While porting some of my libraries to Swift 6 in preparation for my next projects, it struck me for the first time in my 5-6 years of Swift programming, and maybe echoing some of the posts on this forum: has Swift become an overengineered language? Can it be simplified, or will it only get more and more complex? That happened when I was looking at the Task constructor's signature: @discardableResult public init(priority: TaskPriority? = nil, operation: sending @escaping @isolated(any) () a...

@dataclass class TaskConfig: description: str expected_output: str priority: TaskPriority max_execution_time: int = 300 requires_human_input: bool = False class SupervisorFramework: """ Advanced Supervisor Agent Framework using CrewAI Manages multiple specialized agents with hierarchical coordination """ def __init__(self, gemini_api_key: str, serper_api_key: str = None): """ Initialize the supervisor framework Args: gemini_api_key: Google Gemini API key (free tier) serper_api_key: Serper API key for web search (optional, has free tier) """ os.environ["GOOGLE_API_KEY"] = gemini_api_key if serper_api_key: os.environ["SERPER_API_KEY"] = serper_api_key self.llm = ChatGoogleGenerativeAI( model="gemini-1.5-flash", temperature=0.7, max_tokens=2048 ) self.tools = [] if serper_api_key: self.tools.append(SerperDevTool()) self.agents = {} self.supervisor = None self.crew = None print("🚀 SupervisorFramework initialized successfully!") print(f"📡 LLM Model: {self.llm.model}") print(f"🛠️ Available tools: {len(self.tools)}") def create_research_agent(self) -> Agent: """Create a specialized research agent""" return Agent( role="Senior Research Analyst", goal="Conduct comprehensive research and gather accurate information on any given topic", backstory="""You are an expert research analyst with years of experience in information gathering, fact-checking, and synthesizing complex data from multiple sources.

@dataclass class TaskConfig: description: str expected_output: str priority: TaskPriority max_execution_time: int = 300 requires_human_input: bool = False class SupervisorFramework: """ Advanced Supervisor Agent Framework using CrewAI Manages multiple specialized agents with hierarchical coordination """ def __init__(self, gemini_api_key: str, serper_api_key: str = None): """ Initialize the supervisor framework Args: gemini_api_key: Google Gemini API key (free tier) serper_api_key: Serper API key for web search (optional, has free tier) """ os.environ["GOOGLE_API_KEY"] = gemini_api_key if serper_api_key: os.environ["SERPER_API_KEY"] = serper_api_key self.llm = ChatGoogleGenerativeAI( model="gemini-1.5-flash", temperature=0.7, max_tokens=2048 ) self.tools = [] if serper_api_key: self.tools.append(SerperDevTool()) self.agents = {} self.supervisor = None self.crew = None print("🚀 SupervisorFramework initialized successfully!") print(f"📡 LLM Model: {self.llm.model}") print(f"🛠️ Available tools: {len(self.tools)}") def create_research_agent(self) -> Agent: """Create a specialized research agent""" return Agent( role="Senior Research Analyst", goal="Conduct comprehensive research and gather accurate information on any given topic", backstory="""You are an expert research analyst with years of experience in information gathering, fact-checking, and synthesizing complex data from multiple sources.