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"""
Multi-Agent Collaboration Framework for Cyber-LLM
Advanced agent-to-agent communication and swarm intelligence
Author: Muzan Sano <[email protected]>
"""
import asyncio
import json
import logging
import uuid
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple, Union, Callable
from dataclasses import dataclass, field
from enum import Enum
import numpy as np
from collections import defaultdict, deque
import websockets
import aiohttp
from ..utils.logging_system import CyberLLMLogger, CyberLLMError, ErrorCategory
from ..memory.persistent_memory import PersistentMemoryManager
from ..cognitive.meta_cognitive import MetaCognitiveEngine
class MessageType(Enum):
"""Agent communication message types"""
TASK_REQUEST = "task_request"
TASK_RESPONSE = "task_response"
INFORMATION_SHARE = "information_share"
COORDINATION_REQUEST = "coordination_request"
CONSENSUS_PROPOSAL = "consensus_proposal"
CONSENSUS_VOTE = "consensus_vote"
CAPABILITY_ANNOUNCEMENT = "capability_announcement"
RESOURCE_REQUEST = "resource_request"
RESOURCE_OFFER = "resource_offer"
SWARM_DIRECTIVE = "swarm_directive"
EMERGENCY_ALERT = "emergency_alert"
class AgentRole(Enum):
"""Agent roles in the collaboration framework"""
LEADER = "leader"
SPECIALIST = "specialist"
COORDINATOR = "coordinator"
SCOUT = "scout"
ANALYZER = "analyzer"
EXECUTOR = "executor"
MONITOR = "monitor"
class ConsensusAlgorithm(Enum):
"""Consensus algorithms for decision making"""
MAJORITY_VOTE = "majority_vote"
WEIGHTED_VOTE = "weighted_vote"
BYZANTINE_FAULT_TOLERANT = "byzantine_fault_tolerant"
PROOF_OF_EXPERTISE = "proof_of_expertise"
RAFT = "raft"
@dataclass
class AgentMessage:
"""Inter-agent communication message"""
message_id: str
sender_id: str
recipient_id: Optional[str] # None for broadcast
message_type: MessageType
timestamp: datetime
# Content
content: Dict[str, Any]
priority: int = 5 # 1-10, 10 = highest
# Routing and delivery
ttl: int = 300 # Time to live in seconds
requires_acknowledgment: bool = False
correlation_id: Optional[str] = None
# Security
signature: Optional[str] = None
encrypted: bool = False
@dataclass
class AgentCapability:
"""Agent capability description"""
capability_id: str
name: str
description: str
# Performance metrics
accuracy: float
speed: float # Operations per second
resource_cost: float
# Availability
available: bool = True
current_load: float = 0.0
max_concurrent: int = 10
# Requirements
required_resources: Dict[str, float] = field(default_factory=dict)
dependencies: List[str] = field(default_factory=list)
@dataclass
class SwarmTask:
"""Task for swarm execution"""
task_id: str
description: str
task_type: str
# Requirements
required_capabilities: List[str]
estimated_complexity: float
deadline: Optional[datetime] = None
# Decomposition
subtasks: List['SwarmTask'] = field(default_factory=list)
dependencies: List[str] = field(default_factory=list)
# Assignment
assigned_agents: List[str] = field(default_factory=list)
status: str = "pending"
# Results
results: Dict[str, Any] = field(default_factory=dict)
completion_time: Optional[datetime] = None
class AgentCommunicationProtocol:
"""Standardized protocol for agent communication"""
def __init__(self, agent_id: str, logger: Optional[CyberLLMLogger] = None):
self.agent_id = agent_id
self.logger = logger or CyberLLMLogger(name="agent_protocol")
# Communication infrastructure
self.message_queue = asyncio.Queue()
self.active_connections = {}
self.message_handlers = {}
self.acknowledgments = {}
# Protocol state
self.capabilities = {}
self.peer_agents = {}
self.conversation_contexts = {}
# Security
self.trusted_agents = set()
self.encryption_keys = {}
self.logger.info("Agent Communication Protocol initialized", agent_id=agent_id)
async def send_message(self, message: AgentMessage) -> bool:
"""Send message to another agent or broadcast"""
try:
