chore: update crewAI and crewAI-tools dependencies to version 0.203.0 and 0.76.0 respectively (#3674)

- Updated the `crewai-tools` dependency in `pyproject.toml` and `uv.lock` to version 0.76.0.
- Updated the `crewai` version in `__init__.py` to 0.203.0.
- Updated the dependency versions in the crew, flow, and tool templates to reflect the new `crewai` version.

.cursorrules

@@ -260,7 +260,7 @@ def handle_success(self):
     # Handle success case
     pass
 
-@listen("failure_path")  
+@listen("failure_path")
 def handle_failure(self):
     # Handle failure case
     pass
@@ -288,7 +288,7 @@ class SelectiveFlow(Flow):
     def critical_step(self):
         # Only this method's state is persisted
         self.state["important_data"] = "value"
-    
+
     @start()
     def temporary_step(self):
         # This method's state is not persisted
@@ -322,20 +322,20 @@ flow.plot("workflow_diagram")  # Generates HTML visualization
 class CyclicFlow(Flow):
     max_iterations = 5
     current_iteration = 0
-    
+
     @start("loop")
     def process_iteration(self):
         if self.current_iteration >= self.max_iterations:
             return
         # Process current iteration
         self.current_iteration += 1
-    
+
     @router(process_iteration)
     def check_continue(self):
         if self.current_iteration < self.max_iterations:
             return "loop"  # Continue cycling
         return "complete"
-    
+
     @listen("complete")
     def finalize(self):
         # Final processing
@@ -369,7 +369,7 @@ def risky_operation(self):
         self.state["success"] = False
         return None
 
-@listen(risky_operation)  
+@listen(risky_operation)
 def handle_result(self, result):
     if self.state.get("success", False):
         # Handle success case
@@ -390,7 +390,7 @@ class CrewOrchestrationFlow(Flow[WorkflowState]):
         result = research_crew.crew().kickoff(inputs={"topic": self.state.research_topic})
         self.state.research_results = result.raw
         return result
-    
+
     @listen(research_phase)
     def analysis_phase(self, research_results):
         analysis_crew = AnalysisCrew()
@@ -400,13 +400,13 @@ class CrewOrchestrationFlow(Flow[WorkflowState]):
         })
         self.state.analysis_results = result.raw
         return result
-    
+
     @router(analysis_phase)
     def decide_next_action(self):
         if self.state.analysis_results.confidence > 0.7:
             return "generate_report"
         return "additional_research"
-    
+
     @listen("generate_report")
     def final_report(self):
         reporting_crew = ReportingCrew()
@@ -439,7 +439,7 @@ class CrewOrchestrationFlow(Flow[WorkflowState]):
 ## CrewAI Version Compatibility:
 - Stay updated with CrewAI releases for new features and bug fixes
 - Test crew functionality when upgrading CrewAI versions
-- Use version constraints in pyproject.toml (e.g., "crewai[tools]>=0.134.0,<1.0.0")
+- Use version constraints in pyproject.toml (e.g., "crewai[tools]>=0.140.0,<1.0.0")
 - Monitor deprecation warnings for future compatibility
 
