Merge branch 'main' into gl/feat/pydantic-refactor-llms

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

@@ -618,22 +618,22 @@ class Agent(BaseAgent):
             response_template=self.response_template,
         ).task_execution()
 
-        stop_words = [self.i18n.slice("observation")]
+        stop_sequences = [self.i18n.slice("observation")]
 
         if self.response_template:
-            stop_words.append(
+            stop_sequences.append(
                 self.response_template.split("{{ .Response }}")[1].strip()
             )
 
         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,
             tools=parsed_tools,
             prompt=prompt,
             original_tools=raw_tools,
-            stop_words=stop_words,
+            stop_sequences=stop_sequences,
             max_iter=self.max_iter,
             tools_handler=self.tools_handler,
             tools_names=get_tool_names(parsed_tools),
@@ -974,7 +974,9 @@ 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]] | Any:
         """Get tool schemas from MCP server for wrapper creation with caching."""
         server_url = server_params["url"]
 
@@ -1006,7 +1008,7 @@ 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 +1016,7 @@ 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 +1047,7 @@ 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 +1151,13 @@ 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.
@@ -1175,12 +1177,12 @@ 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]
 
@@ -1193,10 +1195,10 @@ class Agent(BaseAgent):
             "object": dict,
         }
 
-        return type_mapping.get(json_type, Any)
+        return type_mapping.get(json_type, Any)  # type: ignore[arg-type]
 
     @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
@@ -1435,7 +1437,7 @@ class Agent(BaseAgent):
             goal=self.goal,
             backstory=self.backstory,
             llm=self.llm,
-            tools=self.tools or [],
+            tools=self.tools,
             max_iterations=self.max_iter,
             max_execution_time=self.max_execution_time,
             respect_context_window=self.respect_context_window,

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(

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

@@ -73,7 +73,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         max_iter: int,
         tools: list[CrewStructuredTool],
         tools_names: str,
-        stop_words: list[str],
+        stop_sequences: list[str],
         tools_description: str,
         tools_handler: ToolsHandler,
         step_callback: Any = None,
@@ -95,7 +95,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             max_iter: Maximum iterations.
             tools: Available tools.
             tools_names: Tool names string.
-            stop_words: Stop word list.
+            stop_sequences: Stop sequences list for halting generation.
             tools_description: Tool descriptions.
             tools_handler: Tool handler instance.
             step_callback: Optional step callback.
@@ -114,7 +114,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.prompt = prompt
         self.tools = tools
         self.tools_names = tools_names
-        self.stop = stop_words
         self.max_iter = max_iter
         self.callbacks = callbacks or []
         self._printer: Printer = Printer()
@@ -131,15 +130,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.iterations = 0
         self.log_error_after = 3
         if self.llm:
-            # This may be mutating the shared llm object and needs further evaluation
-            existing_stop = getattr(self.llm, "stop", [])
-            self.llm.stop = list(
-                set(
-                    existing_stop + self.stop
-                    if isinstance(existing_stop, list)
-                    else self.stop
-                )
-            )
+            self.llm.stop_sequences.extend(stop_sequences)
 
     @property
     def use_stop_words(self) -> bool:
@@ -148,7 +139,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             bool: True if tool should be used or not.
         """
-        return self.llm.supports_stop_words() if self.llm else False
+        return self.llm.supports_stop_words if self.llm else False
 
     def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
         """Execute the agent with given inputs.

lib/crewai/src/crewai/llm.py

@@ -20,8 +20,7 @@ from typing import (
 )
 
 from dotenv import load_dotenv
-import httpx
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 from typing_extensions import Self
 
 from crewai.events.event_bus import crewai_event_bus
@@ -54,7 +53,6 @@ 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
@@ -320,7 +318,138 @@ class AccumulatedToolArgs(BaseModel):
 
 
 class LLM(BaseLLM):
-    completion_cost: float | None = None
+    completion_cost: float | None = Field(
+        default=None, description="The completion cost of the LLM."
+    )
+    top_p: float | None = Field(
+        default=None, description="Sampling probability threshold."
+    )
+    n: int | None = Field(
+        default=None, description="Number of completions to generate."
+    )
+    max_completion_tokens: int | None = Field(
+        default=None,
+        description="Maximum number of tokens to generate in the completion.",
+    )
+    max_tokens: int | None = Field(
+        default=None,
+        description="Maximum number of tokens allowed in the prompt + completion.",
+    )
+    presence_penalty: float | None = Field(
+        default=None, description="Penalty on the presence penalty."
+    )
+    frequency_penalty: float | None = Field(
+        default=None, description="Penalty on the frequency penalty."
+    )
+    logit_bias: dict[int, float] | None = Field(
+        default=None,
+        description="Modifies the likelihood of specified tokens appearing in the completion.",
+    )
+    response_format: type[BaseModel] | None = Field(
+        default=None,
+        description="Pydantic model class for structured response parsing.",
+    )
+    seed: int | None = Field(
+        default=None,
+        description="Random seed for reproducibility.",
+    )
+    logprobs: int | None = Field(
+        default=None,
+        description="Number of top logprobs to return.",
+    )
+    top_logprobs: int | None = Field(
+        default=None,
+        description="Number of top logprobs to return.",
+    )
+    api_base: str | None = Field(
+        default=None,
+        description="Base URL for the API endpoint.",
+    )
+    api_version: str | None = Field(
+        default=None,
+        description="API version to use.",
+    )
+    callbacks: list[Any] = Field(
+        default_factory=list,
+        description="List of callback handlers for LLM events.",
+    )
+    reasoning_effort: Literal["none", "low", "medium", "high"] | None = Field(
+        default=None,
+        description="Level of reasoning effort for the LLM.",
+    )
+    context_window_size: int = Field(
+        default=0,
+        description="The context window size of the LLM.",
+    )
+    is_anthropic: bool = Field(
+        default=False,
+        description="Indicates if the model is from Anthropic provider.",
+    )
+    supports_function_calling: bool = Field(
+        default=False,
+        description="Indicates if the model supports function calling.",
+    )
+    supports_stop_words: bool = Field(
+        default=False,
+        description="Indicates if the model supports stop words.",
+    )
+
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        self.is_anthropic = any(
+            prefix in self.model.lower() for prefix in ANTHROPIC_PREFIXES
+        )
+        try:
+            provider = self._get_custom_llm_provider()
+            self.supports_function_calling = litellm.utils.supports_function_calling(
+                self.model, custom_llm_provider=provider
+            )
+        except Exception as e:
+            logging.error(f"Failed to check function calling support: {e!s}")
+            self.supports_function_calling = False
+        try:
+            params = get_supported_openai_params(model=self.model)
+            self.supports_stop_words = params is not None and "stop" in params
+        except Exception as e:
+            logging.error(f"Failed to get supported params: {e!s}")
+            self.supports_stop_words = False
+
+        with suppress_warnings():
+            callback_types = [type(callback) for callback in self.callbacks]
+            for callback in litellm.success_callback[:]:
+                if type(callback) in callback_types:
+                    litellm.success_callback.remove(callback)
+
+            for callback in litellm._async_success_callback[:]:
+                if type(callback) in callback_types:
+                    litellm._async_success_callback.remove(callback)
+
+            litellm.callbacks = self.callbacks
+
+        with suppress_warnings():
+            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
+            success_callbacks: list[str | Callable[..., Any] | CustomLogger] = []
+            if success_callbacks_str:
+                success_callbacks = [
+                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
+                ]
+
+            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
+            if failure_callbacks_str:
+                failure_callbacks: list[str | Callable[..., Any] | CustomLogger] = [
+                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
+                ]
+
+                litellm.success_callback = success_callbacks
+                litellm.failure_callback = failure_callbacks
+        return self
+
+    # @computed_field
+    # @property
+    # def is_anthropic(self) -> bool:
+    #     """Determine if the model is from Anthropic provider."""
+    #     anthropic_prefixes = ("anthropic/", "claude-", "claude/")
+    #     return any(prefix in self.model.lower() for prefix in anthropic_prefixes)
 
     def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
         """Factory method that routes to native SDK or falls back to LiteLLM."""
@@ -383,98 +512,6 @@ class LLM(BaseLLM):
 
