fix: remove unused imports from DeepSeek integration tests

- Remove unused pytest import
- Remove unused MagicMock import

Fixes lint errors in CI

Co-Authored-By: João <joao@crewai.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

pyproject.toml

@@ -33,7 +33,7 @@ dependencies = [
     "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/constants.py

@@ -103,6 +103,12 @@ ENV_VARS = {
             "key_name": "SAMBANOVA_API_KEY",
         }
     ],
+    "deepseek": [
+        {
+            "prompt": "Enter your DeepSeek API key (press Enter to skip)",
+            "key_name": "DEEPSEEK_API_KEY",
+        }
+    ],
 }
 
 
@@ -119,6 +125,7 @@ PROVIDERS = [
     "azure",
     "cerebras",
     "sambanova",
+    "deepseek",
 ]
 
 MODELS = {
@@ -314,6 +321,13 @@ MODELS = {
         "sambanova/Meta-Llama-3.2-3B-Instruct",
         "sambanova/Meta-Llama-3.2-1B-Instruct",
     ],
+    "deepseek": [
+        "deepseek/deepseek-chat",
+        "deepseek/deepseek-coder",
+        "deepseek/deepseek-r1",
+        "deepseek/deepseek-v3",
+        "deepseek/deepseek-reasoner",
+    ],
 }
 
 DEFAULT_LLM_MODEL = "gpt-4o-mini"

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/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/llm.py

@@ -154,6 +154,11 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "gemini/gemma-3-27b-it": 128000,
     # deepseek
     "deepseek-chat": 128000,
+    "deepseek/deepseek-chat": 128000,
+    "deepseek/deepseek-coder": 128000,
+    "deepseek/deepseek-r1": 128000,
+    "deepseek/deepseek-v3": 128000,
+    "deepseek/deepseek-reasoner": 128000,
     # groq
     "gemma2-9b-it": 8192,
     "gemma-7b-it": 8192,

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/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

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/test_deepseek_integration.py

@@ -0,0 +1,114 @@
+"""Tests for DeepSeek integration in CrewAI."""
+
+from unittest.mock import patch
+
+from crewai.llm import LLM
+from crewai.cli.constants import ENV_VARS, PROVIDERS, MODELS
+
+
+class TestDeepSeekIntegration:
+    """Test DeepSeek integration in CrewAI."""
+
+    def test_deepseek_in_providers(self):
+        """Test that DeepSeek is included in the providers list."""
+        assert "deepseek" in PROVIDERS
+
+    def test_deepseek_in_env_vars(self):
+        """Test that DeepSeek API key configuration is in ENV_VARS."""
+        assert "deepseek" in ENV_VARS
+        deepseek_config = ENV_VARS["deepseek"]
+        assert len(deepseek_config) == 1
+        assert deepseek_config[0]["key_name"] == "DEEPSEEK_API_KEY"
+        assert "DeepSeek API key" in deepseek_config[0]["prompt"]
+
+    def test_deepseek_in_models(self):
+        """Test that DeepSeek models are included in the models dictionary."""
+        assert "deepseek" in MODELS
+        deepseek_models = MODELS["deepseek"]
+        expected_models = [
+            "deepseek/deepseek-chat",
+            "deepseek/deepseek-coder",
+            "deepseek/deepseek-r1",
+            "deepseek/deepseek-v3",
+            "deepseek/deepseek-reasoner",
+        ]
+        for model in expected_models:
+            assert model in deepseek_models
+
+    def test_llm_creation_with_deepseek_chat(self):
+        """Test creating LLM instance with deepseek-chat model."""
+        llm = LLM(model="deepseek-chat")
+        assert llm.model == "deepseek-chat"
+        assert llm.get_context_window_size() > 0
+
+    def test_llm_creation_with_deepseek_prefix(self):
+        """Test creating LLM instance with deepseek/ prefix."""
+        llm = LLM(model="deepseek/deepseek-chat")
+        assert llm.model == "deepseek/deepseek-chat"
+        assert llm._get_custom_llm_provider() == "deepseek"
+        assert llm.get_context_window_size() > 0
+
+    def test_deepseek_context_window_sizes(self):
+        """Test that all DeepSeek models have context window sizes defined."""
+        from crewai.llm import LLM_CONTEXT_WINDOW_SIZES
+        
+        deepseek_models = [
+            "deepseek-chat",
+            "deepseek/deepseek-chat",
+            "deepseek/deepseek-coder",
+            "deepseek/deepseek-r1",
+            "deepseek/deepseek-v3",
+            "deepseek/deepseek-reasoner",
+        ]
+        
+        for model in deepseek_models:
+            assert model in LLM_CONTEXT_WINDOW_SIZES
+            assert LLM_CONTEXT_WINDOW_SIZES[model] > 0
+
+    def test_deepseek_models_context_window_consistency(self):
+        """Test that DeepSeek models have consistent context window sizes."""
+        from crewai.llm import LLM_CONTEXT_WINDOW_SIZES
+        
+        expected_size = 128000
+        deepseek_models = [
+            "deepseek-chat",
+            "deepseek/deepseek-chat",
+            "deepseek/deepseek-coder",
+            "deepseek/deepseek-r1",
+            "deepseek/deepseek-v3",
+            "deepseek/deepseek-reasoner",
+        ]
+        
+        for model in deepseek_models:
+            assert LLM_CONTEXT_WINDOW_SIZES[model] == expected_size
+
+    @patch.dict("os.environ", {"DEEPSEEK_API_KEY": "test-key"})
+    def test_llm_with_deepseek_api_key(self):
+        """Test LLM creation with DeepSeek API key in environment."""
+        llm = LLM(model="deepseek/deepseek-chat")
+        assert llm.model == "deepseek/deepseek-chat"
+        assert llm._get_custom_llm_provider() == "deepseek"
+
+    def test_deepseek_provider_detection(self):
+        """Test that DeepSeek provider is correctly detected from model name."""
+        llm = LLM(model="deepseek/deepseek-chat")
+        provider = llm._get_custom_llm_provider()
+        assert provider == "deepseek"
+
+    def test_deepseek_vs_openrouter_provider_detection(self):
+        """Test provider detection for DeepSeek vs OpenRouter DeepSeek models."""
+        deepseek_llm = LLM(model="deepseek/deepseek-chat")
+        openrouter_llm = LLM(model="openrouter/deepseek/deepseek-chat")
+        
+        assert deepseek_llm._get_custom_llm_provider() == "deepseek"
+        assert openrouter_llm._get_custom_llm_provider() == "openrouter"
+
+    def test_all_deepseek_models_can_be_instantiated(self):
+        """Test that all DeepSeek models in MODELS can be instantiated."""
+        deepseek_models = MODELS["deepseek"]
+        
+        for model in deepseek_models:
+            llm = LLM(model=model)
+            assert llm.model == model
+            assert llm._get_custom_llm_provider() == "deepseek"
+            assert llm.get_context_window_size() > 0

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