chore: refactor llms to base models

docs/en/concepts/llms.mdx

@@ -1212,7 +1212,7 @@ Learn how to get the most out of your LLM configuration:
     ```python
 import httpx
 from crewai import LLM
-from crewai.llms.hooks import BaseInterceptor
+from crewai.llm.hooks import BaseInterceptor
 
 class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
     """Custom interceptor to modify requests and responses."""

lib/crewai/src/crewai/__init__.py

@@ -9,7 +9,7 @@ from crewai.crews.crew_output import CrewOutput
 from crewai.flow.flow import Flow
 from crewai.knowledge.knowledge import Knowledge
 from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.process import Process
 from crewai.task import Task
 from crewai.tasks.llm_guardrail import LLMGuardrail

lib/crewai/src/crewai/agent/core.py

@@ -39,7 +39,7 @@ from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.lite_agent import LiteAgent
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.mcp import (
     MCPClient,
     MCPServerConfig,
@@ -626,7 +626,7 @@ class Agent(BaseAgent):
             )
 
         self.agent_executor = CrewAgentExecutor(
-            llm=self.llm,
+            llm=self.llm,  # type: ignore[arg-type]
             task=task,  # type: ignore[arg-type]
             agent=self,
             crew=self.crew,
@@ -803,6 +803,7 @@ class Agent(BaseAgent):
         from crewai.tools.base_tool import BaseTool
         from crewai.tools.mcp_native_tool import MCPNativeTool
 
+        transport: StdioTransport | HTTPTransport | SSETransport
         if isinstance(mcp_config, MCPServerStdio):
             transport = StdioTransport(
                 command=mcp_config.command,
@@ -896,10 +897,12 @@ class Agent(BaseAgent):
                                 server_name=server_name,
                                 run_context=None,
                             )
-                            if mcp_config.tool_filter(context, tool):
+                            # Try new signature first
+                            if mcp_config.tool_filter(context, tool):  # type: ignore[arg-type,call-arg]
                                 filtered_tools.append(tool)
                         except (TypeError, AttributeError):
-                            if mcp_config.tool_filter(tool):
+                            # Fallback to old signature
+                            if mcp_config.tool_filter(tool):  # type: ignore[arg-type,call-arg]
                                 filtered_tools.append(tool)
                     else:
                         # Not callable - include tool
@@ -974,7 +977,9 @@ class Agent(BaseAgent):
         path = parsed.path.replace("/", "_").strip("_")
         return f"{domain}_{path}" if path else domain
 
-    def _get_mcp_tool_schemas(self, server_params: dict) -> dict[str, dict]:
+    def _get_mcp_tool_schemas(
+        self, server_params: dict[str, Any]
+    ) -> dict[str, dict[str, Any]]:
         """Get tool schemas from MCP server for wrapper creation with caching."""
         server_url = server_params["url"]
 
@@ -988,7 +993,7 @@ class Agent(BaseAgent):
                 self._logger.log(
                     "debug", f"Using cached MCP tool schemas for {server_url}"
                 )
-                return cached_data
+                return cast(dict[str, dict[str, Any]], cached_data)
 
         try:
             schemas = asyncio.run(self._get_mcp_tool_schemas_async(server_params))
@@ -1006,7 +1011,7 @@ class Agent(BaseAgent):
 
     async def _get_mcp_tool_schemas_async(
         self, server_params: dict[str, Any]
-    ) -> dict[str, dict]:
+    ) -> dict[str, dict[str, Any]]:
         """Async implementation of MCP tool schema retrieval with timeouts and retries."""
         server_url = server_params["url"]
         return await self._retry_mcp_discovery(
@@ -1014,7 +1019,7 @@ class Agent(BaseAgent):
         )
 
     async def _retry_mcp_discovery(
-        self, operation_func, server_url: str
+        self, operation_func: Any, server_url: str
     ) -> dict[str, dict[str, Any]]:
         """Retry MCP discovery operation with exponential backoff, avoiding try-except in loop."""
         last_error = None
@@ -1045,7 +1050,7 @@ class Agent(BaseAgent):
 
     @staticmethod
     async def _attempt_mcp_discovery(
-        operation_func, server_url: str
+        operation_func: Any, server_url: str
     ) -> tuple[dict[str, dict[str, Any]] | None, str, bool]:
         """Attempt single MCP discovery operation and return (result, error_message, should_retry)."""
         try:
@@ -1149,13 +1154,13 @@ class Agent(BaseAgent):
                     Field(..., description=field_description),
                 )
             else:
-                field_definitions[field_name] = (
+                field_definitions[field_name] = (  # type: ignore[assignment]
                     field_type | None,
                     Field(default=None, description=field_description),
                 )
 
         model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
-        return create_model(model_name, **field_definitions)
+        return create_model(model_name, **field_definitions)  # type: ignore[no-any-return,call-overload]
 
     def _json_type_to_python(self, field_schema: dict[str, Any]) -> type:
         """Convert JSON Schema type to Python type.
@@ -1175,16 +1180,16 @@ class Agent(BaseAgent):
                 if "const" in option:
                     types.append(str)
                 else:
-                    types.append(self._json_type_to_python(option))
+                    types.append(self._json_type_to_python(option))  # type: ignore[arg-type]
             unique_types = list(set(types))
             if len(unique_types) > 1:
                 result = unique_types[0]
                 for t in unique_types[1:]:
-                    result = result | t
+                    result = result | t  # type: ignore[assignment]
                 return result
             return unique_types[0]
 
-        type_mapping = {
+        type_mapping: dict[str, type] = {
             "string": str,
             "number": float,
             "integer": int,
@@ -1193,10 +1198,10 @@ class Agent(BaseAgent):
             "object": dict,
         }
 
-        return type_mapping.get(json_type, Any)
+        return type_mapping.get(json_type or "", Any)
 
     @staticmethod
-    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict]:
+    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict[str, Any]]:
         """Fetch MCP server configurations from CrewAI AMP API."""
         # TODO: Implement AMP API call to "integrations/mcps" endpoint
         # Should return list of server configs with URLs

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

@@ -137,7 +137,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: list[BaseTool] | None = Field(
+    tools: list[BaseTool] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: int = Field(
@@ -161,7 +161,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         description="An instance of the ToolsHandler class.",
     )
     tools_results: list[dict[str, Any]] = Field(
-        default=[], description="Results of the tools used by the agent."
+        default_factory=list, description="Results of the tools used by the agent."
     )
     max_tokens: int | None = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
@@ -265,7 +265,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         if not mcps:
             return mcps
 
-        validated_mcps = []
+        validated_mcps: list[str | MCPServerConfig] = []
         for mcp in mcps:
             if isinstance(mcp, str):
                 if mcp.startswith(("https://", "crewai-amp:")):

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

@@ -47,7 +47,7 @@ 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.llm.base_llm import BaseLLM
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.tools.structured_tool import CrewStructuredTool

lib/crewai/src/crewai/crew.py

@@ -57,7 +57,7 @@ from crewai.flow.flow_trackable import FlowTrackable
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.external.external_memory import ExternalMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory

lib/crewai/src/crewai/lite_agent.py

@@ -40,7 +40,7 @@ from crewai.events.types.logging_events import AgentLogsExecutionEvent
 from crewai.flow.flow_trackable import FlowTrackable
 from crewai.lite_agent_output import LiteAgentOutput
 from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities.agent_utils import (
@@ -503,7 +503,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             AgentFinish: The final result of the agent execution.
         """
         # Execute the agent loop
-        formatted_answer = None
+        formatted_answer: AgentAction | AgentFinish | None = None
         while not isinstance(formatted_answer, AgentFinish):
             try:
                 if has_reached_max_iterations(self._iterations, self.max_iterations):
@@ -551,6 +551,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                         show_logs=self._show_logs,
                     )
 
+                if formatted_answer is not None:
                     self._append_message(formatted_answer.text, role="assistant")
             except OutputParserError as e:  # noqa: PERF203
                 self._printer.print(

lib/crewai/src/crewai/llm/__init__.py

@@ -0,0 +1,4 @@
+from crewai.llm.core import LLM
+
+
+__all__ = ["LLM"]

lib/crewai/src/crewai/llm/base_llm.py

@@ -11,9 +11,10 @@ from datetime import datetime
 import json
 import logging
 import re
-from typing import TYPE_CHECKING, Any, Final
+from typing import TYPE_CHECKING, Any, ClassVar, Final
 
-from pydantic import BaseModel
+import httpx
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.llm_events import (
@@ -28,6 +29,8 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
+from crewai.llm.hooks.base import BaseInterceptor
+from crewai.llm.internal.meta import LLMMeta
 from crewai.types.usage_metrics import UsageMetrics
 
 
@@ -43,7 +46,7 @@ DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True
 _JSON_EXTRACTION_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{.*}", re.DOTALL)
 
 
-class BaseLLM(ABC):
+class BaseLLM(BaseModel, ABC, metaclass=LLMMeta):
     """Abstract base class for LLM implementations.
 
     This class defines the interface that all LLM implementations must follow.
@@ -62,47 +65,32 @@ class BaseLLM(ABC):
         additional_params: Additional provider-specific parameters.
     """
 
-    is_litellm: bool = False
+    model_config: ClassVar[ConfigDict] = ConfigDict(
+        arbitrary_types_allowed=True, extra="allow", validate_assignment=True
+    )
 
-    def __init__(
+    # Core fields
-        self,
+    model: str = Field(..., description="The model identifier/name")
-        model: str,
+    temperature: float | None = Field(
-        temperature: float | None = None,
+        None, description="Temperature setting for response generation"
-        api_key: str | None = None,
+    )
-        base_url: str | None = None,
+    api_key: str | None = Field(None, description="API key for authentication")
-        provider: str | None = None,
+    base_url: str | None = Field(None, description="Base URL for API requests")
-        **kwargs: Any,
+    provider: str = Field(
-    ) -> None:
+        default="openai", description="Provider name (openai, anthropic, etc.)"
-        """Initialize the BaseLLM with default attributes.
+    )
+    stop: list[str] = Field(
+        default_factory=list, description="Stop sequences for generation"
+    )
 
-        Args:
+    # Internal fields
-            model: The model identifier/name.
+    is_litellm: bool = Field(
-            temperature: Optional temperature setting for response generation.
+        default=False, description="Whether this instance uses LiteLLM"
-            stop: Optional list of stop sequences for generation.
+    )
-            **kwargs: Additional provider-specific parameters.
+    interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = Field(
-        """
+        None, description="HTTP request/response interceptor"
-        if not model:
+    )
-            raise ValueError("Model name is required and cannot be empty")
+    _token_usage: dict[str, int] = {
-
-        self.model = model
-        self.temperature = temperature
-        self.api_key = api_key
-        self.base_url = base_url
-        # 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,
@@ -110,15 +98,70 @@ class BaseLLM(ABC):
         "cached_prompt_tokens": 0,
     }
 
-    @property
+    @model_validator(mode="before")
-    def provider(self) -> str:
+    @classmethod
-        """Get the provider of the LLM."""
+    def _extract_stop_and_validate(cls, values: dict[str, Any]) -> dict[str, Any]:
-        return self._provider
+        """Extract and normalize stop sequences before model initialization.
 