         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
-
-        litellm.drop_params = True
-
-        # Normalize self.stop to always be a list[str]
-        if stop is None:
-            self.stop: list[str] = []
-        elif isinstance(stop, str):
-            self.stop = [stop]
-        else:
-            self.stop = stop
-
-        self.set_callbacks(callbacks or [])
-        self.set_env_callbacks()
-
-    @staticmethod
-    def _is_anthropic_model(model: str) -> bool:
-        """Determine if the model is from Anthropic provider.
-
-        Args:
-            model: The model identifier string.
-
-        Returns:
-            bool: True if the model is from Anthropic, False otherwise.
-        """
-        anthropic_prefixes = ("anthropic/", "claude-", "claude/")
-        return any(prefix in model.lower() for prefix in anthropic_prefixes)
-
     def _prepare_completion_params(
         self,
         messages: str | list[LLMMessage],
@@ -1188,8 +1225,6 @@ class LLM(BaseLLM):
                     message["role"] = msg_role
         # --- 5) Set up callbacks if provided
         with suppress_warnings():
-            if callbacks and len(callbacks) > 0:
-                self.set_callbacks(callbacks)
             try:
                 # --- 6) Prepare parameters for the completion call
                 params = self._prepare_completion_params(messages, tools)
@@ -1378,24 +1413,6 @@ class LLM(BaseLLM):
                 "Please remove response_format or use a supported model."
             )
 
-    def supports_function_calling(self) -> bool:
-        try:
-            provider = self._get_custom_llm_provider()
-            return litellm.utils.supports_function_calling(
-                self.model, custom_llm_provider=provider
-            )
-        except Exception as e:
-            logging.error(f"Failed to check function calling support: {e!s}")
-            return False
-
-    def supports_stop_words(self) -> bool:
-        try:
-            params = get_supported_openai_params(model=self.model)
-            return params is not None and "stop" in params
-        except Exception as e:
-            logging.error(f"Failed to get supported params: {e!s}")
-            return False
-
     def get_context_window_size(self) -> int:
         """
         Returns the context window size, using 75% of the maximum to avoid
@@ -1425,60 +1442,6 @@ class LLM(BaseLLM):
                 self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
         return self.context_window_size
 
-    @staticmethod
-    def set_callbacks(callbacks: list[Any]) -> None:
-        """
-        Attempt to keep a single set of callbacks in litellm by removing old
-        duplicates and adding new ones.
-        """
-        with suppress_warnings():
-            callback_types = [type(callback) for callback in callbacks]
-            for callback in litellm.success_callback[:]:
-                if type(callback) in callback_types:
-                    litellm.success_callback.remove(callback)
-
-            for callback in litellm._async_success_callback[:]:
-                if type(callback) in callback_types:
-                    litellm._async_success_callback.remove(callback)
-
-            litellm.callbacks = callbacks
-
-    @staticmethod
-    def set_env_callbacks() -> None:
-        """Sets the success and failure callbacks for the LiteLLM library from environment variables.
-
-        This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS`
-        environment variables, which should contain comma-separated lists of callback names.
-        It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`,
-        respectively.
-
-        If the environment variables are not set or are empty, the corresponding callback lists
-        will be set to empty lists.
-
-        Examples:
-            LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
-            LITELLM_FAILURE_CALLBACKS="langfuse"
-
-        This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
-        `litellm.failure_callback` to ["langfuse"].
-        """
-        with suppress_warnings():
-            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-            success_callbacks: list[str | Callable[..., Any] | CustomLogger] = []
-            if success_callbacks_str:
-                success_callbacks = [
-                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
-                ]
-
-            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
-            if failure_callbacks_str:
-                failure_callbacks: list[str | Callable[..., Any] | CustomLogger] = [
-                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
-                ]
-
-                litellm.success_callback = success_callbacks
-                litellm.failure_callback = failure_callbacks
-
     def __copy__(self) -> LLM:
         """Create a shallow copy of the LLM instance."""
         # Filter out parameters that are already explicitly passed to avoid conflicts
@@ -1539,7 +1502,7 @@ 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/llms/base_llm.py

@@ -13,8 +13,9 @@ import logging
 import re
 from typing import TYPE_CHECKING, Any, Final
 
-from pydantic import BaseModel
+from pydantic import AliasChoices, BaseModel, Field, PrivateAttr, field_validator
 
+from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.llm_events import (
     LLMCallCompletedEvent,
@@ -28,6 +29,7 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
+from crewai.llms.hooks import BaseInterceptor
 from crewai.types.usage_metrics import UsageMetrics
 
 
@@ -43,7 +45,7 @@ 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):
     """Abstract base class for LLM implementations.
 
     This class defines the interface that all LLM implementations must follow.
@@ -55,70 +57,105 @@ class BaseLLM(ABC):
     implement proper validation for input parameters and provide clear error
     messages when things go wrong.
 
+
     Attributes:
         model: The model identifier/name.
         temperature: Optional temperature setting for response generation.
-        stop: A list of stop sequences that the LLM should use to stop generation.
-        additional_params: Additional provider-specific parameters.
     """
 
-    is_litellm: bool = False
+    provider: str | re.Pattern[str] = Field(
+        default="openai", description="The provider of the LLM."
+    )
+    model: str = Field(description="The model identifier/name.")
+    temperature: float | None = Field(
+        default=None, ge=0, le=2, description="Temperature for response generation."
+    )
+    api_key: str | None = Field(default=None, description="API key for authentication.")
+    base_url: str | None = Field(default=None, description="Base URL for API calls.")
+    timeout: float | None = Field(default=None, description="Timeout for API calls.")
+    max_retries: int = Field(
+        default=2, description="Maximum number of API requests to make."
+    )
+    max_tokens: int | None = Field(
+        default=None, description="Maximum tokens for response generation."
+    )
+    stream: bool | None = Field(default=False, description="Stream the API requests.")
+    client: Any = Field(description="Underlying LLM client instance.")
+    interceptor: BaseInterceptor[Any, Any] | None = Field(
+        default=None,
+        description="An optional HTTPX interceptor for modifying requests/responses.",
+    )
+    client_params: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional parameters for the underlying LLM client.",
+    )
+    supports_stop_words: bool = Field(
+        default=DEFAULT_SUPPORTS_STOP_WORDS,
+        description="Whether or not to support stop words.",
+    )
+    stop_sequences: list[str] = Field(
+        default_factory=list,
+        validation_alias=AliasChoices("stop_sequences", "stop"),
+        description="Stop sequences for generation (synchronized with stop).",
+    )
+    is_litellm: bool = Field(
+        default=False, description="Is this LLM implementation in litellm?"
+    )
+    additional_params: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional parameters for LLM calls.",
+    )
+    _token_usage: TokenProcess = PrivateAttr(default_factory=TokenProcess)
 
-    def __init__(
-        self,
-        model: str,
-        temperature: float | None = None,
-        api_key: str | None = None,
-        base_url: str | None = None,
-        provider: str | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize the BaseLLM with default attributes.
+    @field_validator("provider", mode="before")
+    @classmethod
+    def extract_provider_from_model(
+        cls, v: str | re.Pattern[str] | None, info: Any
+    ) -> str | re.Pattern[str]:
+        """Extract provider from model string if not explicitly provided.
 
