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updating

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This commit is contained in:
João Moura
2025-05-28 20:49:05 -07:00
parent 224ba1fb69
commit e0cd41e9f9
7 changed files with 307 additions and 114 deletions
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99
src/crewai/utilities/reasoning_handler.py
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@@ -57,7 +57,7 @@ class AgentReasoning:
"""
import time
start_time = time.time()
# Emit a reasoning started event (attempt 1)
try:
crewai_event_bus.emit(
@@ -74,7 +74,7 @@ class AgentReasoning:
try:
output = self.__handle_agent_reasoning()
duration_seconds = time.time() - start_time
# Emit reasoning completed event
@@ -391,14 +391,14 @@ class AgentReasoning:
"The _handle_agent_reasoning method is deprecated. Use handle_agent_reasoning instead."
)
return self.handle_agent_reasoning()
def _emit_reasoning_event(self, event_class, **kwargs):
"""Centralized method for emitting reasoning events."""
try:
reasoning_trigger = "interval"
if hasattr(self.agent, 'adaptive_reasoning') and self.agent.adaptive_reasoning:
reasoning_trigger = "adaptive"
crewai_event_bus.emit(
self.agent,
event_class(
@@ -411,9 +411,9 @@ class AgentReasoning:
except Exception:
# Ignore event bus errors to avoid breaking execution
pass
def handle_mid_execution_reasoning(
self,
self,
current_steps: int,
tools_used: list,
current_progress: str,
@@ -421,21 +421,21 @@ class AgentReasoning:
) -> AgentReasoningOutput:
"""
Handle reasoning during task execution with context about current progress.
Args:
current_steps: Number of steps executed so far
tools_used: List of tools that have been used
current_progress: Summary of progress made so far
iteration_messages: Recent conversation messages
Returns:
AgentReasoningOutput: Updated reasoning plan based on current context
"""
import time
start_time = time.time()
from crewai.utilities.events.reasoning_events import AgentMidExecutionReasoningStartedEvent
self._emit_reasoning_event(
AgentMidExecutionReasoningStartedEvent,
current_step=current_steps
@@ -445,12 +445,12 @@ class AgentReasoning:
output = self.__handle_mid_execution_reasoning(
current_steps, tools_used, current_progress, iteration_messages
)
duration_seconds = time.time() - start_time
# Emit completed event
from crewai.utilities.events.reasoning_events import AgentMidExecutionReasoningCompletedEvent
self._emit_reasoning_event(
AgentMidExecutionReasoningCompletedEvent,
current_step=current_steps,
@@ -462,7 +462,7 @@ class AgentReasoning:
except Exception as e:
# Emit failed event
from crewai.utilities.events.reasoning_events import AgentReasoningFailedEvent
self._emit_reasoning_event(
AgentReasoningFailedEvent,
error=str(e),
@@ -470,9 +470,9 @@ class AgentReasoning:
)
raise
def __handle_mid_execution_reasoning(
self,
self,
current_steps: int,
tools_used: list,
current_progress: str,
@@ -480,13 +480,13 @@ class AgentReasoning:
) -> AgentReasoningOutput:
"""
Private method that handles the mid-execution reasoning process.
Args:
current_steps: Number of steps executed so far
tools_used: List of tools that have been used
current_progress: Summary of progress made so far
iteration_messages: Recent conversation messages
Returns:
AgentReasoningOutput: The output of the mid-execution reasoning process.
"""
@@ -515,9 +515,9 @@ class AgentReasoning:
reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
return AgentReasoningOutput(plan=reasoning_plan)
def __create_mid_execution_prompt(
self,
self,
current_steps: int,
tools_used: list,
current_progress: str,
@@ -525,18 +525,18 @@ class AgentReasoning:
) -> str:
"""
Creates a prompt for the agent to reason during task execution.
Args:
current_steps: Number of steps executed so far
tools_used: List of tools that have been used
current_progress: Summary of progress made so far
iteration_messages: Recent conversation messages
Returns:
str: The mid-execution reasoning prompt.