-    @provider.setter
+        Args:
-    def provider(self, value: str) -> None:
+            values: Input values dictionary
-        """Set the provider of the LLM."""
+
-        self._provider = value
+        Returns:
+            Processed values dictionary
+        """
+        if not values.get("model"):
+            raise ValueError("Model name is required and cannot be empty")
+
+        # Handle stop sequences
+        stop = values.get("stop")
+        if stop is None:
+            values["stop"] = []
+        elif isinstance(stop, str):
+            values["stop"] = [stop]
+        elif not isinstance(stop, list):
+            values["stop"] = []
+
+        # Set default provider if not specified
+        if "provider" not in values or values["provider"] is None:
+            values["provider"] = "openai"
+
+        return values
+
+    @property
+    def additional_params(self) -> dict[str, Any]:
+        """Get additional parameters stored as extra fields.
+
+        Returns:
+            Dictionary of additional parameters
+        """
+        return self.__pydantic_extra__ or {}
+
+    @additional_params.setter
+    def additional_params(self, value: dict[str, Any]) -> None:
+        """Set additional parameters as extra fields.
+
+        Args:
+            value: Dictionary of additional parameters to set
+        """
+        if not isinstance(value, dict):
+            raise ValueError("additional_params must be a dictionary")
+        if self.__pydantic_extra__ is None:
+            self.__pydantic_extra__ = {}
+        self.__pydantic_extra__.update(value)
+
+    def model_post_init(self, __context: Any) -> None:
+        """Initialize token usage tracking after model initialization.
+
+        Args:
+            __context: Pydantic context (unused)
+        """
+        self._token_usage = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+            "cached_prompt_tokens": 0,
+        }
 
     @abstractmethod
     def call(

lib/crewai/src/crewai/llm/core.py

@@ -20,9 +20,7 @@ from typing import (
 )
 
 from dotenv import load_dotenv
-import httpx
 from pydantic import BaseModel, Field
-from typing_extensions import Self
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.llm_events import (
@@ -37,14 +35,7 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
-from crewai.llms.constants import (
-    ANTHROPIC_MODELS,
-    AZURE_MODELS,
-    BEDROCK_MODELS,
-    GEMINI_MODELS,
-    OPENAI_MODELS,
-)
 from crewai.utilities import InternalInstructor
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -61,7 +52,6 @@ if TYPE_CHECKING:
     from litellm.utils import supports_response_schema
 
     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
     from crewai.utilities.types import LLMMessage
@@ -327,249 +317,57 @@ class AccumulatedToolArgs(BaseModel):
 
 
 class LLM(BaseLLM):
-    completion_cost: float | None = None
+    """LiteLLM-based LLM implementation for CrewAI.
 
-    def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
+    This class provides LiteLLM integration for models not covered by native providers.
-        """Factory method that routes to native SDK or falls back to LiteLLM.
+    The metaclass (LLMMeta) automatically routes to native providers when appropriate.
-
-        Routing priority:
-            1. If 'provider' kwarg is present, use that provider with constants
-            2. If only 'model' kwarg, use constants to infer provider
-            3. If "/" in model name:
-               - Check if prefix is a native provider (openai/anthropic/azure/bedrock/gemini)
-               - If yes, validate model against constants
-               - If valid, route to native SDK; otherwise route to LiteLLM
     """
-        if not model or not isinstance(model, str):
-            raise ValueError("Model must be a non-empty string")
 
-        explicit_provider = kwargs.get("provider")
+    # LiteLLM-specific fields
-
+    completion_cost: float | None = Field(None, description="Cost of completion")
-        if explicit_provider:
+    timeout: float | int | None = Field(None, description="Request timeout")
-            provider = explicit_provider
+    top_p: float | None = Field(None, description="Top-p sampling parameter")
-            use_native = True
+    n: int | None = Field(None, description="Number of completions to generate")
-            model_string = model
+    max_completion_tokens: int | None = Field(
-        elif "/" in model:
+        None, description="Maximum completion tokens"
-            prefix, _, model_part = model.partition("/")
-
-            provider_mapping = {
-                "openai": "openai",
-                "anthropic": "anthropic",
-                "claude": "anthropic",
-                "azure": "azure",
-                "azure_openai": "azure",
-                "google": "gemini",
-                "gemini": "gemini",
-                "bedrock": "bedrock",
-                "aws": "bedrock",
-            }
-
-            canonical_provider = provider_mapping.get(prefix.lower())
-
-            if canonical_provider and cls._validate_model_in_constants(
-                model_part, canonical_provider
-            ):
-                provider = canonical_provider
-                use_native = True
-                model_string = model_part
-            else:
-                provider = prefix
-                use_native = False
-                model_string = model_part
-        else:
-            provider = cls._infer_provider_from_model(model)
-            use_native = True
-            model_string = model
-
-        native_class = cls._get_native_provider(provider) if use_native else None
-        if native_class and not is_litellm and provider in SUPPORTED_NATIVE_PROVIDERS:
-            try:
-                # Remove 'provider' from kwargs if it exists to avoid duplicate keyword argument
-                kwargs_copy = {k: v for k, v in kwargs.items() if k != 'provider'}
-                return cast(
-                    Self, native_class(model=model_string, provider=provider, **kwargs_copy)
     )
-            except NotImplementedError:
+    max_tokens: int | float | None = Field(None, description="Maximum total tokens")
-                raise
+    presence_penalty: float | None = Field(None, description="Presence penalty")
-            except Exception as e:
+    frequency_penalty: float | None = Field(None, description="Frequency penalty")
-                raise ImportError(f"Error importing native provider: {e}") from e
+    logit_bias: dict[int, float] | None = Field(None, description="Logit bias")
+    response_format: type[BaseModel] | None = Field(
+        None, description="Response format model"
+    )
+    seed: int | None = Field(None, description="Random seed for reproducibility")
+    logprobs: int | None = Field(None, description="Log probabilities to return")
+    top_logprobs: int | None = Field(None, description="Top log probabilities")
+    api_base: str | None = Field(None, description="API base URL (alias for base_url)")
+    api_version: str | None = Field(None, description="API version")
+    callbacks: list[Any] | None = Field(None, description="Callback functions")
+    context_window_size: int = Field(0, description="Context window size in tokens")
+    reasoning_effort: Literal["none", "low", "medium", "high"] | None = Field(
+        None, description="Reasoning effort level"
+    )
+    is_anthropic: bool = Field(False, description="Whether model is from Anthropic")
+    stream: bool = Field(False, description="Whether to stream responses")
 
-        # FALLBACK to LiteLLM
+    def model_post_init(self, __context: Any) -> None:
-        if not LITELLM_AVAILABLE:
+        """Initialize LiteLLM-specific settings after model initialization.
-            logger.error("LiteLLM is not available, falling back to LiteLLM")
-            raise ImportError("Fallback to LiteLLM is not available") from None
-
-        instance = object.__new__(cls)
-        super(LLM, instance).__init__(model=model, is_litellm=True, **kwargs)
-        instance.is_litellm = True
-        return instance
-
-    @classmethod
-    def _validate_model_in_constants(cls, model: str, provider: str) -> bool:
-        """Validate if a model name exists in the provider's constants.
 
         Args:
-            model: The model name to validate
+            __context: Pydantic context
-            provider: The provider to check against (canonical name)
-
-        Returns:
-            True if the model exists in the provider's constants, False otherwise
         """
-        if provider == "openai":
+        super().model_post_init(__context)
-            return model in OPENAI_MODELS
-
-        if provider == "anthropic" or provider == "claude":
-            return model in ANTHROPIC_MODELS
-
-        if provider == "gemini":
-            return model in GEMINI_MODELS
-
-        if provider == "bedrock":
-            return model in BEDROCK_MODELS
-
-        if provider == "azure":
-            # azure does not provide a list of available models, determine a better way to handle this
-            return True
-
-        return False
-
-    @classmethod
-    def _infer_provider_from_model(cls, model: str) -> str:
-        """Infer the provider from the model name.
-
-        Args:
-            model: The model name without provider prefix
-
-        Returns:
-            The inferred provider name, defaults to "openai"
-        """
-
-        if model in OPENAI_MODELS:
-            return "openai"
-
-        if model in ANTHROPIC_MODELS:
-            return "anthropic"
-
-        if model in GEMINI_MODELS:
-            return "gemini"
-
-        if model in BEDROCK_MODELS:
-            return "bedrock"
-
-        if model in AZURE_MODELS:
-            return "azure"
-
-        return "openai"
-
-    @classmethod
-    def _get_native_provider(cls, provider: str) -> type | None:
-        """Get native provider class if available."""
-        if provider == "openai":
-            from crewai.llms.providers.openai.completion import OpenAICompletion
-
-            return OpenAICompletion
-
-        if provider == "anthropic" or provider == "claude":
-            from crewai.llms.providers.anthropic.completion import (
-                AnthropicCompletion,
-            )
-
-            return AnthropicCompletion
-
-        if provider == "azure" or provider == "azure_openai":
-            from crewai.llms.providers.azure.completion import AzureCompletion
-
-            return AzureCompletion
-
-        if provider == "google" or provider == "gemini":
-            from crewai.llms.providers.gemini.completion import GeminiCompletion
-
-            return GeminiCompletion
-
-        if provider == "bedrock":
-            from crewai.llms.providers.bedrock.completion import BedrockCompletion
-
-            return BedrockCompletion
-
-        return None
-
-    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,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize LLM instance.
-
-        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.additional_params = kwargs
-        self.is_anthropic = self._is_anthropic_model(model)
-        self.stream = stream
-        self.interceptor = interceptor
 
+        # Configure LiteLLM
+        if LITELLM_AVAILABLE:
             litellm.drop_params = True
 
-        # Normalize self.stop to always be a list[str]
+        # Determine if this is an Anthropic model
-        if stop is None:
+        self.is_anthropic = self._is_anthropic_model(self.model)
-            self.stop: list[str] = []
-        elif isinstance(stop, str):
-            self.stop = [stop]
-        else:
-            self.stop = stop
 
-        self.set_callbacks(callbacks or [])
+        # Set up callbacks
+        self.set_callbacks(self.callbacks or [])
         self.set_env_callbacks()
 
     @staticmethod
@@ -1649,7 +1447,7 @@ class LLM(BaseLLM):
             **filtered_params,
         )
 
-    def __deepcopy__(self, memo: dict[int, Any] | None) -> LLM:
+    def __deepcopy__(self, memo: dict[int, Any] | None) -> LLM:  # type: ignore[override]
         """Create a deep copy of the LLM instance."""
         import copy
 

lib/crewai/src/crewai/llm/hooks/__init__.py

@@ -1,6 +1,6 @@
 """Interceptor contracts for crewai"""
 
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llm.hooks.base import BaseInterceptor
 
 
 __all__ = ["BaseInterceptor"]

lib/crewai/src/crewai/llm/hooks/transport.py

@@ -22,7 +22,7 @@ if TYPE_CHECKING:
     from httpx import Limits, Request, Response
     from httpx._types import CertTypes, ProxyTypes
 
