diff --git a/lib/crewai/src/crewai/agent/core.py b/lib/crewai/src/crewai/agent/core.py index eb73ba719..597b69dc9 100644 --- a/lib/crewai/src/crewai/agent/core.py +++ b/lib/crewai/src/crewai/agent/core.py @@ -1341,7 +1341,6 @@ class Agent(BaseAgent): raw_tools: list[BaseTool] = self.tools or [] - # Inject memory tools for standalone kickoff (crew path handles its own) agent_memory = getattr(self, "memory", None) if agent_memory is not None: from crewai.tools.memory_tools import create_memory_tools @@ -1399,7 +1398,6 @@ class Agent(BaseAgent): if input_files: all_files.update(input_files) - # Inject memory context for standalone kickoff (recall before execution) if agent_memory is not None: try: crewai_event_bus.emit( @@ -1485,8 +1483,6 @@ class Agent(BaseAgent): Note: For explicit async usage outside of Flow, use kickoff_async() directly. """ - # Magic auto-async: if inside event loop (e.g., inside a Flow), - # return coroutine for Flow to await if is_inside_event_loop(): return self.kickoff_async(messages, response_format, input_files) @@ -1637,7 +1633,7 @@ class Agent(BaseAgent): if isinstance(conversion_result, BaseModel): formatted_result = conversion_result except ConverterError: - pass # Keep raw output if conversion fails + pass else: raw_output = str(output) if not isinstance(output, str) else output @@ -1719,7 +1715,6 @@ class Agent(BaseAgent): elif callable(self.guardrail): guardrail_callable = self.guardrail else: - # Should not happen if called from kickoff with guardrail check return output guardrail_result = process_guardrail( diff --git a/lib/crewai/src/crewai/agent/planning_config.py b/lib/crewai/src/crewai/agent/planning_config.py index d30b0eb46..cd8124b9c 100644 --- a/lib/crewai/src/crewai/agent/planning_config.py +++ b/lib/crewai/src/crewai/agent/planning_config.py @@ -41,7 +41,6 @@ class PlanningConfig(BaseModel): from crewai import Agent from crewai.agent.planning_config import PlanningConfig - # Simple usage — fast, linear execution (default) agent = Agent( role="Researcher", goal="Research topics", @@ -49,7 +48,6 @@ class PlanningConfig(BaseModel): planning_config=PlanningConfig(), ) - # Balanced — replan only when steps fail agent = Agent( role="Researcher", goal="Research topics", @@ -59,7 +57,6 @@ class PlanningConfig(BaseModel): ), ) - # Full adaptive planning with refinement and replanning agent = Agent( role="Researcher", goal="Research topics", @@ -69,7 +66,7 @@ class PlanningConfig(BaseModel): max_attempts=3, max_steps=10, plan_prompt="Create a focused plan for: {description}", - llm="gpt-4o-mini", # Use cheaper model for planning + llm="gpt-4o-mini", ), ) ``` diff --git a/lib/crewai/src/crewai/agent/utils.py b/lib/crewai/src/crewai/agent/utils.py index 31f08903f..93c861835 100644 --- a/lib/crewai/src/crewai/agent/utils.py +++ b/lib/crewai/src/crewai/agent/utils.py @@ -39,7 +39,6 @@ def handle_reasoning(agent: Agent, task: Task) -> None: agent: The agent performing the task. task: The task to execute. """ - # Check if planning is enabled using the planning_enabled property if not getattr(agent, "planning_enabled", False): return