Lorenze/better tracing events (#3382)

* feat: implement tool usage limit exception handling

- Introduced `ToolUsageLimitExceeded` exception to manage maximum usage limits for tools.
- Enhanced `CrewStructuredTool` to check and raise this exception when the usage limit is reached.
- Updated `_run` and `_execute` methods to include usage limit checks and handle exceptions appropriately, improving reliability and user feedback.

* feat: enhance PlusAPI and ToolUsage with task metadata

- Removed the `send_trace_batch` method from PlusAPI to streamline the API.
- Added timeout parameters to trace event methods in PlusAPI for improved reliability.
- Updated ToolUsage to include task metadata (task name and ID) in event emissions, enhancing traceability and context during tool usage.
- Refactored event handling in LLM and ToolUsage events to ensure task information is consistently captured.

* feat: enhance memory and event handling with task and agent metadata

- Added task and agent metadata to various memory and event classes, improving traceability and context during memory operations.
- Updated the `ContextualMemory` and `Memory` classes to associate tasks and agents, allowing for better context management.
- Enhanced event emissions in `LLM`, `ToolUsage`, and memory events to include task and agent information, facilitating improved debugging and monitoring.
- Refactored event handling to ensure consistent capture of task and agent details across the system.

* drop

* refactor: clean up unused imports in memory and event modules

- Removed unused TYPE_CHECKING imports from long_term_memory.py to streamline the code.
- Eliminated unnecessary import from memory_events.py, enhancing clarity and maintainability.

* fix memory tests

* fix task_completed payload

* fix: remove unused test agent variable in external memory tests

* refactor: remove unused agent parameter from Memory class save method

- Eliminated the agent parameter from the save method in the Memory class to streamline the code and improve clarity.
- Updated the TraceBatchManager class by moving initialization of attributes into the constructor for better organization and readability.

* refactor: enhance ExecutionState and ReasoningEvent classes with optional task and agent identifiers

- Added optional `current_agent_id` and `current_task_id` attributes to the `ExecutionState` class for better tracking of agent and task states.
- Updated the `from_task` attribute in the `ReasoningEvent` class to use `Optional[Any]` instead of a specific type, improving flexibility in event handling.

* refactor: update ExecutionState class by removing unused agent and task identifiers

- Removed the `current_agent_id` and `current_task_id` attributes from the `ExecutionState` class to simplify the code and enhance clarity.
- Adjusted the import statements to include `Optional` for better type handling.

* refactor: streamline LLM event handling in LiteAgent

- Removed unused LLM event emissions (LLMCallStartedEvent, LLMCallCompletedEvent, LLMCallFailedEvent) from the LiteAgent class to simplify the code and improve performance.
- Adjusted the flow of LLM response handling by eliminating unnecessary event bus interactions, enhancing clarity and maintainability.

* flow ownership and not emitting events when a crew is done

* refactor: remove unused agent parameter from ShortTermMemory save method

- Eliminated the agent parameter from the save method in the ShortTermMemory class to streamline the code and improve clarity.
- This change enhances the maintainability of the memory management system by reducing unnecessary complexity.

* runtype check fix

* fixing tests

* fix lints

* fix: update event assertions in test_llm_emits_event_with_lite_agent

- Adjusted the expected counts for completed and started events in the test to reflect the correct behavior of the LiteAgent.
- Updated assertions for agent roles and IDs to match the expected values after recent changes in event handling.

* fix: update task name assertions in event tests

- Modified assertions in `test_stream_llm_emits_event_with_task_and_agent_info` and `test_llm_emits_event_with_task_and_agent_info` to use `task.description` as a fallback for `task.name`. This ensures that the tests correctly validate the task name even when it is not explicitly set.

* fix: update test assertions for output values and improve readability

- Updated assertions in `test_output_json_dict_hierarchical` to reflect the correct expected score value.
- Enhanced readability of assertions in `test_output_pydantic_to_another_task` and `test_key` by formatting the error messages for clarity.
- These changes ensure that the tests accurately validate the expected outputs and improve overall code quality.

* test fixes

* fix crew_test

* added another fixture

* fix: ensure agent and task assignments in contextual memory are conditional

- Updated the ContextualMemory class to check for the existence of short-term, long-term, external, and extended memory before assigning agent and task attributes. This prevents potential attribute errors when memory types are not initialized.

tests/test_task.py

@@ -159,7 +159,7 @@ def test_task_callback_returns_task_output():
             "json_dict": None,
             "agent": researcher.role,
             "summary": "Give me a list of 5 interesting ideas to explore...",
-            "name": None,
+            "name": task.name or task.description,
             "expected_output": "Bullet point list of 5 interesting ideas.",
             "output_format": OutputFormat.RAW,
         }
@@ -340,7 +340,7 @@ def test_output_pydantic_hierarchical():
     )
     result = crew.kickoff()
     assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
+    assert result.to_dict() == {"score": 5}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -401,8 +401,8 @@ def test_output_json_hierarchical():
         manager_llm="gpt-4o",
     )
     result = crew.kickoff()
-    assert result.json == '{"score": 4}'
-    assert result.to_dict() == {"score": 4}
+    assert result.json == '{"score": 5}'
+    assert result.to_dict() == {"score": 5}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -560,8 +560,8 @@ def test_output_json_dict_hierarchical():
         manager_llm="gpt-4o",
     )
     result = crew.kickoff()
-    assert {"score": 4} == result.json_dict
-    assert result.to_dict() == {"score": 4}
+    assert {"score": 5} == result.json_dict
+    assert result.to_dict() == {"score": 5}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -596,9 +596,9 @@ def test_output_pydantic_to_another_task():
     crew = Crew(agents=[scorer], tasks=[task1, task2], verbose=True)
     result = crew.kickoff()
     pydantic_result = result.pydantic
-    assert isinstance(
-        pydantic_result, ScoreOutput
-    ), "Expected pydantic result to be of type ScoreOutput"
+    assert isinstance(pydantic_result, ScoreOutput), (
+        "Expected pydantic result to be of type ScoreOutput"
+    )
     assert pydantic_result.score == 5
 
 
@@ -1081,9 +1081,9 @@ def test_key():
     assert task.key == hash, "The key should be the hash of the description."
 
     task.interpolate_inputs_and_add_conversation_history(inputs={"topic": "AI"})
-    assert (
-        task.key == hash
-    ), "The key should be the hash of the non-interpolated description."
+    assert task.key == hash, (
+        "The key should be the hash of the non-interpolated description."
+    )
 
 
 def test_output_file_validation():