-    from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.llm.hooks.base import BaseInterceptor
 
 
 class HTTPTransportKwargs(TypedDict, total=False):

lib/crewai/src/crewai/llm/internal/meta.py

@@ -0,0 +1,232 @@
+"""Metaclass for LLM provider routing.
+
+This metaclass enables automatic routing to native provider implementations
+based on the model parameter at instantiation time.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from pydantic._internal._model_construction import ModelMetaclass
+
+
+# Provider constants imported from crewai.llm.constants
+SUPPORTED_NATIVE_PROVIDERS: list[str] = [
+    "openai",
+    "anthropic",
+    "claude",
+    "azure",
+    "azure_openai",
+    "google",
+    "gemini",
+    "bedrock",
+    "aws",
+]
+
+
+class LLMMeta(ModelMetaclass):
+    """Metaclass for LLM that handles provider routing.
+
+    This metaclass intercepts LLM instantiation and routes to the appropriate
+    native provider implementation based on the model parameter.
+    """
+
+    def __call__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> Any:  # noqa: N805
+        """Route to appropriate provider implementation at instantiation time.
+
+        Args:
+            model: The model identifier (e.g., "gpt-4", "claude-3-opus")
+            is_litellm: Force use of LiteLLM instead of native provider
+            **kwargs: Additional parameters for the LLM
+
+        Returns:
+            Instance of the appropriate provider class or LLM class
+
+        Raises:
+            ValueError: If model is not a valid string
+        """
+        if not model or not isinstance(model, str):
+            raise ValueError("Model must be a non-empty string")
+
+        # Only perform routing if called on the base LLM class
+        # Subclasses (OpenAICompletion, etc.) should create normally
+        from crewai.llm import LLM
+
+        if cls is not LLM:
+            # Direct instantiation of provider class, skip routing
+            return super().__call__(model=model, **kwargs)
+
+        # Extract provider information
+        explicit_provider = kwargs.get("provider")
+
+        if explicit_provider:
+            provider = explicit_provider
+            use_native = True
+            model_string = model
+        elif "/" in model:
+            prefix, _, model_part = model.partition("/")
+
+            provider_mapping = {
+                "openai": "openai",
+                "anthropic": "anthropic",
+                "claude": "anthropic",
+                "azure": "azure",
+                "azure_openai": "azure",
+                "google": "gemini",
+                "gemini": "gemini",
+                "bedrock": "bedrock",
+                "aws": "bedrock",
+            }
+
+            canonical_provider = provider_mapping.get(prefix.lower())
+
+            if canonical_provider and cls._validate_model_in_constants(
+                model_part, canonical_provider
+            ):
+                provider = canonical_provider
+                use_native = True
+                model_string = model_part
+            else:
+                provider = prefix
+                use_native = False
+                model_string = model_part
+        else:
+            provider = cls._infer_provider_from_model(model)
+            use_native = True
+            model_string = model
+
+        # Route to native provider if available
+        native_class = cls._get_native_provider(provider) if use_native else None
+        if native_class and not is_litellm and provider in SUPPORTED_NATIVE_PROVIDERS:
+            try:
+                # Remove 'provider' from kwargs to avoid duplicate keyword argument
+                kwargs_copy = {k: v for k, v in kwargs.items() if k != "provider"}
+                return native_class(
+                    model=model_string, provider=provider, **kwargs_copy
+                )
+            except NotImplementedError:
+                raise
+            except Exception as e:
+                raise ImportError(f"Error importing native provider: {e}") from e
+
+        # Fallback to LiteLLM
+        try:
+            import litellm  # noqa: F401
+        except ImportError:
+            logging.error("LiteLLM is not available, falling back to LiteLLM")
+            raise ImportError("Fallback to LiteLLM is not available") from None
+
+        # Create actual LLM instance with is_litellm=True
+        return super().__call__(model=model, is_litellm=True, **kwargs)
+
+    @staticmethod
+    def _validate_model_in_constants(model: str, provider: str) -> bool:
+        """Validate if a model name exists in the provider's constants.
+
+        Args:
+            model: The model name to validate
+            provider: The provider to check against (canonical name)
+
+        Returns:
+            True if the model exists in the provider's constants, False otherwise
+        """
+        from crewai.llm.constants import (
+            ANTHROPIC_MODELS,
+            BEDROCK_MODELS,
+            GEMINI_MODELS,
+            OPENAI_MODELS,
+        )
+
+        if provider == "openai":
+            return model in OPENAI_MODELS
+
+        if provider == "anthropic" or provider == "claude":
+            return model in ANTHROPIC_MODELS
+
+        if provider == "gemini":
+            return model in GEMINI_MODELS
+
+        if provider == "bedrock":
+            return model in BEDROCK_MODELS
+
+        if provider == "azure":
+            # azure does not provide a list of available models
+            return True
+
+        return False
+
+    @staticmethod
+    def _infer_provider_from_model(model: str) -> str:
+        """Infer the provider from the model name.
+
+        Args:
+            model: The model name without provider prefix
+
+        Returns:
+            The inferred provider name, defaults to "openai"
+        """
+        from crewai.llm.constants import (
+            ANTHROPIC_MODELS,
+            AZURE_MODELS,
+            BEDROCK_MODELS,
+            GEMINI_MODELS,
+            OPENAI_MODELS,
+        )
+
+        if model in OPENAI_MODELS:
+            return "openai"
+
+        if model in ANTHROPIC_MODELS:
+            return "anthropic"
+
+        if model in GEMINI_MODELS:
+            return "gemini"
+
+        if model in BEDROCK_MODELS:
+            return "bedrock"
+
+        if model in AZURE_MODELS:
+            return "azure"
+
+        return "openai"
+
+    @staticmethod
+    def _get_native_provider(provider: str) -> type | None:
+        """Get native provider class if available.
+
+        Args:
+            provider: The provider name
+
+        Returns:
+            The provider class or None if not available
+        """
+        if provider == "openai":
+            from crewai.llm.providers.openai.completion import OpenAICompletion
+
+            return OpenAICompletion
+
+        if provider == "anthropic" or provider == "claude":
+            from crewai.llm.providers.anthropic.completion import (
+                AnthropicCompletion,
+            )
+
+            return AnthropicCompletion
+
+        if provider == "azure" or provider == "azure_openai":
+            from crewai.llm.providers.azure.completion import AzureCompletion
+
+            return AzureCompletion
+
+        if provider == "google" or provider == "gemini":
+            from crewai.llm.providers.gemini.completion import GeminiCompletion
+
+            return GeminiCompletion
+
+        if provider == "bedrock":
+            from crewai.llm.providers.bedrock.completion import BedrockCompletion
+
+            return BedrockCompletion
+
+        return None

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

@@ -3,13 +3,15 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any, cast
+from typing import TYPE_CHECKING, Any, ClassVar, cast
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
-from crewai.llms.hooks.transport import HTTPTransport
+from crewai.llm.core import CONTEXT_WINDOW_USAGE_RATIO
+from crewai.llm.hooks.transport import HTTPTransport
+from crewai.llm.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -18,7 +20,7 @@ from crewai.utilities.types import LLMMessage
 
 
 if TYPE_CHECKING:
-    from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.llm.hooks.base import BaseInterceptor
 
 try:
     from anthropic import Anthropic
@@ -38,6 +40,8 @@ class AnthropicCompletion(BaseLLM):
     offering native tool use, streaming support, and proper message formatting.
     """
 
+    model_config: ClassVar[ConfigDict] = ConfigDict(ignored_types=(property,))
+
     def __init__(
         self,
         model: str = "claude-3-5-sonnet-20241022",
@@ -94,29 +98,30 @@ class AnthropicCompletion(BaseLLM):
         self.is_claude_3 = "claude-3" in model.lower()
         self.supports_tools = self.is_claude_3  # Claude 3+ supports tool use
 
-    @property
+    #
-    def stop(self) -> list[str]:
+    # @property
-        """Get stop sequences sent to the API."""
+    # def stop(self) -> list[str]:  # type: ignore[misc]
-        return self.stop_sequences
+    #     """Get stop sequences sent to the API."""
+    #     return self.stop_sequences
 
-    @stop.setter
+    # @stop.setter
-    def stop(self, value: list[str] | str | None) -> None:
+    # def stop(self, value: list[str] | str | None) -> None:
-        """Set stop sequences.
+    #     """Set stop sequences.
-
+    #
-        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+    #     Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
-        are properly sent to the Anthropic API.
+    #     are properly sent to the Anthropic API.
-
+    #
-        Args:
+    #     Args:
-            value: Stop sequences as a list, single string, or None
+    #         value: Stop sequences as a list, single string, or None
-        """
+    #     """
-        if value is None:
+    #     if value is None:
-            self.stop_sequences = []
+    #         self.stop_sequences = []
-        elif isinstance(value, str):
+    #     elif isinstance(value, str):
-            self.stop_sequences = [value]
+    #         self.stop_sequences = [value]
-        elif isinstance(value, list):
+    #     elif isinstance(value, list):
-            self.stop_sequences = value
+    #         self.stop_sequences = value
-        else:
+    #     else:
-            self.stop_sequences = []
+    #         self.stop_sequences = []
 
     def _get_client_params(self) -> dict[str, Any]:
         """Get client parameters."""
@@ -266,8 +271,6 @@ class AnthropicCompletion(BaseLLM):
                 continue
 
             try:
-                from crewai.llms.providers.utils.common import safe_tool_conversion
-
                 name, description, parameters = safe_tool_conversion(tool, "Anthropic")
             except (ImportError, KeyError, ValueError) as e:
                 logging.error(f"Error converting tool to Anthropic format: {e}")
@@ -636,7 +639,6 @@ class AnthropicCompletion(BaseLLM):
 
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""
-        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO
 
         # Context window sizes for Anthropic models
         context_windows = {

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

@@ -7,6 +7,8 @@ from typing import TYPE_CHECKING, Any
 
 from pydantic import BaseModel
 
+from crewai.llm.core import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
+from crewai.llm.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -15,7 +17,7 @@ from crewai.utilities.types import LLMMessage
 
 
 if TYPE_CHECKING:
-    from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.llm.hooks.base import BaseInterceptor
     from crewai.tools.base_tool import BaseTool
 
 
@@ -36,7 +38,7 @@ try:
     )
 
     from crewai.events.types.llm_events import LLMCallType
-    from crewai.llms.base_llm import BaseLLM
+    from crewai.llm.base_llm import BaseLLM
 
 except ImportError:
     raise ImportError(
@@ -317,8 +319,6 @@ class AzureCompletion(BaseLLM):
     ) -> list[dict[str, Any]]:
         """Convert CrewAI tool format to Azure OpenAI function calling format."""
 