         Args:
-            model: The model identifier/name.
-            temperature: Optional temperature setting for response generation.
-            stop: Optional list of stop sequences for generation.
-            **kwargs: Additional provider-specific parameters.
+            v: Provided provider value (can be str, Pattern, or None)
+            info: Validation info containing other field values
+
+        Returns:
+            Provider name (str) or Pattern
         """
-        if not model:
-            raise ValueError("Model name is required and cannot be empty")
+        # If provider explicitly provided, validate and return it
+        if v is not None:
+            if not isinstance(v, (str, re.Pattern)):
+                raise ValueError(f"Provider must be str or Pattern, got {type(v)}")
+            return v
 
-        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"
+        model: str = info.data.get("model", "")
+        if "/" in model:
+            return model.partition("/")[0]
+        return "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 = []
+    @field_validator("stop_sequences", mode="before")
+    @classmethod
+    def normalize_stop_sequences(
+        cls, v: str | list[str] | set[str] | None
+    ) -> list[str]:
+        """Validate and normalize stop sequences.
 
-        self._token_usage = {
-            "total_tokens": 0,
-            "prompt_tokens": 0,
-            "completion_tokens": 0,
-            "successful_requests": 0,
-            "cached_prompt_tokens": 0,
-        }
+        Converts string to list and handles None values.
+        AliasChoices handles accepting both 'stop' and 'stop_sequences' parameter names.
+        """
+        if v is None:
+            return []
+        if isinstance(v, str):
+            return [v]
+        if isinstance(v, set):
+            return list(v)
+        if isinstance(v, list):
+            return v
+        return []
 
     @property
-    def provider(self) -> str:
-        """Get the provider of the LLM."""
-        return self._provider
-
-    @provider.setter
-    def provider(self, value: str) -> None:
-        """Set the provider of the LLM."""
-        self._provider = value
+    def stop(self) -> list[str]:
+        """Alias for stop_sequences to maintain backward compatibility."""
+        return self.stop_sequences
 
     @abstractmethod
     def call(
@@ -171,14 +208,6 @@ class BaseLLM(ABC):
         """
         return tools
 
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        return DEFAULT_SUPPORTS_STOP_WORDS
-
     def _supports_stop_words_implementation(self) -> bool:
         """Check if stop words are configured for this LLM instance.
 
@@ -506,7 +535,7 @@ class BaseLLM(ABC):
         """
         if "/" in model:
             return model.partition("/")[0]
-        return "openai"  # Default provider
+        return "openai"
 
     def _track_token_usage_internal(self, usage_data: dict[str, Any]) -> None:
         """Track token usage internally in the LLM instance.
@@ -535,11 +564,11 @@ class BaseLLM(ABC):
             or 0
         )
 
-        self._token_usage["prompt_tokens"] += prompt_tokens
-        self._token_usage["completion_tokens"] += completion_tokens
-        self._token_usage["total_tokens"] += prompt_tokens + completion_tokens
-        self._token_usage["successful_requests"] += 1
-        self._token_usage["cached_prompt_tokens"] += cached_tokens
+        self._token_usage.prompt_tokens += prompt_tokens
+        self._token_usage.completion_tokens += completion_tokens
+        self._token_usage.total_tokens += prompt_tokens + completion_tokens
+        self._token_usage.successful_requests += 1
+        self._token_usage.cached_prompt_tokens += cached_tokens
 
     def get_token_usage_summary(self) -> UsageMetrics:
         """Get summary of token usage for this LLM instance.
@@ -547,4 +576,10 @@ class BaseLLM(ABC):
         Returns:
             Dictionary with token usage totals
         """
-        return UsageMetrics(**self._token_usage)
+        return UsageMetrics(
+            prompt_tokens=self._token_usage.prompt_tokens,
+            completion_tokens=self._token_usage.completion_tokens,
+            total_tokens=self._token_usage.total_tokens,
+            successful_requests=self._token_usage.successful_requests,
+            cached_prompt_tokens=self._token_usage.cached_prompt_tokens,
+        )

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

@@ -5,11 +5,14 @@ import logging
 import os
 from typing import TYPE_CHECKING, Any, cast
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, PrivateAttr, computed_field, model_validator
+from typing_extensions import Self
 
 from crewai.events.types.llm_events import LLMCallType
+from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO
 from crewai.llms.base_llm import BaseLLM
 from crewai.llms.hooks.transport import HTTPTransport
+from crewai.llms.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 +21,8 @@ from crewai.utilities.types import LLMMessage
 
 
 if TYPE_CHECKING:
-    from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.agent import Agent
+    from crewai.task import Task
 
 try:
     from anthropic import Anthropic
@@ -31,6 +35,19 @@ except ImportError:
     ) from None
 
 
+ANTHROPIC_CONTEXT_WINDOWS: dict[str, int] = {
+    "claude-3-5-sonnet": 200000,
+    "claude-3-5-haiku": 200000,
+    "claude-3-opus": 200000,
+    "claude-3-sonnet": 200000,
+    "claude-3-haiku": 200000,
+    "claude-3-7-sonnet": 200000,
+    "claude-2.1": 200000,
+    "claude-2": 100000,
+    "claude-instant": 100000,
+}
+
+
 class AnthropicCompletion(BaseLLM):
     """Anthropic native completion implementation.
 
