From abf2814a7c669bf2fec35630f56653218cef7821 Mon Sep 17 00:00:00 2001 From: Greyson LaLonde Date: Sun, 12 Apr 2026 16:32:25 +0800 Subject: [PATCH] chore: remove remaining redundant inline docs in agent module --- lib/crewai/src/crewai/agent/core.py | 5 +---- lib/crewai/src/crewai/agent/planning_config.py | 5 +---- 2 files changed, 2 insertions(+), 8 deletions(-) diff --git a/lib/crewai/src/crewai/agent/core.py b/lib/crewai/src/crewai/agent/core.py index 176178554..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( @@ -1635,7 +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 @@ -1717,7 +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", ), ) ```