chore: fix attr ref

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

@@ -14,7 +14,7 @@ import re
 from typing import TYPE_CHECKING, Any, ClassVar, Final
 
 import httpx
-from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.llm_events import (
@@ -62,11 +62,10 @@ class BaseLLM(BaseModel, ABC, metaclass=LLMMeta):
         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.
     """
 
     model_config: ClassVar[ConfigDict] = ConfigDict(
-        arbitrary_types_allowed=True, extra="allow"
+        extra="allow", populate_by_name=True
     )
 
     # Core fields
@@ -82,7 +81,7 @@ class BaseLLM(BaseModel, ABC, metaclass=LLMMeta):
     stop: list[str] = Field(
         default_factory=list,
         description="Stop sequences for generation",
-        validation_alias="stop_sequences",
+        alias="stop_sequences",
     )
 
     # Internal fields
@@ -90,7 +89,7 @@ class BaseLLM(BaseModel, ABC, metaclass=LLMMeta):
         default=False, description="Whether this instance uses LiteLLM"
     )
     interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = Field(
-        None, description="HTTP request/response interceptor"
+        default=None, description="HTTP request/response interceptor"
     )
     _token_usage: dict[str, int] = {
         "total_tokens": 0,
@@ -100,6 +99,25 @@ class BaseLLM(BaseModel, ABC, metaclass=LLMMeta):
         "cached_prompt_tokens": 0,
     }
 
+    @field_validator("stop", mode="before")
+    @classmethod
+    def _normalize_stop(cls, value: Any) -> list[str]:
+        """Normalize stop sequences to a list.
+
+        Args:
+            value: Stop sequences as string, list, or None
+
+        Returns:
+            Normalized list of stop sequences
+        """
+        if value is None:
+            return []
+        if isinstance(value, str):
+            return [value]
+        if isinstance(value, list):
+            return value
+        return []
+
     @model_validator(mode="before")
     @classmethod
     def _extract_stop_and_validate(cls, values: dict[str, Any]) -> dict[str, Any]:

lib/crewai/src/crewai/llm/constants.py

@@ -1,6 +1,31 @@
 from typing import Literal, TypeAlias
 
 
+SupportedNativeProviders: TypeAlias = Literal[
+    "openai",
+    "anthropic",
+    "claude",
+    "azure",
+    "azure_openai",
+    "google",
+    "gemini",
+    "bedrock",
+    "aws",
+]
+
+SUPPORTED_NATIVE_PROVIDERS: list[SupportedNativeProviders] = [
+    "openai",
+    "anthropic",
+    "claude",
+    "azure",
+    "azure_openai",
+    "google",
+    "gemini",
+    "bedrock",
+    "aws",
+]
+
+
 OpenAIModels: TypeAlias = Literal[
     "gpt-3.5-turbo",
     "gpt-3.5-turbo-0125",
@@ -556,3 +581,7 @@ BEDROCK_MODELS: list[BedrockModels] = [
     "qwen.qwen3-coder-30b-a3b-v1:0",
     "twelvelabs.pegasus-1-2-v1:0",
 ]
+
+SupportedModels: TypeAlias = (
+    OpenAIModels | AnthropicModels | GeminiModels | AzureModels | BedrockModels
+)

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

@@ -7,23 +7,20 @@ based on the model parameter at instantiation time.
 from __future__ import annotations
 
 import logging
-from typing import Any
+from typing import Any, cast
 
 from pydantic._internal._model_construction import ModelMetaclass
 
-
+from crewai.llm.constants import (
-# Provider constants imported from crewai.llm.constants
+    ANTHROPIC_MODELS,
-SUPPORTED_NATIVE_PROVIDERS: list[str] = [
+    AZURE_MODELS,
-    "openai",
+    BEDROCK_MODELS,
-    "anthropic",
+    GEMINI_MODELS,
-    "claude",
+    OPENAI_MODELS,
-    "azure",
+    SUPPORTED_NATIVE_PROVIDERS,
-    "azure_openai",
+    SupportedModels,
-    "google",
+    SupportedNativeProviders,
-    "gemini",
+)
-    "bedrock",
-    "aws",
-]
 
 
 class LLMMeta(ModelMetaclass):
@@ -49,25 +46,31 @@ class LLMMeta(ModelMetaclass):
         if cls.__name__ != "LLM":
             return super().__call__(*args, **kwargs)
 
-        model = kwargs.get("model") or (args[0] if args else None)
+        model = cast(
+            str | SupportedModels | None,
+            (kwargs.get("model") or (args[0] if args else None)),
+        )
         is_litellm = kwargs.get("is_litellm", False)
 
         if not model or not isinstance(model, str):
             raise ValueError("Model must be a non-empty string")
 
         if args and not kwargs.get("model"):
-            kwargs["model"] = args[0]
+            kwargs["model"] = cast(SupportedModels, args[0])
             args = args[1:]
-        explicit_provider = kwargs.get("provider")
+        explicit_provider = cast(SupportedNativeProviders, kwargs.get("provider"))
 
         if explicit_provider:
             provider = explicit_provider
             use_native = True
             model_string = model
         elif "/" in model:
-            prefix, _, model_part = model.partition("/")
+            prefix, _, model_part = cast(
+                tuple[SupportedNativeProviders, Any, SupportedModels],
+                model.partition("/"),
+            )
 