@@ -38,110 +55,69 @@ class AnthropicCompletion(BaseLLM):
     offering native tool use, streaming support, and proper message formatting.
     """
 
-    def __init__(
-        self,
-        model: str = "claude-3-5-sonnet-20241022",
-        api_key: str | None = None,
-        base_url: str | None = None,
-        timeout: float | None = None,
-        max_retries: int = 2,
-        temperature: float | None = None,
-        max_tokens: int = 4096,  # Required for Anthropic
-        top_p: float | None = None,
-        stop_sequences: list[str] | None = None,
-        stream: bool = False,
-        client_params: dict[str, Any] | None = None,
-        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
-        **kwargs: Any,
-    ):
-        """Initialize Anthropic chat completion client.
+    model: str = Field(
+        default="claude-3-5-sonnet-20241022",
+        description="Anthropic model name (e.g., 'claude-3-5-sonnet-20241022')",
+    )
+    max_tokens: int = Field(
+        default=4096,
+        description="Maximum number of allowed tokens in response.",
+    )
+    top_p: float | None = Field(
+        default=None,
+        description="Nucleus sampling parameter.",
+    )
+    _client: Anthropic = PrivateAttr(
+        default_factory=Anthropic,
+    )
 
-        Args:
-            model: Anthropic model name (e.g., 'claude-3-5-sonnet-20241022')
-            api_key: Anthropic API key (defaults to ANTHROPIC_API_KEY env var)
-            base_url: Custom base URL for Anthropic API
-            timeout: Request timeout in seconds
-            max_retries: Maximum number of retries
-            temperature: Sampling temperature (0-1)
-            max_tokens: Maximum tokens in response (required for Anthropic)
-            top_p: Nucleus sampling parameter
-            stop_sequences: Stop sequences (Anthropic uses stop_sequences, not stop)
-            stream: Enable streaming responses
-            client_params: Additional parameters for the Anthropic client
-            interceptor: HTTP interceptor for modifying requests/responses at transport level.
-            **kwargs: Additional parameters
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        """Initialize the Anthropic client after Pydantic validation.
+
+        This runs after all field validation is complete, ensuring that:
+        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
+        - Field validators have run (stop_sequences is normalized to set[str])
+        - API key and other configuration is ready
         """
-        super().__init__(
-            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
-        )
-
-        # Client params
-        self.interceptor = interceptor
-        self.client_params = client_params
-        self.base_url = base_url
-        self.timeout = timeout
-        self.max_retries = max_retries
-
-        self.client = Anthropic(**self._get_client_params())
-
-        # Store completion parameters
-        self.max_tokens = max_tokens
-        self.top_p = top_p
-        self.stream = stream
-        self.stop_sequences = stop_sequences or []
-
-        # Model-specific settings
-        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]:
-        """Get stop sequences sent to the API."""
-        return self.stop_sequences
-
-    @stop.setter
-    def stop(self, value: list[str] | str | None) -> None:
-        """Set stop sequences.
-
-        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
-        are properly sent to the Anthropic API.
-
-        Args:
-            value: Stop sequences as a list, single string, or None
-        """
-        if value is None:
-            self.stop_sequences = []
-        elif isinstance(value, str):
-            self.stop_sequences = [value]
-        elif isinstance(value, list):
-            self.stop_sequences = value
-        else:
-            self.stop_sequences = []
-
-    def _get_client_params(self) -> dict[str, Any]:
-        """Get client parameters."""
-
         if self.api_key is None:
             self.api_key = os.getenv("ANTHROPIC_API_KEY")
             if self.api_key is None:
                 raise ValueError("ANTHROPIC_API_KEY is required")
 
-        client_params = {
-            "api_key": self.api_key,
-            "base_url": self.base_url,
-            "timeout": self.timeout,
-            "max_retries": self.max_retries,
-        }
+        params = self.model_dump(
+            include={"api_key", "base_url", "timeout", "max_retries"},
+            exclude_none=True,
+        )
 
         if self.interceptor:
             transport = HTTPTransport(interceptor=self.interceptor)
             http_client = httpx.Client(transport=transport)
-            client_params["http_client"] = http_client  # type: ignore[assignment]
+            params["http_client"] = http_client
 
         if self.client_params:
-            client_params.update(self.client_params)
+            params.update(self.client_params)
 
-        return client_params
+        self._client = Anthropic(**params)
+        return self
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def is_claude_3(self) -> bool:
+        """Check if the model is Claude 3 or higher."""
+        return "claude-3" in self.model.lower()
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def supports_tools(self) -> bool:
+        """Check if the model supports tool use."""
+        return self.is_claude_3
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def supports_function_calling(self) -> bool:
+        """Check if the model supports function calling."""
+        return self.supports_tools
 
     def call(
         self,
@@ -149,8 +125,8 @@ class AnthropicCompletion(BaseLLM):
         tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call Anthropic messages API.
@@ -229,25 +205,21 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Parameters dictionary for Anthropic API
         """
-        params = {
-            "model": self.model,
-            "messages": messages,
-            "max_tokens": self.max_tokens,
-            "stream": self.stream,
-        }
-
+        params = self.model_dump(
+            include={
+                "model",
+                "max_tokens",
+                "stream",
+                "temperature",
+                "top_p",
+                "stop_sequences",
+            },
+        )
+        params["messages"] = messages
         # Add system message if present
         if system_message:
             params["system"] = system_message
 
-        # Add optional parameters if set
-        if self.temperature is not None:
-            params["temperature"] = self.temperature
-        if self.top_p is not None:
-            params["top_p"] = self.top_p
-        if self.stop_sequences:
-            params["stop_sequences"] = self.stop_sequences
-
         # Handle tools for Claude 3+
         if tools and self.supports_tools:
             params["tools"] = self._convert_tools_for_interference(tools)
@@ -266,8 +238,6 @@ 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}")
@@ -341,8 +311,8 @@ class AnthropicCompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming message completion."""
@@ -357,7 +327,7 @@ class AnthropicCompletion(BaseLLM):
             params["tool_choice"] = {"type": "tool", "name": "structured_output"}
 
         try:
-            response: Message = self.client.messages.create(**params)
+            response: Message = self._client.messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -429,8 +399,8 @@ class AnthropicCompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming message completion."""
@@ -451,7 +421,7 @@ class AnthropicCompletion(BaseLLM):
         stream_params = {k: v for k, v in params.items() if k != "stream"}
 
         # Make streaming API call
-        with self.client.messages.stream(**stream_params) as stream:
+        with self._client.messages.stream(**stream_params) as stream:
             for event in stream:
                 if hasattr(event, "delta") and hasattr(event.delta, "text"):
                     text_delta = event.delta.text
@@ -525,8 +495,8 @@ class AnthropicCompletion(BaseLLM):
         tool_uses: list[ToolUseBlock],
         params: dict[str, Any],
         available_functions: dict[str, Any],
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
     ) -> str:
         """Handle the complete tool use conversation flow.
 
@@ -579,7 +549,7 @@ class AnthropicCompletion(BaseLLM):
 
         try:
             # Send tool results back to Claude for final response
-            final_response: Message = self.client.messages.create(**follow_up_params)
+            final_response: Message = self._client.messages.create(**follow_up_params)
 
             # Track token usage for follow-up call
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
@@ -626,48 +596,24 @@ class AnthropicCompletion(BaseLLM):
                 return tool_results[0]["content"]
             raise e
 
-    def supports_function_calling(self) -> bool:
-        """Check if the model supports function calling."""
-        return self.supports_tools
-
-    def supports_stop_words(self) -> bool:
-        """Check if the model supports stop words."""
-        return True  # All Claude models support stop sequences
-
     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 = {
-            "claude-3-5-sonnet": 200000,
-            "claude-3-5-haiku": 200000,
-            "claude-3-opus": 200000,
-            "claude-3-sonnet": 200000,
-            "claude-3-haiku": 200000,
-            "claude-3-7-sonnet": 200000,
-            "claude-2.1": 200000,
-            "claude-2": 100000,
-            "claude-instant": 100000,
-        }
-
         # Find the best match for the model name
-        for model_prefix, size in context_windows.items():
+        for model_prefix, size in ANTHROPIC_CONTEXT_WINDOWS.items():
             if self.model.startswith(model_prefix):
                 return int(size * CONTEXT_WINDOW_USAGE_RATIO)
 