-        from crewai.llms.providers.utils.common import safe_tool_conversion
-
         azure_tools = []
 
         for tool in tools:
@@ -554,7 +554,6 @@ class AzureCompletion(BaseLLM):
 
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""
-        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 
         min_context = 1024
         max_context = 2097152

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

@@ -3,13 +3,15 @@ from __future__ import annotations
 from collections.abc import Mapping, Sequence
 import logging
 import os
-from typing import TYPE_CHECKING, Any, TypedDict, cast
+from typing import TYPE_CHECKING, Any, ClassVar, TypedDict, cast
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 from typing_extensions import Required
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
+from crewai.llm.core import CONTEXT_WINDOW_USAGE_RATIO
+from crewai.llm.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -30,7 +32,7 @@ if TYPE_CHECKING:
         ToolTypeDef,
     )
 
-    from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.llm.hooks.base import BaseInterceptor
 
 
 try:
@@ -143,6 +145,8 @@ class BedrockCompletion(BaseLLM):
     - Model-specific conversation format handling (e.g., Cohere requirements)
     """
 
+    model_config: ClassVar[ConfigDict] = ConfigDict(ignored_types=(property,))
+
     def __init__(
         self,
         model: str = "anthropic.claude-3-5-sonnet-20241022-v2:0",
@@ -243,29 +247,29 @@ class BedrockCompletion(BaseLLM):
         # Handle inference profiles for newer models
         self.model_id = model
 
-    @property
+    # @property
-    def stop(self) -> list[str]:
+    # def stop(self) -> list[str]:  # type: ignore[misc]
-        """Get stop sequences sent to the API."""
+    #     """Get stop sequences sent to the API."""
-        return list(self.stop_sequences)
+    #     return list(self.stop_sequences)
 
-    @stop.setter
+    # @stop.setter
-    def stop(self, value: Sequence[str] | str | None) -> None:
+    # def stop(self, value: Sequence[str] | str | None) -> None:
-        """Set stop sequences.
+    #     """Set stop sequences.
-
+    #
-        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+    #     Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
-        are properly sent to the Bedrock API.
+    #     are properly sent to the Bedrock API.
-
+    #
-        Args:
+    #     Args:
-            value: Stop sequences as a Sequence, single string, or None
+    #         value: Stop sequences as a Sequence, single string, or None
-        """
+    #     """
-        if value is None:
+    #     if value is None:
-            self.stop_sequences = []
+    #         self.stop_sequences = []
-        elif isinstance(value, str):
+    #     elif isinstance(value, str):
-            self.stop_sequences = [value]
+    #         self.stop_sequences = [value]
-        elif isinstance(value, Sequence):
+    #     elif isinstance(value, Sequence):
-            self.stop_sequences = list(value)
+    #         self.stop_sequences = list(value)
-        else:
+    #     else:
-            self.stop_sequences = []
+    #         self.stop_sequences = []
 
     def call(
         self,
@@ -778,7 +782,6 @@ class BedrockCompletion(BaseLLM):
         tools: list[dict[str, Any]],
     ) -> list[ConverseToolTypeDef]:
         """Convert CrewAI tools to Converse API format following AWS specification."""
-        from crewai.llms.providers.utils.common import safe_tool_conversion
 
         converse_tools: list[ConverseToolTypeDef] = []
 
@@ -871,7 +874,6 @@ class BedrockCompletion(BaseLLM):
 
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""
-        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO
 
         # Context window sizes for common Bedrock models
         context_windows = {

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

@@ -1,12 +1,14 @@
 import logging
 import os
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llm.core import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
+from crewai.llm.hooks.base import BaseInterceptor
+from crewai.llm.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -31,6 +33,8 @@ class GeminiCompletion(BaseLLM):
     offering native function calling, streaming support, and proper Gemini formatting.
     """
 
+    model_config: ClassVar[ConfigDict] = ConfigDict(ignored_types=(property,))
+
     def __init__(
         self,
         model: str = "gemini-2.0-flash-001",
@@ -104,29 +108,29 @@ class GeminiCompletion(BaseLLM):
         self.is_gemini_1_5 = "gemini-1.5" in model.lower()
         self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
 
-    @property
+    # @property
-    def stop(self) -> list[str]:
+    # def stop(self) -> list[str]:  # type: ignore[misc]
-        """Get stop sequences sent to the API."""
+    #     """Get stop sequences sent to the API."""
-        return self.stop_sequences
+    #     return self.stop_sequences
 
-    @stop.setter
+    # @stop.setter
-    def stop(self, value: list[str] | str | None) -> None:
+    # def stop(self, value: list[str] | str | None) -> None:
-        """Set stop sequences.
+    #     """Set stop sequences.
-
+    #
-        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+    #     Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
-        are properly sent to the Gemini API.
+    #     are properly sent to the Gemini API.
-
+    #
-        Args:
+    #     Args:
-            value: Stop sequences as a list, single string, or None
+    #         value: Stop sequences as a list, single string, or None
-        """
+    #     """
-        if value is None:
+    #     if value is None:
-            self.stop_sequences = []
+    #         self.stop_sequences = []
-        elif isinstance(value, str):
+    #     elif isinstance(value, str):
-            self.stop_sequences = [value]
+    #         self.stop_sequences = [value]
-        elif isinstance(value, list):
+    #     elif isinstance(value, list):
-            self.stop_sequences = value
+    #         self.stop_sequences = value
-        else:
+    #     else:
-            self.stop_sequences = []
+    #         self.stop_sequences = []
 
     def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:  # type: ignore[no-any-unimported]
         """Initialize the Google Gen AI client with proper parameter handling.
@@ -335,8 +339,6 @@ class GeminiCompletion(BaseLLM):
         """Convert CrewAI tool format to Gemini function declaration format."""
         gemini_tools = []
 
-        from crewai.llms.providers.utils.common import safe_tool_conversion
-
         for tool in tools:
             name, description, parameters = safe_tool_conversion(tool, "Gemini")
 
@@ -547,7 +549,6 @@ class GeminiCompletion(BaseLLM):
 
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""
-        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 
         min_context = 1024
         max_context = 2097152

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

@@ -11,11 +11,13 @@ from openai import APIConnectionError, NotFoundError, OpenAI
 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 pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
-from crewai.llms.hooks.transport import HTTPTransport
+from crewai.llm.core import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
+from crewai.llm.hooks.transport import HTTPTransport
+from crewai.llm.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -25,7 +27,6 @@ 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
 
@@ -37,61 +38,61 @@ class OpenAICompletion(BaseLLM):
     offering native structured outputs, function calling, and streaming support.
     """
 
-    def __init__(
+    # Client configuration fields
-        self,
+    organization: str | None = Field(None, description="OpenAI organization ID")
-        model: str = "gpt-4o",
+    project: str | None = Field(None, description="OpenAI project ID")
-        api_key: str | None = None,
+    max_retries: int = Field(2, description="Maximum number of retries")
-        base_url: str | None = None,
+    default_headers: dict[str, str] | None = Field(
-        organization: str | None = None,
+        None, description="Default headers for requests"
-        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,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize OpenAI chat completion client."""
-
-        if provider is None:
-            provider = kwargs.pop("provider", "openai")
-
-        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,
     )
+    default_query: dict[str, Any] | None = Field(
+        None, description="Default query parameters"
+    )
+    client_params: dict[str, Any] | None = Field(
+        None, description="Additional client parameters"
+    )
+    timeout: float | None = Field(None, description="Request timeout")
+    api_base: str | None = Field(None, description="API base URL (deprecated)")
 
+    # Completion parameters
+    top_p: float | None = Field(None, description="Top-p sampling parameter")
+    frequency_penalty: float | None = Field(None, description="Frequency penalty")
+    presence_penalty: float | None = Field(None, description="Presence penalty")
+    max_tokens: int | None = Field(None, description="Maximum tokens")
+    max_completion_tokens: int | None = Field(
+        None, description="Maximum completion tokens"
+    )
+    seed: int | None = Field(None, description="Random seed")
+    stream: bool = Field(False, description="Enable streaming")
+    response_format: dict[str, Any] | type[BaseModel] | None = Field(
+        None, description="Response format"
+    )
+    logprobs: bool | None = Field(None, description="Return log probabilities")
+    top_logprobs: int | None = Field(
+        None, description="Number of top log probabilities"
+    )
+    reasoning_effort: str | None = Field(None, description="Reasoning effort level")
+
+    # Internal state
+    client: OpenAI = Field(
+        default_factory=OpenAI, exclude=True, description="OpenAI client instance"
+    )
+    is_o1_model: bool = Field(False, description="Whether this is an O1 model")
+    is_gpt4_model: bool = Field(False, description="Whether this is a GPT-4 model")
+
+    def model_post_init(self, __context: Any) -> None:
+        """Initialize OpenAI client after model initialization.
+
+        Args:
+            __context: Pydantic context
+        """
+        super().model_post_init(__context)
+
+        # Set API key from environment if not provided
+        if self.api_key is None:
+            self.api_key = os.getenv("OPENAI_API_KEY")
+
+        # Initialize client
         client_config = self._get_client_params()
         if self.interceptor:
             transport = HTTPTransport(interceptor=self.interceptor)
@@ -100,20 +101,9 @@ class OpenAICompletion(BaseLLM):
 
         self.client = OpenAI(**client_config)
 
-        # Completion parameters
+        # Set model flags
-        self.top_p = top_p
+        self.is_o1_model = "o1" in self.model.lower()
-        self.frequency_penalty = frequency_penalty
+        self.is_gpt4_model = "gpt-4" in self.model.lower()
-        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()
 
     def _get_client_params(self) -> dict[str, Any]:
         """Get OpenAI client parameters."""
@@ -268,7 +258,6 @@ class OpenAICompletion(BaseLLM):
         self, tools: list[dict[str, BaseTool]]
     ) -> list[dict[str, Any]]:
         """Convert CrewAI tool format to OpenAI function calling format."""
-        from crewai.llms.providers.utils.common import safe_tool_conversion
 
         openai_tools = []
 
@@ -560,7 +549,6 @@ class OpenAICompletion(BaseLLM):
 
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""
-        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 
         min_context = 1024
         max_context = 2097152

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

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

lib/crewai/src/crewai/tasks/llm_guardrail.py

@@ -4,7 +4,7 @@ from pydantic import BaseModel, Field
 
 from crewai.agent import Agent
 from crewai.lite_agent_output import LiteAgentOutput
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.tasks.task_output import TaskOutput
 
 

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

@@ -16,7 +16,7 @@ from crewai.agents.parser import (
     parse,
 )
 from crewai.cli.config import Settings
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.tools import BaseTool as CrewAITool
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool

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

@@ -19,7 +19,7 @@ if TYPE_CHECKING:
     from crewai.agent import Agent
     from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.llm import LLM
-    from crewai.llms.base_llm import BaseLLM
+    from crewai.llm.base_llm import BaseLLM
 
 _JSON_PATTERN: Final[re.Pattern[str]] = re.compile(r"({.*})", re.DOTALL)
 _I18N = get_i18n()

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

@@ -11,7 +11,7 @@ from rich.table import Table
 from crewai.agent import Agent
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.crew_events import CrewTestResultEvent
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 

lib/crewai/src/crewai/utilities/internal_instructor.py

@@ -10,7 +10,7 @@ from crewai.utilities.logger_utils import suppress_warnings
 if TYPE_CHECKING:
     from crewai.agent import Agent
     from crewai.llm import LLM
-    from crewai.llms.base_llm import BaseLLM
+    from crewai.llm.base_llm import BaseLLM
     from crewai.utilities.types import LLMMessage
 
 

lib/crewai/src/crewai/utilities/llm_utils.py

@@ -4,7 +4,7 @@ from typing import Any, Final
 
 from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
 from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 
 
 logger = logging.getLogger(__name__)

lib/crewai/src/crewai/utilities/planning_handler.py

@@ -5,7 +5,7 @@ import logging
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.task import Task
 
 

lib/crewai/src/crewai/utilities/tool_utils.py

@@ -15,7 +15,7 @@ if TYPE_CHECKING:
     from crewai.agent import Agent
     from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.llm import LLM
-    from crewai.llms.base_llm import BaseLLM
+    from crewai.llm.base_llm import BaseLLM
     from crewai.task import Task
 
