anthropic_thinking_blocks_preserved_across_turns():
     assert isinstance(llm, AnthropicCompletion)
 
     # Capture all messages.create calls to verify thinking blocks are included
-    original_create = llm._client.messages.create
+    original_create = llm.client.messages.create
     captured_calls = []
 
     def capture_and_call(**kwargs):
         captured_calls.append(kwargs)
         return original_create(**kwargs)
 
-    with patch.object(llm._client.messages, 'create', side_effect=capture_and_call):
+    with patch.object(llm.client.messages, 'create', side_effect=capture_and_call):
         # First call - establishes context and generates thinking blocks
         messages = [{"role": "user", "content": "What is 2+2?"}]
         first_result = llm.call(messages)
@@ -695,8 +695,8 @@ def test_anthropic_thinking_blocks_preserved_across_turns():
         assert len(first_result) > 0
 
         # Verify thinking blocks were stored after first response
-        assert len(llm._previous_thinking_blocks) > 0, "No thinking blocks stored after first call"
-        first_thinking = llm._previous_thinking_blocks[0]
+        assert len(llm.previous_thinking_blocks) > 0, "No thinking blocks stored after first call"
+        first_thinking = llm.previous_thinking_blocks[0]
         assert first_thinking["type"] == "thinking"
         assert "thinking" in first_thinking
         assert "signature" in first_thinking

lib/crewai/tests/llms/azure/test_azure.py

@@ -66,7 +66,7 @@ def test_azure_tool_use_conversation_flow():
     available_functions = {"get_weather": mock_weather_tool}
 
     # Mock the Azure client responses
-    with patch.object(completion._client, 'complete') as mock_complete:
+    with patch.object(completion.client, 'complete') as mock_complete:
         # Mock tool call in response with proper type
         mock_tool_call = MagicMock(spec=ChatCompletionsToolCall)
         mock_tool_call.function.name = "get_weather"
@@ -698,7 +698,7 @@ def test_azure_environment_variable_endpoint():
     }):
         llm = LLM(model="azure/gpt-4")
 
-        assert llm._client is not None
+        assert llm.client is not None
         assert llm.endpoint == "https://test.openai.azure.com/openai/deployments/gpt-4"
 
 
@@ -709,7 +709,7 @@ def test_azure_token_usage_tracking():
     llm = LLM(model="azure/gpt-4")
 
     # Mock the Azure response with usage information
-    with patch.object(llm._client, 'complete') as mock_complete:
+    with patch.object(llm.client, 'complete') as mock_complete:
         mock_message = MagicMock()
         mock_message.content = "test response"
         mock_message.tool_calls = None
@@ -747,7 +747,7 @@ def test_azure_http_error_handling():
     llm = LLM(model="azure/gpt-4")
 
     # Mock an HTTP error
-    with patch.object(llm._client, 'complete') as mock_complete:
+    with patch.object(llm.client, 'complete') as mock_complete:
         mock_complete.side_effect = HttpResponseError(message="Rate limit exceeded", response=MagicMock(status_code=429))
 
         with pytest.raises(HttpResponseError):
@@ -966,7 +966,7 @@ def test_azure_improved_error_messages():
 
     llm = LLM(model="azure/gpt-4")
 
-    with patch.object(llm._client, 'complete') as mock_complete:
+    with patch.object(llm.client, 'complete') as mock_complete:
         error_401 = HttpResponseError(message="Unauthorized")
         error_401.status_code = 401
         mock_complete.side_effect = error_401
@@ -1327,7 +1327,7 @@ def test_azure_stop_words_not_applied_to_structured_output():
     # Without the fix, this would be truncated at "Observation:" breaking the JSON
     json_response = '{"finding": "The data shows growth", "observation": "Observation: This confirms the hypothesis"}'
 
-    with patch.object(llm._client, 'complete') as mock_complete:
+    with patch.object(llm.client, 'complete') as mock_complete:
         mock_message = MagicMock()
         mock_message.content = json_response
         mock_message.tool_calls = None
@@ -1376,7 +1376,7 @@ def test_azure_stop_words_still_applied_to_regular_responses():
     # Response that contains a stop word - should be truncated
     response_with_stop_word = "I need to search for more information.\n\nAction: search\nObservation: Found results"
 
