chore: add commitizen to pre-commit hooks

Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>

.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/workflows/tests.yml

@@ -7,14 +7,18 @@ permissions:
 
 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
@@ -23,6 +27,9 @@ jobs:
         uses: astral-sh/setup-uv@v3
         with:
           enable-cache: true
+          cache-dependency-glob: |
+            **/pyproject.toml
+            **/uv.lock
 
       - name: Set up Python ${{ matrix.python-version }}
         run: uv python install ${{ matrix.python-version }}
@@ -30,5 +37,14 @@ jobs:
       - name: Install the project
         run: uv sync --dev --all-extras
 
-      - name: Run tests
-        run: uv run pytest --block-network --timeout=60 -vv
+      - name: Run tests (group ${{ matrix.group }} of 8)
+        run: |
+          uv run pytest \
+            --block-network \
+            --timeout=30 \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            --durations=10 \
+            -n auto \
+            --maxfail=3

.pre-commit-config.yaml

@@ -5,3 +5,7 @@ repos:
       - id: ruff
         args: ["--fix"]
       - id: ruff-format
+  - repo: https://github.com/commitizen-tools/commitizen
+    rev: v3.13.0
+    hooks:
+      - id: commitizen

docs/docs.json

@@ -94,7 +94,7 @@
                     "pages": [
                       "en/guides/advanced/customizing-prompts",
                       "en/guides/advanced/fingerprinting"
-                 
+
                     ]
                   }
                 ]
@@ -296,7 +296,8 @@
                   "en/enterprise/features/webhook-streaming",
                   "en/enterprise/features/traces",
                   "en/enterprise/features/hallucination-guardrail",
-                  "en/enterprise/features/integrations"
+                  "en/enterprise/features/integrations",
+                  "en/enterprise/features/agent-repositories"
                 ]
               },
               {
@@ -373,7 +374,7 @@
               }
             ]
           }
- 
+
         ]
       },
       {
@@ -730,7 +731,7 @@
               }
             ]
           }
- 
+
         ]
       }
     ]
@@ -774,7 +775,7 @@
       "destination": "/en/introduction"
     },
     {
-      "source": "/installation", 
+      "source": "/installation",
       "destination": "/en/installation"
     },
     {

docs/en/concepts/agents.mdx

@@ -526,6 +526,103 @@ 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

@@ -4,6 +4,8 @@ description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
 ---
 
+<Warning>Since release 0.140.0, CrewAI Enterprise 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.
@@ -186,10 +188,7 @@ def crew(self) -> Crew:
 Deploy the crew or flow to [CrewAI Enterprise](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:
+    You can login or create an account with:
     ```shell Terminal
     crewai login
     ```

docs/en/concepts/event-listener.mdx

@@ -255,6 +255,17 @@ 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:

docs/en/concepts/memory.mdx

@@ -9,7 +9,7 @@ icon: database
 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:
 
 1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
-2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)  
+2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)
 3. **External Memory** - Standalone external memory providers (new approach)
 
 ## Memory System Components
@@ -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
@@ -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
 ```
@@ -596,7 +596,7 @@ providers_to_test = [
     {
         "name": "Ollama",
         "config": {
-            "provider": "ollama", 
+            "provider": "ollama",
             "config": {"model": "mxbai-embed-large"}
         }
     }
@@ -604,7 +604,7 @@ 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 +612,7 @@ 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")
@@ -655,17 +655,17 @@ 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 +676,7 @@ openai_time = test_embedding_performance({
 })
 
 ollama_time = test_embedding_performance({
-    "provider": "ollama", 
+    "provider": "ollama",
     "config": {"model": "mxbai-embed-large"}
 })
 
@@ -783,7 +783,7 @@ os.environ["MEM0_API_KEY"] = "your-api-key"
 # Create external memory instance
 external_memory = ExternalMemory(
     embedder_config={
-        "provider": "mem0", 
+        "provider": "mem0",
         "config": {"user_id": "U-123"}
     }
 )
@@ -808,8 +808,8 @@ class CustomStorage(Storage):
 
     def save(self, value, metadata=None, agent=None):
         self.memories.append({
-            "value": value, 
-            "metadata": metadata, 
+            "value": value,
+            "metadata": metadata,
             "agent": agent
         })
 
@@ -986,7 +986,201 @@ 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.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events import (
+    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.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events import (
+    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.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events import (
+    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.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events import (
+    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/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: 'database'
+---
+
+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.
+
+## 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
+
+## Using Agent Repositories
+
+### Prerequisites
+
+1. You must have an account at CrewAI, try the [free plan](https://app.crewai.com).
+2. You need to be authenticated using the CrewAI CLI.
+3. If you have more than one organization, make sure you are switched to the correct organization using the CLI command:
+
+```bash
+crewai org switch <org_id>
+```
+
+### Creating and Managing Agents in Repositories
+
+To create and manage agents in repositories,Enterprise Dashboard.
+
+### Loading Agents from Repositories
+
+You can load agents from repositories in your code using the `from_repository` parameter:
+
+```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>

docs/en/enterprise/guides/deploy-crew.mdx

@@ -41,11 +41,8 @@ The CLI provides the fastest way to deploy locally developed crews to the Enterp
     First, you need to authenticate your CLI with the CrewAI Enterprise platform:
 
     ```bash
-    # If you already have a CrewAI Enterprise account
+    # If you already have a CrewAI Enterprise account, or want to create one:
     crewai login
-
-    # If you're creating a new account
-    crewai signup
     ```
 
     When you run either command, the CLI will:

docs/pt-BR/concepts/agents.mdx

@@ -149,34 +149,33 @@ from crewai_tools import SerperDevTool
 
 # Crie um agente com todos os parâmetros disponíveis
 agent = Agent(
-    role="Senior Data Scientist",
-    goal="Analyze and interpret complex datasets to provide actionable insights",
-    backstory="With over 10 years of experience in data science and machine learning, "
-              "you excel at finding patterns in complex datasets.",
-    llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
-    function_calling_llm=None,  # Optional: Separate LLM for tool calling
-    verbose=False,  # Default: False
-    allow_delegation=False,  # Default: False
-    max_iter=20,  # Default: 20 iterations
-    max_rpm=None,  # Optional: Rate limit for API calls
-    max_execution_time=None,  # Optional: Maximum execution time in seconds
-    max_retry_limit=2,  # Default: 2 retries on error
-    allow_code_execution=False,  # Default: False
-    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
-    respect_context_window=True,  # Default: True
-    use_system_prompt=True,  # Default: True
-    multimodal=False,  # Default: False
-    inject_date=False,  # Default: False
-    date_format="%Y-%m-%d",  # Default: ISO format
-    reasoning=False,  # Default: False
-    max_reasoning_attempts=None,  # Default: None
-    tools=[SerperDevTool()],  # Optional: List of tools
-    knowledge_sources=None,  # Optional: List of knowledge sources
-    embedder=None,  # Optional: Custom embedder configuration
-    system_template=None,  # Optional: Custom system prompt template
-    prompt_template=None,  # Optional: Custom prompt template
-    response_template=None,  # Optional: Custom response template
-    step_callback=None,  # Optional: Callback function for monitoring
+    role="Cientista de Dados Sênior",
+    goal="Analisar e interpretar conjuntos de dados complexos para fornecer insights acionáveis",
+    backstory="Com mais de 10 anos de experiência em ciência de dados e aprendizado de máquina, você é especialista em encontrar padrões em grandes volumes de dados.",
+    llm="gpt-4",  # Padrão: OPENAI_MODEL_NAME ou "gpt-4"
+    function_calling_llm=None,  # Opcional: LLM separado para chamadas de ferramentas
+    verbose=False,  # Padrão: False
+    allow_delegation=False,  # Padrão: False
+    max_iter=20,  # Padrão: 20 iterações
+    max_rpm=None,  # Opcional: Limite de requisições por minuto
+    max_execution_time=None,  # Opcional: Tempo máximo de execução em segundos
+    max_retry_limit=2,  # Padrão: 2 tentativas em caso de erro
+    allow_code_execution=False,  # Padrão: False
+    code_execution_mode="safe",  # Padrão: "safe" (opções: "safe", "unsafe")
+    respect_context_window=True,  # Padrão: True
+    use_system_prompt=True,  # Padrão: True
+    multimodal=False,  # Padrão: False
+    inject_date=False,  # Padrão: False
+    date_format="%Y-%m-%d",  # Padrão: formato ISO
+    reasoning=False,  # Padrão: False
+    max_reasoning_attempts=None,  # Padrão: None
+    tools=[SerperDevTool()],  # Opcional: Lista de ferramentas
+    knowledge_sources=None,  # Opcional: Lista de fontes de conhecimento
+    embedder=None,  # Opcional: Configuração de embedder customizado
+    system_template=None,  # Opcional: Template de prompt de sistema
+    prompt_template=None,  # Opcional: Template de prompt customizado
+    response_template=None,  # Opcional: Template de resposta customizado
+    step_callback=None,  # Opcional: Função de callback para monitoramento
 )
 ```
 
@@ -185,65 +184,62 @@ Vamos detalhar algumas combinações de parâmetros-chave para casos de uso comu
 #### Agente de Pesquisa Básico
 ```python Code
 research_agent = Agent(
-    role="Research Analyst",
-    goal="Find and summarize information about specific topics",
-    backstory="You are an experienced researcher with attention to detail",
+    role="Analista de Pesquisa",
+    goal="Encontrar e resumir informações sobre tópicos específicos",
+    backstory="Você é um pesquisador experiente com atenção aos detalhes",
     tools=[SerperDevTool()],
-    verbose=True  # Enable logging for debugging
+    verbose=True  # Ativa logs para depuração
 )
 ```
 
 #### Agente de Desenvolvimento de Código
 ```python Code
 dev_agent = Agent(
-    role="Senior Python Developer",
-    goal="Write and debug Python code",
-    backstory="Expert Python developer with 10 years of experience",
+    role="Desenvolvedor Python Sênior",
+    goal="Escrever e depurar códigos Python",
+    backstory="Desenvolvedor Python especialista com 10 anos de experiência",
     allow_code_execution=True,
-    code_execution_mode="safe",  # Uses Docker for safety
-    max_execution_time=300,  # 5-minute timeout
-    max_retry_limit=3  # More retries for complex code tasks
+    code_execution_mode="safe",  # Usa Docker para segurança
+    max_execution_time=300,  # Limite de 5 minutos
+    max_retry_limit=3  # Mais tentativas para tarefas complexas
 )
 ```
 
 #### Agente de Análise de Longa Duração
 ```python Code
 analysis_agent = Agent(
-    role="Data Analyst",
-    goal="Perform deep analysis of large datasets",
-    backstory="Specialized in big data analysis and pattern recognition",
+    role="Analista de Dados",
+    goal="Realizar análise aprofundada de grandes conjuntos de dados",
+    backstory="Especialista em análise de big data e reconhecimento de padrões",
     memory=True,
     respect_context_window=True,
-    max_rpm=10,  # Limit API calls
-    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
+    max_rpm=10,  # Limite de requisições por minuto
+    function_calling_llm="gpt-4o-mini"  # Modelo mais econômico para chamadas de ferramentas
 )
 ```
 
 #### Agente com Template Personalizado
 ```python Code
 custom_agent = Agent(
-    role="Customer Service Representative",
-    goal="Assist customers with their inquiries",
-    backstory="Experienced in customer support with a focus on satisfaction",
-    system_template="""<|start_header_id|>system<|end_header_id|>
-                        {{ .System }}<|eot_id|>""",
-    prompt_template="""<|start_header_id|>user<|end_header_id|>
-                        {{ .Prompt }}<|eot_id|>""",
-    response_template="""<|start_header_id|>assistant<|end_header_id|>
-                        {{ .Response }}<|eot_id|>""",
+    role="Atendente de Suporte ao Cliente",
+    goal="Auxiliar clientes com suas dúvidas e solicitações",
+    backstory="Experiente em atendimento ao cliente com foco em satisfação",
+    system_template="""<|start_header_id|>system<|end_header_id|>\n                        {{ .System }}<|eot_id|>""",
+    prompt_template="""<|start_header_id|>user<|end_header_id|>\n                        {{ .Prompt }}<|eot_id|>""",
+    response_template="""<|start_header_id|>assistant<|end_header_id|>\n                        {{ .Response }}<|eot_id|>""",
 )
 ```
 
 #### Agente Ciente de Data, com Raciocínio
 ```python Code
 strategic_agent = Agent(
-    role="Market Analyst",
-    goal="Track market movements with precise date references and strategic planning",
-    backstory="Expert in time-sensitive financial analysis and strategic reporting",
-    inject_date=True,  # Automatically inject current date into tasks
-    date_format="%B %d, %Y",  # Format as "May 21, 2025"
-    reasoning=True,  # Enable strategic planning
-    max_reasoning_attempts=2,  # Limit planning iterations
+    role="Analista de Mercado",
+    goal="Acompanhar movimentos do mercado com referências de datas precisas e planejamento estratégico",
+    backstory="Especialista em análise financeira sensível ao tempo e relatórios estratégicos",
+    inject_date=True,  # Injeta automaticamente a data atual nas tarefas
+    date_format="%d de %B de %Y",  # Exemplo: "21 de maio de 2025"
+    reasoning=True,  # Ativa planejamento estratégico
+    max_reasoning_attempts=2,  # Limite de iterações de planejamento
     verbose=True
 )
 ```
@@ -251,12 +247,12 @@ strategic_agent = Agent(
 #### Agente de Raciocínio
 ```python Code
 reasoning_agent = Agent(
-    role="Strategic Planner",
-    goal="Analyze complex problems and create detailed execution plans",
-    backstory="Expert strategic planner who methodically breaks down complex challenges",
-    reasoning=True,  # Enable reasoning and planning
-    max_reasoning_attempts=3,  # Limit reasoning attempts
-    max_iter=30,  # Allow more iterations for complex planning
+    role="Planejador Estratégico",
+    goal="Analisar problemas complexos e criar planos de execução detalhados",
+    backstory="Especialista em planejamento estratégico que desmembra desafios complexos metodicamente",
+    reasoning=True,  # Ativa raciocínio e planejamento
+    max_reasoning_attempts=3,  # Limite de tentativas de raciocínio
+    max_iter=30,  # Permite mais iterações para planejamento complexo
     verbose=True
 )
 ```
@@ -264,10 +260,10 @@ reasoning_agent = Agent(
 #### Agente Multimodal
 ```python Code
 multimodal_agent = Agent(
-    role="Visual Content Analyst",
-    goal="Analyze and process both text and visual content",
-    backstory="Specialized in multimodal analysis combining text and image understanding",
-    multimodal=True,  # Enable multimodal capabilities
+    role="Analista de Conteúdo Visual",
+    goal="Analisar e processar tanto conteúdo textual quanto visual",
+    backstory="Especialista em análise multimodal combinando compreensão de texto e imagem",
+    multimodal=True,  # Ativa capacidades multimodais
     verbose=True
 )
 ```
@@ -336,8 +332,8 @@ wiki_tool = WikipediaTools()
 
 # Adicionar ferramentas ao agente
 researcher = Agent(
-    role="AI Technology Researcher",
-    goal="Research the latest AI developments",
+    role="Pesquisador de Tecnologia em IA",
+    goal="Pesquisar os últimos avanços em IA",
     tools=[search_tool, wiki_tool],
     verbose=True
 )
@@ -351,9 +347,9 @@ Agentes podem manter a memória de suas interações e usar contexto de tarefas
 from crewai import Agent
 
 analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze and remember complex data patterns",
-    memory=True,  # Enable memory
+    role="Analista de Dados",
+    goal="Analisar e memorizar padrões complexos de dados",
+    memory=True,  # Ativa memória
     verbose=True
 )
 ```
@@ -380,10 +376,10 @@ Esta é a **configuração padrão e recomendada** para a maioria dos casos. Qua
 ```python Code
 # Agente com gerenciamento automático de contexto (padrão)
 smart_agent = Agent(
-    role="Research Analyst",
-    goal="Analyze large documents and datasets",
-    backstory="Expert at processing extensive information",
-    respect_context_window=True,  # 🔑 Default: auto-handle context limits
+    role="Analista de Pesquisa",
+    goal="Analisar grandes documentos e conjuntos de dados",
+    backstory="Especialista em processar informações extensas",
+    respect_context_window=True,  # 🔑 Padrão: gerencia limites de contexto automaticamente
     verbose=True
 )
 ```

docs/pt-BR/concepts/cli.mdx

@@ -3,6 +3,7 @@ title: CLI
 description: Aprenda a usar o CLI do CrewAI para interagir com o CrewAI.
 icon: terminal
 ---
+<Warning>A partir da versão 0.140.0, a plataforma CrewAI Enterprise iniciou um processo de migração de seu provedor de login. Como resultado, o fluxo de autenticação via CLI foi atualizado. Usuários que utlizam o Google para fazer login, ou que criaram conta após 3 de julho de 2025 não poderão fazer login com versões anteriores da biblioteca `crewai`.</Warning>
 
 ## Visão Geral
 
@@ -75,6 +76,20 @@ Exemplo:
 crewai train -n 10 -f my_training_data.pkl
 ```
 
+# Exemplo de uso programático do comando train
+n_iterations = 2
+inputs = {"topic": "Treinamento CrewAI"}
+filename = "seu_modelo.pkl"
+
+try:
+    SuaCrew().crew().train(
+      n_iterations=n_iterations, 
+      inputs=inputs, 
+      filename=filename
+    )
+except Exception as e:
+    raise Exception(f"Ocorreu um erro ao treinar a crew: {e}")
+
 ### 4. Replay
 
 Reexecute a execução do crew a partir de uma tarefa específica.

docs/pt-BR/concepts/collaboration.mdx

@@ -15,18 +15,18 @@ from crewai import Agent, Crew, Task
 
 # Enable collaboration for agents
 researcher = Agent(
-    role="Research Specialist",
-    goal="Conduct thorough research on any topic",
-    backstory="Expert researcher with access to various sources",
-    allow_delegation=True,  # 🔑 Key setting for collaboration
+    role="Especialista em Pesquisa",
+    goal="Realizar pesquisas aprofundadas sobre qualquer tema",
+    backstory="Pesquisador especialista com acesso a diversas fontes",
+    allow_delegation=True,  # 🔑 Configuração chave para colaboração
     verbose=True
 )
 
 writer = Agent(
-    role="Content Writer", 
-    goal="Create engaging content based on research",
-    backstory="Skilled writer who transforms research into compelling content",
-    allow_delegation=True,  # 🔑 Enables asking questions to other agents
+    role="Redator de Conteúdo",
+    goal="Criar conteúdo envolvente com base em pesquisas",
+    backstory="Redator habilidoso que transforma pesquisas em conteúdo atraente",
+    allow_delegation=True,  # 🔑 Permite fazer perguntas a outros agentes
     verbose=True
 )
 
@@ -67,19 +67,17 @@ from crewai import Agent, Crew, Task, Process
 
 # Create collaborative agents
 researcher = Agent(
-    role="Research Specialist",
-    goal="Find accurate, up-to-date information on any topic",
-    backstory="""You're a meticulous researcher with expertise in finding 
-    reliable sources and fact-checking information across various domains.""",
+    role="Especialista em Pesquisa",
+    goal="Realizar pesquisas aprofundadas sobre qualquer tema",
+    backstory="Pesquisador especialista com acesso a diversas fontes",
     allow_delegation=True,
     verbose=True
 )
 
 writer = Agent(
-    role="Content Writer",
-    goal="Create engaging, well-structured content",
-    backstory="""You're a skilled content writer who excels at transforming 
-    research into compelling, readable content for different audiences.""",
+    role="Redator de Conteúdo",
+    goal="Criar conteúdo envolvente com base em pesquisas",
+    backstory="Redator habilidoso que transforma pesquisas em conteúdo atraente",
     allow_delegation=True,
     verbose=True
 )
@@ -95,17 +93,17 @@ editor = Agent(
 
 # Create a task that encourages collaboration
 article_task = Task(
-    description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.
-    
-    The article should include:
-    - Current AI applications in healthcare
-    - Emerging trends and technologies  
-    - Potential challenges and ethical considerations
-    - Expert predictions for the next 5 years
-    
-    Collaborate with your teammates to ensure accuracy and quality.""",
-    expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
-    agent=writer  # Writer leads, but can delegate research to researcher
+    description="""Escreva um artigo abrangente de 1000 palavras sobre 'O Futuro da IA na Saúde'.
+
+O artigo deve incluir:
+- Aplicações atuais de IA na saúde
+- Tendências e tecnologias emergentes
+- Desafios potenciais e considerações éticas
+- Previsões de especialistas para os próximos 5 anos
+
+Colabore com seus colegas para garantir precisão e qualidade.""",
+    expected_output="Um artigo bem pesquisado, envolvente, com 1000 palavras, estrutura adequada e citações",
+    agent=writer  # O redator lidera, mas pode delegar pesquisa ao pesquisador
 )
 
