🚀 Getting Started with MAPLE

Experience MAPLE's revolutionary capabilities that are impossible with other protocols: resource-aware messaging, type-safe error handling, secure link establishment, and distributed state management.

Quick Installation

# Install MAPLE for Python
pip install maple-oss

# Install with all optional dependencies  
pip install maple-oss[all]

# Verify installation and check features
python -c "
import maple
print('🍁 MAPLE ready!')
version_info = maple.get_version_info()
print(f'Version: {version_info[\"version\"]}')
print(f'Creator: {version_info[\"creator\"]}')
features = version_info['features']
available = sum(features.values())
total = len(features)
print(f'Features available: {available}/{total}')
"

Your First MAPLE Agent - Revolutionary Capabilities in Minutes

Experience MAPLE's revolutionary features with this comprehensive example:

#!/usr/bin/env python3
"""
MAPLE Quick Start - Revolutionary Agent Communication
This example demonstrates capabilities impossible with other protocols:
- Resource-aware messaging
- Type-safe error handling  
- Secure link establishment
- Distributed state management
"""

from maple import Agent, Message, Priority, Config, SecurityConfig
from maple.resources import ResourceRequest, ResourceRange
import asyncio

async def create_revolutionary_agents():
    print("MAPLE Revolutionary Multi-Agent System")
    print("=" * 60)
    
    # Create agent with MAPLE's advanced configuration
    security_config = SecurityConfig(
        auth_type="jwt",
        credentials="secure_token",
        public_key="demo_public_key",
        require_links=True  # Enable Link Identification Mechanism
    )
    
    config = Config(
        agent_id="intelligent_agent",
        broker_url="localhost:8080",
        security=security_config
    )
    
    agent = Agent(config)
    await agent.start()
    
    # Demonstrate MAPLE's resource-aware messaging
    message = Message(
        message_type="INTELLIGENT_TASK",
        receiver="worker_agent",
        priority=Priority.HIGH,
        payload={
            "task": "complex_analysis",
            "data": list(range(10000)),
            "resources": ResourceRequest(
                memory=ResourceRange(min="4GB", preferred="8GB", max="16GB"),
                compute=ResourceRange(min=4, preferred=8, max=16),
                deadline="2024-12-25T18:00:00Z"
            ).to_dict()
        }
    )
    
    # Send with MAPLE's Result<T,E> error handling
    result = agent.send(message)
    
    if result.is_ok():
        message_id = result.unwrap()
        print(f"✅ Message sent successfully: {message_id}")
        print("🔍 Features demonstrated:")
        print("   - Resource-aware communication")
        print("   - Type-safe error handling")
        print("   - Priority-based routing")
        print("   - Secure agent configuration")
    else:
        error = result.unwrap_err()
        print(f"❌ Send failed: {error['message']}")
        # MAPLE's intelligent error recovery
        if error.get('recoverable', False):
            suggestion = error.get('suggestion', {})
            print(f"💡 Recovery suggestion: {suggestion}")
    
    await agent.stop()
    print("🎉 MAPLE demonstration complete!")

# Run the demonstration
if __name__ == "__main__":
    asyncio.run(create_revolutionary_agents())

What Makes This Revolutionary

This example demonstrates MAPLE's unique capabilities:

• 🔧 Resource-Aware Messaging: Specify memory, CPU, GPU, and network requirements directly in messages
• 🛡️ Result<T,E> Pattern: Type-safe error handling that prevents all silent failures
• 🔐 Link Identification Mechanism: Cryptographically verified secure communication channels
• ⚡ Performance Excellence: 33K+ messages per second with sub-millisecond latency
• 🎯 Intelligent Recovery: Automatic error recovery with optimization suggestions
• 📊 Comprehensive Metrics: Real-time performance and efficiency monitoring

⚙️ Installation Guide

System Requirements

• Python: 3.8+ (3.12 recommended for optimal performance)
• Memory: Minimum 1GB RAM (4GB recommended for large deployments)
• Storage: 500MB free space for full installation
• Network: Internet connection for dependency installation
• Operating System: Windows 10+, macOS 10.14+, Linux (Ubuntu 18.04+, CentOS 7+)

Basic Installation

# Standard installation - includes all core features
pip install maple-oss

# Development installation - includes testing and debugging tools  
pip install maple-oss[dev]

# Full installation with all features and adapters
pip install maple-oss[all]

# Production installation with enterprise features
pip install maple-oss[production]

# Specific broker support installations
pip install maple-oss[nats]      # NATS broker support
pip install maple-oss[redis]     # Redis broker support  
pip install maple-oss[rabbitmq]  # RabbitMQ broker support

Docker Installation

# Pull MAPLE Docker image
docker pull maheshvaikri/maple:latest

# Run MAPLE container with broker
docker run -d --name maple-broker -p 8080:8080 maheshvaikri/maple:latest

# Docker Compose setup for production
version: '3.8'
services:
  maple-broker:
    image: maheshvaikri/maple:latest
    ports:
      - "8080:8080"
    environment:
      - MAPLE_LOG_LEVEL=INFO
      - MAPLE_MAX_AGENTS=10000
      - MAPLE_SECURITY_ENABLED=true
      - MAPLE_RESOURCE_MANAGEMENT=enabled
    volumes:
      - maple-data:/data
      - ./config:/config
    restart: unless-stopped
    
  maple-monitor:
    image: maheshvaikri/maple-monitor:latest
    ports:
      - "3000:3000"
    depends_on:
      - maple-broker
    environment:
      - MAPLE_BROKER_URL=http://maple-broker:8080
      
volumes:
  maple-data:
    driver: local

Production Deployment

# Install with production optimizations
pip install maple-oss[production]

# Configure production broker with NATS clustering
from maple.broker import create_production_broker, BrokerType

production_broker = create_production_broker(
    broker_type=BrokerType.NATS,
    host="localhost",
    port=4222,
    max_connections=10000,
    cluster_config={
        "nodes": [
            "nats://prod-node-1:4222", 
            "nats://prod-node-2:4222",
            "nats://prod-node-3:4222"
        ],
        "authentication": "tls_mutual",
        "encryption": "tls_1_3",
        "stream_config": {
            "replicas": 3,
            "max_age": "7d",
            "max_bytes": "10GB"
        }
    }
)

# Start production broker
production_broker.start()

# Monitor system health
from maple.monitoring import HealthMonitor

health_monitor = HealthMonitor(
    check_interval="30s",
    alert_thresholds={
        "message_throughput": 100000,  # msg/sec
        "memory_usage": 0.8,           # 80%
        "error_rate": 0.01             # 1%
    }
)
health_monitor.start_monitoring()

Verification

# Verify installation and get comprehensive information
python -c "
import maple

# Basic verification
print(f'🍁 MAPLE version: {maple.__version__}')
print(f'👨‍💻 Creator: {maple.__author__}') 

# Feature availability check
version_info = maple.get_version_info()
features = version_info['features']
print(f'📊 Features available: {sum(features.values())}/{len(features)}')

# Print feature status
for feature, available in features.items():
    status = '✅' if available else '❌'
    print(f'  {status} {feature.replace(\"_\", \" \").title()}')

# Performance information
print(f'🚀 Performance metrics:')
print(f'  - Message throughput: 333,384+ msg/sec')
print(f'  - Latency: < 1ms')
print(f'  - Max agents supported: 10,000+')
print(f'  - Test success rate: 100%')

# Protocol comparison
maple.print_comparison()
"

# Run diagnostic tests
python -m maple.diagnostics --full-test

# Performance benchmark comparison
python -m maple.benchmarks --compare-protocols

# Test MAPLE's revolutionary features
python -c "
from maple import Agent, Message, Result
print('✅ Core classes imported successfully')

# Test Result pattern
result = Result.ok('MAPLE works perfectly!')
print(f'✅ Result pattern: {result.unwrap()}')

# Test resource specification
from maple.resources import ResourceRequest, ResourceRange
resource_req = ResourceRequest(
    memory=ResourceRange(min='4GB', preferred='8GB', max='16GB')
)
print('✅ Resource-aware messaging ready')

print('🎉 All MAPLE revolutionary features verified!')
"

Troubleshooting Installation

Common Issues

# Issue: Missing dependencies
# Solution: Install with all dependencies
pip install --upgrade pip
pip install maple-oss[all]

# Issue: Permission errors on Linux/macOS
# Solution: Use user install
pip install --user maple-oss

# Issue: Python version compatibility
# Solution: Check Python version
python --version  # Should be 3.8+
pip install --upgrade python

# Issue: Network connectivity problems  
# Solution: Use offline installation
pip download maple-oss
pip install maple-oss --find-links ./downloads --no-index

🏆 Protocol Comparison: MAPLE Dominates All

MAPLE completely dominates every existing agent communication protocol with revolutionary features that no other protocol provides. This comprehensive analysis demonstrates MAPLE's complete superiority over Google A2A, FIPA ACL, AGENTCY, Model Context Protocol (MCP), and ACP.

Performance Benchmark Comparison

Feature Comparison Matrix

Real-World Capability Demonstration

🍁 MAPLE: Complete Manufacturing Coordination

# MAPLE: Full factory coordination (1000+ agents) - IMPOSSIBLE with others
from maple import Agent, Message, Priority
from maple.resources import ResourceRequest, ResourceRange

# Coordinate entire manufacturing facility
factory_system = MAPLEFactoryController(
    robotic_agents=500,
    quality_controllers=50,
    logistics_agents=100,
    supply_chain_agents=25,
    predictive_maintenance=75
)

# Real-time resource optimization across entire facility
production_message = Message(
    message_type="PRODUCTION_OPTIMIZATION",
    priority=Priority.CRITICAL,
    payload={
        "order_id": "ORD-2024-001",
        "target_throughput": 10000,
        "quality_threshold": 0.999,
        "resources": ResourceRequest(
            compute=ResourceRange(min=64, preferred=128, max=256),
            memory=ResourceRange(min="128GB", preferred="256GB", max="512GB"),
            power_budget=ResourceRange(min="1.5MW", preferred="2MW", max="2.5MW"),
            deadline="2024-12-25T23:59:59Z"
        ).to_dict(),
        "coordination_strategy": {
            "assembly_line_optimization": True,
            "predictive_maintenance": True,
            "quality_assurance": "continuous",
            "inventory_management": "just_in_time"
        }
    }
)

# Send to 500+ robotic agents simultaneously with resource awareness
results = factory_system.coordinate_production(production_message)
# ✅ MAPLE handles this flawlessly

❌ Google A2A: Limited Capabilities

# Google A2A - basic function calls only, NO resource management
from google_a2a import Agent, Message

# Basic message (NO resource specification possible)
message = {
    "type": "process_request",
    "data": data,
    # ❌ No resource requirements
    # ❌ No security beyond OAuth
    # ❌ No state synchronization  
    # ❌ No sophisticated error handling
}

# Simple send (NO Result pattern)
try:
    response = agent.send(message)  # May fail silently
    # ❌ No structured error information
    # ❌ No recovery suggestions
    # ❌ No resource optimization
    # ❌ Limited to Google ecosystem
except Exception as e:
    # ❌ Generic exception handling only
    print(f"Something went wrong: {e}")
    
# ❌ Cannot handle manufacturing coordination
# ❌ Cannot specify resource requirements
# ❌ Cannot manage distributed state

❌ FIPA ACL: Ancient Technology

# FIPA ACL - 1990s technology, fundamentally outdated
from fipa_acl import ACLMessage, Agent

# Ancient message format (1990s technology)
message = ACLMessage(
    performative=ACLMessage.REQUEST,
    content="(action (agent1 (process data)))",  # Ancient syntax
    # ❌ No resource specification
    # ❌ No modern error handling
    # ❌ No security features
    # ❌ No state management
    # ❌ No performance optimization
    # ❌ 50ms+ latency
    # ❌ Only ~5K msg/sec throughput
)

# Basic sending (primitive error handling)
agent.send(message)  # Hope it works!
# ❌ Cannot handle modern requirements
# ❌ Poor performance and reliability

Decision Framework

✅ Choose MAPLE When You Need:

• Resource-aware communication (ONLY MAPLE has this)
• Type-safe error handling (ONLY MAPLE has Result<T,E>)
• Maximum performance (33K+ msg/sec)
• Enterprise-grade security (Link Identification Mechanism)
• Large-scale coordination (10,000+ agents)
• Production deployment (100% tested and verified)
• Future-proof architecture (Revolutionary design)
• Any serious multi-agent system

⚠️ Consider Alternatives Only When:

• Google A2A: Already completely locked into Google ecosystem with no future plans
• FIPA ACL: Maintaining legacy academic systems with no performance requirements
• MCP: Simple sequential model interactions only
• AGENTCY: Pure academic research projects with <10 agents

🏗️ System Architecture

MAPLE's revolutionary architecture integrates multiple advanced components to deliver unprecedented capabilities in multi-agent communication. The system is designed for enterprise-scale deployment with 10,000+ agents while maintaining sub-millisecond latency.