 ## Code Examples and Implementation Patterns
@@ -464,22 +464,22 @@ class ResearchOutput(BaseModel):
 @CrewBase
 class ResearchCrew():
     """Advanced research crew with structured outputs and validation"""
-    
+
     agents: List[BaseAgent]
     tasks: List[Task]
-    
+
     @before_kickoff
     def setup_environment(self):
         """Initialize environment before crew execution"""
         print("🚀 Setting up research environment...")
         # Validate API keys, create directories, etc.
-        
+
     @after_kickoff
     def cleanup_and_report(self, output):
         """Handle post-execution tasks"""
         print(f"✅ Research completed. Generated {len(output.tasks_output)} task outputs")
         print(f"📊 Token usage: {output.token_usage}")
-        
+
     @agent
     def researcher(self) -> Agent:
         return Agent(
@@ -490,7 +490,7 @@ class ResearchCrew():
             max_iter=15,
             max_execution_time=1800
         )
-    
+
     @agent
     def analyst(self) -> Agent:
         return Agent(
@@ -499,7 +499,7 @@ class ResearchCrew():
             verbose=True,
             memory=True
         )
-    
+
     @task
     def research_task(self) -> Task:
         return Task(
@@ -507,7 +507,7 @@ class ResearchCrew():
             agent=self.researcher(),
             output_pydantic=ResearchOutput
         )
-    
+
     @task
     def validation_task(self) -> Task:
         return Task(
@@ -517,7 +517,7 @@ class ResearchCrew():
             guardrail=self.validate_research_quality,
             max_retries=3
         )
-    
+
     def validate_research_quality(self, output) -> tuple[bool, str]:
         """Custom guardrail to ensure research quality"""
         content = output.raw
@@ -526,7 +526,7 @@ class ResearchCrew():
         if not any(keyword in content.lower() for keyword in ['conclusion', 'finding', 'result']):
             return False, "Missing key analytical elements."
         return True, content
-    
+
     @crew
     def crew(self) -> Crew:
         return Crew(
@@ -557,13 +557,13 @@ class RobustSearchTool(BaseTool):
     name: str = "robust_search"
     description: str = "Perform web search with retry logic and error handling"
     args_schema: Type[BaseModel] = SearchInput
-    
+
     def __init__(self, api_key: Optional[str] = None, **kwargs):
         super().__init__(**kwargs)
         self.api_key = api_key or os.getenv("SEARCH_API_KEY")
         self.rate_limit_delay = 1.0
         self.last_request_time = 0
-        
+
     @retry(
         stop=stop_after_attempt(3),
         wait=wait_exponential(multiplier=1, min=4, max=10)
@@ -575,43 +575,43 @@ class RobustSearchTool(BaseTool):
             time_since_last = time.time() - self.last_request_time
             if time_since_last < self.rate_limit_delay:
                 time.sleep(self.rate_limit_delay - time_since_last)
-            
+
             # Input validation
             if not query or len(query.strip()) == 0:
                 return "Error: Empty search query provided"
-            
+
             if len(query) > 500:
                 return "Error: Search query too long (max 500 characters)"
-            
+
             # Perform search
             results = self._perform_search(query, max_results, timeout)
             self.last_request_time = time.time()
-            
+
             return self._format_results(results)
-            
+
         except requests.exceptions.Timeout:
             return f"Search timed out after {timeout} seconds"
         except requests.exceptions.RequestException as e:
             return f"Search failed due to network error: {str(e)}"
         except Exception as e:
             return f"Unexpected error during search: {str(e)}"
-    
+
     def _perform_search(self, query: str, max_results: int, timeout: int) -> List[dict]:
         """Implement actual search logic here"""
         # Your search API implementation
         pass
-    
+
     def _format_results(self, results: List[dict]) -> str:
         """Format search results for LLM consumption"""
         if not results:
             return "No results found for the given query."
-        
+
         formatted = "Search Results:\n\n"
         for i, result in enumerate(results[:10], 1):
             formatted += f"{i}. {result.get('title', 'No title')}\n"
             formatted += f"   URL: {result.get('url', 'No URL')}\n"
             formatted += f"   Summary: {result.get('snippet', 'No summary')}\n\n"
-        
+
         return formatted
 ```
 
@@ -623,20 +623,20 @@ from crewai.memory.storage.mem0_storage import Mem0Storage
 
 class AdvancedMemoryManager:
     """Enhanced memory management for CrewAI applications"""
-    
+
     def __init__(self, crew, config: dict = None):
         self.crew = crew
         self.config = config or {}
         self.setup_memory_systems()
-    
+
     def setup_memory_systems(self):
         """Configure multiple memory systems"""
         # Short-term memory for current session
         self.short_term = ShortTermMemory()
-        
+
         # Long-term memory for cross-session persistence
         self.long_term = LongTermMemory()
-        
+
         # External memory with Mem0 (if configured)
         if self.config.get('use_external_memory'):
             self.external = ExternalMemory.create_storage(
@@ -649,8 +649,8 @@ class AdvancedMemoryManager:
                     }
                 }
             )
-    
-    def save_with_context(self, content: str, memory_type: str = "short_term", 
+
+    def save_with_context(self, content: str, memory_type: str = "short_term",
                          metadata: dict = None, agent: str = None):
         """Save content with enhanced metadata"""
         enhanced_metadata = {
@@ -659,14 +659,14 @@ class AdvancedMemoryManager:
             "crew_type": self.crew.__class__.__name__,
             **(metadata or {})
         }
-        
+
         if memory_type == "short_term":
             self.short_term.save(content, enhanced_metadata, agent)
         elif memory_type == "long_term":
             self.long_term.save(content, enhanced_metadata, agent)
         elif memory_type == "external" and hasattr(self, 'external'):
             self.external.save(content, enhanced_metadata, agent)
-    
+
     def search_across_memories(self, query: str, limit: int = 5) -> dict:
         """Search across all memory systems"""
         results = {
@@ -674,23 +674,23 @@ class AdvancedMemoryManager:
             "long_term": [],
             "external": []
         }
-        
+
         # Search short-term memory
         results["short_term"] = self.short_term.search(query, limit=limit)
-        
+
         # Search long-term memory
         results["long_term"] = self.long_term.search(query, limit=limit)
-        
+
         # Search external memory (if available)
         if hasattr(self, 'external'):
             results["external"] = self.external.search(query, limit=limit)
-        
+
         return results
-    
+
     def cleanup_old_memories(self, days_threshold: int = 30):
         """Clean up old memories based on age"""
         cutoff_time = time.time() - (days_threshold * 24 * 60 * 60)
-        
+
         # Implement cleanup logic based on timestamps in metadata
         # This would vary based on your specific storage implementation
         pass
@@ -719,12 +719,12 @@ class TaskMetrics:
 
 class CrewMonitor:
     """Comprehensive monitoring for CrewAI applications"""
-    
+
     def __init__(self, crew_name: str, log_level: str = "INFO"):
         self.crew_name = crew_name
         self.metrics: List[TaskMetrics] = []
         self.session_start = time.time()
-        
+
         # Setup logging
         logging.basicConfig(
             level=getattr(logging, log_level),
@@ -735,7 +735,7 @@ class CrewMonitor:
             ]
         )
         self.logger = logging.getLogger(f"CrewAI.{crew_name}")
-        
+
     def start_task_monitoring(self, task_name: str, agent_name: str) -> dict:
         """Start monitoring a task execution"""
         context = {
@@ -743,16 +743,16 @@ class CrewMonitor:
             "agent_name": agent_name,
             "start_time": time.time()
         }
-        
+
         self.logger.info(f"Task started: {task_name} by {agent_name}")
         return context
-        
-    def end_task_monitoring(self, context: dict, success: bool = True, 
+
+    def end_task_monitoring(self, context: dict, success: bool = True,
                            tokens_used: int = 0, error: str = None):
         """End monitoring and record metrics"""
         end_time = time.time()
         duration = end_time - context["start_time"]
-        
+
         # Get memory usage (if psutil is available)
         memory_usage = None
         try:
@@ -761,7 +761,7 @@ class CrewMonitor:
             memory_usage = process.memory_info().rss / 1024 / 1024  # MB
         except ImportError:
             pass
-        
+
         metrics = TaskMetrics(
             task_name=context["task_name"],
             agent_name=context["agent_name"],
@@ -773,29 +773,29 @@ class CrewMonitor:
             error_message=error,
             memory_usage_mb=memory_usage
         )
-        
+
         self.metrics.append(metrics)
-        
+
         # Log the completion
         status = "SUCCESS" if success else "FAILED"
         self.logger.info(f"Task {status}: {context['task_name']} "
                         f"(Duration: {duration:.2f}s, Tokens: {tokens_used})")
-        
+
         if error:
             self.logger.error(f"Task error: {error}")
-    
+
     def get_performance_summary(self) -> Dict[str, Any]:
         """Generate comprehensive performance summary"""
         if not self.metrics:
             return {"message": "No metrics recorded yet"}
-        
+
         successful_tasks = [m for m in self.metrics if m.success]
         failed_tasks = [m for m in self.metrics if not m.success]
-        
+
         total_duration = sum(m.duration for m in self.metrics)
         total_tokens = sum(m.tokens_used for m in self.metrics)
         avg_duration = total_duration / len(self.metrics)
-        
+
         return {
             "crew_name": self.crew_name,
             "session_duration": time.time() - self.session_start,
@@ -811,7 +811,7 @@ class CrewMonitor:
             "most_token_intensive": max(self.metrics, key=lambda x: x.tokens_used).task_name if self.metrics else None,
             "common_errors": self._get_common_errors()
         }
-    
+
     def _get_common_errors(self) -> Dict[str, int]:
         """Get frequency of common errors"""
         error_counts = {}
@@ -819,20 +819,20 @@ class CrewMonitor:
             if metric.error_message:
                 error_counts[metric.error_message] = error_counts.get(metric.error_message, 0) + 1
         return dict(sorted(error_counts.items(), key=lambda x: x[1], reverse=True))
-    
+
     def export_metrics(self, filename: str = None) -> str:
         """Export metrics to JSON file"""
         if not filename:
             filename = f"crew_metrics_{self.crew_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
-        
+
         export_data = {
             "summary": self.get_performance_summary(),
             "detailed_metrics": [asdict(m) for m in self.metrics]
         }
-        
+
         with open(filename, 'w') as f:
             json.dump(export_data, f, indent=2, default=str)
-        
+
         self.logger.info(f"Metrics exported to {filename}")
         return filename
 
@@ -847,10 +847,10 @@ def monitored_research_task(self) -> Task:
         if context:
             tokens = getattr(task_output, 'token_usage', {}).get('total', 0)
             monitor.end_task_monitoring(context, success=True, tokens_used=tokens)
-    
+
     # Start monitoring would be called before task execution
     # This is a simplified example - in practice you'd integrate this into the task execution flow
-    
+
     return Task(
         config=self.tasks_config['research_task'],
         agent=self.researcher(),
@@ -872,7 +872,7 @@ class ErrorSeverity(Enum):
 
 class CrewError(Exception):
     """Base exception for CrewAI applications"""
-    def __init__(self, message: str, severity: ErrorSeverity = ErrorSeverity.MEDIUM, 
+    def __init__(self, message: str, severity: ErrorSeverity = ErrorSeverity.MEDIUM,
                  context: dict = None):
         super().__init__(message)
         self.severity = severity
@@ -893,19 +893,19 @@ class ConfigurationError(CrewError):
 
 class ErrorHandler:
     """Centralized error handling for CrewAI applications"""
-    
+
     def __init__(self, crew_name: str):
         self.crew_name = crew_name
         self.error_log: List[CrewError] = []
         self.recovery_strategies: Dict[type, Callable] = {}
-        
+
     def register_recovery_strategy(self, error_type: type, strategy: Callable):
         """Register a recovery strategy for specific error types"""
         self.recovery_strategies[error_type] = strategy
-        
+
     def handle_error(self, error: Exception, context: dict = None) -> Any:
         """Handle errors with appropriate recovery strategies"""
-        
+
         # Convert to CrewError if needed
         if not isinstance(error, CrewError):
             crew_error = CrewError(
@@ -915,11 +915,11 @@ class ErrorHandler:
             )
         else:
             crew_error = error
-            
+
         # Log the error
         self.error_log.append(crew_error)
         self._log_error(crew_error)
-        
+
         # Apply recovery strategy if available
         error_type = type(error)
         if error_type in self.recovery_strategies:
@@ -931,21 +931,21 @@ class ErrorHandler:
                     ErrorSeverity.HIGH,
                     {"original_error": str(error), "recovery_error": str(recovery_error)}
                 ))
-        
+
         # If critical, re-raise
         if crew_error.severity == ErrorSeverity.CRITICAL:
             raise crew_error
-            
+
         return None
-    
+
     def _log_error(self, error: CrewError):
         """Log error with appropriate level based on severity"""
         logger = logging.getLogger(f"CrewAI.{self.crew_name}.ErrorHandler")
-        
+
         error_msg = f"[{error.severity.value.upper()}] {error}"
         if error.context:
             error_msg += f" | Context: {error.context}"
-            
+
         if error.severity in [ErrorSeverity.HIGH, ErrorSeverity.CRITICAL]:
             logger.error(error_msg)
             logger.error(f"Stack trace: {traceback.format_exc()}")
@@ -953,16 +953,16 @@ class ErrorHandler:
             logger.warning(error_msg)
         else:
             logger.info(error_msg)
-    
+
     def get_error_summary(self) -> Dict[str, Any]:
         """Get summary of errors encountered"""
         if not self.error_log:
             return {"total_errors": 0}
-            
+
         severity_counts = {}
         for error in self.error_log:
             severity_counts[error.severity.value] = severity_counts.get(error.severity.value, 0) + 1
-            
+
         return {
             "total_errors": len(self.error_log),
             "severity_breakdown": severity_counts,
@@ -1004,7 +1004,7 @@ def robust_task(self) -> Task:
                     # Use fallback response
                     return "Task failed, using fallback response"
         return wrapper
-    
+
     return Task(
         config=self.tasks_config['research_task'],
         agent=self.researcher()
@@ -1020,60 +1020,60 @@ from pydantic import BaseSettings, Field, validator
 
 class Environment(str, Enum):
     DEVELOPMENT = "development"
-    TESTING = "testing"  
+    TESTING = "testing"
     STAGING = "staging"
     PRODUCTION = "production"
 
 class CrewAISettings(BaseSettings):
     """Comprehensive settings management for CrewAI applications"""
-    
+
     # Environment
     environment: Environment = Field(default=Environment.DEVELOPMENT)
     debug: bool = Field(default=True)
-    
+
     # API Keys (loaded from environment)
     openai_api_key: Optional[str] = Field(default=None, env="OPENAI_API_KEY")
     anthropic_api_key: Optional[str] = Field(default=None, env="ANTHROPIC_API_KEY")
     serper_api_key: Optional[str] = Field(default=None, env="SERPER_API_KEY")
     mem0_api_key: Optional[str] = Field(default=None, env="MEM0_API_KEY")
-    
+
     # CrewAI Configuration
     crew_max_rpm: int = Field(default=100)
     crew_max_execution_time: int = Field(default=3600)  # 1 hour
     default_llm_model: str = Field(default="gpt-4")
     fallback_llm_model: str = Field(default="gpt-3.5-turbo")
-    
+
     # Memory and Storage
     crewai_storage_dir: str = Field(default="./storage", env="CREWAI_STORAGE_DIR")