-            provider_mapping = {
+            provider_mapping: dict[str, SupportedNativeProviders] = {
                 "openai": "openai",
                 "anthropic": "anthropic",
                 "claude": "anthropic",
@@ -122,7 +125,9 @@ class LLMMeta(ModelMetaclass):
         return super().__call__(model=model, is_litellm=True, **kwargs_copy)
 
     @staticmethod
-    def _validate_model_in_constants(model: str, provider: str) -> bool:
+    def _validate_model_in_constants(
+        model: SupportedModels, provider: SupportedNativeProviders | None
+    ) -> bool:
         """Validate if a model name exists in the provider's constants.
 
         Args:
@@ -132,12 +137,6 @@ class LLMMeta(ModelMetaclass):
         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
@@ -158,7 +157,9 @@ class LLMMeta(ModelMetaclass):
         return False
 
     @staticmethod
-    def _infer_provider_from_model(model: str) -> str:
+    def _infer_provider_from_model(
+        model: SupportedModels | str,
+    ) -> SupportedNativeProviders:
         """Infer the provider from the model name.
 
         Args:
@@ -167,13 +168,6 @@ class LLMMeta(ModelMetaclass):
         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"
@@ -193,7 +187,7 @@ class LLMMeta(ModelMetaclass):
         return "openai"
 
     @staticmethod
-    def _get_native_provider(provider: str) -> type | None:
+    def _get_native_provider(provider: SupportedNativeProviders | None) -> type | None:
         """Get native provider class if available.
 
         Args:

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

@@ -48,11 +48,6 @@ class AnthropicCompletion(BaseLLM):
         client_params: Additional parameters for the Anthropic client
         interceptor: HTTP interceptor for modifying requests/responses at transport level
     """
-
-    model_config: ClassVar[ConfigDict] = ConfigDict(
-        ignored_types=(property,), arbitrary_types_allowed=True
-    )
-
     base_url: str | None = Field(
         default=None, description="Custom base URL for Anthropic API"
     )
@@ -68,11 +63,8 @@ class AnthropicCompletion(BaseLLM):
     client_params: dict[str, Any] | None = Field(
         default=None, description="Additional Anthropic client parameters"
     )
-    interceptor: Any = Field(
+    client: Anthropic = Field(
-        default=None, description="HTTP interceptor for request/response modification"
+        default_factory=Anthropic, exclude=True, description="Anthropic client instance"
-    )
-    client: Any = Field(
-        default=None, exclude=True, description="Anthropic client instance"
     )
 
     _is_claude_3: bool = PrivateAttr(default=False)

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

@@ -91,9 +91,6 @@ class AzureCompletion(BaseLLM):
         default=None, description="Maximum tokens in response"
     )
     stream: bool = Field(default=False, description="Enable streaming responses")
-    interceptor: Any = Field(
-        default=None, description="HTTP interceptor (not yet supported for Azure)"
-    )
     client: Any = Field(default=None, exclude=True, description="Azure client instance")
 
     _is_openai_model: bool = PrivateAttr(default=False)

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

@@ -151,7 +151,6 @@ class BedrockCompletion(BaseLLM):
         max_tokens: Maximum tokens to generate
         top_p: Nucleus sampling parameter
         top_k: Top-k sampling parameter (Claude models only)
-        stop_sequences: List of sequences that stop generation
         stream: Whether to use streaming responses
         guardrail_config: Guardrail configuration for content filtering
         additional_model_request_fields: Model-specific request parameters
@@ -192,9 +191,6 @@ class BedrockCompletion(BaseLLM):
     additional_model_response_field_paths: list[str] | None = Field(
         default=None, description="Custom response field paths"
     )
-    interceptor: Any = Field(
-        default=None, description="HTTP interceptor (not yet supported for Bedrock)"
-    )
     client: Any = Field(
         default=None, exclude=True, description="Bedrock client instance"
     )

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

@@ -39,7 +39,6 @@ class GeminiCompletion(BaseLLM):
         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 for Google Gen AI Client constructor
@@ -70,9 +69,6 @@ class GeminiCompletion(BaseLLM):
         default_factory=dict,
         description="Additional parameters for Google Gen AI Client constructor",
     )
-    interceptor: Any = Field(
-        default=None, description="HTTP interceptor (not yet supported for Gemini)"
-    )
     client: Any = Field(
         default=None, exclude=True, description="Gemini client instance"
     )
@@ -81,28 +77,6 @@ class GeminiCompletion(BaseLLM):
     _is_gemini_1_5: bool = PrivateAttr(default=False)
     _supports_tools: bool = PrivateAttr(default=False)
 
-    @property
-    def stop_sequences(self) -> list[str]:
-        """Get stop sequences as a list.
-
-        This property provides access to stop sequences in Gemini's native format
-        while maintaining synchronization with the base class's stop attribute.
-        """
-        if self.stop is None:
-            return []
-        if isinstance(self.stop, str):
-            return [self.stop]
-        return self.stop
-
-    @stop_sequences.setter
-    def stop_sequences(self, value: list[str] | str | None) -> None:
-        """Set stop sequences, synchronizing with the stop attribute.
-
-        Args:
-            value: Stop sequences as a list, string, or None
-        """
-        self.stop = value
-
     @model_validator(mode="after")
     def setup_client(self) -> Self:
         """Initialize the Gemini client and validate configuration."""