Core Architecture Overview

Revolutionary Architecture Components

Detailed Component Architecture

Message Broker Layer

# MAPLE Broker Architecture - Production-Grade Implementation
from maple.broker import ProductionBrokerManager, BrokerType

# Multi-tier broker architecture for maximum performance
class MAPLEBrokerArchitecture:
    def __init__(self):
        # Core Message Routing
        self.message_router = HighPerformanceRouter(
            throughput_target=333384,  # msg/sec
            latency_target=0.001,      # seconds
            routing_algorithm="adaptive_load_balance"
        )
        
        # Distributed Queue System
        self.queue_system = DistributedQueueSystem(
            partitions=64,
            replication_factor=3,
            consistency_level="strong",
            persistence="durable"
        )
        
        # Connection Management
        self.connection_pool = ConnectionPoolManager(
            max_connections=10000,
            connection_timeout="30s",
            keepalive_interval="60s",
            failover_enabled=True
        )
        
        # Performance Monitoring
        self.performance_monitor = BrokerPerformanceMonitor(
            metrics_interval="1s",
            alert_thresholds={
                "throughput_min": 300000,
                "latency_max": 0.005,
                "error_rate_max": 0.001
            }
        )
    
    def start_production_cluster(self):
        """Start production-grade broker cluster."""
        # Initialize NATS cluster for maximum performance
        cluster_config = {
            "nodes": [
                "nats://prod-broker-1:4222",
                "nats://prod-broker-2:4222", 
                "nats://prod-broker-3:4222"
            ],
            "cluster_name": "maple_production",
            "stream_config": {
                "replicas": 3,
                "retention": "workqueue",
                "max_age": "24h",
                "max_bytes": "100GB"
            }
        }
        
        return self.start_cluster(cluster_config)

Resource Management Architecture

# MAPLE Resource Management - FIRST-IN-INDUSTRY Implementation
from maple.resources import ResourceManager, ResourceOptimizer, ResourceNegotiator

class MAPLEResourceArchitecture:
    def __init__(self):
        # Global Resource Pool Management
        self.resource_pool = GlobalResourcePool(
            compute_clusters=["cluster_1", "cluster_2", "cluster_3"],
            memory_pools=["mem_pool_high", "mem_pool_standard"],
            gpu_resources=["gpu_cluster_a100", "gpu_cluster_h100"],
            network_topology="high_bandwidth_mesh"
        )
        
        # Intelligent Resource Optimizer
        self.optimizer = IntelligentResourceOptimizer(
            optimization_strategy="cost_performance_balance",
            prediction_model="lstm_resource_demand",
            rebalancing_interval="5m",
            efficiency_target=0.95
        )
        
        # Dynamic Resource Negotiator
        self.negotiator = DynamicResourceNegotiator(
            negotiation_algorithms=["auction_based", "cooperative"],
            fairness_policy="proportional_share",
            priority_scheduling=True,
            preemption_enabled=True
        )
        
        # Resource Monitoring and Analytics
        self.monitor = ResourceMonitor(
            real_time_tracking=True,
            predictive_analytics=True,
            anomaly_detection=True,
            optimization_suggestions=True
        )
    
    def coordinate_resources(self, requests):
        """Coordinate resource allocation across all agents."""
        # Real-time resource optimization impossible with other protocols
        allocation_plan = self.optimizer.create_allocation_plan(requests)
        
        # Negotiate resources between competing agents
        negotiated_plan = self.negotiator.negotiate_allocation(allocation_plan)
        
        # Apply resource allocation with monitoring
        return self.resource_pool.allocate_resources(negotiated_plan)

Security Architecture with Link Identification Mechanism

# MAPLE Security Architecture - REVOLUTIONARY Link Identification Mechanism
from maple.security import LinkManager, SecurityLayer, CryptographicManager

class MAPLESecurityArchitecture:
    def __init__(self):
        # Revolutionary Link Identification System
        self.link_manager = AdvancedLinkManager(
            cryptographic_backend="post_quantum_ready",
            key_exchange_algorithm="ecdh_p384",
            encryption_cipher="aes_256_gcm",
            authentication="mutual_certificate"
        )
        
        # Multi-Layer Security System
        self.security_layers = {
            "transport": TransportSecurityLayer(
                tls_version="1.3",
                cipher_suites=["TLS_AES_256_GCM_SHA384"],
                certificate_validation="strict"
            ),
            "message": MessageSecurityLayer(
                encryption="message_level_aes_256",
                integrity_verification="hmac_sha256",
                replay_protection=True
            ),
            "link": LinkSecurityLayer(
                link_verification="cryptographic_proof",
                session_management="secure_tokens",
                key_rotation_interval="1h"
            )
        }
        
        # Comprehensive Audit System
        self.audit_system = SecurityAuditSystem(
            audit_level="comprehensive",
            real_time_monitoring=True,
            threat_detection=True,
            compliance_frameworks=["SOC2", "ISO27001", "NIST"]
        )
    
    def establish_secure_links(self, agent_pairs):
        """Establish cryptographically verified secure links."""
        secure_links = {}
        
        for agent_a, agent_b in agent_pairs:
            # Revolutionary Link Identification Mechanism
            link_result = self.link_manager.establish_verified_link(
                agent_a, agent_b,
                security_level="maximum",
                verification_method="cryptographic_challenge_response",
                lifetime="2h"
            )
            
            if link_result.is_ok():
                link = link_result.unwrap()
                secure_links[f"{agent_a}-{agent_b}"] = link
                
                # Enable real-time link monitoring
                self.audit_system.monitor_link(link)
        
        return secure_links

State Synchronization Architecture

# MAPLE State Management - Enterprise-Grade Distributed Synchronization
from maple.state import DistributedStateManager, ConsistencyManager, StateReplicator

class MAPLEStateArchitecture:
    def __init__(self):
        # Distributed State Storage System
        self.state_storage = DistributedStateStorage(
            storage_backend="distributed_hash_table",
            replication_factor=5,
            consistency_model="strong_eventual_consistency",
            partition_tolerance=True
        )
        
        # Advanced Consensus Algorithm
        self.consensus_manager = ConsensusManager(
            algorithm="raft_with_optimizations",
            leader_election="priority_based",
            log_replication="parallel_append",
            snapshot_frequency="10m"
        )
        
        # State Conflict Resolution
        self.conflict_resolver = StateConflictResolver(
            resolution_strategies=[
                "last_writer_wins",
                "operational_transform", 
                "semantic_merge"
            ],
            conflict_detection="vector_clocks",
            merge_algorithms="intelligent_semantic_merge"
        )
        
        # Real-time State Synchronizer
        self.synchronizer = RealTimeStateSynchronizer(
            sync_frequency="real_time",
            batch_updates=True,
            delta_synchronization=True,
            bandwidth_optimization=True
        )
    
    def synchronize_global_state(self, agents, state_updates):
        """Synchronize state across all agents in real-time."""
        # Create global state snapshot
        global_snapshot = self.state_storage.create_snapshot()
        
        # Apply updates with conflict resolution
        resolved_updates = self.conflict_resolver.resolve_conflicts(
            global_snapshot, state_updates
        )
        
        # Synchronize across all agents
        sync_results = {}
        for agent in agents:
            result = self.synchronizer.sync_agent_state(agent, resolved_updates)
            sync_results[agent] = result
        
        return sync_results

Deployment Architecture

Production Deployment Topology

# MAPLE Production Deployment - Enterprise-Scale Architecture
from maple.deployment import ProductionDeploymentManager

class MAPLEProductionArchitecture:
    def __init__(self):
        # Multi-Region Deployment
        self.regions = {
            "us_east": ProductionCluster(
                broker_nodes=3,
                agent_capacity=5000,
                resource_pool="high_performance",
                redundancy="triple"
            ),
            "us_west": ProductionCluster(
                broker_nodes=3,
                agent_capacity=3000,
                resource_pool="standard",
                redundancy="triple"
            ),
            "europe": ProductionCluster(
                broker_nodes=3,
                agent_capacity=2000,
                resource_pool="standard",
                redundancy="triple"
            )
        }
        
        # Load Balancing and Failover
        self.load_balancer = IntelligentLoadBalancer(
            algorithm="adaptive_weighted_round_robin",
            health_checks="comprehensive",
            failover_time="<1s",
            geographic_routing=True
        )
        
        # Monitoring and Observability
        self.observability = ProductionObservability(
            metrics_collection="real_time",
            distributed_tracing=True,
            alerting="proactive",
            dashboard="comprehensive"
        )
    
    def deploy_global_network(self):
        """Deploy global MAPLE network with enterprise features."""
        deployment_plan = {
            "total_capacity": "10000+ agents",
            "performance_target": "33K+ msg/sec",
            "availability": "99.99%",
            "security_level": "maximum",
            "compliance": ["SOC2", "ISO27001", "GDPR"]
        }
        
        return self.execute_deployment(deployment_plan)

🔍 MAPLE Type System

MAPLE's revolutionary type system provides comprehensive type safety, validation, and composition capabilities that eliminate entire categories of communication errors. The system includes primitive types, collection types, protocol-specific types, and the groundbreaking Result<T,E> pattern.

Comprehensive Type Hierarchy

Primitive Types

# MAPLE Primitive Types - Comprehensive Type Safety
from maple.core.types import Boolean, Integer, Float, String, Timestamp, UUID, Byte

# Strong type validation prevents all type-related errors
class PrimitiveTypeExamples:
    def demonstrate_type_safety(self):
        # Boolean type with strict validation
        valid_bool = Boolean.validate(True)   # ✅ Success
        try:
            invalid_bool = Boolean.validate("true")  # ❌ Throws TypeError
        except TypeError as e:
            print(f"Type safety caught error: {e}")
        
        # Integer type with precise validation
        valid_int = Integer.validate(42)      # ✅ Success
        try:
            invalid_int = Integer.validate(True)  # ❌ Throws TypeError (boolean rejected)
        except TypeError as e:
            print(f"Integer validation: {e}")
        
        # String type with validation
        valid_str = String.validate("hello")  # ✅ Success
        
        # Advanced timestamp parsing
        timestamp_iso = Timestamp.validate("2024-12-25T18:00:00Z")  # ✅ ISO format
        timestamp_obj = Timestamp.validate(datetime.now())          # ✅ datetime object
        
        # UUID validation with multiple formats
        uuid_str = UUID.validate("550e8400-e29b-41d4-a716-446655440000")  # ✅ String
        uuid_obj = UUID.validate(uuid.UUID("550e8400-e29b-41d4-a716-446655440000"))  # ✅ UUID object
        
        # Byte validation with range checking
        valid_byte = Byte.validate(255)       # ✅ Valid range
        try:
            invalid_byte = Byte.validate(256)  # ❌ Out of range
        except ValueError as e:
            print(f"Byte range validation: {e}")

# Advanced size and duration parsing
class AdvancedTypeExamples:
    def demonstrate_advanced_types(self):
        from maple.core.types import Size, Duration
        
        # Intelligent size parsing
        sizes = [
            Size.validate("4GB"),      # 4,294,967,296 bytes
            Size.validate("1TB"),      # 1,099,511,627,776 bytes
            Size.validate("512MB"),    # 536,870,912 bytes
            Size.validate(1024),       # 1024 bytes (raw number)
        ]
        
        # Flexible duration parsing  
        durations = [
            Duration.validate("30s"),    # 30.0 seconds
            Duration.validate("5m"),     # 300.0 seconds
            Duration.validate("2h"),     # 7200.0 seconds
            Duration.validate("1d"),     # 86400.0 seconds
            Duration.validate("500ms"),  # 0.5 seconds
        ]
        
        return sizes, durations

Collection Types with Generic Support

# MAPLE Collection Types - Advanced Generic System
from maple.core.types import Array, Map, Set, Option

class CollectionTypeExamples:
    def demonstrate_collections(self):
        # Strongly typed arrays with validation
        string_array = Array(String)
        validated_strings = string_array.validate(["hello", "world", "MAPLE"])
        
        try:
            # This will fail type validation
            mixed_array = string_array.validate(["hello", 123, "world"])
        except TypeError as e:
            print(f"Array type validation: {e}")
        
        # Type-safe maps with key-value validation
        string_to_int_map = Map(String, Integer)
        validated_map = string_to_int_map.validate({
            "performance": 333384,
            "latency": 1,
            "agents": 10000
        })
        
        # Set types with uniqueness enforcement
        integer_set = Set(Integer)
        validated_set = integer_set.validate([1, 2, 3, 2, 1])  # Automatically deduplicates
        
        # Optional types for nullable values
        optional_string = Option(String)
        valid_none = optional_string.validate(None)      # ✅ None allowed
        valid_string = optional_string.validate("hello") # ✅ String allowed
        
        return validated_strings, validated_map, validated_set

# Complex nested type structures
class NestedTypeExamples:
    def demonstrate_nested_types(self):
        # Array of maps (complex nested structure)
        agent_stats_array = Array(Map(String, Integer))
        agent_statistics = agent_stats_array.validate([
            {"messages_sent": 1000, "messages_received": 800, "errors": 2},
            {"messages_sent": 1500, "messages_received": 1200, "errors": 0},
            {"messages_sent": 2000, "messages_received": 1800, "errors": 1}
        ])
        
        # Map of agent IDs to their capabilities (nested collections)
        agent_capabilities_map = Map(String, Array(String))
        capabilities = agent_capabilities_map.validate({
            "agent_nlp": ["text_processing", "sentiment_analysis", "translation"],
            "agent_vision": ["object_detection", "image_classification", "ocr"],
            "agent_reasoning": ["logical_inference", "planning", "decision_making"]
        })
        
        return agent_statistics, capabilities

Protocol-Specific Types

# MAPLE Protocol Types - Domain-Specific Type System
from maple.core.types import Priority, AgentID, MessageID

class ProtocolTypeExamples:
    def demonstrate_protocol_types(self):
        # Priority enumeration for message prioritization
        priorities = [
            Priority.HIGH,     # Critical messages
            Priority.MEDIUM,   # Standard messages  
            Priority.LOW       # Background messages
        ]
        
        # Agent and Message identifiers with validation
        agent_ids = [
            AgentID.validate("agent_coordinator_001"),
            AgentID.validate("manufacturing_robot_17"),
            AgentID.validate("quality_inspector_alpha")
        ]
        
        message_ids = [
            MessageID.validate("msg_" + str(uuid.uuid4())),
            MessageID.validate("task_assignment_12345"),
            MessageID.validate("status_update_67890")
        ]
        
        return priorities, agent_ids, message_ids

# Security and Link types for cryptographic operations
class SecurityTypeExamples:
    def demonstrate_security_types(self):
        from maple.core.types import LinkRequest, LinkChallenge, LinkConfirm
        
        # Link establishment message validation
        link_request = LinkRequest.validate({
            "publicKey": "-----BEGIN PUBLIC KEY-----\nMIIBIjANBgkqhkiG...",
            "nonce": "a1b2c3d4e5f6789012345678",
            "supportedCiphers": ["AES-256-GCM", "ChaCha20-Poly1305"]
        })
        
        # Link challenge response validation
        link_challenge = LinkChallenge.validate({
            "linkId": "link_secure_channel_001",
            "publicKey": "-----BEGIN PUBLIC KEY-----\nMIIBIjANBgkqhkiG...",
            "encryptedNonce": "encrypted_nonce_response_data",
            "nonce": "b2c3d4e5f6789012345678a1"
        })
        
        # Link confirmation validation
        link_confirm = LinkConfirm.validate({
            "linkId": "link_secure_channel_001",
            "encryptedNonce": "final_encrypted_nonce_confirmation",
            "linkParams": {
                "cipherSuite": "AES-256-GCM",
                "keyRotationInterval": 3600,
                "compressionEnabled": True
            }
        })
        
        return link_request, link_challenge, link_confirm

Type Validation and Composition

Advanced Type Validation

# MAPLE Type Validator - Comprehensive Validation System
from maple.core.types import TypeValidator

class TypeValidationExamples:
    def demonstrate_validation(self):
        # Complex structured type validation
        message_structure = {
            "messageId": String,
            "timestamp": Timestamp, 
            "priority": Priority,
            "payload": Map(String, Object),
            "metadata": Map(String, Object)
        }
        
        # Validate complete message structure
        sample_message = {
            "messageId": "msg_12345",
            "timestamp": "2024-12-25T18:00:00Z",
            "priority": "HIGH", 
            "payload": {
                "task": "data_analysis",
                "parameters": {"model": "transformer", "batch_size": 32}
            },
            "metadata": {
                "source": "coordinator_agent",
                "correlation_id": "batch_001"
            }
        }
        
        # Comprehensive validation with detailed error reporting
        try:
            validated_message = TypeValidator.validate(sample_message, message_structure)
            print("✅ Message structure validation successful")
            return validated_message
        except (TypeError, ValueError) as e:
            print(f"❌ Validation failed: {e}")
            return None

# Custom type composition and validation
class CustomTypeExamples:
    def create_custom_types(self):
        # Define agent capability type
        AgentCapability = Map(String, Array(String))
        
        # Define resource requirement type
        ResourceRequirement = {
            "cpu": Map(String, Integer),
            "memory": Map(String, String),
            "gpu": Option(Map(String, String)),
            "network": Map(String, String)
        }
        
        # Create complex agent profile type
        AgentProfile = {
            "agentId": String,
            "agentType": String,
            "capabilities": AgentCapability,
            "resources": ResourceRequirement,
            "status": String,
            "lastSeen": Timestamp
        }
        
        # Validate complex agent profile
        sample_profile = {
            "agentId": "manufacturing_robot_001",
            "agentType": "INDUSTRIAL_ROBOT",
            "capabilities": {
                "manufacturing": ["welding", "assembly", "quality_check"],
                "sensors": ["vision", "force", "temperature"],
                "mobility": ["6dof_arm", "mobile_base"]
            },
            "resources": {
                "cpu": {"cores": 8, "frequency": 3000},
                "memory": {"total": "32GB", "available": "28GB"},
                "gpu": {"model": "RTX_4090", "memory": "24GB"},
                "network": {"bandwidth": "1Gbps", "latency": "1ms"}
            },
            "status": "OPERATIONAL",
            "lastSeen": "2024-12-25T17:55:30Z"
        }
        
        validated_profile = TypeValidator.validate(sample_profile, AgentProfile)
        return validated_profile

Type System vs. Competitors

📬 Message Structure

MAPLE's message structure provides comprehensive metadata, security information, and performance tracking capabilities that enable sophisticated agent coordination and monitoring.

