#!/usr/bin/env python3

"""

Advanced Multi-Agent System with 40+ Agents across 8 Teams

Using Claude Code SDK for sophisticated AI collaboration

"""

import asyncio

import json

import random

from dataclasses import dataclass, field

from datetime import datetime

from enum import Enum

from typing import Dict, List, Optional, Any, Set

from collections import defaultdict

import uuid

from claude_code_sdk import query, ClaudeCodeOptions

# Configuration

MAX_THINKING_TOKENS = 12000

class TeamType(Enum):

"""Different types of teams with specialized capabilities"""

RESEARCH = "research"

DEVELOPMENT = "development"

ANALYSIS = "analysis"

CREATIVE = "creative"

OPERATIONS = "operations"

QUALITY = "quality"

SECURITY = "security"

STRATEGY = "strategy"

class Priority(Enum):

"""Task priority levels"""

CRITICAL = 5

HIGH = 4

MEDIUM = 3

LOW = 2

TRIVIAL = 1

@dataclass

class Task:

"""Represents a task that can be assigned to agents"""

id: str

title: str

description: str

priority: Priority

required_skills: Set[str]

team_type: Optional[TeamType] = None

assigned_to: Optional[str] = None

status: str = "pending"

created_at: datetime = field(default_factory=datetime.now)

completed_at: Optional[datetime] = None

result: Optional[Dict[str, Any]] = None

dependencies: List[str] = field(default_factory=list)

def __hash__(self):

return hash(self.id)

@dataclass

class Message:

"""Inter-agent communication message"""

id: str

sender: str

recipient: str

content: str

message_type: str

timestamp: datetime = field(default_factory=datetime.now)

metadata: Dict[str, Any] = field(default_factory=dict)

class Agent:

"""Base agent class with core capabilities"""

def __init__(self, agent_id: str, name: str, team: str, skills: Set[str],

specialization: str, claude_options: ClaudeCodeOptions):

self.id = agent_id

self.name = name

self.team = team

self.skills = skills

self.specialization = specialization

self.claude_options = claude_options

self.current_task: Optional[Task] = None

self.task_history: List[Task] = []

self.message_queue: List[Message] = []

self.performance_score = 1.0

self.workload = 0

self.knowledge_base: Dict[str, Any] = {}

async def process_task(self, task: Task) -> Dict[str, Any]:

"""Process a task using Claude"""

self.current_task = task

self.workload += 1

prompt = f"""

You are {self.name}, a {self.specialization} agent in the {self.team} team.

Task: {task.title}

Description: {task.description}

Priority: {task.priority.name}

Required Skills: {', '.join(task.required_skills)}

Your skills: {', '.join(self.skills)}

Please complete this task and provide a structured response.

"""

result = {"agent": self.name, "task_id": task.id, "outputs": []}

try:

message_count = 0

async for message in query(prompt=prompt, options=self.claude_options):

message_count += 1

# Handle different message types

if hasattr(message, 'content') and message.content:

for block in message.content:

if hasattr(block, 'text'):

result["outputs"].append(block.text)

elif hasattr(block, 'name'): # ToolUseBlock

result["outputs"].append({

"tool": block.name,

"tool_use": True

})

elif hasattr(message, 'subtype'): # System or Result message

if message.subtype == "error":

result["error"] = f"System error: {getattr(message, 'data', {})}"

break

# Limit messages processed

if message_count >= 5:

break

if "error" not in result:

task.status = "completed"

task.completed_at = datetime.now()

task.result = result

self.performance_score = min(1.0, self.performance_score + 0.1)

else:

task.status = "failed"

self.performance_score = max(0.1, self.performance_score - 0.1)

except Exception as e:

result["error"] = f"Exception: {type(e).__name__}: {str(e)}"

task.status = "failed"

self.performance_score = max(0.1, self.performance_score - 0.1)

print(f"Agent {self.name} encountered error: {type(e).__name__}: {e}")

finally:

self.current_task = None

self.workload -= 1

self.task_history.append(task)

return result

async def communicate(self, recipient: 'Agent', content: str,

message_type: str = "info") -> None:

"""Send a message to another agent"""

message = Message(

id=str(uuid.uuid4()),

sender=self.id,

recipient=recipient.id,

content=content,

message_type=message_type

)

recipient.receive_message(message)

def receive_message(self, message: Message) -> None:

"""Receive a message from another agent"""

self.message_queue.append(message)

def can_handle_task(self, task: Task) -> bool:

"""Check if agent has required skills for a task"""

return task.required_skills.issubset(self.skills)

class Team:

"""Represents a team of agents with shared goals"""

def __init__(self, team_id: str, name: str, team_type: TeamType):

self.id = team_id

self.name = name

self.team_type = team_type

self.agents: List[Agent] = []

self.team_lead: Optional[Agent] = None

self.task_queue: List[Task] = []

self.completed_tasks: List[Task] = []

self.team_knowledge: Dict[str, Any] = {}

def add_agent(self, agent: Agent) -> None:

"""Add an agent to the team"""

self.agents.append(agent)

if not self.team_lead and "leadership" in agent.skills:

self.team_lead = agent

async def assign_task(self, task: Task) -> Optional[Agent]:

"""Assign a task to the most suitable agent"""

suitable_agents = [

agent for agent in self.agents

if agent.can_handle_task(task) and agent.workload < 3

]

if not suitable_agents:

return None

# Sort by performance and workload

suitable_agents.sort(

key=lambda a: (a.performance_score, -a.workload),

reverse=True

)

chosen_agent = suitable_agents[0]

task.assigned_to = chosen_agent.id

return chosen_agent

async def collaborate_on_task(self, task: Task) -> Dict[str, Any]:

"""Multiple agents collaborate on a complex task"""

collaboration_result = {

"task_id": task.id,

"team": self.name,

"agents_involved": [],

"results": []

}

# Find agents with required skills

participating_agents = [

agent for agent in self.agents

if any(skill in agent.skills for skill in task.required_skills)

][:3] # Limit to 3 agents for efficiency

if not participating_agents:

return collaboration_result

# Agents work in parallel

tasks = []

for agent in participating_agents:

collaboration_result["agents_involved"].append(agent.name)

tasks.append(agent.process_task(task))

results = await asyncio.gather(*tasks)

collaboration_result["results"] = results

return collaboration_result

class MultiAgentSystem:

"""Main system coordinating all teams and agents"""

def __init__(self):

self.teams: Dict[str, Team] = {}

self.agents: Dict[str, Agent] = {}

self.global_task_queue: List[Task] = []

self.completed_tasks: List[Task] = []

self.system_knowledge: Dict[str, Any] = {}

self.inter_team_messages: List[Message] = []

self._initialize_teams_and_agents()

def _initialize_teams_and_agents(self):

"""Initialize 8 teams with 40+ specialized agents"""

# Define team configurations

team_configs = {

TeamType.RESEARCH: {

"name": "Research & Discovery",

"agents": [

("Dr. Researcher", {"research", "analysis", "documentation"}, "Senior Research Scientist"),

("Data Hunter", {"data_mining", "web_scraping", "research"}, "Data Research Specialist"),

("Scholar", {"academic_research", "literature_review", "citation"}, "Academic Researcher"),

("Trend Analyst", {"trend_analysis", "market_research", "forecasting"}, "Market Research Analyst"),

("Patent Expert", {"patent_research", "ip_analysis", "legal_research"}, "IP Research Specialist"),

("Science Scout", {"scientific_research", "experiment_design", "hypothesis"}, "Scientific Researcher")

]

},

TeamType.DEVELOPMENT: {

"name": "Engineering & Development",

"agents": [

("Code Master", {"python", "javascript", "architecture", "leadership"}, "Lead Developer"),

("Backend Pro", {"backend", "databases", "api", "python"}, "Backend Engineer"),

("Frontend Wizard", {"frontend", "ui", "react", "design"}, "Frontend Developer"),

("Full Stack Hero", {"fullstack", "devops", "cloud"}, "Full Stack Engineer"),

("Mobile Dev", {"mobile", "ios", "android", "react_native"}, "Mobile Developer"),

("DevOps Ninja", {"devops", "ci_cd", "kubernetes", "automation"}, "DevOps Engineer"),

("ML Engineer", {"machine_learning", "tensorflow", "pytorch", "data_science"}, "ML Engineer")

]

},

TeamType.ANALYSIS: {

"name": "Data & Analytics",

"agents": [

("Data Scientist", {"data_analysis", "statistics", "visualization", "leadership"}, "Lead Data Scientist"),

("Business Analyst", {"business_analysis", "requirements", "process_mapping"}, "Senior Business Analyst"),

("Quant Analyst", {"quantitative_analysis", "financial_modeling", "risk"}, "Quantitative Analyst"),

("Performance Analyst", {"performance_analysis", "metrics", "optimization"}, "Performance Analyst"),

("Market Analyst", {"market_analysis", "competitive_intelligence", "strategy"}, "Market Analyst")