         # Default context window size for Claude models
         return int(200000 * CONTEXT_WINDOW_USAGE_RATIO)
 
-    def _extract_anthropic_token_usage(self, response: Message) -> dict[str, Any]:
+    @staticmethod
+    def _extract_anthropic_token_usage(response: Message) -> dict[str, Any]:
         """Extract token usage from Anthropic response."""
-        if hasattr(response, "usage") and response.usage:
+        if response.usage:
             usage = response.usage
-            input_tokens = getattr(usage, "input_tokens", 0)
-            output_tokens = getattr(usage, "output_tokens", 0)
             return {
-                "input_tokens": input_tokens,
-                "output_tokens": output_tokens,
-                "total_tokens": input_tokens + output_tokens,
+                "input_tokens": usage.input_tokens,
+                "output_tokens": usage.output_tokens,
+                "total_tokens": usage.input_tokens + usage.output_tokens,
             }
         return {"total_tokens": 0}

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

@@ -1,12 +1,14 @@
-import logging
-import os
-from typing import Any, cast
+from __future__ import annotations
 
-from pydantic import BaseModel
+import logging
+from typing import TYPE_CHECKING, Any, cast
+
+from pydantic import BaseModel, Field, PrivateAttr, computed_field, model_validator
+from typing_extensions import Self
 
 from crewai.events.types.llm_events import LLMCallType
+from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 from crewai.llms.base_llm import BaseLLM
-from crewai.llms.hooks.base import BaseInterceptor
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -14,6 +16,11 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 from crewai.utilities.types import LLMMessage
 
 
+if TYPE_CHECKING:
+    from crewai.agent import Agent
+    from crewai.task import Task
+
+
 try:
     from google import genai  # type: ignore[import-untyped]
     from google.genai import types  # type: ignore[import-untyped]
@@ -24,6 +31,27 @@ except ImportError:
     ) from None
 
 
+GEMINI_CONTEXT_WINDOWS: dict[str, int] = {
+    "gemini-2.0-flash": 1048576,  # 1M tokens
+    "gemini-2.0-flash-thinking": 32768,
+    "gemini-2.0-flash-lite": 1048576,
+    "gemini-2.5-flash": 1048576,
+    "gemini-2.5-pro": 1048576,
+    "gemini-1.5-pro": 2097152,  # 2M tokens
+    "gemini-1.5-flash": 1048576,
+    "gemini-1.5-flash-8b": 1048576,
+    "gemini-1.0-pro": 32768,
+    "gemma-3-1b": 32000,
+    "gemma-3-4b": 128000,
+    "gemma-3-12b": 128000,
+    "gemma-3-27b": 128000,
+}
+
+# Context window validation constraints
+MIN_CONTEXT_WINDOW: int = 1024
+MAX_CONTEXT_WINDOW: int = 2097152
+
+
 class GeminiCompletion(BaseLLM):
     """Google Gemini native completion implementation.
 
@@ -31,78 +59,140 @@ class GeminiCompletion(BaseLLM):
     offering native function calling, streaming support, and proper Gemini formatting.
     """
 
-    def __init__(
-        self,
-        model: str = "gemini-2.0-flash-001",
-        api_key: str | None = None,
-        project: str | None = None,
-        location: str | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        top_k: int | None = None,
-        max_output_tokens: int | None = None,
-        stop_sequences: list[str] | None = None,
-        stream: bool = False,
-        safety_settings: dict[str, Any] | None = None,
-        client_params: dict[str, Any] | None = None,
-        interceptor: BaseInterceptor[Any, Any] | None = None,
-        **kwargs: Any,
-    ):
-        """Initialize Google Gemini chat completion client.
+    model: str = Field(
+        default="gemini-2.0-flash-001",
+        description="Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')",
+    )
+    project: str | None = Field(
+        default=None,
+        description="Google Cloud project ID (for Vertex AI)",
+    )
+    location: str = Field(
+        default="us-central1",
+        description="Google Cloud location (for Vertex AI)",
+    )
+    top_p: float | None = Field(
+        default=None,
+        description="Nucleus sampling parameter",
+    )
+    top_k: int | None = Field(
+        default=None,
+        description="Top-k sampling parameter",
+    )
+    max_output_tokens: int | None = Field(
+        default=None,
+        description="Maximum tokens in response",
+    )
+    safety_settings: dict[str, Any] | None = Field(
+        default=None,
+        description="Safety filter settings",
+    )
+    _client: genai.Client = PrivateAttr(  # type: ignore[no-any-unimported]
+        default_factory=genai.Client,
+    )
 
-        Args:
-            model: Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')
-            api_key: Google API key (defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var)
-            project: Google Cloud project ID (for Vertex AI)
-            location: Google Cloud location (for Vertex AI, defaults to 'us-central1')
-            temperature: Sampling temperature (0-2)
-            top_p: Nucleus sampling parameter
-            top_k: Top-k sampling parameter
-            max_output_tokens: Maximum tokens in response
-            stop_sequences: Stop sequences
-            stream: Enable streaming responses
-            safety_settings: Safety filter settings
-            client_params: Additional parameters to pass to the Google Gen AI Client constructor.
-                          Supports parameters like http_options, credentials, debug_config, etc.
-            interceptor: HTTP interceptor (not yet supported for Gemini).
-            **kwargs: Additional parameters
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        """Initialize the Anthropic client after Pydantic validation.
+
+        This runs after all field validation is complete, ensuring that:
+        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
+        - Field validators have run (stop_sequences is normalized to set[str])
+        - API key and other configuration is ready
         """
-        if interceptor is not None:
-            raise NotImplementedError(
-                "HTTP interceptors are not yet supported for Google Gemini provider. "
-                "Interceptors are currently supported for OpenAI and Anthropic providers only."
-            )
+        self._client = genai.Client(**self._get_client_params())
+        return self
 