     memory_enabled: bool = Field(default=True)
     memory_cleanup_interval: int = Field(default=86400)  # 24 hours in seconds
-    
+
     # Performance
     enable_caching: bool = Field(default=True)
     max_retries: int = Field(default=3)
     retry_delay: float = Field(default=1.0)
-    
+
     # Monitoring
     enable_monitoring: bool = Field(default=True)
     log_level: str = Field(default="INFO")
     metrics_export_interval: int = Field(default=3600)  # 1 hour
-    
+
     # Security
     input_sanitization: bool = Field(default=True)
     max_input_length: int = Field(default=10000)
     allowed_file_types: list = Field(default=["txt", "md", "pdf", "docx"])
-    
+
     @validator('environment', pre=True)
     def set_debug_based_on_env(cls, v):
         return v
-    
+
     @validator('debug')
     def set_debug_from_env(cls, v, values):
         env = values.get('environment')
         if env == Environment.PRODUCTION:
             return False
         return v
-    
+
     @validator('openai_api_key')
     def validate_openai_key(cls, v):
         if not v:
@@ -1081,15 +1081,15 @@ class CrewAISettings(BaseSettings):
         if not v.startswith('sk-'):
             raise ValueError("Invalid OpenAI API key format")
         return v
-    
+
     @property
     def is_production(self) -> bool:
         return self.environment == Environment.PRODUCTION
-    
+
     @property
     def is_development(self) -> bool:
         return self.environment == Environment.DEVELOPMENT
-        
+
     def get_llm_config(self) -> Dict[str, Any]:
         """Get LLM configuration based on environment"""
         config = {
@@ -1098,12 +1098,12 @@ class CrewAISettings(BaseSettings):
             "max_tokens": 4000 if self.is_production else 2000,
             "timeout": 60
         }
-        
+
         if self.is_development:
             config["model"] = self.fallback_llm_model
-            
+
         return config
-    
+
     def get_memory_config(self) -> Dict[str, Any]:
         """Get memory configuration"""
         return {
@@ -1112,7 +1112,7 @@ class CrewAISettings(BaseSettings):
             "cleanup_interval": self.memory_cleanup_interval,
             "provider": "mem0" if self.mem0_api_key and self.is_production else "local"
         }
-        
+
     class Config:
         env_file = ".env"
         env_file_encoding = 'utf-8'
@@ -1125,25 +1125,25 @@ settings = CrewAISettings()
 @CrewBase
 class ConfigurableCrew():
     """Crew that uses centralized configuration"""
-    
+
     def __init__(self):
         self.settings = settings
         self.validate_configuration()
-        
+
     def validate_configuration(self):
         """Validate configuration before crew execution"""
         required_keys = [self.settings.openai_api_key]
         if not all(required_keys):
             raise ConfigurationError("Missing required API keys")
-            
+
         if not os.path.exists(self.settings.crewai_storage_dir):
             os.makedirs(self.settings.crewai_storage_dir, exist_ok=True)
-    
+
     @agent
     def adaptive_agent(self) -> Agent:
         """Agent that adapts to configuration"""
         llm_config = self.settings.get_llm_config()
-        
+
         return Agent(
             config=self.agents_config['researcher'],
             llm=llm_config["model"],
@@ -1163,7 +1163,7 @@ from crewai.tasks.task_output import TaskOutput
 
 class CrewAITestFramework:
     """Comprehensive testing framework for CrewAI applications"""
-    
+
     @staticmethod
     def create_mock_agent(role: str = "test_agent", tools: list = None) -> Mock:
         """Create a mock agent for testing"""
@@ -1175,9 +1175,9 @@ class CrewAITestFramework:
         mock_agent.llm = "gpt-3.5-turbo"
         mock_agent.verbose = False
         return mock_agent
-    
+
     @staticmethod
-    def create_mock_task_output(content: str, success: bool = True, 
+    def create_mock_task_output(content: str, success: bool = True,
                                tokens: int = 100) -> TaskOutput:
         """Create a mock task output for testing"""
         return TaskOutput(
@@ -1187,13 +1187,13 @@ class CrewAITestFramework:
             pydantic=None,
             json_dict=None
         )
-    
+
     @staticmethod
     def create_test_crew(agents: list = None, tasks: list = None) -> Crew:
         """Create a test crew with mock components"""
         test_agents = agents or [CrewAITestFramework.create_mock_agent()]
         test_tasks = tasks or []
-        
+
         return Crew(
             agents=test_agents,
             tasks=test_tasks,
@@ -1203,53 +1203,53 @@ class CrewAITestFramework:
 # Example test cases
 class TestResearchCrew:
     """Test cases for research crew functionality"""
-    
+
     def setup_method(self):
         """Setup test environment"""
         self.framework = CrewAITestFramework()
         self.mock_serper = Mock()
-        
+
     @patch('crewai_tools.SerperDevTool')
     def test_agent_creation(self, mock_serper_tool):
         """Test agent creation with proper configuration"""
         mock_serper_tool.return_value = self.mock_serper
-        
+
         crew = ResearchCrew()
         researcher = crew.researcher()
-        
+
         assert researcher.role == "Senior Research Analyst"
         assert len(researcher.tools) > 0
         assert researcher.verbose is True
-        
+
     def test_task_validation(self):
         """Test task validation logic"""
         crew = ResearchCrew()
-        
+
         # Test valid output
         valid_output = self.framework.create_mock_task_output(
             "This is a comprehensive research summary with conclusions and findings."
         )
         is_valid, message = crew.validate_research_quality(valid_output)
         assert is_valid is True
-        
+
         # Test invalid output (too short)
         invalid_output = self.framework.create_mock_task_output("Too short")
         is_valid, message = crew.validate_research_quality(invalid_output)
         assert is_valid is False
         assert "brief" in message.lower()
-        
+
     @patch('requests.get')
     def test_tool_error_handling(self, mock_requests):
         """Test tool error handling and recovery"""
         # Simulate network error
         mock_requests.side_effect = requests.exceptions.RequestException("Network error")
-        
+
         tool = RobustSearchTool()
         result = tool._run("test query")
-        
+
         assert "network error" in result.lower()
         assert "failed" in result.lower()
-        
+
     @pytest.mark.asyncio
     async def test_crew_execution_flow(self):
         """Test complete crew execution with mocked dependencies"""
@@ -1257,18 +1257,18 @@ class TestResearchCrew:
             mock_execute.return_value = self.framework.create_mock_task_output(
                 "Research completed successfully with findings and recommendations."
             )
-            
+
             crew = ResearchCrew()
             result = crew.crew().kickoff(inputs={"topic": "AI testing"})
-            
+
             assert result is not None
             assert "successfully" in result.raw.lower()
-            
+
     def test_memory_integration(self):
         """Test memory system integration"""
         crew = ResearchCrew()
         memory_manager = AdvancedMemoryManager(crew)
-        
+
         # Test saving to memory
         test_content = "Important research finding about AI"
         memory_manager.save_with_context(
@@ -1277,34 +1277,34 @@ class TestResearchCrew:
             metadata={"importance": "high"},
             agent="researcher"
         )
-        
+
         # Test searching memory
         results = memory_manager.search_across_memories("AI research")
         assert "short_term" in results
-        
+
     def test_error_handling_workflow(self):
         """Test error handling and recovery mechanisms"""
         error_handler = ErrorHandler("test_crew")
-        
+
         # Test error registration and handling
         test_error = TaskExecutionError("Test task failed", ErrorSeverity.MEDIUM)
         result = error_handler.handle_error(test_error)
-        
+
         assert len(error_handler.error_log) == 1
         assert error_handler.error_log[0].severity == ErrorSeverity.MEDIUM
-        
+
     def test_configuration_validation(self):
         """Test configuration validation"""
         # Test with missing API key
         with patch.dict(os.environ, {}, clear=True):
             with pytest.raises(ValueError):
                 settings = CrewAISettings()
-                
+
         # Test with valid configuration
         with patch.dict(os.environ, {"OPENAI_API_KEY": "sk-test-key"}):
             settings = CrewAISettings()
             assert settings.openai_api_key == "sk-test-key"
-            
+
     @pytest.mark.integration
     def test_end_to_end_workflow(self):
         """Integration test for complete workflow"""
@@ -1315,41 +1315,41 @@ class TestResearchCrew:
 # Performance testing
 class TestCrewPerformance:
     """Performance tests for CrewAI applications"""
-    
+
     def test_memory_usage(self):
         """Test memory usage during crew execution"""
         import psutil
         import gc
-        
+
         process = psutil.Process()
         initial_memory = process.memory_info().rss
-        
+
         # Create and run crew multiple times
         for i in range(10):
             crew = ResearchCrew()
             # Simulate crew execution
             del crew
             gc.collect()
-        
+
         final_memory = process.memory_info().rss
         memory_increase = final_memory - initial_memory
-        
+
         # Assert memory increase is reasonable (less than 100MB)
         assert memory_increase < 100 * 1024 * 1024
-        
+
     def test_concurrent_execution(self):
         """Test concurrent crew execution"""
         import concurrent.futures
-        
+
         def run_crew(crew_id):
             crew = ResearchCrew()
             # Simulate execution
             return f"crew_{crew_id}_completed"
-        
+
         with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
             futures = [executor.submit(run_crew, i) for i in range(5)]
             results = [future.result() for future in futures]
-        
+
         assert len(results) == 5
         assert all("completed" in result for result in results)
 
@@ -1400,7 +1400,7 @@ class TestCrewPerformance:
 
 ### Development:
 1. Always use .env files for sensitive configuration
-2. Implement comprehensive error handling and logging  
+2. Implement comprehensive error handling and logging
 3. Use structured outputs with Pydantic for reliability
 4. Test crew functionality with different input scenarios
 5. Follow CrewAI patterns and conventions consistently
@@ -1426,4 +1426,4 @@ class TestCrewPerformance:
 5. Use async patterns for I/O-bound operations
 6. Implement proper connection pooling and resource management
 7. Profile and optimize critical paths
-8. Plan for horizontal scaling when needed 
+8. Plan for horizontal scaling when needed

.github/codeql/codeql-config.yml

@@ -0,0 +1,21 @@
+name: "CodeQL Config"
+
+paths-ignore:
+  # Ignore template files - these are boilerplate code that shouldn't be analyzed
+  - "src/crewai/cli/templates/**"
+  # Ignore test cassettes - these are test fixtures/recordings
+  - "tests/cassettes/**"
+  # Ignore cache and build artifacts
+  - ".cache/**"
+  # Ignore documentation build artifacts
+  - "docs/.cache/**"
+  
+paths:
+  # Include all Python source code
+  - "src/**"
+  # Include tests (but exclude cassettes)
+  - "tests/**"
+
+# Configure specific queries or packs if needed
+# queries:
+#   - uses: security-and-quality

.github/workflows/build-uv-cache.yml

@@ -0,0 +1,48 @@
+name: Build uv cache
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "uv.lock"
+      - "pyproject.toml"
+  schedule:
+    - cron: "0 0 */5 * *"  # Run every 5 days at midnight UTC to prevent cache expiration
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  build-cache:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
+
+      - name: Install dependencies and populate cache
+        run: |
+          echo "Building global UV cache for Python ${{ matrix.python-version }}..."
+          uv sync --all-groups --all-extras --no-install-project
+          echo "Cache populated successfully"
+
+      - name: Save uv caches
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/codeql.yml

@@ -0,0 +1,103 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL Advanced"
+
+on:
+  push:
+    branches: [ "main" ]
+    paths-ignore:
+      - "src/crewai/cli/templates/**"
+  pull_request:
+    branches: [ "main" ]
+    paths-ignore:
+      - "src/crewai/cli/templates/**"
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    # Runner size impacts CodeQL analysis time. To learn more, please see:
+    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
+    #   - https://gh.io/supported-runners-and-hardware-resources
+    #   - https://gh.io/using-larger-runners (GitHub.com only)
+    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    permissions:
+      # required for all workflows
+      security-events: write
+
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+      # only required for workflows in private repositories
+      actions: read
+      contents: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        - language: actions
+          build-mode: none
+        - language: python
+          build-mode: none
+        # CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
+        # Use `c-cpp` to analyze code written in C, C++ or both
+        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
+        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
+        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
+        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
+        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
+        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Add any setup steps before running the `github/codeql-action/init` action.
+    # This includes steps like installing compilers or runtimes (`actions/setup-node`
+    # or others). This is typically only required for manual builds.
+    # - name: Setup runtime (example)
+    #   uses: actions/setup-example@v1
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        config-file: ./.github/codeql/codeql-config.yml
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"

.github/workflows/linter.yml

@@ -2,6 +2,9 @@ name: Lint
 
 on: [pull_request]
 
+permissions:
+  contents: read
+
 jobs:
   lint:
     runs-on: ubuntu-latest
@@ -15,8 +18,27 @@ jobs:
       - name: Fetch Target Branch
         run: git fetch origin $TARGET_BRANCH --depth=1
 
-      - name: Install Ruff
-        run: pip install ruff
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py3.11-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: "3.11"
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras --no-install-project
 
       - name: Get Changed Python Files
         id: changed-files
@@ -33,4 +55,14 @@ jobs:
           echo "${{ steps.changed-files.outputs.files }}" \
             | tr ' ' '\n' \
             | grep -v 'src/crewai/cli/templates/' \
-            | xargs -I{} ruff check "{}"
+            | xargs -I{} uv run ruff check "{}"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}

.github/workflows/security-checker.yml

@@ -1,23 +0,0 @@
-name: Security Checker
-
-on: [pull_request]
-
-jobs:
-  security-check:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11.9"
-
-      - name: Install dependencies
-        run: pip install bandit
-
-      - name: Run Bandit
-        run: bandit -c pyproject.toml -r src/ -ll
-

.github/workflows/tests.yml

@@ -3,32 +3,95 @@ name: Run Tests
 on: [pull_request]
 
 permissions:
-  contents: write
+  contents: read
 
 env:
   OPENAI_API_KEY: fake-api-key
+  PYTHONUNBUFFERED: 1
 
 jobs:
   tests:
+    name: tests (${{ matrix.python-version }})
     runs-on: ubuntu-latest
     timeout-minutes: 15
     strategy:
+      fail-fast: true
       matrix:
         python-version: ['3.10', '3.11', '3.12', '3.13']
+        group: [1, 2, 3, 4, 5, 6, 7, 8]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0  # Fetch all history for proper diff
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@v6
         with:
-          enable-cache: true
-
-      - name: Set up Python ${{ matrix.python-version }}
-        run: uv python install ${{ matrix.python-version }}
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
 
       - name: Install the project
-        run: uv sync --dev --all-extras
+        run: uv sync --all-groups --all-extras
 
-      - name: Run tests
