Merge branch 'main' into lg-track-task-from-llm-calls

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

docs/en/concepts/llms.mdx

@@ -749,9 +749,58 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     ```
 
     <Tip>
-      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details
     </Tip>
   </Tab>
+
+  <Tab title="Agent & Task Tracking">
+    All LLM events in CrewAI include agent and task information, allowing you to track and filter LLM interactions by specific agents or tasks:
+
+    ```python
+    from crewai import LLM, Agent, Task, Crew
+    from crewai.utilities.events import LLMStreamChunkEvent
+    from crewai.utilities.events.base_event_listener import BaseEventListener
+
+    class MyCustomListener(BaseEventListener):
+        def setup_listeners(self, crewai_event_bus):
+            @crewai_event_bus.on(LLMStreamChunkEvent)
+            def on_llm_stream_chunk(source, event):
+                if researcher.id == event.agent_id:
+                    print("\n==============\n Got event:", event, "\n==============\n")
+
+
+    my_listener = MyCustomListener()
+
+    llm = LLM(model="gpt-4o-mini", temperature=0, stream=True)
+
+    researcher = Agent(
+        role="About User",
+        goal="You know everything about the user.",
+        backstory="""You are a master at understanding people and their preferences.""",
+        llm=llm,
+    )
+
+    search = Task(
+        description="Answer the following questions about the user: {question}",
+        expected_output="An answer to the question.",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[search])
+
+    result = crew.kickoff(
+        inputs={"question": "..."}
+    )
+    ```
+
+    <Info>
+      This feature is particularly useful for:
+      - Debugging specific agent behaviors
+      - Logging LLM usage by task type
+      - Auditing which agents are making what types of LLM calls
+      - Performance monitoring of specific tasks
+    </Info>
+  </Tab>
 </Tabs>
 
 ## Structured LLM Calls
@@ -847,7 +896,7 @@ Learn how to get the most out of your LLM configuration:
       Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
     </Info>
   </Accordion>
-  
+
   <Accordion title="Drop Additional Parameters">
     CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
     For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:

src/crewai/agent.py

@@ -775,6 +775,7 @@ class Agent(BaseAgent):
             LiteAgentOutput: The result of the agent execution.
         """
         lite_agent = LiteAgent(
+            id=self.id,
             role=self.role,
             goal=self.goal,
             backstory=self.backstory,

src/crewai/agents/crew_agent_executor.py

@@ -159,6 +159,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     messages=self.messages,
                     callbacks=self.callbacks,
                     printer=self._printer,
+                    from_task=self.task
                 )
                 formatted_answer = process_llm_response(answer, self.use_stop_words)
 

src/crewai/lite_agent.py

@@ -15,12 +15,14 @@ from typing import (
     get_origin,
 )
 
+
 try:
     from typing import Self
 except ImportError:
     from typing_extensions import Self
 
 from pydantic import (
+    UUID4,
     BaseModel,
     Field,
     InstanceOf,
@@ -129,6 +131,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     model_config = {"arbitrary_types_allowed": True}
 
     # Core Agent Properties
+    id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Goal of the agent")
     backstory: str = Field(description="Backstory of the agent")
@@ -517,6 +520,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                         messages=self._messages,
                         tools=None,
                         callbacks=self._callbacks,
+                        from_agent=self,
                     ),
                 )
 
@@ -526,6 +530,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                         messages=self._messages,
                         callbacks=self._callbacks,
                         printer=self._printer,
+                        from_agent=self,
                     )
 
                     # Emit LLM call completed event
@@ -534,13 +539,14 @@ class LiteAgent(FlowTrackable, BaseModel):
                         event=LLMCallCompletedEvent(
                             response=answer,
                             call_type=LLMCallType.LLM_CALL,
+                            from_agent=self,
                         ),
                     )
                 except Exception as e:
                     # Emit LLM call failed event
                     crewai_event_bus.emit(
                         self,
-                        event=LLMCallFailedEvent(error=str(e)),
+                        event=LLMCallFailedEvent(error=str(e), from_agent=self),
                     )
                     raise e
 

src/crewai/llm.py

@@ -419,6 +419,8 @@ class LLM(BaseLLM):
         params: Dict[str, Any],
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> str:
         """Handle a streaming response from the LLM.
 