-    with patch.object(llm._client, 'complete') as mock_complete:
+    with patch.object(llm.client, 'complete') as mock_complete:
         mock_message = MagicMock()
         mock_message.content = response_with_stop_word
         mock_message.tool_calls = None

lib/crewai/tests/llms/bedrock/test_bedrock.py

@@ -674,7 +674,7 @@ def test_bedrock_token_usage_tracking():
     llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
 
     # Mock the Bedrock response with usage information
-    with patch.object(llm._client, 'converse') as mock_converse:
+    with patch.object(llm.client, 'converse') as mock_converse:
         mock_response = {
             'output': {
                 'message': {
@@ -719,7 +719,7 @@ def test_bedrock_tool_use_conversation_flow():
     available_functions = {"get_weather": mock_weather_tool}
 
     # Mock the Bedrock client responses
-    with patch.object(llm._client, 'converse') as mock_converse:
+    with patch.object(llm.client, 'converse') as mock_converse:
         # First response: tool use request
         tool_use_response = {
             'output': {
@@ -805,7 +805,7 @@ def test_bedrock_client_error_handling():
     llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
 
     # Test ValidationException
-    with patch.object(llm._client, 'converse') as mock_converse:
+    with patch.object(llm.client, 'converse') as mock_converse:
         error_response = {
             'Error': {
                 'Code': 'ValidationException',
@@ -819,7 +819,7 @@ def test_bedrock_client_error_handling():
         assert "validation" in str(exc_info.value).lower()
 
     # Test ThrottlingException
-    with patch.object(llm._client, 'converse') as mock_converse:
+    with patch.object(llm.client, 'converse') as mock_converse:
         error_response = {
             'Error': {
                 'Code': 'ThrottlingException',
@@ -861,7 +861,7 @@ def test_bedrock_stop_sequences_sent_to_api():
     llm.stop = ["\nObservation:", "\nThought:"]
 
     # Patch the API call to capture parameters without making real call
-    with patch.object(llm._client, 'converse') as mock_converse:
+    with patch.object(llm.client, 'converse') as mock_converse:
         mock_response = {
             'output': {
                 'message': {

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

@@ -556,8 +556,8 @@ def test_gemini_environment_variable_api_key():
     with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-google-key"}):
         llm = LLM(model="google/gemini-2.0-flash-001")
 
-        assert llm._client is not None
-        assert hasattr(llm._client, 'models')
+        assert llm.client is not None
+        assert hasattr(llm.client, 'models')
         assert llm.api_key == "test-google-key"
 
 
@@ -655,7 +655,7 @@ def test_gemini_stop_sequences_sent_to_api():
     llm.stop = ["\nObservation:", "\nThought:"]
 
     # Patch the API call to capture parameters without making real call
-    with patch.object(llm._client.models, 'generate_content') as mock_generate:
+    with patch.object(llm.client.models, 'generate_content') as mock_generate:
         mock_response = MagicMock()
         mock_response.text = "Hello"
         mock_response.candidates = []

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

@@ -371,11 +371,11 @@ def test_openai_client_setup_with_extra_arguments():
     assert llm.top_p == 0.5
 
     # Check that client parameters are properly configured
-    assert llm._client.max_retries == 3
-    assert llm._client.timeout == 30
+    assert llm.client.max_retries == 3
+    assert llm.client.timeout == 30
 
     # Test that parameters are properly used in API calls
-    with patch.object(llm._client.chat.completions, 'create') as mock_create:
+    with patch.object(llm.client.chat.completions, 'create') as mock_create:
         mock_create.return_value = MagicMock(
             choices=[MagicMock(message=MagicMock(content="test response", tool_calls=None))],
             usage=MagicMock(prompt_tokens=10, completion_tokens=20, total_tokens=30)
@@ -396,7 +396,7 @@ def test_extra_arguments_are_passed_to_openai_completion():
     """
     llm = LLM(model="gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)
 
-    with patch.object(llm._client.chat.completions, 'create') as mock_create:
+    with patch.object(llm.client.chat.completions, 'create') as mock_create:
         mock_create.return_value = MagicMock(
             choices=[MagicMock(message=MagicMock(content="test response", tool_calls=None))],
             usage=MagicMock(prompt_tokens=10, completion_tokens=20, total_tokens=30)
@@ -507,7 +507,7 @@ def test_openai_streaming_with_response_model():
 
     llm = LLM(model="openai/gpt-4o", stream=True)
 