# Validate message
if not self._validate_message(message):
self.logger.error("Invalid message", message_id=message.message_id)
return False
# Add timestamp and sender
message.timestamp = datetime.now()
message.sender_id = self.agent_id
# Sign message if required
if message.encrypted or message.signature:
message = await self._secure_message(message)
# Route message
if message.recipient_id:
# Direct message
success = await self._send_direct_message(message)
else:
# Broadcast message
success = await self._broadcast_message(message)
# Handle acknowledgment requirement
if message.requires_acknowledgment and success:
asyncio.create_task(self._wait_for_acknowledgment(message))
self.logger.info("Message sent",
message_id=message.message_id,
recipient=message.recipient_id or "broadcast",
type=message.message_type.value)
return success
except Exception as e:
self.logger.error("Failed to send message", error=str(e))
return False
async def receive_message(self) -> Optional[AgentMessage]:
"""Receive next message from queue"""
try:
# Get message from queue (with timeout)
message = await asyncio.wait_for(self.message_queue.get(), timeout=1.0)
# Validate and process message
if self._validate_received_message(message):
await self._process_received_message(message)
return message
return None
except asyncio.TimeoutError:
return None
except Exception as e:
self.logger.error("Failed to receive message", error=str(e))
return None
async def register_capability(self, capability: AgentCapability):
"""Register agent capability"""
self.capabilities[capability.capability_id] = capability
# Announce capability to other agents
announcement = AgentMessage(
message_id=str(uuid.uuid4()),
sender_id=self.agent_id,
recipient_id=None, # Broadcast
message_type=MessageType.CAPABILITY_ANNOUNCEMENT,
timestamp=datetime.now(),
content={
"capability": {
"id": capability.capability_id,
"name": capability.name,
"description": capability.description,
"accuracy": capability.accuracy,
"speed": capability.speed,
"available": capability.available
}
}
)
await self.send_message(announcement)
self.logger.info("Capability registered and announced",
capability_id=capability.capability_id,
name=capability.name)
class DistributedConsensus:
"""Distributed consensus mechanisms for multi-agent decisions"""
def __init__(self,
agent_id: str,
communication_protocol: AgentCommunicationProtocol,
logger: Optional[CyberLLMLogger] = None):
self.agent_id = agent_id
self.protocol = communication_protocol
self.logger = logger or CyberLLMLogger(name="consensus")
# Consensus state
self.active_proposals = {}
self.voting_history = deque(maxlen=1000)
self.consensus_results = {}
# Agent weights for weighted voting
self.agent_weights = {}
self.logger.info("Distributed Consensus initialized", agent_id=agent_id)
async def propose_consensus(self,
proposal_id: str,
proposal_content: Dict[str, Any],
algorithm: ConsensusAlgorithm = ConsensusAlgorithm.MAJORITY_VOTE,
timeout: int = 300) -> Dict[str, Any]:
"""Propose a decision for consensus"""
try:
proposal = {
"proposal_id": proposal_id,
"proposer": self.agent_id,
"content": proposal_content,
"algorithm": algorithm.value,
"created_at": datetime.now().isoformat(),
"timeout": timeout,
"votes": {},
"status": "active"
}
self.active_proposals[proposal_id] = proposal
# Broadcast proposal
message = AgentMessage(
message_id=str(uuid.uuid4()),
sender_id=self.agent_id,
recipient_id=None, # Broadcast
message_type=MessageType.CONSENSUS_PROPOSAL,
timestamp=datetime.now(),
content=proposal,
ttl=timeout
)
await self.protocol.send_message(message)
# Wait for consensus or timeout
result = await self._wait_for_consensus(proposal_id, timeout)
self.logger.info("Consensus proposal completed",
proposal_id=proposal_id,
result=result.get("decision"),
votes_received=len(result.get("votes", {})))
return result
except Exception as e:
self.logger.error("Consensus proposal failed", error=str(e))
