from crewai import Agent, Task, Crew

# Define specialized agents
developer = Agent(
    name="Developer",
    role="Writes and reviews code",
    goal="Implement features and fix bugs",
    backstory="Experienced software engineer with 5 years of Python development"
)

tester = Agent(
    name="Tester",
    role="Quality assurance specialist",
    goal="Identify and report bugs",
    backstory="QA engineer focused on test automation and coverage"
)

# Create a crew with defined roles
crew = Crew(
    agents=[developer, tester],
    context="Building a REST API for user management",
    verbose=True
)

# Define tasks that leverage agent specialization
tasks = [
    Task(description="Implement user authentication endpoints", agent=developer),
    Task(description="Write unit tests for authentication", agent=tester),
    Task(description="Review test coverage and suggest improvements", agent=developer)
]

# Execute the crew workflow
crew.kickoff(tasks)

from crewai import Agent, Task, Crew
from datetime import datetime

class ContentCreationCrew:
    def __init__(self):
        # Define specialized agents
        self.researcher = Agent(
            name="Researcher",
            role="Gathers information and sources",
            goal="Provide comprehensive research for content",
            backstory="Academic researcher with expertise in information synthesis"
        )
        
        self.writer = Agent(
            name="Writer",
            role="Creates initial drafts",
            goal="Produce engaging, well-structured content",
            backstory="Professional writer with SEO expertise"
        )
        
        self.editor = Agent(
            name="Editor",
            role="Refines and polishes content",
            goal="Ensure clarity, accuracy, and quality",
            backstory="Experienced editor with attention to detail"
        )
        
        self.seo_specialist = Agent(
            name="SEO Specialist",
            role="Optimizes for search engines",
            goal="Maximize content visibility and ranking",
            backstory="Digital marketing expert with SEO certification"
        )
    
    def create_article(self, topic):
        # Create the crew
        crew = Crew(
            agents=[self.researcher, self.writer, self.editor, self.seo_specialist],
            context=f"Creating article about {topic}",
            verbose=True
        )
        
        # Define the workflow
        tasks = [
            Task(description=f"Research {topic} comprehensively", agent=self.researcher),
            Task(description=f"Write initial draft about {topic}", agent=self.writer),
            Task(description=f"Edit and refine the draft", agent=self.editor),
            Task(description=f"Optimize for SEO with focus on {topic}", agent=self.seo_specialist)
        ]
        
        # Execute
        results = crew.kickoff(tasks)
        
        # Compile final output
        final_content = "\n\n".join([result.output for result in results])
        return final_content

# Usage
crew = ContentCreationCrew()
article = crew.create_article("multi-agent AI systems")
print(article)

# Manager agents overseeing specialized teams
class ManagerAgent(Agent):
    def __init__(self, name, team):
        super().__init__(name=name, role="Manager", goal="Coordinate team efforts")
        self.team = team
    
    def coordinate(self, task):
        # Delegate subtasks to team members
        subtasks = self.breakdown(task)
        results = []
        
        for subtask in subtasks:
            # Assign to appropriate team member
            best_agent = self.select_agent(subtask)
            result = best_agent.execute(subtask)
            results.append(result)
        
        return self.combine_results(results)

class SpecializedTeam:
    def __init__(self, members):
        self.members = members
    
    def execute_task(self, task):
        # Parallel execution of subtasks
        results = [agent.execute(subtask) 
                   for agent, subtask in zip(self.members, task.subtasks)]
        return results

from typing import Dict, List

class DynamicCrew:
    def __init__(self, agents: List[Agent]):
        self.agents = agents
        self.skills_matrix = self.build_skills_matrix()
    
    def build_skills_matrix(self):
        matrix = {}
        for agent in self.agents:
            matrix[agent.name] = agent.skills
        return matrix
    
    def assign_roles(self, task_requirements: Dict[str, float]):
        # Match agent skills to task requirements
        assignments = {}
        
        for skill, required_score in task_requirements.items():
            best_match = None
            best_score = 0
            
            for agent_name, skills in self.skills_matrix.items():
                if skill in skills:
                    skill_score = skills[skill]
                    if skill_score > required_score and skill_score > best_score:
                        best_match = agent_name
                        best_score = skill_score
            
            if best_match:
                assignments[skill] = best_match
        
        return assignments
    
    def execute_dynamic_task(self, task):
        # Dynamically assign agents based on task needs
        requirements = task.get_requirements()
        assignments = self.assign_roles(requirements)
        
        # Create specialized crew
        specialized_crew = [self.get_agent_by_name(name) 
                           for name in assignments.values()]
        
        # Execute with specialized team
        return Crew(agents=specialized_crew).kickoff(task.steps)

class OptimizedCommunication:
    def __init__(self, agents):
        self.agents = agents
        self.message_queue = Queue()
        self.communication_protocol = self.setup_protocol()
    
    def setup_protocol(self):
        # Implement efficient communication patterns
        return {
            'broadcast': self.broadcast_message,
            'direct': self.direct_message,
            'context_share': self.share_context
        }
    
    def broadcast_message(self, message):
        # Send message to all agents efficiently
        for agent in self.agents:
            agent.receive_message(message)
    
    def direct_message(self, sender, recipient_name, message):
        # Direct communication between specific agents
        recipient = next(a for a in self.agents 
                        if a.name == recipient_name)
        recipient.receive_message(f"{sender.name}: {message}")
    
    def share_context(self, context):
        # Share relevant context across agents
        for agent in self.agents:
            agent.update_context(context)

class ResourceManager:
    def __init__(self, agents):
        self.agents = agents
        self.resource_pool = self.initialize_resources()
    
    def initialize_resources(self):
        return {
            'compute': Resource(capacity=100, allocated=0),
            'memory': Resource(capacity=64, allocated=0),
            'bandwidth': Resource(capacity=1000, allocated=0)
        }
    
    def allocate_resources(self, task):
        # Calculate resource requirements
        requirements = task.calculate_resource_needs()
        
        # Check availability
        if self.can_allocate(requirements):
            self.allocate(requirements)
            return True
        return False
    
    def can_allocate(self, requirements):
        return (self.resource_pool['compute'].available >= requirements['compute'] and
                self.resource_pool['memory'].available >= requirements['memory'] and
                self.resource_pool['bandwidth'].available >= requirements['bandwidth'])
    
    def allocate(self, requirements):
        self.resource_pool['compute'].allocate(requirements['compute'])
        self.resource_pool['memory'].allocate(requirements['memory'])
        self.resource_pool['bandwidth'].allocate(requirements['bandwidth'])