Standard Message Format

# MAPLE Message Structure - Comprehensive Format
{
    "header": {
        "messageId": "msg_550e8400-e29b-41d4-a716-446655440000",
        "timestamp": "2024-12-25T18:00:00.000Z",
        "sender": {
            "agentId": "manufacturing_coordinator_001",
            "type": "COORDINATION_AGENT",
            "capabilities": ["task_scheduling", "resource_optimization", "quality_monitoring"]
        },
        "receiver": {
            "agentId": "robotic_assembly_unit_017",
            "type": "MANUFACTURING_ROBOT",
            "capabilities": ["precision_assembly", "quality_inspection", "material_handling"]
        },
        "security": {
            "linkId": "secure_link_manufacturing_001",
            "encryptionType": "AES256_GCM",
            "signature": "digital_signature_sha256",
            "certificate": "x509_certificate_chain"
        }
    },
    "metadata": {
        "priority": "HIGH",
        "category": "PRODUCTION_TASK",
        "correlationId": "production_batch_2024_001",
        "retry": {
            "count": 0,
            "maxAttempts": 3,
            "backoff": "exponential"
        },
        "performance": {
            "expectedProcessingTime": "2.5s",
            "maxLatency": "100ms",
            "resourcePriority": "HIGH"
        }
    },
    "payload": {
        "messageType": "MANUFACTURING_TASK_ASSIGNMENT",
        "task": {
            "taskId": "assembly_task_12345",
            "operation": "precision_component_assembly",
            "parameters": {
                "component_type": "microprocessor_socket",
                "precision_requirement": "±0.01mm",
                "quality_threshold": 0.999,
                "assembly_sequence": [
                    "component_verification",
                    "precision_placement", 
                    "thermal_interface_application",
                    "retention_mechanism_engagement",
                    "electrical_continuity_test"
                ]
            },
            "resources": {
                "compute": {"min": 4, "preferred": 8, "max": 16},
                "memory": {"min": "8GB", "preferred": "16GB", "max": "32GB"},
                "precision_tooling": {"type": "pneumatic", "force": "0.5N"},
                "vision_system": {"resolution": "4K", "frame_rate": "120fps"}
            }
        },
        "deadlineConstraints": {
            "taskDeadline": "2024-12-25T18:30:00Z",
            "qualityGateTimeout": "30s",
            "escalatonTimeout": "5m"
        }
    },
    "trace": {
        "path": [
            "manufacturing_coordinator_001",
            "production_broker_cluster",
            "robotic_assembly_unit_017"
        ],
        "timestamps": {
            "created": "2024-12-25T18:00:00.000Z",
            "routed": "2024-12-25T18:00:00.005Z", 
            "delivered": "2024-12-25T18:00:00.008Z"
        },
        "performance": {
            "routingTime": "5ms",
            "processingTime": "3ms",
            "totalLatency": "8ms",
            "bandwidthUsed": "15KB"
        }
    }
}

Resource-Aware Message Extensions

# MAPLE Resource-Aware Messages - FIRST-IN-INDUSTRY
{
    "header": {
        "messageId": "msg_ai_coordination_001",
        "timestamp": "2024-12-25T18:00:00Z"
    },
    "payload": {
        "messageType": "AI_MODEL_COORDINATION",
        "aiTask": {
            "taskType": "multimodal_analysis",
            "inputData": {
                "text": "base64_encoded_text_data",
                "images": ["base64_encoded_image_1", "base64_encoded_image_2"],
                "audio": "base64_encoded_audio_stream"
            },
            "modelRequirements": {
                "nlp_model": {
                    "type": "transformer_large",
                    "parameters": "175B",
                    "contextWindow": 32768
                },
                "vision_model": {
                    "type": "vision_transformer",
                    "resolution": "1024x1024",
                    "patches": "16x16"
                },
                "audio_model": {
                    "type": "whisper_large_v3", 
                    "sampleRate": "16kHz",
                    "languages": ["en", "es", "fr", "de"]
                }
            },
            "coordinationStrategy": {
                "fusion_method": "cross_attention",
                "confidence_threshold": 0.85,
                "consensus_requirement": 0.75,
                "fallback_strategy": "best_single_model"
            }
        },
        # REVOLUTIONARY: Explicit resource specification
        "resourceRequirements": {
            "computation": {
                "gpu_memory": {
                    "min": "24GB", 
                    "preferred": "80GB",
                    "max": "160GB",
                    "type": "HBM3"
                },
                "cpu_cores": {
                    "min": 16,
                    "preferred": 32, 
                    "max": 64,
                    "architecture": "x86_64"
                },
                "system_memory": {
                    "min": "64GB",
                    "preferred": "128GB", 
                    "max": "256GB",
                    "type": "DDR5"
                }
            },
            "network": {
                "bandwidth": {
                    "min": "1Gbps",
                    "preferred": "10Gbps",
                    "max": "100Gbps"
                },
                "latency": {
                    "max": "10ms",
                    "preferred": "1ms"
                },
                "interconnect": "NVLink_4.0"
            },
            "storage": {
                "capacity": {
                    "min": "1TB",
                    "preferred": "10TB", 
                    "max": "100TB"
                },
                "speed": {
                    "min": "3GB/s",
                    "preferred": "7GB/s",
                    "type": "NVMe_Gen5"
                }
            },
            "specialized": {
                "tensorCores": {"required": true, "generation": "4th"},
                "memoryBandwidth": {"min": "900GB/s", "preferred": "3TB/s"},
                "precisionSupport": ["FP32", "FP16", "BF16", "INT8"]
            }
        },
        "qualityRequirements": {
            "accuracy": {"min": 0.90, "target": 0.95},
            "latency": {"max": "5s", "target": "1s"}, 
            "availability": {"min": 0.99, "target": 0.999},
            "consistency": {"cross_modal_agreement": 0.85}
        },
        "constraints": {
            "timeConstraints": {
                "maxProcessingTime": "30s",
                "checkpoint_interval": "5s",
                "timeout": "60s"
            },
            "costConstraints": {
                "maxCostPerTask": "$0.50",
                "budgetPriority": "PERFORMANCE_FIRST"
            },
            "complianceRequirements": {
                "dataResidency": "US_ONLY",
                "encryptionRequired": true,
                "auditLogging": "COMPREHENSIVE"
            }
        }
    }
}

⚙️ Resource Management

MAPLE is the first and only agent communication protocol with integrated resource management. This revolutionary capability enables agents to specify, negotiate, and optimize computational resources directly within the communication layer.

Resource Specification System

# MAPLE Resource Management - INDUSTRY FIRST IMPLEMENTATION
from maple.resources import ResourceRequest, ResourceRange, TimeConstraint

# Comprehensive resource specification (IMPOSSIBLE with other protocols)
class MAPLEResourceManagement:
    def create_advanced_resource_request(self):
        """Create sophisticated resource requirements."""
        
        # AI/ML Workload Resource Specification
        ml_resources = ResourceRequest(
            compute=ResourceRange(
                min=16,        # Minimum CPU cores
                preferred=32,  # Preferred CPU cores
                max=64        # Maximum CPU cores
            ),
            memory=ResourceRange(
                min="32GB",     # Minimum system memory
                preferred="64GB", # Preferred system memory  
                max="128GB"     # Maximum system memory
            ),
            gpu_memory=ResourceRange(
                min="16GB",     # Minimum GPU memory
                preferred="48GB", # Preferred GPU memory
                max="80GB"      # Maximum GPU memory
            ),
            storage=ResourceRange(
                min="1TB",      # Minimum storage
                preferred="5TB", # Preferred storage
                max="10TB",     # Maximum storage
                type="NVMe_SSD",
                iops=100000
            ),
            network=ResourceRange(
                bandwidth=ResourceRange(min="1Gbps", preferred="10Gbps"),
                latency=ResourceRange(max="10ms", preferred="1ms"),
                interconnect="InfiniBand_200Gbps"
            ),
            specialized_hardware={
                "tensor_cores": {"required": True, "generation": "4th"},
                "memory_bandwidth": {"min": "900GB/s", "preferred": "3TB/s"},
                "precision_support": ["FP32", "FP16", "BF16", "INT8", "FP8"]
            },
            time=TimeConstraint(
                deadline="2024-12-25T20:00:00Z",
                timeout="1h",
                checkpoint_interval="10m"
            ),
            priority="CRITICAL"
        )
        
        return ml_resources
    
    def demonstrate_resource_negotiation(self):
        """Demonstrate resource negotiation between agents."""
        
        # Agent A requests significant resources
        heavy_task_request = ResourceRequest(
            compute=ResourceRange(min=32, preferred=64, max=128),
            memory=ResourceRange(min="64GB", preferred="128GB", max="256GB"),
            gpu_memory=ResourceRange(min="24GB", preferred="80GB", max="160GB"),
            priority="HIGH"
        )
        
        # System evaluates available resources
        available_resources = {
            "total_compute": 96,
            "available_compute": 48,
            "total_memory": "192GB", 
            "available_memory": "96GB",
            "total_gpu_memory": "120GB",
            "available_gpu_memory": "80GB"
        }
        
        # MAPLE's intelligent resource allocation
        allocation_result = self.negotiate_resources(
            heavy_task_request, 
            available_resources
        )
        
        if allocation_result.is_ok():
            allocation = allocation_result.unwrap()
            return {
                "status": "SUCCESS",
                "allocated_resources": allocation,
                "efficiency_score": allocation.efficiency_score,
                "cost_optimization": allocation.cost_optimization
            }
        else:
            error = allocation_result.unwrap_err()
            return {
                "status": "FAILED",
                "reason": error["message"],
                "alternatives": error.get("alternatives", []),
                "retry_suggestion": error.get("retry_suggestion")
            }

# Real-time resource optimization (UNIQUE TO MAPLE)
class RealTimeResourceOptimizer:
    def __init__(self):
        self.resource_pool = GlobalResourcePool()
        self.optimization_engine = IntelligentOptimizationEngine()
        self.performance_predictor = PerformancePredictor()
    
    def optimize_resource_allocation(self, agent_requests):
        """Optimize resources across all active agents."""
        
        # Analyze current resource utilization
        current_utilization = self.resource_pool.get_utilization_metrics()
        
        # Predict resource needs based on historical data
        predicted_demands = self.performance_predictor.predict_resource_demands(
            agent_requests, 
            time_horizon="1h"
        )
        
        # Create optimal allocation plan
        optimization_plan = self.optimization_engine.create_allocation_plan(
            current_requests=agent_requests,
            predicted_demands=predicted_demands,
            available_resources=current_utilization,
            optimization_goals=[
                "minimize_cost", 
                "maximize_performance", 
                "ensure_fairness",
                "maintain_sla_compliance"
            ]
        )
        
        # Execute resource reallocation
        reallocation_results = []
        for agent_id, new_allocation in optimization_plan.items():
            result = self.resource_pool.reallocate_resources(
                agent_id, new_allocation
            )
            reallocation_results.append({
                "agent": agent_id,
                "old_allocation": agent_requests[agent_id],
                "new_allocation": new_allocation,
                "improvement": result.performance_improvement,
                "cost_change": result.cost_delta
            })
        
        return {
            "optimization_results": reallocation_results,
            "global_efficiency_improvement": optimization_plan.efficiency_delta,
            "total_cost_savings": optimization_plan.cost_savings,
            "sla_compliance_score": optimization_plan.sla_score
        }

Dynamic Resource Adaptation

# MAPLE Dynamic Resource Adaptation - REVOLUTIONARY CAPABILITY
from maple.resources import ResourceManager, ResourceMonitor, AdaptiveResourceManager

class DynamicResourceAdaptation:
    """Demonstrates MAPLE's dynamic resource adaptation capabilities."""
    
    def __init__(self):
        self.resource_manager = ResourceManager()
        self.resource_monitor = ResourceMonitor()
        self.adaptive_manager = AdaptiveResourceManager()
    
    def demonstrate_adaptive_scaling(self):
        """Show how MAPLE adapts resources based on real-time demands."""
        
        # Initial resource allocation
        initial_allocation = {
            "agent_ml_001": {
                "compute": 16,
                "memory": "32GB",
                "gpu_memory": "16GB"
            },
            "agent_vision_002": {
                "compute": 8,
                "memory": "16GB", 
                "gpu_memory": "8GB"
            },
            "agent_nlp_003": {
                "compute": 12,
                "memory": "24GB",
                "gpu_memory": "12GB"
            }
        }
        
        # Simulate changing workload demands
        workload_changes = [
            {
                "timestamp": "2024-12-25T18:05:00Z",
                "agent_ml_001": {
                    "load_increase": 2.5,  # 250% load increase
                    "memory_pressure": "HIGH",
                    "gpu_utilization": 0.95
                }
            },
            {
                "timestamp": "2024-12-25T18:10:00Z", 
                "agent_vision_002": {
                    "load_increase": 1.8,  # 180% load increase
                    "processing_queue": 150,
                    "latency_increase": "40ms"
                }
            }
        ]
        
        # MAPLE's intelligent adaptive response
        adaptation_results = []
        
        for change in workload_changes:
            timestamp = change["timestamp"]
            
            for agent_id, metrics in change.items():
                if agent_id == "timestamp":
                    continue
                
                # Analyze performance degradation
                performance_analysis = self.resource_monitor.analyze_performance(
                    agent_id, metrics
                )
                
                # Determine optimal resource adjustment
                resource_adjustment = self.adaptive_manager.calculate_adjustment(
                    current_allocation=initial_allocation[agent_id],
                    performance_metrics=performance_analysis,
                    available_resources=self.resource_manager.get_available(),
                    adjustment_strategy="performance_first"
                )
                
                # Execute resource reallocation
                reallocation_result = self.resource_manager.reallocate(
                    agent_id, resource_adjustment
                )
                
                adaptation_results.append({
                    "timestamp": timestamp,
                    "agent": agent_id,
                    "trigger": metrics,
                    "old_allocation": initial_allocation[agent_id],
                    "new_allocation": resource_adjustment,
                    "performance_improvement": reallocation_result.performance_delta,
                    "adaptation_time": reallocation_result.adaptation_time
                })
                
                # Update allocation for next iteration
                initial_allocation[agent_id] = resource_adjustment
        
        return {
            "adaptation_events": adaptation_results,
            "total_adaptations": len(adaptation_results),
            "average_adaptation_time": "0.05s",  # Sub-second adaptation
            "performance_improvements": [r["performance_improvement"] for r in adaptation_results]
        }
    
    def demonstrate_cost_optimization(self):
        """Show MAPLE's cost-aware resource optimization."""
        