 # Create collaborative crew
@@ -124,37 +122,37 @@ result = crew.kickoff()
 ### Padrão 1: Pesquisa → Redação → Edição
 ```python
 research_task = Task(
-    description="Research the latest developments in quantum computing",
-    expected_output="Comprehensive research summary with key findings and sources",
+    description="Pesquise os últimos avanços em computação quântica",
+    expected_output="Resumo abrangente da pesquisa com principais descobertas e fontes",
     agent=researcher
 )
 
 writing_task = Task(
-    description="Write an article based on the research findings",
-    expected_output="Engaging 800-word article about quantum computing",
+    description="Escreva um artigo com base nos achados da pesquisa",
+    expected_output="Artigo envolvente de 800 palavras sobre computação quântica",
     agent=writer,
-    context=[research_task]  # Gets research output as context
+    context=[research_task]  # Recebe a saída da pesquisa como contexto
 )
 
 editing_task = Task(
-    description="Edit and polish the article for publication",
-    expected_output="Publication-ready article with improved clarity and flow",
+    description="Edite e revise o artigo para publicação",
+    expected_output="Artigo pronto para publicação, com clareza e fluidez aprimoradas",
     agent=editor,
-    context=[writing_task]  # Gets article draft as context
+    context=[writing_task]  # Recebe o rascunho do artigo como contexto
 )
 ```
 
 ### Padrão 2: Tarefa Única Colaborativa
 ```python
 collaborative_task = Task(
-    description="""Create a marketing strategy for a new AI product.
-    
-    Writer: Focus on messaging and content strategy
-    Researcher: Provide market analysis and competitor insights
-    
-    Work together to create a comprehensive strategy.""",
-    expected_output="Complete marketing strategy with research backing",
-    agent=writer  # Lead agent, but can delegate to researcher
+    description="""Crie uma estratégia de marketing para um novo produto de IA.
+
+Redator: Foque em mensagens e estratégia de conteúdo
+Pesquisador: Forneça análise de mercado e insights de concorrentes
+
+Trabalhem juntos para criar uma estratégia abrangente.""",
+    expected_output="Estratégia de marketing completa com embasamento em pesquisa",
+    agent=writer  # Agente líder, mas pode delegar ao pesquisador
 )
 ```
 
@@ -167,35 +165,35 @@ from crewai import Agent, Crew, Task, Process
 
 # Manager agent coordinates the team
 manager = Agent(
-    role="Project Manager",
-    goal="Coordinate team efforts and ensure project success",
-    backstory="Experienced project manager skilled at delegation and quality control",
+    role="Gerente de Projetos",
+    goal="Coordenar esforços da equipe e garantir o sucesso do projeto",
+    backstory="Gerente de projetos experiente, habilidoso em delegação e controle de qualidade",
     allow_delegation=True,
     verbose=True
 )
 
 # Specialist agents
 researcher = Agent(
-    role="Researcher",
-    goal="Provide accurate research and analysis",
-    backstory="Expert researcher with deep analytical skills",
-    allow_delegation=False,  # Specialists focus on their expertise
+    role="Pesquisador",
+    goal="Fornecer pesquisa e análise precisas",
+    backstory="Pesquisador especialista com habilidades analíticas profundas",
+    allow_delegation=False,  # Especialistas focam em sua expertise
     verbose=True
 )
 
 writer = Agent(
-    role="Writer", 
-    goal="Create compelling content",
-    backstory="Skilled writer who creates engaging content",
+    role="Redator",
+    goal="Criar conteúdo envolvente",
+    backstory="Redator habilidoso que cria conteúdo atraente",
     allow_delegation=False,
     verbose=True
 )
 
 # Manager-led task
 project_task = Task(
-    description="Create a comprehensive market analysis report with recommendations",
-    expected_output="Executive summary, detailed analysis, and strategic recommendations",
-    agent=manager  # Manager will delegate to specialists
+    description="Crie um relatório de análise de mercado completo com recomendações",
+    expected_output="Resumo executivo, análise detalhada e recomendações estratégicas",
+    agent=manager  # O gerente delega para especialistas
 )
 
 # Hierarchical crew

docs/pt-BR/concepts/crews.mdx

@@ -153,32 +153,32 @@ from crewai_tools import YourCustomTool
 class YourCrewName:
     def agent_one(self) -> Agent:
         return Agent(
-            role="Data Analyst",
-            goal="Analyze data trends in the market",
-            backstory="An experienced data analyst with a background in economics",
+            role="Analista de Dados",
+            goal="Analisar tendências de dados no mercado brasileiro",
+            backstory="Analista experiente com formação em economia",
             verbose=True,
             tools=[YourCustomTool()]
         )
 
     def agent_two(self) -> Agent:
         return Agent(
-            role="Market Researcher",
-            goal="Gather information on market dynamics",
-            backstory="A diligent researcher with a keen eye for detail",
+            role="Pesquisador de Mercado",
+            goal="Coletar informações sobre a dinâmica do mercado nacional",
+            backstory="Pesquisador dedicado com olhar atento aos detalhes",
             verbose=True
         )
 
     def task_one(self) -> Task:
         return Task(
-            description="Collect recent market data and identify trends.",
-            expected_output="A report summarizing key trends in the market.",
+            description="Coletar dados recentes do mercado brasileiro e identificar tendências.",
+            expected_output="Um relatório resumido com as principais tendências do mercado.",
             agent=self.agent_one()
         )
 
     def task_two(self) -> Task:
         return Task(
-            description="Research factors affecting market dynamics.",
-            expected_output="An analysis of factors influencing the market.",
+            description="Pesquisar fatores que afetam a dinâmica do mercado nacional.",
+            expected_output="Uma análise dos fatores que influenciam o mercado.",
             agent=self.agent_two()
         )
 

docs/pt-BR/concepts/event-listener.mdx

@@ -51,24 +51,24 @@ from crewai.utilities.events import (
 )
 from crewai.utilities.events.base_event_listener import BaseEventListener
 
-class MyCustomListener(BaseEventListener):
+class MeuListenerPersonalizado(BaseEventListener):
     def __init__(self):
         super().__init__()
 
     def setup_listeners(self, crewai_event_bus):
         @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event):
-            print(f"Crew '{event.crew_name}' has started execution!")
+        def ao_iniciar_crew(source, event):
+            print(f"Crew '{event.crew_name}' iniciou a execução!")
 
         @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event):
-            print(f"Crew '{event.crew_name}' has completed execution!")
-            print(f"Output: {event.output}")
+        def ao_finalizar_crew(source, event):
+            print(f"Crew '{event.crew_name}' finalizou a execução!")
+            print(f"Saída: {event.output}")
 
         @crewai_event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_execution_completed(source, event):
-            print(f"Agent '{event.agent.role}' completed task")
-            print(f"Output: {event.output}")
+        def ao_finalizar_execucao_agente(source, event):
+            print(f"Agente '{event.agent.role}' concluiu a tarefa")
+            print(f"Saída: {event.output}")
 ```
 
 ## Registrando Corretamente Seu Listener

docs/pt-BR/concepts/flows.mdx

@@ -486,8 +486,9 @@ Existem duas formas de executar um flow:
 Você pode executar um flow programaticamente criando uma instância da sua classe de flow e chamando o método `kickoff()`:
 
 ```python
-flow = ExampleFlow()
-result = flow.kickoff()
+# Exemplo de execução de flow em português
+flow = ExemploFlow()
+resultado = flow.kickoff()
 ```
 
 ### Usando a CLI

docs/pt-BR/concepts/knowledge.mdx

@@ -39,17 +39,17 @@ llm = LLM(model="gpt-4o-mini", temperature=0)
 
 # Create an agent with the knowledge store
 agent = Agent(
-    role="About User",
-    goal="You know everything about the user.",
-    backstory="You are a master at understanding people and their preferences.",
+    role="Sobre o Usuário",
+    goal="Você sabe tudo sobre o usuário.",
+    backstory="Você é mestre em entender pessoas e suas preferências.",
     verbose=True,
     allow_delegation=False,
     llm=llm,
 )
 
 task = Task(
-    description="Answer the following questions about the user: {question}",
-    expected_output="An answer to the question.",
+    description="Responda às seguintes perguntas sobre o usuário: {question}",
+    expected_output="Uma resposta para a pergunta.",
     agent=agent,
 )
 
@@ -87,17 +87,17 @@ llm = LLM(model="gpt-4o-mini", temperature=0)
 
 # Create an agent with the knowledge store
 agent = Agent(
-    role="About papers",
-    goal="You know everything about the papers.",
-    backstory="You are a master at understanding papers and their content.",
+    role="Sobre artigos",
+    goal="Você sabe tudo sobre os artigos.",
+    backstory="Você é mestre em entender artigos e seus conteúdos.",
     verbose=True,
     allow_delegation=False,
     llm=llm,
 )
 
 task = Task(
-    description="Answer the following questions about the papers: {question}",
-    expected_output="An answer to the question.",
+    description="Responda às seguintes perguntas sobre os artigos: {question}",
+    expected_output="Uma resposta para a pergunta.",
     agent=agent,
 )
 
@@ -201,16 +201,16 @@ specialist_knowledge = StringKnowledgeSource(
 )
 
 specialist_agent = Agent(
-    role="Technical Specialist",
-    goal="Provide technical expertise",
-    backstory="Expert in specialized technical domains",
-    knowledge_sources=[specialist_knowledge]  # Agent-specific knowledge
+    role="Especialista Técnico",
+    goal="Fornecer expertise técnica",
+    backstory="Especialista em domínios técnicos especializados",
+    knowledge_sources=[specialist_knowledge]  # Conhecimento específico do agente
 )
 
 task = Task(
-    description="Answer technical questions",
+    description="Responda perguntas técnicas",
     agent=specialist_agent,
-    expected_output="Technical answer"
+    expected_output="Resposta técnica"
 )
 
 # No crew-level knowledge required
@@ -240,7 +240,7 @@ Cada nível de knowledge usa coleções de armazenamento independentes:
 
 ```python
 # Agent knowledge storage
-agent_collection_name = agent.role  # e.g., "Technical Specialist"
+agent_collection_name = agent.role  # e.g., "Especialista Técnico"
 
 # Crew knowledge storage
 crew_collection_name = "crew"
@@ -248,7 +248,7 @@ crew_collection_name = "crew"
 # Both stored in same ChromaDB instance but different collections
 # Path: ~/.local/share/CrewAI/{project}/knowledge/
 #   ├── crew/                    # Crew knowledge collection
-#   ├── Technical Specialist/    # Agent knowledge collection
+#   ├── Especialista Técnico/    # Agent knowledge collection
 #   └── Another Agent Role/      # Another agent's collection
 ```
 
@@ -265,7 +265,7 @@ agent_knowledge = StringKnowledgeSource(
 )
 
 agent = Agent(
-    role="Specialist",
+    role="Especialista",
     goal="Use specialized knowledge",
     backstory="Expert with specific knowledge",
     knowledge_sources=[agent_knowledge],
@@ -299,10 +299,10 @@ specialist_knowledge = StringKnowledgeSource(
 )
 
 specialist = Agent(
-    role="Technical Specialist",
-    goal="Provide technical expertise",
-    backstory="Technical expert",
-    knowledge_sources=[specialist_knowledge]  # Agent-specific
+    role="Especialista Técnico",
+    goal="Fornecer expertise técnica",
+    backstory="Especialista em domínios técnicos especializados",
+    knowledge_sources=[specialist_knowledge]  # Conhecimento específico do agente
 )
 
 generalist = Agent(

docs/pt-BR/concepts/llms.mdx

@@ -78,15 +78,15 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod
 
     # Configuração avançada com parâmetros detalhados
     llm = LLM(
-        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
-        temperature=0.7,        # Mais alto para saídas criativas
-        timeout=120,            # Segundos para aguardar resposta
-        max_tokens=4000,        # Comprimento máximo da resposta
-        top_p=0.9,              # Parâmetro de amostragem nucleus
-        frequency_penalty=0.1 , # Reduz repetição
-        presence_penalty=0.1,   # Incentiva diversidade de tópicos
-        response_format={"type": "json"},  # Para respostas estruturadas
-        seed=42                 # Para resultados reproduzíveis
+        model="openai/gpt-4", 
+        temperature=0.8,
+        max_tokens=150,
+        top_p=0.9,
+        frequency_penalty=0.1,
+        presence_penalty=0.1,
+        response_format={"type":"json"},
+        stop=["FIM"],
+        seed=42
     )
     ```
 
@@ -127,13 +127,13 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     from crewai import LLM
 
     llm = LLM(
-        model="openai/gpt-4", # chamar modelo por provider/model_name
+        model="openai/gpt-4",
         temperature=0.8,
         max_tokens=150,
         top_p=0.9,
         frequency_penalty=0.1,
         presence_penalty=0.1,
-        stop=["END"],
+        stop=["FIM"],
         seed=42
     )
     ```
@@ -169,7 +169,7 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     llm = LLM(
         model="meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8",
         temperature=0.8,
-        stop=["END"],
+        stop=["FIM"],
         seed=42
     )
     ```

docs/pt-BR/concepts/planning.mdx

@@ -17,7 +17,7 @@ Começar a usar o recurso de planejamento é muito simples, o único passo neces
 from crewai import Crew, Agent, Task, Process
 
 # Monte sua crew com capacidades de planejamento
-my_crew = Crew(
+minha_crew = Crew(
     agents=self.agents,
     tasks=self.tasks,
     process=Process.sequential,

docs/pt-BR/concepts/processes.mdx

@@ -28,23 +28,23 @@ from crewai import Crew, Process
 
 # Exemplo: Criando uma crew com processo sequencial
 crew = Crew(
-    agents=my_agents,
-    tasks=my_tasks,
+    agents=meus_agentes,
+    tasks=minhas_tarefas,
     process=Process.sequential
 )
 
 # Exemplo: Criando uma crew com processo hierárquico
 # Certifique-se de fornecer um manager_llm ou manager_agent
 crew = Crew(
-    agents=my_agents,
-    tasks=my_tasks,
+    agents=meus_agentes,
+    tasks=minhas_tarefas,
     process=Process.hierarchical,
     manager_llm="gpt-4o"
     # ou
-    # manager_agent=my_manager_agent
+    # manager_agent=meu_agente_gerente
 )
 ```
-**Nota:** Certifique-se de que `my_agents` e `my_tasks` estejam definidos antes de criar o objeto `Crew`, e para o processo hierárquico, é necessário também fornecer o `manager_llm` ou `manager_agent`.
+**Nota:** Certifique-se de que `meus_agentes` e `minhas_tarefas` estejam definidos antes de criar o objeto `Crew`, e para o processo hierárquico, é necessário também fornecer o `manager_llm` ou `manager_agent`.
 
 ## Processo Sequencial
 

docs/pt-BR/concepts/reasoning.mdx

@@ -15,12 +15,12 @@ Para habilitar o reasoning para um agente, basta definir `reasoning=True` ao cri
 ```python
 from crewai import Agent
 
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze complex datasets and provide insights",
-    backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
-    reasoning=True,  # Enable reasoning
-    max_reasoning_attempts=3  # Optional: Set a maximum number of reasoning attempts
+analista = Agent(
+    role="Analista de Dados",
+    goal="Analisar dados e fornecer insights",
+    backstory="Você é um analista de dados especialista.",
+    reasoning=True,
+    max_reasoning_attempts=3  # Opcional: Defina um limite de tentativas de reasoning
 )
 ```
 
@@ -53,23 +53,23 @@ Aqui está um exemplo completo:
 from crewai import Agent, Task, Crew
 
 # Create an agent with reasoning enabled
-analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
-    backstory="You are an expert data analyst.",
+analista = Agent(
+    role="Analista de Dados",
+    goal="Analisar dados e fornecer insights",
+    backstory="Você é um analista de dados especialista.",
     reasoning=True,
-    max_reasoning_attempts=3  # Optional: Set a limit on reasoning attempts
+    max_reasoning_attempts=3  # Opcional: Defina um limite de tentativas de reasoning
 )
 
 # Create a task
 analysis_task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
-    agent=analyst
+    description="Analise os dados de vendas fornecidos e identifique as principais tendências.",
+    expected_output="Um relatório destacando as 3 principais tendências de vendas.",
+    agent=analista
 )
 
 # Create a crew and run the task
-crew = Crew(agents=[analyst], tasks=[analysis_task])
+crew = Crew(agents=[analista], tasks=[analysis_task])
 result = crew.kickoff()
 
 print(result)
@@ -90,16 +90,16 @@ logging.basicConfig(level=logging.INFO)
 
 # Create an agent with reasoning enabled
 agent = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
+    role="Analista de Dados",
+    goal="Analisar dados e fornecer insights",
     reasoning=True,
     max_reasoning_attempts=3
 )
 
 # Create a task
 task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
+    description="Analise os dados de vendas fornecidos e identifique as principais tendências.",
+    expected_output="Um relatório destacando as 3 principais tendências de vendas.",
     agent=agent
 )
 
@@ -113,7 +113,7 @@ result = agent.execute_task(task)
 Veja um exemplo de como pode ser um plano de reasoning para uma tarefa de análise de dados:
 
 ```
-Task: Analyze the provided sales data and identify key trends.
+Task: Analise os dados de vendas fornecidos e identifique as principais tendências.
 
 Reasoning Plan:
 I'll analyze the sales data to identify the top 3 trends.

docs/pt-BR/concepts/tasks.mdx

@@ -386,7 +386,7 @@ def validate_with_context(result: TaskOutput) -> Tuple[bool, Any]:
         validated_data = perform_validation(result)
         return (True, validated_data)
     except ValidationError as e:
-        return (False, f"VALIDATION_ERROR: {str(e)}")
+        return (False, f"ERRO_DE_VALIDACAO: {str(e)}")
     except Exception as e:
         return (False, str(e))
 ```

docs/pt-BR/concepts/tools.mdx

@@ -67,17 +67,17 @@ web_rag_tool = WebsiteSearchTool()
 
 # Criar agentes
 researcher = Agent(
-    role='Market Research Analyst',
-    goal='Provide up-to-date market analysis of the AI industry',
-    backstory='An expert analyst with a keen eye for market trends.',
+    role='Analista de Mercado',
+    goal='Fornecer análise de mercado atualizada da indústria de IA',
+    backstory='Analista especialista com olhar atento para tendências de mercado.',
     tools=[search_tool, web_rag_tool],
     verbose=True
 )
 
 writer = Agent(
-    role='Content Writer',
-    goal='Craft engaging blog posts about the AI industry',
-    backstory='A skilled writer with a passion for technology.',
+    role='Redator de Conteúdo',
+    goal='Criar posts de blog envolventes sobre a indústria de IA',
+    backstory='Redator habilidoso com paixão por tecnologia.',
     tools=[docs_tool, file_tool],
     verbose=True
 )

docs/pt-BR/concepts/training.mdx

@@ -36,19 +36,18 @@ Para treinar sua crew de forma programática, siga estes passos:
 3. Execute o comando de treinamento dentro de um bloco try-except para tratar possíveis erros.
 
 ```python Code
-n_iterations = 2
-inputs = {"topic": "CrewAI Training"}
-filename = "your_model.pkl"
+n_iteracoes = 2
+entradas = {"topic": "Treinamento CrewAI"}
+nome_arquivo = "seu_modelo.pkl"
 
 try:
-    YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
-      filename=filename
+    SuaCrew().crew().train(
+      n_iterations=n_iteracoes, 
+      inputs=entradas, 
+      filename=nome_arquivo
     )
-
 except Exception as e:
-    raise Exception(f"An error occurred while training the crew: {e}")
+    raise Exception(f"Ocorreu um erro ao treinar a crew: {e}")
 ```
 
 ### Pontos Importantes

docs/pt-BR/enterprise/features/hallucination-guardrail.mdx

@@ -26,13 +26,13 @@ from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 from crewai import LLM
 
 # Uso básico - utiliza o expected_output da tarefa como contexto
-guardrail = HallucinationGuardrail(
+protecao = HallucinationGuardrail(
     llm=LLM(model="gpt-4o-mini")
 )
 
 # Com contexto de referência explícito
-context_guardrail = HallucinationGuardrail(
-    context="AI helps with various tasks including analysis and generation.",
+protecao_com_contexto = HallucinationGuardrail(
+    context="IA ajuda em várias tarefas, incluindo análise e geração.",
     llm=LLM(model="gpt-4o-mini")
 )
 ```
@@ -43,11 +43,11 @@ context_guardrail = HallucinationGuardrail(
 from crewai import Task
 
 # Crie sua tarefa com a proteção
-task = Task(
-    description="Write a summary about AI capabilities",
-    expected_output="A factual summary based on the provided context",
-    agent=my_agent,
-    guardrail=guardrail  # Adiciona a proteção para validar a saída
+minha_tarefa = Task(
+    description="Escreva um resumo sobre as capacidades da IA",
+    expected_output="Um resumo factual baseado no contexto fornecido",
+    agent=meu_agente,
+    guardrail=protecao  # Adiciona a proteção para validar a saída
 )
 ```
 
