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https://github.com/crewAIInc/crewAI.git
synced 2026-01-11 00:58:30 +00:00
Fixed core invoke loop logic and relevant tests (#1865)
* Fixed core invoke loop logic and relevant tests * Fix failing tests * Clean up final print statements * Additional clean up for PR review
This commit is contained in:
Brandon Hancock (bhancock_ai)
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GitHub
GitHub
parent
b3504e768c
commit
2131b94ddb
@@ -19,15 +19,10 @@ class CrewAgentExecutorMixin:
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agent: Optional["BaseAgent"]
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task: Optional["Task"]
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iterations: int
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have_forced_answer: bool
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max_iter: int
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_i18n: I18N
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_printer: Printer = Printer()
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def _should_force_answer(self) -> bool:
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"""Determine if a forced answer is required based on iteration count."""
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return (self.iterations >= self.max_iter) and not self.have_forced_answer
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def _create_short_term_memory(self, output) -> None:
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"""Create and save a short-term memory item if conditions are met."""
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if (
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@@ -1,7 +1,7 @@
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import json
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import re
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from dataclasses import dataclass
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from typing import Any, Dict, List, Union
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from typing import Any, Callable, Dict, List, Optional, Union
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from crewai.agents.agent_builder.base_agent import BaseAgent
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from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
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@@ -50,7 +50,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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original_tools: List[Any] = [],
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function_calling_llm: Any = None,
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respect_context_window: bool = False,
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request_within_rpm_limit: Any = None,
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request_within_rpm_limit: Optional[Callable[[], bool]] = None,
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callbacks: List[Any] = [],
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):
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self._i18n: I18N = I18N()
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@@ -77,7 +77,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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self.messages: List[Dict[str, str]] = []
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self.iterations = 0
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self.log_error_after = 3
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self.have_forced_answer = False
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self.tool_name_to_tool_map: Dict[str, BaseTool] = {
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tool.name: tool for tool in self.tools
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}
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@@ -108,106 +107,151 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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self._create_long_term_memory(formatted_answer)
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return {"output": formatted_answer.output}
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def _invoke_loop(self, formatted_answer=None):
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try:
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while not isinstance(formatted_answer, AgentFinish):
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if not self.request_within_rpm_limit or self.request_within_rpm_limit():
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answer = self.llm.call(
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self.messages,
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callbacks=self.callbacks,
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def _invoke_loop(self):
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"""
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Main loop to invoke the agent's thought process until it reaches a conclusion
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or the maximum number of iterations is reached.
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"""
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formatted_answer = None
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while not isinstance(formatted_answer, AgentFinish):
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try:
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if self._has_reached_max_iterations():
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formatted_answer = self._handle_max_iterations_exceeded(
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formatted_answer
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)
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break
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self._enforce_rpm_limit()
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answer = self._get_llm_response()
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formatted_answer = self._process_llm_response(answer)
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if isinstance(formatted_answer, AgentAction):
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tool_result = self._execute_tool_and_check_finality(
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formatted_answer
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)
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formatted_answer = self._handle_agent_action(
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formatted_answer, tool_result
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)
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if answer is None or answer == "":
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self._printer.print(
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content="Received None or empty response from LLM call.",
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color="red",
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)
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raise ValueError(
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"Invalid response from LLM call - None or empty."
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)
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self._invoke_step_callback(formatted_answer)
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self._append_message(formatted_answer.text, role="assistant")
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if not self.use_stop_words:
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try:
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self._format_answer(answer)
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except OutputParserException as e:
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if (
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FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE
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in e.error
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):
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answer = answer.split("Observation:")[0].strip()
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except OutputParserException as e:
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formatted_answer = self._handle_output_parser_exception(e)
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self.iterations += 1
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formatted_answer = self._format_answer(answer)
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if isinstance(formatted_answer, AgentAction):
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tool_result = self._execute_tool_and_check_finality(
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formatted_answer
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)
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# Directly append the result to the messages if the
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# tool is "Add image to content" in case of multimodal
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# agents
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if formatted_answer.tool == self._i18n.tools("add_image")["name"]:
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self.messages.append(tool_result.result)
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continue
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else:
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if self.step_callback:
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self.step_callback(tool_result)
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formatted_answer.text += f"\nObservation: {tool_result.result}"
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formatted_answer.result = tool_result.result
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if tool_result.result_as_answer:
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return AgentFinish(
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thought="",
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output=tool_result.result,
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text=formatted_answer.text,
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)
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self._show_logs(formatted_answer)
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if self.step_callback:
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self.step_callback(formatted_answer)
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if self._should_force_answer():
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if self.have_forced_answer:
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return AgentFinish(
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thought="",
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output=self._i18n.errors(
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"force_final_answer_error"
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).format(formatted_answer.text),
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text=formatted_answer.text,
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)
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else:
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formatted_answer.text += (
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f'\n{self._i18n.errors("force_final_answer")}'
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)
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self.have_forced_answer = True
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self.messages.append(
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self._format_msg(formatted_answer.text, role="assistant")
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)
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except OutputParserException as e:
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self.messages.append({"role": "user", "content": e.error})
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if self.iterations > self.log_error_after:
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self._printer.print(
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content=f"Error parsing LLM output, agent will retry: {e.error}",
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color="red",
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)
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return self._invoke_loop(formatted_answer)
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except Exception as e:
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if LLMContextLengthExceededException(str(e))._is_context_limit_error(
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str(e)
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):
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self._handle_context_length()
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return self._invoke_loop(formatted_answer)
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else:
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raise e
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except Exception as e:
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if self._is_context_length_exceeded(e):
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self._handle_context_length()
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continue
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else:
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raise e
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self._show_logs(formatted_answer)
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return formatted_answer
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def _has_reached_max_iterations(self) -> bool:
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"""Check if the maximum number of iterations has been reached."""