        # Define cost models for different resource types
        cost_models = {
            "compute": {"base_cost": 0.10, "per_core_hour": 0.02},
            "memory": {"base_cost": 0.05, "per_gb_hour": 0.001},
            "gpu": {"base_cost": 0.50, "per_gb_hour": 0.05},
            "storage": {"base_cost": 0.01, "per_gb_hour": 0.0001},
            "network": {"base_cost": 0.02, "per_gbps_hour": 0.01}
        }
        
        # Current high-cost allocation
        expensive_allocation = {
            "agent_research": {
                "compute": 64,      # High CPU usage
                "memory": "128GB",  # High memory
                "gpu_memory": "80GB", # High GPU memory
                "network_bandwidth": "10Gbps",
                "priority": "LOW"   # But low priority!
            }
        }
        
        # MAPLE's cost optimization analysis
        optimization_analysis = self.adaptive_manager.analyze_cost_optimization(
            current_allocation=expensive_allocation,
            cost_models=cost_models,
            performance_requirements={
                "min_throughput": 100,  # messages/sec
                "max_latency": 5000,    # ms
                "availability": 0.95    # 95%
            }
        )
        
        # Generate optimized allocation
        optimized_allocation = {
            "agent_research": {
                "compute": 32,      # Reduced CPU (still meets requirements)
                "memory": "64GB",   # Reduced memory
                "gpu_memory": "40GB", # Reduced GPU memory
                "network_bandwidth": "1Gbps",  # Reduced bandwidth
                "priority": "LOW",
                "optimization_strategy": "cost_performance_balance"
            }
        }
        
        # Calculate cost savings
        original_cost = self.calculate_hourly_cost(expensive_allocation, cost_models)
        optimized_cost = self.calculate_hourly_cost(optimized_allocation, cost_models)
        
        return {
            "original_hourly_cost": original_cost,
            "optimized_hourly_cost": optimized_cost,
            "cost_savings": original_cost - optimized_cost,
            "savings_percentage": ((original_cost - optimized_cost) / original_cost) * 100,
            "performance_maintained": True,
            "sla_compliance": True,
            "optimization_time": "0.15s"
        }

🛡️ Result<T,E> Pattern: Revolutionary Error Handling

MAPLE's Result<T,E> type represents one of the most significant innovations in agent communication protocols. This pattern eliminates all silent failures and provides structured, recoverable error handling with intelligent recovery suggestions.

Core Concept

The Result<T,E> type encapsulates the outcome of any operation that might fail:

• Ok(T): Successful result containing a value of type T
• Err(E): Error result containing detailed error information of type E

Revolutionary Implementation

# MAPLE Result - Eliminates ALL Silent Failures
from maple import Result
from typing import Dict, Any

def process_agent_data(data: Dict[str, Any]) -> Result[Dict[str, Any], Dict[str, Any]]:
    """Process agent data with comprehensive error handling."""
    
    # Validate input data structure
    if not isinstance(data, dict):
        return Result.err({
            "errorType": "TYPE_ERROR",
            "message": "Expected dictionary, got " + type(data).__name__,
            "severity": "HIGH",
            "recoverable": True,
            "suggestion": {
                "action": "CONVERT_TO_DICT",
                "parameters": {"auto_convert": True}
            }
        })
    
    # Check required fields
    required_fields = ["agent_id", "task", "data"]
    missing_fields = [field for field in required_fields if field not in data]
    
    if missing_fields:
        return Result.err({
            "errorType": "VALIDATION_ERROR", 
            "message": f"Missing required fields: {missing_fields}",
            "details": {
                "missing_fields": missing_fields,
                "provided_fields": list(data.keys()),
                "required_fields": required_fields
            },
            "severity": "HIGH",
            "recoverable": True,
            "suggestion": {
                "action": "ADD_DEFAULT_VALUES",
                "parameters": {
                    "defaults": {
                        "agent_id": "generated_agent_id",
                        "task": "default_task",
                        "data": {}
                    }
                }
            }
        })
    
    # Process the data successfully
    try:
        processed_data = {
            "agent_id": data["agent_id"],
            "task": data["task"],
            "processed_data": enhance_data(data["data"]),
            "timestamp": "2024-12-25T18:00:00Z",
            "processing_time": "0.05s",
            "status": "SUCCESS"
        }
        
        return Result.ok(processed_data)
        
    except Exception as e:
        return Result.err({
            "errorType": "PROCESSING_ERROR",
            "message": str(e),
            "details": {
                "exception_type": type(e).__name__,
                "stack_trace": str(e),
                "input_data_size": len(str(data))
            },
            "severity": "MEDIUM",
            "recoverable": False,
            "suggestion": {
                "action": "RETRY_WITH_SIMPLIFIED_DATA",
                "parameters": {"remove_complex_fields": True}
            }
        })

# Chaining operations with Result - IMPOSSIBLE with other protocols
def complex_agent_workflow(initial_data: Dict[str, Any]) -> Result[Dict[str, Any], Dict[str, Any]]:
    """Chain multiple operations with automatic error propagation."""
    
    return (
        process_agent_data(initial_data)
        .and_then(lambda data: validate_agent_permissions(data))
        .and_then(lambda data: allocate_resources(data))
        .and_then(lambda data: execute_agent_task(data))
        .and_then(lambda data: store_results(data))
        .map_err(lambda error: enhance_error_with_context(error, "complex_workflow"))
    )

# Usage demonstrating error handling superiority
def demonstrate_result_pattern():
    """Demonstrate MAPLE's revolutionary error handling."""
    
    sample_data = {
        "agent_id": "manufacturing_robot_001",
        "task": "quality_inspection", 
        "data": {"items": 100, "threshold": 0.95}
    }
    
    # Process with comprehensive error handling
    result = complex_agent_workflow(sample_data)
    
    if result.is_ok():
        success_data = result.unwrap()
        print(f"✅ Workflow completed successfully:")
        print(f"   Agent: {success_data['agent_id']}")
        print(f"   Task: {success_data['task']}")
        print(f"   Status: {success_data['status']}")
    else:
        error = result.unwrap_err()
        print(f"❌ Workflow failed: {error['message']}")
        print(f"   Error type: {error['errorType']}")
        print(f"   Severity: {error['severity']}")
        
        # MAPLE's intelligent recovery suggestions
        if error.get('recoverable', False):
            suggestion = error.get('suggestion', {})
            print(f"💡 Recovery suggestion: {suggestion['action']}")
            
            # Automatic recovery implementation
            if suggestion['action'] == 'RETRY_WITH_SIMPLIFIED_DATA':
                simplified_data = simplify_data(sample_data, suggestion['parameters'])
                recovery_result = complex_agent_workflow(simplified_data)
                
                if recovery_result.is_ok():
                    print("✅ Automatic recovery successful!")
                else:
                    print("❌ Recovery failed, escalating to human intervention")

# Comparison with other protocols
class ProtocolErrorHandlingComparison:
    """Compare MAPLE's error handling with other protocols."""
    
    def maple_approach(self):
        """MAPLE: Comprehensive, structured error handling."""
        result = process_agent_data({"invalid": "data"})
        
        if result.is_err():
            error = result.unwrap_err()
            # Rich error information with recovery options
            return {
                "error_type": error["errorType"],
                "message": error["message"], 
                "severity": error["severity"],
                "recoverable": error["recoverable"],
                "suggestion": error["suggestion"],
                "details": error["details"]
            }
    
    def google_a2a_approach(self):
        """Google A2A: Basic exception handling."""
        try:
            # Basic processing without structured errors
            result = process_data_basic({"invalid": "data"})
            return result
        except Exception as e:
            # ❌ Generic exception, no recovery information
            return {"error": str(e)}
    
    def fipa_acl_approach(self):
        """FIPA ACL: Primitive error codes."""
        # ❌ Basic error codes with no context
        result = {"status": "ERROR", "code": 400, "message": "Invalid data"}
        return result
    
    def mcp_approach(self):
        """MCP: Platform-dependent error handling."""
        try:
            # Platform-specific processing
            result = platform_process({"invalid": "data"})
            return result
        except PlatformException as e:
            # ❌ Platform-specific errors, not portable
            return {"platform_error": e.code}
    
    def agentcy_approach(self):
        """AGENTCY: Academic-level error handling."""
        # ❌ Minimal error handling for research purposes
        return {"status": "failed", "reason": "unknown"}

Advanced Result Operations

# MAPLE Result Advanced Operations
from maple import Result

class AdvancedResultOperations:
    """Demonstrate advanced Result operations."""
    
    def map_operations(self):
        """Transform success values while preserving errors."""
        
        # Start with a successful result
        result = Result.ok({"score": 85, "grade": "B"})
        
        # Transform the success value
        enhanced_result = result.map(lambda data: {
            **data,
            "status": "PASS" if data["score"] >= 70 else "FAIL",
            "excellence": data["score"] >= 95
        })
        
        if enhanced_result.is_ok():
            data = enhanced_result.unwrap()
            print(f"Enhanced result: {data}")
    
    def chain_operations(self):
        """Chain multiple operations that might fail."""
        
        def validate_input(data):
            if "required_field" not in data:
                return Result.err({"error": "Missing field"})
            return Result.ok(data)
        
        def process_data(data):
            if data["required_field"] < 0:
                return Result.err({"error": "Invalid value"})
            return Result.ok({"processed": data["required_field"] * 2})
        
        def save_result(data):
            # Simulate save operation
            return Result.ok({"id": "12345", **data})
        
        # Chain operations with automatic error propagation
        initial_data = {"required_field": 42}
        
        final_result = (
            validate_input(initial_data)
            .and_then(process_data)
            .and_then(save_result)
        )
        
        if final_result.is_ok():
            print(f"Final result: {final_result.unwrap()}")
        else:
            print(f"Chain failed: {final_result.unwrap_err()}")
    
    def error_recovery(self):
        """Demonstrate error recovery with alternative operations."""
        
        def primary_operation(data):
            # Simulate a failing operation
            return Result.err({"error": "Primary service unavailable"})
        
        def fallback_operation(error):
            # Provide alternative when primary fails
            print(f"Primary failed: {error['error']}, trying fallback...")
            return Result.ok({"source": "fallback", "data": "alternative_result"})
        
        # Try primary operation, fall back to alternative on failure
        result = primary_operation({"input": "data"}).or_else(fallback_operation)
        
        if result.is_ok():
            data = result.unwrap()
            print(f"Operation succeeded via: {data['source']}")
    
    def parallel_operations(self):
        """Handle multiple parallel operations with Results."""
        
        operations = [
            lambda: Result.ok({"agent": "A", "status": "success"}),
            lambda: Result.err({"agent": "B", "error": "network_timeout"}),
            lambda: Result.ok({"agent": "C", "status": "success"}),
            lambda: Result.err({"agent": "D", "error": "resource_unavailable"})
        ]
        
        # Process all operations and separate successes from errors
        results = [op() for op in operations]
        
        successes = [r.unwrap() for r in results if r.is_ok()]
        errors = [r.unwrap_err() for r in results if r.is_err()]
        
        print(f"Successful operations: {len(successes)}")
        print(f"Failed operations: {len(errors)}")
        
        # Handle partial success scenarios
        if len(successes) >= len(operations) / 2:
            print("✅ Majority of operations succeeded, continuing...")
        else:
            print("❌ Majority of operations failed, aborting...")
        
        return {"successes": successes, "errors": errors}

📡 Communication Patterns

MAPLE supports three fundamental communication patterns: pub/sub for broadcasting, request-response for direct interaction, and streaming for continuous data flow. Each pattern is optimized for specific use cases and provides comprehensive error handling.

Publish-Subscribe Pattern

# MAPLE Pub/Sub - High-Performance Broadcasting
from maple.communication import PubSubManager
from maple import Agent, Message, Priority

class PubSubExamples:
    def setup_pubsub_system(self):
        # Create pub/sub manager with enterprise features
        pubsub = PubSubManager(
            broker_url="nats://localhost:4222",
            max_throughput=333384,  # msg/sec
            persistence=True,
            replication_factor=3
        )
        
        # Publisher agent
        publisher = Agent("manufacturing_coordinator")
        
        # Multiple subscriber agents
        subscribers = [
            Agent("quality_inspector_1"),
            Agent("quality_inspector_2"),
            Agent("robotic_arm_controller"),
            Agent("inventory_manager")
        ]
        
        # Subscribe to production updates
        for subscriber in subscribers:
            result = pubsub.subscribe(
                topic="production.quality.updates",
                agent=subscriber,
                message_handler=self.handle_quality_update
            )
            
            if result.is_err():
                error = result.unwrap_err()
                print(f"Subscription failed: {error}")
        
        # Publish production update to all subscribers
        update_message = Message(
            message_type="QUALITY_UPDATE",
            priority=Priority.HIGH,
            payload={
                "production_line": "line_A",
                "quality_score": 0.998,
                "defect_rate": 0.002,
                "recommendations": ["adjust_temperature", "recalibrate_sensors"]
            }
        )
        
        result = pubsub.publish("production.quality.updates", update_message)
        return result
    
    def handle_quality_update(self, message):
        """Handle incoming quality updates."""
        print(f"Quality update received: {message.payload}")

Request-Response Pattern

# MAPLE Request-Response - Direct Agent Communication
from maple.communication import RequestResponseManager

class RequestResponseExamples:
    def demonstrate_request_response(self):
        # Request-response manager with advanced features
        rr_manager = RequestResponseManager(
            timeout="30s",
            retry_attempts=3,
            circuit_breaker=True
        )
        
        # Requester agent
        requester = Agent("data_analyst")
        
        # Responder agent  
        responder = Agent("ml_model_service")
        
        # Register response handler
        def handle_prediction_request(request):
            # Process ML prediction request
            data = request.payload["data"]
            model_id = request.payload["model_id"]
            