 

lib/crewai/tests/agents/test_agent.py

@@ -14,7 +14,7 @@ from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.process import Process
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage

lib/crewai/tests/agents/test_lite_agent.py

@@ -9,7 +9,7 @@ from crewai.events.types.agent_events import LiteAgentExecutionStartedEvent
 from crewai.events.types.tool_usage_events import ToolUsageStartedEvent
 from crewai.lite_agent import LiteAgent
 from crewai.lite_agent_output import LiteAgentOutput
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from pydantic import BaseModel, Field
 import pytest
 

lib/crewai/tests/cassettes/test_agent_function_calling_llm.yaml

@@ -590,7 +590,7 @@ interactions:
       "<function BaseTool.<lambda> at 0x107389260>", "result_as_answer": "False",
       "max_usage_count": "None", "current_usage_count": "0"}], "max_iter": 2, "agent_executor":
       "<crewai.agents.crew_agent_executor.CrewAgentExecutor object at 0x130de6540>",
-      "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion object at
+      "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion object at
       0x130db6de0>", "crew": {"parent_flow": null, "name": "crew", "cache": true,
       "tasks": ["{''used_tools'': 0, ''tools_errors'': 0, ''delegations'': 0, ''i18n'':
       {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''', ''description'':
@@ -605,7 +605,7 @@ interactions:
       ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
       at 0x107389260>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'':
       0}], ''max_iter'': 2, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x130de6540>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x130de6540>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x130db6de0>, ''crew'': Crew(id=991ac83f-9a29-411f-b0a0-0a335c7a2d0e,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -634,7 +634,7 @@ interactions:
       ''abc.Learn_About_Ai''>, ''description_updated'': False, ''cache_function'':
       <function BaseTool.<lambda> at 0x107389260>, ''result_as_answer'': False, ''max_usage_count'':
       None, ''current_usage_count'': 0}], ''max_iter'': 2, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x130de6540>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x130de6540>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x130db6de0>, ''crew'': Crew(id=991ac83f-9a29-411f-b0a0-0a335c7a2d0e,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -657,7 +657,7 @@ interactions:
       {"fingerprint": {"metadata": "{}"}}, "callbacks": [], "adapted_agent": false,
       "knowledge_config": null, "max_execution_time": null, "agent_ops_agent_name":
       "test role", "agent_ops_agent_id": null, "step_callback": null, "use_system_prompt":
-      true, "function_calling_llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      true, "function_calling_llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x130db7020>", "system_template": null, "prompt_template": null, "response_template":
       null, "allow_code_execution": false, "respect_context_window": true, "max_retry_limit":
       2, "multimodal": false, "inject_date": false, "date_format": "%Y-%m-%d", "code_execution_mode":
@@ -1068,7 +1068,7 @@ interactions:
       "<function BaseTool.<lambda> at 0x107e394e0>", "result_as_answer": "False",
       "max_usage_count": "None", "current_usage_count": "0"}], "max_iter": 2, "agent_executor":
       "<crewai.agents.crew_agent_executor.CrewAgentExecutor object at 0x13b37c980>",
-      "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion object at
+      "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion object at
       0x13b7563c0>", "crew": {"parent_flow": null, "name": "crew", "cache": true,
       "tasks": ["{''used_tools'': 0, ''tools_errors'': 0, ''delegations'': 0, ''i18n'':
       {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''', ''description'':
@@ -1083,7 +1083,7 @@ interactions:
       ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
       at 0x107e394e0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'':
       0}], ''max_iter'': 2, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13b37c980>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13b37c980>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13b7563c0>, ''crew'': Crew(id=f38365e9-3206-45b6-8754-950cb03fe57e,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1112,7 +1112,7 @@ interactions:
       ''abc.Learn_About_Ai''>, ''description_updated'': False, ''cache_function'':
       <function BaseTool.<lambda> at 0x107e394e0>, ''result_as_answer'': False, ''max_usage_count'':
       None, ''current_usage_count'': 0}], ''max_iter'': 2, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13b37c980>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13b37c980>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13b7563c0>, ''crew'': Crew(id=f38365e9-3206-45b6-8754-950cb03fe57e,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1135,7 +1135,7 @@ interactions:
       {"fingerprint": {"metadata": "{}"}}, "callbacks": [], "adapted_agent": false,
       "knowledge_config": null, "max_execution_time": null, "agent_ops_agent_name":
       "test role", "agent_ops_agent_id": null, "step_callback": null, "use_system_prompt":
-      true, "function_calling_llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      true, "function_calling_llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13b756690>", "system_template": null, "prompt_template": null, "response_template":
       null, "allow_code_execution": false, "respect_context_window": true, "max_retry_limit":
       2, "multimodal": false, "inject_date": false, "date_format": "%Y-%m-%d", "code_execution_mode":

lib/crewai/tests/cassettes/test_agent_remembers_output_format_after_using_tools_too_many_times.yaml

@@ -1274,7 +1274,7 @@ interactions:
       "b6cf723e-04c8-40c5-a927-e2078cfbae59", "role": "test role", "goal": "test goal",
       "backstory": "test backstory", "cache": true, "verbose": true, "max_rpm": null,
       "allow_delegation": false, "tools": [], "max_iter": 6, "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>", "crew": {"parent_flow": null, "name": "crew", "cache":
       true, "tasks": ["{''used_tools'': 0, ''tools_errors'': 0, ''delegations'': 0,
       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -1285,7 +1285,7 @@ interactions:
       ''test role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>, ''crew'': Crew(id=004dd8a0-dd87-43fa-bdc8-07f449808028,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1309,7 +1309,7 @@ interactions:
       role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>, ''crew'': Crew(id=004dd8a0-dd87-43fa-bdc8-07f449808028,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1491,7 +1491,7 @@ interactions:
       "goal": "test goal", "backstory": "test backstory", "cache": true, "verbose":
       true, "max_rpm": null, "allow_delegation": false, "tools": [], "max_iter": 6,
       "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor object
-      at 0x13ab0abd0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      at 0x13ab0abd0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>", "crew": {"parent_flow": null, "name": "crew", "cache":
       true, "tasks": ["{''used_tools'': 2, ''tools_errors'': 0, ''delegations'': 0,
       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -1502,7 +1502,7 @@ interactions:
       ''test role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>, ''crew'': Crew(id=004dd8a0-dd87-43fa-bdc8-07f449808028,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1528,7 +1528,7 @@ interactions:
       role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>, ''crew'': Crew(id=004dd8a0-dd87-43fa-bdc8-07f449808028,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1660,7 +1660,7 @@ interactions:
       role", "goal": "test goal", "backstory": "test backstory", "cache": true, "verbose":
       true, "max_rpm": null, "allow_delegation": false, "tools": [], "max_iter": 6,
       "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor object
-      at 0x13ab0abd0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      at 0x13ab0abd0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>", "crew": {"parent_flow": null, "name": "crew", "cache":
       true, "tasks": ["{''used_tools'': 3, ''tools_errors'': 0, ''delegations'': 0,
       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -1671,7 +1671,7 @@ interactions:
       ''test role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>, ''crew'': Crew(id=004dd8a0-dd87-43fa-bdc8-07f449808028,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1698,7 +1698,7 @@ interactions:
       role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>, ''crew'': Crew(id=004dd8a0-dd87-43fa-bdc8-07f449808028,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1839,7 +1839,7 @@ interactions:
       "goal": "test goal", "backstory": "test backstory", "cache": true, "verbose":
       true, "max_rpm": null, "allow_delegation": false, "tools": [], "max_iter": 6,
       "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor object
-      at 0x13ab0abd0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      at 0x13ab0abd0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
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       true, "tasks": ["{''used_tools'': 4, ''tools_errors'': 0, ''delegations'': 0,
       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -1850,7 +1850,7 @@ interactions:
       ''test role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>, ''crew'': Crew(id=004dd8a0-dd87-43fa-bdc8-07f449808028,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1879,7 +1879,7 @@ interactions:
       role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab0b050>, ''crew'': Crew(id=004dd8a0-dd87-43fa-bdc8-07f449808028,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -2029,7 +2029,7 @@ interactions:
       "goal": "test goal", "backstory": "test backstory", "cache": true, "verbose":
       true, "max_rpm": null, "allow_delegation": false, "tools": [], "max_iter": 6,
       "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor object
-      at 0x13ab0abd0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
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       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -2040,7 +2040,7 @@ interactions:
       ''test role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
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-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
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       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -2070,7 +2070,7 @@ interactions:
       role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': None, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 6, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab0abd0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab0abd0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
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       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler

lib/crewai/tests/cassettes/test_agent_respect_the_max_rpm_set_over_crew_rpm.yaml

@@ -1082,7 +1082,7 @@ interactions:
       "role": "test role", "goal": "test goal", "backstory": "test backstory", "cache":
       true, "verbose": true, "max_rpm": 10, "allow_delegation": false, "tools": [],
       "max_iter": 4, "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x133d41100>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x133d41100>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
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@@ -1093,7 +1093,7 @@ interactions:
       ''test role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': 10, ''allow_delegation'': False, ''tools'':
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-      object at 0x133d41100>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x133d41100>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
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       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1117,7 +1117,7 @@ interactions:
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       True, ''verbose'': True, ''max_rpm'': 10, ''allow_delegation'': False, ''tools'':
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-      object at 0x133d41100>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x133d41100>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
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       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1910,7 +1910,7 @@ interactions:
       "role": "test role", "goal": "test goal", "backstory": "test backstory", "cache":
       true, "verbose": true, "max_rpm": 10, "allow_delegation": false, "tools": [],
       "max_iter": 4, "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x10308d610>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
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       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -1921,7 +1921,7 @@ interactions:
       ''test role'', ''goal'': ''test goal'', ''backstory'': ''test backstory'', ''cache'':
       True, ''verbose'': True, ''max_rpm'': 10, ''allow_delegation'': False, ''tools'':
       [], ''max_iter'': 4, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x10308d610>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x10308d610>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
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       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1945,7 +1945,7 @@ interactions:
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       True, ''verbose'': True, ''max_rpm'': 10, ''allow_delegation'': False, ''tools'':
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-      object at 0x10308d610>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x10308d610>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x129201640>, ''crew'': Crew(id=1a07d718-fed5-49fa-bee2-de2db91c9f33,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler

lib/crewai/tests/cassettes/test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical.yaml

