mirror of
https://github.com/crewAIInc/crewAI.git
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99% done. Need to make docs match new example
This commit is contained in:
Brandon Hancock
@@ -54,9 +54,6 @@ from crewai.utilities.events.tool_usage_events import (
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ToolUsageFinishedEvent,
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ToolUsageStartedEvent,
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)
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from crewai.utilities.exceptions.context_window_exceeding_exception import (
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LLMContextLengthExceededException,
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)
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from crewai.utilities.llm_utils import create_llm
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from crewai.utilities.printer import Printer
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from crewai.utilities.token_counter_callback import TokenCalcHandler
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@@ -116,8 +113,8 @@ class LiteAgent(BaseModel):
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role: str = Field(description="Role of the agent")
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goal: str = Field(description="Goal of the agent")
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backstory: str = Field(description="Backstory of the agent")
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llm: Union[str, InstanceOf[LLM], Any] = Field(
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description="Language model that will run the agent"
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llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
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default=None, description="Language model that will run the agent"
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)
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tools: List[BaseTool] = Field(
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default_factory=list, description="Tools at agent's disposal"
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@@ -199,6 +196,117 @@ class LiteAgent(BaseModel):
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"""Return the original role for compatibility with tool interfaces."""
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return self.role
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def kickoff(self, messages: Union[str, List[Dict[str, str]]]) -> LiteAgentOutput:
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"""
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Execute the agent with the given messages.
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Args:
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messages: Either a string query or a list of message dictionaries.
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If a string is provided, it will be converted to a user message.
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If a list is provided, each dict should have 'role' and 'content' keys.
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Returns:
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LiteAgentOutput: The result of the agent execution.
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"""
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# Create agent info for event emission
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agent_info = {
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"role": self.role,
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"goal": self.goal,
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"backstory": self.backstory,
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"tools": self._parsed_tools,
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"verbose": self.verbose,
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}
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try:
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# Reset state for this run
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self._iterations = 0
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self.tools_results = []
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# Format messages for the LLM
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self._messages = self._format_messages(messages)
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# Emit event for agent execution start
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crewai_event_bus.emit(
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self,
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event=LiteAgentExecutionStartedEvent(
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agent_info=agent_info,
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tools=self._parsed_tools,
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messages=messages,
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),
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)
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# Execute the agent using invoke loop
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agent_finish = self._invoke_loop()
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formatted_result: Optional[BaseModel] = None
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if self.response_format:
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try:
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# Cast to BaseModel to ensure type safety
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result = self.response_format.model_validate_json(
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agent_finish.output
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)
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if isinstance(result, BaseModel):
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formatted_result = result
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except Exception as e:
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self._printer.print(
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content=f"Failed to parse output into response format: {str(e)}",
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color="yellow",
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)
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# Calculate token usage metrics
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usage_metrics = self._token_process.get_summary()
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# Create output
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output = LiteAgentOutput(
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raw=agent_finish.output,
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pydantic=formatted_result,
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agent_role=self.role,
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usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
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)
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# Emit completion event
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crewai_event_bus.emit(
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self,
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event=LiteAgentExecutionCompletedEvent(
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agent_info=agent_info,
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output=agent_finish.output,
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),
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)
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return output
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except Exception as e:
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self._printer.print(
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content="Agent failed to reach a final answer. This is likely a bug - please report it.",
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color="red",
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)
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handle_unknown_error(self._printer, e)
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# Emit error event
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crewai_event_bus.emit(
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self,
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event=LiteAgentExecutionErrorEvent(
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agent_info=agent_info,
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error=str(e),
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),
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)
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raise e
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async def kickoff_async(
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self, messages: Union[str, List[Dict[str, str]]]
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) -> LiteAgentOutput:
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"""
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Execute the agent asynchronously with the given messages.
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Args:
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messages: Either a string query or a list of message dictionaries.
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If a string is provided, it will be converted to a user message.
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If a list is provided, each dict should have 'role' and 'content' keys.
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Returns:
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LiteAgentOutput: The result of the agent execution.
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"""
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return await asyncio.to_thread(self.kickoff, messages)
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def _get_default_system_prompt(self) -> str:
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"""Get the default system prompt for the agent."""