-        super().__init__(
-            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
-        )
+    # def __init__(
+    #     self,
+    # model: str = "gemini-2.0-flash-001",
+    # api_key: str | None = None,
+    # project: str | None = None,
+    # location: str | None = None,
+    # temperature: float | None = None,
+    # top_p: float | None = None,
+    # top_k: int | None = None,
+    # max_output_tokens: int | None = None,
+    # stop_sequences: list[str] | None = None,
+    # stream: bool = False,
+    # safety_settings: dict[str, Any] | None = None,
+    # client_params: dict[str, Any] | None = None,
+    # interceptor: BaseInterceptor[Any, Any] | None = None,
+    # **kwargs: Any,
+    # # ):
+    #     """Initialize Google Gemini chat completion client.
+    #
+    #     Args:
+    #         model: Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')
+    #         api_key: Google API key (defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var)
+    #         project: Google Cloud project ID (for Vertex AI)
+    #         location: Google Cloud location (for Vertex AI, defaults to 'us-central1')
+    #         temperature: Sampling temperature (0-2)
+    #         top_p: Nucleus sampling parameter
+    #         top_k: Top-k sampling parameter
+    #         max_output_tokens: Maximum tokens in response
+    #         stop_sequences: Stop sequences
+    #         stream: Enable streaming responses
+    #         safety_settings: Safety filter settings
+    #         client_params: Additional parameters to pass to the Google Gen AI Client constructor.
+    #                       Supports parameters like http_options, credentials, debug_config, etc.
+    #         interceptor: HTTP interceptor (not yet supported for Gemini).
+    #         **kwargs: Additional parameters
+    #     """
+    #     if interceptor is not None:
+    #         raise NotImplementedError(
+    #             "HTTP interceptors are not yet supported for Google Gemini provider. "
+    #             "Interceptors are currently supported for OpenAI and Anthropic providers only."
+    #         )
+    #
+    #     super().__init__(
+    #         model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
+    #     )
+    #
+    #     # Store client params for later use
+    #     self.client_params = client_params or {}
+    #
+    #     # Get API configuration with environment variable fallbacks
+    #     self.api_key = (
+    #         api_key or os.getenv("GOOGLE_API_KEY") or os.getenv("GEMINI_API_KEY")
+    #     )
+    #     self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
+    #     self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
+    #
+    #     use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
+    #
+    #     self.client = self._initialize_client(use_vertexai)
+    #
+    #     # Store completion parameters
+    #     self.top_p = top_p
+    #     self.top_k = top_k
+    #     self.max_output_tokens = max_output_tokens
+    #     self.stream = stream
+    #     self.safety_settings = safety_settings or {}
+    #     self.stop_sequences = stop_sequences or []
+    #
+    #     # Model-specific settings
+    #     self.is_gemini_2 = "gemini-2" in model.lower()
+    #     self.is_gemini_1_5 = "gemini-1.5" in model.lower()
+    #     self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
 
-        # Store client params for later use
-        self.client_params = client_params or {}
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def is_gemini_2(self) -> bool:
+        """Check if the model is Gemini 2.x."""
+        return "gemini-2" in self.model.lower()
 
-        # Get API configuration with environment variable fallbacks
-        self.api_key = (
-            api_key or os.getenv("GOOGLE_API_KEY") or os.getenv("GEMINI_API_KEY")
-        )
-        self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
-        self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def is_gemini_1_5(self) -> bool:
+        """Check if the model is Gemini 1.5.x."""
+        return "gemini-1.5" in self.model.lower()
 
-        use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
-
-        self.client = self._initialize_client(use_vertexai)
-
-        # Store completion parameters
-        self.top_p = top_p
-        self.top_k = top_k
-        self.max_output_tokens = max_output_tokens
-        self.stream = stream
-        self.safety_settings = safety_settings or {}
-        self.stop_sequences = stop_sequences or []
-
-        # Model-specific settings
-        self.is_gemini_2 = "gemini-2" in model.lower()
-        self.is_gemini_1_5 = "gemini-1.5" in model.lower()
-        self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def supports_tools(self) -> bool:
+        """Check if the model supports tool/function calling."""
+        return self.is_gemini_1_5 or self.is_gemini_2
 
     @property
     def stop(self) -> list[str]:
@@ -142,6 +232,12 @@ class GeminiCompletion(BaseLLM):
         if self.client_params:
             client_params.update(self.client_params)
 
+        if self.interceptor:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for Google Gemini provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
+
         if use_vertexai or self.project:
             client_params.update(
                 {
@@ -181,7 +277,7 @@ class GeminiCompletion(BaseLLM):
 
         if (
             hasattr(self, "client")
-            and hasattr(self.client, "vertexai")
+            and hasattr(self._client, "vertexai")
             and self.client.vertexai
         ):
             # Vertex AI configuration
@@ -206,8 +302,8 @@ class GeminiCompletion(BaseLLM):
         tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call Google Gemini generate content API.
@@ -294,7 +390,16 @@ class GeminiCompletion(BaseLLM):
             GenerateContentConfig object for Gemini API
         """
         self.tools = tools
-        config_params = {}
+        config_params = self.model_dump(
+            include={
+                "temperature",
+                "top_p",
+                "top_k",
+                "max_output_tokens",
+                "stop_sequences",
+                "safety_settings",
+            }
+        )
 
         # Add system instruction if present
         if system_instruction:
@@ -304,18 +409,6 @@ class GeminiCompletion(BaseLLM):
             )
             config_params["system_instruction"] = system_content
 
-        # Add generation config parameters
-        if self.temperature is not None:
-            config_params["temperature"] = self.temperature
-        if self.top_p is not None:
-            config_params["top_p"] = self.top_p
-        if self.top_k is not None:
-            config_params["top_k"] = self.top_k
-        if self.max_output_tokens is not None:
-            config_params["max_output_tokens"] = self.max_output_tokens
-        if self.stop_sequences:
-            config_params["stop_sequences"] = self.stop_sequences
-
         if response_model:
             config_params["response_mime_type"] = "application/json"
             config_params["response_schema"] = response_model.model_json_schema()
@@ -324,9 +417,6 @@ class GeminiCompletion(BaseLLM):
         if tools and self.supports_tools:
             config_params["tools"] = self._convert_tools_for_interference(tools)
 
-        if self.safety_settings:
-            config_params["safety_settings"] = self.safety_settings
-
         return types.GenerateContentConfig(**config_params)
 
     def _convert_tools_for_interference(  # type: ignore[no-any-unimported]
@@ -404,8 +494,8 @@ class GeminiCompletion(BaseLLM):
         system_instruction: str | None,
         config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming content generation."""
@@ -416,7 +506,7 @@ class GeminiCompletion(BaseLLM):
         }
 
         try:
-            response = self.client.models.generate_content(**api_params)
+            response = self._client.models.generate_content(**api_params)
 
             usage = self._extract_token_usage(response)
         except Exception as e:
@@ -470,8 +560,8 @@ class GeminiCompletion(BaseLLM):
         contents: list[types.Content],
         config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming content generation."""
@@ -484,7 +574,7 @@ class GeminiCompletion(BaseLLM):
             "config": config,
         }
 
-        for chunk in self.client.models.generate_content_stream(**api_params):
+        for chunk in self._client.models.generate_content_stream(**api_params):
             if hasattr(chunk, "text") and chunk.text:
                 full_response += chunk.text
                 self._emit_stream_chunk_event(
@@ -537,52 +627,30 @@ class GeminiCompletion(BaseLLM):
 
         return full_response
 
+    @computed_field  # type: ignore[prop-decorator]
+    @property
     def supports_function_calling(self) -> bool:
         """Check if the model supports function calling."""
         return self.supports_tools
 
-    def supports_stop_words(self) -> bool:
-        """Check if the model supports stop words."""
-        return True
-
     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
-
         for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if value < min_context or value > max_context:
+            if value < MIN_CONTEXT_WINDOW or value > MAX_CONTEXT_WINDOW:
                 raise ValueError(
-                    f"Context window for {key} must be between {min_context} and {max_context}"
+                    f"Context window for {key} must be between {MIN_CONTEXT_WINDOW} and {MAX_CONTEXT_WINDOW}"
                 )
 