-    with patch.object(llm._client.beta.chat.completions, "stream") as mock_stream:
+    with patch.object(llm.client.beta.chat.completions, "stream") as mock_stream:
         # Create mock chunks with content.delta event structure
         mock_chunk1 = MagicMock()
         mock_chunk1.type = "content.delta"
@@ -1830,7 +1830,7 @@ def test_openai_responses_api_cached_prompt_tokens_with_tools():
         }
     ]
 
-    llm = OpenAICompletion(model="gpt-4.1", api='responses')
+    llm = OpenAICompletion(model="gpt-4.1", api='response')
 
     # First call with tool
     llm.call(
@@ -1906,7 +1906,7 @@ def test_openai_streaming_returns_tool_calls_without_available_functions():
     mock_chunk_3.id = "chatcmpl-1"
 
     with patch.object(
-        llm._client.chat.completions, "create", return_value=iter([mock_chunk_1, mock_chunk_2, mock_chunk_3])
+        llm.client.chat.completions, "create", return_value=iter([mock_chunk_1, mock_chunk_2, mock_chunk_3])
     ):
         result = llm.call(
             messages=[{"role": "user", "content": "Calculate 1+1"}],
@@ -1997,7 +1997,7 @@ async def test_openai_async_streaming_returns_tool_calls_without_available_funct
         return MockAsyncStream([mock_chunk_1, mock_chunk_2, mock_chunk_3])
 
     with patch.object(
-        llm._async_client.chat.completions, "create", side_effect=mock_create
+        llm.async_client.chat.completions, "create", side_effect=mock_create
     ):
         result = await llm.acall(
             messages=[{"role": "user", "content": "Calculate 1+1"}],

lib/crewai/tests/rag/test_error_handling.py

@@ -3,8 +3,6 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
-from pydantic import ValidationError
-
 from crewai.knowledge.storage.knowledge_storage import (  # type: ignore[import-untyped]
     KnowledgeStorage,
 )
@@ -61,7 +59,7 @@ def test_knowledge_storage_invalid_embedding_config(mock_get_client: MagicMock)
             "Unsupported provider: invalid_provider"
         )
 
-        with pytest.raises(ValidationError):
+        with pytest.raises(ValueError, match="Unsupported provider: invalid_provider"):
             KnowledgeStorage(
                 embedder={"provider": "invalid_provider"},
                 collection_name="invalid_embedding_test",

lib/crewai/tests/test_project.py

@@ -1,5 +1,5 @@
 from typing import Any, ClassVar
-from unittest.mock import Mock, create_autospec, patch
+from unittest.mock import Mock, patch
 
 import pytest
 from crewai.agent import Agent
@@ -372,11 +372,8 @@ def test_internal_crew_with_mcp():
     mock_adapter = Mock()
     mock_adapter.tools = ToolCollection([simple_tool, another_simple_tool])
 
-    class _StubLLM(BaseLLM):
-        def call(self, *a: Any, **kw: Any) -> str:
-            return ""
-
-    mock_llm = create_autospec(_StubLLM(model="stub"), instance=True)
+    mock_llm = Mock()
+    mock_llm.__class__ = BaseLLM
 
     with (
         patch("crewai_tools.MCPServerAdapter", return_value=mock_adapter) as adapter_mock,