-        run: uv run pytest --block-network --timeout=60 -vv
+      - name: Restore test durations
+        uses: actions/cache/restore@v4
+        with:
+          path: .test_durations_py*
+          key: test-durations-py${{ matrix.python-version }}
+
+      - name: Run tests (group ${{ matrix.group }} of 8)
+        run: |
+          PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
+          DURATION_FILE=".test_durations_py${PYTHON_VERSION_SAFE}"
+          
+          # Temporarily always skip cached durations to fix test splitting
+          # When durations don't match, pytest-split runs duplicate tests instead of splitting
+          echo "Using even test splitting (duration cache disabled until fix merged)"
+          DURATIONS_ARG=""
+          
+          # Original logic (disabled temporarily):
+          # if [ ! -f "$DURATION_FILE" ]; then
+          #   echo "No cached durations found, tests will be split evenly"
+          #   DURATIONS_ARG=""
+          # elif git diff origin/${{ github.base_ref }}...HEAD --name-only 2>/dev/null | grep -q "^tests/.*\.py$"; then
+          #   echo "Test files have changed, skipping cached durations to avoid mismatches"
+          #   DURATIONS_ARG=""
+          # else
+          #   echo "No test changes detected, using cached test durations for optimal splitting"
+          #   DURATIONS_ARG="--durations-path=${DURATION_FILE}"
+          # fi
+          
+          uv run pytest \
+            --block-network \
+            --timeout=30 \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            $DURATIONS_ARG \
+            --durations=10 \
+            -n auto \
+            --maxfail=3
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/type-checker.yml

@@ -3,24 +3,99 @@ name: Run Type Checks
 on: [pull_request]
 
 permissions:
-  contents: write
+  contents: read
 
 jobs:
-  type-checker:
+  type-checker-matrix:
+    name: type-checker (${{ matrix.python-version }})
     runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
         with:
-          python-version: "3.11.9"
+          fetch-depth: 0 # Fetch all history for proper diff
 
-      - name: Install Requirements
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras
+
+      - name: Get changed Python files
+        id: changed-files
         run: |
-          pip install mypy
+          # Get the list of changed Python files compared to the base branch
+          echo "Fetching changed files..."
+          git diff --name-only --diff-filter=ACMRT origin/${{ github.base_ref }}...HEAD -- '*.py' > changed_files.txt
 
-      - name: Run type checks
-        run: mypy src
+          # Filter for files in src/ directory only (excluding tests/)
+          grep -E "^src/" changed_files.txt > filtered_changed_files.txt || true
+
+          # Check if there are any changed files
+          if [ -s filtered_changed_files.txt ]; then
+            echo "Changed Python files in src/:"
+            cat filtered_changed_files.txt
+            echo "has_changes=true" >> $GITHUB_OUTPUT
+            # Convert newlines to spaces for mypy command
+            echo "files=$(cat filtered_changed_files.txt | tr '\n' ' ')" >> $GITHUB_OUTPUT
+          else
+            echo "No Python files changed in src/"
+            echo "has_changes=false" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Run type checks on changed files
+        if: steps.changed-files.outputs.has_changes == 'true'
+        run: |
+          echo "Running mypy on changed files with Python ${{ matrix.python-version }}..."
+          uv run mypy ${{ steps.changed-files.outputs.files }}
+
+      - name: No files to check
+        if: steps.changed-files.outputs.has_changes == 'false'
+        run: echo "No Python files in src/ were modified - skipping type checks"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+
+  # Summary job to provide single status for branch protection
+  type-checker:
+    name: type-checker
+    runs-on: ubuntu-latest
+    needs: type-checker-matrix
+    if: always()
+    steps:
+      - name: Check matrix results
+        run: |
+          if [ "${{ needs.type-checker-matrix.result }}" == "success" ] || [ "${{ needs.type-checker-matrix.result }}" == "skipped" ]; then
+            echo "✅ All type checks passed"
+          else
+            echo "❌ Type checks failed"
+            exit 1
+          fi

.github/workflows/update-test-durations.yml

@@ -0,0 +1,71 @@
+name: Update Test Durations
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'tests/**/*.py'
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  update-durations:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.10', '3.11', '3.12', '3.13']
+    env:
+      OPENAI_API_KEY: fake-api-key
+      PYTHONUNBUFFERED: 1
+    
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
+
+      - name: Install the project
+        run: uv sync --all-groups --all-extras
+
+      - name: Run all tests and store durations
+        run: |
+          PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
+          uv run pytest --store-durations --durations-path=.test_durations_py${PYTHON_VERSION_SAFE} -n auto
+        continue-on-error: true
+
+      - name: Save durations to cache
+        if: always()
+        uses: actions/cache/save@v4
+        with:
+          path: .test_durations_py*
+          key: test-durations-py${{ matrix.python-version }}
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.gitignore

@@ -21,9 +21,9 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
 test_flow.html
 crewairules.mdc
 plan.md
 conceptual_plan.md
-build_image
+build_image
+chromadb-*.lock

.pre-commit-config.yaml

@@ -1,7 +1,19 @@
 repos:
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.2
+  - repo: local
     hooks:
       - id: ruff
-        args: ["--fix"]
+        name: ruff
+        entry: uv run ruff check
+        language: system
+        types: [python]
       - id: ruff-format
+        name: ruff-format
+        entry: uv run ruff format
+        language: system
+        types: [python]
+      - id: mypy
+        name: mypy
+        entry: uv run mypy
+        language: system
+        types: [python]
+        exclude: ^tests/

.ruff.toml

@@ -1,4 +0,0 @@
-exclude = [
-    "templates",
-    "__init__.py",
-]

README.md

@@ -62,9 +62,9 @@
 With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
 standard for enterprise-ready AI automation.
 
-# CrewAI Enterprise Suite
+# CrewAI AMP Suite
 
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
 
 You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
 
@@ -76,9 +76,9 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
 - **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
 - **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
 - **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
-- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI AMP on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
+CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
 ## Table of contents
@@ -418,10 +418,10 @@ Choose CrewAI to easily build powerful, adaptable, and production-ready AI autom
 
 You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
 
-- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
 
 ### Quick Tutorial
 
@@ -429,19 +429,19 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
@@ -674,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 
 ### Enterprise Features
 
-- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
-- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
-- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
+- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-amp-offer)
+- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free)
 
 ### Q: What exactly is CrewAI?
 
@@ -732,17 +732,17 @@ A: Check out practical examples in the [CrewAI-examples repository](https://gith
 
 A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
 
-### Q: What additional features does CrewAI Enterprise offer?
+### Q: What additional features does CrewAI AMP offer?
 
-A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
 
-### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
+### Q: Is CrewAI AMP available for cloud and on-premise deployments?
 
-A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
 
-### Q: Can I try CrewAI Enterprise for free?
+### Q: Can I try CrewAI AMP for free?
 
-A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
+A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
 
 ### Q: Does CrewAI support fine-tuning or training custom models?
 
@@ -762,7 +762,7 @@ A: CrewAI is highly scalable, supporting simple automations and large-scale ente
 
 ### Q: Does CrewAI offer debugging and monitoring tools?
 
-A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
 
 ### Q: What programming languages does CrewAI support?
 

docs/en/api-reference/inputs.mdx

@@ -0,0 +1,8 @@
+---
+title: "GET /inputs"
+description: "Get required inputs for your crew"
+openapi: "/enterprise-api.en.yaml GET /inputs"
+mode: "wide"
+---
+
+

docs/en/api-reference/introduction.mdx

@@ -1,28 +1,29 @@
 ---
 title: "Introduction"
-description: "Complete reference for the CrewAI Enterprise REST API"
+description: "Complete reference for the CrewAI AMP REST API"
 icon: "code"
+mode: "wide"
 ---
 
-# CrewAI Enterprise API
+# CrewAI AMP API
 
-Welcome to the CrewAI Enterprise API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
+Welcome to the CrewAI AMP API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
 
 ## Quick Start
 
 <Steps>
   <Step title="Get Your API Credentials">
-    Navigate to your crew's detail page in the CrewAI Enterprise dashboard and copy your Bearer Token from the Status tab.
+    Navigate to your crew's detail page in the CrewAI AMP dashboard and copy your Bearer Token from the Status tab.
   </Step>
-  
+
   <Step title="Discover Required Inputs">
     Use the `GET /inputs` endpoint to see what parameters your crew expects.
   </Step>
-  
+
   <Step title="Start a Crew Execution">
     Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
   </Step>
-  
+
   <Step title="Monitor Progress">
     Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
   </Step>
@@ -45,7 +46,7 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
 | **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
 
 <Tip>
-You can find both token types in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
+You can find both token types in the Status tab of your crew's detail page in the CrewAI AMP dashboard.
 </Tip>
 
 ## Base URL
@@ -61,7 +62,7 @@ Replace `your-crew-name` with your actual crew's URL from the dashboard.
 ## Typical Workflow
 
 1. **Discovery**: Call `GET /inputs` to understand what your crew needs
-2. **Execution**: Submit inputs via `POST /kickoff` to start processing  
+2. **Execution**: Submit inputs via `POST /kickoff` to start processing
 3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
 4. **Results**: Extract the final output from the completed response
 
@@ -81,12 +82,12 @@ The API uses standard HTTP status codes:
 ## Interactive Testing
 
 <Info>
-**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
+**Why no "Send" button?** Since each CrewAI AMP user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
 </Info>
 
 Each endpoint page shows you:
 - ✅ **Exact request format** with all parameters
-- ✅ **Response examples** for success and error cases  
+- ✅ **Response examples** for success and error cases
 - ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
 - ✅ **Authentication examples** with proper Bearer token format
 
@@ -103,7 +104,7 @@ Each endpoint page shows you:
 
 **Example workflow:**
 1. **Copy this cURL example** from any endpoint page
-2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL  
+2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL
 3. **Replace the Bearer token** with your real token from the dashboard
 4. **Run the request** in your terminal or API client
 

docs/en/api-reference/kickoff.mdx

@@ -0,0 +1,8 @@
+---
+title: "POST /kickoff"
+description: "Start a crew execution"
+openapi: "/enterprise-api.en.yaml POST /kickoff"
+mode: "wide"
+---
+
+

docs/en/api-reference/resume.mdx

@@ -0,0 +1,6 @@
+---
+title: "POST /resume"
+description: "Resume crew execution with human feedback"
+openapi: "/enterprise-api.en.yaml POST /resume"
+mode: "wide"
+---

docs/en/api-reference/status.mdx

@@ -0,0 +1,8 @@
+---
+title: "GET /status/{kickoff_id}"
+description: "Get execution status"
+openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
+mode: "wide"
+---
+
+

docs/en/concepts/agents.mdx

@@ -2,6 +2,7 @@
 title: Agents
 description: Detailed guide on creating and managing agents within the CrewAI framework.
 icon: robot
+mode: "wide"
 ---
 
 ## Overview of an Agent
@@ -19,7 +20,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 </Tip>
 
 <Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
-CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
+CrewAI AMP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
 
 ![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 
@@ -526,6 +527,103 @@ agent = Agent(
 The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
 </Note>
 
+## Direct Agent Interaction with `kickoff()`
+
+Agents can be used directly without going through a task or crew workflow using the `kickoff()` method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.
+
+### How `kickoff()` Works
+
+The `kickoff()` method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool
+
+# Create an agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[SerperDevTool()],
+    verbose=True
+)
+
+# Use kickoff() to interact directly with the agent
+result = researcher.kickoff("What are the latest developments in language models?")
+
+# Access the raw response
+print(result.raw)
+```
+
+### Parameters and Return Values
+
+| Parameter         | Type                                | Description                                                               |
+| :---------------- | :---------------------------------- | :------------------------------------------------------------------------ |
+| `messages`        | `Union[str, List[Dict[str, str]]]`  | Either a string query or a list of message dictionaries with role/content |
+| `response_format` | `Optional[Type[Any]]`               | Optional Pydantic model for structured output                             |
+
+The method returns a `LiteAgentOutput` object with the following properties:
+
+- `raw`: String containing the raw output text
+- `pydantic`: Parsed Pydantic model (if a `response_format` was provided)
+- `agent_role`: Role of the agent that produced the output
+- `usage_metrics`: Token usage metrics for the execution
+
+### Structured Output
+
+You can get structured output by providing a Pydantic model as the `response_format`:
+
+```python Code
+from pydantic import BaseModel
+from typing import List
+
+class ResearchFindings(BaseModel):
+    main_points: List[str]
+    key_technologies: List[str]
+    future_predictions: str
+
+# Get structured output
+result = researcher.kickoff(
+    "Summarize the latest developments in AI for 2025",
+    response_format=ResearchFindings
+)
+
+# Access structured data
+print(result.pydantic.main_points)
+print(result.pydantic.future_predictions)
+```
+
+### Multiple Messages
+
+You can also provide a conversation history as a list of message dictionaries:
+
+```python Code
+messages = [
+    {"role": "user", "content": "I need information about large language models"},
+    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
+    {"role": "user", "content": "What are the latest developments in 2025?"}
+]
+
+result = researcher.kickoff(messages)
+```
+
+### Async Support
+
+An asynchronous version is available via `kickoff_async()` with the same parameters:
+
+```python Code
+import asyncio
+
+async def main():
+    result = await researcher.kickoff_async("What are the latest developments in AI?")
+    print(result.raw)
+
+asyncio.run(main())
+```
+
+<Note>
+The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler execution flow while preserving all of the agent's configuration (role, goal, backstory, tools, etc.).
+</Note>
+
 ## Important Considerations and Best Practices
 
 ### Security and Code Execution

docs/en/concepts/cli.mdx

@@ -2,8 +2,11 @@
 title: CLI
 description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
+mode: "wide"
 ---
 
+<Warning>Since release 0.140.0, CrewAI AMP started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
+
 ## Overview
 
 The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
@@ -86,7 +89,7 @@ crewai replay [OPTIONS]
 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
 
 Example:
-```shell Terminal    
+```shell Terminal
 crewai replay -t task_123456
 ```
 