@@ -426,6 +428,8 @@ class LLM(BaseLLM):
             params: Parameters for the completion call
             callbacks: Optional list of callback functions
             available_functions: Dict of available functions
+            from_task: Optional task object
+            from_agent: Optional agent object
 
         Returns:
             str: The complete response text
@@ -510,6 +514,8 @@ class LLM(BaseLLM):
                                         tool_calls=tool_calls,
                                         accumulated_tool_args=accumulated_tool_args,
                                         available_functions=available_functions,
+                                        from_task=from_task,
+                                        from_agent=from_agent,
                                     )
                                     if result is not None:
                                         chunk_content = result
@@ -527,7 +533,7 @@ class LLM(BaseLLM):
                     assert hasattr(crewai_event_bus, "emit")
                     crewai_event_bus.emit(
                         self,
-                        event=LLMStreamChunkEvent(chunk=chunk_content),
+                        event=LLMStreamChunkEvent(chunk=chunk_content, from_task=from_task, from_agent=from_agent),
                     )
             # --- 4) Fallback to non-streaming if no content received
             if not full_response.strip() and chunk_count == 0:
@@ -540,7 +546,7 @@ class LLM(BaseLLM):
                     "stream_options", None
                 )  # Remove stream_options for non-streaming call
                 return self._handle_non_streaming_response(
-                    non_streaming_params, callbacks, available_functions
+                    non_streaming_params, callbacks, available_functions, from_task, from_agent
                 )
 
             # --- 5) Handle empty response with chunks
@@ -625,7 +631,7 @@ class LLM(BaseLLM):
                 # Log token usage if available in streaming mode
                 self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
                 # Emit completion event and return response
-                self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
+                self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL, from_task, from_agent)
                 return full_response
 
             # --- 9) Handle tool calls if present
@@ -637,7 +643,7 @@ class LLM(BaseLLM):
             self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
 
             # --- 11) Emit completion event and return response
-            self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
+            self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL, from_task, from_agent)
             return full_response
 
         except ContextWindowExceededError as e:
@@ -649,14 +655,14 @@ class LLM(BaseLLM):
             logging.error(f"Error in streaming response: {str(e)}")
             if full_response.strip():
                 logging.warning(f"Returning partial response despite error: {str(e)}")
-                self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
+                self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL, from_task, from_agent)
                 return full_response
 
             # Emit failed event and re-raise the exception
             assert hasattr(crewai_event_bus, "emit")
             crewai_event_bus.emit(
                 self,
-                event=LLMCallFailedEvent(error=str(e)),
+                event=LLMCallFailedEvent(error=str(e), from_task=from_task, from_agent=from_agent),
             )
             raise Exception(f"Failed to get streaming response: {str(e)}")
 
@@ -665,6 +671,8 @@ class LLM(BaseLLM):
         tool_calls: List[ChatCompletionDeltaToolCall],
         accumulated_tool_args: DefaultDict[int, AccumulatedToolArgs],
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> None | str:
         for tool_call in tool_calls:
             current_tool_accumulator = accumulated_tool_args[tool_call.index]
@@ -682,6 +690,8 @@ class LLM(BaseLLM):
                 event=LLMStreamChunkEvent(
                     tool_call=tool_call.to_dict(),
                     chunk=tool_call.function.arguments,
+                    from_task=from_task,
+                    from_agent=from_agent,
                 ),
             )
 
@@ -748,6 +758,8 @@ class LLM(BaseLLM):
         params: Dict[str, Any],
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
+        from_task: Optional[Any] = None,
+        from_agent: Optional[Any] = None,
     ) -> str:
         """Handle a non-streaming response from the LLM.
 
@@ -755,6 +767,8 @@ class LLM(BaseLLM):
             params: Parameters for the completion call
             callbacks: Optional list of callback functions
             available_functions: Dict of available functions
+            from_task: Optional Task that invoked the LLM
+            from_agent: Optional Agent that invoked the LLM
 
         Returns:
             str: The response text
@@ -795,7 +809,7 @@ class LLM(BaseLLM):
 
         # --- 5) If no tool calls or no available functions, return the text response directly
         if not tool_calls or not available_functions:
-            self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
+            self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL, from_task, from_agent)
             return text_response
 
         # --- 6) Handle tool calls if present
@@ -804,7 +818,7 @@ class LLM(BaseLLM):
             return tool_result
 
         # --- 7) If tool call handling didn't return a result, emit completion event and return text response
-        self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
+        self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL, from_task, from_agent)
         return text_response
 
     def _handle_tool_call(
@@ -889,6 +903,8 @@ class LLM(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]:
         """High-level LLM call method.
 