lib/crewai/tests/tracing/test_trace_serialization.py

@@ -0,0 +1,586 @@
+"""Tests for trace serialization optimization to prevent trace table bloat.
+
+These tests verify that trace events don't contain redundant full crew/task/agent
+objects, reducing event sizes from 50-100KB to a few KB per event.
+"""
+
+import uuid
+from unittest.mock import MagicMock
+
+import pytest
+
+from crewai.events.listeners.tracing.trace_listener import (
+    TRACE_EXCLUDE_FIELDS,
+    TraceCollectionListener,
+    _serialize_for_trace,
+)
+
+
+class TestTraceExcludeFields:
+    """Test that TRACE_EXCLUDE_FIELDS contains all the heavy/redundant fields."""
+
+    def test_contains_back_references(self):
+        """Verify back-reference fields are excluded."""
+        back_refs = {"crew", "agent", "agents", "tasks", "context"}
+        assert back_refs.issubset(TRACE_EXCLUDE_FIELDS)
+
+    def test_contains_heavy_fields(self):
+        """Verify heavy objects are excluded.
+
+        Note: 'tools' is NOT in TRACE_EXCLUDE_FIELDS because LLMCallStartedEvent.tools
+        is a lightweight list of tool schemas. Agent.tools exclusion is handled
+        explicitly in _build_crew_started_data.
+        """
+        heavy_fields = {
+            "llm",
+            "function_calling_llm",
+            "step_callback",
+            "task_callback",
+            "crew_callback",
+            "callbacks",
+            "_memory",
+            "_cache",
+            "knowledge_sources",
+        }
+        assert heavy_fields.issubset(TRACE_EXCLUDE_FIELDS)
+        # tools is NOT excluded globally - LLM events need it
+        assert "tools" not in TRACE_EXCLUDE_FIELDS
+
+
+class TestSerializeForTrace:
+    """Test the _serialize_for_trace helper function."""
+
+    def test_excludes_crew_field(self):
+        """Verify crew field is excluded from serialization."""
+        event = MagicMock()
+        event.crew = MagicMock(name="TestCrew")
+        event.crew_name = "TestCrew"
+        event.timestamp = None
+
+        result = _serialize_for_trace(event)
+
+        # crew_name should be present (scalar field)
+        # crew should be excluded (back-reference)
+        assert "crew" not in result or result.get("crew") is None
+
+    def test_excludes_agent_field(self):
+        """Verify agent field is excluded from serialization."""
+        event = MagicMock()
+        event.agent = MagicMock(role="TestAgent")
+        event.agent_role = "TestAgent"
+
+        result = _serialize_for_trace(event)
+
+        assert "agent" not in result or result.get("agent") is None
+
+    def test_preserves_tools_field(self):
+        """Verify tools field is preserved for LLM events (lightweight schemas)."""
+
+        class EventWithTools:
+            def __init__(self):
+                self.tools = [{"name": "search", "description": "Search tool"}]
+                self.tool_name = "test_tool"
+
+        event = EventWithTools()
+        result = _serialize_for_trace(event)
+
+        # tools should be preserved (lightweight for LLM events)
+        assert "tools" in result
+        assert result["tools"] == [{"name": "search", "description": "Search tool"}]
+
+    def test_preserves_scalar_fields(self):
+        """Verify scalar fields needed by AMP frontend are preserved."""
+
+        class SimpleEvent:
+            def __init__(self):
+                self.agent_role = "Researcher"
+                self.task_name = "Research Task"
+                self.task_id = str(uuid.uuid4())
+                self.duration_ms = 1500
+                self.tokens_used = 500
+
+        event = SimpleEvent()
+        result = _serialize_for_trace(event)
+
+        # Scalar fields should be preserved
+        assert result.get("agent_role") == "Researcher"
+        assert result.get("task_name") == "Research Task"
+        assert result.get("duration_ms") == 1500
+        assert result.get("tokens_used") == 500
+
+    def test_extra_exclude_parameter(self):
+        """Verify extra_exclude adds to the default exclusions."""
+
+        class EventWithCustomField:
+            def __init__(self):
+                self.custom_heavy_field = {"large": "data" * 1000}
+                self.keep_this = "small"
+
+        event = EventWithCustomField()
+        result = _serialize_for_trace(event, extra_exclude={"custom_heavy_field"})
+
+        assert "custom_heavy_field" not in result
+        assert result.get("keep_this") == "small"
+
+