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return self.iterations >= self.max_iter
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def _enforce_rpm_limit(self) -> None:
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"""Enforce the requests per minute (RPM) limit if applicable."""
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if self.request_within_rpm_limit:
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self.request_within_rpm_limit()
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def _get_llm_response(self) -> str:
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"""Call the LLM and return the response, handling any invalid responses."""
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answer = self.llm.call(
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self.messages,
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callbacks=self.callbacks,
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)
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if not answer:
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self._printer.print(
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content="Received None or empty response from LLM call.",
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color="red",
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)
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raise ValueError("Invalid response from LLM call - None or empty.")
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return answer
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def _process_llm_response(self, answer: str) -> Union[AgentAction, AgentFinish]:
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"""Process the LLM response and format it into an AgentAction or AgentFinish."""
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if not self.use_stop_words:
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try:
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# Preliminary parsing to check for errors.
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self._format_answer(answer)
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except OutputParserException as e:
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if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
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answer = answer.split("Observation:")[0].strip()
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self.iterations += 1
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return self._format_answer(answer)
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def _handle_agent_action(
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self, formatted_answer: AgentAction, tool_result: ToolResult
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) -> Union[AgentAction, AgentFinish]:
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"""Handle the AgentAction, execute tools, and process the results."""
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add_image_tool = self._i18n.tools("add_image")
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if (
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isinstance(add_image_tool, dict)
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and formatted_answer.tool.casefold().strip()
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== add_image_tool.get("name", "").casefold().strip()
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):
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self.messages.append(tool_result.result)
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return formatted_answer # Continue the loop
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if self.step_callback:
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self.step_callback(tool_result)
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formatted_answer.text += f"\nObservation: {tool_result.result}"
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formatted_answer.result = tool_result.result
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if tool_result.result_as_answer:
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return AgentFinish(
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thought="",
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output=tool_result.result,
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text=formatted_answer.text,
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)
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self._show_logs(formatted_answer)
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return formatted_answer
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def _invoke_step_callback(self, formatted_answer) -> None:
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"""Invoke the step callback if it exists."""
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if self.step_callback:
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self.step_callback(formatted_answer)
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def _append_message(self, text: str, role: str = "assistant") -> None:
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"""Append a message to the message list with the given role."""
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self.messages.append(self._format_msg(text, role=role))
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def _handle_output_parser_exception(self, e: OutputParserException) -> AgentAction:
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"""Handle OutputParserException by updating messages and formatted_answer."""
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self.messages.append({"role": "user", "content": e.error})
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formatted_answer = AgentAction(
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text=e.error,
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tool="",
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tool_input="",
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thought="",
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)
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if self.iterations > self.log_error_after:
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self._printer.print(
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content=f"Error parsing LLM output, agent will retry: {e.error}",
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color="red",
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)
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return formatted_answer
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def _is_context_length_exceeded(self, exception: Exception) -> bool:
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"""Check if the exception is due to context length exceeding."""
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return LLMContextLengthExceededException(
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str(exception)
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)._is_context_limit_error(str(exception))
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def _show_start_logs(self):
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if self.agent is None:
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raise ValueError("Agent cannot be None")
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@@ -487,3 +531,45 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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self.ask_for_human_input = False
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return formatted_answer
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def _handle_max_iterations_exceeded(self, formatted_answer):
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"""
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Handles the case when the maximum number of iterations is exceeded.
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Performs one more LLM call to get the final answer.
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Parameters:
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formatted_answer: The last formatted answer from the agent.
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Returns:
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The final formatted answer after exceeding max iterations.
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"""
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self._printer.print(
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content="Maximum iterations reached. Requesting final answer.",
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color="yellow",
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)
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if formatted_answer and hasattr(formatted_answer, "text"):
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assistant_message = (
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formatted_answer.text + f'\n{self._i18n.errors("force_final_answer")}'
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)
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else:
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assistant_message = self._i18n.errors("force_final_answer")
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self.messages.append(self._format_msg(assistant_message, role="assistant"))
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# Perform one more LLM call to get the final answer
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answer = self.llm.call(
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self.messages,
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callbacks=self.callbacks,
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)
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if answer is None or answer == "":
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self._printer.print(
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content="Received None or empty response from LLM call.",
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color="red",
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)
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raise ValueError("Invalid response from LLM call - None or empty.")
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formatted_answer = self._format_answer(answer)
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# Return the formatted answer, regardless of its type
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return formatted_answer
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