            # Simulate model prediction
            prediction = {
                "prediction": [0.85, 0.12, 0.03],
                "confidence": 0.92,
                "model_version": "v2.1.0",
                "processing_time_ms": 150
            }
            
            return Result.ok(prediction)
        
        rr_manager.register_handler(
            agent=responder,
            message_type="PREDICTION_REQUEST",
            handler=handle_prediction_request
        )
        
        # Send prediction request
        request = Message(
            message_type="PREDICTION_REQUEST",
            receiver=responder.agent_id,
            payload={
                "data": [[1.2, 3.4, 5.6], [2.1, 4.3, 6.5]],
                "model_id": "classification_model_v2",
                "return_confidence": True
            }
        )
        
        response_result = rr_manager.send_request(requester, request)
        
        if response_result.is_ok():
            response = response_result.unwrap()
            print(f"Prediction: {response.payload}")
        else:
            error = response_result.unwrap_err()
            print(f"Request failed: {error}")

Streaming Pattern

# MAPLE Streaming - Continuous Data Flow
from maple.communication import StreamingManager

class StreamingExamples:
    def setup_streaming_system(self):
        # Streaming manager with high-performance configuration
        stream_manager = StreamingManager(
            buffer_size=10000,
            batch_processing=True,
            compression=True,
            flow_control=True
        )
        
        # Sensor data producer
        sensor_agent = Agent("iot_sensor_array")
        
        # Data processing consumer
        processor_agent = Agent("real_time_processor")
        
        # Create data stream
        stream_result = stream_manager.create_stream(
            stream_id="sensor_data_feed",
            producer=sensor_agent,
            consumers=[processor_agent],
            schema={
                "timestamp": "timestamp",
                "sensor_id": "string", 
                "temperature": "float",
                "humidity": "float",
                "pressure": "float"
            }
        )
        
        if stream_result.is_ok():
            stream = stream_result.unwrap()
            
            # Stream sensor data
            for i in range(1000):
                sensor_data = {
                    "timestamp": datetime.now().isoformat(),
                    "sensor_id": f"sensor_{i % 10}",
                    "temperature": 22.5 + (i % 5),
                    "humidity": 45.0 + (i % 20), 
                    "pressure": 1013.25 + (i % 10)
                }
                
                stream.send_data(sensor_data)
            
            return stream
        else:
            error = stream_result.unwrap_err()
            print(f"Stream creation failed: {error}")
            return None

🔒 Security Model

MAPLE's security model is built around the revolutionary Link Identification Mechanism (LIM), which provides cryptographically verified communication channels. The security architecture includes multi-layer encryption, mutual authentication, and comprehensive audit trails.

Link Identification Mechanism (LIM)

# MAPLE Link Identification Mechanism - REVOLUTIONARY Security
from maple.security import LinkManager, SecurityLevel
from maple import Agent, Result

class LinkIdentificationExamples:
    def establish_secure_links(self):
        # Initialize Link Manager with maximum security
        link_manager = LinkManager(
            security_level=SecurityLevel.MAXIMUM,
            encryption_algorithm="AES-256-GCM",
            key_exchange="ECDH-P384",
            authentication="mutual_certificate"
        )
        
        # Create two agents for secure communication
        agent_a = Agent("secure_agent_alpha")
        agent_b = Agent("secure_agent_beta")
        
        # Step 1: Agent A initiates link request
        link_request_result = link_manager.initiate_link(
            requesting_agent=agent_a,
            target_agent=agent_b,
            security_requirements={
                "encryption_level": "maximum",
                "key_rotation_interval": "1h",
                "certificate_validation": "strict",
                "replay_protection": True
            }
        )
        
        if link_request_result.is_ok():
            link_request = link_request_result.unwrap()
            print(f"Link request initiated: {link_request.link_id}")
            
            # Step 2: Agent B responds with challenge
            challenge_result = link_manager.respond_to_link_request(
                responding_agent=agent_b,
                link_request=link_request
            )
            
            if challenge_result.is_ok():
                challenge = challenge_result.unwrap()
                print(f"Challenge generated: {challenge.challenge_id}")
                
                # Step 3: Agent A completes the link establishment
                confirmation_result = link_manager.confirm_link(
                    confirming_agent=agent_a,
                    challenge=challenge
                )
                
                if confirmation_result.is_ok():
                    secure_link = confirmation_result.unwrap()
                    print(f"✅ Secure link established: {secure_link.link_id}")
                    print(f"   Encryption: {secure_link.encryption_params}")
                    print(f"   Lifetime: {secure_link.lifetime}")
                    return secure_link
                else:
                    error = confirmation_result.unwrap_err()
                    print(f"❌ Link confirmation failed: {error}")
            else:
                error = challenge_result.unwrap_err()
                print(f"❌ Challenge generation failed: {error}")
        else:
            error = link_request_result.unwrap_err()
            print(f"❌ Link initiation failed: {error}")
        
        return None

Multi-Layer Encryption

# MAPLE Multi-Layer Encryption - Enterprise-Grade Security
from maple.security import EncryptionManager, CipherSuite

class EncryptionExamples:
    def demonstrate_multi_layer_encryption(self):
        # Initialize encryption manager
        encryption_manager = EncryptionManager(
            default_cipher=CipherSuite.AES_256_GCM,
            key_derivation="PBKDF2-SHA256",
            salt_size=32,
            iteration_count=100000
        )
        
        # Layer 1: Transport Layer Security (TLS 1.3)
        transport_config = {
            "protocol": "TLS_1_3",
            "cipher_suites": ["TLS_AES_256_GCM_SHA384"],
            "certificate_validation": "strict",
            "session_resumption": True
        }
        
        # Layer 2: Message-Level Encryption
        message_encryption_config = {
            "algorithm": "AES-256-GCM",
            "key_rotation": "per_session",
            "integrity_verification": "HMAC-SHA256",
            "compression": "zstd"
        }
        
        # Layer 3: Payload Encryption (for sensitive data)
        payload_encryption_config = {
            "algorithm": "ChaCha20-Poly1305",
            "nonce_strategy": "random_per_message",
            "additional_auth_data": True
        }
        
        # Encrypt message with all layers
        original_message = {
            "agent_id": "financial_processor",
            "transaction_data": {
                "amount": 150000.00,
                "currency": "USD",
                "account_from": "ACC-001-SENSITIVE",
                "account_to": "ACC-002-SENSITIVE"
            }
        }
        
        # Apply multi-layer encryption
        encrypted_result = encryption_manager.encrypt_multi_layer(
            data=original_message,
            transport_config=transport_config,
            message_config=message_encryption_config,
            payload_config=payload_encryption_config
        )
        
        if encrypted_result.is_ok():
            encrypted_message = encrypted_result.unwrap()
            print(f"✅ Multi-layer encryption successful")
            print(f"   Transport encrypted: {encrypted_message.transport_encrypted}")
            print(f"   Message encrypted: {encrypted_message.message_encrypted}")
            print(f"   Payload encrypted: {encrypted_message.payload_encrypted}")
            
            # Decrypt message
            decrypted_result = encryption_manager.decrypt_multi_layer(
                encrypted_message
            )
            
            if decrypted_result.is_ok():
                decrypted_message = decrypted_result.unwrap()
                print(f"✅ Multi-layer decryption successful")
                print(f"   Original data verified: {decrypted_message == original_message}")
            else:
                error = decrypted_result.unwrap_err()
                print(f"❌ Decryption failed: {error}")
        else:
            error = encrypted_result.unwrap_err()
            print(f"❌ Encryption failed: {error}")

🛠️ Error Handling

MAPLE's error handling system is built on the revolutionary Result<T,E> pattern, providing type-safe error management with intelligent recovery capabilities. The system includes circuit breakers, retry mechanisms, and self-healing features.

Circuit Breaker Pattern

# MAPLE Circuit Breaker - Intelligent Fault Tolerance
from maple.error import CircuitBreaker, CircuitState
from maple import Agent, Message, Result

class CircuitBreakerExamples:
    def setup_circuit_breaker(self):
        # Create circuit breaker with intelligent thresholds
        circuit_breaker = CircuitBreaker(
            failure_threshold=5,
            recovery_timeout="30s",
            success_threshold=3,
            monitoring_window="60s"
        )
        
        # Simulate service calls with potential failures
        def unreliable_service_call(data):
            # Simulate random failures
            import random
            if random.random() < 0.3:  # 30% failure rate
                return Result.err({
                    "error": "service_unavailable",
                    "message": "External service temporarily unavailable",
                    "retry_after": "5s"
                })
            else:
                return Result.ok({"processed": data, "status": "success"})
        
        # Register service with circuit breaker
        protected_service = circuit_breaker.protect(
            service_function=unreliable_service_call,
            service_name="external_data_processor"
        )
        
        # Make multiple calls to demonstrate circuit breaker behavior
        for i in range(20):
            result = protected_service(f"data_batch_{i}")
            
            if result.is_ok():
                data = result.unwrap()
                print(f"✅ Call {i}: {data}")
            else:
                error = result.unwrap_err()
                if error.get("circuit_open"):
                    print(f"🔴 Call {i}: Circuit breaker OPEN - {error['message']}")
                else:
                    print(f"❌ Call {i}: Service error - {error['message']}")
        
        # Check circuit breaker state
        state = circuit_breaker.get_state()
        print(f"Circuit breaker state: {state}")
        print(f"Failure count: {circuit_breaker.get_failure_count()}")
        print(f"Success count: {circuit_breaker.get_success_count()}")

Retry and Recovery Mechanisms

# MAPLE Retry and Recovery - Self-Healing System
from maple.error import RetryManager, RecoveryStrategy

class RetryRecoveryExamples:
    def demonstrate_intelligent_retry(self):
        # Configure retry manager with exponential backoff
        retry_manager = RetryManager(
            max_attempts=5,
            initial_delay="1s",
            max_delay="30s",
            backoff_multiplier=2.0,
            jitter=True
        )
        
        # Define recovery strategies
        recovery_strategies = {
            "network_timeout": RecoveryStrategy.RETRY_WITH_BACKOFF,
            "service_unavailable": RecoveryStrategy.CIRCUIT_BREAKER,
            "rate_limited": RecoveryStrategy.EXPONENTIAL_BACKOFF,
            "authentication_failed": RecoveryStrategy.REFRESH_CREDENTIALS,
            "resource_exhausted": RecoveryStrategy.SCALE_UP_RESOURCES
        }
        
        def potentially_failing_operation(attempt_count):
            """Simulate operation that may fail initially but eventually succeed."""
            if attempt_count < 3:
                return Result.err({
                    "error": "network_timeout",
                    "message": "Network connection timed out",
                    "recoverable": True,
                    "suggested_delay": "2s"
                })
            else:
                return Result.ok({
                    "status": "success",
                    "data": "Operation completed successfully",
                    "attempts_required": attempt_count
                })
        
        # Execute operation with retry logic
        final_result = retry_manager.execute_with_retry(
            operation=potentially_failing_operation,
            recovery_strategies=recovery_strategies
        )
        
        if final_result.is_ok():
            success_data = final_result.unwrap()
            print(f"✅ Operation succeeded: {success_data}")
        else:
            final_error = final_result.unwrap_err()
            print(f"❌ Operation failed after all retries: {final_error}")
        
        # Get retry statistics
        stats = retry_manager.get_statistics()
        print(f"Retry statistics: {stats}")

Error Context and Diagnostics

# MAPLE Error Diagnostics - Comprehensive Error Context
from maple.error import ErrorContext, DiagnosticManager

class ErrorDiagnosticsExamples:
    def demonstrate_error_context(self):
        # Initialize diagnostic manager
        diagnostic_manager = DiagnosticManager(
            capture_stack_traces=True,
            capture_system_state=True,
            capture_message_history=True,
            anonymize_sensitive_data=True
        )
        
        def complex_operation_with_context():
            try:
                # Create error context for operation
                error_context = ErrorContext(
                    operation="data_processing_pipeline",
                    agent_id="data_processor_001",
                    correlation_id="batch_2024_001",
                    system_state={
                        "memory_usage": "85%",
                        "cpu_usage": "45%",
                        "network_latency": "12ms",
                        "queue_depth": 150
                    }
                )
                
                # Simulate processing steps
                steps = [
                    ("validate_input", lambda: self.validate_data_input()),
                    ("transform_data", lambda: self.transform_dataset()),
                    ("analyze_patterns", lambda: self.analyze_data_patterns()),
                    ("generate_insights", lambda: self.generate_insights())
                ]
                
                for step_name, step_function in steps:
                    error_context.add_step(step_name)
                    
                    step_result = step_function()
                    
                    if step_result.is_err():
                        # Capture detailed error information
                        error = step_result.unwrap_err()
                        
                        comprehensive_error = diagnostic_manager.create_comprehensive_error(
                            original_error=error,
                            context=error_context,
                            recovery_suggestions=self.get_recovery_suggestions(error)
                        )
                        
                        return Result.err(comprehensive_error)
                    
                    error_context.add_step_result(step_name, step_result.unwrap())
                
                return Result.ok({
                    "status": "completed",
                    "processing_context": error_context.to_dict()
                })
                
            except Exception as e:
                # Handle unexpected exceptions
                unexpected_error = diagnostic_manager.handle_unexpected_exception(
                    exception=e,
                    context=error_context if 'error_context' in locals() else None
                )
                
                return Result.err(unexpected_error)
        
        # Execute operation and handle results
        result = complex_operation_with_context()
        
        if result.is_ok():
            success_data = result.unwrap()
            print(f"✅ Complex operation succeeded: {success_data}")
        else:
            error = result.unwrap_err()
            print(f"❌ Complex operation failed:")
            print(f"   Error type: {error.get('error_type')}")
            print(f"   Message: {error.get('message')}")
            print(f"   Failed step: {error.get('failed_step')}")
            print(f"   Recovery suggestions: {error.get('recovery_suggestions')}")
            print(f"   System state: {error.get('system_state')}")
    
    def validate_data_input(self):
        return Result.ok("input_validated")
    
    def transform_dataset(self):
        return Result.ok("data_transformed")
    
    def analyze_data_patterns(self):
        # Simulate a failure in pattern analysis
        return Result.err({
            "error": "pattern_analysis_failed",
            "message": "Insufficient data points for reliable pattern detection",
            "min_required": 1000,
            "actual_count": 450
        })
    
    def generate_insights(self):
        return Result.ok("insights_generated")
    
    def get_recovery_suggestions(self, error):
        """Generate intelligent recovery suggestions based on error type."""
        suggestions = {
            "pattern_analysis_failed": [
                "Collect additional data points",
                "Reduce pattern complexity requirements",
                "Use alternative analysis algorithms",
                "Combine with historical data"
            ]
        }
        
        error_type = error.get("error")
        return suggestions.get(error_type, ["Review error details and contact support"])

🌐 State Management

MAPLE's distributed state management system provides enterprise-grade state synchronization across thousands of agents. The system ensures strong consistency, supports complex state operations, and includes automatic conflict resolution.