@@ -937,7 +937,7 @@ interactions:
       "description_updated": "False", "cache_function": "<function BaseTool.<lambda>
       at 0x10614d3a0>", "result_as_answer": "False", "max_usage_count": "None", "current_usage_count":
       "0"}], "max_iter": 25, "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
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+      object at 0x10f6c3bc0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
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       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -977,7 +977,7 @@ interactions:
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+      object at 0x10f6c3bc0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x10f6c27e0>, ''crew'': Crew(id=49cbb747-f055-4636-bbca-9e8a450c05f6,
       process=Process.hierarchical, number_of_agents=2, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -996,7 +996,7 @@ interactions:
       ''First Agent'', ''goal'': ''First goal'', ''backstory'': ''First backstory'',
       ''cache'': True, ''verbose'': False, ''max_rpm'': None, ''allow_delegation'':
       False, ''tools'': [], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x11059ca10>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x11059ca10>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
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       process=Process.hierarchical, number_of_agents=2, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1007,7 +1007,7 @@ interactions:
       ''role'': ''Second Agent'', ''goal'': ''Second goal'', ''backstory'': ''Second
       backstory'', ''cache'': True, ''verbose'': False, ''max_rpm'': None, ''allow_delegation'':
       False, ''tools'': [], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x10f6c3500>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x10f6c3500>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x10f6d2000>, ''crew'': Crew(id=49cbb747-f055-4636-bbca-9e8a450c05f6,
       process=Process.hierarchical, number_of_agents=2, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1017,7 +1017,7 @@ interactions:
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       false, "memory": false, "short_term_memory": null, "long_term_memory": null,
       "entity_memory": null, "external_memory": null, "embedder": null, "usage_metrics":
-      null, "manager_llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      null, "manager_llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x10f6c27e0>", "manager_agent": {"id": "UUID(''b0898472-5e3b-45bb-bd90-05bad0b5a8ce'')",
       "role": "''Crew Manager''", "goal": "''Manage the team to complete the task
       in the best way possible.''", "backstory": "\"You are a seasoned manager with
@@ -1053,7 +1053,7 @@ interactions:
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-      object at 0x10f6c3bc0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x10f6c3bc0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x10f6c27e0>", "crew": "Crew(id=49cbb747-f055-4636-bbca-9e8a450c05f6,
       process=Process.hierarchical, number_of_agents=2, number_of_tasks=1)", "i18n":
       "{''prompt_file'': None}", "cache_handler": "{}", "tools_handler": "<crewai.agents.tools_handler.ToolsHandler
@@ -1805,7 +1805,7 @@ interactions:
       "description_updated": "False", "cache_function": "<function BaseTool.<lambda>
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       "0"}], "max_iter": 25, "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x1388bedb0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
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       object at 0x1388bf710>", "crew": {"parent_flow": null, "name": "crew", "cache":
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       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -1845,7 +1845,7 @@ interactions:
       ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
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       0}], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
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+      object at 0x1388bedb0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
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       process=Process.hierarchical, number_of_agents=2, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1864,7 +1864,7 @@ interactions:
       ''First Agent'', ''goal'': ''First goal'', ''backstory'': ''First backstory'',
       ''cache'': True, ''verbose'': False, ''max_rpm'': None, ''allow_delegation'':
       False, ''tools'': [], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x1388d5c70>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x1388d5c70>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
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       process=Process.hierarchical, number_of_agents=2, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1875,7 +1875,7 @@ interactions:
       ''role'': ''Second Agent'', ''goal'': ''Second goal'', ''backstory'': ''Second
       backstory'', ''cache'': True, ''verbose'': False, ''max_rpm'': None, ''allow_delegation'':
       False, ''tools'': [], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x1388bf7d0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x1388bf7d0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x1388bfb90>, ''crew'': Crew(id=4d744f3e-0589-4d1d-b1c1-6aa8b52478ac,
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       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -1885,7 +1885,7 @@ interactions:
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       false, "memory": false, "short_term_memory": null, "long_term_memory": null,
       "entity_memory": null, "external_memory": null, "embedder": null, "usage_metrics":
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+      null, "manager_llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x1388bf710>", "manager_agent": {"id": "UUID(''09794b42-447f-4b7a-b634-3a861f457357'')",
       "role": "''Crew Manager''", "goal": "''Manage the team to complete the task
       in the best way possible.''", "backstory": "\"You are a seasoned manager with
@@ -1921,7 +1921,7 @@ interactions:
       ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
       at 0x107e394e0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'':
       0}]", "max_iter": "25", "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x1388bedb0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x1388bedb0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x1388bf710>", "crew": "Crew(id=4d744f3e-0589-4d1d-b1c1-6aa8b52478ac,
       process=Process.hierarchical, number_of_agents=2, number_of_tasks=1)", "i18n":
       "{''prompt_file'': None}", "cache_handler": "{}", "tools_handler": "<crewai.agents.tools_handler.ToolsHandler

lib/crewai/tests/cassettes/test_sets_parent_flow_when_inside_flow.yaml

@@ -126,7 +126,7 @@ interactions:
       a freelancer and is now working on doing research and analysis for a new customer.\",
       ''cache'': True, ''verbose'': False, ''max_rpm'': None, ''allow_delegation'':
       False, ''tools'': [], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x12b973fe0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x12b973fe0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x12b910290>, ''crew'': None, ''i18n'': {''prompt_file'': None}, ''cache_handler'':
       {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler object at 0x12b9934d0>,
       ''tools_results'': [], ''max_tokens'': None, ''knowledge'': None, ''knowledge_sources'':
@@ -149,7 +149,7 @@ interactions:
       writing content for a new customer.\", ''cache'': True, ''verbose'': False,
       ''max_rpm'': None, ''allow_delegation'': False, ''tools'': [], ''max_iter'':
       25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x12b7bbbf0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x12b7bbbf0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x12b9903b0>, ''crew'': None, ''i18n'': {''prompt_file'': None}, ''cache_handler'':
       {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler object at 0x12b631bb0>,
       ''tools_results'': [], ''max_tokens'': None, ''knowledge'': None, ''knowledge_sources'':
@@ -169,7 +169,7 @@ interactions:
       a freelancer and is now working on doing research and analysis for a new customer.\"",
       "cache": "True", "verbose": "False", "max_rpm": "None", "allow_delegation":
       "False", "tools": "[]", "max_iter": "25", "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x12b973fe0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x12b973fe0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x12b910290>", "crew": "None", "i18n": "{''prompt_file'': None}",
       "cache_handler": "{}", "tools_handler": "<crewai.agents.tools_handler.ToolsHandler
       object at 0x12b9934d0>", "tools_results": "[]", "max_tokens": "None", "knowledge":
@@ -182,7 +182,7 @@ interactions:
       You work as a freelancer and are now working on writing content for a new customer.\"",
       "cache": "True", "verbose": "False", "max_rpm": "None", "allow_delegation":
       "False", "tools": "[]", "max_iter": "25", "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x12b7bbbf0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x12b7bbbf0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x12b9903b0>", "crew": "None", "i18n": "{''prompt_file'': None}",
       "cache_handler": "{}", "tools_handler": "<crewai.agents.tools_handler.ToolsHandler
       object at 0x12b631bb0>", "tools_results": "[]", "max_tokens": "None", "knowledge":
@@ -214,7 +214,7 @@ interactions:
       a freelancer and is now working on doing research and analysis for a new customer.\",
       ''cache'': True, ''verbose'': False, ''max_rpm'': None, ''allow_delegation'':
       False, ''tools'': [], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x12b973fe0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x12b973fe0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x12b910290>, ''crew'': None, ''i18n'': {''prompt_file'': None}, ''cache_handler'':
       {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler object at 0x12b9934d0>,
       ''tools_results'': [], ''max_tokens'': None, ''knowledge'': None, ''knowledge_sources'':
@@ -237,7 +237,7 @@ interactions:
       writing content for a new customer.\", ''cache'': True, ''verbose'': False,
       ''max_rpm'': None, ''allow_delegation'': False, ''tools'': [], ''max_iter'':
       25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x12b7bbbf0>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x12b7bbbf0>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x12b9903b0>, ''crew'': None, ''i18n'': {''prompt_file'': None}, ''cache_handler'':
       {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler object at 0x12b631bb0>,
       ''tools_results'': [], ''max_tokens'': None, ''knowledge'': None, ''knowledge_sources'':
@@ -257,7 +257,7 @@ interactions:
       a freelancer and is now working on doing research and analysis for a new customer.\"",
       "cache": "True", "verbose": "False", "max_rpm": "None", "allow_delegation":
       "False", "tools": "[]", "max_iter": "25", "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x12b973fe0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x12b973fe0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x12b910290>", "crew": "None", "i18n": "{''prompt_file'': None}",
       "cache_handler": "{}", "tools_handler": "<crewai.agents.tools_handler.ToolsHandler
       object at 0x12b9934d0>", "tools_results": "[]", "max_tokens": "None", "knowledge":
@@ -270,7 +270,7 @@ interactions:
       You work as a freelancer and are now working on writing content for a new customer.\"",
       "cache": "True", "verbose": "False", "max_rpm": "None", "allow_delegation":
       "False", "tools": "[]", "max_iter": "25", "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x12b7bbbf0>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x12b7bbbf0>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x12b9903b0>", "crew": "None", "i18n": "{''prompt_file'': None}",
       "cache_handler": "{}", "tools_handler": "<crewai.agents.tools_handler.ToolsHandler
       object at 0x12b631bb0>", "tools_results": "[]", "max_tokens": "None", "knowledge":

lib/crewai/tests/cassettes/test_tool_result_as_answer_is_the_final_answer_for_the_agent.yaml

@@ -468,7 +468,7 @@ interactions:
       "description_updated": "False", "cache_function": "<function BaseTool.<lambda>
       at 0x107ff9440>", "result_as_answer": "True", "max_usage_count": "None", "current_usage_count":
       "0"}], "max_iter": 25, "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab2e030>", "llm": "<crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab2e030>", "llm": "<crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab2e5d0>", "crew": {"parent_flow": null, "name": "crew", "cache":
       true, "tasks": ["{''used_tools'': 0, ''tools_errors'': 0, ''delegations'': 0,
       ''i18n'': {''prompt_file'': None}, ''name'': None, ''prompt_context'': '''',
@@ -484,7 +484,7 @@ interactions:
       <class ''abc.MyCustomToolSchema''>, ''description_updated'': False, ''cache_function'':
       <function BaseTool.<lambda> at 0x107ff9440>, ''result_as_answer'': True, ''max_usage_count'':
       None, ''current_usage_count'': 0}], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab2e030>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab2e030>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab2e5d0>, ''crew'': Crew(id=f74956dd-60d0-402a-a703-2cc3d767397f,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
@@ -512,7 +512,7 @@ interactions:
       ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
       at 0x107ff9440>, ''result_as_answer'': True, ''max_usage_count'': None, ''current_usage_count'':
       0}], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
-      object at 0x13ab2e030>, ''llm'': <crewai.llms.providers.openai.completion.OpenAICompletion
+      object at 0x13ab2e030>, ''llm'': <crewai.llm.providers.openai.completion.OpenAICompletion
       object at 0x13ab2e5d0>, ''crew'': Crew(id=f74956dd-60d0-402a-a703-2cc3d767397f,
       process=Process.sequential, number_of_agents=1, number_of_tasks=1), ''i18n'':
       {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler

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

@@ -34,7 +34,7 @@ def test_anthropic_completion_is_used_when_claude_provider():
     """
     llm = LLM(model="claude/claude-3-5-sonnet-20241022")
 
-    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+    from crewai.llm.providers.anthropic.completion import AnthropicCompletion
     assert isinstance(llm, AnthropicCompletion)
     assert llm.provider == "anthropic"
     assert llm.model == "claude-3-5-sonnet-20241022"
@@ -47,7 +47,7 @@ def test_anthropic_tool_use_conversation_flow():
     Test that the Anthropic completion properly handles tool use conversation flow
     """
     from unittest.mock import Mock, patch
-    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+    from crewai.llm.providers.anthropic.completion import AnthropicCompletion
     from anthropic.types.tool_use_block import ToolUseBlock
 
     # Create AnthropicCompletion instance
@@ -123,7 +123,7 @@ def test_anthropic_completion_module_is_imported():
     """
     Test that the completion module is properly imported when using Anthropic provider
     """
-    module_name = "crewai.llms.providers.anthropic.completion"
+    module_name = "crewai.llm.providers.anthropic.completion"
 
     # Remove module from cache if it exists
     if module_name in sys.modules:
@@ -175,7 +175,7 @@ def test_anthropic_completion_initialization_parameters():
         api_key="test-key"
     )
 
-    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+    from crewai.llm.providers.anthropic.completion import AnthropicCompletion
     assert isinstance(llm, AnthropicCompletion)
     assert llm.model == "claude-3-5-sonnet-20241022"
     assert llm.temperature == 0.7
@@ -195,7 +195,7 @@ def test_anthropic_specific_parameters():
         timeout=60
     )
 
-    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+    from crewai.llm.providers.anthropic.completion import AnthropicCompletion
     assert isinstance(llm, AnthropicCompletion)
     assert llm.stop_sequences == ["Human:", "Assistant:"]
     assert llm.stream == True
@@ -390,7 +390,7 @@ def test_anthropic_raises_error_when_model_not_supported():
     """Test that AnthropicCompletion raises ValueError when model not supported"""
 