return {"decision": "failed", "error": str(e)}
async def vote_on_proposal(self,
proposal_id: str,
vote: Union[bool, float, str],
justification: Optional[str] = None) -> bool:
"""Vote on an active proposal"""
try:
if proposal_id not in self.active_proposals:
self.logger.warning("Unknown proposal", proposal_id=proposal_id)
return False
proposal = self.active_proposals[proposal_id]
# Create vote message
vote_content = {
"proposal_id": proposal_id,
"vote": vote,
"voter": self.agent_id,
"timestamp": datetime.now().isoformat(),
"justification": justification
}
message = AgentMessage(
message_id=str(uuid.uuid4()),
sender_id=self.agent_id,
recipient_id=proposal["proposer"],
message_type=MessageType.CONSENSUS_VOTE,
timestamp=datetime.now(),
content=vote_content
)
await self.protocol.send_message(message)
# Record vote locally
self.voting_history.append((datetime.now(), proposal_id, vote))
self.logger.info("Vote submitted",
proposal_id=proposal_id,
vote=vote)
return True
except Exception as e:
self.logger.error("Failed to vote on proposal", error=str(e))
return False
class SwarmIntelligence:
"""Swarm intelligence capabilities for emergent behavior"""
def __init__(self,
agent_id: str,
communication_protocol: AgentCommunicationProtocol,
memory_manager: PersistentMemoryManager,
logger: Optional[CyberLLMLogger] = None):
self.agent_id = agent_id
self.protocol = communication_protocol
self.memory_manager = memory_manager
self.logger = logger or CyberLLMLogger(name="swarm_intelligence")
# Swarm state
self.swarm_members = set()
self.role = AgentRole.SPECIALIST
self.current_tasks = {}
# Intelligence mechanisms
self.pheromone_trails = defaultdict(float)
self.collective_knowledge = {}
self.emergence_patterns = {}
# Task distribution
self.task_queue = asyncio.Queue()
self.completed_tasks = deque(maxlen=1000)
self.logger.info("Swarm Intelligence initialized", agent_id=agent_id)
async def join_swarm(self, swarm_id: str, role: AgentRole = AgentRole.SPECIALIST):
"""Join a swarm with specified role"""
try:
self.role = role
self.swarm_members.add(self.agent_id)
# Announce joining
message = AgentMessage(
message_id=str(uuid.uuid4()),
sender_id=self.agent_id,
recipient_id=None, # Broadcast
message_type=MessageType.INFORMATION_SHARE,
timestamp=datetime.now(),
content={
"action": "join_swarm",
"swarm_id": swarm_id,
"role": role.value,
"agent_capabilities": list(self.protocol.capabilities.keys())
}
)
await self.protocol.send_message(message)
# Start swarm behaviors
asyncio.create_task(self._run_swarm_behaviors())
self.logger.info("Joined swarm",
swarm_id=swarm_id,
role=role.value)
except Exception as e:
self.logger.error("Failed to join swarm", error=str(e))
async def distribute_task(self, task: SwarmTask) -> str:
"""Distribute task across swarm members"""
try:
# Analyze task requirements
task_requirements = await self._analyze_task_requirements(task)
# Find suitable agents
suitable_agents = await self._find_suitable_agents(task_requirements)
if not suitable_agents:
self.logger.warning("No suitable agents found for task", task_id=task.task_id)
return "failed"
# Decompose task if needed
if len(task.required_capabilities) > 1 or task.estimated_complexity > 0.7:
subtasks = await self._decompose_task(task)
if subtasks:
# Distribute subtasks
for subtask in subtasks:
await self.distribute_task(subtask)
return "distributed"
# Assign task to best agent
best_agent = await self._select_best_agent(suitable_agents, task_requirements)
# Send task assignment
task_message = AgentMessage(
message_id=str(uuid.uuid4()),
sender_id=self.agent_id,
recipient_id=best_agent,
message_type=MessageType.TASK_REQUEST,
timestamp=datetime.now(),
content={
"task": {
"id": task.task_id,
"description": task.description,
"type": task.task_type,
"complexity": task.estimated_complexity,
"deadline": task.deadline.isoformat() if task.deadline else None,
"requirements": task_requirements