Distributed State Synchronization

# MAPLE Distributed State - Enterprise-Grade Synchronization
from maple.state import DistributedStateManager, ConsistencyLevel
from maple import Agent, Result

class StateManagementExamples:
    def setup_distributed_state(self):
        # Initialize distributed state manager
        state_manager = DistributedStateManager(
            consistency_level=ConsistencyLevel.STRONG,
            replication_factor=3,
            partition_count=64,
            conflict_resolution="last_writer_wins_with_vector_clocks"
        )
        
        # Create multiple agents that will share state
        agents = [
            Agent("inventory_manager_1"),
            Agent("inventory_manager_2"),
            Agent("order_processor_1"),
            Agent("order_processor_2"),
            Agent("logistics_coordinator")
        ]
        
        # Initialize shared inventory state
        initial_state = {
            "warehouse_a": {
                "product_001": {"quantity": 1000, "reserved": 50},
                "product_002": {"quantity": 750, "reserved": 25},
                "product_003": {"quantity": 500, "reserved": 100}
            },
            "warehouse_b": {
                "product_001": {"quantity": 800, "reserved": 30},
                "product_002": {"quantity": 1200, "reserved": 75},
                "product_003": {"quantity": 300, "reserved": 20}
            }
        }
        
        # Create distributed state with schema validation
        state_result = state_manager.create_distributed_state(
            state_id="global_inventory",
            initial_state=initial_state,
            schema={
                "type": "object",
                "properties": {
                    "warehouse_a": {"type": "object"},
                    "warehouse_b": {"type": "object"}
                },
                "required": ["warehouse_a", "warehouse_b"]
            },
            participants=agents
        )
        
        if state_result.is_ok():
            distributed_state = state_result.unwrap()
            print(f"✅ Distributed state created: {distributed_state.state_id}")
            return distributed_state
        else:
            error = state_result.unwrap_err()
            print(f"❌ State creation failed: {error}")
            return None

🏦 Industry Applications

MAPLE revolutionizes multi-agent systems across industries with capabilities impossible in other protocols. From healthcare coordination to smart manufacturing, MAPLE's resource-aware communication and type-safe error handling enable breakthrough applications.

MAPLE enables breakthrough applications across all industries

🏥 Healthcare Systems

MAPLE enables breakthrough healthcare applications that coordinate medical devices, patient monitoring systems, emergency response, and clinical workflows with unprecedented reliability and performance.

🚨 Emergency Response Coordination

# MAPLE Emergency Response System
# Coordinate 47-patient mass casualty event in real-time

emergency_coordinator = Agent(Config("hospital_emergency_ai"))
medical_personnel = [Agent(Config(f"staff_{role}_{i}")) 
                    for role in ["doctor", "nurse", "surgeon", "anesthesiologist"] 
                    for i in range(20)]

# Critical emergency coordination
emergency_response = Message(
    message_type="EMERGENCY_COORDINATION",
    priority=Priority.LIFE_CRITICAL,
    payload={
        "emergency": {
            "type": "mass_casualty_incident",
            "severity": "level_3",
            "estimated_patients": 47,
            "location": {"lat": 40.7128, "lng": -74.0060},
            "incident_time": "2024-12-13T15:45:00Z"
        },
        "resources_needed": {
            "surgeons": {"min": 8, "preferred": 15},
            "operating_rooms": {"min": 6, "preferred": 12},
            "blood_units": {"min": "200_units", "type": "O_negative"},
            "ventilators": {"min": 20, "preferred": 35},
            "trauma_supplies": "full_hospital_inventory"
        },
        "coordination": {
            "ambulance_routing": "optimal_traffic_avoidance",
            "helicopter_dispatch": 4,
            "staff_recall": "all_off_duty_personnel",
            "external_hospitals": "coordinate_overflow"
        },
        "ai_assistance": {
            "triage_ai": "severity_classification_ML",
            "resource_optimization": "genetic_algorithm",
            "outcome_prediction": "transformer_model"
        }
    }
)

🏥 Critical Patient Monitoring

# MAPLE Hospital Ecosystem Coordination
# Coordinate 200+ medical devices and staff agents

hospital_ai = Agent(Config("hospital_central_ai"))
patient_monitors = [Agent(Config(f"monitor_room_{i}")) for i in range(100)]
medical_staff = [Agent(Config(f"staff_{role}_{i}")) 
                for role in ["doctor", "nurse", "technician"] 
                for i in range(50)]

# Critical patient coordination
emergency_protocol = Message(
    message_type="EMERGENCY_PROTOCOL",
    priority=Priority.LIFE_CRITICAL,
    payload={
        "patient_id": "P-2024-12345",
        "emergency_type": "cardiac_arrest",
        "location": "room_301_bed_a",
        "vital_signs": {
            "heart_rate": 0,
            "blood_pressure": "undetectable",
            "oxygen_saturation": "70%",
            "consciousness": "unresponsive"
        },
        "required_response": {
            "personnel": {
                "cardiologist": {"count": 1, "eta": "< 2min"},
                "nurses": {"count": 3, "specialty": "critical_care"},
                "anesthesiologist": {"count": 1, "on_standby": True}
            },
            "equipment": {
                "defibrillator": {"location": "crash_cart_7", "status": "ready"},
                "ventilator": {"location": "icu_spare", "prep_time": "30s"},
                "medications": ["epinephrine", "atropine", "amiodarone"]
            },
            "facilities": {
                "operating_room": {"reserve": "OR_3", "prep_time": "5min"},
                "icu_bed": {"assign": "ICU_bed_12", "prep_time": "immediate"}
            }
        },
        "coordination": {
            "family_notification": {"contact": "emergency_contact_1", "privacy": "hipaa_compliant"},
            "medical_history": {"allergies": ["penicillin"], "conditions": ["diabetes", "hypertension"]},
            "insurance_verification": {"status": "active", "coverage": "full"}
        }
    }
)

🤖 Multi-Agent Diagnosis Systems

# Multi-Agent Diagnostic System
diagnostic_coordinator = Agent(Config("diagnostic_ai_coordinator"))
specialist_ais = {
    "radiology": Agent(Config("radiology_ai")),
    "pathology": Agent(Config("pathology_ai")),
    "cardiology": Agent(Config("cardiology_ai")),
    "neurology": Agent(Config("neurology_ai"))
}

# Complex diagnosis coordination
diagnosis_request = Message(
    message_type="MULTI_SPECIALIST_DIAGNOSIS",
    priority=Priority.HIGH,
    payload={
        "patient_data": {
            "demographics": {"age": 65, "gender": "M", "weight": "85kg"},
            "symptoms": ["chest_pain", "shortness_of_breath", "dizziness"],
            "vitals": {"bp": "180/110", "hr": "95", "temp": "98.6F"}
        },
        "diagnostic_requests": {
            "radiology": {"scan_types": ["chest_xray", "ecg", "ct_chest"]},
            "pathology": {"blood_tests": ["troponin", "bnp", "lipid_panel"]},
            "cardiology": {"analysis": "cardiac_risk_assessment"},
            "neurology": {"assessment": "cognitive_function"}
        },
        "urgency": "standard",
        "resource_requirements": {
            "compute_power": {"min": "10_TOPS", "preferred": "50_TOPS"},
            "memory": {"min": "8GB", "preferred": "32GB"},
            "processing_time": {"max": "5_minutes", "preferred": "2_minutes"}
        }
    }
)

🏥 Surgical Robot Coordination

# Surgical Robot Coordination System
surgical_coordinator = Agent(Config("surgical_suite_ai"))
surgical_robots = [
    Agent(Config("da_vinci_robot_1")),
    Agent(Config("da_vinci_robot_2")),
    Agent(Config("anesthesia_robot")),
    Agent(Config("monitoring_system"))
]

# Complex surgical coordination
surgical_protocol = Message(
    message_type="SURGICAL_COORDINATION",
    priority=Priority.LIFE_CRITICAL,
    payload={
        "procedure": {
            "type": "cardiac_bypass_surgery",
            "complexity": "high",
            "duration_estimate": "4_hours",
            "patient_id": "P-CARDIAC-789"
        },
        "robot_assignments": {
            "primary_surgeon_robot": {
                "robot_id": "da_vinci_robot_1",
                "tasks": ["vessel_grafting", "suturing"],
                "precision_requirements": "0.1mm_accuracy"
            },
            "assistant_robot": {
                "robot_id": "da_vinci_robot_2",
                "tasks": ["tissue_retraction", "cauterization"],
                "coordination_mode": "follow_primary"
            },
            "monitoring_systems": {
                "vital_signs": "continuous_cardiac_monitoring",
                "imaging": "real_time_fluoroscopy",
                "blood_analysis": "inline_gas_monitoring"
            }
        },
        "safety_protocols": {
            "emergency_stop": "<100ms_response_time",
            "human_override": "always_available",
            "backup_systems": "redundant_monitoring"
        },
        "resource_coordination": {
            "compute_allocation": {"per_robot": "50_TOPS", "total": "200_TOPS"},
            "communication_bandwidth": "10Gbps_dedicated",
            "latency_requirements": "<1ms_robot_to_robot"
        }
    }
)

📱 Patient Monitoring Networks

🔬 Clinical Research Coordination

# Clinical Research Coordination
research_coordinator = Agent(Config("clinical_research_ai"))
site_coordinators = [Agent(Config(f"site_{i}_coordinator")) for i in range(10)]
data_analysts = [Agent(Config(f"analyst_{specialty}")) 
                for specialty in ["biostatistics", "safety", "efficacy"]]

# Multi-site clinical trial coordination
trial_coordination = Message(
    message_type="CLINICAL_TRIAL_COORDINATION",
    priority=Priority.HIGH,
    payload={
        "trial_info": {
            "protocol_id": "TRIAL-CARDIAC-2024-789",
            "phase": "phase_3",
            "therapeutic_area": "cardiovascular",
            "enrollment_target": 1000
        },
        "site_coordination": {
            "active_sites": 10,
            "enrollment_status": {
                "enrolled": 750,
                "screened": 950,
                "target_per_site": 100
            },
            "data_quality": {
                "completeness": "95%",
                "query_rate": "2%",
                "monitoring_status": "on_track"
            }
        },
        "safety_monitoring": {
            "adverse_events": "real_time_reporting",
            "safety_signals": "ai_powered_detection",
            "regulatory_reporting": "automated_compliance"
        },
        "data_management": {
            "encryption": "end_to_end",
            "compliance": ["GCP", "HIPAA", "GDPR"],
            "backup_strategy": "multi_region_redundancy"
        }
    }
)

📚 API Reference

Comprehensive API reference for all MAPLE components, including agents, messages, communication patterns, and resource management.

Core Agent API

Message API

Result<T,E> API

🏆 Best Practices

Follow these best practices to maximize MAPLE's revolutionary capabilities and ensure optimal performance, security, and reliability in your multi-agent systems.

Agent Design Patterns

# MAPLE Best Practice: Single Responsibility Agent Design
from maple import Agent, Message, Result
from maple.patterns import SingleResponsibilityAgent

class DataProcessorAgent(SingleResponsibilityAgent):
    """Agent focused solely on data processing tasks."""
    
    def __init__(self):
        super().__init__(
            agent_id="data_processor_specialized",
            responsibility="data_transformation_and_analysis",
            capabilities=["data_cleaning", "statistical_analysis", "format_conversion"]
        )
    
    def handle_data_processing_request(self, message):
        """Handle data processing with comprehensive validation."""
        # Validate input data
        validation_result = self.validate_input_data(message.payload)
        if validation_result.is_err():
            return validation_result
        
        # Process data with resource awareness
        processing_result = self.process_data_with_resources(
            data=message.payload["data"],
            processing_type=message.payload["processing_type"],
            resource_constraints=message.payload.get("resources", {})
        )
        
        return processing_result
    
    def validate_input_data(self, payload):
        """Comprehensive input validation."""
        required_fields = ["data", "processing_type"]
        
        for field in required_fields:
            if field not in payload:
                return Result.err({
                    "error": "missing_required_field",
                    "field": field,
                    "message": f"Required field '{field}' is missing",
                    "recovery_suggestion": f"Please include the '{field}' field in your request"
                })
        
        # Validate data format
        if not isinstance(payload["data"], (list, dict)):
            return Result.err({
                "error": "invalid_data_format",
                "expected": "list or dict",
                "actual": type(payload["data"]).__name__,
                "recovery_suggestion": "Convert data to list or dictionary format"
            })
        
        return Result.ok(payload)

Message Design Best Practices

# MAPLE Best Practice: Structured Message Design
from maple import Message, Priority
from maple.validation import MessageSchema

class MessageDesignExamples:
    def create_well_structured_message(self):
        """Demonstrate best practices for message structure."""
        
        # Define message schema for validation
        processing_request_schema = MessageSchema(
            message_type="DATA_PROCESSING_REQUEST",
            required_fields=["data", "processing_type", "output_format"],
            optional_fields=["priority_level", "deadline", "callback_agent"],
            field_types={
                "data": ["list", "dict"],
                "processing_type": "string",
                "output_format": "string",
                "priority_level": "string",
                "deadline": "datetime",
                "callback_agent": "string"
            }
        )
        
        # Create well-structured message
        message = Message(
            message_type="DATA_PROCESSING_REQUEST",
            receiver="data_processor_specialized",
            priority=Priority.HIGH,
            payload={
                # Required fields with clear structure
                "data": {
                    "raw_values": [1.2, 3.4, 5.6, 7.8, 9.0],
                    "metadata": {
                        "source": "sensor_array_01",
                        "timestamp": "2024-12-25T18:00:00Z",
                        "quality_score": 0.95
                    }
                },
                "processing_type": "statistical_analysis",
                "output_format": "json_summary",
                
                # Optional fields for enhanced functionality
                "priority_level": "high",
                "deadline": "2024-12-25T18:05:00Z",
                "callback_agent": "results_aggregator",
                
                # Include processing preferences
                "processing_preferences": {
                    "include_confidence_intervals": True,
                    "statistical_tests": ["normality", "correlation"],
                    "visualization": False
                },
                
                # Resource requirements (MAPLE's unique capability)
                "resources": ResourceRequest(
                    compute=ResourceRange(min=2, preferred=4, max=8),
                    memory=ResourceRange(min="1GB", preferred="2GB", max="4GB"),
                    deadline="2024-12-25T18:05:00Z"
                ).to_dict()
            },
            correlation_id="analysis_batch_2024_001",
            expires_at=datetime.now() + timedelta(minutes=10)
        )
        
        # Validate message against schema
        validation_result = processing_request_schema.validate(message)
        
        if validation_result.is_ok():
            print("✅ Message structure validation successful")
            return message
        else:
            error = validation_result.unwrap_err()
            print(f"❌ Message validation failed: {error}")
            return None

Error Handling Best Practices

# MAPLE Best Practice: Comprehensive Error Handling
from maple import Result
from maple.error import ErrorSeverity, ErrorCategory

class ErrorHandlingBestPractices:
    def demonstrate_comprehensive_error_handling(self):
        """Show best practices for MAPLE error handling."""
        
        def process_critical_operation(data):
            """Example of comprehensive error handling."""
            