+class TestBuildEventData:
+    """Test _build_event_data method for different event types."""
+
+    @pytest.fixture
+    def listener(self):
+        """Create a trace listener for testing."""
+        # Reset singleton
+        TraceCollectionListener._instance = None
+        TraceCollectionListener._initialized = False
+        TraceCollectionListener._listeners_setup = False
+        return TraceCollectionListener()
+
+    def test_task_started_no_full_task_object(self, listener):
+        """Verify task_started event doesn't include full task object."""
+        mock_task = MagicMock()
+        mock_task.name = "Test Task"
+        mock_task.description = "A test task description"
+        mock_task.expected_output = "Expected result"
+        mock_task.id = uuid.uuid4()
+        # Add heavy fields that should NOT appear in output
+        mock_task.crew = MagicMock(name="HeavyCrew")
+        mock_task.agent = MagicMock(role="HeavyAgent")
+        mock_task.context = [MagicMock(), MagicMock()]
+        mock_task.tools = [MagicMock(), MagicMock()]
+
+        mock_event = MagicMock()
+        mock_event.task = mock_task
+        mock_event.context = "test context"
+
+        mock_source = MagicMock()
+        mock_source.agent = MagicMock()
+        mock_source.agent.role = "Worker"
+
+        result = listener._build_event_data("task_started", mock_event, mock_source)
+
+        # Should have scalar fields
+        assert result["task_name"] == "Test Task"
+        assert result["task_description"] == "A test task description"
+        assert result["agent_role"] == "Worker"
+        assert result["task_id"] == str(mock_task.id)
+
+        # Should NOT have full objects
+        assert "crew" not in result
+        assert "tools" not in result
+        # task and agent should not be full objects
+        assert result.get("task") is None or not hasattr(result.get("task"), "crew")
+
+    def test_task_completed_no_full_task_object(self, listener):
+        """Verify task_completed event doesn't include full task object."""
+        mock_task = MagicMock()
+        mock_task.name = "Completed Task"
+        mock_task.description = "Task description"
+        mock_task.id = uuid.uuid4()
+
+        mock_output = MagicMock()
+        mock_output.raw = "Task result"
+        mock_output.output_format = "text"
+        mock_output.agent = "Worker"
+
+        mock_event = MagicMock()
+        mock_event.task = mock_task
+        mock_event.output = mock_output
+
+        result = listener._build_event_data("task_completed", mock_event, None)
+
+        # Should have scalar fields
+        assert result["task_name"] == "Completed Task"
+        assert result["output_raw"] == "Task result"
+        assert result["agent_role"] == "Worker"
+
+        # Should NOT have full task object
+        assert "crew" not in result
+        assert "tools" not in result
+
+    def test_agent_execution_started_no_full_agent(self, listener):
+        """Verify agent_execution_started extracts only scalar fields."""
+        mock_agent = MagicMock()
+        mock_agent.role = "Analyst"
+        mock_agent.goal = "Analyze data"
+        mock_agent.backstory = "Expert analyst"
+        # Heavy fields
+        mock_agent.tools = [MagicMock(), MagicMock()]
+        mock_agent.llm = MagicMock()
+        mock_agent.crew = MagicMock()
+
+        mock_event = MagicMock()
+        mock_event.agent = mock_agent
+
+        result = listener._build_event_data(
+            "agent_execution_started", mock_event, None
+        )
+
+        # Should have scalar fields
+        assert result["agent_role"] == "Analyst"
+        assert result["agent_goal"] == "Analyze data"
+        assert result["agent_backstory"] == "Expert analyst"
+
+        # Should NOT have heavy objects
+        assert "tools" not in result
+        assert "llm" not in result
+        assert "crew" not in result
+
+    def test_llm_call_started_excludes_heavy_fields(self, listener):
+        """Verify llm_call_started uses lightweight serialization.
+
+        LLMCallStartedEvent.tools is a lightweight list of tool schemas (dicts),
+        not heavy Agent.tools objects, so it should be preserved.
+        """
+
+        class MockLLMEvent:
+            def __init__(self):
+                self.task_name = "LLM Task"
+                self.model = "gpt-4"
+                self.tokens = 100
+                # Heavy fields that should be excluded