@@ -903,6 +919,8 @@ class LLM(BaseLLM):
                       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 that invoked the LLM
+            from_agent: Optional Agent that invoked the LLM
 
         Returns:
             Union[str, Any]: Either a text response from the LLM (str) or
@@ -922,6 +940,8 @@ class LLM(BaseLLM):
                 tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
             ),
         )
 
@@ -950,11 +970,11 @@ class LLM(BaseLLM):
                 # --- 7) Make the completion call and handle response
                 if self.stream:
                     return self._handle_streaming_response(
-                        params, callbacks, available_functions
+                        params, callbacks, available_functions, from_task, from_agent
                     )
                 else:
                     return self._handle_non_streaming_response(
-                        params, callbacks, available_functions
+                        params, callbacks, available_functions, from_task, from_agent
                     )
 
             except LLMContextLengthExceededException:
@@ -966,12 +986,12 @@ class LLM(BaseLLM):
                 assert hasattr(crewai_event_bus, "emit")
                 crewai_event_bus.emit(
                     self,
-                    event=LLMCallFailedEvent(error=str(e)),
+                    event=LLMCallFailedEvent(error=str(e), from_task=from_task, from_agent=from_agent),
                 )
                 logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
-    def _handle_emit_call_events(self, response: Any, call_type: LLMCallType):
+    def _handle_emit_call_events(self, response: Any, call_type: LLMCallType, from_task: Optional[Any] = None, from_agent: Optional[Any] = None):
         """Handle the events for the LLM call.
 
         Args:
@@ -981,7 +1001,7 @@ class LLM(BaseLLM):
         assert hasattr(crewai_event_bus, "emit")
         crewai_event_bus.emit(
             self,
-            event=LLMCallCompletedEvent(response=response, call_type=call_type),
+            event=LLMCallCompletedEvent(response=response, call_type=call_type, from_task=from_task, from_agent=from_agent),
         )
 
     def _format_messages_for_provider(

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)

src/crewai/utilities/agent_utils.py

@@ -145,12 +145,16 @@ def get_llm_response(
     messages: List[Dict[str, str]],
     callbacks: List[Any],
     printer: Printer,
+    from_task: Optional[Any] = None,
+    from_agent: Optional[Any] = None,
 ) -> str:
     """Call the LLM and return the response, handling any invalid responses."""
     try:
         answer = llm.call(
             messages,
             callbacks=callbacks,
+            from_task=from_task,
+            from_agent=from_agent,
         )
     except Exception as e:
         printer.print(

src/crewai/utilities/events/llm_events.py

@@ -5,6 +5,32 @@ from pydantic import BaseModel
 
 from crewai.utilities.events.base_events import BaseEvent
 
+class LLMEventBase(BaseEvent):
+    task_name: Optional[str] = None
+    task_id: Optional[str] = None
+
+    agent_id: Optional[str] = None
+    agent_role: Optional[str] = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+    def _set_agent_params(self, data: Dict[str, Any]):
+        task = data.get("from_task", None)
+        agent = task.agent if task else data.get("from_agent", None)
+
+        if not agent:
+            return
+
+        self.agent_id = agent.id
+        self.agent_role = agent.role
+
+    def _set_task_params(self, data: Dict[str, Any]):
+        if "from_task" in data and (task := data["from_task"]):
+            self.task_id = task.id
+            self.task_name = task.name
 
 class LLMCallType(Enum):
     """Type of LLM call being made"""
@@ -13,7 +39,7 @@ class LLMCallType(Enum):
     LLM_CALL = "llm_call"
 
 
-class LLMCallStartedEvent(BaseEvent):
+class LLMCallStartedEvent(LLMEventBase):
     """Event emitted when a LLM call starts
 
     Attributes:
@@ -28,7 +54,7 @@ class LLMCallStartedEvent(BaseEvent):
     available_functions: Optional[Dict[str, Any]] = None
 