]

},

TeamType.CREATIVE: {

"name": "Creative & Design",

"agents": [

("Creative Director", {"creative_direction", "branding", "leadership", "strategy"}, "Creative Director"),

("UX Designer", {"ux_design", "user_research", "prototyping"}, "Senior UX Designer"),

("UI Artist", {"ui_design", "visual_design", "animation"}, "UI Designer"),

("Content Creator", {"content_creation", "copywriting", "storytelling"}, "Content Strategist"),

("Brand Expert", {"branding", "marketing", "identity_design"}, "Brand Specialist"),

("Video Producer", {"video_production", "editing", "motion_graphics"}, "Video Producer")

]

},

TeamType.OPERATIONS: {

"name": "Operations & Management",

"agents": [

("Ops Manager", {"operations", "management", "leadership", "planning"}, "Operations Manager"),

("Project Lead", {"project_management", "agile", "scrum", "coordination"}, "Project Manager"),

("Process Expert", {"process_improvement", "six_sigma", "efficiency"}, "Process Engineer"),

("Resource Planner", {"resource_planning", "capacity_management", "scheduling"}, "Resource Manager"),

("Supply Chain Pro", {"supply_chain", "logistics", "inventory"}, "Supply Chain Manager")

]

},

TeamType.QUALITY: {

"name": "Quality & Testing",

"agents": [

("QA Lead", {"quality_assurance", "test_strategy", "leadership"}, "QA Lead"),

("Test Automation", {"test_automation", "selenium", "pytest", "ci"}, "Automation Engineer"),

("Manual Tester", {"manual_testing", "test_cases", "bug_tracking"}, "QA Analyst"),

("Performance Tester", {"performance_testing", "load_testing", "jmeter"}, "Performance Test Engineer"),

("Security Tester", {"security_testing", "penetration_testing", "vulnerability"}, "Security Test Engineer")

]

},

TeamType.SECURITY: {

"name": "Security & Compliance",

"agents": [

("Security Chief", {"security_architecture", "leadership", "risk_management"}, "Chief Security Officer"),

("Ethical Hacker", {"penetration_testing", "ethical_hacking", "vulnerability_assessment"}, "Senior Penetration Tester"),

("Compliance Officer", {"compliance", "regulations", "audit", "gdpr"}, "Compliance Manager"),

("Incident Handler", {"incident_response", "forensics", "threat_hunting"}, "Incident Response Lead"),

("Crypto Expert", {"cryptography", "blockchain", "secure_coding"}, "Cryptography Specialist")

]

},

TeamType.STRATEGY: {

"name": "Strategy & Innovation",

"agents": [

("Strategy Chief", {"strategic_planning", "leadership", "vision", "innovation"}, "Chief Strategy Officer"),

("Innovation Lead", {"innovation", "r&d", "emerging_tech", "ideation"}, "Innovation Manager"),

("Business Strategist", {"business_strategy", "competitive_analysis", "growth"}, "Senior Strategist"),

("Digital Transformer", {"digital_transformation", "change_management", "modernization"}, "Digital Transformation Lead"),

("Future Thinker", {"futurism", "trend_forecasting", "scenario_planning"}, "Futurist")

]

}

}

# Create teams and agents

for team_type, config in team_configs.items():

team_id = f"team_{team_type.value}"

team = Team(team_id, config["name"], team_type)

for i, (name, skills, specialization) in enumerate(config["agents"]):

agent_id = f"{team_type.value}_{i+1}"

# Configure Claude options based on agent specialization

tools = self._get_tools_for_specialization(specialization)

claude_options = ClaudeCodeOptions(

allowed_tools=tools,

max_thinking_tokens=MAX_THINKING_TOKENS,

permission_mode="bypassPermissions",

model="claude-3-haiku-20240307", # Use fast model for agents

max_turns=1 # Limit to single turn per task

)

agent = Agent(

agent_id=agent_id,

name=name,

team=team.name,

skills=skills,

specialization=specialization,

claude_options=claude_options

)

team.add_agent(agent)

self.agents[agent_id] = agent

self.teams[team_id] = team

def _get_tools_for_specialization(self, specialization: str) -> List[str]:

"""Get appropriate tools based on agent specialization"""

base_tools = ["Read", "Edit", "Write"]

if "Developer" in specialization or "Engineer" in specialization:

return base_tools + ["Bash", "Grep", "MultiEdit"]

elif "Analyst" in specialization or "Researcher" in specialization:

return base_tools + ["WebSearch", "Grep"]

elif "Designer" in specialization or "Creative" in specialization:

return base_tools + ["WebFetch"]

elif "Security" in specialization:

return base_tools + ["Bash", "Grep"]

else:

return base_tools

async def route_task(self, task: Task) -> Dict[str, Any]:

"""Route a task to the appropriate team and agent"""

# Determine best team based on task requirements

if task.team_type:

team_id = f"team_{task.team_type.value}"

team = self.teams.get(team_id)

else:

# Find team with most matching skills

best_team = None

best_score = 0

for team in self.teams.values():

score = sum(

1 for agent in team.agents

if agent.can_handle_task(task)

)

if score > best_score:

best_score = score

best_team = team

team = best_team

if not team:

return {"error": "No suitable team found"}

# Complex tasks require collaboration

if task.priority == Priority.CRITICAL or len(task.required_skills) > 3:

return await team.collaborate_on_task(task)

# Simple tasks go to individual agents

agent = await team.assign_task(task)

if agent:

return await agent.process_task(task)

return {"error": "No available agent"}

async def execute_workflow(self, tasks: List[Task]) -> List[Dict[str, Any]]:

"""Execute a workflow with multiple tasks"""

# Sort tasks by priority and dependencies

sorted_tasks = self._topological_sort(tasks)

results = []

for task in sorted_tasks:

# Check if dependencies are completed

if all(

dep in [t.id for t in self.completed_tasks]

for dep in task.dependencies

):

result = await self.route_task(task)

results.append(result)

if task.status == "completed":

self.completed_tasks.append(task)

return results

def _topological_sort(self, tasks: List[Task]) -> List[Task]:

"""Sort tasks based on dependencies"""

# Build dependency graph

graph = defaultdict(list)

in_degree = defaultdict(int)

for task in tasks:

for dep in task.dependencies:

graph[dep].append(task.id)

in_degree[task.id] += 1

# Find tasks with no dependencies

queue = [task for task in tasks if in_degree[task.id] == 0]

sorted_tasks = []

while queue:

# Sort by priority

queue.sort(key=lambda t: t.priority.value, reverse=True)

task = queue.pop(0)

sorted_tasks.append(task)

# Update dependencies

for dependent_id in graph[task.id]:

in_degree[dependent_id] -= 1

if in_degree[dependent_id] == 0:

dependent_task = next(t for t in tasks if t.id == dependent_id)

queue.append(dependent_task)

return sorted_tasks

def get_system_status(self) -> Dict[str, Any]:

"""Get current system status and metrics"""

status = {

"teams": {},

"total_agents": len(self.agents),

"active_tasks": 0,

"completed_tasks": len(self.completed_tasks),

"system_performance": 0

}

for team_id, team in self.teams.items():

team_status = {

"name": team.name,

"type": team.team_type.value,

"agents": len(team.agents),

"active_agents": sum(1 for a in team.agents if a.current_task),

"completed_tasks": len(team.completed_tasks),

"average_performance": sum(a.performance_score for a in team.agents) / len(team.agents)

}

status["teams"][team_id] = team_status

status["active_tasks"] += team_status["active_agents"]

# Calculate overall system performance

all_performances = [a.performance_score for a in self.agents.values()]

status["system_performance"] = sum(all_performances) / len(all_performances)

return status

async def inter_team_collaboration(self, task_description: str,

teams_involved: List[TeamType]) -> Dict[str, Any]:

"""Facilitate collaboration between multiple teams"""

collaboration_id = str(uuid.uuid4())

results = {

"collaboration_id": collaboration_id,

"teams": [],

"messages": [],

"outcomes": []

}

# Create a complex task requiring multiple teams

main_task = Task(

id=f"collab_{collaboration_id}",

title=f"Inter-team Collaboration: {task_description}",

description=task_description,

priority=Priority.HIGH,

required_skills=set()

)

# Each team contributes their expertise

for team_type in teams_involved:

team = self.teams[f"team_{team_type.value}"]

results["teams"].append(team.name)

# Select representative agents

if team.team_lead:

lead = team.team_lead

else:

lead = team.agents[0]

# Process task from team perspective

team_result = await lead.process_task(main_task)

results["outcomes"].append(team_result)

# Share results with other teams

for other_team_type in teams_involved:

if other_team_type != team_type:

other_team = self.teams[f"team_{other_team_type.value}"]

if other_team.team_lead:

await lead.communicate(

other_team.team_lead,

f"Team {team.name} findings: {json.dumps(team_result)}",

"collaboration"