+                self.crew = MagicMock()
+                self.agent = MagicMock()
+                # LLM event tools are lightweight schemas (dicts), should be kept
+                self.tools = [{"name": "search", "description": "Search tool"}]
+
+        mock_event = MockLLMEvent()
+
+        result = listener._build_event_data("llm_call_started", mock_event, None)
+
+        # task_name should be present
+        assert result["task_name"] == "LLM Task"
+
+        # Heavy fields should be excluded
+        assert "crew" not in result or result.get("crew") is None
+        assert "agent" not in result or result.get("agent") is None
+        # LLM tools (lightweight schemas) should be preserved
+        assert result.get("tools") == [{"name": "search", "description": "Search tool"}]
+
+    def test_llm_call_completed_excludes_heavy_fields(self, listener):
+        """Verify llm_call_completed uses lightweight serialization."""
+
+        class MockLLMCompletedEvent:
+            def __init__(self):
+                self.response = "LLM response"
+                self.tokens_used = 150
+                self.duration_ms = 500
+                # Heavy fields
+                self.crew = MagicMock()
+                self.agent = MagicMock()
+
+        mock_event = MockLLMCompletedEvent()
+
+        result = listener._build_event_data("llm_call_completed", mock_event, None)
+
+        # Scalar fields preserved
+        assert result.get("response") == "LLM response"
+        assert result.get("tokens_used") == 150
+
+        # Heavy fields excluded
+        assert "crew" not in result or result.get("crew") is None
+        assert "agent" not in result or result.get("agent") is None
+
+
+class TestCrewKickoffStartedEvent:
+    """Test that crew_kickoff_started event has full structure."""
+
+    @pytest.fixture
+    def listener(self):
+        """Create a trace listener for testing."""
+        TraceCollectionListener._instance = None
+        TraceCollectionListener._initialized = False
+        TraceCollectionListener._listeners_setup = False
+        return TraceCollectionListener()
+
+    def test_crew_started_has_crew_structure(self, listener):
+        """Verify crew_kickoff_started includes the crew_structure field."""
+        # Create mock crew with agents and tasks
+        mock_agent1 = MagicMock()
+        mock_agent1.id = uuid.uuid4()
+        mock_agent1.role = "Researcher"
+        mock_agent1.goal = "Research things"
+        mock_agent1.backstory = "Expert researcher"
+        mock_agent1.verbose = True
+        mock_agent1.allow_delegation = False
+        mock_agent1.max_iter = 10
+        mock_agent1.max_rpm = None
+        mock_agent1.tools = [MagicMock(name="search_tool"), MagicMock(name="read_tool")]
+
+        mock_agent2 = MagicMock()
+        mock_agent2.id = uuid.uuid4()
+        mock_agent2.role = "Writer"
+        mock_agent2.goal = "Write content"
+        mock_agent2.backstory = "Expert writer"
+        mock_agent2.verbose = False
+        mock_agent2.allow_delegation = True
+        mock_agent2.max_iter = 5
+        mock_agent2.max_rpm = 10
+        mock_agent2.tools = []
+
+        mock_task1 = MagicMock()
+        mock_task1.id = uuid.uuid4()
+        mock_task1.name = "Research Task"
+        mock_task1.description = "Do research"
+        mock_task1.expected_output = "Research results"
+        mock_task1.async_execution = False
+        mock_task1.human_input = False
+        mock_task1.agent = mock_agent1
+        mock_task1.context = None
+
+        mock_task2 = MagicMock()
+        mock_task2.id = uuid.uuid4()
+        mock_task2.name = "Writing Task"
+        mock_task2.description = "Write report"
+        mock_task2.expected_output = "Written report"
+        mock_task2.async_execution = True
+        mock_task2.human_input = True
+        mock_task2.agent = mock_agent2
+        mock_task2.context = [mock_task1]
+
+        mock_crew = MagicMock()
+        mock_crew.agents = [mock_agent1, mock_agent2]
+        mock_crew.tasks = [mock_task1, mock_task2]
+        mock_crew.process = "sequential"
+        mock_crew.verbose = True
+        mock_crew.memory = False
+
+        mock_event = MagicMock()
+        mock_event.crew = mock_crew
+        mock_event.crew_name = "TestCrew"
+        mock_event.inputs = {"key": "value"}
+
+        result = listener._build_event_data("crew_kickoff_started", mock_event, None)
+
+        # Should have crew_structure