     # Mock the Anthropic client to raise an error
-    with patch('crewai.llms.providers.anthropic.completion.Anthropic') as mock_anthropic_class:
+    with patch('crewai.llm.providers.anthropic.completion.Anthropic') as mock_anthropic_class:
         mock_client = MagicMock()
         mock_anthropic_class.return_value = mock_client
 
@@ -427,7 +427,7 @@ def test_anthropic_client_params_setup():
             client_params=custom_client_params
         )
 
-        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+        from crewai.llm.providers.anthropic.completion import AnthropicCompletion
         assert isinstance(llm, AnthropicCompletion)
 
         assert llm.client_params == custom_client_params
@@ -462,7 +462,7 @@ def test_anthropic_client_params_override_defaults():
         )
 
         # Verify this is actually AnthropicCompletion, not LiteLLM fallback
-        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+        from crewai.llm.providers.anthropic.completion import AnthropicCompletion
         assert isinstance(llm, AnthropicCompletion)
 
         merged_params = llm._get_client_params()
@@ -487,7 +487,7 @@ def test_anthropic_client_params_none():
             client_params=None
         )
 
-        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+        from crewai.llm.providers.anthropic.completion import AnthropicCompletion
         assert isinstance(llm, AnthropicCompletion)
 
         assert llm.client_params is None
@@ -515,7 +515,7 @@ def test_anthropic_client_params_empty_dict():
             client_params={}
         )
 
-        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+        from crewai.llm.providers.anthropic.completion import AnthropicCompletion
         assert isinstance(llm, AnthropicCompletion)
 
         assert llm.client_params == {}
@@ -538,7 +538,7 @@ def test_anthropic_model_detection():
 
     for model_name in anthropic_test_cases:
         llm = LLM(model=model_name)
-        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+        from crewai.llm.providers.anthropic.completion import AnthropicCompletion
         assert isinstance(llm, AnthropicCompletion), f"Failed for model: {model_name}"
 
 

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

@@ -37,7 +37,7 @@ def test_azure_completion_is_used_when_azure_openai_provider():
     """
     llm = LLM(model="azure_openai/gpt-4")
 
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
     assert isinstance(llm, AzureCompletion)
     assert llm.provider == "azure"
     assert llm.model == "gpt-4"
@@ -47,7 +47,7 @@ def test_azure_tool_use_conversation_flow():
     """
     Test that the Azure completion properly handles tool use conversation flow
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
     from azure.ai.inference.models import ChatCompletionsToolCall
 
     # Create AzureCompletion instance
@@ -105,7 +105,7 @@ def test_azure_completion_module_is_imported():
     """
     Test that the completion module is properly imported when using Azure provider
     """
-    module_name = "crewai.llms.providers.azure.completion"
+    module_name = "crewai.llm.providers.azure.completion"
 
     # Remove module from cache if it exists
     if module_name in sys.modules:
@@ -160,7 +160,7 @@ def test_azure_completion_initialization_parameters():
         endpoint="https://test.openai.azure.com"
     )
 
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
     assert isinstance(llm, AzureCompletion)
     assert llm.model == "gpt-4"
     assert llm.temperature == 0.7
@@ -182,7 +182,7 @@ def test_azure_specific_parameters():
         endpoint="https://test.openai.azure.com"
     )
 
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
     assert isinstance(llm, AzureCompletion)
     assert llm.stop == ["Human:", "Assistant:"]
     assert llm.stream == True
@@ -374,7 +374,7 @@ def test_azure_completion_with_tools():
 
 def test_azure_raises_error_when_endpoint_missing():
     """Test that AzureCompletion raises ValueError when endpoint is missing"""
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     # Clear environment variables
     with patch.dict(os.environ, {}, clear=True):
@@ -383,7 +383,7 @@ def test_azure_raises_error_when_endpoint_missing():
 
 def test_azure_raises_error_when_api_key_missing():
     """Test that AzureCompletion raises ValueError when API key is missing"""
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     # Clear environment variables
     with patch.dict(os.environ, {}, clear=True):
@@ -400,7 +400,7 @@ def test_azure_endpoint_configuration():
     }):
         llm = LLM(model="azure/gpt-4")
 
-        from crewai.llms.providers.azure.completion import AzureCompletion
+        from crewai.llm.providers.azure.completion import AzureCompletion
         assert isinstance(llm, AzureCompletion)
         assert llm.endpoint == "https://test1.openai.azure.com/openai/deployments/gpt-4"
 
@@ -426,7 +426,7 @@ def test_azure_api_key_configuration():
     }):
         llm = LLM(model="azure/gpt-4")
 
-        from crewai.llms.providers.azure.completion import AzureCompletion
+        from crewai.llm.providers.azure.completion import AzureCompletion
         assert isinstance(llm, AzureCompletion)
         assert llm.api_key == "test-azure-key"
 
@@ -437,7 +437,7 @@ def test_azure_model_capabilities():
     """
     # Test GPT-4 model (supports function calling)
     llm_gpt4 = LLM(model="azure/gpt-4")
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
     assert isinstance(llm_gpt4, AzureCompletion)
     assert llm_gpt4.is_openai_model == True
     assert llm_gpt4.supports_function_calling() == True
@@ -466,7 +466,7 @@ def test_azure_completion_params_preparation():
             max_tokens=1000
         )
 
-        from crewai.llms.providers.azure.completion import AzureCompletion
+        from crewai.llm.providers.azure.completion import AzureCompletion
         assert isinstance(llm, AzureCompletion)
 
         messages = [{"role": "user", "content": "Hello"}]
@@ -494,7 +494,7 @@ def test_azure_model_detection():
 
     for model_name in azure_test_cases:
         llm = LLM(model=model_name)
-        from crewai.llms.providers.azure.completion import AzureCompletion
+        from crewai.llm.providers.azure.completion import AzureCompletion
         assert isinstance(llm, AzureCompletion), f"Failed for model: {model_name}"
 
 
@@ -662,7 +662,7 @@ def test_azure_streaming_completion():
     """
     Test that streaming completions work properly
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
     from azure.ai.inference.models import StreamingChatCompletionsUpdate
 
     llm = LLM(model="azure/gpt-4", stream=True)
@@ -698,7 +698,7 @@ def test_azure_api_version_default():
     """
     llm = LLM(model="azure/gpt-4")
 
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
     assert isinstance(llm, AzureCompletion)
     # Should use default or environment variable
     assert llm.api_version is not None
@@ -721,7 +721,7 @@ def test_azure_openai_endpoint_url_construction():
     """
     Test that Azure OpenAI endpoint URLs are automatically constructed correctly
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     with patch.dict(os.environ, {
         "AZURE_API_KEY": "test-key",
@@ -738,7 +738,7 @@ def test_azure_openai_endpoint_url_with_trailing_slash():
     """
     Test that trailing slashes are handled correctly in endpoint URLs
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     with patch.dict(os.environ, {
         "AZURE_API_KEY": "test-key",
@@ -804,7 +804,7 @@ def test_non_azure_openai_model_parameter_included():
     """
     Test that model parameter IS included for non-Azure OpenAI endpoints
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     with patch.dict(os.environ, {
         "AZURE_API_KEY": "test-key",
@@ -824,7 +824,7 @@ def test_azure_message_formatting_with_role():
     """
     Test that messages are formatted with both 'role' and 'content' fields
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     llm = LLM(model="azure/gpt-4")
 
@@ -886,7 +886,7 @@ def test_azure_improved_error_messages():
     """
     Test that improved error messages are provided for common HTTP errors
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
     from azure.core.exceptions import HttpResponseError
 
     llm = LLM(model="azure/gpt-4")
@@ -918,7 +918,7 @@ def test_azure_api_version_properly_passed():
     """
     Test that api_version is properly passed to the client
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     with patch.dict(os.environ, {
         "AZURE_API_KEY": "test-key",
@@ -940,7 +940,7 @@ def test_azure_timeout_and_max_retries_stored():
     """
     Test that timeout and max_retries parameters are stored
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     with patch.dict(os.environ, {
         "AZURE_API_KEY": "test-key",
@@ -960,7 +960,7 @@ def test_azure_complete_params_include_optional_params():
     """
     Test that optional parameters are included in completion params when set
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     with patch.dict(os.environ, {
         "AZURE_API_KEY": "test-key",
@@ -992,7 +992,7 @@ def test_azure_endpoint_validation_with_azure_prefix():
     """
     Test that 'azure/' prefix is properly stripped when constructing endpoint
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     with patch.dict(os.environ, {
         "AZURE_API_KEY": "test-key",
@@ -1009,7 +1009,7 @@ def test_azure_message_formatting_preserves_all_roles():
     """
     Test that all message roles (system, user, assistant) are preserved correctly
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
+    from crewai.llm.providers.azure.completion import AzureCompletion
 
     llm = LLM(model="azure/gpt-4")
 

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

@@ -19,7 +19,7 @@ def mock_aws_credentials():
         "AWS_DEFAULT_REGION": "us-east-1"
     }):
         # Mock boto3 Session to prevent actual AWS connections
-        with patch('crewai.llms.providers.bedrock.completion.Session') as mock_session_class:
+        with patch('crewai.llm.providers.bedrock.completion.Session') as mock_session_class:
             # Create mock session instance
             mock_session_instance = MagicMock()
             mock_client = MagicMock()
@@ -67,7 +67,7 @@ def test_bedrock_completion_module_is_imported():
     """
     Test that the completion module is properly imported when using Bedrock provider
     """
-    module_name = "crewai.llms.providers.bedrock.completion"
+    module_name = "crewai.llm.providers.bedrock.completion"
 
     # Remove module from cache if it exists
     if module_name in sys.modules:
@@ -124,7 +124,7 @@ def test_bedrock_completion_initialization_parameters():
         region_name="us-west-2"
     )
 
-    from crewai.llms.providers.bedrock.completion import BedrockCompletion
+    from crewai.llm.providers.bedrock.completion import BedrockCompletion
     assert isinstance(llm, BedrockCompletion)
     assert llm.model == "anthropic.claude-3-5-sonnet-20241022-v2:0"
     assert llm.temperature == 0.7
@@ -145,7 +145,7 @@ def test_bedrock_specific_parameters():
         region_name="us-east-1"
     )
 
-    from crewai.llms.providers.bedrock.completion import BedrockCompletion
+    from crewai.llm.providers.bedrock.completion import BedrockCompletion
     assert isinstance(llm, BedrockCompletion)
     assert llm.stop_sequences == ["Human:", "Assistant:"]
     assert llm.stream == True
@@ -369,7 +369,7 @@ def test_bedrock_aws_credentials_configuration():
     }):
         llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
 
-        from crewai.llms.providers.bedrock.completion import BedrockCompletion
+        from crewai.llm.providers.bedrock.completion import BedrockCompletion
         assert isinstance(llm, BedrockCompletion)
         assert llm.region_name == "us-east-1"
 
@@ -390,7 +390,7 @@ def test_bedrock_model_capabilities():
     """
     # Test Claude model
     llm_claude = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
-    from crewai.llms.providers.bedrock.completion import BedrockCompletion
+    from crewai.llm.providers.bedrock.completion import BedrockCompletion
     assert isinstance(llm_claude, BedrockCompletion)
     assert llm_claude.is_claude_model == True
     assert llm_claude.supports_tools == True
@@ -413,7 +413,7 @@ def test_bedrock_inference_config():
         max_tokens=1000
     )
 
-    from crewai.llms.providers.bedrock.completion import BedrockCompletion
+    from crewai.llm.providers.bedrock.completion import BedrockCompletion
     assert isinstance(llm, BedrockCompletion)
 
