feat: add capability to track LLM calls by task and agent (#3087)

* feat: add capability to track LLM calls by task and agent

This makes it possible to filter or scope LLM events by specific agents or tasks, which can be very useful for debugging or analytics in real-time application

* feat: add docs about LLM tracking by Agents and Tasks

* fix incompatible BaseLLM.call method signature

* feat: support to filter LLM Events from Lite Agent

src/crewai/llms/base_llm.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 
 class BaseLLM(ABC):
@@ -47,6 +47,8 @@ class BaseLLM(ABC):
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> Union[str, Any]:
         """Call the LLM with the given messages.
 
@@ -61,6 +63,7 @@ class BaseLLM(ABC):
                       during and after the LLM call.
             available_functions: Optional dict mapping function names to callables
                                that can be invoked by the LLM.
+            from_task: Optional task caller to be used for the LLM call.
 
         Returns:
             Either a text response from the LLM (str) or

src/crewai/llms/third_party/ai_suite.py

@@ -16,6 +16,8 @@ class AISuiteLLM(BaseLLM):
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> Union[str, Any]:
         completion_params = self._prepare_completion_params(messages, tools)
         response = self.client.chat.completions.create(**completion_params)