@@ -132,7 +135,7 @@ crewai test [OPTIONS]
 - `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
 
 Example:
-```shell Terminal    
+```shell Terminal
 crewai test -n 5 -m gpt-3.5-turbo
 ```
 
@@ -149,7 +152,7 @@ Starting from version 0.103.0, the `crewai run` command can be used to run both
 </Note>
 
 <Note>
-Make sure to run these commands from the directory where your CrewAI project is set up. 
+Make sure to run these commands from the directory where your CrewAI project is set up.
 Some commands may require additional configuration or setup within your project structure.
 </Note>
 
@@ -183,13 +186,10 @@ def crew(self) -> Crew:
 
 ### 10. Deploy
 
-Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
+Deploy the crew or flow to [CrewAI AMP](https://app.crewai.com).
 
-- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
-    ```shell Terminal
-    crewai signup
-    ```
-    If you already have an account, you can login with:
+- **Authentication**: You need to be authenticated to deploy to CrewAI AMP.
+    You can login or create an account with:
     ```shell Terminal
     crewai login
     ```
@@ -203,7 +203,7 @@ Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
 
 ### 11. Organization Management
 
-Manage your CrewAI Enterprise organizations.
+Manage your CrewAI AMP organizations.
 
 ```shell Terminal
 crewai org [COMMAND] [OPTIONS]
@@ -227,17 +227,17 @@ crewai org switch <organization_id>
 ```
 
 <Note>
-You must be authenticated to CrewAI Enterprise to use these organization management commands.
+You must be authenticated to CrewAI AMP to use these organization management commands.
 </Note>
 
 - **Create a deployment** (continued):
     - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
 
-- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
+- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AMP.
     ```shell Terminal
     crewai deploy push
-    ``` 
-    - Initiates the deployment process on the CrewAI Enterprise platform.
+    ```
+    - Initiates the deployment process on the CrewAI AMP platform.
     - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
 
 - **Deployment Status**: You can check the status of your deployment with:
@@ -262,7 +262,7 @@ You must be authenticated to CrewAI Enterprise to use these organization managem
     ```shell Terminal
     crewai deploy remove
     ```
-    This deletes the deployment from the CrewAI Enterprise platform.
+    This deletes the deployment from the CrewAI AMP platform.
 
 - **Help Command**: You can get help with the CLI with:
     ```shell Terminal
@@ -270,20 +270,36 @@ You must be authenticated to CrewAI Enterprise to use these organization managem
     ```
     This shows the help message for the CrewAI Deploy CLI.
 
-Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
+Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI.
 
 <iframe
-  width="100%"
-  height="400"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/3EqSV-CYDZA"
   title="CrewAI Deployment Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
+  frameBorder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
+  allowFullScreen
 ></iframe>
 
-### 11. API Keys
+### 11. Login
+
+Authenticate with CrewAI AMP using a secure device code flow (no email entry required).
+
+```shell Terminal
+crewai login
+```
+
+What happens:
+- A verification URL and short code are displayed in your terminal
+- Your browser opens to the verification URL
+- Enter/confirm the code to complete authentication
+
+Notes:
+- The OAuth2 provider and domain are configured via `crewai config` (defaults use `login.crewai.com`)
+- After successful login, the CLI also attempts to authenticate to the Tool Repository automatically
+- If you reset your configuration, run `crewai login` again to re-authenticate
+
+### 12. API Keys
 
 When running ```crewai create crew``` command, the CLI will show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.
 
@@ -310,3 +326,86 @@ When you select a provider, the CLI will prompt you to enter the Key name and th
 See the following link for each provider's key name:
 
 * [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
+
+### 13. Configuration Management
+
+Manage CLI configuration settings for CrewAI.
+
+```shell Terminal
+crewai config [COMMAND] [OPTIONS]
+```
+
+#### Commands:
+
+- `list`: Display all CLI configuration parameters
+```shell Terminal
+crewai config list
+```
+
+- `set`: Set a CLI configuration parameter
+```shell Terminal
+crewai config set <key> <value>
+```
+
+- `reset`: Reset all CLI configuration parameters to default values
+```shell Terminal
+crewai config reset
+```
+
+#### Available Configuration Parameters
+
+- `enterprise_base_url`: Base URL of the CrewAI AMP instance
+- `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
+- `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource
+- `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests
+- `oauth2_domain`: OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens
+
+#### Examples
+
+Display current configuration:
+```shell Terminal
+crewai config list
+```
+
+Example output:
+| Setting             | Value                    | Description                                                 |
+| :------------------ | :----------------------- | :---------------------------------------------------------- |
+| enterprise_base_url | https://app.crewai.com   | Base URL of the CrewAI AMP instance                  |
+| org_name            | Not set                  | Name of the currently active organization                   |
+| org_uuid            | Not set                  | UUID of the currently active organization                   |
+| oauth2_provider     | workos                   | OAuth2 provider (e.g., workos, okta, auth0)                 |
+| oauth2_audience     | client_01YYY             | Audience identifying the target API/resource                |
+| oauth2_client_id    | client_01XXX             | OAuth2 client ID issued by the provider                     |
+| oauth2_domain       | login.crewai.com         | Provider domain (e.g., your-org.auth0.com)                  |
+
+Set the enterprise base URL:
+```shell Terminal
+crewai config set enterprise_base_url https://my-enterprise.crewai.com
+```
+
+Set OAuth2 provider:
+```shell Terminal
+crewai config set oauth2_provider auth0
+```
+
+Set OAuth2 domain:
+```shell Terminal
+crewai config set oauth2_domain my-company.auth0.com
+```
+
+Reset all configuration to defaults:
+```shell Terminal
+crewai config reset
+```
+
+<Tip>
+After resetting configuration, re-run `crewai login` to authenticate again.
+</Tip>
+
+<Tip>
+CrewAI CLI handles authentication to the Tool Repository automatically when adding packages to your project. Just append `crewai` before any `uv` command to use it. E.g. `crewai uv add requests`. For more information, see [Tool Repository](https://docs.crewai.com/enterprise/features/tool-repository) docs.
+</Tip>
+
+<Note>
+Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
+</Note>

docs/en/concepts/collaboration.mdx

@@ -2,6 +2,7 @@
 title: Collaboration
 description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
 icon: screen-users
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/crews.mdx

@@ -2,6 +2,7 @@
 title: Crews
 description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
 icon: people-group
+mode: "wide"
 ---
 
 ## Overview
@@ -20,8 +21,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
 | **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
-| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
-| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |                                                                                                                                                                                       |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
@@ -32,6 +32,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
+| **Knowledge Sources** _(optional)_    | `knowledge_sources`    | Knowledge sources available at the crew level, accessible to all the agents.                                                                                                                                                                                    |
 
 <Tip>
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.

docs/en/concepts/event-listener.mdx

@@ -2,6 +2,7 @@
 title: 'Event Listeners'
 description: 'Tap into CrewAI events to build custom integrations and monitoring'
 icon: spinner
+mode: "wide"
 ---
 
 ## Overview
@@ -19,7 +20,7 @@ CrewAI uses an event bus architecture to emit events throughout the execution li
 When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
 
 <Note type="info" title="Enterprise Enhancement: Prompt Tracing">
-CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
+CrewAI AMP provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
 
 ![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
 
@@ -44,12 +45,12 @@ To create a custom event listener, you need to:
 Here's a simple example of a custom event listener class:
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     CrewKickoffStartedEvent,
     CrewKickoffCompletedEvent,
     AgentExecutionCompletedEvent,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 
 class MyCustomListener(BaseEventListener):
     def __init__(self):
@@ -146,7 +147,7 @@ my_project/
 
 ```python
 # my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 # ... import events ...
 
 class MyCustomListener(BaseEventListener):
@@ -177,14 +178,7 @@ class MyCustomCrew:
     # Your crew implementation...
 ```
 
-This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
+This is how third-party event listeners are registered in the CrewAI codebase.
 
 ## Available Event Types
 
@@ -255,6 +249,17 @@ CrewAI provides a wide range of events that you can listen for:
 - **LLMCallFailedEvent**: Emitted when an LLM call fails
 - **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
 
+### Memory Events
+
+- **MemoryQueryStartedEvent**: Emitted when a memory query is started. Contains the query, limit, and optional score threshold.
+- **MemoryQueryCompletedEvent**: Emitted when a memory query is completed successfully. Contains the query, results, limit, score threshold, and query execution time.
+- **MemoryQueryFailedEvent**: Emitted when a memory query fails. Contains the query, limit, score threshold, and error message.
+- **MemorySaveStartedEvent**: Emitted when a memory save operation is started. Contains the value to be saved, metadata, and optional agent role.
+- **MemorySaveCompletedEvent**: Emitted when a memory save operation is completed successfully. Contains the saved value, metadata, agent role, and save execution time.
+- **MemorySaveFailedEvent**: Emitted when a memory save operation fails. Contains the value, metadata, agent role, and error message.
+- **MemoryRetrievalStartedEvent**: Emitted when memory retrieval for a task prompt starts. Contains the optional task ID.
+- **MemoryRetrievalCompletedEvent**: Emitted when memory retrieval for a task prompt completes successfully. Contains the task ID, memory content, and retrieval execution time.
+
 ## Event Handler Structure
 
 Each event handler receives two parameters:
@@ -269,84 +274,13 @@ The structure of the event object depends on the event type, but all events inhe
 
 Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
 
-## Real-World Example: Integration with AgentOps
-
-CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
-
-The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
 
 ## Advanced Usage: Scoped Handlers
 
 For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
 
 ```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
+from crewai.events import crewai_event_bus, CrewKickoffStartedEvent
 
 with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)

docs/en/concepts/flows.mdx

@@ -2,6 +2,7 @@
 title: Flows
 description: Learn how to create and manage AI workflows using CrewAI Flows.
 icon: arrow-progress
+mode: "wide"
 ---
 
 ## Overview
@@ -97,7 +98,13 @@ The state's unique ID and stored data can be useful for tracking flow executions
 
 ### @start()
 
-The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
+The `@start()` decorator marks entry points for a Flow. You can:
+
+- Declare multiple unconditional starts: `@start()`
+- Gate a start on a prior method or router label: `@start("method_or_label")`
+- Provide a callable condition to control when a start should fire
+
+All satisfied `@start()` methods will execute (often in parallel) when the Flow begins or resumes.
 
 ### @listen()
 
@@ -868,14 +875,13 @@ By exploring these examples, you can gain insights into how to leverage CrewAI F
 Also, check out our YouTube video on how to use flows in CrewAI below!
 
 <iframe
-  width="560"
-  height="315"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/MTb5my6VOT8"
-  title="YouTube video player"
-  frameborder="0"
+  title="CrewAI Flows overview"
+  frameBorder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowfullscreen
+  referrerPolicy="strict-origin-when-cross-origin"
+  allowFullScreen
 ></iframe>
 
 ## Running Flows

docs/en/concepts/knowledge.mdx

@@ -2,6 +2,7 @@
 title: Knowledge
 description: What is knowledge in CrewAI and how to use it.
 icon: book
+mode: "wide"
 ---
 
 ## Overview
@@ -24,6 +25,41 @@ For file-based Knowledge Sources, make sure to place your files in a `knowledge`
 Also, use relative paths from the `knowledge` directory when creating the source.
 </Tip>
 
+### Vector store (RAG) client configuration
+
+CrewAI exposes a provider-neutral RAG client abstraction for vector stores. The default provider is ChromaDB, and Qdrant is supported as well. You can switch providers using configuration utilities.
+
+Supported today:
+- ChromaDB (default)
+- Qdrant
+
+```python Code
+from crewai.rag.config.utils import set_rag_config, get_rag_client, clear_rag_config
+
+# ChromaDB (default)
+from crewai.rag.chromadb.config import ChromaDBConfig
+set_rag_config(ChromaDBConfig())
+chromadb_client = get_rag_client()
+
+# Qdrant
+from crewai.rag.qdrant.config import QdrantConfig
+set_rag_config(QdrantConfig())
+qdrant_client = get_rag_client()
+
+# Example operations (same API for any provider)
+client = qdrant_client  # or chromadb_client
+client.create_collection(collection_name="docs")
+client.add_documents(
+    collection_name="docs",
+    documents=[{"id": "1", "content": "CrewAI enables collaborative AI agents."}],
+)
+results = client.search(collection_name="docs", query="collaborative agents", limit=3)
+
+clear_rag_config()  # optional reset
+```
+
+This RAG client is separate from Knowledge’s built-in storage. Use it when you need direct vector-store control or custom retrieval pipelines.
+
 ### Basic String Knowledge Example
 
 ```python Code
@@ -681,11 +717,11 @@ CrewAI emits events during the knowledge retrieval process that you can listen f
 #### Example: Monitoring Knowledge Retrieval
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     KnowledgeRetrievalStartedEvent,
     KnowledgeRetrievalCompletedEvent,
+    BaseEventListener,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
 
 class KnowledgeMonitorListener(BaseEventListener):
     def setup_listeners(self, crewai_event_bus):

docs/en/concepts/llms.mdx

@@ -2,6 +2,7 @@
 title: 'LLMs'
 description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
 icon: 'microchip-ai'
+mode: "wide"
 ---
 
 ## Overview
@@ -270,7 +271,7 @@ In this section, you'll find detailed examples that help you select, configure,
     from crewai import LLM
 
     llm = LLM(
-        model="gemini/gemini-1.5-pro-latest",
+        model="gemini-1.5-pro-latest", # or vertex_ai/gemini-1.5-pro-latest
         temperature=0.7,
         vertex_credentials=vertex_credentials_json
     )
@@ -733,10 +734,10 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     CrewAI emits events for each chunk received during streaming:
 
     ```python
-    from crewai.utilities.events import (
+    from crewai.events import (
       LLMStreamChunkEvent
     )
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):
@@ -758,8 +759,8 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
 
     ```python
     from crewai import LLM, Agent, Task, Crew
-    from crewai.utilities.events import LLMStreamChunkEvent
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import LLMStreamChunkEvent
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):

docs/en/concepts/memory.mdx

@@ -2,15 +2,15 @@
 title: Memory
 description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
 icon: database
+mode: "wide"
 ---
 
 ## Overview
 
-The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **three distinct memory approaches** that serve different use cases:
+The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **two distinct memory approaches** that serve different use cases:
 
 1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
-2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)  
-3. **External Memory** - Standalone external memory providers (new approach)
+2. **External Memory** - Standalone external memory providers
 
 ## Memory System Components
 
@@ -19,7 +19,7 @@ The CrewAI framework provides a sophisticated memory system designed to signific
 | **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
 | **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
-| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
+| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, `ExternalMemory` and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
 
 ## 1. Basic Memory System (Recommended)
 
@@ -62,7 +62,7 @@ By default, CrewAI uses the `appdirs` library to determine storage locations fol
 ```
 ~/Library/Application Support/CrewAI/{project_name}/
 ├── knowledge/           # Knowledge base ChromaDB files
-├── short_term_memory/   # Short-term memory ChromaDB files  
+├── short_term_memory/   # Short-term memory ChromaDB files
 ├── long_term_memory/    # Long-term memory ChromaDB files
 ├── entities/            # Entity memory ChromaDB files
 └── long_term_memory_storage.db  # SQLite database
@@ -202,7 +202,7 @@ crew = Crew(
     tasks=[task],
     memory=True,
     embedder={
-        "provider": "anthropic",  # Match your LLM provider
+        "provider": "anthropic", # Match your LLM provider
         "config": {
             "api_key": "your-anthropic-key",
             "model": "text-embedding-3-small"
@@ -252,7 +252,7 @@ chroma_path = os.path.join(storage_path, "knowledge")
 if os.path.exists(chroma_path):
     client = chromadb.PersistentClient(path=chroma_path)
     collections = client.list_collections()
-    
+
     print("ChromaDB Collections:")
     for collection in collections:
         print(f"  - {collection.name}: {collection.count()} documents")
@@ -269,7 +269,7 @@ crew = Crew(agents=[...], tasks=[...], memory=True)
 
 # Reset specific memory types
 crew.reset_memories(command_type='short')     # Short-term memory
-crew.reset_memories(command_type='long')      # Long-term memory  
+crew.reset_memories(command_type='long')      # Long-term memory
 crew.reset_memories(command_type='entity')    # Entity memory
 crew.reset_memories(command_type='knowledge') # Knowledge storage
 ```
@@ -540,16 +540,71 @@ crew = Crew(
 )
 ```
 