@@ -59,8 +59,8 @@ Para validação mais rigorosa, é possível definir um limiar de fidelidade per
 
 ```python
 # Proteção rigorosa exigindo alta pontuação de fidelidade
-strict_guardrail = HallucinationGuardrail(
-    context="Quantum computing uses qubits that exist in superposition states.",
+protecao_rigorosa = HallucinationGuardrail(
+    context="Computação quântica utiliza qubits que existem em estados de superposição.",
     llm=LLM(model="gpt-4o-mini"),
     threshold=8.0  # Requer pontuação >= 8 para validar
 )
@@ -72,10 +72,10 @@ Se sua tarefa utiliza ferramentas, você pode incluir as respostas das ferrament
 
 ```python
 # Proteção com contexto de resposta da ferramenta
-weather_guardrail = HallucinationGuardrail(
-    context="Current weather information for the requested location",
+protecao_clima = HallucinationGuardrail(
+    context="Informações meteorológicas atuais para o local solicitado",
     llm=LLM(model="gpt-4o-mini"),
-    tool_response="Weather API returned: Temperature 22°C, Humidity 65%, Clear skies"
+    tool_response="API do Clima retornou: Temperatura 22°C, Umidade 65%, Céu limpo"
 )
 ```
 
@@ -123,15 +123,15 @@ Quando uma proteção é adicionada à tarefa, ela valida automaticamente a saí
 
 ```python
 # Fluxo de validação de saída da tarefa
-task_output = agent.execute_task(task)
-validation_result = guardrail(task_output)
+task_output = meu_agente.execute_task(minha_tarefa)
+resultado_validacao = protecao(task_output)
 
-if validation_result.valid:
+if resultado_validacao.valid:
     # Tarefa concluída com sucesso
     return task_output
 else:
     # Tarefa falha com feedback de validação
-    raise ValidationError(validation_result.feedback)
+    raise ValidationError(resultado_validacao.feedback)
 ```
 
 ### Rastreamento de Eventos
@@ -151,10 +151,10 @@ A proteção se integra ao sistema de eventos do CrewAI para fornecer observabil
     Inclua todas as informações factuais relevantes nas quais a IA deve basear sua saída:
 
     ```python
-    context = """
-    Company XYZ was founded in 2020 and specializes in renewable energy solutions.
-    They have 150 employees and generated $50M revenue in 2023.
-    Their main products include solar panels and wind turbines.
+    contexto = """
+    Empresa XYZ foi fundada em 2020 e é especializada em soluções de energia renovável.
+    Possui 150 funcionários e faturou R$ 50 milhões em 2023.
+    Seus principais produtos incluem painéis solares e turbinas eólicas.
     """
     ```
   </Step>
@@ -164,10 +164,10 @@ A proteção se integra ao sistema de eventos do CrewAI para fornecer observabil
 
     ```python
     # Bom: Contexto focado
-    context = "The current weather in New York is 18°C with light rain."
+    contexto = "O clima atual em Nova York é 18°C com chuva leve."
 
     # Evite: Informações irrelevantes
-    context = "The weather is 18°C. The city has 8 million people. Traffic is heavy."
+    contexto = "The weather is 18°C. The city has 8 million people. Traffic is heavy."
     ```
   </Step>
 

docs/pt-BR/enterprise/features/integrations.mdx

@@ -84,31 +84,31 @@ from crewai import Agent, Task, Crew
 from crewai_tools import CrewaiEnterpriseTools
 
 # Obtenha ferramentas enterprise (a ferramenta Gmail será incluída)
-enterprise_tools = CrewaiEnterpriseTools(
-    enterprise_token="your_enterprise_token"
+ferramentas_enterprise = CrewaiEnterpriseTools(
+    enterprise_token="seu_token_enterprise"
 )
 # imprima as ferramentas
-print(enterprise_tools)
+printf(ferramentas_enterprise)
 
 # Crie um agente com capacidades do Gmail
-email_agent = Agent(
-    role="Email Manager",
-    goal="Manage and organize email communications",
-    backstory="An AI assistant specialized in email management and communication.",
-    tools=enterprise_tools
+agente_email = Agent(
+    role="Gerente de E-mails",
+    goal="Gerenciar e organizar comunicações por e-mail",
+    backstory="Um assistente de IA especializado em gestão de e-mails e comunicação.",
+    tools=ferramentas_enterprise
 )
 
 # Tarefa para enviar um e-mail
-email_task = Task(
-    description="Draft and send a follow-up email to john@example.com about the project update",
-    agent=email_agent,
-    expected_output="Confirmation that email was sent successfully"
+tarefa_email = Task(
+    description="Redigir e enviar um e-mail de acompanhamento para john@example.com sobre a atualização do projeto",
+    agent=agente_email,
+    expected_output="Confirmação de que o e-mail foi enviado com sucesso"
 )
 
 # Execute a tarefa
 crew = Crew(
-    agents=[email_agent],
-    tasks=[email_task]
+    agents=[agente_email],
+    tasks=[tarefa_email]
 )
 
 # Execute o crew
@@ -125,23 +125,23 @@ enterprise_tools = CrewaiEnterpriseTools(
 )
 gmail_tool = enterprise_tools["gmail_find_email"]
 
-gmail_agent = Agent(
-    role="Gmail Manager",
-    goal="Manage gmail communications and notifications",
-    backstory="An AI assistant that helps coordinate gmail communications.",
+agente_gmail = Agent(
+    role="Gerente do Gmail",
+    goal="Gerenciar comunicações e notificações do gmail",
+    backstory="Um assistente de IA que ajuda a coordenar comunicações no gmail.",
     tools=[gmail_tool]
 )
 
-notification_task = Task(
-    description="Find the email from john@example.com",
-    agent=gmail_agent,
-    expected_output="Email found from john@example.com"
+tarefa_notificacao = Task(
+    description="Encontrar o e-mail de john@example.com",
+    agent=agente_gmail,
+    expected_output="E-mail encontrado de john@example.com"
 )
 
 # Execute a tarefa
 crew = Crew(
-    agents=[slack_agent],
-    tasks=[notification_task]
+    agents=[agente_gmail],
+    tasks=[tarefa_notificacao]
 )
 ```
 

docs/pt-BR/enterprise/features/tool-repository.mdx

@@ -30,7 +30,7 @@ Antes de usar o Repositório de Ferramentas, certifique-se de que você possui:
 Para instalar uma ferramenta:
 
 ```bash
-crewai tool install <tool-name>
+crewai tool install <nome-da-ferramenta>
 ```
 
 Isso instala a ferramenta e a adiciona ao `pyproject.toml`.
@@ -40,7 +40,7 @@ Isso instala a ferramenta e a adiciona ao `pyproject.toml`.
 Para criar um novo projeto de ferramenta:
 
 ```bash
-crewai tool create <tool-name>
+crewai tool create <nome-da-ferramenta>
 ```
 
 Isso gera um projeto de ferramenta estruturado localmente.
@@ -76,7 +76,7 @@ Para atualizar uma ferramenta publicada:
 3. Faça o commit das alterações e publique
 
 ```bash
-git commit -m "Update version to 0.1.1"
+git commit -m "Atualizar versão para 0.1.1"
 crewai tool publish
 ```
 

docs/pt-BR/enterprise/features/webhook-streaming.mdx

@@ -12,16 +12,17 @@ O Enterprise Event Streaming permite que você receba atualizações em tempo re
 
 Ao utilizar a API Kickoff, inclua um objeto `webhooks` em sua requisição, por exemplo:
 
+# Exemplo de uso da API Kickoff com webhooks
 ```json
 {
   "inputs": {"foo": "bar"},
   "webhooks": {
     "events": ["crew_kickoff_started", "llm_call_started"],
-    "url": "https://your.endpoint/webhook",
+    "url": "https://seu.endpoint/webhook",
     "realtime": false,
     "authentication": {
       "strategy": "bearer",
-      "token": "my-secret-token"
+      "token": "meu-token-secreto"
     }
   }
 }
@@ -33,19 +34,20 @@ Se `realtime` estiver definido como `true`, cada evento será entregue individua
 
 Cada webhook envia uma lista de eventos:
 
+# Exemplo de evento enviado pelo webhook
 ```json
 {
   "events": [
     {
-      "id": "event-id",
-      "execution_id": "crew-run-id",
+      "id": "id-do-evento",
+      "execution_id": "id-da-execucao-do-crew",
       "timestamp": "2025-02-16T10:58:44.965Z",
       "type": "llm_call_started",
       "data": {
         "model": "gpt-4",
         "messages": [
-          {"role": "system", "content": "You are an assistant."},
-          {"role": "user", "content": "Summarize this article."}
+          {"role": "system", "content": "Você é um assistente."},
+          {"role": "user", "content": "Resuma este artigo."}
         ]
       }
     }

docs/pt-BR/enterprise/guides/deploy-crew.mdx

@@ -41,11 +41,8 @@ A CLI fornece a maneira mais rápida de implantar crews desenvolvidos localmente
     Primeiro, você precisa autenticar sua CLI com a plataforma CrewAI Enterprise:
 
     ```bash
-    # Se já possui uma conta CrewAI Enterprise
+    # Se já possui uma conta CrewAI Enterprise, ou deseja criar uma:
     crewai login
-
-    # Se vai criar uma nova conta
-    crewai signup
     ```
 
     Ao executar qualquer um dos comandos, a CLI irá:

docs/pt-BR/learn/before-and-after-kickoff-hooks.mdx

@@ -16,17 +16,17 @@ from crewai import CrewBase
 from crewai.project import before_kickoff
 
 @CrewBase
-class MyCrew:
+class MinhaEquipe:
     @before_kickoff
-    def prepare_data(self, inputs):
-        # Preprocess or modify inputs
-        inputs['processed'] = True
-        return inputs
+    def preparar_dados(self, entradas):
+        # Pré-processa ou modifica as entradas
+        entradas['processado'] = True
+        return entradas
 
 #...
 ```
 
-Neste exemplo, a função prepare_data modifica as entradas adicionando um novo par chave-valor indicando que as entradas foram processadas.
+Neste exemplo, a função preparar_dados modifica as entradas adicionando um novo par chave-valor indicando que as entradas foram processadas.
 
 ## Hook Depois do Kickoff
 
@@ -39,17 +39,17 @@ from crewai import CrewBase
 from crewai.project import after_kickoff
 
 @CrewBase
-class MyCrew:
+class MinhaEquipe:
     @after_kickoff
-    def log_results(self, result):
-        # Log or modify the results
-        print("Crew execution completed with result:", result)
-        return result
+    def registrar_resultados(self, resultado):
+        # Registra ou modifica os resultados
+        print("Execução da equipe concluída com resultado:", resultado)
+        return resultado
 
 # ...
 ```
 
-Na função `log_results`, os resultados da execução da crew são simplesmente impressos. Você pode estender isso para realizar operações mais complexas, como enviar notificações ou integrar com outros serviços.
+Na função `registrar_resultados`, os resultados da execução da crew são simplesmente impressos. Você pode estender isso para realizar operações mais complexas, como enviar notificações ou integrar com outros serviços.
 
 ## Utilizando Ambos os Hooks
 

docs/pt-BR/learn/customizing-agents.mdx

@@ -77,9 +77,9 @@ search_tool = SerperDevTool()
 
 # Inicialize o agente com opções avançadas
 agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
+  role='Analista de Pesquisa',
+  goal='Fornecer análises de mercado atualizadas',
+  backstory='Um analista especialista com olhar atento para tendências de mercado.',
   tools=[search_tool],
   memory=True, # Ativa memória
   verbose=True,
@@ -98,14 +98,9 @@ eficiência dentro do ecossistema CrewAI. Se necessário, a delegação pode ser
 
 ```python Code
 agent = Agent(
-  role='Content Writer',
-  goal='Write engaging content on market trends',
-  backstory='A seasoned writer with expertise in market analysis.',
+  role='Redator de Conteúdo',
+  goal='Escrever conteúdo envolvente sobre tendências de mercado',
+  backstory='Um redator experiente com expertise em análise de mercado.',
   allow_delegation=True # Habilitando delegação
 )
-```
-
-## Conclusão
-
-Personalizar agentes no CrewAI definindo seus papéis, objetivos, histórias e ferramentas, juntamente com opções avançadas como personalização de modelo de linguagem, memória, ajustes de performance e preferências de delegação, 
-proporciona uma equipe de IA sofisticada e preparada para enfrentar desafios complexos.
+```

docs/pt-BR/learn/kickoff-async.mdx

@@ -45,17 +45,17 @@ from crewai import Crew, Agent, Task
 
 # Create an agent with code execution enabled
 coding_agent = Agent(
-    role="Python Data Analyst",
-    goal="Analyze data and provide insights using Python",
-    backstory="You are an experienced data analyst with strong Python skills.",
+    role="Analista de Dados Python",
+    goal="Analisar dados e fornecer insights usando Python",
+    backstory="Você é um analista de dados experiente com fortes habilidades em Python.",
     allow_code_execution=True
 )
 
 # Create a task that requires code execution
 data_analysis_task = Task(
-    description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
+    description="Analise o conjunto de dados fornecido e calcule a idade média dos participantes. Idades: {ages}",
     agent=coding_agent,
-    expected_output="The average age of the participants."
+    expected_output="A idade média dos participantes."
 )
 
 # Create a crew and add the task
@@ -83,23 +83,23 @@ from crewai import Crew, Agent, Task
 
 # Create an agent with code execution enabled
 coding_agent = Agent(
-    role="Python Data Analyst",
-    goal="Analyze data and provide insights using Python",
-    backstory="You are an experienced data analyst with strong Python skills.",
+    role="Analista de Dados Python",
+    goal="Analisar dados e fornecer insights usando Python",
+    backstory="Você é um analista de dados experiente com fortes habilidades em Python.",
     allow_code_execution=True
 )
 
 # Create tasks that require code execution
 task_1 = Task(
-    description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
+    description="Analise o primeiro conjunto de dados e calcule a idade média dos participantes. Idades: {ages}",
     agent=coding_agent,
-    expected_output="The average age of the participants."
+    expected_output="A idade média dos participantes."
 )
 
 task_2 = Task(
-    description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
+    description="Analise o segundo conjunto de dados e calcule a idade média dos participantes. Idades: {ages}",
     agent=coding_agent,
-    expected_output="The average age of the participants."
+    expected_output="A idade média dos participantes."
 )
 
 # Create two crews and add tasks

docs/pt-BR/mcp/multiple-servers.mdx

@@ -43,11 +43,11 @@ try:
     with MCPServerAdapter(server_params_list) as aggregated_tools:
         print(f"Available aggregated tools: {[tool.name for tool in aggregated_tools]}")
 
-        multi_server_agent = Agent(
-            role="Versatile Assistant",
-            goal="Utilize tools from local Stdio, remote SSE, and remote HTTP MCP servers.",
-            backstory="An AI agent capable of leveraging a diverse set of tools from multiple sources.",
-            tools=aggregated_tools, # All tools are available here
+        agente_multiservidor = Agent(
+            role="Assistente Versátil",
+            goal="Utilizar ferramentas de servidores MCP locais Stdio, remotos SSE e remotos HTTP.",
+            backstory="Um agente de IA capaz de aproveitar um conjunto diversificado de ferramentas de múltiplas fontes.",
+            tools=aggregated_tools, # Todas as ferramentas estão disponíveis aqui
             verbose=True,
         )
 

docs/pt-BR/mcp/overview.mdx

@@ -73,10 +73,10 @@ server_params = {
 with MCPServerAdapter(server_params) as mcp_tools:
     print(f"Available tools: {[tool.name for tool in mcp_tools]}")
 
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
+    meu_agente = Agent(
+        role="Usuário de Ferramentas MCP",
+        goal="Utilizar ferramentas de um servidor MCP.",
+        backstory="Posso conectar a servidores MCP e usar suas ferramentas.",
         tools=mcp_tools, # Passe as ferramentas carregadas para o seu agente
         reasoning=True,
         verbose=True
@@ -91,10 +91,10 @@ Este padrão geral mostra como integrar ferramentas. Para exemplos específicos
 with MCPServerAdapter(server_params) as mcp_tools:
     print(f"Available tools: {[tool.name for tool in mcp_tools]}")
 
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
+    meu_agente = Agent(
+        role="Usuário de Ferramentas MCP",
+        goal="Utilizar ferramentas de um servidor MCP.",
+        backstory="Posso conectar a servidores MCP e usar suas ferramentas.",
         tools=mcp_tools["tool_name"], # Passe as ferramentas filtradas para o seu agente
         reasoning=True,
         verbose=True

docs/pt-BR/mcp/sse.mdx

@@ -37,24 +37,24 @@ try:
         print(f"Available tools from SSE MCP server: {[tool.name for tool in tools]}")
 
         # Example: Using a tool from the SSE MCP server
-        sse_agent = Agent(
-            role="Remote Service User",
-            goal="Utilize a tool provided by a remote SSE MCP server.",
-            backstory="An AI agent that connects to external services via SSE.",
+        agente_sse = Agent(
+            role="Usuário de Serviço Remoto",
+            goal="Utilizar uma ferramenta fornecida por um servidor MCP remoto via SSE.",
+            backstory="Um agente de IA que conecta a serviços externos via SSE.",
             tools=tools,
             reasoning=True,
             verbose=True,
         )
 
         sse_task = Task(
-            description="Fetch real-time stock updates for 'AAPL' using an SSE tool.",
-            expected_output="The latest stock price for AAPL.",
-            agent=sse_agent,
+            description="Buscar atualizações em tempo real das ações 'AAPL' usando uma ferramenta SSE.",
+            expected_output="O preço mais recente da ação AAPL.",
+            agent=agente_sse,
             markdown=True
         )
 
         sse_crew = Crew(
-            agents=[sse_agent],
+            agents=[agente_sse],
             tasks=[sse_task],
             verbose=True,
             process=Process.sequential
@@ -101,16 +101,16 @@ try:
     print(f"Available tools (manual SSE): {[tool.name for tool in tools]}")
 
     manual_sse_agent = Agent(
-        role="Remote Data Analyst",
-        goal="Analyze data fetched from a remote SSE MCP server using manual connection management.",
-        backstory="An AI skilled in handling SSE connections explicitly.",
+        role="Analista Remoto de Dados",
+        goal="Analisar dados obtidos de um servidor MCP remoto SSE usando gerenciamento manual de conexão.",
+        backstory="Um agente de IA especializado em gerenciar conexões SSE explicitamente.",
         tools=tools,
         verbose=True
     )
     
     analysis_task = Task(
-        description="Fetch and analyze the latest user activity trends from the SSE server.",
-        expected_output="A summary report of user activity trends.",
+        description="Buscar e analisar as tendências mais recentes de atividade de usuários do servidor SSE.",
+        expected_output="Um relatório resumido das tendências de atividade dos usuários.",
         agent=manual_sse_agent
     )
     

docs/pt-BR/mcp/stdio.mdx

@@ -38,24 +38,24 @@ with MCPServerAdapter(server_params) as tools:
     print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}")
 
     # Exemplo: Usando as ferramentas do servidor MCP Stdio em um Agente CrewAI
-    research_agent = Agent(
-        role="Local Data Processor",
-        goal="Process data using a local Stdio-based tool.",
-        backstory="An AI that leverages local scripts via MCP for specialized tasks.",
+    pesquisador_local = Agent(
+        role="Processador Local de Dados",
+        goal="Processar dados usando uma ferramenta local baseada em Stdio.",
+        backstory="Uma IA que utiliza scripts locais via MCP para tarefas especializadas.",
         tools=tools,
         reasoning=True,
         verbose=True,
     )
     
     processing_task = Task(
-        description="Process the input data file 'data.txt' and summarize its contents.",
-        expected_output="A summary of the processed data.",
-        agent=research_agent,
+        description="Processar o arquivo de dados de entrada 'data.txt' e resumir seu conteúdo.",
+        expected_output="Um resumo dos dados processados.",
+        agent=pesquisador_local,
         markdown=True
     )
     
     data_crew = Crew(
-        agents=[research_agent],
+        agents=[pesquisador_local],
         tasks=[processing_task],
         verbose=True,
         process=Process.sequential 
@@ -95,16 +95,16 @@ try:
 
     # Exemplo: Usando as ferramentas com sua configuração de Agent, Task, Crew
     manual_agent = Agent(
-        role="Local Task Executor",
-        goal="Execute a specific local task using a manually managed Stdio tool.",
-        backstory="An AI proficient in controlling local processes via MCP.",
+        role="Executor Local de Tarefas",
+        goal="Executar uma tarefa local específica usando uma ferramenta Stdio gerenciada manualmente.",
+        backstory="Uma IA proficiente em controlar processos locais via MCP.",
         tools=tools,
         verbose=True
     )
     
     manual_task = Task(
-        description="Execute the 'perform_analysis' command via the Stdio tool.",
-        expected_output="Results of the analysis.",
+        description="Executar o comando 'perform_analysis' via ferramenta Stdio.",
+        expected_output="Resultados da análise.",
         agent=manual_agent
     )
     

docs/pt-BR/mcp/streamable-http.mdx

@@ -35,22 +35,22 @@ try:
     with MCPServerAdapter(server_params) as tools:
         print(f"Available tools from Streamable HTTP MCP server: {[tool.name for tool in tools]}")
 
-        http_agent = Agent(
-            role="HTTP Service Integrator",
-            goal="Utilize tools from a remote MCP server via Streamable HTTP.",
-            backstory="An AI agent adept at interacting with complex web services.",
+        agente_http = Agent(
+            role="Integrador de Serviços HTTP",
+            goal="Utilizar ferramentas de um servidor MCP remoto via Streamable HTTP.",
+            backstory="Um agente de IA especializado em interagir com serviços web complexos.",
             tools=tools,
             verbose=True,
         )
 
         http_task = Task(
-            description="Perform a complex data query using a tool from the Streamable HTTP server.",
-            expected_output="The result of the complex data query.",
-            agent=http_agent,
+            description="Realizar uma consulta de dados complexa usando uma ferramenta do servidor Streamable HTTP.",
+            expected_output="O resultado da consulta de dados complexa.",
+            agent=agente_http,
         )
 
         http_crew = Crew(
-            agents=[http_agent],
+            agents=[agente_http],
             tasks=[http_task],
             verbose=True,
             process=Process.sequential
@@ -91,16 +91,16 @@ try:
     print(f"Available tools (manual Streamable HTTP): {[tool.name for tool in tools]}")
 
     manual_http_agent = Agent(
-        role="Advanced Web Service User",
-        goal="Interact with an MCP server using manually managed Streamable HTTP connections.",
-        backstory="An AI specialist in fine-tuning HTTP-based service integrations.",
+        role="Usuário Avançado de Serviços Web",
+        goal="Interagir com um servidor MCP usando conexões HTTP Streamable gerenciadas manualmente.",
+        backstory="Um especialista em IA em ajustar integrações baseadas em HTTP.",
         tools=tools,
         verbose=True
     )
     
     data_processing_task = Task(
-        description="Submit data for processing and retrieve results via Streamable HTTP.",
-        expected_output="Processed data or confirmation.",
+        description="Enviar dados para processamento e recuperar resultados via Streamable HTTP.",
+        expected_output="Dados processados ou confirmação.",
         agent=manual_http_agent
     )
     

docs/pt-BR/observability/arize-phoenix.mdx

@@ -78,47 +78,40 @@ CrewAIInstrumentor().instrument(skip_dep_check=True, tracer_provider=tracer_prov
 search_tool = SerperDevTool()
 
 # Defina seus agentes com papéis e objetivos
-researcher = Agent(
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI and data science",
-    backstory="""You work at a leading tech think tank.
-    Your expertise lies in identifying emerging trends.
-    You have a knack for dissecting complex data and presenting actionable insights.""",
+pesquisador = Agent(
+    role="Analista Sênior de Pesquisa",
+    goal="Descobrir os avanços mais recentes em IA e ciência de dados",
+    backstory="""
+Você trabalha em um importante think tank de tecnologia. Sua especialidade é identificar tendências emergentes. Você tem habilidade para dissecar dados complexos e apresentar insights acionáveis.
+""",
     verbose=True,
     allow_delegation=False,
-    # You can pass an optional llm attribute specifying what model you wanna use.
-    # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
     tools=[search_tool],
 )
 writer = Agent(
-    role="Tech Content Strategist",
-    goal="Craft compelling content on tech advancements",
-    backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
-    You transform complex concepts into compelling narratives.""",
+    role="Estrategista de Conteúdo Técnico",
+    goal="Criar conteúdo envolvente sobre avanços tecnológicos",
+    backstory="Você é um Estrategista de Conteúdo renomado, conhecido por seus artigos perspicazes e envolventes. Você transforma conceitos complexos em narrativas atraentes.",
     verbose=True,
     allow_delegation=True,
 )
 
 # Crie tarefas para seus agentes
 task1 = Task(
-    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-    Identify key trends, breakthrough technologies, and potential industry impacts.""",
-    expected_output="Full analysis report in bullet points",
-    agent=researcher,
+    description="Realize uma análise abrangente dos avanços mais recentes em IA em 2024. Identifique tendências-chave, tecnologias inovadoras e impactos potenciais na indústria.",
+    expected_output="Relatório analítico completo em tópicos",
+    agent=pesquisador,
 )
 
 task2 = Task(
-    description="""Using the insights provided, develop an engaging blog
-    post that highlights the most significant AI advancements.
-    Your post should be informative yet accessible, catering to a tech-savvy audience.
-    Make it sound cool, avoid complex words so it doesn't sound like AI.""",
-    expected_output="Full blog post of at least 4 paragraphs",
+    description="Utilizando os insights fornecidos, desenvolva um blog envolvente destacando os avanços mais significativos em IA. O post deve ser informativo e acessível, voltado para um público técnico. Dê um tom interessante, evite palavras complexas para não soar como IA.",
+    expected_output="Post de blog completo com pelo menos 4 parágrafos",
     agent=writer,
 )
 
 # Instancie seu crew com um processo sequencial
 crew = Crew(
-    agents=[researcher, writer], tasks=[task1, task2], verbose=1, process=Process.sequential
+    agents=[pesquisador, writer], tasks=[task1, task2], verbose=1, process=Process.sequential
 )
 
 # Coloque seu crew para trabalhar!

docs/pt-BR/observability/langfuse.mdx

@@ -76,20 +76,20 @@ from crewai_tools import (
 
 web_rag_tool = WebsiteSearchTool()
 
-writer = Agent(
-        role="Writer",
-        goal="Você torna a matemática envolvente e compreensível para crianças pequenas através de poesias",
-        backstory="Você é especialista em escrever haicais mas não sabe nada de matemática.",
-        tools=[web_rag_tool],  
-    )
+escritor = Agent(
+    role="Escritor",
+    goal="Você torna a matemática envolvente e compreensível para crianças pequenas através de poesias",
+    backstory="Você é especialista em escrever haicais mas não sabe nada de matemática.",
+    tools=[web_rag_tool],  
+)
 
-task = Task(description=("O que é {multiplicação}?"),
-            expected_output=("Componha um haicai que inclua a resposta."),
-            agent=writer)
+tarefa = Task(description=("O que é {multiplicação}?"),
+              expected_output=("Componha um haicai que inclua a resposta."),
+              agent=escritor)
 
-crew = Crew(
-  agents=[writer],
-  tasks=[task],
+equipe = Crew(
+  agents=[escritor],
+  tasks=[tarefa],
   share_crew=False
 )
 ```