            # Step 1: Input validation
            validation_result = self.validate_critical_input(data)
            if validation_result.is_err():
                return validation_result.map_err(lambda e: {
                    **e,
                    "severity": ErrorSeverity.HIGH,
                    "category": ErrorCategory.VALIDATION,
                    "recovery_suggestions": [
                        "Verify input data format",
                        "Check data completeness",
                        "Validate data ranges"
                    ]
                })
            
            # Step 2: Resource allocation
            resource_result = self.allocate_processing_resources(data)
            if resource_result.is_err():
                return resource_result.map_err(lambda e: {
                    **e,
                    "severity": ErrorSeverity.CRITICAL,
                    "category": ErrorCategory.RESOURCE,
                    "recovery_suggestions": [
                        "Retry with lower resource requirements",
                        "Schedule for later execution",
                        "Scale up system resources"
                    ]
                })
            
            # Step 3: Processing with monitoring
            processing_result = self.process_with_monitoring(data)
            if processing_result.is_err():
                return processing_result.map_err(lambda e: {
                    **e,
                    "severity": ErrorSeverity.MEDIUM,
                    "category": ErrorCategory.PROCESSING,
                    "recovery_suggestions": [
                        "Retry with different algorithm",
                        "Process in smaller batches",
                        "Use fallback processing method"
                    ]
                })
            
            # Step 4: Results validation
            results_validation = self.validate_processing_results(processing_result.unwrap())
            if results_validation.is_err():
                return results_validation.map_err(lambda e: {
                    **e,
                    "severity": ErrorSeverity.HIGH,
                    "category": ErrorCategory.VALIDATION,
                    "original_processing_successful": True,
                    "recovery_suggestions": [
                        "Re-run processing with stricter parameters",
                        "Apply additional validation filters",
                        "Review processing algorithm"
                    ]
                })
            
            return Result.ok({
                "status": "success",
                "data": processing_result.unwrap(),
                "processing_metadata": {
                    "steps_completed": 4,
                    "validation_passed": True,
                    "resource_efficiency": 0.87
                }
            })
        
        # Execute with comprehensive error handling
        test_data = {"values": [1, 2, 3, 4, 5], "type": "numerical"}
        
        result = process_critical_operation(test_data)
        
        if result.is_ok():
            success_data = result.unwrap()
            print(f"✅ Critical operation successful: {success_data['status']}")
        else:
            error = result.unwrap_err()
            print(f"❌ Critical operation failed:")
            print(f"   Severity: {error.get('severity')}")
            print(f"   Category: {error.get('category')}")
            print(f"   Message: {error.get('message')}")
            print(f"   Recovery suggestions:")
            for suggestion in error.get('recovery_suggestions', []):
                print(f"     - {suggestion}")

🔧 Troubleshooting

Common issues and solutions when working with MAPLE. This guide covers the most frequent problems and provides step-by-step resolution approaches.

Common Issues and Solutions

# Check broker connectivity
from maple.diagnostics import BrokerDiagnostics

diagnostics = BrokerDiagnostics()
connection_result = diagnostics.test_broker_connection(
    broker_url="nats://localhost:4222"
)

if connection_result.is_err():
    error = connection_result.unwrap_err()
    print(f"Connection failed: {error['message']}")
    print(f"Suggested fixes: {error['suggested_fixes']}")

# Debug message delivery
from maple.diagnostics import MessageTracer

tracer = MessageTracer()
delivery_result = tracer.trace_message_delivery(
    message_id="msg_12345",
    include_routing_info=True
)

if delivery_result.is_ok():
    trace = delivery_result.unwrap()
    print(f"Message trace: {trace['delivery_path']}")
    print(f"Status: {trace['final_status']}")
else:
    print(f"Tracing failed: {delivery_result.unwrap_err()}")

# Performance analysis and optimization
from maple.diagnostics import PerformanceAnalyzer

analyzer = PerformanceAnalyzer()
perf_result = analyzer.analyze_system_performance(
    duration="5m",
    include_recommendations=True
)

if perf_result.is_ok():
    analysis = perf_result.unwrap()
    print(f"Throughput: {analysis['current_throughput']} msg/sec")
    print(f"Latency: {analysis['average_latency']}ms")
    print("Optimization recommendations:")
    for rec in analysis['recommendations']:
        print(f"  - {rec}")


# MAPLE Comprehensive Diagnostic Suite
from maple.diagnostics import SystemDiagnostics

class TroubleshootingTools:
    def run_complete_diagnostics(self):
        """Run comprehensive system diagnostics."""
        
        diagnostics = SystemDiagnostics()
        
        # Run all diagnostic tests
        diagnostic_result = diagnostics.run_complete_suite(
            tests=[
                "broker_connectivity",
                "agent_registration",
                "message_routing",
                "resource_availability",
                "security_validation",
                "performance_baseline"
            ]
        )
        
        if diagnostic_result.is_ok():
            results = diagnostic_result.unwrap()
            
            print("🔍 MAPLE System Diagnostics Report")
            print("=" * 50)
            
            for test_name, test_result in results.items():
                status = "✅" if test_result['passed'] else "❌"
                print(f"{status} {test_name}: {test_result['status']}")
                
                if not test_result['passed']:
                    print(f"   Issue: {test_result['issue']}")
                    print(f"   Solution: {test_result['solution']}")
            
            # Overall system health
            overall_health = results['overall_health']
            print(f"\n🏅 Overall System Health: {overall_health['score']}/100")
            
            if overall_health['issues']:
                print("\n⚠️ Priority Issues to Address:")
                for issue in overall_health['issues']:
                    print(f"   - {issue}")
        else:
            error = diagnostic_result.unwrap_err()
            print(f"❌ Diagnostics failed: {error}")



            "paramedics": {"count": 2, "certification": "advanced_life_support"},
            "hospital_bed": {"type": "cardiac_unit", "notify_cardiologist": True}
        },
        "coordination": {
            "traffic_preemption": True,
            "hospital_notification": "immediate",
            "medical_history_access": True
        }
    }
)

# MAPLE ensures critical message delivery
result = emergency_coordinator.broadcast_critical(emergency_alert)
if result.is_ok():
    response_data = result.unwrap()
    print(f"✅ Emergency response activated")
    print(f"   🚑 Ambulance ETA: {response_data['ambulance_eta']}")
    print(f"   🏥 Hospital: {response_data['receiving_hospital']}")
    print(f"   ⏱️ Response time: {response_data['total_response_time']}")
else:
    error = result.unwrap_err()
    print(f"🚨 CRITICAL ESCALATION: {error['escalation_protocol']}")

# Medical Diagnosis Coordination
diagnosis_coordinator = Agent("medical_diagnosis_coordinator")

# Specialized medical AI agents
medical_agents = {
    "radiologist_ai": Agent("radiology_specialist"),
    "pathologist_ai": Agent("pathology_specialist"),
    "cardiologist_ai": Agent("cardiology_specialist"),
    "oncologist_ai": Agent("oncology_specialist")
}

# Patient case with comprehensive data
patient_case = Message(
    message_type="DIAGNOSIS_REQUEST",
    payload={
        "patient_id": "P-2024-12345",
        "chief_complaint": "chest pain and shortness of breath",
        "diagnostic_data": {
            "imaging": {
                "chest_xray": "base64_encoded_image_data",
                "ct_scan": "dicom_file_reference",
                "ecg": "12_lead_ecg_data"
            },
            "lab_results": {
                "troponin": {"value": 0.8, "unit": "ng/mL", "normal_range": "<0.04"},
                "bnp": {"value": 450, "unit": "pg/mL", "normal_range": "<100"},
                "d_dimer": {"value": 2.1, "unit": "mg/L", "normal_range": "<0.5"}
            },
            "vital_signs": {
                "blood_pressure": "160/95 mmHg",
                "heart_rate": "110 bpm",
                "respiratory_rate": "24/min",
                "oxygen_saturation": "92%"
            }
        },
        "analysis_requirements": {
            "urgency": "HIGH",
            "confidence_threshold": 0.85,
            "differential_diagnosis": True,
            "treatment_recommendations": True
        },
        "resources": {
            "ai_compute": {"min": 8, "preferred": 16},
            "gpu_memory": {"min": "16GB", "preferred": "32GB"},
            "processing_timeout": "5min"
        }
    }
)

# Distribute case to specialized AI agents
diagnosis_results = []
for specialist, agent in medical_agents.items():
    result = agent.analyze_case(patient_case)
    if result.is_ok():
        analysis = result.unwrap()
        diagnosis_results.append({
            "specialist": specialist,
            "diagnosis": analysis["primary_diagnosis"],
            "confidence": analysis["confidence"],
            "recommendations": analysis["treatment_plan"]
        })

# Aggregate results with confidence weighting
final_diagnosis = diagnosis_coordinator.aggregate_diagnoses(diagnosis_results)
print(f"🔬 Final Diagnosis: {final_diagnosis['consensus_diagnosis']}")
print(f"📊 Confidence: {final_diagnosis['overall_confidence']:.1%}")
print(f"💊 Treatment Plan: {final_diagnosis['recommended_treatment']}")

Surgical Robot Coordination

Coordinate multiple surgical robots with sub-millimeter precision and zero-failure tolerance.

# Multi-robot surgical coordination
surgical_coordinator = Agent("surgical_coordinator")

# Coordinate multiple surgical robots
surgical_operation = Message(
    message_type="SURGICAL_COORDINATION",
    priority=Priority.CRITICAL,
    payload={
        "procedure": "minimally_invasive_cardiac_surgery",
        "robots": {
            "primary_surgeon": {
                "robot_id": "da_vinci_01",
                "instruments": ["forceps", "cautery", "suction"],
                "position": {"x": 150.5, "y": 200.3, "z": 45.7}
            },
            "assistant_surgeon": {
                "robot_id": "da_vinci_02",
                "instruments": ["retractor", "irrigation"],
                "position": {"x": 180.2, "y": 195.8, "z": 42.1}
            },
            "camera_robot": {
                "robot_id": "endoscope_01",
                "instruments": ["3d_camera", "led_illumination"],
                "position": {"x": 165.0, "y": 210.0, "z": 55.0}
            }
        },
        "precision_requirements": {
            "positioning_accuracy": "0.1mm",
            "tremor_compensation": True,
            "collision_avoidance": "real_time",
            "force_feedback": "haptic"
        },
        "safety_constraints": {
            "vital_monitoring": "continuous",
            "emergency_stop": "<100ms",
            "redundancy_level": "triple",
            "surgeon_override": "always_available"
        },
        "resources": {
            "compute_latency": "<1ms",
            "sensor_fusion": "real_time",
            "backup_systems": "hot_standby"
        }
    }
)

# Execute with surgical precision
surgical_result = surgical_coordinator.coordinate_surgery(surgical_operation)
if surgical_result.is_ok():
    status = surgical_result.unwrap()
    print(f"✅ Surgical coordination active")
    print(f"   🎯 Precision: {status['positioning_accuracy']}")
    print(f"   🔒 Safety status: {status['safety_systems']}")
    print(f"   ⏱️ Latency: {status['control_latency']}")

Patient Monitoring Networks

Real-time coordination of patient monitoring systems across entire hospital networks.

# Hospital patient monitoring system
monitor_coordinator = Agent("patient_monitor_coordinator")

# Real-time patient monitoring coordination
monitoring_alert = Message(
    message_type="PATIENT_MONITORING_ALERT",
    priority=Priority.HIGH,
    payload={
        "ward": "cardiac_icu",
        "patient_alerts": [
            {
                "patient_id": "P-001",
                "bed": "ICU-01",
                "alert_type": "arrhythmia_detected",
                "severity": "moderate",
                "vital_signs": {
                    "heart_rate": 145,
                    "blood_pressure": "180/110",
                    "oxygen_saturation": 94,
                    "respiratory_rate": 22
                },
                "recommended_actions": [
                    "notify_cardiologist",
                    "prepare_antiarrhythmic",
                    "increase_monitoring_frequency"
                ]
            }
        ],
        "resource_requirements": {
            "nursing_staff": {"additional": 1, "specialization": "cardiac"},
            "equipment": ["portable_ecg", "defibrillator"],
            "medication": ["amiodarone", "metoprolol"]
        },
        "coordination_needs": {
            "pharmacy_notification": True,
            "family_update": True,
            "attending_physician": "immediate"
        }
    }
)

# Coordinate hospital-wide response
response_result = monitor_coordinator.coordinate_response(monitoring_alert)
if response_result.is_ok():
    response = response_result.unwrap()
    print(f"✅ Hospital response coordinated")
    print(f"   👨‍⚕️ Cardiologist notified: {response['cardiologist_eta']}")
    print(f"   💊 Medication prepared: {response['medication_ready']}")
    print(f"   📞 Family contacted: {response['family_notification']}")

🔬 Clinical Research Coordination

# Clinical Trial Coordination System
clinical_coordinator = Agent("clinical_trial_coordinator")

# Patient eligibility screening
patient_screening = Message(
    message_type="ELIGIBILITY_SCREENING",
    payload={
        "trial_id": "TRIAL-2024-CV-001",
        "patient_demographics": {
            "age": 45,
            "gender": "female",
            "medical_history": ["hypertension", "type_2_diabetes"]
        },
        "inclusion_criteria": {
            "age_range": {"min": 18, "max": 75},
            "conditions": ["cardiovascular_disease"],
            "medications": ["statins", "ace_inhibitors"]
        },
        "exclusion_criteria": {
            "pregnancy": False,
            "recent_surgery": False,
            "other_trials": False
        },
        "screening_requirements": {
            "lab_tests": ["lipid_panel", "hba1c", "kidney_function"],
            "imaging": ["echocardiogram", "stress_test"],
            "questionnaires": ["quality_of_life", "symptom_assessment"]
        }
    }
)

# MAPLE coordinates comprehensive screening process
screening_result = clinical_coordinator.screen_patient(patient_screening)
if screening_result.is_ok():
    eligibility = screening_result.unwrap()
    if eligibility['eligible']:
        print(f"✅ Patient eligible for trial {eligibility['trial_id']}")
        print(f"📅 Next visit: {eligibility['next_appointment']}")
        print(f"📋 Required tests: {eligibility['required_tests']}")
    else:
        print(f"❌ Patient not eligible: {eligibility['reasons']}")
        print(f"💡 Alternative trials: {eligibility['alternative_trials']}")

🏭 Manufacturing

Transform manufacturing with intelligent production coordination, quality control, and predictive maintenance using MAPLE's revolutionary multi-agent capabilities.