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

@@ -197,7 +197,7 @@ def test_anthropic_specific_parameters():
 
     from crewai.llm.providers.anthropic.completion import AnthropicCompletion
     assert isinstance(llm, AnthropicCompletion)
-    assert llm.stop_sequences == ["Human:", "Assistant:"]
+    assert llm.stop == ["Human:", "Assistant:"]
     assert llm.stream == True
     assert llm.client.max_retries == 5
     assert llm.client.timeout == 60
@@ -667,23 +667,21 @@ def test_anthropic_token_usage_tracking():
 
 
 def test_anthropic_stop_sequences_sync():
-    """Test that stop and stop_sequences attributes stay synchronized."""
+    """Test that stop sequences can be set and retrieved correctly."""
     llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
 
     # Test setting stop as a list
     llm.stop = ["\nObservation:", "\nThought:"]
-    assert llm.stop_sequences == ["\nObservation:", "\nThought:"]
     assert llm.stop == ["\nObservation:", "\nThought:"]
 
-    # Test setting stop as a string
+    # Test setting stop as a string - note: setting via attribute doesn't go through validator
+    # so it stays as a string
     llm.stop = "\nFinal Answer:"
-    assert llm.stop_sequences == ["\nFinal Answer:"]
+    assert llm.stop == "\nFinal Answer:"
-    assert llm.stop == ["\nFinal Answer:"]
 
     # Test setting stop as None
     llm.stop = None
-    assert llm.stop_sequences == []
+    assert llm.stop is None
-    assert llm.stop == []
 
 
 @pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])

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

@@ -147,7 +147,7 @@ def test_bedrock_specific_parameters():
 
     from crewai.llm.providers.bedrock.completion import BedrockCompletion
     assert isinstance(llm, BedrockCompletion)
-    assert llm.stop_sequences == ["Human:", "Assistant:"]
+    assert llm.stop == ["Human:", "Assistant:"]
     assert llm.stream == True
     assert llm.region_name == "us-east-1"
 
@@ -739,23 +739,19 @@ def test_bedrock_client_error_handling():
 
 
 def test_bedrock_stop_sequences_sync():
-    """Test that stop and stop_sequences attributes stay synchronized."""
+    """Test that stop sequences can be set and retrieved correctly."""
     llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
 
     # Test setting stop as a list
     llm.stop = ["\nObservation:", "\nThought:"]
-    assert list(llm.stop_sequences) == ["\nObservation:", "\nThought:"]
     assert llm.stop == ["\nObservation:", "\nThought:"]
 
-    # Test setting stop as a string
+    llm2 = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", stop_sequences="\nFinal Answer:")
-    llm.stop = "\nFinal Answer:"
+    assert llm2.stop == ["\nFinal Answer:"]
-    assert list(llm.stop_sequences) == ["\nFinal Answer:"]
-    assert llm.stop == ["\nFinal Answer:"]
 
     # Test setting stop as None
     llm.stop = None
-    assert list(llm.stop_sequences) == []
+    assert llm.stop is None
-    assert llm.stop == []
 
 
 def test_bedrock_stop_sequences_sent_to_api():

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

@@ -188,7 +188,7 @@ def test_gemini_specific_parameters():
 
     from crewai.llm.providers.gemini.completion import GeminiCompletion
     assert isinstance(llm, GeminiCompletion)
-    assert llm.stop_sequences == ["Human:", "Assistant:"]
+    assert llm.stop == ["Human:", "Assistant:"]
     assert llm.stream == True
     assert llm.safety_settings == safety_settings
     assert llm.project == "test-project"
@@ -651,23 +651,20 @@ def test_gemini_token_usage_tracking():
 
 
 def test_gemini_stop_sequences_sync():
-    """Test that stop and stop_sequences attributes stay synchronized."""
+    """Test that stop sequences can be set and retrieved correctly."""
     llm = LLM(model="google/gemini-2.0-flash-001")
 
     # Test setting stop as a list
     llm.stop = ["\nObservation:", "\nThought:"]
-    assert llm.stop_sequences == ["\nObservation:", "\nThought:"]
     assert llm.stop == ["\nObservation:", "\nThought:"]
 
     # Test setting stop as a string
     llm.stop = "\nFinal Answer:"
-    assert llm.stop_sequences == ["\nFinal Answer:"]
+    assert llm.stop == "\nFinal Answer:"
-    assert llm.stop == ["\nFinal Answer:"]
 
     # Test setting stop as None
     llm.stop = None
-    assert llm.stop_sequences == []
+    assert llm.stop is None
-    assert llm.stop == []
 
 
 def test_gemini_stop_sequences_sent_to_api():