docs/pt-BR/observability/langtrace.mdx

@@ -35,7 +35,7 @@ Essa integração permite o registro de hiperparâmetros, o monitoramento de reg
 
     ```python
     from langtrace_python_sdk import langtrace
-    langtrace.init(api_key='<LANGTRACE_API_KEY>')
+    langtrace.init(api_key='<SUA_CHAVE_LANGTRACE>')
 
     # Agora importe os módulos do CrewAI
     from crewai import Agent, Task, Crew

docs/pt-BR/observability/maxim.mdx

@@ -73,26 +73,24 @@ instrument_crewai(logger)
 ### 4. Crie e execute sua aplicação CrewAI normalmente
 
 ```python
-
-# Crie seu agente
-researcher = Agent(
-    role='Senior Research Analyst',
-    goal='Uncover cutting-edge developments in AI',
-    backstory="You are an expert researcher at a tech think tank...",
+pesquisador = Agent(
+    role='Pesquisador Sênior',
+    goal='Descobrir os avanços mais recentes em IA',
+    backstory="Você é um pesquisador especialista em um think tank de tecnologia...",
     verbose=True,
     llm=llm
 )
 
 # Defina a tarefa
 research_task = Task(
-    description="Research the latest AI advancements...",
+    description="Pesquise os avanços mais recentes em IA...",
     expected_output="",
-    agent=researcher
+    agent=pesquisador
 )
 
 # Configure e execute a crew
 crew = Crew(
-    agents=[researcher],
+    agents=[pesquisador],
     tasks=[research_task],
     verbose=True
 )

docs/pt-BR/observability/mlflow.mdx

@@ -70,22 +70,19 @@ O tracing fornece uma forma de registrar os inputs, outputs e metadados associad
 
       class TripAgents:
           def city_selection_agent(self):
-              return Agent(
-                  role="City Selection Expert",
-                  goal="Select the best city based on weather, season, and prices",
-                  backstory="An expert in analyzing travel data to pick ideal destinations",
-                  tools=[
-                      search_tool,
-                  ],
+              especialista_cidades = Agent(
+                  role="Especialista em Seleção de Cidades",
+                  goal="Selecionar a melhor cidade com base no clima, estação e preços",
+                  backstory="Especialista em analisar dados de viagem para escolher destinos ideais",
+                  tools=[search_tool],
                   verbose=True,
               )
 
           def local_expert(self):
-              return Agent(
-                  role="Local Expert at this city",
-                  goal="Provide the BEST insights about the selected city",
-                  backstory="""A knowledgeable local guide with extensive information
-              about the city, it's attractions and customs""",
+              especialista_local = Agent(
+                  role="Especialista Local nesta cidade",
+                  goal="Fornecer as MELHORES informações sobre a cidade selecionada",
+                  backstory="Um guia local experiente com amplo conhecimento sobre a cidade, suas atrações e costumes",
                   tools=[search_tool],
                   verbose=True,
               )
@@ -96,53 +93,36 @@ O tracing fornece uma forma de registrar os inputs, outputs e metadados associad
               return Task(
                   description=dedent(
                       f"""
-                      Analyze and select the best city for the trip based
-                      on specific criteria such as weather patterns, seasonal
-                      events, and travel costs. This task involves comparing
-                      multiple cities, considering factors like current weather
-                      conditions, upcoming cultural or seasonal events, and
-                      overall travel expenses.
-                      Your final answer must be a detailed
-                      report on the chosen city, and everything you found out
-                      about it, including the actual flight costs, weather
-                      forecast and attractions.
+                      Analise e selecione a melhor cidade para a viagem com base em critérios específicos como padrões climáticos, eventos sazonais e custos de viagem. Esta tarefa envolve comparar várias cidades, considerando fatores como condições climáticas atuais, eventos culturais ou sazonais e despesas gerais de viagem.
+                      Sua resposta final deve ser um relatório detalhado sobre a cidade escolhida e tudo o que você descobriu sobre ela, incluindo custos reais de voo, previsão do tempo e atrações.
 
-                      Traveling from: {origin}
-                      City Options: {cities}
-                      Trip Date: {range}
-                      Traveler Interests: {interests}
+                      Saindo de: {origin}
+                      Opções de cidades: {cities}
+                      Data da viagem: {range}
+                      Interesses do viajante: {interests}
                   """
                   ),
                   agent=agent,
-                  expected_output="Detailed report on the chosen city including flight costs, weather forecast, and attractions",
+                  expected_output="Relatório detalhado sobre a cidade escolhida incluindo custos de voo, previsão do tempo e atrações",
               )
 
           def gather_task(self, agent, origin, interests, range):
               return Task(
                   description=dedent(
                       f"""
-                      As a local expert on this city you must compile an
-                      in-depth guide for someone traveling there and wanting
-                      to have THE BEST trip ever!
-                      Gather information about key attractions, local customs,
-                      special events, and daily activity recommendations.
-                      Find the best spots to go to, the kind of place only a
-                      local would know.
-                      This guide should provide a thorough overview of what
-                      the city has to offer, including hidden gems, cultural
-                      hotspots, must-visit landmarks, weather forecasts, and
-                      high level costs.
-                      The final answer must be a comprehensive city guide,
-                      rich in cultural insights and practical tips,
-                      tailored to enhance the travel experience.
+                      Como especialista local nesta cidade, você deve compilar um guia aprofundado para alguém que está viajando para lá e quer ter a MELHOR viagem possível!
+                      Reúna informações sobre principais atrações, costumes locais, eventos especiais e recomendações de atividades diárias.
+                      Encontre os melhores lugares para ir, aqueles que só um local conhece.
+                      Este guia deve fornecer uma visão abrangente do que a cidade tem a oferecer, incluindo joias escondidas, pontos culturais, marcos imperdíveis, previsão do tempo e custos gerais.
+                      A resposta final deve ser um guia completo da cidade, rico em insights culturais e dicas práticas, adaptado para aprimorar a experiência de viagem.
 
-                      Trip Date: {range}
-                      Traveling from: {origin}
-                      Traveler Interests: {interests}
+                      Data da viagem: {range}
+                      Saindo de: {origin}
+                      Interesses do viajante: {interests}
                   """
                   ),
                   agent=agent,
-                  expected_output="Comprehensive city guide including hidden gems, cultural hotspots, and practical travel tips",
+                  expected_output="Guia completo da cidade incluindo joias escondidas, pontos culturais e dicas práticas",
               )
 
 
@@ -189,7 +169,7 @@ O tracing fornece uma forma de registrar os inputs, outputs e metadados associad
       trip_crew = TripCrew("California", "Tokyo", "Dec 12 - Dec 20", "sports")
       result = trip_crew.run()
 
-      print(result)
+      print("Resultado da equipe:", result)
       ```
       Consulte a [Documentação de Tracing do MLflow](https://mlflow.org/docs/latest/llms/tracing/index.html) para mais configurações e casos de uso.
     </Step>

docs/pt-BR/observability/openlit.mdx

@@ -69,10 +69,10 @@ Essa configuração permite acompanhar hiperparâmetros e monitorar problemas de
 
           openlit.init(disable_metrics=True)
           # Definir seus agentes
-          researcher = Agent(
-              role="Researcher",
-              goal="Conduct thorough research and analysis on AI and AI agents",
-              backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
+          pesquisador = Agent(
+              role="Pesquisador",
+              goal="Realizar pesquisas e análises aprofundadas sobre IA e agentes de IA",
+              backstory="Você é um pesquisador especialista em tecnologia, engenharia de software, IA e startups. Trabalha como freelancer e está atualmente pesquisando para um novo cliente.",
               allow_delegation=False,
               llm='command-r'
           )
@@ -80,24 +80,24 @@ Essa configuração permite acompanhar hiperparâmetros e monitorar problemas de
 
           # Definir sua task
           task = Task(
-              description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.",
-              expected_output="5 bullet points, each with a paragraph and accompanying notes.",
+              description="Gere uma lista com 5 ideias interessantes para um artigo e escreva um parágrafo cativante para cada ideia, mostrando o potencial de um artigo completo sobre o tema. Retorne a lista de ideias com seus parágrafos e suas anotações.",
+              expected_output="5 tópicos, cada um com um parágrafo e notas complementares.",
           )
 
           # Definir o agente gerente
-          manager = Agent(
-              role="Project Manager",
-              goal="Efficiently manage the crew and ensure high-quality task completion",
-              backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
+          gerente = Agent(
+              role="Gerente de Projeto",
+              goal="Gerenciar eficientemente a equipe e garantir a conclusão de tarefas de alta qualidade",
+              backstory="Você é um gerente de projetos experiente, habilidoso em supervisionar projetos complexos e guiar equipes para o sucesso. Sua função é coordenar os esforços dos membros da equipe, garantindo que cada tarefa seja concluída no prazo e com o mais alto padrão.",
               allow_delegation=True,
               llm='command-r'
           )
 
           # Instanciar sua crew com um manager personalizado
           crew = Crew(
-              agents=[researcher],
+              agents=[pesquisador],
               tasks=[task],
-              manager_agent=manager,
+              manager_agent=gerente,
               process=Process.hierarchical,
           )
 
@@ -132,18 +132,18 @@ Essa configuração permite acompanhar hiperparâmetros e monitorar problemas de
 
           # Criar um agente com execução de código habilitada
           coding_agent = Agent(
-            role="Python Data Analyst",
-            goal="Analyze data and provide insights using Python",
-            backstory="You are an experienced data analyst with strong Python skills.",
+            role="Analista de Dados Python",
+            goal="Analisar dados e fornecer insights usando Python",
+            backstory="Você é um analista de dados experiente com fortes habilidades em Python.",
             allow_code_execution=True,
             llm="command-r"
           )
 
           # Criar uma task que exige execução de código
           data_analysis_task = Task(
-            description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
+            description="Analise o conjunto de dados fornecido e calcule a idade média dos participantes. Idades: {ages}",
             agent=coding_agent,
-            expected_output="5 bullet points, each with a paragraph and accompanying notes.",
+            expected_output="5 tópicos, cada um com um parágrafo e notas complementares.",
           )
 
           # Criar uma crew e adicionar a task

docs/pt-BR/observability/opik.mdx

@@ -58,43 +58,43 @@ Neste guia, utilizaremos o exemplo de início rápido da CrewAI.
       from crewai import Agent, Crew, Task, Process
 
 
-      class YourCrewName:
-          def agent_one(self) -> Agent:
+      class NomeDaEquipe:
+          def agente_um(self) -> Agent:
               return Agent(
-                  role="Data Analyst",
-                  goal="Analyze data trends in the market",
-                  backstory="An experienced data analyst with a background in economics",
+                  role="Analista de Dados",
+                  goal="Analisar tendências de dados no mercado",
+                  backstory="Analista de dados experiente com formação em economia",
                   verbose=True,
               )
 
-          def agent_two(self) -> Agent:
+          def agente_dois(self) -> Agent:
               return Agent(
-                  role="Market Researcher",
-                  goal="Gather information on market dynamics",
-                  backstory="A diligent researcher with a keen eye for detail",
+                  role="Pesquisador de Mercado",
+                  goal="Coletar informações sobre a dinâmica do mercado",
+                  backstory="Pesquisador dedicado com olhar atento para detalhes",
                   verbose=True,
               )
 
-          def task_one(self) -> Task:
+          def tarefa_um(self) -> Task:
               return Task(
-                  name="Collect Data Task",
-                  description="Collect recent market data and identify trends.",
-                  expected_output="A report summarizing key trends in the market.",
-                  agent=self.agent_one(),
+                  name="Tarefa de Coleta de Dados",
+                  description="Coletar dados recentes do mercado e identificar tendências.",
+                  expected_output="Um relatório resumindo as principais tendências do mercado.",
+                  agent=self.agente_um(),
               )
 
-          def task_two(self) -> Task:
+          def tarefa_dois(self) -> Task:
               return Task(
-                  name="Market Research Task",
-                  description="Research factors affecting market dynamics.",
-                  expected_output="An analysis of factors influencing the market.",
-                  agent=self.agent_two(),
+                  name="Tarefa de Pesquisa de Mercado",
+                  description="Pesquisar fatores que afetam a dinâmica do mercado.",
+                  expected_output="Uma análise dos fatores que influenciam o mercado.",
+                  agent=self.agente_dois(),
               )
 
-          def crew(self) -> Crew:
+          def equipe(self) -> Crew:
               return Crew(
-                  agents=[self.agent_one(), self.agent_two()],
-                  tasks=[self.task_one(), self.task_two()],
+                  agents=[self.agente_um(), self.agente_dois()],
+                  tasks=[self.tarefa_um(), self.tarefa_dois()],
                   process=Process.sequential,
                   verbose=True,
               )
@@ -108,7 +108,7 @@ Neste guia, utilizaremos o exemplo de início rápido da CrewAI.
 
       track_crewai(project_name="crewai-integration-demo")
 
-      my_crew = YourCrewName().crew()
+      my_crew = NomeDaEquipe().equipe()
       result = my_crew.kickoff()
 
       print(result)

docs/pt-BR/observability/patronus-evaluation.mdx

@@ -64,17 +64,17 @@ patronus_eval_tool = PatronusEvalTool()
 
 # Define an agent that uses the tool
 coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code and verify that the output is code",
-    backstory="An experienced coder who can generate high quality python code.",
+    role="Agente de Programação",
+    goal="Gerar código de alta qualidade e verificar se a saída é código",
+    backstory="Um programador experiente que pode gerar código Python de alta qualidade.",
     tools=[patronus_eval_tool],
     verbose=True,
 )
 
 # Example task to generate and evaluate code
 generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence. Select the most appropriate evaluator and criteria for evaluating your output.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
+    description="Crie um programa simples para gerar os N primeiros números da sequência de Fibonacci. Selecione o avaliador e os critérios mais apropriados para avaliar sua saída.",
+    expected_output="Programa que gera os N primeiros números da sequência de Fibonacci.",
     agent=coding_agent,
 )
 
@@ -98,17 +98,17 @@ patronus_eval_tool = PatronusPredefinedCriteriaEvalTool(
 
 # Define an agent that uses the tool
 coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code",
-    backstory="An experienced coder who can generate high quality python code.",
+    role="Agente de Programação",
+    goal="Gerar código de alta qualidade",
+    backstory="Um programador experiente que pode gerar código Python de alta qualidade.",
     tools=[patronus_eval_tool],
     verbose=True,
 )
 
 # Example task to generate code
 generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
+    description="Crie um programa simples para gerar os N primeiros números da sequência de Fibonacci.",
+    expected_output="Programa que gera os N primeiros números da sequência de Fibonacci.",
     agent=coding_agent,
 )
 
@@ -149,17 +149,17 @@ patronus_eval_tool = PatronusLocalEvaluatorTool(
 
 # Define an agent that uses the tool
 coding_agent = Agent(
-    role="Coding Agent",
-    goal="Generate high quality code",
-    backstory="An experienced coder who can generate high quality python code.",
+    role="Agente de Programação",
+    goal="Gerar código de alta qualidade",
+    backstory="Um programador experiente que pode gerar código Python de alta qualidade.",
     tools=[patronus_eval_tool],
     verbose=True,
 )
 
 # Example task to generate code
 generate_code_task = Task(
-    description="Create a simple program to generate the first N numbers in the Fibonacci sequence.",
-    expected_output="Program that generates the first N numbers in the Fibonacci sequence.",
+    description="Crie um programa simples para gerar os N primeiros números da sequência de Fibonacci.",
+    expected_output="Programa que gera os N primeiros números da sequência de Fibonacci.",
     agent=coding_agent,
 )
 

docs/pt-BR/observability/weave.mdx

@@ -50,48 +50,48 @@ O Weave captura automaticamente rastreamentos (traces) de suas aplicações Crew
       llm = LLM(model="gpt-4o", temperature=0)
 
       # Crie os agentes
-      researcher = Agent(
-          role='Research Analyst',
-          goal='Find and analyze the best investment opportunities',
-          backstory='Expert in financial analysis and market research',
+      pesquisador = Agent(
+          role='Analista de Pesquisa',
+          goal='Encontrar e analisar as melhores oportunidades de investimento',
+          backstory='Especialista em análise financeira e pesquisa de mercado',
           llm=llm,
           verbose=True,
           allow_delegation=False,
       )
 
-      writer = Agent(
-          role='Report Writer',
-          goal='Write clear and concise investment reports',
-          backstory='Experienced in creating detailed financial reports',
+      redator = Agent(
+          role='Redator de Relatórios',
+          goal='Escrever relatórios de investimento claros e concisos',
+          backstory='Experiente na criação de relatórios financeiros detalhados',
           llm=llm,
           verbose=True,
           allow_delegation=False,
       )
 
       # Crie as tarefas
-      research_task = Task(
-          description='Deep research on the {topic}',
-          expected_output='Comprehensive market data including key players, market size, and growth trends.',
-          agent=researcher
+      pesquisa = Task(
+          description='Pesquisa aprofundada sobre o {tema}',
+          expected_output='Dados de mercado abrangentes incluindo principais players, tamanho de mercado e tendências de crescimento.',
+          agent=pesquisador
       )
 
-      writing_task = Task(
-          description='Write a detailed report based on the research',
-          expected_output='The report should be easy to read and understand. Use bullet points where applicable.',
-          agent=writer
+      redacao = Task(
+          description='Escreva um relatório detalhado com base na pesquisa',
+          expected_output='O relatório deve ser fácil de ler e entender. Use tópicos quando aplicável.',
+          agent=redator
       )
 
       # Crie o crew
-      crew = Crew(
-          agents=[researcher, writer],
-          tasks=[research_task, writing_task],
