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* 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.
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
from datetime import datetime
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from typing import Any, Callable, Dict, Optional
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from .base_events import BaseEvent
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class ToolUsageEvent(BaseEvent):
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"""Base event for tool usage tracking"""
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agent_key: Optional[str] = None
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agent_role: Optional[str] = None
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agent_id: Optional[str] = None
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tool_name: str
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tool_args: Dict[str, Any] | str
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tool_class: Optional[str] = None
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run_attempts: int | None = None
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delegations: int | None = None
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agent: Optional[Any] = None
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task_name: Optional[str] = None
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task_id: Optional[str] = None
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from_task: Optional[Any] = None
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from_agent: Optional[Any] = None
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model_config = {"arbitrary_types_allowed": True}
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def __init__(self, **data):
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super().__init__(**data)
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self._set_agent_params(data)
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self._set_task_params(data)
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# Set fingerprint data from the agent
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if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
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self.source_fingerprint = self.agent.fingerprint.uuid_str
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self.source_type = "agent"
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if (
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hasattr(self.agent.fingerprint, "metadata")
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and self.agent.fingerprint.metadata
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):
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self.fingerprint_metadata = self.agent.fingerprint.metadata
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class ToolUsageStartedEvent(ToolUsageEvent):
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"""Event emitted when a tool execution is started"""
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type: str = "tool_usage_started"
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class ToolUsageFinishedEvent(ToolUsageEvent):
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"""Event emitted when a tool execution is completed"""
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started_at: datetime
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finished_at: datetime
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from_cache: bool = False
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output: Any
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type: str = "tool_usage_finished"
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class ToolUsageErrorEvent(ToolUsageEvent):
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"""Event emitted when a tool execution encounters an error"""
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error: Any
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type: str = "tool_usage_error"
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class ToolValidateInputErrorEvent(ToolUsageEvent):
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"""Event emitted when a tool input validation encounters an error"""
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error: Any
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type: str = "tool_validate_input_error"
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class ToolSelectionErrorEvent(ToolUsageEvent):
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"""Event emitted when a tool selection encounters an error"""
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error: Any
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type: str = "tool_selection_error"
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class ToolExecutionErrorEvent(BaseEvent):
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"""Event emitted when a tool execution encounters an error"""
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error: Any
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type: str = "tool_execution_error"
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tool_name: str
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tool_args: Dict[str, Any]
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tool_class: Callable
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agent: Optional[Any] = None
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def __init__(self, **data):
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super().__init__(**data)
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# Set fingerprint data from the agent
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if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
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self.source_fingerprint = self.agent.fingerprint.uuid_str
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self.source_type = "agent"
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if (
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hasattr(self.agent.fingerprint, "metadata")
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and self.agent.fingerprint.metadata
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):
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self.fingerprint_metadata = self.agent.fingerprint.metadata
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