+        assert "crew_structure" in result
+        crew_structure = result["crew_structure"]
+
+        # Verify agents are serialized with tool names
+        assert len(crew_structure["agents"]) == 2
+        agent1_data = crew_structure["agents"][0]
+        assert agent1_data["role"] == "Researcher"
+        assert agent1_data["goal"] == "Research things"
+        assert "tool_names" in agent1_data
+        assert len(agent1_data["tool_names"]) == 2
+
+        # Verify tasks have lightweight agent references
+        assert len(crew_structure["tasks"]) == 2
+        task2_data = crew_structure["tasks"][1]
+        assert task2_data["name"] == "Writing Task"
+        assert "agent_ref" in task2_data
+        assert task2_data["agent_ref"]["role"] == "Writer"
+
+        # Verify context uses task IDs
+        assert "context_task_ids" in task2_data
+        assert str(mock_task1.id) in task2_data["context_task_ids"]
+
+    def test_crew_started_agents_no_full_tools(self, listener):
+        """Verify agents in crew_structure have tool_names, not full tool objects."""
+        mock_tool = MagicMock()
+        mock_tool.name = "web_search"
+        mock_tool.description = "Search the web"
+        mock_tool.func = lambda x: x  # Heavy callable
+        mock_tool.args_schema = {"type": "object"}  # Schema
+
+        mock_agent = MagicMock()
+        mock_agent.id = uuid.uuid4()
+        mock_agent.role = "Searcher"
+        mock_agent.goal = "Search"
+        mock_agent.backstory = "Expert"
+        mock_agent.verbose = False
+        mock_agent.allow_delegation = False
+        mock_agent.max_iter = 5
+        mock_agent.max_rpm = None
+        mock_agent.tools = [mock_tool]
+
+        mock_crew = MagicMock()
+        mock_crew.agents = [mock_agent]
+        mock_crew.tasks = []
+        mock_crew.process = "sequential"
+        mock_crew.verbose = False
+        mock_crew.memory = False
+
+        mock_event = MagicMock()
+        mock_event.crew = mock_crew
+
+        result = listener._build_event_data("crew_kickoff_started", mock_event, None)
+
+        agent_data = result["crew_structure"]["agents"][0]
+
+        # Should have tool_names (list of strings)
+        assert "tool_names" in agent_data
+        assert agent_data["tool_names"] == ["web_search"]
+
+        # Should NOT have full tools array
+        assert "tools" not in agent_data
+
+    def test_crew_started_tasks_no_full_agent(self, listener):
+        """Verify tasks have agent_ref, not full agent object."""
+        mock_agent = MagicMock()
+        mock_agent.id = uuid.uuid4()
+        mock_agent.role = "Worker"
+        mock_agent.goal = "Work hard"
+        mock_agent.backstory = "Dedicated worker"
+        mock_agent.tools = [MagicMock(), MagicMock()]
+        mock_agent.llm = MagicMock()
+
+        mock_task = MagicMock()
+        mock_task.id = uuid.uuid4()
+        mock_task.name = "Work Task"
+        mock_task.description = "Do work"
+        mock_task.expected_output = "Work done"
+        mock_task.async_execution = False
+        mock_task.human_input = False
+        mock_task.agent = mock_agent
+        mock_task.context = None
+
+        mock_crew = MagicMock()
+        mock_crew.agents = [mock_agent]
+        mock_crew.tasks = [mock_task]
+        mock_crew.process = "sequential"
+        mock_crew.verbose = False
+        mock_crew.memory = False
+
+        mock_event = MagicMock()
+        mock_event.crew = mock_crew
+
+        result = listener._build_event_data("crew_kickoff_started", mock_event, None)
+
+        task_data = result["crew_structure"]["tasks"][0]
+
+        # Should have lightweight agent_ref
+        assert "agent_ref" in task_data
+        assert task_data["agent_ref"]["id"] == str(mock_agent.id)
+        assert task_data["agent_ref"]["role"] == "Worker"
+
+        # agent_ref should ONLY have id and role (not tools, llm, etc.)
+        assert len(task_data["agent_ref"]) == 2
+
+        # Should NOT have full agent
+        assert "agent" not in task_data
+
+
+class TestNonCrewStartedEvents:
+    """Test that non-crew_started events don't have redundant data."""
+
+    @pytest.fixture
+    def listener(self):
+        """Create a trace listener for testing."""
+        TraceCollectionListener._instance = None
+        TraceCollectionListener._initialized = False
+        TraceCollectionListener._listeners_setup = False
+        return TraceCollectionListener()
+