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base_prompt = ""
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@@ -247,140 +355,13 @@ class LiteAgent(BaseModel):
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return formatted_messages
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def kickoff(self, messages: Union[str, List[Dict[str, str]]]) -> LiteAgentOutput:
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"""
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Execute the agent with the given messages.
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Args:
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messages: Either a string query or a list of message dictionaries.
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If a string is provided, it will be converted to a user message.
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If a list is provided, each dict should have 'role' and 'content' keys.
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Returns:
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LiteAgentOutput: The result of the agent execution.
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"""
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return asyncio.run(self.kickoff_async(messages))
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async def kickoff_async(
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self, messages: Union[str, List[Dict[str, str]]]
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) -> LiteAgentOutput:
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"""
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Execute the agent asynchronously with the given messages.
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Args:
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messages: Either a string query or a list of message dictionaries.
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If a string is provided, it will be converted to a user message.
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If a list is provided, each dict should have 'role' and 'content' keys.
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Returns:
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LiteAgentOutput: The result of the agent execution.
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Raises:
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Exception: If agent execution fails
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"""
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# Create agent info for event emission
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agent_info = {
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"role": self.role,
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"goal": self.goal,
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"backstory": self.backstory,
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"tools": self._parsed_tools,
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"verbose": self.verbose,
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}
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try:
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# Reset state for this run
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self._iterations = 0
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self.tools_results = []
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# Format messages for the LLM
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self._messages = self._format_messages(messages)
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# Emit event for agent execution start
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crewai_event_bus.emit(
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self,
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event=LiteAgentExecutionStartedEvent(
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agent_info=agent_info,
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tools=self._parsed_tools,
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messages=messages,
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),
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)
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# Execute the agent using invoke loop
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try:
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agent_finish = await self._invoke_loop()
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except Exception as e:
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self._printer.print(
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content="Agent failed to reach a final answer. This is likely a bug - please report it.",
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color="red",
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)
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handle_unknown_error(self._printer, e)
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# Emit error event
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crewai_event_bus.emit(
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self,
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event=LiteAgentExecutionErrorEvent(
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agent_info=agent_info,
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error=str(e),
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),
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)
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raise e
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formatted_result: Optional[BaseModel] = None
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if self.response_format:
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try:
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# Cast to BaseModel to ensure type safety
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result = self.response_format.model_validate_json(
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agent_finish.output
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)
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if isinstance(result, BaseModel):
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formatted_result = result
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except Exception as e:
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self._printer.print(
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content=f"Failed to parse output into response format: {str(e)}",
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color="yellow",
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)
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# Calculate token usage metrics
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usage_metrics = self._token_process.get_summary()
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# Create output
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output = LiteAgentOutput(
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raw=agent_finish.output,
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pydantic=formatted_result,
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agent_role=self.role,
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usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
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)
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# Emit completion event
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crewai_event_bus.emit(
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self,
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event=LiteAgentExecutionCompletedEvent(
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agent_info=agent_info,
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output=agent_finish.output,
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),
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)
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return output
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except Exception as e:
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handle_unknown_error(self._printer, e)
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# Emit error event
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crewai_event_bus.emit(
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self,
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event=LiteAgentExecutionErrorEvent(
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agent_info=agent_info,
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error=str(e),
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),
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)
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raise e
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async def _invoke_loop(self) -> AgentFinish:
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def _invoke_loop(self) -> AgentFinish:
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"""
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Run the agent's thought process until it reaches a conclusion or max iterations.
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Returns:
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str: The final result of the agent execution.
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AgentFinish: The final result of the agent execution.
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"""
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# Execute the agent loop
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formatted_answer = None
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while not isinstance(formatted_answer, AgentFinish):
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@@ -518,10 +499,6 @@ class LiteAgent(BaseModel):
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finally:
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self._iterations += 1
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# During the invoke loop, formatted_answer alternates between AgentAction
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# (when the agent is using tools) and eventually becomes AgentFinish
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# (when the agent reaches a final answer). This assertion confirms we've
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# reached a final answer and helps type checking understand this transition.
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assert isinstance(formatted_answer, AgentFinish)
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self._show_logs(formatted_answer)
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return formatted_answer
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