+      equipe = Crew(
+          agents=[pesquisador, redator],
+          tasks=[pesquisa, redacao],
           verbose=True,
           process=Process.sequential,
       )
 
       # Execute o crew
-      result = crew.kickoff(inputs={"topic": "AI in material science"})
-      print(result)
+      resultado = equipe.kickoff(inputs={"tema": "IA em ciência dos materiais"})
+      print(resultado)
       ```
     </Step>
     <Step title="Visualize rastreamentos no Weave">

docs/pt-BR/quickstart.mdx

@@ -39,23 +39,19 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
     # src/latest_ai_development/config/agents.yaml
     researcher:
       role: >
-        {topic} Senior Data Researcher
+        Pesquisador Sênior de Dados em {topic}
       goal: >
-        Uncover cutting-edge developments in {topic}
+        Descobrir os avanços mais recentes em {topic}
       backstory: >
-        You're a seasoned researcher with a knack for uncovering the latest
-        developments in {topic}. Known for your ability to find the most relevant
-        information and present it in a clear and concise manner.
+        Você é um pesquisador experiente com talento para descobrir os últimos avanços em {topic}. Conhecido por sua habilidade em encontrar as informações mais relevantes e apresentá-las de forma clara e concisa.
 
     reporting_analyst:
       role: >
-        {topic} Reporting Analyst
+        Analista de Relatórios em {topic}
       goal: >
-        Create detailed reports based on {topic} data analysis and research findings
+        Criar relatórios detalhados com base na análise de dados e descobertas de pesquisa em {topic}
       backstory: >
-        You're a meticulous analyst with a keen eye for detail. You're known for
-        your ability to turn complex data into clear and concise reports, making
-        it easy for others to understand and act on the information you provide.
+        Você é um analista meticuloso com um olhar atento aos detalhes. É conhecido por sua capacidade de transformar dados complexos em relatórios claros e concisos, facilitando o entendimento e a tomada de decisão por parte dos outros.
     ```
   </Step>
   <Step title="Modifique seu arquivo `tasks.yaml`">
@@ -63,20 +59,19 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
     # src/latest_ai_development/config/tasks.yaml
     research_task:
       description: >
-        Conduct a thorough research about {topic}
-        Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        Realize uma pesquisa aprofundada sobre {topic}.
+        Certifique-se de encontrar informações interessantes e relevantes considerando que o ano atual é 2025.
       expected_output: >
-        A list with 10 bullet points of the most relevant information about {topic}
+        Uma lista com 10 tópicos dos dados mais relevantes sobre {topic}
       agent: researcher
 
     reporting_task:
       description: >
-        Review the context you got and expand each topic into a full section for a report.
-        Make sure the report is detailed and contains any and all relevant information.
+        Revise o contexto obtido e expanda cada tópico em uma seção completa para um relatório.
+        Certifique-se de que o relatório seja detalhado e contenha todas as informações relevantes.
       expected_output: >
-        A fully fledge reports with the mains topics, each with a full section of information.
-        Formatted as markdown without '```'
+        Um relatório completo com os principais tópicos, cada um com uma seção detalhada de informações.
+        Formate como markdown sem usar '```'
       agent: reporting_analyst
       output_file: report.md
     ```
@@ -122,15 +117,15 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
       def reporting_task(self) -> Task:
         return Task(
           config=self.tasks_config['reporting_task'], # type: ignore[index]
-          output_file='output/report.md' # This is the file that will be contain the final report.
+          output_file='output/report.md' # Este é o arquivo que conterá o relatório final.
         )
 
       @crew
       def crew(self) -> Crew:
         """Creates the LatestAiDevelopment crew"""
         return Crew(
-          agents=self.agents, # Automatically created by the @agent decorator
-          tasks=self.tasks, # Automatically created by the @task decorator
+          agents=self.agents, # Criado automaticamente pelo decorador @agent
+          tasks=self.tasks, # Criado automaticamente pelo decorador @task
           process=Process.sequential,
           verbose=True,
         )
@@ -229,7 +224,7 @@ Siga os passos abaixo para começar a tripular! 🚣‍♂️
 
   <CodeGroup>
     ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2025
+    # Relatório Abrangente sobre a Ascensão e o Impacto dos Agentes de IA em 2025
 
     ## 1. Introduction to AI Agents
     In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.

docs/pt-BR/tools/search-research/linkupsearchtool.mdx

@@ -35,78 +35,18 @@ from crewai_tools import LinkupSearchTool
 from crewai import Agent
 import os
 
-# Initialize the tool with your API key
-linkup_tool = LinkupSearchTool(api_key=os.getenv("LINKUP_API_KEY"))
+# Inicialize a ferramenta com sua chave de API
+linkup_ferramenta = LinkupSearchTool(api_key=os.getenv("LINKUP_API_KEY"))
 
-# Define an agent that uses the tool
+# Defina um agente que usa a ferramenta
 @agent
-def researcher(self) -> Agent:
+def pesquisador(self) -> Agent:
     '''
-    This agent uses the LinkupSearchTool to retrieve contextual information
-    from the Linkup API.
+    Este agente usa o LinkupSearchTool para recuperar informações contextuais
+    da API do Linkup.
     '''
     return Agent(
-        config=self.agents_config["researcher"],
-        tools=[linkup_tool]
+        config=self.agentes_config["pesquisador"],
+        tools=[linkup_ferramenta]
     )
-```
-
-## Parâmetros
-
-O `LinkupSearchTool` aceita os seguintes parâmetros:
-
-### Parâmetros do Construtor
-- **api_key**: Obrigatório. Sua chave de API do Linkup.
-
-### Parâmetros de Execução
-- **query**: Obrigatório. O termo ou frase de busca.
-- **depth**: Opcional. A profundidade da busca. O padrão é "standard".
-- **output_type**: Opcional. O tipo de saída. O padrão é "searchResults".
-
-## Uso Avançado
-
-Você pode personalizar os parâmetros de busca para resultados mais específicos:
-
-```python Code
-# Perform a search with custom parameters
-results = linkup_tool.run(
-    query="Women Nobel Prize Physics",
-    depth="deep",
-    output_type="searchResults"
-)
-```
-
-## Formato de Retorno
-
-A ferramenta retorna resultados no seguinte formato:
-
-```json
-{
-  "success": true,
-  "results": [
-    {
-      "name": "Result Title",
-      "url": "https://example.com/result",
-      "content": "Content of the result..."
-    },
-    // Additional results...
-  ]
-}
-```
-
-Se ocorrer um erro, a resposta será:
-
-```json
-{
-  "success": false,
-  "error": "Error message"
-}
-```
-
-## Tratamento de Erros
-
-A ferramenta lida com erros de API de forma amigável e fornece feedback estruturado. Se a requisição à API falhar, a ferramenta retornará um dicionário com `success: false` e uma mensagem de erro.
-
-## Conclusão
-
-O `LinkupSearchTool` oferece uma forma integrada de incorporar as capacidades de busca de informações contextuais do Linkup aos seus agentes CrewAI. Ao utilizar esta ferramenta, os agentes podem acessar informações relevantes e atualizadas para aprimorar sua tomada de decisão e execução de tarefas.
+```

pyproject.toml

@@ -27,13 +27,13 @@ dependencies = [
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
     # Authentication and Security
-    "auth0-python>=4.7.1",
     "python-dotenv>=1.0.0",
+    "pyjwt>=2.9.0",
     # Configuration and Utils
     "click>=8.1.7",
     "appdirs>=1.4.4",
     "jsonref>=1.1.0",
-    "json-repair>=0.25.2",
+    "json-repair==0.25.2",
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
@@ -47,11 +47,11 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.48.0"]
+tools = ["crewai-tools~=0.49.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
-agentops = ["agentops>=0.3.0"]
+agentops = ["agentops==0.3.18"]
 pdfplumber = [
     "pdfplumber>=0.11.4",
 ]
@@ -83,6 +83,8 @@ dev-dependencies = [
     "pytest-recording>=0.13.2",
     "pytest-randomly>=3.16.0",
     "pytest-timeout>=2.3.1",
+    "pytest-xdist>=3.6.1",
+    "pytest-split>=0.9.0",
 ]
 
 [project.scripts]
@@ -123,3 +125,15 @@ path = "src/crewai/__init__.py"
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+exclude = [
+"docs/**",
+"docs/",
+]
+
+[tool.hatch.build.targets.sdist]
+exclude = [
+"docs/**",
+"docs/",
+]

src/crewai/__init__.py

@@ -28,19 +28,19 @@ _telemetry_submitted = False
 def _track_install():
     """Track package installation/first-use via Scarf analytics."""
     global _telemetry_submitted
-    
+
     if _telemetry_submitted or Telemetry._is_telemetry_disabled():
         return
-    
+
     try:
         pixel_url = "https://api.scarf.sh/v2/packages/CrewAI/crewai/docs/00f2dad1-8334-4a39-934e-003b2e1146db"
-        
+
         req = urllib.request.Request(pixel_url)
         req.add_header('User-Agent', f'CrewAI-Python/{__version__}')
-        
+
         with urllib.request.urlopen(req, timeout=2):  # nosec B310
             _telemetry_submitted = True
-            
+
     except Exception:
         pass
 
@@ -54,7 +54,7 @@ def _track_install_async():
 
 _track_install_async()
 
-__version__ = "0.134.0"
+__version__ = "0.140.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/cli/authentication/constants.py

@@ -2,3 +2,7 @@ ALGORITHMS = ["RS256"]
 AUTH0_DOMAIN = "crewai.us.auth0.com"
 AUTH0_CLIENT_ID = "DEVC5Fw6NlRoSzmDCcOhVq85EfLBjKa8"
 AUTH0_AUDIENCE = "https://crewai.us.auth0.com/api/v2/"
+
+WORKOS_DOMAIN = "login.crewai.com"
+WORKOS_CLI_CONNECT_APP_ID = "client_01JYT06R59SP0NXYGD994NFXXX"
+WORKOS_ENVIRONMENT_ID = "client_01JNJQWB4HG8T5980R5VHP057C"

src/crewai/cli/authentication/main.py

@@ -5,37 +5,72 @@ from typing import Any, Dict
 import requests
 from rich.console import Console
 
-from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN
-from .utils import TokenManager, validate_token
+from .constants import (
+    AUTH0_AUDIENCE,
+    AUTH0_CLIENT_ID,
+    AUTH0_DOMAIN,
+    WORKOS_DOMAIN,
+    WORKOS_CLI_CONNECT_APP_ID,
+    WORKOS_ENVIRONMENT_ID,
+)
+
+from .utils import TokenManager, validate_jwt_token
+from urllib.parse import quote
+from crewai.cli.plus_api import PlusAPI
+from crewai.cli.config import Settings
 
 console = Console()
 
 
 class AuthenticationCommand:
-    DEVICE_CODE_URL = f"https://{AUTH0_DOMAIN}/oauth/device/code"
-    TOKEN_URL = f"https://{AUTH0_DOMAIN}/oauth/token"
+    AUTH0_DEVICE_CODE_URL = f"https://{AUTH0_DOMAIN}/oauth/device/code"
+    AUTH0_TOKEN_URL = f"https://{AUTH0_DOMAIN}/oauth/token"
+
+    WORKOS_DEVICE_CODE_URL = f"https://{WORKOS_DOMAIN}/oauth2/device_authorization"
+    WORKOS_TOKEN_URL = f"https://{WORKOS_DOMAIN}/oauth2/token"
 
     def __init__(self):
         self.token_manager = TokenManager()
+        # TODO: WORKOS - This variable is temporary until migration to WorkOS is complete.
+        self.user_provider = "workos"
 
-    def signup(self) -> None:
+    def login(self) -> None:
         """Sign up to CrewAI+"""
-        console.print("Signing Up to CrewAI+ \n", style="bold blue")
-        device_code_data = self._get_device_code()
+
+        device_code_url = self.WORKOS_DEVICE_CODE_URL
+        token_url = self.WORKOS_TOKEN_URL
+        client_id = WORKOS_CLI_CONNECT_APP_ID
+        audience = None
+
+        console.print("Signing in to CrewAI Enterprise...\n", style="bold blue")
+
+        # TODO: WORKOS - Next line and conditional are temporary until migration to WorkOS is complete.
+        user_provider = self._determine_user_provider()
+        if user_provider == "auth0":
+            device_code_url = self.AUTH0_DEVICE_CODE_URL
+            token_url = self.AUTH0_TOKEN_URL
+            client_id = AUTH0_CLIENT_ID
+            audience = AUTH0_AUDIENCE
+            self.user_provider = "auth0"
+        # End of temporary code.
+
+        device_code_data = self._get_device_code(client_id, device_code_url, audience)
         self._display_auth_instructions(device_code_data)
 
-        return self._poll_for_token(device_code_data)
+        return self._poll_for_token(device_code_data, client_id, token_url)
 
-    def _get_device_code(self) -> Dict[str, Any]:
+    def _get_device_code(
+        self, client_id: str, device_code_url: str, audience: str | None = None
+    ) -> Dict[str, Any]:
         """Get the device code to authenticate the user."""
 
         device_code_payload = {
-            "client_id": AUTH0_CLIENT_ID,
+            "client_id": client_id,
             "scope": "openid",
-            "audience": AUTH0_AUDIENCE,
+            "audience": audience,
         }
         response = requests.post(
-            url=self.DEVICE_CODE_URL, data=device_code_payload, timeout=20
+            url=device_code_url, data=device_code_payload, timeout=20
         )
         response.raise_for_status()
         return response.json()
@@ -46,38 +81,33 @@ class AuthenticationCommand:
         console.print("2. Enter the following code: ", device_code_data["user_code"])
         webbrowser.open(device_code_data["verification_uri_complete"])
 
-    def _poll_for_token(self, device_code_data: Dict[str, Any]) -> None:
-        """Poll the server for the token."""
+    def _poll_for_token(
+        self, device_code_data: Dict[str, Any], client_id: str, token_poll_url: str
+    ) -> None:
+        """Polls the server for the token until it is received, or max attempts are reached."""
+
         token_payload = {
             "grant_type": "urn:ietf:params:oauth:grant-type:device_code",
             "device_code": device_code_data["device_code"],
-            "client_id": AUTH0_CLIENT_ID,
+            "client_id": client_id,
         }
 
+        console.print("\nWaiting for authentication... ", style="bold blue", end="")
+
         attempts = 0
-        while True and attempts < 5:
-            response = requests.post(self.TOKEN_URL, data=token_payload, timeout=30)
+        while True and attempts < 10:
+            response = requests.post(token_poll_url, data=token_payload, timeout=30)
             token_data = response.json()
 
             if response.status_code == 200:
-                validate_token(token_data["id_token"])
-                expires_in = 360000  # Token expiration time in seconds
-                self.token_manager.save_tokens(token_data["access_token"], expires_in)
+                self._validate_and_save_token(token_data)
 
-                try:
-                    from crewai.cli.tools.main import ToolCommand
-                    ToolCommand().login()
-                except Exception:
-                    console.print(
-                        "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.",
-                        style="yellow",
-                    )
-                    console.print(
-                        "Other features will work normally, but you may experience limitations "
-                        "with downloading and publishing tools."
-                        "\nRun [bold]crewai login[/bold] to try logging in again.\n",
-                        style="yellow",
-                    )
+                console.print(
+                    "Success!",
+                    style="bold green",
+                )
+
+                self._login_to_tool_repository()
 
                 console.print(
                     "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
@@ -93,3 +123,88 @@ class AuthenticationCommand:
         console.print(
             "Timeout: Failed to get the token. Please try again.", style="bold red"
         )
+
+    def _validate_and_save_token(self, token_data: Dict[str, Any]) -> None:
+        """Validates the JWT token and saves the token to the token manager."""
+
+        jwt_token = token_data["access_token"]
+        jwt_token_data = {
+            "jwt_token": jwt_token,
+            "jwks_url": f"https://{WORKOS_DOMAIN}/oauth2/jwks",
+            "issuer": f"https://{WORKOS_DOMAIN}",
+            "audience": WORKOS_ENVIRONMENT_ID,
+        }
+
+        # TODO: WORKOS - The following conditional is temporary until migration to WorkOS is complete.
+        if self.user_provider == "auth0":
+            jwt_token_data["jwks_url"] = f"https://{AUTH0_DOMAIN}/.well-known/jwks.json"
+            jwt_token_data["issuer"] = f"https://{AUTH0_DOMAIN}/"
+            jwt_token_data["audience"] = AUTH0_AUDIENCE
+
+        decoded_token = validate_jwt_token(**jwt_token_data)
+
+        expires_at = decoded_token.get("exp", 0)
+        self.token_manager.save_tokens(jwt_token, expires_at)
+
+    def _login_to_tool_repository(self) -> None:
+        """Login to the tool repository."""
+
+        from crewai.cli.tools.main import ToolCommand
+
+        try:
+            console.print(
+                "Now logging you in to the Tool Repository... ",
+                style="bold blue",
+                end="",
+            )
+
+            ToolCommand().login()
+
+            console.print(
+                "Success!\n",
+                style="bold green",
+            )
+
+            settings = Settings()
+            console.print(
+                f"You are authenticated to the tool repository as [bold cyan]'{settings.org_name}'[/bold cyan] ({settings.org_uuid})",
+                style="green",
+            )
+        except Exception:
+            console.print(
+                "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.",
+                style="yellow",
+            )
+            console.print(
+                "Other features will work normally, but you may experience limitations "
+                "with downloading and publishing tools."
+                "\nRun [bold]crewai login[/bold] to try logging in again.\n",
+                style="yellow",
+            )
+
+    # TODO: WORKOS - This method is temporary until migration to WorkOS is complete.
+    def _determine_user_provider(self) -> str:
+        """Determine which provider to use for authentication."""
+
+        console.print(
+            "Enter your CrewAI Enterprise account email: ", style="bold blue", end=""
+        )
+        email = input()
+        email_encoded = quote(email)
+
+        # It's not correct to call this method directly, but it's temporary until migration is complete.
+        response = PlusAPI("")._make_request(
+            "GET", f"/crewai_plus/api/v1/me/provider?email={email_encoded}"
+        )
+
+        if response.status_code == 200:
+            if response.json().get("provider") == "auth0":
+                return "auth0"
+            else:
+                return "workos"
+        else:
+            console.print(
+                "Error: Failed to authenticate with crewai enterprise. Ensure that you are using the latest crewai version and please try again. If the problem persists, contact support@crewai.com.",
+                style="red",
+            )
+            raise SystemExit

src/crewai/cli/authentication/utils.py

@@ -1,32 +1,63 @@
 import json
 import os
 import sys
-from datetime import datetime, timedelta
+from datetime import datetime
 from pathlib import Path
 from typing import Optional
-
-from auth0.authentication.token_verifier import (
-    AsymmetricSignatureVerifier,
-    TokenVerifier,
-)
+import jwt
+from jwt import PyJWKClient
 from cryptography.fernet import Fernet
 
-from .constants import AUTH0_CLIENT_ID, AUTH0_DOMAIN
 
-
-def validate_token(id_token: str) -> None:
+def validate_jwt_token(
+    jwt_token: str, jwks_url: str, issuer: str, audience: str
+) -> dict:
     """
-    Verify the token and its precedence
-
-    :param id_token:
+    Verify the token's signature and claims using PyJWT.
+    :param jwt_token: The JWT (JWS) string to validate.
+    :param jwks_url: The URL of the JWKS endpoint.
+    :param issuer: The expected issuer of the token.
+    :param audience: The expected audience of the token.
+    :return: The decoded token.
+    :raises Exception: If the token is invalid for any reason (e.g., signature mismatch,
+                       expired, incorrect issuer/audience, JWKS fetching error,
+                       missing required claims).
     """
-    jwks_url = f"https://{AUTH0_DOMAIN}/.well-known/jwks.json"
-    issuer = f"https://{AUTH0_DOMAIN}/"
-    signature_verifier = AsymmetricSignatureVerifier(jwks_url)
-    token_verifier = TokenVerifier(
-        signature_verifier=signature_verifier, issuer=issuer, audience=AUTH0_CLIENT_ID
-    )
-    token_verifier.verify(id_token)
+
+    decoded_token = None
+
+    try:
+        jwk_client = PyJWKClient(jwks_url)
+        signing_key = jwk_client.get_signing_key_from_jwt(jwt_token)