 
-class LLMCallCompletedEvent(BaseEvent):
+class LLMCallCompletedEvent(LLMEventBase):
     """Event emitted when a LLM call completes"""
 
     type: str = "llm_call_completed"
@@ -36,7 +62,7 @@ class LLMCallCompletedEvent(BaseEvent):
     call_type: LLMCallType
 
 
-class LLMCallFailedEvent(BaseEvent):
+class LLMCallFailedEvent(LLMEventBase):
     """Event emitted when a LLM call fails"""
 
     error: str
@@ -55,7 +81,7 @@ class ToolCall(BaseModel):
     index: int
 
 
-class LLMStreamChunkEvent(BaseEvent):
+class LLMStreamChunkEvent(LLMEventBase):
     """Event emitted when a streaming chunk is received"""
 
     type: str = "llm_stream_chunk"

tests/custom_llm_test.py

@@ -1,5 +1,4 @@
 from typing import Any, Dict, List, Optional, Union
-from unittest.mock import Mock
 
 import pytest
 
@@ -31,6 +30,8 @@ class CustomLLM(BaseLLM):
         tools=None,
         callbacks=None,
         available_functions=None,
+        from_task=None,
+        from_agent=None,
     ):
         """
         Mock LLM call that returns a predefined response.

tests/utilities/cassettes/test_llm_emits_event_with_lite_agent.yaml

@@ -0,0 +1,171 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are Speaker. You are a
+      helpful assistant that just says hi\nYour personal goal is: Just say hi\n\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "say hi!"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"],
+      "stream": true, "stream_options": {"include_usage": true}}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '602'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.78.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.78.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-read-timeout:
+      - '600.0'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.12
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: 'data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"role":"assistant","content":"","refusal":null},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"Thought"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"
+        I"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"
+        now"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"
+        can"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"
+        give"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"
+        a"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"
+        great"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"
+        answer"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"  \n"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
+        data: {"id":"chatcmpl-BoGFzpBc0nuAKcVrYlEEztNwzrUG6","object":"chat.completion.chunk","created":1751318591,"model":"gpt-4o-mini-2024-07-18","service_tier":"default","system_fingerprint":"fp_34a54ae93c","choices":[{"index":0,"delta":{"content":"Final"},"logprobs":null,"finish_reason":null}],"usage":null}
+
+
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tests/utilities/test_events.py

@@ -57,23 +57,28 @@ def vcr_config(request) -> dict:
     }
 
 
-base_agent = Agent(
-    role="base_agent",
-    llm="gpt-4o-mini",
-    goal="Just say hi",
-    backstory="You are a helpful assistant that just says hi",
+@pytest.fixture(scope="module")
+def base_agent():
+    return Agent(
+        role="base_agent",
+        llm="gpt-4o-mini",
+        goal="Just say hi",
+        backstory="You are a helpful assistant that just says hi",
 )
 
-base_task = Task(
-    description="Just say hi",
-    expected_output="hi",
-    agent=base_agent,
-)
+@pytest.fixture(scope="module")
+def base_task(base_agent):
+    return Task(
+        description="Just say hi",
+        expected_output="hi",
+        agent=base_agent,
+    )
+
 event_listener = EventListener()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_start_kickoff_event():
+def test_crew_emits_start_kickoff_event(base_agent, base_task):
     received_events = []
     mock_span = Mock()
 
@@ -101,7 +106,7 @@ def test_crew_emits_start_kickoff_event():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_end_kickoff_event():
+def test_crew_emits_end_kickoff_event(base_agent, base_task):
     received_events = []
 
     @crewai_event_bus.on(CrewKickoffCompletedEvent)
@@ -119,7 +124,7 @@ def test_crew_emits_end_kickoff_event():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_test_kickoff_type_event():
+def test_crew_emits_test_kickoff_type_event(base_agent, base_task):
     received_events = []
     mock_span = Mock()
 
@@ -165,7 +170,7 @@ def test_crew_emits_test_kickoff_type_event():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_kickoff_failed_event():
+def test_crew_emits_kickoff_failed_event(base_agent, base_task):
     received_events = []
 
     with crewai_event_bus.scoped_handlers():
@@ -190,7 +195,7 @@ def test_crew_emits_kickoff_failed_event():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_start_task_event():
+def test_crew_emits_start_task_event(base_agent, base_task):
     received_events = []
 