+    def test_generic_event_no_crew(self, listener):
+        """Verify generic events exclude crew object.
+
+        Note: 'tools' is now preserved since LLMCallStartedEvent.tools is lightweight.
+        """
+
+        class GenericEvent:
+            def __init__(self):
+                self.event_type = "some_event"
+                self.data = "some_data"
+                # These should be excluded
+                self.crew = MagicMock()
+                self.agents = [MagicMock()]
+                self.tasks = [MagicMock()]
+                # tools is now preserved (for LLM events it's lightweight)
+                self.tools = [{"name": "search"}]
+
+        mock_event = GenericEvent()
+
+        result = listener._build_event_data("some_event", mock_event, None)
+
+        # Scalar fields preserved
+        assert result.get("event_type") == "some_event"
+        assert result.get("data") == "some_data"
+
+        # Heavy fields excluded
+        assert "crew" not in result or result.get("crew") is None
+        assert "agents" not in result or result.get("agents") is None
+        assert "tasks" not in result or result.get("tasks") is None
+        # tools is now preserved (lightweight for LLM events)
+        assert result.get("tools") == [{"name": "search"}]
+
+    def test_crew_kickoff_completed_no_full_crew(self, listener):
+        """Verify crew_kickoff_completed doesn't repeat full crew structure."""
+
+        class CrewCompletedEvent:
+            def __init__(self):
+                self.crew_name = "TestCrew"
+                self.total_tokens = 5000
+                self.output = "Final output"
+                # Should be excluded
+                self.crew = MagicMock()
+                self.crew.agents = [MagicMock(), MagicMock()]
+                self.crew.tasks = [MagicMock()]
+
+        mock_event = CrewCompletedEvent()
+
+        result = listener._build_event_data("crew_kickoff_completed", mock_event, None)
+
+        # Scalar fields preserved
+        assert result.get("crew_name") == "TestCrew"
+        assert result.get("total_tokens") == 5000
+
+        # Should NOT have full crew object
+        assert "crew" not in result or result.get("crew") is None
+        # Should NOT have crew_structure (that's only for crew_started)
+        assert "crew_structure" not in result
+
+
+class TestSizeReduction:
+    """Test that the optimization actually reduces serialized size."""
+
+    @pytest.fixture
+    def listener(self):
+        """Create a trace listener for testing."""
+        TraceCollectionListener._instance = None
+        TraceCollectionListener._initialized = False
+        TraceCollectionListener._listeners_setup = False
+        return TraceCollectionListener()
+
+    def test_task_event_size_reduction(self, listener):
+        """Verify task events are much smaller than naive serialization."""
+        import json
+
+        # Create a realistic task with many fields
+        mock_agent = MagicMock()
+        mock_agent.id = uuid.uuid4()
+        mock_agent.role = "Researcher"
+        mock_agent.goal = "Research" * 50  # Longer goal
+        mock_agent.backstory = "Expert" * 100  # Longer backstory
+        mock_agent.tools = [MagicMock() for _ in range(5)]
+        mock_agent.llm = MagicMock()
+        mock_agent.crew = MagicMock()
+
+        mock_task = MagicMock()
+        mock_task.name = "Research Task"
+        mock_task.description = "Detailed description" * 20
+        mock_task.expected_output = "Expected" * 10
+        mock_task.id = uuid.uuid4()
+        mock_task.agent = mock_agent
+        mock_task.context = [MagicMock() for _ in range(3)]
+        mock_task.crew = MagicMock()
+        mock_task.tools = [MagicMock() for _ in range(3)]
+
+        mock_event = MagicMock()
+        mock_event.task = mock_task
+        mock_event.context = "test context"
+
+        mock_source = MagicMock()
+        mock_source.agent = mock_agent
+
+        result = listener._build_event_data("task_started", mock_event, mock_source)
+
+        # The result should be relatively small
+        serialized = json.dumps(result, default=str)
+
+        # Should be under 2KB for task_started (was potentially 50-100KB before)
+        assert len(serialized) < 2000, f"task_started too large: {len(serialized)} bytes"
+
+        # Should have the essential fields
+        assert "task_name" in result
+        assert "task_id" in result
+        assert "agent_role" in result