+
+        decoded_token = jwt.decode(
+            jwt_token,
+            signing_key.key,
+            algorithms=["RS256"],
+            audience=audience,
+            issuer=issuer,
+            options={
+                "verify_signature": True,
+                "verify_exp": True,
+                "verify_nbf": True,
+                "verify_iat": True,
+                "require": ["exp", "iat", "iss", "aud", "sub"],
+            },
+        )
+        return decoded_token
+
+    except jwt.ExpiredSignatureError:
+        raise Exception("Token has expired.")
+    except jwt.InvalidAudienceError:
+        raise Exception(f"Invalid token audience. Expected: '{audience}'")
+    except jwt.InvalidIssuerError:
+        raise Exception(f"Invalid token issuer. Expected: '{issuer}'")
+    except jwt.MissingRequiredClaimError as e:
+        raise Exception(f"Token is missing required claims: {str(e)}")
+    except jwt.exceptions.PyJWKClientError as e:
+        raise Exception(f"JWKS or key processing error: {str(e)}")
+    except jwt.InvalidTokenError as e:
+        raise Exception(f"Invalid token: {str(e)}")
 
 
 class TokenManager:
@@ -56,14 +87,14 @@ class TokenManager:
         self.save_secure_file(key_filename, new_key)
         return new_key
 
-    def save_tokens(self, access_token: str, expires_in: int) -> None:
+    def save_tokens(self, access_token: str, expires_at: int) -> None:
         """
         Save the access token and its expiration time.
 
         :param access_token: The access token to save.
-        :param expires_in: The expiration time of the access token in seconds.
+        :param expires_at: The UNIX timestamp of the expiration time.
         """
-        expiration_time = datetime.now() + timedelta(seconds=expires_in)
+        expiration_time = datetime.fromtimestamp(expires_at)
         data = {
             "access_token": access_token,
             "expiration": expiration_time.isoformat(),

src/crewai/cli/cli.py

@@ -2,7 +2,7 @@ from importlib.metadata import version as get_version
 from typing import Optional
 
 import click
-
+from crewai.cli.config import Settings
 from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
@@ -138,8 +138,12 @@ def log_tasks_outputs() -> None:
 @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
 @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
 @click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage")
-@click.option("-akn", "--agent-knowledge", is_flag=True, help="Reset AGENT KNOWLEDGE storage")
-@click.option("-k","--kickoff-outputs",is_flag=True,help="Reset LATEST KICKOFF TASK OUTPUTS")
+@click.option(
+    "-akn", "--agent-knowledge", is_flag=True, help="Reset AGENT KNOWLEDGE storage"
+)
+@click.option(
+    "-k", "--kickoff-outputs", is_flag=True, help="Reset LATEST KICKOFF TASK OUTPUTS"
+)
 @click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
 def reset_memories(
     long: bool,
@@ -154,13 +158,23 @@ def reset_memories(
     Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs, knowledge, agent_knowledge). This will delete all the data saved.
     """
     try:
-        memory_types = [long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all]
+        memory_types = [
+            long,
+            short,
+            entities,
+            knowledge,
+            agent_knowledge,
+            kickoff_outputs,
+            all,
+        ]
         if not any(memory_types):
             click.echo(
                 "Please specify at least one memory type to reset using the appropriate flags."
             )
             return
-        reset_memories_command(long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all)
+        reset_memories_command(
+            long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all
+        )
     except Exception as e:
         click.echo(f"An error occurred while resetting memories: {e}", err=True)
 
@@ -210,16 +224,11 @@ def update():
     update_crew()
 
 
-@crewai.command()
-def signup():
-    """Sign Up/Login to CrewAI+."""
-    AuthenticationCommand().signup()
-
-
 @crewai.command()
 def login():
-    """Sign Up/Login to CrewAI+."""
-    AuthenticationCommand().signup()
+    """Sign Up/Login to CrewAI Enterprise."""
+    Settings().clear()
+    AuthenticationCommand().login()
 
 
 # DEPLOY CREWAI+ COMMANDS

src/crewai/cli/config.py

@@ -37,6 +37,10 @@ class Settings(BaseModel):
         merged_data = {**file_data, **data}
         super().__init__(config_path=config_path, **merged_data)
 
+    def clear(self) -> None:
+        """Clear all settings"""
+        self.config_path.unlink(missing_ok=True)
+
     def dump(self) -> None:
         """Save current settings to settings.json"""
         if self.config_path.is_file():

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.134.0,<1.0.0"
+    "crewai[tools]>=0.140.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.134.0,<1.0.0",
+    "crewai[tools]>=0.140.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.134.0"
+    "crewai[tools]>=0.140.0"
 ]
 
 [tool.crewai]

src/crewai/cli/tools/main.py

@@ -156,7 +156,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         console.print(f"Successfully installed {handle}", style="bold green")
 
-    def login(self):
+    def login(self) -> None:
         login_response = self.plus_api_client.login_to_tool_repository()
 
         if login_response.status_code != 200:
@@ -175,18 +175,10 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings.tool_repository_password = login_response_json["credential"][
             "password"
         ]
-        settings.org_uuid = login_response_json["current_organization"][
-            "uuid"
-        ]
-        settings.org_name = login_response_json["current_organization"][
-            "name"
-        ]
+        settings.org_uuid = login_response_json["current_organization"]["uuid"]
+        settings.org_name = login_response_json["current_organization"]["name"]
         settings.dump()
 
-        console.print(
-            f"Successfully authenticated to the tool repository as {settings.org_name} ({settings.org_uuid}).", style="bold green"
-        )
-
     def _add_package(self, tool_details: dict[str, Any]):
         is_from_pypi = tool_details.get("source", None) == "pypi"
         tool_handle = tool_details["handle"]
@@ -243,9 +235,15 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         return env
 
-    def _print_current_organization(self):
+    def _print_current_organization(self) -> None:
         settings = Settings()
         if settings.org_uuid:
-            console.print(f"Current organization: {settings.org_name} ({settings.org_uuid})", style="bold blue")
+            console.print(
+                f"Current organization: {settings.org_name} ({settings.org_uuid})",
+                style="bold blue",
+            )
         else:
-            console.print("No organization currently set. We recommend setting one before using: `crewai org switch <org_id>` command.", style="yellow")
+            console.print(
+                "No organization currently set. We recommend setting one before using: `crewai org switch <org_id>` command.",
+                style="yellow",
+            )

src/crewai/crew.py

@@ -18,6 +18,11 @@ from typing import (
     cast,
 )
 
+from opentelemetry import baggage
+from opentelemetry.context import attach, detach
+
+from crewai.utilities.crew.models import CrewContext
+
 from pydantic import (
     UUID4,
     BaseModel,
@@ -616,6 +621,11 @@ class Crew(FlowTrackable, BaseModel):
         self,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
+        ctx = baggage.set_baggage(
+            "crew_context", CrewContext(id=str(self.id), key=self.key)
+        )
+        token = attach(ctx)
+
         try:
             for before_callback in self.before_kickoff_callbacks:
                 if inputs is None:
@@ -676,6 +686,8 @@ class Crew(FlowTrackable, BaseModel):
                 CrewKickoffFailedEvent(error=str(e), crew_name=self.name or "crew"),
             )
             raise
+        finally:
+            detach(token)
 
     def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
         """Executes the Crew's workflow for each input in the list and aggregates results."""

src/crewai/task.py

@@ -97,7 +97,7 @@ class Task(BaseModel):
     )
     context: Union[List["Task"], None, _NotSpecified] = Field(
         description="Other tasks that will have their output used as context for this task.",
-        default=NOT_SPECIFIED
+        default=NOT_SPECIFIED,
     )
     async_execution: Optional[bool] = Field(
         description="Whether the task should be executed asynchronously or not.",
@@ -158,9 +158,7 @@ class Task(BaseModel):
     end_time: Optional[datetime.datetime] = Field(
         default=None, description="End time of the task execution"
     )
-    model_config = {
-        "arbitrary_types_allowed": True
-    }
+    model_config = {"arbitrary_types_allowed": True}
 
     @field_validator("guardrail")
     @classmethod
@@ -204,7 +202,6 @@ class Task(BaseModel):
             # Check return annotation if present, but don't require it
             return_annotation = sig.return_annotation
             if return_annotation != inspect.Signature.empty:
-
                 return_annotation_args = get_args(return_annotation)
                 if not (
                     get_origin(return_annotation) is tuple
@@ -437,7 +434,7 @@ class Task(BaseModel):
                 guardrail_result = process_guardrail(
                     output=task_output,
                     guardrail=self._guardrail,
-                    retry_count=self.retry_count
+                    retry_count=self.retry_count,
                 )
                 if not guardrail_result.success:
                     if self.retry_count >= self.max_retries:
@@ -510,8 +507,6 @@ class Task(BaseModel):
         )
         from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 
-        result = self._guardrail(task_output)
-
         crewai_event_bus.emit(
             self,
             LLMGuardrailStartedEvent(
@@ -519,7 +514,13 @@ class Task(BaseModel):
             ),
         )
 
-        guardrail_result = GuardrailResult.from_tuple(result)
+        try:
+            result = self._guardrail(task_output)
+            guardrail_result = GuardrailResult.from_tuple(result)
+        except Exception as e:
+            guardrail_result = GuardrailResult(
+                success=False, result=None, error=f"Guardrail execution error: {str(e)}"
+            )
 
         crewai_event_bus.emit(
             self,

src/crewai/utilities/crew/__init__.py

@@ -0,0 +1 @@
+"""Crew-specific utilities."""

src/crewai/utilities/crew/crew_context.py

@@ -0,0 +1,16 @@
+"""Context management utilities for tracking crew and task execution context using OpenTelemetry baggage."""
+
+from typing import Optional
+
+from opentelemetry import baggage
+
+from crewai.utilities.crew.models import CrewContext
+
+
+def get_crew_context() -> Optional[CrewContext]:
+    """Get the current crew context from OpenTelemetry baggage.
+
+    Returns:
+        CrewContext instance containing crew context information, or None if no context is set
+    """
+    return baggage.get_baggage("crew_context")

src/crewai/utilities/crew/models.py

@@ -0,0 +1,16 @@
+"""Models for crew-related data structures."""
+
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class CrewContext(BaseModel):
+    """Model representing crew context information."""
+
+    id: Optional[str] = Field(
+        default=None, description="Unique identifier for the crew"
+    )
+    key: Optional[str] = Field(
+        default=None, description="Optional crew key/name for identification"
+    )

src/crewai/utilities/events/event_listener.py

@@ -22,6 +22,10 @@ from crewai.utilities.events.llm_events import (
     LLMCallStartedEvent,
     LLMStreamChunkEvent,
 )
+from crewai.utilities.events.llm_guardrail_events import (
+    LLMGuardrailStartedEvent,
+    LLMGuardrailCompletedEvent,
+)
 from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
 
 from .agent_events import (
@@ -370,6 +374,23 @@ class EventListener(BaseEventListener):
             print(content, end="", flush=True)
             self.next_chunk = self.text_stream.tell()
 
+        # ----------- LLM GUARDRAIL EVENTS -----------
+
+        @crewai_event_bus.on(LLMGuardrailStartedEvent)
+        def on_llm_guardrail_started(source, event: LLMGuardrailStartedEvent):
+            guardrail_str = str(event.guardrail)
+            guardrail_name = (
+                guardrail_str[:50] + "..." if len(guardrail_str) > 50 else guardrail_str
+            )
+
+            self.formatter.handle_guardrail_started(guardrail_name, event.retry_count)
+
+        @crewai_event_bus.on(LLMGuardrailCompletedEvent)
+        def on_llm_guardrail_completed(source, event: LLMGuardrailCompletedEvent):
+            self.formatter.handle_guardrail_completed(
+                event.success, event.error, event.retry_count
+            )
+
         @crewai_event_bus.on(CrewTestStartedEvent)
         def on_crew_test_started(source, event: CrewTestStartedEvent):
             cloned_crew = source.copy()

src/crewai/utilities/events/llm_guardrail_events.py

@@ -1,3 +1,4 @@
+from inspect import getsource
 from typing import Any, Callable, Optional, Union
 
 from crewai.utilities.events.base_events import BaseEvent
@@ -16,23 +17,26 @@ class LLMGuardrailStartedEvent(BaseEvent):
     retry_count: int
 
     def __init__(self, **data):
-        from inspect import getsource
-
         from crewai.tasks.llm_guardrail import LLMGuardrail
         from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 
         super().__init__(**data)
 
-        if isinstance(self.guardrail, LLMGuardrail) or isinstance(
-            self.guardrail, HallucinationGuardrail
-        ):
+        if isinstance(self.guardrail, (LLMGuardrail, HallucinationGuardrail)):
             self.guardrail = self.guardrail.description.strip()
         elif isinstance(self.guardrail, Callable):
             self.guardrail = getsource(self.guardrail).strip()
 
 
 class LLMGuardrailCompletedEvent(BaseEvent):
-    """Event emitted when a guardrail task completes"""
+    """Event emitted when a guardrail task completes
+
+    Attributes:
+        success: Whether the guardrail validation passed
+        result: The validation result
+        error: Error message if validation failed
+        retry_count: The number of times the guardrail has been retried
+    """
 
     type: str = "llm_guardrail_completed"
     success: bool

src/crewai/utilities/events/utils/console_formatter.py

@@ -1473,9 +1473,7 @@ class ConsoleFormatter:
                 return None
 
         memory_branch = branch_to_use.add("")
-        self.update_tree_label(
-            memory_branch, "🧠", "Memory Retrieval Started", "blue"
-        )
+        self.update_tree_label(memory_branch, "🧠", "Memory Retrieval Started", "blue")
 
         self.print(tree_to_use)
         self.print()
@@ -1549,7 +1547,6 @@ class ConsoleFormatter:
         if memory_content:
             add_panel()
 
-
     def handle_memory_query_completed(
         self,
         agent_branch: Optional[Tree],
@@ -1616,11 +1613,8 @@ class ConsoleFormatter:
                     sources_branch.add(f"❌ {memory_type} - Error: {error}")
                     break
 
-
     def handle_memory_save_started(
-        self,
-        agent_branch: Optional[Tree],
-        crew_tree: Optional[Tree]
+        self, agent_branch: Optional[Tree], crew_tree: Optional[Tree]
     ) -> None:
         if not self.verbose:
             return None
@@ -1633,7 +1627,7 @@ class ConsoleFormatter:
 
         for child in tree_to_use.children:
             if "Memory Update" in str(child.label):
-               break
+                break
         else:
             memory_branch = tree_to_use.add("")
             self.update_tree_label(
@@ -1700,4 +1694,62 @@ class ConsoleFormatter:
             memory_branch.add(content)
 
         self.print(tree_to_use)
-        self.print()
+        self.print()
+
+    def handle_guardrail_started(
+        self,
+        guardrail_name: str,
+        retry_count: int,
+    ) -> None:
+        """Display guardrail evaluation started status.
+
+        Args:
+            guardrail_name: Name/description of the guardrail being evaluated.
+            retry_count: Zero-based retry count (0 = first attempt).
+        """
+        if not self.verbose:
+            return
+
+        content = self.create_status_content(
+            "Guardrail Evaluation Started",
+            guardrail_name,
+            "yellow",
+            Status="🔄 Evaluating",
+            Attempt=f"{retry_count + 1}",
+        )
+        self.print_panel(content, "🛡️ Guardrail Check", "yellow")
+
+    def handle_guardrail_completed(
+        self,
+        success: bool,
+        error: Optional[str],
+        retry_count: int,
+    ) -> None:
+        """Display guardrail evaluation result.
+
+        Args:
+            success: Whether validation passed.
+            error: Error message if validation failed.
+            retry_count: Zero-based retry count.
+        """
+        if not self.verbose:
+            return
+
+        if success:
+            content = self.create_status_content(
+                "Guardrail Passed",
+                "Validation Successful",
+                "green",
+                Status="✅ Validated",
+                Attempts=f"{retry_count + 1}",
+            )
+            self.print_panel(content, "🛡️ Guardrail Success", "green")
+        else:
+            content = self.create_status_content(
+                "Guardrail Failed",
+                "Validation Error",
+                "red",
+                Error=str(error) if error else "Unknown error",
+                Attempts=f"{retry_count + 1}",
+            )
+            self.print_panel(content, "🛡️ Guardrail Failed", "red")

tests/cli/authentication/test_auth_main.py

@@ -1,103 +1,419 @@
-import unittest
-from unittest.mock import MagicMock, patch
-
+import pytest
+from datetime import datetime, timedelta
 import requests
-
+from unittest.mock import MagicMock, patch, call
 from crewai.cli.authentication.main import AuthenticationCommand
+from crewai.cli.authentication.constants import (
+    AUTH0_AUDIENCE,
+    AUTH0_CLIENT_ID,
+    AUTH0_DOMAIN,
+    WORKOS_DOMAIN,
+    WORKOS_CLI_CONNECT_APP_ID,
+    WORKOS_ENVIRONMENT_ID,
+)
 
 
-class TestAuthenticationCommand(unittest.TestCase):
-    def setUp(self):
+class TestAuthenticationCommand:
+    def setup_method(self):
         self.auth_command = AuthenticationCommand()
 
-    @patch("crewai.cli.authentication.main.requests.post")
-    def test_get_device_code(self, mock_post):
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "device_code": "123456",
-            "user_code": "ABCDEF",
-            "verification_uri_complete": "https://example.com",
-            "interval": 5,
-        }
-        mock_post.return_value = mock_response
-
-        device_code_data = self.auth_command._get_device_code()
-
-        self.assertEqual(device_code_data["device_code"], "123456")
-        self.assertEqual(device_code_data["user_code"], "ABCDEF")
-        self.assertEqual(
-            device_code_data["verification_uri_complete"], "https://example.com"
-        )
-        self.assertEqual(device_code_data["interval"], 5)
-
+    @pytest.mark.parametrize(
+        "user_provider,expected_urls",
+        [
+            (
+                "auth0",
+                {
+                    "device_code_url": f"https://{AUTH0_DOMAIN}/oauth/device/code",
+                    "token_url": f"https://{AUTH0_DOMAIN}/oauth/token",
+                    "client_id": AUTH0_CLIENT_ID,
+                    "audience": AUTH0_AUDIENCE,
+                },
+            ),
+            (
+                "workos",
+                {
+                    "device_code_url": f"https://{WORKOS_DOMAIN}/oauth2/device_authorization",
+                    "token_url": f"https://{WORKOS_DOMAIN}/oauth2/token",
+                    "client_id": WORKOS_CLI_CONNECT_APP_ID,
+                },
+            ),
+        ],
+    )
+    @patch(
+        "crewai.cli.authentication.main.AuthenticationCommand._determine_user_provider"
+    )
+    @patch("crewai.cli.authentication.main.AuthenticationCommand._get_device_code")
+    @patch(
+        "crewai.cli.authentication.main.AuthenticationCommand._display_auth_instructions"
+    )
+    @patch("crewai.cli.authentication.main.AuthenticationCommand._poll_for_token")
     @patch("crewai.cli.authentication.main.console.print")
-    @patch("crewai.cli.authentication.main.webbrowser.open")
-    def test_display_auth_instructions(self, mock_open, mock_print):
+    def test_login(
+        self,
+        mock_console_print,
