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https://github.com/crewAIInc/crewAI.git
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This commit is contained in:
João Moura
@@ -45,35 +45,35 @@ class AgentReasoning:
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"""
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Public method for the reasoning process that creates and refines a plan
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for the task until the agent is ready to execute it.
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Returns:
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AgentReasoningOutput: The output of the agent reasoning process.
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"""
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return self.__handle_agent_reasoning()
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def __handle_agent_reasoning(self) -> AgentReasoningOutput:
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"""
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Private method that handles the agent reasoning process.
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Returns:
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AgentReasoningOutput: The output of the agent reasoning process.
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"""
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plan, ready = self.__create_initial_plan()
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plan, ready = self.__refine_plan_if_needed(plan, ready)
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reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
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return AgentReasoningOutput(plan=reasoning_plan)
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def __create_initial_plan(self) -> Tuple[str, bool]:
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"""
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Creates the initial reasoning plan for the task.
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Returns:
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Tuple[str, bool]: The initial plan and whether the agent is ready to execute the task.
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"""
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reasoning_prompt = self.__create_reasoning_prompt()
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if self.llm.supports_function_calling():
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plan, ready = self.__call_with_function(reasoning_prompt, "initial_plan")
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return plan, ready
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@@ -83,33 +83,33 @@ class AgentReasoning:
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goal=self.agent.goal,
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backstory=self.__get_agent_backstory()
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)
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response = self.llm.call(
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[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": reasoning_prompt}
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]
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)
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return self.__parse_reasoning_response(str(response))
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def __refine_plan_if_needed(self, plan: str, ready: bool) -> Tuple[str, bool]:
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"""
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Refines the reasoning plan if the agent is not ready to execute the task.
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Args:
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plan: The current reasoning plan.
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ready: Whether the agent is ready to execute the task.
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Returns:
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Tuple[str, bool]: The refined plan and whether the agent is ready to execute the task.
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"""
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attempt = 1
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max_attempts = self.agent.max_reasoning_attempts
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while not ready and (max_attempts is None or attempt < max_attempts):
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refine_prompt = self.__create_refine_prompt(plan)
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if self.llm.supports_function_calling():
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plan, ready = self.__call_with_function(refine_prompt, "refine_plan")
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else:
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@@ -118,7 +118,7 @@ class AgentReasoning:
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goal=self.agent.goal,
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backstory=self.__get_agent_backstory()
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)
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response = self.llm.call(
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[
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{"role": "system", "content": system_prompt},
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@@ -126,56 +126,59 @@ class AgentReasoning:
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]
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)
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plan, ready = self.__parse_reasoning_response(str(response))
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attempt += 1
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if max_attempts is not None and attempt >= max_attempts:
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self.logger.warning(
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f"Agent reasoning reached maximum attempts ({max_attempts}) without being ready. Proceeding with current plan."
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)
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break
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return plan, ready
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def __call_with_function(self, prompt: str, prompt_type: str) -> Tuple[str, bool]:
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"""
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Calls the LLM with function calling to get a reasoning plan.
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Args:
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prompt: The prompt to send to the LLM.
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prompt_type: The type of prompt (initial_plan or refine_plan).
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Returns:
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Tuple[str, bool]: A tuple containing the plan and whether the agent is ready.
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"""
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self.logger.debug(f"Using function calling for {prompt_type} reasoning")
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function_schema = {
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"name": "create_reasoning_plan",
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"description": "Create or refine a reasoning plan for a task",
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"parameters": {
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"type": "object",
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"properties": {
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"plan": {
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"type": "string",
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"description": "The detailed reasoning plan for the task."
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"type": "function",
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"function": {
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"name": "create_reasoning_plan",
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"description": "Create or refine a reasoning plan for a task",
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"parameters": {
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"type": "object",
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"properties": {
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"plan": {
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"type": "string",
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"description": "The detailed reasoning plan for the task."
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},
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"ready": {
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"type": "boolean",
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"description": "Whether the agent is ready to execute the task."
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}
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},
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"ready": {
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"type": "boolean",
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"description": "Whether the agent is ready to execute the task."
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}
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},
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"required": ["plan", "ready"]
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"required": ["plan", "ready"]
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}
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}
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}
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try:
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system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
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role=self.agent.role,
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goal=self.agent.goal,
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backstory=self.__get_agent_backstory()
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)
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response = self.llm.call(
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[
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{"role": "system", "content": system_prompt},
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@@ -183,36 +186,36 @@ class AgentReasoning:
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],
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tools=[function_schema]
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)
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self.logger.debug(f"Function calling response: {response[:100]}...")
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try:
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result = json.loads(response)
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if "plan" in result and "ready" in result:
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return result["plan"], result["ready"]
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except (json.JSONDecodeError, KeyError):
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pass
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response_str = str(response)
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return response_str, "READY: I am ready to execute the task." in response_str
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except Exception as e:
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self.logger.warning(f"Error during function calling: {str(e)}. Falling back to text parsing.")
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try:
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system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
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role=self.agent.role,
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goal=self.agent.goal,
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backstory=self.__get_agent_backstory()
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)
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fallback_response = self.llm.call(
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[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt}
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]
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)
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fallback_str = str(fallback_response)
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return fallback_str, "READY: I am ready to execute the task." in fallback_str
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except Exception as inner_e:
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@@ -222,7 +225,7 @@ class AgentReasoning:
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def __get_agent_backstory(self) -> str:
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"""
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Safely gets the agent's backstory, providing a default if not available.
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Returns:
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str: The agent's backstory or a default value.
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"""
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@@ -231,12 +234,12 @@ class AgentReasoning:
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def __create_reasoning_prompt(self) -> str:
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"""
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Creates a prompt for the agent to reason about the task.
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Returns:
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str: The reasoning prompt.
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"""
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available_tools = self.__format_available_tools()
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return self.i18n.retrieve("reasoning", "create_plan_prompt").format(
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role=self.agent.role,
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goal=self.agent.goal,
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@@ -249,7 +252,7 @@ class AgentReasoning:
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def __format_available_tools(self) -> str:
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"""
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Formats the available tools for inclusion in the prompt.
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Returns:
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str: Comma-separated list of tool names.
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"""
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@@ -261,10 +264,10 @@ class AgentReasoning:
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def __create_refine_prompt(self, current_plan: str) -> str:
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"""
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Creates a prompt for the agent to refine its reasoning plan.
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Args:
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current_plan: The current reasoning plan.
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Returns:
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str: The refine prompt.
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"""
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@@ -279,29 +282,29 @@ class AgentReasoning:
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"""
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Parses the reasoning response to extract the plan and whether
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the agent is ready to execute the task.
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Args:
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response: The LLM response.
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Returns:
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Tuple[str, bool]: The plan and whether the agent is ready to execute the task.
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"""
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if not response:
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return "No plan was generated.", False
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plan = response
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ready = False
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if "READY: I am ready to execute the task." in response:
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ready = True
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return plan, ready
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def _handle_agent_reasoning(self) -> AgentReasoningOutput:
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"""
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Deprecated method for backward compatibility.
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Use handle_agent_reasoning() instead.
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Returns:
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AgentReasoningOutput: The output of the agent reasoning process.
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"""
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