+### Mem0 Provider
+
+Short-Term Memory and Entity Memory both supports a tight integration with both Mem0 OSS and Mem0 Client as a provider. Here is how you can use Mem0 as a provider.
+
+```python
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.memory.entity_entity_memory import EntityMemory
+
+mem0_oss_embedder_config = {
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "local_mem0_config": {
+                "vector_store": {"provider": "qdrant","config": {"host": "localhost", "port": 6333}},
+                "llm": {"provider": "openai","config": {"api_key": "your-api-key", "model": "gpt-4"}},
+                "embedder": {"provider": "openai","config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}}
+            },
+            "infer": True # Optional defaults to True
+        },
+    }
+
+
+mem0_client_embedder_config = {
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "org_id": "my_org_id",        # Optional
+            "project_id": "my_project_id", # Optional
+            "api_key": "custom-api-key"    # Optional - overrides env var
+            "run_id": "my_run_id",        # Optional - for short-term memory
+            "includes": "include1",       # Optional 
+            "excludes": "exclude1",       # Optional
+            "infer": True                 # Optional defaults to True
+            "custom_categories": new_categories  # Optional - custom categories for user memory
+        },
+    }
+
+
+short_term_memory_mem0_oss = ShortTermMemory(embedder_config=mem0_oss_embedder_config) # Short Term Memory with Mem0 OSS
+short_term_memory_mem0_client = ShortTermMemory(embedder_config=mem0_client_embedder_config) # Short Term Memory with Mem0 Client
+entity_memory_mem0_oss = EntityMemory(embedder_config=mem0_oss_embedder_config) # Entity Memory with Mem0 OSS
+entity_memory_mem0_client = EntityMemory(embedder_config=mem0_client_embedder_config) # Short Term Memory with Mem0 Client
+
+crew = Crew(
+    memory=True,
+    short_term_memory=short_term_memory_mem0_oss, # or short_term_memory_mem0_client
+    entity_memory=entity_memory_mem0_oss # or entity_memory_mem0_client
+)
+```
+
 ### Choosing the Right Embedding Provider
 
-| Provider | Best For | Pros | Cons |
-|:---------|:----------|:------|:------|
-| **OpenAI** | General use, reliability | High quality, well-tested | Cost, requires API key |
-| **Ollama** | Privacy, cost savings | Free, local, private | Requires local setup |
-| **Google AI** | Google ecosystem | Good performance | Requires Google account |
-| **Azure OpenAI** | Enterprise, compliance | Enterprise features | Complex setup |
-| **Cohere** | Multilingual content | Great language support | Specialized use case |
-| **VoyageAI** | Retrieval tasks | Optimized for search | Newer provider |
+When selecting an embedding provider, consider factors like performance, privacy, cost, and integration needs.  
+Below is a comparison to help you decide:
+
+| Provider       | Best For                       | Pros                              | Cons                      |
+| -------------- | ------------------------------ | --------------------------------- | ------------------------- |
+| **OpenAI**     | General use, high reliability  | High quality, widely tested       | Paid service, API key required |
+| **Ollama**     | Privacy-focused, cost savings  | Free, runs locally, fully private | Requires local installation/setup |
+| **Google AI**  | Integration in Google ecosystem| Strong performance, good support  | Google account required   |
+| **Azure OpenAI** | Enterprise & compliance needs| Enterprise-grade features, security | More complex setup process |
+| **Cohere**     | Multilingual content handling  | Excellent language support        | More niche use cases      |
+| **VoyageAI**   | Information retrieval & search | Optimized for retrieval tasks     | Relatively new provider   |
+| **Mem0**       | Per-user personalization       | Search-optimized embeddings       | Paid service, API key required |
+
 
 ### Environment Variable Configuration
 
@@ -596,7 +651,7 @@ providers_to_test = [
     {
         "name": "Ollama",
         "config": {
-            "provider": "ollama", 
+            "provider": "ollama",
             "config": {"model": "mxbai-embed-large"}
         }
     }
@@ -604,7 +659,7 @@ providers_to_test = [
 
 for provider in providers_to_test:
     print(f"\nTesting {provider['name']} embeddings...")
-    
+
     # Create crew with specific embedder
     crew = Crew(
         agents=[...],
@@ -612,7 +667,7 @@ for provider in providers_to_test:
         memory=True,
         embedder=provider['config']
     )
-    
+
     # Run your test and measure performance
     result = crew.kickoff()
     print(f"{provider['name']} completed successfully")
@@ -623,7 +678,7 @@ for provider in providers_to_test:
 **Model not found errors:**
 ```python
 # Verify model availability
-from crewai.utilities.embedding_configurator import EmbeddingConfigurator
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
 
 configurator = EmbeddingConfigurator()
 try:
@@ -655,17 +710,17 @@ import time
 
 def test_embedding_performance(embedder_config, test_text="This is a test document"):
     start_time = time.time()
-    
+
     crew = Crew(
         agents=[...],
         tasks=[...],
         memory=True,
         embedder=embedder_config
     )
-    
+
     # Simulate memory operation
     crew.kickoff()
-    
+
     end_time = time.time()
     return end_time - start_time
 
@@ -676,7 +731,7 @@ openai_time = test_embedding_performance({
 })
 
 ollama_time = test_embedding_performance({
-    "provider": "ollama", 
+    "provider": "ollama",
     "config": {"model": "mxbai-embed-large"}
 })
 
@@ -684,67 +739,29 @@ print(f"OpenAI: {openai_time:.2f}s")
 print(f"Ollama: {ollama_time:.2f}s")
 ```
 
-## 2. User Memory with Mem0 (Legacy)
+### Entity Memory batching behavior
 
-<Warning>
-**Legacy Approach**: While fully functional, this approach is considered legacy. For new projects requiring user-specific memory, consider using External Memory instead.
-</Warning>
+Entity Memory supports batching when saving multiple entities at once. When you pass a list of `EntityMemoryItem`, the system:
 
-User Memory integrates with [Mem0](https://mem0.ai/) to provide user-specific memory that persists across sessions and integrates with the crew's contextual memory system.
+- Emits a single MemorySaveStartedEvent with `entity_count`
+- Saves each entity internally, collecting any partial errors
+- Emits MemorySaveCompletedEvent with aggregate metadata (saved count, errors)
+- Raises a partial-save exception if some entities failed (includes counts)
 
-### Prerequisites
-```bash
-pip install mem0ai
-```
+This improves performance and observability when writing many entities in one operation.
 
-### Mem0 Cloud Configuration
+## 2. External Memory 
+External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
+
+### Basic External Memory with Mem0
 ```python
 import os
-from crewai import Crew, Process
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
 
-# Set your Mem0 API key
-os.environ["MEM0_API_KEY"] = "m0-your-api-key"
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,  # Required for contextual memory integration
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john"},
-        "user_memory": {}  # Required - triggers user memory initialization
-    },
-    process=Process.sequential,
-    verbose=True
-)
-```
-
-### Advanced Mem0 Configuration
-```python
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {
-            "user_id": "john",
-            "org_id": "my_org_id",        # Optional
-            "project_id": "my_project_id", # Optional
-            "api_key": "custom-api-key"    # Optional - overrides env var
-        },
-        "user_memory": {}
-    }
-)
-```
-
-### Local Mem0 Configuration
-```python
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,
-    memory_config={
+# Create external memory instance with local Mem0 Configuration
+external_memory = ExternalMemory(
+    embedder_config={
         "provider": "mem0",
         "config": {
             "user_id": "john",
@@ -761,37 +778,60 @@ crew = Crew(
                     "provider": "openai",
                     "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}
                 }
-            }
+            },
+            "infer": True # Optional defaults to True
         },
-        "user_memory": {}
-    }
-)
-```
-
-## 3. External Memory (New Approach)
-
-External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
-
-### Basic External Memory with Mem0
-```python
-import os
-from crewai import Agent, Crew, Process, Task
-from crewai.memory.external.external_memory import ExternalMemory
-
-os.environ["MEM0_API_KEY"] = "your-api-key"
-
-# Create external memory instance
-external_memory = ExternalMemory(
-    embedder_config={
-        "provider": "mem0", 
-        "config": {"user_id": "U-123"}
     }
 )
 
 crew = Crew(
     agents=[...],
     tasks=[...],
-    external_memory=external_memory,  # Separate from basic memory
+    external_memory=external_memory, # Separate from basic memory
+    process=Process.sequential,
+    verbose=True
+)
+```
+
+### Advanced External Memory with Mem0 Client
+When using Mem0 Client, you can customize the memory configuration further, by using parameters like 'includes', 'excludes', 'custom_categories', 'infer' and 'run_id' (this is only for short-term memory).
+You can find more details in the [Mem0 documentation](https://docs.mem0.ai/).
+
+```python
+import os
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
+
+new_categories = [
+    {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+]
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+# Create external memory instance with Mem0 Client
+external_memory = ExternalMemory(
+    embedder_config={
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "org_id": "my_org_id",        # Optional
+            "project_id": "my_project_id", # Optional
+            "api_key": "custom-api-key"    # Optional - overrides env var
+            "run_id": "my_run_id",        # Optional - for short-term memory
+            "includes": "include1",       # Optional 
+            "excludes": "exclude1",       # Optional
+            "infer": True                 # Optional defaults to True
+            "custom_categories": new_categories  # Optional - custom categories for user memory
+        },
+    }
+)
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    external_memory=external_memory, # Separate from basic memory
     process=Process.sequential,
     verbose=True
 )
@@ -808,8 +848,8 @@ class CustomStorage(Storage):
 
     def save(self, value, metadata=None, agent=None):
         self.memories.append({
-            "value": value, 
-            "metadata": metadata, 
+            "value": value,
+            "metadata": metadata,
             "agent": agent
         })
 
@@ -830,17 +870,18 @@ crew = Crew(
 )
 ```
 
-## Memory System Comparison
+## 🧠 Memory System Comparison
+
+| **Category**        | **Feature**            | **Basic Memory**            | **External Memory**         |
+|---------------------|------------------------|-----------------------------|------------------------------|
+| **Ease of Use**     | Setup Complexity       | Simple                      | Moderate                     |
+|                     | Integration            | Built-in (contextual)       | Standalone                   |
+| **Persistence**     | Storage                | Local files                 | Custom / Mem0                |
+|                     | Cross-session Support  | ✅                           | ✅                           |
+| **Personalization** | User-specific Memory   | ❌                          |  ✅                           |
+|                     | Custom Providers       | Limited                     | Any provider                 |
+| **Use Case Fit**    | Recommended For        | Most general use cases      | Specialized / custom needs   |
 
-| Feature | Basic Memory | User Memory (Legacy) | External Memory |
-|---------|-------------|---------------------|----------------|
-| **Setup Complexity** | Simple | Medium | Medium |
-| **Integration** | Built-in contextual | Contextual + User-specific | Standalone |
-| **Storage** | Local files | Mem0 Cloud/Local | Custom/Mem0 |
-| **Cross-session** | ✅ | ✅ | ✅ |
-| **User-specific** | ❌ | ✅ | ✅ |
-| **Custom providers** | Limited | Mem0 only | Any provider |
-| **Recommended for** | Most use cases | Legacy projects | Specialized needs |
 
 ## Supported Embedding Providers
 
@@ -986,7 +1027,201 @@ crew = Crew(
 - 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
 - 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
 