+        mock_poll,
+        mock_display,
+        mock_get_device,
+        mock_determine_provider,
+        user_provider,
+        expected_urls,
+    ):
+        mock_determine_provider.return_value = user_provider
+        mock_get_device.return_value = {
+            "device_code": "test_code",
+            "user_code": "123456",
+        }
+
+        self.auth_command.login()
+
+        mock_console_print.assert_called_once_with(
+            "Signing in to CrewAI Enterprise...\n", style="bold blue"
+        )
+        mock_determine_provider.assert_called_once()
+        mock_get_device.assert_called_once_with(
+            expected_urls["client_id"],
+            expected_urls["device_code_url"],
+            expected_urls.get("audience", None),
+        )
+        mock_display.assert_called_once_with(
+            {"device_code": "test_code", "user_code": "123456"}
+        )
+        mock_poll.assert_called_once_with(
+            {"device_code": "test_code", "user_code": "123456"},
+            expected_urls["client_id"],
+            expected_urls["token_url"],
+        )
+
+    @patch("crewai.cli.authentication.main.webbrowser")
+    @patch("crewai.cli.authentication.main.console.print")
+    def test_display_auth_instructions(self, mock_console_print, mock_webbrowser):
         device_code_data = {
-            "verification_uri_complete": "https://example.com",
-            "user_code": "ABCDEF",
+            "verification_uri_complete": "https://example.com/auth",
+            "user_code": "123456",
         }
 
         self.auth_command._display_auth_instructions(device_code_data)
 
-        mock_print.assert_any_call("1. Navigate to: ", "https://example.com")
-        mock_print.assert_any_call("2. Enter the following code: ", "ABCDEF")
-        mock_open.assert_called_once_with("https://example.com")
+        expected_calls = [
+            call("1. Navigate to: ", "https://example.com/auth"),
+            call("2. Enter the following code: ", "123456"),
+        ]
+        mock_console_print.assert_has_calls(expected_calls)
+        mock_webbrowser.open.assert_called_once_with("https://example.com/auth")
+
+    @pytest.mark.parametrize(
+        "user_provider,jwt_config",
+        [
+            (
+                "auth0",
+                {
+                    "jwks_url": f"https://{AUTH0_DOMAIN}/.well-known/jwks.json",
+                    "issuer": f"https://{AUTH0_DOMAIN}/",
+                    "audience": AUTH0_AUDIENCE,
+                },
+            ),
+            (
+                "workos",
+                {
+                    "jwks_url": f"https://{WORKOS_DOMAIN}/oauth2/jwks",
+                    "issuer": f"https://{WORKOS_DOMAIN}",
+                    "audience": WORKOS_ENVIRONMENT_ID,
+                },
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("has_expiration", [True, False])
+    @patch("crewai.cli.authentication.main.validate_jwt_token")
+    @patch("crewai.cli.authentication.main.TokenManager.save_tokens")
+    def test_validate_and_save_token(
+        self,
+        mock_save_tokens,
+        mock_validate_jwt,
+        user_provider,
+        jwt_config,
+        has_expiration,
+    ):
+        self.auth_command.user_provider = user_provider
+        token_data = {"access_token": "test_access_token", "id_token": "test_id_token"}
+
+        if has_expiration:
+            future_timestamp = int((datetime.now() + timedelta(days=100)).timestamp())
+            decoded_token = {"exp": future_timestamp}
+        else:
+            decoded_token = {}
+
+        mock_validate_jwt.return_value = decoded_token
+
+        self.auth_command._validate_and_save_token(token_data)
+
+        mock_validate_jwt.assert_called_once_with(
+            jwt_token="test_access_token",
+            jwks_url=jwt_config["jwks_url"],
+            issuer=jwt_config["issuer"],
+            audience=jwt_config["audience"],
+        )
+
+        if has_expiration:
+            mock_save_tokens.assert_called_once_with(
+                "test_access_token", future_timestamp
+            )
+        else:
+            mock_save_tokens.assert_called_once_with("test_access_token", 0)
 
     @patch("crewai.cli.tools.main.ToolCommand")
-    @patch("crewai.cli.authentication.main.requests.post")
-    @patch("crewai.cli.authentication.main.validate_token")
+    @patch("crewai.cli.authentication.main.Settings")
     @patch("crewai.cli.authentication.main.console.print")
-    def test_poll_for_token_success(
-        self, mock_print, mock_validate_token, mock_post, mock_tool
+    def test_login_to_tool_repository_success(
+        self, mock_console_print, mock_settings, mock_tool_command
     ):
+        mock_tool_instance = MagicMock()
+        mock_tool_command.return_value = mock_tool_instance
+
+        mock_settings_instance = MagicMock()
+        mock_settings_instance.org_name = "Test Org"
+        mock_settings_instance.org_uuid = "test-uuid-123"
+        mock_settings.return_value = mock_settings_instance
+
+        self.auth_command._login_to_tool_repository()
+
+        mock_tool_command.assert_called_once()
+        mock_tool_instance.login.assert_called_once()
+
+        expected_calls = [
+            call(
+                "Now logging you in to the Tool Repository... ",
+                style="bold blue",
+                end="",
+            ),
+            call("Success!\n", style="bold green"),
+            call(
+                "You are authenticated to the tool repository as [bold cyan]'Test Org'[/bold cyan] (test-uuid-123)",
+                style="green",
+            ),
+        ]
+        mock_console_print.assert_has_calls(expected_calls)
+
+    @patch("crewai.cli.tools.main.ToolCommand")
+    @patch("crewai.cli.authentication.main.console.print")
+    def test_login_to_tool_repository_error(
+        self, mock_console_print, mock_tool_command
+    ):
+        mock_tool_instance = MagicMock()
+        mock_tool_instance.login.side_effect = Exception("Tool repository error")
+        mock_tool_command.return_value = mock_tool_instance
+
+        self.auth_command._login_to_tool_repository()
+
+        mock_tool_command.assert_called_once()
+        mock_tool_instance.login.assert_called_once()
+
+        expected_calls = [
+            call(
+                "Now logging you in to the Tool Repository... ",
+                style="bold blue",
+                end="",
+            ),
+            call(
+                "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.",
+                style="yellow",
+            ),
+            call(
+                "Other features will work normally, but you may experience limitations with downloading and publishing tools.\nRun [bold]crewai login[/bold] to try logging in again.\n",
+                style="yellow",
+            ),
+        ]
+        mock_console_print.assert_has_calls(expected_calls)
+
+    @pytest.mark.parametrize(
+        "api_response,expected_provider",
+        [
+            ({"provider": "auth0"}, "auth0"),
+            ({"provider": "workos"}, "workos"),
+            ({"provider": "none"}, "workos"),  # Default to workos for any other value
+            (
+                {},
+                "workos",
+            ),  # Default to workos if no provider key is sent in the response
+        ],
+    )
+    @patch("crewai.cli.authentication.main.PlusAPI")
+    @patch("crewai.cli.authentication.main.console.print")
+    @patch("builtins.input", return_value="test@example.com")
+    def test_determine_user_provider_success(
+        self,
+        mock_input,
+        mock_console_print,
+        mock_plus_api,
+        api_response,
+        expected_provider,
+    ):
+        mock_api_instance = MagicMock()
         mock_response = MagicMock()
         mock_response.status_code = 200
-        mock_response.json.return_value = {
-            "id_token": "TOKEN",
-            "access_token": "ACCESS_TOKEN",
-        }
-        mock_post.return_value = mock_response
+        mock_response.json.return_value = api_response
+        mock_api_instance._make_request.return_value = mock_response
+        mock_plus_api.return_value = mock_api_instance
 
-        mock_instance = mock_tool.return_value
-        mock_instance.login.return_value = None
+        result = self.auth_command._determine_user_provider()
 
-        self.auth_command._poll_for_token({"device_code": "123456"})
+        mock_input.assert_called_once()
 
-        mock_validate_token.assert_called_once_with("TOKEN")
-        mock_print.assert_called_once_with(
-            "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
+        mock_plus_api.assert_called_once_with("")
+        mock_api_instance._make_request.assert_called_once_with(
+            "GET", "/crewai_plus/api/v1/me/provider?email=test%40example.com"
         )
 
-    @patch("crewai.cli.authentication.main.requests.post")
+        assert result == expected_provider
+
+    @patch("crewai.cli.authentication.main.PlusAPI")
     @patch("crewai.cli.authentication.main.console.print")
-    def test_poll_for_token_error(self, mock_print, mock_post):
+    @patch("builtins.input", return_value="test@example.com")
+    def test_determine_user_provider_error(
+        self, mock_input, mock_console_print, mock_plus_api
+    ):
+        mock_api_instance = MagicMock()
+        mock_response = MagicMock()
+        mock_response.status_code = 500
+        mock_api_instance._make_request.return_value = mock_response
+        mock_plus_api.return_value = mock_api_instance
+
+        with pytest.raises(SystemExit):
+            self.auth_command._determine_user_provider()
+
+        mock_input.assert_called_once()
+
+        mock_plus_api.assert_called_once_with("")
+        mock_api_instance._make_request.assert_called_once_with(
+            "GET", "/crewai_plus/api/v1/me/provider?email=test%40example.com"
+        )
+
+        mock_console_print.assert_has_calls(
+            [
+                call(
+                    "Enter your CrewAI Enterprise account email: ",
+                    style="bold blue",
+                    end="",
+                ),
+                call(
+                    "Error: Failed to authenticate with crewai enterprise. Ensure that you are using the latest crewai version and please try again. If the problem persists, contact support@crewai.com.",
+                    style="red",
+                ),
+            ]
+        )
+
+    @patch("requests.post")
+    def test_get_device_code(self, mock_post):
         mock_response = MagicMock()
-        mock_response.status_code = 400
         mock_response.json.return_value = {
-            "error": "invalid_request",
-            "error_description": "Invalid request",
+            "device_code": "test_device_code",
+            "user_code": "123456",
+            "verification_uri_complete": "https://example.com/auth",
         }
         mock_post.return_value = mock_response
 
-        with self.assertRaises(requests.HTTPError):
-            self.auth_command._poll_for_token({"device_code": "123456"})
+        result = self.auth_command._get_device_code(
+            client_id="test_client",
+            device_code_url="https://example.com/device",
+            audience="test_audience",
+        )
 
-        mock_print.assert_not_called()
+        mock_post.assert_called_once_with(
+            url="https://example.com/device",
+            data={
+                "client_id": "test_client",
+                "scope": "openid",
+                "audience": "test_audience",
+            },
+            timeout=20,
+        )
 
-    @patch("crewai.cli.authentication.main.requests.post")
-    @patch("crewai.cli.authentication.main.console.print")
-    def test_poll_for_token_timeout(self, mock_print, mock_post):
-        mock_response = MagicMock()
-        mock_response.status_code = 400
-        mock_response.json.return_value = {
-            "error": "authorization_pending",
-            "error_description": "Authorization pending",
+        assert result == {
+            "device_code": "test_device_code",
+            "user_code": "123456",
+            "verification_uri_complete": "https://example.com/auth",
         }
-        mock_post.return_value = mock_response
 
-        self.auth_command._poll_for_token({"device_code": "123456", "interval": 0.01})
+    @patch("requests.post")
+    @patch("crewai.cli.authentication.main.console.print")
+    def test_poll_for_token_success(self, mock_console_print, mock_post):
+        mock_response_success = MagicMock()
+        mock_response_success.status_code = 200
+        mock_response_success.json.return_value = {
+            "access_token": "test_access_token",
+            "id_token": "test_id_token",
+        }
+        mock_post.return_value = mock_response_success
 
-        mock_print.assert_called_once_with(
+        device_code_data = {"device_code": "test_device_code", "interval": 1}
+
+        with (
+            patch.object(
+                self.auth_command, "_validate_and_save_token"
+            ) as mock_validate,
+            patch.object(
+                self.auth_command, "_login_to_tool_repository"
+            ) as mock_tool_login,
+        ):
+            self.auth_command._poll_for_token(
+                device_code_data, "test_client", "https://example.com/token"
+            )
+
+            mock_post.assert_called_once_with(
+                "https://example.com/token",
+                data={
+                    "grant_type": "urn:ietf:params:oauth:grant-type:device_code",
+                    "device_code": "test_device_code",
+                    "client_id": "test_client",
+                },
+                timeout=30,
+            )
+
+            mock_validate.assert_called_once()
+            mock_tool_login.assert_called_once()
+
+            expected_calls = [
+                call("\nWaiting for authentication... ", style="bold blue", end=""),
+                call("Success!", style="bold green"),
+                call("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"),
+            ]
+            mock_console_print.assert_has_calls(expected_calls)
+
+    @patch("requests.post")
+    @patch("crewai.cli.authentication.main.console.print")
+    def test_poll_for_token_timeout(self, mock_console_print, mock_post):
+        mock_response_pending = MagicMock()
+        mock_response_pending.status_code = 400
+        mock_response_pending.json.return_value = {"error": "authorization_pending"}
+        mock_post.return_value = mock_response_pending
+
+        device_code_data = {
+            "device_code": "test_device_code",
+            "interval": 0.1,  # Short interval for testing
+        }
+
+        self.auth_command._poll_for_token(
+            device_code_data, "test_client", "https://example.com/token"
+        )
+
+        mock_console_print.assert_any_call(
             "Timeout: Failed to get the token. Please try again.", style="bold red"
         )
+
+    @patch("requests.post")
+    def test_poll_for_token_error(self, mock_post):
+        """Test the method to poll for token (error path)."""
+        # Setup mock to return error
+        mock_response_error = MagicMock()
+        mock_response_error.status_code = 400
+        mock_response_error.json.return_value = {
+            "error": "access_denied",
+            "error_description": "User denied access",
+        }
+        mock_post.return_value = mock_response_error
+
+        device_code_data = {"device_code": "test_device_code", "interval": 1}
+
+        with pytest.raises(requests.HTTPError):
+            self.auth_command._poll_for_token(
+                device_code_data, "test_client", "https://example.com/token"
+            )

tests/cli/authentication/test_utils.py

@@ -1,31 +1,110 @@
 import json
+import jwt
 import unittest
 from datetime import datetime, timedelta
 from unittest.mock import MagicMock, patch
 
 from cryptography.fernet import Fernet
 
-from crewai.cli.authentication.utils import TokenManager, validate_token
+from crewai.cli.authentication.utils import TokenManager, validate_jwt_token
 
 
+@patch("crewai.cli.authentication.utils.PyJWKClient", return_value=MagicMock())
+@patch("crewai.cli.authentication.utils.jwt")
 class TestValidateToken(unittest.TestCase):
-    @patch("crewai.cli.authentication.utils.AsymmetricSignatureVerifier")
-    @patch("crewai.cli.authentication.utils.TokenVerifier")
-    def test_validate_token(self, mock_token_verifier, mock_asymmetric_verifier):
-        mock_verifier_instance = mock_token_verifier.return_value
-        mock_id_token = "mock_id_token"
+    def test_validate_jwt_token(self, mock_jwt, mock_pyjwkclient):
+        mock_jwt.decode.return_value = {"exp": 1719859200}
 
-        validate_token(mock_id_token)
+        # Create signing key object mock with a .key attribute
+        mock_pyjwkclient.return_value.get_signing_key_from_jwt.return_value = MagicMock(
+            key="mock_signing_key"
+        )
 
-        mock_asymmetric_verifier.assert_called_once_with(
-            "https://crewai.us.auth0.com/.well-known/jwks.json"
+        decoded_token = validate_jwt_token(
+            jwt_token="aaaaa.bbbbbb.cccccc",
+            jwks_url="https://mock_jwks_url",
+            issuer="https://mock_issuer",
+            audience="app_id_xxxx",
         )
-        mock_token_verifier.assert_called_once_with(
-            signature_verifier=mock_asymmetric_verifier.return_value,
-            issuer="https://crewai.us.auth0.com/",
-            audience="DEVC5Fw6NlRoSzmDCcOhVq85EfLBjKa8",
+
+        mock_jwt.decode.assert_called_once_with(
+            "aaaaa.bbbbbb.cccccc",
+            "mock_signing_key",
+            algorithms=["RS256"],
+            audience="app_id_xxxx",
+            issuer="https://mock_issuer",
+            options={
+                "verify_signature": True,
+                "verify_exp": True,
+                "verify_nbf": True,
+                "verify_iat": True,
+                "require": ["exp", "iat", "iss", "aud", "sub"],
+            },
         )
-        mock_verifier_instance.verify.assert_called_once_with(mock_id_token)
+        mock_pyjwkclient.assert_called_once_with("https://mock_jwks_url")
+        self.assertEqual(decoded_token, {"exp": 1719859200})
+
+    def test_validate_jwt_token_expired(self, mock_jwt, mock_pyjwkclient):
+        mock_jwt.decode.side_effect = jwt.ExpiredSignatureError
+        with self.assertRaises(Exception):
+            validate_jwt_token(
+                jwt_token="aaaaa.bbbbbb.cccccc",
+                jwks_url="https://mock_jwks_url",
+                issuer="https://mock_issuer",
+                audience="app_id_xxxx",
+            )
+
+    def test_validate_jwt_token_invalid_audience(self, mock_jwt, mock_pyjwkclient):
+        mock_jwt.decode.side_effect = jwt.InvalidAudienceError
+        with self.assertRaises(Exception):
+            validate_jwt_token(
+                jwt_token="aaaaa.bbbbbb.cccccc",
+                jwks_url="https://mock_jwks_url",
+                issuer="https://mock_issuer",
+                audience="app_id_xxxx",
+            )
+
+    def test_validate_jwt_token_invalid_issuer(self, mock_jwt, mock_pyjwkclient):
+        mock_jwt.decode.side_effect = jwt.InvalidIssuerError
+        with self.assertRaises(Exception):
+            validate_jwt_token(
+                jwt_token="aaaaa.bbbbbb.cccccc",
+                jwks_url="https://mock_jwks_url",
+                issuer="https://mock_issuer",
+                audience="app_id_xxxx",
+            )
+
+    def test_validate_jwt_token_missing_required_claims(
+        self, mock_jwt, mock_pyjwkclient
+    ):
+        mock_jwt.decode.side_effect = jwt.MissingRequiredClaimError
+        with self.assertRaises(Exception):
+            validate_jwt_token(
+                jwt_token="aaaaa.bbbbbb.cccccc",
+                jwks_url="https://mock_jwks_url",
+                issuer="https://mock_issuer",
+                audience="app_id_xxxx",
+            )
+
+    def test_validate_jwt_token_jwks_error(self, mock_jwt, mock_pyjwkclient):
+        mock_jwt.decode.side_effect = jwt.exceptions.PyJWKClientError
+        with self.assertRaises(Exception):
+            validate_jwt_token(
+                jwt_token="aaaaa.bbbbbb.cccccc",
+                jwks_url="https://mock_jwks_url",
+                issuer="https://mock_issuer",
+                audience="app_id_xxxx",
+            )
+
+    def test_validate_jwt_token_invalid_token(self, mock_jwt, mock_pyjwkclient):
+        mock_jwt.decode.side_effect = jwt.InvalidTokenError
+        with self.assertRaises(Exception):
+            validate_jwt_token(
+                jwt_token="aaaaa.bbbbbb.cccccc",
+                jwks_url="https://mock_jwks_url",
+                issuer="https://mock_issuer",
+                audience="app_id_xxxx",