-        context_windows = {
-            "gemini-2.0-flash": 1048576,  # 1M tokens
-            "gemini-2.0-flash-thinking": 32768,
-            "gemini-2.0-flash-lite": 1048576,
-            "gemini-2.5-flash": 1048576,
-            "gemini-2.5-pro": 1048576,
-            "gemini-1.5-pro": 2097152,  # 2M tokens
-            "gemini-1.5-flash": 1048576,
-            "gemini-1.5-flash-8b": 1048576,
-            "gemini-1.0-pro": 32768,
-            "gemma-3-1b": 32000,
-            "gemma-3-4b": 128000,
-            "gemma-3-12b": 128000,
-            "gemma-3-27b": 128000,
-        }
-
         # Find the best match for the model name
-        for model_prefix, size in context_windows.items():
+        for model_prefix, size in GEMINI_CONTEXT_WINDOWS.items():
             if self.model.startswith(model_prefix):
                 return int(size * CONTEXT_WINDOW_USAGE_RATIO)
 
         # Default context window size for Gemini models
         return int(1048576 * CONTEXT_WINDOW_USAGE_RATIO)  # 1M tokens
 
-    def _extract_token_usage(self, response: dict[str, Any]) -> dict[str, Any]:
+    @staticmethod
+    def _extract_token_usage(response: dict[str, Any]) -> dict[str, Any]:
         """Extract token usage from Gemini response."""
         if hasattr(response, "usage_metadata"):
             usage = response.usage_metadata
@@ -594,8 +662,8 @@ class GeminiCompletion(BaseLLM):
             }
         return {"total_tokens": 0}
 
+    @staticmethod
     def _convert_contents_to_dict(  # type: ignore[no-any-unimported]
-        self,
         contents: list[types.Content],
     ) -> list[dict[str, str]]:
         """Convert contents to dict format."""

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

@@ -4,16 +4,23 @@ from collections.abc import Iterator
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Final
 
 import httpx
 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,
+    PrivateAttr,
+    model_validator,
+)
+from typing_extensions import Self
 
 from crewai.events.types.llm_events import LLMCallType
+from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 from crewai.llms.base_llm import BaseLLM
 from crewai.llms.hooks.transport import HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
@@ -25,11 +32,28 @@ 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
 
 
+OPENAI_CONTEXT_WINDOWS: dict[str, int] = {
+    "gpt-4": 8192,
+    "gpt-4o": 128000,
+    "gpt-4o-mini": 200000,
+    "gpt-4-turbo": 128000,
+    "gpt-4.1": 1047576,
+    "gpt-4.1-mini-2025-04-14": 1047576,
+    "gpt-4.1-nano-2025-04-14": 1047576,
+    "o1-preview": 128000,
+    "o1-mini": 128000,
+    "o3-mini": 200000,
+    "o4-mini": 200000,
+}
+
+MIN_CONTEXT_WINDOW: Final[int] = 1024
+MAX_CONTEXT_WINDOW: Final[int] = 2097152
+
+
 class OpenAICompletion(BaseLLM):
     """OpenAI native completion implementation.
 
@@ -37,112 +61,125 @@ class OpenAICompletion(BaseLLM):
     offering native structured outputs, function calling, and streaming support.
     """
 
-    def __init__(
-        self,
-        model: str = "gpt-4o",
-        api_key: str | None = None,
-        base_url: str | None = None,
-        organization: str | None = None,
-        project: str | None = None,
-        timeout: float | None = None,
-        max_retries: int = 2,
-        default_headers: dict[str, str] | None = None,
-        default_query: dict[str, Any] | None = None,
-        client_params: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        frequency_penalty: float | None = None,
-        presence_penalty: float | None = None,
-        max_tokens: int | None = None,
-        max_completion_tokens: int | None = None,
-        seed: int | None = None,
-        stream: bool = False,
-        response_format: dict[str, Any] | type[BaseModel] | None = None,
-        logprobs: bool | None = None,
-        top_logprobs: int | None = None,
-        reasoning_effort: str | None = None,
-        provider: str | None = None,
-        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize OpenAI chat completion client."""
+    model: str = Field(
+        default="gpt-4o",
+        description="OpenAI model name (e.g., 'gpt-4o')",
+    )
+    organization: str | None = Field(
+        default=None,
+        description="Name of the OpenAI organization",
+    )
+    project: str | None = Field(
+        default=None,
+        description="Name of the OpenAI project",
+    )
+    api_base: str | None = Field(
+        default=os.getenv("OPENAI_BASE_URL"),
+        description="Base URL for OpenAI API",
+    )
+    default_headers: dict[str, str] | None = Field(
+        default=None,
+        description="Default headers for OpenAI API requests",
+    )
+    default_query: dict[str, Any] | None = Field(
+        default=None,
+        description="Default query parameters for OpenAI API requests",
+    )
+    top_p: float | None = Field(
+        default=None,
+        description="Top-p sampling parameter",
+    )
+    frequency_penalty: float | None = Field(
+        default=None,
+        description="Frequency penalty parameter",
+    )
+    presence_penalty: float | None = Field(
+        default=None,
+        description="Presence penalty parameter",
+    )
+    max_completion_tokens: int | None = Field(
+        default=None,
+        description="Maximum tokens for completion",
+    )
+    seed: int | None = Field(
+        default=None,
+        description="Random seed for reproducibility",
+    )
+    response_format: dict[str, Any] | type[BaseModel] | None = Field(
+        default=None,
+        description="Response format for structured output",
+    )
+    logprobs: bool | None = Field(
+        default=None,
+        description="Whether to include log probabilities",
+    )
+    top_logprobs: int | None = Field(
+        default=None,
+        description="Number of top log probabilities to return",
+    )
+    reasoning_effort: str | None = Field(
+        default=None,
+        description="Reasoning effort level for o1 models",
+    )
+    supports_function_calling: bool = Field(
+        default=True,
+        description="Whether the model supports function calling",
+    )
+    is_o1_model: bool = Field(
+        default=False,
+        description="Whether the model is an o1 model",
+    )
+    is_gpt4_model: bool = Field(
+        default=False,
+        description="Whether the model is a GPT-4 model",
+    )
+    _client: OpenAI = PrivateAttr(
+        default_factory=OpenAI,
+    )
 
-        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,
-        )
-
-        client_config = self._get_client_params()
-        if self.interceptor:
-            transport = HTTPTransport(interceptor=self.interceptor)
-            http_client = httpx.Client(transport=transport)
-            client_config["http_client"] = http_client
-
-        self.client = OpenAI(**client_config)
-
-        # Completion parameters
-        self.top_p = top_p
-        self.frequency_penalty = frequency_penalty
-        self.presence_penalty = presence_penalty
-        self.max_tokens = max_tokens
-        self.max_completion_tokens = max_completion_tokens
-        self.seed = seed
-        self.stream = stream
-        self.response_format = response_format
-        self.logprobs = logprobs
-        self.top_logprobs = top_logprobs
-        self.reasoning_effort = reasoning_effort
-        self.is_o1_model = "o1" in model.lower()
-        self.is_gpt4_model = "gpt-4" in model.lower()
-
-    def _get_client_params(self) -> dict[str, Any]:
-        """Get OpenAI client parameters."""
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        """Initialize the Anthropic client after Pydantic validation.
 