     @crewai_event_bus.on(TaskStartedEvent)
@@ -207,7 +212,7 @@ def test_crew_emits_start_task_event():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_emits_end_task_event():
+def test_crew_emits_end_task_event(base_agent, base_task):
     received_events = []
 
     @crewai_event_bus.on(TaskCompletedEvent)
@@ -235,7 +240,7 @@ def test_crew_emits_end_task_event():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_emits_failed_event_on_execution_error():
+def test_task_emits_failed_event_on_execution_error(base_agent, base_task):
     received_events = []
     received_sources = []
 
@@ -272,7 +277,7 @@ def test_task_emits_failed_event_on_execution_error():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_emits_execution_started_and_completed_events():
+def test_agent_emits_execution_started_and_completed_events(base_agent, base_task):
     received_events = []
 
     @crewai_event_bus.on(AgentExecutionStartedEvent)
@@ -301,7 +306,7 @@ def test_agent_emits_execution_started_and_completed_events():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_emits_execution_error_event():
+def test_agent_emits_execution_error_event(base_agent, base_task):
     received_events = []
 
     @crewai_event_bus.on(AgentExecutionErrorEvent)
@@ -501,7 +506,7 @@ def test_flow_emits_method_execution_started_event():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_register_handler_adds_new_handler():
+def test_register_handler_adds_new_handler(base_agent, base_task):
     received_events = []
 
     def custom_handler(source, event):
@@ -519,7 +524,7 @@ def test_register_handler_adds_new_handler():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_multiple_handlers_for_same_event():
+def test_multiple_handlers_for_same_event(base_agent, base_task):
     received_events_1 = []
     received_events_2 = []
 
@@ -613,6 +618,11 @@ def test_llm_emits_call_started_event():
     assert received_events[0].type == "llm_call_started"
     assert received_events[1].type == "llm_call_completed"
 
+    assert received_events[0].task_name is None
+    assert received_events[0].agent_role is None
+    assert received_events[0].agent_id is None
+    assert received_events[0].task_id is None
+
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_emits_call_failed_event():
@@ -632,6 +642,10 @@ def test_llm_emits_call_failed_event():
         assert len(received_events) == 1
         assert received_events[0].type == "llm_call_failed"
         assert received_events[0].error == error_message
+        assert received_events[0].task_name is None
+        assert received_events[0].agent_role is None
+        assert received_events[0].agent_id is None
+        assert received_events[0].task_id is None
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -742,7 +756,6 @@ def test_streaming_empty_response_handling():
     received_chunks = []
 