+## Memory Events
+
+CrewAI's event system provides powerful insights into memory operations. By leveraging memory events, you can monitor, debug, and optimize your memory system's performance and behavior.
+
+### Available Memory Events
+
+CrewAI emits the following memory-related events:
+
+| Event | Description | Key Properties |
+| :---- | :---------- | :------------- |
+| **MemoryQueryStartedEvent** | Emitted when a memory query begins | `query`, `limit`, `score_threshold` |
+| **MemoryQueryCompletedEvent** | Emitted when a memory query completes successfully | `query`, `results`, `limit`, `score_threshold`, `query_time_ms` |
+| **MemoryQueryFailedEvent** | Emitted when a memory query fails | `query`, `limit`, `score_threshold`, `error` |
+| **MemorySaveStartedEvent** | Emitted when a memory save operation begins | `value`, `metadata`, `agent_role` |
+| **MemorySaveCompletedEvent** | Emitted when a memory save operation completes successfully | `value`, `metadata`, `agent_role`, `save_time_ms` |
+| **MemorySaveFailedEvent** | Emitted when a memory save operation fails | `value`, `metadata`, `agent_role`, `error` |
+| **MemoryRetrievalStartedEvent** | Emitted when memory retrieval for a task prompt starts | `task_id` |
+| **MemoryRetrievalCompletedEvent** | Emitted when memory retrieval completes successfully | `task_id`, `memory_content`, `retrieval_time_ms` |
+
+### Practical Applications
+
+#### 1. Memory Performance Monitoring
+
+Track memory operation timing to optimize your application:
+
+```python
+from crewai.events import (
+    BaseEventListener,
+    MemoryQueryCompletedEvent,
+    MemorySaveCompletedEvent
+)
+import time
+
+class MemoryPerformanceMonitor(BaseEventListener):
+    def __init__(self):
+        super().__init__()
+        self.query_times = []
+        self.save_times = []
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
+            self.query_times.append(event.query_time_ms)
+            print(f"Memory query completed in {event.query_time_ms:.2f}ms. Query: '{event.query}'")
+            print(f"Average query time: {sum(self.query_times)/len(self.query_times):.2f}ms")
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
+            self.save_times.append(event.save_time_ms)
+            print(f"Memory save completed in {event.save_time_ms:.2f}ms")
+            print(f"Average save time: {sum(self.save_times)/len(self.save_times):.2f}ms")
+
+# Create an instance of your listener
+memory_monitor = MemoryPerformanceMonitor()
+```
+
+#### 2. Memory Content Logging
+
+Log memory operations for debugging and insights:
+
+```python
+from crewai.events import (
+    BaseEventListener,
+    MemorySaveStartedEvent,
+    MemoryQueryStartedEvent,
+    MemoryRetrievalCompletedEvent
+)
+import logging
+
+# Configure logging
+logger = logging.getLogger('memory_events')
+
+class MemoryLogger(BaseEventListener):
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemorySaveStartedEvent)
+        def on_memory_save_started(source, event: MemorySaveStartedEvent):
+            if event.agent_role:
+                logger.info(f"Agent '{event.agent_role}' saving memory: {event.value[:50]}...")
+            else:
+                logger.info(f"Saving memory: {event.value[:50]}...")
+
+        @crewai_event_bus.on(MemoryQueryStartedEvent)
+        def on_memory_query_started(source, event: MemoryQueryStartedEvent):
+            logger.info(f"Memory query started: '{event.query}' (limit: {event.limit})")
+
+        @crewai_event_bus.on(MemoryRetrievalCompletedEvent)
+        def on_memory_retrieval_completed(source, event: MemoryRetrievalCompletedEvent):
+            if event.task_id:
+                logger.info(f"Memory retrieved for task {event.task_id} in {event.retrieval_time_ms:.2f}ms")
+            else:
+                logger.info(f"Memory retrieved in {event.retrieval_time_ms:.2f}ms")
+            logger.debug(f"Memory content: {event.memory_content}")
+
+# Create an instance of your listener
+memory_logger = MemoryLogger()
+```
+
+#### 3. Error Tracking and Notifications
+
+Capture and respond to memory errors:
+
+```python
+from crewai.events import (
+    BaseEventListener,
+    MemorySaveFailedEvent,
+    MemoryQueryFailedEvent
+)
+import logging
+from typing import Optional
+
+# Configure logging
+logger = logging.getLogger('memory_errors')
+
+class MemoryErrorTracker(BaseEventListener):
+    def __init__(self, notify_email: Optional[str] = None):
+        super().__init__()
+        self.notify_email = notify_email
+        self.error_count = 0
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemorySaveFailedEvent)
+        def on_memory_save_failed(source, event: MemorySaveFailedEvent):
+            self.error_count += 1
+            agent_info = f"Agent '{event.agent_role}'" if event.agent_role else "Unknown agent"
+            error_message = f"Memory save failed: {event.error}. {agent_info}"
+            logger.error(error_message)
+
+            if self.notify_email and self.error_count % 5 == 0:
+                self._send_notification(error_message)
+
+        @crewai_event_bus.on(MemoryQueryFailedEvent)
+        def on_memory_query_failed(source, event: MemoryQueryFailedEvent):
+            self.error_count += 1
+            error_message = f"Memory query failed: {event.error}. Query: '{event.query}'"
+            logger.error(error_message)
+
+            if self.notify_email and self.error_count % 5 == 0:
+                self._send_notification(error_message)
+
+    def _send_notification(self, message):
+        # Implement your notification system (email, Slack, etc.)
+        print(f"[NOTIFICATION] Would send to {self.notify_email}: {message}")
+
+# Create an instance of your listener
+error_tracker = MemoryErrorTracker(notify_email="admin@example.com")
+```
+
+### Integrating with Analytics Platforms
+
+Memory events can be forwarded to analytics and monitoring platforms to track performance metrics, detect anomalies, and visualize memory usage patterns:
+
+```python
+from crewai.events import (
+    BaseEventListener,
+    MemoryQueryCompletedEvent,
+    MemorySaveCompletedEvent
+)
+
+class MemoryAnalyticsForwarder(BaseEventListener):
+    def __init__(self, analytics_client):
+        super().__init__()
+        self.client = analytics_client
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
+            # Forward query metrics to analytics platform
+            self.client.track_metric({
+                "event_type": "memory_query",
+                "query": event.query,
+                "duration_ms": event.query_time_ms,
+                "result_count": len(event.results) if hasattr(event.results, "__len__") else 0,
+                "timestamp": event.timestamp
+            })
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
+            # Forward save metrics to analytics platform
+            self.client.track_metric({
+                "event_type": "memory_save",
+                "agent_role": event.agent_role,
+                "duration_ms": event.save_time_ms,
+                "timestamp": event.timestamp
+            })
+```
+
+### Best Practices for Memory Event Listeners
+
+1. **Keep handlers lightweight**: Avoid complex processing in event handlers to prevent performance impacts
+2. **Use appropriate logging levels**: Use INFO for normal operations, DEBUG for details, ERROR for issues
+3. **Batch metrics when possible**: Accumulate metrics before sending to external systems
+4. **Handle exceptions gracefully**: Ensure your event handlers don't crash due to unexpected data
+5. **Consider memory consumption**: Be mindful of storing large amounts of event data
+
 ## Conclusion
 
-Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, 
+Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations,
 you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/en/concepts/planning.mdx

@@ -2,6 +2,7 @@
 title: Planning
 description: Learn how to add planning to your CrewAI Crew and improve their performance.
 icon: ruler-combined
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/processes.mdx

@@ -2,6 +2,7 @@
 title: Processes
 description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
 icon: bars-staggered
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/reasoning.mdx

@@ -2,6 +2,7 @@
 title: Reasoning
 description: "Learn how to enable and use agent reasoning to improve task execution."
 icon: brain
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/tasks.mdx

@@ -2,6 +2,7 @@
 title: Tasks
 description: Detailed guide on managing and creating tasks within the CrewAI framework.
 icon: list-check
+mode: "wide"
 ---
 
 ## Overview
@@ -13,7 +14,7 @@ Tasks provide all necessary details for execution, such as a description, the ag
 Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
 <Note type="info" title="Enterprise Enhancement: Visual Task Builder">
-CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
+CrewAI AMP includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
 
 ![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 
@@ -54,9 +55,17 @@ crew = Crew(
 | **Markdown** _(optional)_        | `markdown`        | `Optional[bool]`              | Whether the task should instruct the agent to return the final answer formatted in Markdown. Defaults to False.      |
 | **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
 | **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
+| **Create Directory** _(optional)_ | `create_directory` | `Optional[bool]`             | Whether to create the directory for output_file if it doesn't exist. Defaults to True.                               |
 | **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
+| **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
+| **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
+
+<Note type="warning" title="Deprecated: max_retries">
+  The task attribute `max_retries` is deprecated and will be removed in v1.0.0.
+  Use `guardrail_max_retries` instead to control retry attempts when a guardrail fails.
+</Note>
 
 ## Creating Tasks
 
@@ -332,9 +341,11 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-### Using Task Guardrails
+Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
 
-To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
+### Function-Based Guardrails
+
+To add a function-based guardrail to a task, provide a validation function through the `guardrail` parameter:
 
 ```python Code
 from typing import Tuple, Union, Dict, Any
@@ -372,9 +383,7 @@ blog_task = Task(
    - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)`
    - On Failure: it returns a tuple of `(bool, str)`. For example: `(False, "Error message explain the failure")`
 
-### LLMGuardrail
 
-The `LLMGuardrail` class offers a robust mechanism for validating task outputs.
 
 ### Error Handling Best Practices
 
@@ -429,7 +438,7 @@ When a guardrail returns `(False, error)`:
 2. The agent attempts to fix the issue
 3. The process repeats until:
    - The guardrail returns `(True, result)`
-   - Maximum retries are reached
+   - Maximum retries are reached (`guardrail_max_retries`)
 
 Example with retry handling:
 ```python Code
@@ -450,7 +459,7 @@ task = Task(
     expected_output="A valid JSON object",
     agent=analyst,
     guardrail=validate_json_output,
-    max_retries=3  # Limit retry attempts
+    guardrail_max_retries=3  # Limit retry attempts
 )
 ```
 
@@ -798,197 +807,103 @@ While creating and executing tasks, certain validation mechanisms are in place t
 
 These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
 
-## Task Guardrails
 
-Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
-
-### Basic Usage
-
-#### Define your own logic to validate
-
-```python Code
-from typing import Tuple, Union
-from crewai import Task
-
-def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
-    """Validate that the output is valid JSON."""
-    try:
-        json_data = json.loads(result)
-        return (True, json_data)
-    except json.JSONDecodeError:
-        return (False, "Output must be valid JSON")
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=validate_json_output
-)
-```
-
-#### Leverage a no-code approach for validation
-
-```python Code
-from crewai import Task
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail="Ensure the response is a valid JSON object"
-)
-```
-
-#### Using YAML
-
-```yaml
-research_task:
-  ...
-  guardrail: make sure each bullet contains a minimum of 100 words
-  ...
-```
-
-```python Code
-@CrewBase
-class InternalCrew:
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
-
-    ...
-    @task
-    def research_task(self):
-        return Task(config=self.tasks_config["research_task"])  # type: ignore[index]
-    ...
-```
-
-
-#### Use custom models for code generation
-
-```python Code
-from crewai import Task
-from crewai.llm import LLM
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=LLMGuardrail(
-        description="Ensure the response is a valid JSON object",
-        llm=LLM(model="gpt-4o-mini"),
-    )
-)
-```
-
-### How Guardrails Work
-
-1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
-2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
-3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
-   - If `success` is `True`, `data` is the validated/transformed result
-   - If `success` is `False`, `data` is the error message
-4. **Result Routing**:
-   - On success (`True`), the result is automatically passed to the next task
-   - On failure (`False`), the error is sent back to the agent to generate a new answer
-
-### Common Use Cases
-
-#### Data Format Validation
-```python Code
-def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure the output contains a valid email address."""
-    import re
-    email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
-    if re.match(email_pattern, result.strip()):
-        return (True, result.strip())
-    return (False, "Output must be a valid email address")
-```
-
-#### Content Filtering
-```python Code
-def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
-    """Remove or validate sensitive information."""
-    sensitive_patterns = ['SSN:', 'password:', 'secret:']
-    for pattern in sensitive_patterns:
-        if pattern.lower() in result.lower():
-            return (False, f"Output contains sensitive information ({pattern})")
-    return (True, result)
-```
-
-#### Data Transformation
-```python Code
-def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure phone numbers are in a consistent format."""
-    import re
-    digits = re.sub(r'\D', '', result)
-    if len(digits) == 10:
-        formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
-        return (True, formatted)
-    return (False, "Output must be a 10-digit phone number")
-```
-
-### Advanced Features
-
-#### Chaining Multiple Validations
-```python Code
-def chain_validations(*validators):
-    """Chain multiple validators together."""
-    def combined_validator(result):
-        for validator in validators:
-            success, data = validator(result)
-            if not success:
-                return (False, data)
-            result = data
-        return (True, result)
-    return combined_validator
-
-# Usage
-task = Task(
-    description="Get user contact info",
-    expected_output="Email and phone",
-    guardrail=chain_validations(
-        validate_email_format,
-        filter_sensitive_info
-    )
-)
-```
-
-#### Custom Retry Logic
-```python Code
-task = Task(
-    description="Generate data",
-    expected_output="Valid data",
-    guardrail=validate_data,
-    max_retries=5  # Override default retry limit
-)
-```
 
 ## Creating Directories when Saving Files
 
-You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
+The `create_directory` parameter controls whether CrewAI should automatically create directories when saving task outputs to files. This feature is particularly useful for organizing outputs and ensuring that file paths are correctly structured, especially when working with complex project hierarchies.
+
+### Default Behavior
+
+By default, `create_directory=True`, which means CrewAI will automatically create any missing directories in the output file path:
 
 ```python Code
-# ...
-
-save_output_task = Task(
-    description='Save the summarized AI news to a file',
-    expected_output='File saved successfully',
-    agent=research_agent,
-    tools=[file_save_tool],
-    output_file='outputs/ai_news_summary.txt',
-    create_directory=True
+# Default behavior - directories are created automatically
+report_task = Task(
+    description='Generate a comprehensive market analysis report',
+    expected_output='A detailed market analysis with charts and insights',
+    agent=analyst_agent,
+    output_file='reports/2025/market_analysis.md',  # Creates 'reports/2025/' if it doesn't exist
+    markdown=True
 )
+```
 