"""
tools_used_str = ", ".join(tools_used) if tools_used else "No tools used yet"
recent_messages = ""
if iteration_messages:
recent_msgs = iteration_messages[-6:] if len(iteration_messages) > 6 else iteration_messages
@@ -545,7 +545,7 @@ class AgentReasoning:
content = msg.get("content", "")
if content:
recent_messages += f"{role.upper()}: {content[:200]}...\n\n"
return self.i18n.retrieve("reasoning", "mid_execution_reasoning").format(
description=self.task.description,
expected_output=self.task.expected_output,
@@ -554,21 +554,21 @@ class AgentReasoning:
current_progress=current_progress,
recent_messages=recent_messages
)
def should_adaptive_reason_llm(
self,
self,
current_steps: int,
tools_used: list,
current_progress: str
) -> bool:
"""
Use LLM function calling to determine if adaptive reasoning should be triggered.
Args:
current_steps: Number of steps executed so far
tools_used: List of tools that have been used
current_progress: Summary of progress made so far
Returns:
bool: True if reasoning should be triggered, False otherwise.
"""
@@ -578,16 +578,29 @@ class AgentReasoning:
)
if self.llm.supports_function_calling():
should_reason = self.__call_adaptive_reasoning_function(decision_prompt)
should_reason, reasoning_expl = self.__call_adaptive_reasoning_function(decision_prompt)
else:
should_reason = self.__call_adaptive_reasoning_text(decision_prompt)
should_reason, reasoning_expl = self.__call_adaptive_reasoning_text(decision_prompt)
# Emit an event so the UI/console can display the decision
try:
from crewai.utilities.events.reasoning_events import AgentAdaptiveReasoningDecisionEvent
self._emit_reasoning_event(
AgentAdaptiveReasoningDecisionEvent,
should_reason=should_reason,
reasoning=reasoning_expl,
)
except Exception:
# Ignore event bus errors to avoid breaking execution
pass
return should_reason
except Exception as e:
self.logger.warning(f"Error during adaptive reasoning decision: {str(e)}. Defaulting to no reasoning.")
return False
def __call_adaptive_reasoning_function(self, prompt: str) -> bool:
def __call_adaptive_reasoning_function(self, prompt: str) -> tuple[bool, str]:
"""Call LLM with function calling for adaptive reasoning decision."""
function_schema = {
"type": "function",
@@ -602,7 +615,7 @@ class AgentReasoning:
"description": "Whether reasoning/re-planning is needed at this point in task execution."
},
"reasoning": {
"type": "string",
"type": "string",
"description": "Brief explanation of why reasoning is or isn't needed."
}
},
@@ -632,11 +645,11 @@ class AgentReasoning:
try:
result = json.loads(response)
return result.get("should_reason", False)
return result.get("should_reason", False), result.get("reasoning", "No explanation provided")
except (json.JSONDecodeError, KeyError):
return False
return False, "No explanation provided"
def __call_adaptive_reasoning_text(self, prompt: str) -> bool:
def __call_adaptive_reasoning_text(self, prompt: str) -> tuple[bool, str]:
"""Fallback text-based adaptive reasoning decision."""
system_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_decision").format(
role=self.agent.role,
@@ -649,24 +662,24 @@ class AgentReasoning:
{"role": "user", "content": prompt + "\n\nRespond with 'YES' if reasoning is needed, 'NO' if not."}
])
return "YES" in str(response).upper()
return "YES" in str(response).upper(), "No explanation provided"
def __create_adaptive_reasoning_decision_prompt(
self,
self,
current_steps: int,
tools_used: list,
current_progress: str
) -> str:
"""Create prompt for adaptive reasoning decision."""
tools_used_str = ", ".join(tools_used) if tools_used else "No tools used yet"
# Use the prompt from i18n and format it with the current context
base_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_decision").format(
role=self.agent.role,
goal=self.agent.goal,
backstory=self.__get_agent_backstory()
)
context_prompt = self.i18n.retrieve("reasoning", "adaptive_reasoning_context").format(
description=self.task.description,
expected_output=self.task.expected_output,
@@ -674,7 +687,7 @@ class AgentReasoning:
tools_used=tools_used_str,
current_progress=current_progress
)
prompt = base_prompt + context_prompt
return prompt