     with crewai_event_bus.scoped_handlers():
-
         @crewai_event_bus.on(LLMStreamChunkEvent)
         def handle_stream_chunk(source, event):
             received_chunks.append(event.chunk)
@@ -779,3 +792,167 @@ def test_streaming_empty_response_handling():
         finally:
             # Restore the original method
             llm.call = original_call
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_stream_llm_emits_event_with_task_and_agent_info():
+    completed_event = []
+    failed_event = []
+    started_event = []
+    stream_event = []
+
+    with crewai_event_bus.scoped_handlers():
+        @crewai_event_bus.on(LLMCallFailedEvent)
+        def handle_llm_failed(source, event):
+            failed_event.append(event)
+
+        @crewai_event_bus.on(LLMCallStartedEvent)
+        def handle_llm_started(source, event):
+            started_event.append(event)
+
+        @crewai_event_bus.on(LLMCallCompletedEvent)
+        def handle_llm_completed(source, event):
+            completed_event.append(event)
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_llm_stream_chunk(source, event):
+            stream_event.append(event)
+
+        agent = Agent(
+            role="TestAgent",
+            llm=LLM(model="gpt-4o-mini", stream=True),
+            goal="Just say hi",
+            backstory="You are a helpful assistant that just says hi",
+        )
+        task = Task(
+            description="Just say hi",
+            expected_output="hi",
+            llm=LLM(model="gpt-4o-mini", stream=True),
+            agent=agent
+        )
+
+        crew = Crew(agents=[agent], tasks=[task])
+        crew.kickoff()
+
+    assert len(completed_event) == 1
+    assert len(failed_event) == 0
+    assert len(started_event) == 1
+    assert len(stream_event) == 12
+
+    all_events = completed_event + failed_event + started_event + stream_event
+    all_agent_roles = [event.agent_role for event in all_events]
+    all_agent_id = [event.agent_id for event in all_events]
+    all_task_id = [event.task_id for event in all_events]
+    all_task_name = [event.task_name for event in all_events]
+
+    # ensure all events have the agent + task props set
+    assert len(all_agent_roles) == 14
+    assert len(all_agent_id) == 14
+    assert len(all_task_id) == 14
+    assert len(all_task_name) == 14
+
+    assert set(all_agent_roles) == {agent.role}
+    assert set(all_agent_id) == {agent.id}
+    assert set(all_task_id) == {task.id}
+    assert set(all_task_name) == {task.name}
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task):
+    completed_event = []
+    failed_event = []
+    started_event = []
+    stream_event = []
+
+    with crewai_event_bus.scoped_handlers():
+        @crewai_event_bus.on(LLMCallFailedEvent)
+        def handle_llm_failed(source, event):
+            failed_event.append(event)
+
+        @crewai_event_bus.on(LLMCallStartedEvent)
+        def handle_llm_started(source, event):
+            started_event.append(event)
+
+        @crewai_event_bus.on(LLMCallCompletedEvent)
+        def handle_llm_completed(source, event):
+            completed_event.append(event)
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_llm_stream_chunk(source, event):
+            stream_event.append(event)
+
+        crew = Crew(agents=[base_agent], tasks=[base_task])
+        crew.kickoff()
+
+    assert len(completed_event) == 1
+    assert len(failed_event) == 0
+    assert len(started_event) == 1
+    assert len(stream_event) == 0
+
+    all_events = completed_event + failed_event + started_event + stream_event
+    all_agent_roles = [event.agent_role for event in all_events]
+    all_agent_id = [event.agent_id for event in all_events]
+    all_task_id = [event.task_id for event in all_events]
+    all_task_name = [event.task_name for event in all_events]
+
+    # ensure all events have the agent + task props set
+    assert len(all_agent_roles) == 2
+    assert len(all_agent_id) == 2
+    assert len(all_task_id) == 2
+    assert len(all_task_name) == 2
+
+    assert set(all_agent_roles) == {base_agent.role}
+    assert set(all_agent_id) == {base_agent.id}
+    assert set(all_task_id) == {base_task.id}
+    assert set(all_task_name) == {base_task.name}
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_emits_event_with_lite_agent():
+    completed_event = []
+    failed_event = []
+    started_event = []
+    stream_event = []
+
+    with crewai_event_bus.scoped_handlers():
+        @crewai_event_bus.on(LLMCallFailedEvent)
+        def handle_llm_failed(source, event):
+            failed_event.append(event)
+
+        @crewai_event_bus.on(LLMCallStartedEvent)
+        def handle_llm_started(source, event):
+            started_event.append(event)
+
+        @crewai_event_bus.on(LLMCallCompletedEvent)
+        def handle_llm_completed(source, event):
+            completed_event.append(event)
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_llm_stream_chunk(source, event):
+            stream_event.append(event)
+
+        agent = Agent(
+            role="Speaker",
+            llm=LLM(model="gpt-4o-mini", stream=True),
+            goal="Just say hi",
+            backstory="You are a helpful assistant that just says hi",
+        )
+        agent.kickoff(messages=[{"role": "user", "content": "say hi!"}])
+
+
+    assert len(completed_event) == 2
+    assert len(failed_event) == 0
+    assert len(started_event) == 2
+    assert len(stream_event) == 15
+
+    all_events = completed_event + failed_event + started_event + stream_event
+    all_agent_roles = [event.agent_role for event in all_events]
+    all_agent_id = [event.agent_id for event in all_events]
+    all_task_id = [event.task_id for event in all_events if event.task_id]
+    all_task_name = [event.task_name for event in all_events if event.task_name]
+
+    # ensure all events have the agent + task props set
+    assert len(all_agent_roles) == 19
+    assert len(all_agent_id) == 19
+    assert len(all_task_id) == 0
+    assert len(all_task_name) == 0
+
+    assert set(all_agent_roles) == {agent.role}
+    assert set(all_agent_id) == {agent.id}