+            )
 
 
 class TestTokenManager(unittest.TestCase):
@@ -62,9 +141,9 @@ class TestTokenManager(unittest.TestCase):
     @patch("crewai.cli.authentication.utils.TokenManager.save_secure_file")
     def test_save_tokens(self, mock_save):
         access_token = "test_token"
-        expires_in = 3600
+        expires_at = int((datetime.now() + timedelta(seconds=3600)).timestamp())
 
-        self.token_manager.save_tokens(access_token, expires_in)
+        self.token_manager.save_tokens(access_token, expires_at)
 
         mock_save.assert_called_once()
         args = mock_save.call_args[0]
@@ -73,11 +152,7 @@ class TestTokenManager(unittest.TestCase):
         data = json.loads(decrypted_data)
         self.assertEqual(data["access_token"], access_token)
         expiration = datetime.fromisoformat(data["expiration"])
-        self.assertAlmostEqual(
-            expiration,
-            datetime.now() + timedelta(seconds=expires_in),
-            delta=timedelta(seconds=1),
-        )
+        self.assertEqual(expiration, datetime.fromtimestamp(expires_at))
 
     @patch("crewai.cli.authentication.utils.TokenManager.read_secure_file")
     def test_get_token_valid(self, mock_read):

tests/cli/cli_test.py

@@ -13,7 +13,7 @@ from crewai.cli.cli import (
     deply_status,
     flow_add_crew,
     reset_memories,
-    signup,
+    login,
     test,
     train,
     version,
@@ -261,12 +261,12 @@ def test_test_invalid_string_iterations(evaluate_crew, runner):
 
 
 @mock.patch("crewai.cli.cli.AuthenticationCommand")
-def test_signup(command, runner):
+def test_login(command, runner):
     mock_auth = command.return_value
-    result = runner.invoke(signup)
+    result = runner.invoke(login)
 
     assert result.exit_code == 0
-    mock_auth.signup.assert_called_once()
+    mock_auth.login.assert_called_once()
 
 
 @mock.patch("crewai.cli.cli.DeployCommand")

tests/cli/tools/test_main.py

@@ -2,6 +2,7 @@ import os
 import tempfile
 import unittest
 import unittest.mock
+from datetime import datetime, timedelta
 from contextlib import contextmanager
 from unittest import mock
 from unittest.mock import MagicMock, patch
@@ -26,7 +27,9 @@ def in_temp_dir():
 
 @pytest.fixture
 def tool_command():
-    TokenManager().save_tokens("test-token", 36000)
+    TokenManager().save_tokens(
+        "test-token", (datetime.now() + timedelta(seconds=36000)).timestamp()
+    )
     tool_command = ToolCommand()
     with patch.object(tool_command, "login"):
         yield tool_command
@@ -57,7 +60,9 @@ def test_create_success(mock_subprocess, capsys, tool_command):
 @patch("crewai.cli.tools.main.subprocess.run")
 @patch("crewai.cli.plus_api.PlusAPI.get_tool")
 @patch("crewai.cli.tools.main.ToolCommand._print_current_organization")
-def test_install_success(mock_print_org, mock_get, mock_subprocess_run, capsys, tool_command):
+def test_install_success(
+    mock_print_org, mock_get, mock_subprocess_run, capsys, tool_command
+):
     mock_get_response = MagicMock()
     mock_get_response.status_code = 200
     mock_get_response.json.return_value = {
@@ -89,6 +94,7 @@ def test_install_success(mock_print_org, mock_get, mock_subprocess_run, capsys,
     # Verify _print_current_organization was called
     mock_print_org.assert_called_once()
 
+
 @patch("crewai.cli.tools.main.subprocess.run")
 @patch("crewai.cli.plus_api.PlusAPI.get_tool")
 def test_install_success_from_pypi(mock_get, mock_subprocess_run, capsys, tool_command):
@@ -169,7 +175,10 @@ def test_publish_when_not_in_sync(mock_is_synced, capsys, tool_command):
 )
 @patch("crewai.cli.plus_api.PlusAPI.publish_tool")
 @patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=False)
-@patch("crewai.cli.tools.main.extract_available_exports", return_value=[{"name": "SampleTool"}])
+@patch(
+    "crewai.cli.tools.main.extract_available_exports",
+    return_value=[{"name": "SampleTool"}],
+)
 @patch("crewai.cli.tools.main.ToolCommand._print_current_organization")
 def test_publish_when_not_in_sync_and_force(
     mock_print_org,
@@ -223,7 +232,10 @@ def test_publish_when_not_in_sync_and_force(
 )
 @patch("crewai.cli.plus_api.PlusAPI.publish_tool")
 @patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=True)
-@patch("crewai.cli.tools.main.extract_available_exports", return_value=[{"name": "SampleTool"}])
+@patch(
+    "crewai.cli.tools.main.extract_available_exports",
+    return_value=[{"name": "SampleTool"}],
+)
 def test_publish_success(
     mock_available_exports,
     mock_is_synced,
@@ -273,7 +285,10 @@ def test_publish_success(
     read_data=b"sample tarball content",
 )
 @patch("crewai.cli.plus_api.PlusAPI.publish_tool")
-@patch("crewai.cli.tools.main.extract_available_exports", return_value=[{"name": "SampleTool"}])
+@patch(
+    "crewai.cli.tools.main.extract_available_exports",
+    return_value=[{"name": "SampleTool"}],
+)
 def test_publish_failure(
     mock_available_exports,
     mock_publish,
@@ -311,7 +326,10 @@ def test_publish_failure(
     read_data=b"sample tarball content",
 )
 @patch("crewai.cli.plus_api.PlusAPI.publish_tool")
-@patch("crewai.cli.tools.main.extract_available_exports", return_value=[{"name": "SampleTool"}])
+@patch(
+    "crewai.cli.tools.main.extract_available_exports",
+    return_value=[{"name": "SampleTool"}],
+)
 def test_publish_api_error(
     mock_available_exports,
     mock_publish,
@@ -338,7 +356,6 @@ def test_publish_api_error(
     mock_publish.assert_called_once()
 
 
-
 @patch("crewai.cli.tools.main.Settings")
 def test_print_current_organization_with_org(mock_settings, capsys, tool_command):
     mock_settings_instance = MagicMock()

tests/crew_test.py

@@ -52,6 +52,7 @@ from crewai.utilities.events.memory_events import (
     MemoryRetrievalCompletedEvent,
 )
 
+
 @pytest.fixture
 def ceo():
     return Agent(
@@ -935,12 +936,27 @@ def test_cache_hitting_between_agents(researcher, writer, ceo):
         read.return_value = "12"
         crew.kickoff()
         assert read.call_count == 2, "read was not called exactly twice"
-        # Check if read was called with the expected arguments
-        expected_calls = [
-            call(tool="multiplier", input={"first_number": 2, "second_number": 6}),
-            call(tool="multiplier", input={"first_number": 2, "second_number": 6}),
+
+        # Filter the mock calls to only include the ones with 'tool' and 'input' keywords
+        cache_calls = [
+            call
+            for call in read.call_args_list
+            if len(call.kwargs) == 2
+            and "tool" in call.kwargs
+            and "input" in call.kwargs
         ]
-        read.assert_has_calls(expected_calls, any_order=False)
+
+        # Check if we have the expected number of cache calls
+        assert len(cache_calls) == 2, f"Expected 2 cache calls, got {len(cache_calls)}"
+
+        # Check if both calls were made with the expected arguments
+        expected_call = call(
+            tool="multiplier", input={"first_number": 2, "second_number": 6}
+        )
+        assert cache_calls[0] == expected_call, f"First call mismatch: {cache_calls[0]}"
+        assert (
+            cache_calls[1] == expected_call
+        ), f"Second call mismatch: {cache_calls[1]}"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1797,7 +1813,7 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher):
         agent=researcher,  # *regular* agent
     )
 
-    # ── 2.  Stub out each agent’s _token_process.get_summary() ───────────────────
+    # ── 2.  Stub out each agent's _token_process.get_summary() ───────────────────
     researcher_metrics = UsageMetrics(
         total_tokens=120, prompt_tokens=80, completion_tokens=40, successful_requests=2
     )
@@ -1821,7 +1837,7 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher):
         process=Process.hierarchical,
     )
 
-    # We don’t care about LLM output here; patch execute_sync to avoid network
+    # We don't care about LLM output here; patch execute_sync to avoid network
     with patch.object(
         Task,
         "execute_sync",
@@ -2489,17 +2505,19 @@ def test_using_contextual_memory():
         memory=True,
     )
 
-    with patch.object(ContextualMemory, "build_context_for_task", return_value="") as contextual_mem:
+    with patch.object(
+        ContextualMemory, "build_context_for_task", return_value=""
+    ) as contextual_mem:
         crew.kickoff()
         contextual_mem.assert_called_once()
 
 
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_memory_events_are_emitted():
     events = defaultdict(list)
 
     with crewai_event_bus.scoped_handlers():
+
         @crewai_event_bus.on(MemorySaveStartedEvent)
         def handle_memory_save_started(source, event):
             events["MemorySaveStartedEvent"].append(event)
@@ -2562,6 +2580,7 @@ def test_memory_events_are_emitted():
     assert len(events["MemoryRetrievalStartedEvent"]) == 1
     assert len(events["MemoryRetrievalCompletedEvent"]) == 1
 
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_using_contextual_memory_with_long_term_memory():
     from unittest.mock import patch
@@ -2585,7 +2604,9 @@ def test_using_contextual_memory_with_long_term_memory():
         long_term_memory=LongTermMemory(),
     )
 
-    with patch.object(ContextualMemory, "build_context_for_task", return_value="") as contextual_mem:
+    with patch.object(
+        ContextualMemory, "build_context_for_task", return_value=""
+    ) as contextual_mem:
         crew.kickoff()
         contextual_mem.assert_called_once()
         assert crew.memory is False
@@ -2686,7 +2707,9 @@ def test_using_contextual_memory_with_short_term_memory():
         short_term_memory=ShortTermMemory(),
     )
 
-    with patch.object(ContextualMemory, "build_context_for_task", return_value="") as contextual_mem:
+    with patch.object(
+        ContextualMemory, "build_context_for_task", return_value=""
+    ) as contextual_mem:
         crew.kickoff()
         contextual_mem.assert_called_once()
         assert crew.memory is False
@@ -2715,7 +2738,9 @@ def test_disabled_memory_using_contextual_memory():
         memory=False,
     )
 
-    with patch.object(ContextualMemory, "build_context_for_task", return_value="") as contextual_mem:
+    with patch.object(
+        ContextualMemory, "build_context_for_task", return_value=""
+    ) as contextual_mem:
         crew.kickoff()
         contextual_mem.assert_not_called()
 

tests/test_crew_thread_safety.py

@@ -0,0 +1,226 @@
+import asyncio
+import threading
+from concurrent.futures import ThreadPoolExecutor
+from typing import Dict, Any, Callable
+from unittest.mock import patch
+
+import pytest
+
+from crewai import Agent, Crew, Task
+from crewai.utilities.crew.crew_context import get_crew_context
+
+
+@pytest.fixture
+def simple_agent_factory():
+    def create_agent(name: str) -> Agent:
+        return Agent(
+            role=f"{name} Agent",
+            goal=f"Complete {name} task",
+            backstory=f"I am agent for {name}",
+        )
+
+    return create_agent
+
+
+@pytest.fixture
+def simple_task_factory():
+    def create_task(name: str, callback: Callable = None) -> Task:
+        return Task(
+            description=f"Task for {name}", expected_output="Done", callback=callback
+        )
+
+    return create_task
+
+
+@pytest.fixture
+def crew_factory(simple_agent_factory, simple_task_factory):
+    def create_crew(name: str, task_callback: Callable = None) -> Crew:
+        agent = simple_agent_factory(name)
+        task = simple_task_factory(name, callback=task_callback)
+        task.agent = agent
+
+        return Crew(agents=[agent], tasks=[task], verbose=False)
+
+    return create_crew
+
+
+class TestCrewThreadSafety:
+    @patch("crewai.Agent.execute_task")
+    def test_parallel_crews_thread_safety(self, mock_execute_task, crew_factory):
+        mock_execute_task.return_value = "Task completed"
+        num_crews = 5
+
+        def run_crew_with_context_check(crew_id: str) -> Dict[str, Any]:
+            results = {"crew_id": crew_id, "contexts": []}
+
+            def check_context_task(output):
+                context = get_crew_context()
+                results["contexts"].append(
+                    {
+                        "stage": "task_callback",
+                        "crew_id": context.id if context else None,
+                        "crew_key": context.key if context else None,
+                        "thread": threading.current_thread().name,
+                    }
+                )
+                return output
+
+            context_before = get_crew_context()
+            results["contexts"].append(
+                {
+                    "stage": "before_kickoff",
+                    "crew_id": context_before.id if context_before else None,
+                    "thread": threading.current_thread().name,
+                }
+            )
+
+            crew = crew_factory(crew_id, task_callback=check_context_task)
+            output = crew.kickoff()
+
+            context_after = get_crew_context()
+            results["contexts"].append(
+                {
+                    "stage": "after_kickoff",
+                    "crew_id": context_after.id if context_after else None,
+                    "thread": threading.current_thread().name,
+                }
+            )
+
+            results["crew_uuid"] = str(crew.id)
+            results["output"] = output.raw
+
+            return results
+
+        with ThreadPoolExecutor(max_workers=num_crews) as executor:
+            futures = []
+            for i in range(num_crews):
+                future = executor.submit(run_crew_with_context_check, f"crew_{i}")
+                futures.append(future)
+
+            results = [f.result() for f in futures]
+
+        for result in results:
+            crew_uuid = result["crew_uuid"]
+
+            before_ctx = next(
+                ctx for ctx in result["contexts"] if ctx["stage"] == "before_kickoff"
+            )
+            assert (
+                before_ctx["crew_id"] is None
+            ), f"Context should be None before kickoff for {result['crew_id']}"
+
+            task_ctx = next(
+                ctx for ctx in result["contexts"] if ctx["stage"] == "task_callback"
+            )
+            assert (
+                task_ctx["crew_id"] == crew_uuid
+            ), f"Context mismatch during task for {result['crew_id']}"
+
+            after_ctx = next(
+                ctx for ctx in result["contexts"] if ctx["stage"] == "after_kickoff"
+            )
+            assert (
+                after_ctx["crew_id"] is None
+            ), f"Context should be None after kickoff for {result['crew_id']}"
+
+            thread_name = before_ctx["thread"]
+            assert (
+                "ThreadPoolExecutor" in thread_name
+            ), f"Should run in thread pool for {result['crew_id']}"
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.execute_task")
+    async def test_async_crews_thread_safety(self, mock_execute_task, crew_factory):
+        mock_execute_task.return_value = "Task completed"
+        num_crews = 5
+
+        async def run_crew_async(crew_id: str) -> Dict[str, Any]:
+            task_context = {"crew_id": crew_id, "context": None}
+
+            def capture_context(output):
+                ctx = get_crew_context()
+                task_context["context"] = {
+                    "crew_id": ctx.id if ctx else None,
+                    "crew_key": ctx.key if ctx else None,
+                }
+                return output
+
+            crew = crew_factory(crew_id, task_callback=capture_context)
+            output = await crew.kickoff_async()
+
+            return {
+                "crew_id": crew_id,
+                "crew_uuid": str(crew.id),
+                "output": output.raw,
+                "task_context": task_context,
+            }
+
+        tasks = [run_crew_async(f"async_crew_{i}") for i in range(num_crews)]
+        results = await asyncio.gather(*tasks)
+
+        for result in results:
+            crew_uuid = result["crew_uuid"]
+            task_ctx = result["task_context"]["context"]
+
+            assert (
+                task_ctx is not None
+            ), f"Context should exist during task for {result['crew_id']}"
+            assert (
+                task_ctx["crew_id"] == crew_uuid
+            ), f"Context mismatch for {result['crew_id']}"
+
+    @patch("crewai.Agent.execute_task")
+    def test_concurrent_kickoff_for_each(self, mock_execute_task, crew_factory):
+        mock_execute_task.return_value = "Task completed"
+        contexts_captured = []
+
+        def capture_context(output):
+            ctx = get_crew_context()
+            contexts_captured.append(
+                {
+                    "context_id": ctx.id if ctx else None,
+                    "thread": threading.current_thread().name,
+                }
+            )
+            return output
+
+        crew = crew_factory("for_each_test", task_callback=capture_context)
+        inputs = [{"item": f"input_{i}"} for i in range(3)]
+
+        results = crew.kickoff_for_each(inputs=inputs)
+
+        assert len(results) == len(inputs)
+        assert len(contexts_captured) == len(inputs)
+
+        context_ids = [ctx["context_id"] for ctx in contexts_captured]
+        assert len(set(context_ids)) == len(
+            inputs
+        ), "Each execution should have unique context"
+
+    @patch("crewai.Agent.execute_task")
+    def test_no_context_leakage_between_crews(self, mock_execute_task, crew_factory):
+        mock_execute_task.return_value = "Task completed"
+        contexts = []
+
+        def check_context(output):
+            ctx = get_crew_context()
+            contexts.append(
+                {
+                    "context_id": ctx.id if ctx else None,
+                    "context_key": ctx.key if ctx else None,
+                }
+            )
+            return output
+
+        def run_crew(name: str):
+            crew = crew_factory(name, task_callback=check_context)
+            crew.kickoff()
+            return str(crew.id)
+
+        crew1_id = run_crew("First")
+        crew2_id = run_crew("Second")
+
+        assert len(contexts) == 2
+        assert contexts[0]["context_id"] == crew1_id
+        assert contexts[1]["context_id"] == crew2_id
+        assert contexts[0]["context_id"] != contexts[1]["context_id"]

tests/utilities/crew/test_crew_context.py

@@ -0,0 +1,88 @@
+import uuid
+
+import pytest
+from opentelemetry import baggage
+from opentelemetry.context import attach, detach
+
+from crewai.utilities.crew.crew_context import get_crew_context
+from crewai.utilities.crew.models import CrewContext
+
+
+def test_crew_context_creation():
+    crew_id = str(uuid.uuid4())
+    context = CrewContext(id=crew_id, key="test-crew")
+    assert context.id == crew_id
+    assert context.key == "test-crew"
+
+
+def test_get_crew_context_with_baggage():
+    crew_id = str(uuid.uuid4())
+    assert get_crew_context() is None
+
+    crew_ctx = CrewContext(id=crew_id, key="test-key")
+    ctx = baggage.set_baggage("crew_context", crew_ctx)
+    token = attach(ctx)
+
+    try:
+        context = get_crew_context()
+        assert context is not None
+        assert context.id == crew_id
+        assert context.key == "test-key"
+    finally:
+        detach(token)
+
+    assert get_crew_context() is None
+
+
+def test_get_crew_context_empty():
+    assert get_crew_context() is None
+
+
+def test_baggage_nested_contexts():
+    crew_id1 = str(uuid.uuid4())
+    crew_id2 = str(uuid.uuid4())
+
+    crew_ctx1 = CrewContext(id=crew_id1, key="outer")
+    ctx1 = baggage.set_baggage("crew_context", crew_ctx1)
+    token1 = attach(ctx1)
+
+    try:
+        outer_context = get_crew_context()
+        assert outer_context.id == crew_id1
+        assert outer_context.key == "outer"
+
+        crew_ctx2 = CrewContext(id=crew_id2, key="inner")
+        ctx2 = baggage.set_baggage("crew_context", crew_ctx2)
+        token2 = attach(ctx2)
+
+        try:
+            inner_context = get_crew_context()
+            assert inner_context.id == crew_id2
+            assert inner_context.key == "inner"
+        finally:
+            detach(token2)
+
+        restored_context = get_crew_context()
+        assert restored_context.id == crew_id1
+        assert restored_context.key == "outer"
+    finally:
+        detach(token1)
+
+    assert get_crew_context() is None
+
+
+def test_baggage_exception_handling():
+    crew_id = str(uuid.uuid4())
+
+    crew_ctx = CrewContext(id=crew_id, key="test")
+    ctx = baggage.set_baggage("crew_context", crew_ctx)
+    token = attach(ctx)
+
+    with pytest.raises(ValueError):
+        try:
+            assert get_crew_context() is not None
+            raise ValueError("Test exception")
+        finally:
+            detach(token)
+
+    assert get_crew_context() is None