Update AISuiteLLM and LLM utility type handling

- Modify AISuiteLLM to support more flexible input types for messages
- Update type hints in AISuiteLLM to allow string or list of message dictionaries
- Enhance LLM utility function to support broader LLM type annotations
- Remove default `self.stop` attribute from BaseLLM initialization

src/crewai/llms/base_llm.py

@@ -31,7 +31,6 @@ class BaseLLM(ABC):
         All custom LLM implementations should call super().__init__() to ensure
         that these default attributes are properly initialized.
         """
-        self.stop = []
         self.model = model
         self.temperature = temperature
 

src/crewai/llms/third_party/ai_suite.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 import aisuite as ai
 
@@ -12,11 +12,11 @@ class AISuiteLLM(BaseLLM):
 
     def call(
         self,
-        messages: List[Dict[str, str]],
+        messages: Union[str, List[Dict[str, str]]],
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
-    ) -> str:
+    ) -> Union[str, Any]:
         completion_params = self._prepare_completion_params(messages)
         # print(f"Completion params: {completion_params}")
         response = self.client.chat.completions.create(**completion_params)
@@ -26,7 +26,7 @@ class AISuiteLLM(BaseLLM):
         return response.choices[0].message.content
 
     def _prepare_completion_params(
-        self, messages: List[Dict[str, str]]
+        self, messages: Union[str, List[Dict[str, str]]]
     ) -> Dict[str, Any]:
         print(f"Preparing completion params for {self.model}")
         # print(f"Messages: {messages}")

src/crewai/utilities/llm_utils.py

@@ -6,8 +6,8 @@ from crewai.llm import LLM, BaseLLM
 
 
 def create_llm(
-    llm_value: Union[str, BaseLLM, Any, None] = None,
-) -> Optional[BaseLLM]:
+    llm_value: Union[str, BaseLLM, LLM, Any, None] = None,
+) -> Optional[BaseLLM | LLM]:
     """
     Creates or returns an LLM instance based on the given llm_value.