}
},
requires_acknowledgment=True
)
await self.protocol.send_message(task_message)
# Update task status
task.assigned_agents = [best_agent]
task.status = "assigned"
self.current_tasks[task.task_id] = task
self.logger.info("Task distributed",
task_id=task.task_id,
assigned_agent=best_agent)
return "assigned"
except Exception as e:
self.logger.error("Task distribution failed", error=str(e))
return "failed"
async def execute_collective_problem_solving(self,
problem: Dict[str, Any]) -> Dict[str, Any]:
"""Execute collective problem solving using swarm intelligence"""
try:
problem_id = problem.get("id", str(uuid.uuid4()))
self.logger.info("Starting collective problem solving", problem_id=problem_id)
# Phase 1: Problem decomposition
subproblems = await self._decompose_problem(problem)
# Phase 2: Distribute subproblems
partial_solutions = []
for subproblem in subproblems:
solution = await self._solve_subproblem_collectively(subproblem)
partial_solutions.append(solution)
# Phase 3: Solution synthesis
final_solution = await self._synthesize_solutions(partial_solutions, problem)
# Phase 4: Validation through consensus
validation_result = await self._validate_solution_collectively(
final_solution, problem)
# Store in collective knowledge
self.collective_knowledge[problem_id] = {
"problem": problem,
"solution": final_solution,
"validation": validation_result,
"timestamp": datetime.now().isoformat(),
"participating_agents": list(self.swarm_members)
}
# Update pheromone trails for successful patterns
if validation_result.get("valid", False):
await self._update_pheromone_trails(problem, final_solution)
self.logger.info("Collective problem solving completed",
problem_id=problem_id,
solution_quality=validation_result.get("quality", 0.0))
return {
"problem_id": problem_id,
"solution": final_solution,
"validation": validation_result,
"collective_intelligence_applied": True
}
except Exception as e:
self.logger.error("Collective problem solving failed", error=str(e))
return {"problem_id": problem_id, "error": str(e)}
class TaskDistributionEngine:
"""Advanced task distribution and load balancing"""
def __init__(self, logger: Optional[CyberLLMLogger] = None):
self.logger = logger or CyberLLMLogger(name="task_distribution")
self.agent_loads = defaultdict(float)
self.task_history = deque(maxlen=10000)
self.performance_metrics = defaultdict(dict)
async def distribute_workload(self,
tasks: List[SwarmTask],
available_agents: Dict[str, AgentCapability]) -> Dict[str, List[str]]:
"""Distribute workload optimally across agents"""
try:
# Calculate agent scores for each task
task_assignments = {}
for task in tasks:
best_agent = await self._find_optimal_agent(task, available_agents)
if best_agent:
if best_agent not in task_assignments:
task_assignments[best_agent] = []
task_assignments[best_agent].append(task.task_id)
# Update agent load
self.agent_loads[best_agent] += task.estimated_complexity
self.logger.info("Workload distributed",
tasks_count=len(tasks),
agents_used=len(task_assignments))
return task_assignments
except Exception as e:
self.logger.error("Workload distribution failed", error=str(e))
return {}
# Factory functions
def create_communication_protocol(agent_id: str, **kwargs) -> AgentCommunicationProtocol:
"""Create agent communication protocol"""
return AgentCommunicationProtocol(agent_id, **kwargs)
def create_distributed_consensus(agent_id: str,
protocol: AgentCommunicationProtocol,
**kwargs) -> DistributedConsensus:
"""Create distributed consensus manager"""
return DistributedConsensus(agent_id, protocol, **kwargs)
def create_swarm_intelligence(agent_id: str,
protocol: AgentCommunicationProtocol,
memory_manager: PersistentMemoryManager,
**kwargs) -> SwarmIntelligence:
"""Create swarm intelligence engine"""
return SwarmIntelligence(agent_id, protocol, memory_manager, **kwargs)
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