     # Test config preparation
@@ -444,7 +444,7 @@ def test_bedrock_model_detection():
 
     for model_name in bedrock_test_cases:
         llm = LLM(model=model_name)
-        from crewai.llms.providers.bedrock.completion import BedrockCompletion
+        from crewai.llm.providers.bedrock.completion import BedrockCompletion
         assert isinstance(llm, BedrockCompletion), f"Failed for model: {model_name}"
 
 

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

@@ -34,7 +34,7 @@ def test_gemini_completion_is_used_when_gemini_provider():
     """
     llm = LLM(model="gemini/gemini-2.0-flash-001")
 
-    from crewai.llms.providers.gemini.completion import GeminiCompletion
+    from crewai.llm.providers.gemini.completion import GeminiCompletion
     assert isinstance(llm, GeminiCompletion)
     assert llm.provider == "gemini"
     assert llm.model == "gemini-2.0-flash-001"
@@ -47,7 +47,7 @@ def test_gemini_tool_use_conversation_flow():
     Test that the Gemini completion properly handles tool use conversation flow
     """
     from unittest.mock import Mock, patch
-    from crewai.llms.providers.gemini.completion import GeminiCompletion
+    from crewai.llm.providers.gemini.completion import GeminiCompletion
 
     # Create GeminiCompletion instance
     completion = GeminiCompletion(model="gemini-2.0-flash-001")
@@ -102,7 +102,7 @@ def test_gemini_completion_module_is_imported():
     """
     Test that the completion module is properly imported when using Google provider
     """
-    module_name = "crewai.llms.providers.gemini.completion"
+    module_name = "crewai.llm.providers.gemini.completion"
 
     # Remove module from cache if it exists
     if module_name in sys.modules:
@@ -159,7 +159,7 @@ def test_gemini_completion_initialization_parameters():
         api_key="test-key"
     )
 
-    from crewai.llms.providers.gemini.completion import GeminiCompletion
+    from crewai.llm.providers.gemini.completion import GeminiCompletion
     assert isinstance(llm, GeminiCompletion)
     assert llm.model == "gemini-2.0-flash-001"
     assert llm.temperature == 0.7
@@ -186,7 +186,7 @@ def test_gemini_specific_parameters():
         location="us-central1"
     )
 
-    from crewai.llms.providers.gemini.completion import GeminiCompletion
+    from crewai.llm.providers.gemini.completion import GeminiCompletion
     assert isinstance(llm, GeminiCompletion)
     assert llm.stop_sequences == ["Human:", "Assistant:"]
     assert llm.stream == True
@@ -382,7 +382,7 @@ def test_gemini_raises_error_when_model_not_supported():
     """Test that GeminiCompletion raises ValueError when model not supported"""
 
     # Mock the Google client to raise an error
-    with patch('crewai.llms.providers.gemini.completion.genai') as mock_genai:
+    with patch('crewai.llm.providers.gemini.completion.genai') as mock_genai:
         mock_client = MagicMock()
         mock_genai.Client.return_value = mock_client
 
@@ -420,7 +420,7 @@ def test_gemini_vertex_ai_setup():
             location="us-west1"
         )
 
-        from crewai.llms.providers.gemini.completion import GeminiCompletion
+        from crewai.llm.providers.gemini.completion import GeminiCompletion
         assert isinstance(llm, GeminiCompletion)
 
         assert llm.project == "test-project"
@@ -435,7 +435,7 @@ def test_gemini_api_key_configuration():
     with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-google-key"}):
         llm = LLM(model="google/gemini-2.0-flash-001")
 
-        from crewai.llms.providers.gemini.completion import GeminiCompletion
+        from crewai.llm.providers.gemini.completion import GeminiCompletion
         assert isinstance(llm, GeminiCompletion)
         assert llm.api_key == "test-google-key"
 
@@ -453,7 +453,7 @@ def test_gemini_model_capabilities():
     """
     # Test Gemini 2.0 model
     llm_2_0 = LLM(model="google/gemini-2.0-flash-001")
-    from crewai.llms.providers.gemini.completion import GeminiCompletion
+    from crewai.llm.providers.gemini.completion import GeminiCompletion
     assert isinstance(llm_2_0, GeminiCompletion)
     assert llm_2_0.is_gemini_2 == True
     assert llm_2_0.supports_tools == True
@@ -477,7 +477,7 @@ def test_gemini_generation_config():
         max_output_tokens=1000
     )
 
-    from crewai.llms.providers.gemini.completion import GeminiCompletion
+    from crewai.llm.providers.gemini.completion import GeminiCompletion
     assert isinstance(llm, GeminiCompletion)
 
     # Test config preparation
@@ -504,7 +504,7 @@ def test_gemini_model_detection():
 
     for model_name in gemini_test_cases:
         llm = LLM(model=model_name)
-        from crewai.llms.providers.gemini.completion import GeminiCompletion
+        from crewai.llm.providers.gemini.completion import GeminiCompletion
         assert isinstance(llm, GeminiCompletion), f"Failed for model: {model_name}"
 
 

lib/crewai/tests/llms/hooks/test_anthropic_interceptor.py

@@ -6,7 +6,7 @@ import httpx
 import pytest
 
 from crewai.llm import LLM
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llm.hooks.base import BaseInterceptor
 
 
 @pytest.fixture(autouse=True)

lib/crewai/tests/llms/hooks/test_base_interceptor.py

@@ -3,7 +3,7 @@
 import httpx
 import pytest
 
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llm.hooks.base import BaseInterceptor
 
 
 class SimpleInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):

lib/crewai/tests/llms/hooks/test_openai_interceptor.py

@@ -4,7 +4,7 @@ import httpx
 import pytest
 
 from crewai.llm import LLM
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llm.hooks.base import BaseInterceptor
 
 
 class OpenAITestInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):

lib/crewai/tests/llms/hooks/test_transport.py

@@ -5,8 +5,8 @@ from unittest.mock import Mock
 import httpx
 import pytest
 
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llm.hooks.base import BaseInterceptor
-from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
+from crewai.llm.hooks.transport import AsyncHTTPTransport, HTTPTransport
 
 
 class TrackingInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):

lib/crewai/tests/llms/hooks/test_unsupported_providers.py

@@ -6,7 +6,7 @@ import httpx
 import pytest
 
 from crewai.llm import LLM
-from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llm.hooks.base import BaseInterceptor
 
 
 @pytest.fixture(autouse=True)

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

@@ -6,7 +6,7 @@ import openai
 import pytest
 
 from crewai.llm import LLM
-from crewai.llms.providers.openai.completion import OpenAICompletion
+from crewai.llm.providers.openai.completion import OpenAICompletion
 from crewai.crew import Crew
 from crewai.agent import Agent
 from crewai.task import Task
@@ -29,7 +29,7 @@ def test_openai_completion_is_used_when_no_provider_prefix():
     """
     llm = LLM(model="gpt-4o")
 
-    from crewai.llms.providers.openai.completion import OpenAICompletion
+    from crewai.llm.providers.openai.completion import OpenAICompletion
     assert isinstance(llm, OpenAICompletion)
     assert llm.provider == "openai"
     assert llm.model == "gpt-4o"
@@ -63,7 +63,7 @@ def test_openai_completion_module_is_imported():
     """
     Test that the completion module is properly imported when using OpenAI provider
     """
-    module_name = "crewai.llms.providers.openai.completion"
+    module_name = "crewai.llm.providers.openai.completion"
 
     # Remove module from cache if it exists
     if module_name in sys.modules:
@@ -114,7 +114,7 @@ def test_openai_completion_initialization_parameters():
         api_key="test-key"
     )
 
-    from crewai.llms.providers.openai.completion import OpenAICompletion
+    from crewai.llm.providers.openai.completion import OpenAICompletion
     assert isinstance(llm, OpenAICompletion)
     assert llm.model == "gpt-4o"
     assert llm.temperature == 0.7
@@ -335,7 +335,7 @@ def test_openai_completion_call_returns_usage_metrics():
 def test_openai_raises_error_when_model_not_supported():
     """Test that OpenAICompletion raises ValueError when model not supported"""
 
-    with patch('crewai.llms.providers.openai.completion.OpenAI') as mock_openai_class:
+    with patch('crewai.llm.providers.openai.completion.OpenAI') as mock_openai_class:
         mock_client = MagicMock()
         mock_openai_class.return_value = mock_client
 

lib/crewai/tests/test_custom_llm.py

@@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional, Union
 
 import pytest
 from crewai import Agent, Crew, Process, Task
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.utilities.llm_utils import create_llm
 
 

lib/crewai/tests/utilities/test_events.py

@@ -743,7 +743,7 @@ def test_llm_emits_call_failed_event():
     error_message = "OpenAI API call failed: Simulated API failure"
 
     with patch(
-        "crewai.llms.providers.openai.completion.OpenAICompletion._handle_completion"
+        "crewai.llm.providers.openai.completion.OpenAICompletion._handle_completion"
     ) as mock_handle_completion:
         mock_handle_completion.side_effect = Exception("Simulated API failure")
 

lib/crewai/tests/utilities/test_llm_utils.py

@@ -4,7 +4,7 @@ from unittest.mock import patch
 
 from crewai.cli.constants import DEFAULT_LLM_MODEL
 from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm.base_llm import BaseLLM
 from crewai.utilities.llm_utils import create_llm
 import pytest
 

logs.txt

@@ -0,0 +1,20 @@
+lib/crewai/src/crewai/agent/core.py:901: error: Argument 1 has incompatible type "ToolFilterContext"; expected "dict[str, Any]"  [arg-type]
+lib/crewai/src/crewai/agent/core.py:901: note: Error code "arg-type" not covered by "type: ignore" comment
+lib/crewai/src/crewai/agent/core.py:905: error: Argument 1 has incompatible type "dict[str, Any]"; expected "ToolFilterContext"  [arg-type]
+lib/crewai/src/crewai/agent/core.py:905: note: Error code "arg-type" not covered by "type: ignore" comment
+lib/crewai/src/crewai/agent/core.py:996: error: Returning Any from function declared to return "dict[str, dict[str, Any]]"  [no-any-return]
+lib/crewai/src/crewai/agent/core.py:1157: error: Incompatible types in assignment (expression has type "tuple[UnionType, None]", target has type "tuple[type, Any]")  [assignment]
+lib/crewai/src/crewai/agent/core.py:1183: error: Argument 1 to "append" of "list" has incompatible type "type"; expected "type[str]"  [arg-type]
+lib/crewai/src/crewai/agent/core.py:1188: error: Incompatible types in assignment (expression has type "UnionType", variable has type "type[str]")  [assignment]
+lib/crewai/src/crewai/agent/core.py:1201: error: Argument 1 to "get" of "dict" has incompatible type "Any | None"; expected "str"  [arg-type]
+Found 7 errors in 1 file (checked 4 source files)
+Success: no issues found in 4 source files
+lib/crewai/src/crewai/llm/providers/gemini/completion.py:111: error: BaseModel field may only be overridden by another field  [misc]
+Found 1 error in 1 file (checked 4 source files)
+Success: no issues found in 4 source files
+lib/crewai/src/crewai/llm/providers/anthropic/completion.py:101: error: BaseModel field may only be overridden by another field  [misc]
+Found 1 error in 1 file (checked 4 source files)
+lib/crewai/src/crewai/llm/providers/bedrock/completion.py:250: error: BaseModel field may only be overridden by another field  [misc]
+Found 1 error in 1 file (checked 4 source files)
+
+uv-lock..............................................(no files to check)Skipped