+        This runs after all field validation is complete, ensuring that:
+        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
+        - Field validators have run (stop_sequences is normalized to set[str])
+        - API key and other configuration is ready
+        """
         if self.api_key is None:
             self.api_key = os.getenv("OPENAI_API_KEY")
             if self.api_key is None:
                 raise ValueError("OPENAI_API_KEY is required")
 
-        base_params = {
-            "api_key": self.api_key,
-            "organization": self.organization,
-            "project": self.project,
-            "base_url": self.base_url
-            or self.api_base
-            or os.getenv("OPENAI_BASE_URL")
-            or None,
-            "timeout": self.timeout,
-            "max_retries": self.max_retries,
-            "default_headers": self.default_headers,
-            "default_query": self.default_query,
-        }
+        self.is_o1_model = "o1" in self.model.lower()
+        self.supports_function_calling = not self.is_o1_model
+        self.is_gpt4_model = "gpt-4" in self.model.lower()
+        self.supports_stop_words = not self.is_o1_model
 
-        client_params = {k: v for k, v in base_params.items() if v is not None}
+        params = self.model_dump(
+            include={
+                "api_key",
+                "organization",
+                "project",
+                "base_url",
+                "timeout",
+                "max_retries",
+                "default_headers",
+                "default_query",
+            },
+            exclude_none=True,
+        )
+
+        if self.interceptor:
+            transport = HTTPTransport(interceptor=self.interceptor)
+            http_client = httpx.Client(transport=transport)
+            params["http_client"] = http_client
 
         if self.client_params:
-            client_params.update(self.client_params)
+            params.update(self.client_params)
 
-        return client_params
+        self._client = OpenAI(**params)
+        return self
 
     def call(
         self,
@@ -213,38 +250,26 @@ class OpenAICompletion(BaseLLM):
         self, messages: list[LLMMessage], tools: list[dict[str, BaseTool]] | None = None
     ) -> dict[str, Any]:
         """Prepare parameters for OpenAI chat completion."""
-        params: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-        }
-        if self.stream:
-            params["stream"] = self.stream
-
+        params = self.model_dump(
+            include={
+                "model",
+                "stream",
+                "temperature",
+                "top_p",
+                "frequency_penalty",
+                "presence_penalty",
+                "max_completion_tokens",
+                "max_tokens",
+                "seed",
+                "logprobs",
+                "top_logprobs",
+                "reasoning_effort",
+            },
+            exclude_none=True,
+        )
+        params["messages"] = messages
         params.update(self.additional_params)
 
-        if self.temperature is not None:
-            params["temperature"] = self.temperature
-        if self.top_p is not None:
-            params["top_p"] = self.top_p
-        if self.frequency_penalty is not None:
-            params["frequency_penalty"] = self.frequency_penalty
-        if self.presence_penalty is not None:
-            params["presence_penalty"] = self.presence_penalty
-        if self.max_completion_tokens is not None:
-            params["max_completion_tokens"] = self.max_completion_tokens
-        elif self.max_tokens is not None:
-            params["max_tokens"] = self.max_tokens
-        if self.seed is not None:
-            params["seed"] = self.seed
-        if self.logprobs is not None:
-            params["logprobs"] = self.logprobs
-        if self.top_logprobs is not None:
-            params["top_logprobs"] = self.top_logprobs
-
-        # Handle o1 model specific parameters
-        if self.is_o1_model and self.reasoning_effort:
-            params["reasoning_effort"] = self.reasoning_effort
-
         if tools:
             params["tools"] = self._convert_tools_for_interference(tools)
             params["tool_choice"] = "auto"
@@ -296,14 +321,14 @@ class OpenAICompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming chat completion."""
         try:
             if response_model:
-                parsed_response = self.client.beta.chat.completions.parse(
+                parsed_response = self._client.beta.chat.completions.parse(
                     **params,
                     response_format=response_model,
                 )
@@ -327,7 +352,7 @@ class OpenAICompletion(BaseLLM):
                     )
                     return structured_json
 
-            response: ChatCompletion = self.client.chat.completions.create(**params)
+            response: ChatCompletion = self._client.chat.completions.create(**params)
 
             usage = self._extract_openai_token_usage(response)
 
@@ -419,8 +444,8 @@ class OpenAICompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming chat completion."""
@@ -429,7 +454,7 @@ class OpenAICompletion(BaseLLM):
 
         if response_model:
             completion_stream: Iterator[ChatCompletionChunk] = (
-                self.client.chat.completions.create(**params)
+                self._client.chat.completions.create(**params)
             )
 
             accumulated_content = ""
@@ -472,7 +497,7 @@ class OpenAICompletion(BaseLLM):
                 )
                 return accumulated_content
 
-        stream: Iterator[ChatCompletionChunk] = self.client.chat.completions.create(
+        stream: Iterator[ChatCompletionChunk] = self._client.chat.completions.create(
             **params
         )
 
@@ -550,58 +575,31 @@ class OpenAICompletion(BaseLLM):
 
         return full_response
 
-    def supports_function_calling(self) -> bool:
-        """Check if the model supports function calling."""
-        return not self.is_o1_model
-
-    def supports_stop_words(self) -> bool:
-        """Check if the model supports stop words."""
-        return not self.is_o1_model
-
     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
-
         for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if value < min_context or value > max_context:
+            if value < MIN_CONTEXT_WINDOW or value > MAX_CONTEXT_WINDOW:
                 raise ValueError(
-                    f"Context window for {key} must be between {min_context} and {max_context}"
+                    f"Context window for {key} must be between {MIN_CONTEXT_WINDOW} and {MAX_CONTEXT_WINDOW}"
                 )
 
-        # Context window sizes for OpenAI models
-        context_windows = {
-            "gpt-4": 8192,
-            "gpt-4o": 128000,
-            "gpt-4o-mini": 200000,
-            "gpt-4-turbo": 128000,
-            "gpt-4.1": 1047576,
-            "gpt-4.1-mini-2025-04-14": 1047576,
-            "gpt-4.1-nano-2025-04-14": 1047576,
-            "o1-preview": 128000,
-            "o1-mini": 128000,
-            "o3-mini": 200000,
-            "o4-mini": 200000,
-        }
-
         # Find the best match for the model name
-        for model_prefix, size in context_windows.items():
+        for model_prefix, size in OPENAI_CONTEXT_WINDOWS.items():
             if self.model.startswith(model_prefix):
                 return int(size * CONTEXT_WINDOW_USAGE_RATIO)
 
         # Default context window size
         return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
 
-    def _extract_openai_token_usage(self, response: ChatCompletion) -> dict[str, Any]:
+    @staticmethod
+    def _extract_openai_token_usage(response: ChatCompletion) -> dict[str, Any]:
         """Extract token usage from OpenAI ChatCompletion response."""
-        if hasattr(response, "usage") and response.usage:
+        if response.usage:
             usage = response.usage
             return {
-                "prompt_tokens": getattr(usage, "prompt_tokens", 0),
-                "completion_tokens": getattr(usage, "completion_tokens", 0),
-                "total_tokens": getattr(usage, "total_tokens", 0),
+                "prompt_tokens": usage.prompt_tokens,
+                "completion_tokens": usage.completion_tokens,
+                "total_tokens": usage.total_tokens,
             }
         return {"total_tokens": 0}