-#...
+### Disabling Directory Creation
+
+If you want to prevent automatic directory creation and ensure that the directory already exists, set `create_directory=False`:
+
+```python Code
+# Strict mode - directory must already exist
+strict_output_task = Task(
+    description='Save critical data that requires existing infrastructure',
+    expected_output='Data saved to pre-configured location',
+    agent=data_agent,
+    output_file='secure/vault/critical_data.json',
+    create_directory=False  # Will raise RuntimeError if 'secure/vault/' doesn't exist
+)
+```
+
+### YAML Configuration
+
+You can also configure this behavior in your YAML task definitions:
+
+```yaml tasks.yaml
+analysis_task:
+  description: >
+    Generate quarterly financial analysis
+  expected_output: >
+    A comprehensive financial report with quarterly insights
+  agent: financial_analyst
+  output_file: reports/quarterly/q4_2024_analysis.pdf
+  create_directory: true  # Automatically create 'reports/quarterly/' directory
+
+audit_task:
+  description: >
+    Perform compliance audit and save to existing audit directory
+  expected_output: >
+    A compliance audit report
+  agent: auditor
+  output_file: audit/compliance_report.md
+  create_directory: false  # Directory must already exist
+```
+
+### Use Cases
+
+**Automatic Directory Creation (`create_directory=True`):**
+- Development and prototyping environments
+- Dynamic report generation with date-based folders
+- Automated workflows where directory structure may vary
+- Multi-tenant applications with user-specific folders
+
+**Manual Directory Management (`create_directory=False`):**
+- Production environments with strict file system controls
+- Security-sensitive applications where directories must be pre-configured
+- Systems with specific permission requirements
+- Compliance environments where directory creation is audited
+
+### Error Handling
+
+When `create_directory=False` and the directory doesn't exist, CrewAI will raise a `RuntimeError`:
+
+```python Code
+try:
+    result = crew.kickoff()
+except RuntimeError as e:
+    # Handle missing directory error
+    print(f"Directory creation failed: {e}")
+    # Create directory manually or use fallback location
 ```
 
 Check out the video below to see how to use structured outputs in CrewAI:
 
 <iframe
-  width="560"
-  height="315"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/dNpKQk5uxHw"
-  title="YouTube video player"
-  frameborder="0"
+  title="Structured outputs in CrewAI"
+  frameBorder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowfullscreen
+  referrerPolicy="strict-origin-when-cross-origin"
+  allowFullScreen
 ></iframe>
 
 ## Conclusion

docs/en/concepts/testing.mdx

@@ -2,6 +2,7 @@
 title: Testing
 description: Learn how to test your CrewAI Crew and evaluate their performance.
 icon: vial
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/tools.mdx

@@ -2,6 +2,7 @@
 title: Tools
 description: Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.
 icon: screwdriver-wrench
+mode: "wide"
 ---
 
 ## Overview
@@ -16,7 +17,7 @@ This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/cre
 enabling everything from simple searches to complex interactions and effective teamwork among agents.
 
 <Note type="info" title="Enterprise Enhancement: Tools Repository">
-CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
+CrewAI AMP provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
 
 The Enterprise Tools Repository includes:
 - Pre-built connectors for popular enterprise systems
@@ -207,7 +208,7 @@ from crewai.tools import BaseTool
 class AsyncCustomTool(BaseTool):
     name: str = "async_custom_tool"
     description: str = "An asynchronous custom tool"
-    
+
     async def _run(self, query: str = "") -> str:
         """Asynchronously run the tool"""
         # Your async implementation here

docs/en/concepts/training.mdx

@@ -2,6 +2,7 @@
 title: Training
 description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
 icon: dumbbell
+mode: "wide"
 ---
 
 ## Overview
@@ -21,13 +22,17 @@ To use the training feature, follow these steps:
 3. Run the following command:
 
 ```shell
-crewai train -n <n_iterations> <filename> (optional)
+crewai train -n <n_iterations> -f <filename.pkl>
 ```
 <Tip>
   Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
 </Tip>
 
-### Training Your Crew Programmatically
+<Note>
+  If you omit `-f`, the output defaults to `trained_agents_data.pkl` in the current working directory. You can pass an absolute path to control where the file is written.
+</Note>
+
+### Training your Crew programmatically
 
 To train your crew programmatically, use the following steps:
 
@@ -51,19 +56,65 @@ except Exception as e:
     raise Exception(f"An error occurred while training the crew: {e}")
 ```
 
-### Key Points to Note
+## How trained data is used by agents
 
-- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
-- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
-- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
+CrewAI uses the training artifacts in two ways: during training to incorporate your human feedback, and after training to guide agents with consolidated suggestions.
 
-It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
+### Training data flow
 
-Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
+```mermaid
+flowchart TD
+  A["Start training<br/>CLI: crewai train -n -f<br/>or Python: crew.train(...)"] --> B["Setup training mode<br/>- task.human_input = true<br/>- disable delegation<br/>- init training_data.pkl + trained file"]
 
-Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
+  subgraph "Iterations"
+    direction LR
+    C["Iteration i<br/>initial_output"] --> D["User human_feedback"]
+    D --> E["improved_output"]
+    E --> F["Append to training_data.pkl<br/>by agent_id and iteration"]
+  end
 
-Happy training with CrewAI! 🚀
+  B --> C
+  F --> G{"More iterations?"}
+  G -- "Yes" --> C
+  G -- "No" --> H["Evaluate per agent<br/>aggregate iterations"]
+
+  H --> I["Consolidate<br/>suggestions[] + quality + final_summary"]
+  I --> J["Save by agent role to trained file<br/>(default: trained_agents_data.pkl)"]
+
+  J --> K["Normal (non-training) runs"]
+  K --> L["Auto-load suggestions<br/>from trained_agents_data.pkl"]
+  L --> M["Append to prompt<br/>for consistent improvements"]
+```
+
+### During training runs
+
+- On each iteration, the system records for every agent:
+  - `initial_output`: the agent’s first answer
+  - `human_feedback`: your inline feedback when prompted
+  - `improved_output`: the agent’s follow-up answer after feedback
+- This data is stored in a working file named `training_data.pkl` keyed by the agent’s internal ID and iteration.
+- While training is active, the agent automatically appends your prior human feedback to its prompt to enforce those instructions on subsequent attempts within the training session.
+  Training is interactive: tasks set `human_input = true`, so running in a non-interactive environment will block on user input.
+
+### After training completes
+
+- When `train(...)` finishes, CrewAI evaluates the collected training data per agent and produces a consolidated result containing:
+  - `suggestions`: clear, actionable instructions distilled from your feedback and the difference between initial/improved outputs
+  - `quality`: a 0–10 score capturing improvement
+  - `final_summary`: a step-by-step set of action items for future tasks
+- These consolidated results are saved to the filename you pass to `train(...)` (default via CLI is `trained_agents_data.pkl`). Entries are keyed by the agent’s `role` so they can be applied across sessions.
+- During normal (non-training) execution, each agent automatically loads its consolidated `suggestions` and appends them to the task prompt as mandatory instructions. This gives you consistent improvements without changing your agent definitions.
+
+### File summary
+
+- `training_data.pkl` (ephemeral, per-session):
+  - Structure: `agent_id -> { iteration_number: { initial_output, human_feedback, improved_output } }`
+  - Purpose: capture raw data and human feedback during training
+  - Location: saved in the current working directory (CWD)
+- `trained_agents_data.pkl` (or your custom filename):
+  - Structure: `agent_role -> { suggestions: string[], quality: number, final_summary: string }`
+  - Purpose: persist consolidated guidance for future runs
+  - Location: written to the CWD by default; use `-f` to set a custom (including absolute) path
 
 ## Small Language Model Considerations
 
@@ -129,3 +180,18 @@ Happy training with CrewAI! 🚀
     </Warning>
   </Tab>
 </Tabs>
+
+### Key Points to Note
+
+- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
+- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
+- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
+- Trained guidance is applied at prompt time; it does not modify your Python/YAML agent configuration.
+- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, either rename it to `trained_agents_data.pkl` before running, or adjust the loader in code.
+- You can change the output filename when calling `crewai train` with `-f/--filename`. Absolute paths are supported if you want to save outside the CWD.
+
+It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
+
+Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
+
+Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.

docs/en/enterprise/features/agent-repositories.mdx

@@ -0,0 +1,155 @@
+---
+title: 'Agent Repositories'
+description: 'Learn how to use Agent Repositories to share and reuse your agents across teams and projects'
+icon: 'people-group'
+mode: "wide"
+---
+
+Agent Repositories allow enterprise users to store, share, and reuse agent definitions across teams and projects. This feature enables organizations to maintain a centralized library of standardized agents, promoting consistency and reducing duplication of effort.
+
+<Frame>
+  ![Agent Repositories](/images/enterprise/agent-repositories.png)
+</Frame>
+
+## Benefits of Agent Repositories
+
+- **Standardization**: Maintain consistent agent definitions across your organization
+- **Reusability**: Create an agent once and use it in multiple crews and projects
+- **Governance**: Implement organization-wide policies for agent configurations
+- **Collaboration**: Enable teams to share and build upon each other's work
+
+## Creating and Use Agent Repositories
+
+1. You must have an account at CrewAI, try the [free plan](https://app.crewai.com).
+2. Create agents with specific roles and goals for your workflows.
+3. Configure tools and capabilities for each specialized assistant.
+4. Deploy agents across projects via visual interface or API integration.
+
+<Frame>
+  ![Agent Repositories](/images/enterprise/create-agent-repository.png)
+</Frame>
+
+
+### Loading Agents from Repositories
+
+You can load agents from repositories in your code using the `from_repository` parameter to run locally:
+
+```python
+from crewai import Agent
+
+# Create an agent by loading it from a repository
+# The agent is loaded with all its predefined configurations
+researcher = Agent(
+    from_repository="market-research-agent"
+)
+```
+
+### Overriding Repository Settings
+
+You can override specific settings from the repository by providing them in the configuration:
+
+```python
+researcher = Agent(
+    from_repository="market-research-agent",
+    goal="Research the latest trends in AI development",  # Override the repository goal
+    verbose=True  # Add a setting not in the repository
+)
+```
+
+### Example: Creating a Crew with Repository Agents
+
+```python
+from crewai import Crew, Agent, Task
+
+# Load agents from repositories
+researcher = Agent(
+    from_repository="market-research-agent"
+)
+
+writer = Agent(
+    from_repository="content-writer-agent"
+)
+
+# Create tasks
+research_task = Task(
+    description="Research the latest trends in AI",
+    agent=researcher
+)
+
+writing_task = Task(
+    description="Write a comprehensive report based on the research",
+    agent=writer
+)
+
+# Create the crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, writing_task],
+    verbose=True
+)
+
+# Run the crew
+result = crew.kickoff()
+```
+
+### Example: Using `kickoff()` with Repository Agents
+
+You can also use repository agents directly with the `kickoff()` method for simpler interactions:
+
+```python
+from crewai import Agent
+from pydantic import BaseModel
+from typing import List
+
+# Define a structured output format
+class MarketAnalysis(BaseModel):
+    key_trends: List[str]
+    opportunities: List[str]
+    recommendation: str
+
+# Load an agent from repository
+analyst = Agent(
+    from_repository="market-analyst-agent",
+    verbose=True
+)
+
+# Get a free-form response
+result = analyst.kickoff("Analyze the AI market in 2025")
+print(result.raw)  # Access the raw response
+
+# Get structured output
+structured_result = analyst.kickoff(
+    "Provide a structured analysis of the AI market in 2025",
+    response_format=MarketAnalysis
+)
+
+# Access structured data
+print(f"Key Trends: {structured_result.pydantic.key_trends}")
+print(f"Recommendation: {structured_result.pydantic.recommendation}")
+```
+
+## Best Practices
+
+1. **Naming Convention**: Use clear, descriptive names for your repository agents
+2. **Documentation**: Include comprehensive descriptions for each agent
+3. **Tool Management**: Ensure that tools referenced by repository agents are available in your environment
+4. **Access Control**: Manage permissions to ensure only authorized team members can modify repository agents
+
+## Organization Management
+
+To switch between organizations or see your current organization, use the CrewAI CLI:
+
+```bash
+# View current organization
+crewai org current
+
+# Switch to a different organization
+crewai org switch <org_id>
+
+# List all available organizations
+crewai org list
+```
+
+<Note>
+When loading agents from repositories, you must be authenticated and switched to the correct organization. If you receive errors, check your authentication status and organization settings using the CLI commands above.
+</Note>