)

return results

async def demonstrate_system():

"""Demonstrate the multi-agent system capabilities"""

print("🚀 Initializing Advanced Multi-Agent System with 40+ Agents across 8 Teams")

print("=" * 80)

system = MultiAgentSystem()

# Display system overview

status = system.get_system_status()

print(f"\n📊 System Overview:")

print(f"Total Agents: {status['total_agents']}")

print(f"Teams: {len(status['teams'])}")

for team_id, team_info in status["teams"].items():

print(f"\n 🏢 {team_info['name']} ({team_info['type']})")

print(f" Agents: {team_info['agents']}")

print(f" Average Performance: {team_info['average_performance']:.2f}")

# Example 1: Simple task routing

print("\n\n📋 Example 1: Simple Task Routing")

print("-" * 40)

simple_task = Task(

id="task_001",

title="Analyze website performance",

description="Analyze the performance of our website and provide optimization recommendations",

priority=Priority.MEDIUM,

required_skills={"performance_analysis", "web_development"},

team_type=TeamType.ANALYSIS

)

result1 = await system.route_task(simple_task)

print(f"Task assigned to: {result1.get('agent', 'Unknown')}")

print(f"Status: {'Completed' if simple_task.status == 'completed' else 'Failed'}")

# Example 2: Complex workflow with dependencies

print("\n\n📋 Example 2: Complex Workflow with Dependencies")

print("-" * 40)

workflow_tasks = [

Task(

id="research_001",

title="Research AI trends",

description="Research current AI trends and emerging technologies",

priority=Priority.HIGH,

required_skills={"research", "trend_analysis"},

team_type=TeamType.RESEARCH

),

Task(

id="design_001",

title="Design AI product concept",

description="Based on research, design an innovative AI product concept",

priority=Priority.HIGH,

required_skills={"creative_direction", "product_design"},

team_type=TeamType.CREATIVE,

dependencies=["research_001"]

),

Task(

id="develop_001",

title="Create MVP prototype",

description="Develop a minimum viable prototype of the AI product",

priority=Priority.CRITICAL,

required_skills={"python", "machine_learning", "fullstack"},

team_type=TeamType.DEVELOPMENT,

dependencies=["design_001"]

),

Task(

id="test_001",

title="Test and validate prototype",

description="Comprehensive testing of the prototype",

priority=Priority.HIGH,

required_skills={"test_automation", "quality_assurance"},

team_type=TeamType.QUALITY,

dependencies=["develop_001"]

)

]

workflow_results = await system.execute_workflow(workflow_tasks)

print(f"Workflow executed: {len(workflow_results)} tasks completed")

# Example 3: Inter-team collaboration

print("\n\n📋 Example 3: Inter-Team Collaboration")

print("-" * 40)

collab_result = await system.inter_team_collaboration(

"Design and implement a secure, high-performance e-commerce platform",

[TeamType.STRATEGY, TeamType.DEVELOPMENT, TeamType.SECURITY, TeamType.CREATIVE]

)

print(f"Collaboration ID: {collab_result['collaboration_id']}")

print(f"Teams involved: {', '.join(collab_result['teams'])}")

print(f"Outcomes generated: {len(collab_result['outcomes'])}")

# Example 4: Critical incident response

print("\n\n🚨 Example 4: Critical Incident Response")

print("-" * 40)

incident_task = Task(

id="incident_001",

title="Critical Security Breach Response",

description="Respond to detected security breach, assess damage, and implement fixes",

priority=Priority.CRITICAL,

required_skills={"incident_response", "security_architecture", "forensics",

"devops", "communication"},

team_type=TeamType.SECURITY

)

incident_result = await system.route_task(incident_task)

print(f"Incident response team activated")

print(f"Agents involved: {len(incident_result.get('agents_involved', []))}")

# Final system status

print("\n\n📊 Final System Status")

print("=" * 80)

final_status = system.get_system_status()

print(f"Total completed tasks: {final_status['completed_tasks']}")

print(f"Active tasks: {final_status['active_tasks']}")

print(f"System performance: {final_status['system_performance']:.2f}")

# Performance breakdown by team

print("\n🏆 Team Performance Rankings:")

team_rankings = sorted(

final_status["teams"].items(),

key=lambda x: x[1]["average_performance"],

reverse=True

)

for i, (team_id, team_info) in enumerate(team_rankings, 1):

print(f"{i}. {team_info['name']}: {team_info['average_performance']:.2f}")

if __name__ == "__main__":

# Run the demonstration

asyncio.run(demonstrate_system())