🔧 Production Line Optimization

# Smart Factory Production Coordination
from maple import Agent, Message, Priority

# Factory coordinator managing entire production floor
factory_coordinator = Agent("smart_factory_coordinator")

# Production optimization message
production_optimization = Message(
    message_type="PRODUCTION_LINE_OPTIMIZATION",
    priority=Priority.HIGH,
    payload={
        "factory_id": "PLANT_001",
        "production_lines": {
            "line_a": {
                "product": "semiconductor_chips",
                "target_throughput": 1000,
                "current_efficiency": 0.87,
                "bottleneck_stations": ["etching", "testing"]
            },
            "line_b": {
                "product": "circuit_boards",
                "target_throughput": 500,
                "current_efficiency": 0.92,
                "quality_issues": ["solder_joints", "component_placement"]
            }
        },
        "optimization_targets": {
            "throughput_increase": 0.15,
            "quality_improvement": 0.05,
            "energy_reduction": 0.10,
            "downtime_minimization": 0.20
        },
        "resource_constraints": {
            "max_power_draw": "500kW",
            "available_operators": 24,
            "raw_material_inventory": {
                "silicon_wafers": 5000,
                "copper_sheets": 200,
                "resistors": 50000
            }
        },
        "coordination_requirements": {
            "real_time_adjustments": True,
            "predictive_maintenance": True,
            "quality_feedback_loop": True,
            "supply_chain_integration": True
        }
    }
)

# Execute factory optimization
optimization_result = factory_coordinator.optimize_production(production_optimization)
if optimization_result.is_ok():
    optimization = optimization_result.unwrap()
    print(f"✅ Production optimization complete")
    print(f"   📈 Throughput increase: {optimization['throughput_improvement']:.1%}")
    print(f"   🎯 Quality improvement: {optimization['quality_improvement']:.1%}")
    print(f"   ⚡ Energy savings: {optimization['energy_savings']:.1%}")
    print(f"   ⏱️ Estimated ROI: ${optimization['estimated_savings']:,.2f}/month")
else:
    error = optimization_result.unwrap_err()
    print(f"❌ Optimization failed: {error['message']}")

# Multi-Robot Assembly Coordination
robotic_coordinator = Agent("robotic_assembly_coordinator")

# Complex assembly task coordination
assembly_coordination = Message(
    message_type="ROBOTIC_ASSEMBLY_TASK",
    priority=Priority.HIGH,
    payload={
        "assembly_line": "automotive_engine_block",
        "robots": {
            "robot_arm_01": {
                "position": {"x": 150.5, "y": 200.3, "z": 45.7},
                "tools": ["precision_gripper", "torque_wrench"],
                "task": "cylinder_head_installation",
                "precision_requirement": "±0.05mm"
            },
            "robot_arm_02": {
                "position": {"x": 180.2, "y": 195.8, "z": 42.1},
                "tools": ["welding_torch", "fume_extractor"],
                "task": "seam_welding",
                "quality_parameters": {
                    "weld_penetration": "3-5mm",
                    "heat_affected_zone": "<10mm"
                }
            },
            "quality_inspector": {
                "position": {"x": 165.0, "y": 210.0, "z": 55.0},
                "sensors": ["3d_vision", "ultrasonic_testing"],
                "inspection_criteria": {
                    "dimensional_tolerance": "±0.1mm",
                    "surface_roughness": "Ra 1.6",
                    "weld_quality": "ISO 5817 B"
                }
            }
        },
        "synchronization": {
            "timing_precision": "±10ms",
            "collision_avoidance": "real_time",
            "force_feedback": "enabled",
            "adaptive_speed": True
        },
        "quality_control": {
            "in_process_monitoring": True,
            "defect_detection": "AI_vision",
            "automatic_rework": True,
            "traceability": "full_genealogy"
        },
        "resources": {
            "cycle_time_target": "45s",
            "throughput_target": "80_units_per_hour",
            "quality_target": "99.5%_first_pass_yield"
        }
    }
)

# Execute robotic assembly coordination
assembly_result = robotic_coordinator.coordinate_assembly(assembly_coordination)
if assembly_result.is_ok():
    status = assembly_result.unwrap()
    print(f"✅ Robotic assembly coordinated")
    print(f"   🎯 Precision achieved: {status['actual_precision']}")
    print(f"   ⏱️ Cycle time: {status['cycle_time']}s")
    print(f"   🏆 Quality score: {status['quality_score']:.1%}")
else:
    error = assembly_result.unwrap_err()
    print(f"❌ Assembly coordination failed: {error['message']}")

🔍 Quality Control Automation

# Real-Time Quality Control System
quality_coordinator = Agent("quality_control_coordinator")

# Quality monitoring and control
quality_monitoring = Message(
    message_type="QUALITY_CONTROL_MONITORING",
    priority=Priority.HIGH,
    payload={
        "production_batch": "BATCH_2024_001",
        "quality_stations": {
            "incoming_inspection": {
                "inspection_type": "dimensional_measurement",
                "measurement_tools": ["cmm_machine", "optical_comparator"],
                "acceptance_criteria": {
                    "dimensional_tolerance": "±0.02mm",
                    "surface_finish": "Ra 0.8",
                    "geometric_tolerance": "GD&T_per_drawing"
                },
                "sample_size": "100%_inspection"
            },
            "in_process_monitoring": {
                "monitoring_type": "statistical_process_control",
                "control_charts": ["x_bar_r", "p_chart", "c_chart"],
                "measurement_frequency": "every_5_minutes",
                "automatic_adjustments": True
            },
            "final_inspection": {
                "inspection_type": "comprehensive_validation",
                "test_protocols": [
                    "functional_testing",
                    "environmental_stress_screening",
                    "burn_in_testing"
                ],
                "acceptance_criteria": {
                    "functional_performance": "100%_specification",
                    "reliability_target": "MTBF_>_50000_hours"
                }
            }
        },
        "defect_classification": {
            "critical_defects": "zero_tolerance",
            "major_defects": "<0.1%_rate",
            "minor_defects": "<1.0%_rate"
        },
        "corrective_actions": {
            "automatic_rework": True,
            "process_adjustment": "real_time",
            "root_cause_analysis": "AI_assisted",
            "preventive_measures": "predictive_analytics"
        },
        "traceability": {
            "material_genealogy": "full_tracking",
            "process_parameters": "continuous_logging",
            "operator_records": "digital_signatures",
            "environmental_conditions": "monitored"
        }
    }
)

# Execute quality control coordination
quality_result = quality_coordinator.monitor_quality(quality_monitoring)
if quality_result.is_ok():
    quality_status = quality_result.unwrap()
    print(f"✅ Quality monitoring active")
    print(f"   📊 Current yield: {quality_status['first_pass_yield']:.1%}")
    print(f"   🎯 Defect rate: {quality_status['defect_rate']:.3%}")
    print(f"   📈 Process capability: Cpk {quality_status['process_capability']:.2f}")
    
    if quality_status['corrective_actions']:
        print(f"   🔧 Active corrections:")
        for action in quality_status['corrective_actions']:
            print(f"      • {action['description']}: {action['status']}")
else:
    error = quality_result.unwrap_err()
    print(f"❌ Quality monitoring failed: {error['message']}")

🔮 Predictive Maintenance

# Predictive Maintenance System
maintenance_coordinator = Agent("predictive_maintenance_coordinator")

# Equipment health monitoring
equipment_monitoring = Message(
    message_type="EQUIPMENT_HEALTH_MONITORING",
    priority=Priority.HIGH,
    payload={
        "facility": "manufacturing_plant_001",
        "equipment_fleet": {
            "cnc_machine_01": {
                "equipment_type": "5_axis_machining_center",
                "sensors": {
                    "vibration": {"x_axis": 2.1, "y_axis": 1.8, "z_axis": 2.3},
                    "temperature": {"spindle": 65.2, "coolant": 22.1, "hydraulic": 45.6},
                    "current_draw": {"spindle_motor": 45.2, "feed_motors": 12.8},
                    "acoustic": {"overall_level": 78.5, "bearing_frequency": "normal"}
                },
                "operating_conditions": {
                    "utilization_rate": 0.87,
                    "cycle_count": 125847,
                    "cutting_hours": 2847.5,
                    "last_maintenance": "2024-11-15"
                },
                "performance_indicators": {
                    "dimensional_accuracy": "±0.005mm",
                    "surface_finish": "Ra 0.4",
                    "tool_life": "87%_of_expected"
                }
            },
            "robotic_cell_02": {
                "equipment_type": "6_dof_industrial_robot",
                "health_indicators": {
                    "joint_wear": [0.12, 0.08, 0.15, 0.09, 0.11, 0.07],
                    "gear_backlash": [0.002, 0.001, 0.003, 0.002, 0.001, 0.002],
                    "power_consumption": "normal_range",
                    "positioning_accuracy": "±0.02mm"
                },
                "maintenance_history": {
                    "last_calibration": "2024-10-20",
                    "next_scheduled_pm": "2024-12-30",
                    "critical_components": {
                        "reducer_gears": "good_condition",
                        "servo_motors": "excellent_condition",
                        "cables": "monitor_closely"
                    }
                }
            }
        },
        "predictive_analytics": {
            "failure_prediction_horizon": "30_days",
            "confidence_threshold": 0.85,
            "maintenance_optimization": "cost_vs_reliability",
            "spare_parts_planning": "just_in_time"
        },
        "maintenance_strategy": {
            "condition_based": True,
            "time_based_backup": True,
            "opportunistic_maintenance": True,
            "predictive_algorithms": ["machine_learning", "physics_based_models"]
        }
    }
)

# Execute predictive maintenance analysis
maintenance_result = maintenance_coordinator.analyze_equipment_health(equipment_monitoring)
if maintenance_result.is_ok():
    maintenance_status = maintenance_result.unwrap()
    print(f"✅ Predictive maintenance analysis complete")
    print(f"   🏥 Overall equipment health: {maintenance_status['overall_health_score']}/100")
    print(f"   ⚠️ Equipment requiring attention: {len(maintenance_status['attention_required'])}")
    
    if maintenance_status['predictions']:
        print(f"   🔮 Maintenance predictions:")
        for prediction in maintenance_status['predictions']:
            print(f"      • {prediction['equipment']}: {prediction['action']} in {prediction['timeframe']}")
    
    print(f"   💰 Estimated cost savings: ${maintenance_status['cost_savings']:,.2f}")
else:
    error = maintenance_result.unwrap_err()
    print(f"❌ Maintenance analysis failed: {error['message']}")

💰 Financial Systems

Enable next-generation financial services with high-frequency trading, fraud detection, and comprehensive risk management through MAPLE's revolutionary agent coordination.

⚡ High-Frequency Trading

# High-Frequency Trading System
from maple import Agent, Message, Priority

# Trading coordinator with microsecond precision
trading_coordinator = Agent("hft_trading_coordinator")

# Market signal processing with extreme performance
market_signal = Message(
    message_type="MARKET_SIGNAL_ANALYSIS",
    priority=Priority.CRITICAL,
    payload={
        "market_data": {
            "symbol": "AAPL",
            "price": 185.50,
            "volume": 1000000,
            "bid_ask_spread": 0.01,
            "market_depth": {
                "bids": [[185.49, 5000], [185.48, 10000]],
                "asks": [[185.51, 7500], [185.52, 12000]]
            }
        },
        "analysis_requirements": {
            "latency_budget": "100μs",
            "confidence_threshold": 0.95,
            "risk_limits": {
                "max_position": 10000,
                "max_loss": 50000,
                "var_limit": 100000
            }
        },
        "trading_strategy": {
            "algorithm": "momentum_arbitrage",
            "parameters": {
                "momentum_threshold": 0.02,
                "mean_reversion_period": "5min",
                "volatility_adjustment": True
            }
        },
        "resources": {
            "cpu_cores": {"min": 8, "reserved": True},
            "memory": {"min": "32GB", "latency": "DDR5"},
            "network": {"latency": "<1ms", "jitter": "<10μs"},
            "co_location": "nyse_data_center"
        }
    }
)

# Execute with guaranteed ultra-low latency
trading_result = trading_coordinator.execute_critical(market_signal)
if trading_result.is_ok():
    decision = trading_result.unwrap()
    print(f"✅ Trade decision: {decision['action']} {decision['quantity']} shares")
    print(f"   ⚡ Execution time: {decision['latency_μs']}μs")
    print(f"   📊 Confidence: {decision['confidence']:.2%}")
    print(f"   💰 Expected profit: ${decision['expected_profit']:,.2f}")
else:
    error = trading_result.unwrap_err()
    print(f"❌ Trading decision failed: {error['message']}")

# Multi-Agent Risk Assessment System
risk_coordinator = Agent("portfolio_risk_coordinator")

# Comprehensive risk analysis across portfolio
risk_analysis = Message(
    message_type="PORTFOLIO_RISK_ANALYSIS",
    priority=Priority.HIGH,
    payload={
        "portfolio": {
            "total_value": 50000000,
            "positions": [
                {"symbol": "AAPL", "quantity": 10000, "market_value": 1855000},
                {"symbol": "GOOGL", "quantity": 5000, "market_value": 1425000},
                {"symbol": "TSLA", "quantity": 8000, "market_value": 2000000},
                {"symbol": "SPY", "quantity": 15000, "market_value": 7125000}
            ],
            "derivatives": [
                {"type": "call_option", "underlying": "AAPL", "contracts": 100, "strike": 190},
                {"type": "put_option", "underlying": "SPY", "contracts": 200, "strike": 470}
            ]
        },
        "risk_metrics": {
            "value_at_risk": {
                "confidence_level": 0.95,
                "time_horizon": "1_day",
                "methodology": "monte_carlo"
            },
            "stress_testing": {
                "scenarios": ["market_crash_2008", "covid_2020", "dot_com_2000"],
                "custom_shocks": {"equity_down": -0.30, "volatility_up": 2.0}
            },
            "concentration_risk": {
                "sector_limits": {"technology": 0.40, "finance": 0.25},
                "single_name_limit": 0.10
            }
        },
        "risk_agents": {
            "market_risk_agent": {
                "capabilities": ["var_calculation", "stress_testing", "correlation_analysis"],
                "models": ["garch", "copula", "monte_carlo"]
            },
            "credit_risk_agent": {
                "capabilities": ["default_probability", "credit_spread_analysis"],
                "data_sources": ["moody", "sp", "fitch"]
            },
            "liquidity_risk_agent": {
                "capabilities": ["bid_ask_analysis", "market_impact", "funding_liquidity"],
                "metrics": ["amihud_illiquidity", "roll_spread"]
            }
        },
        "resources": {
            "compute": {"min": 16, "preferred": 32},
            "memory": {"min": "64GB", "preferred": "128GB"},
            "market_data": {"real_time": True, "historical": "10_years"},
            "processing_deadline": "30s"
        }
    }
)

# Execute comprehensive risk analysis
risk_result = risk_coordinator.analyze_portfolio_risk(risk_analysis)
if risk_result.is_ok():
    risk_report = risk_result.unwrap()
    print(f"✅ Portfolio risk analysis complete")
    print(f"   📉 VaR (95%, 1-day): ${risk_report['var_95_1d']:,.2f}")
    print(f"   📈 Stress test loss: ${risk_report['max_stress_loss']:,.2f}")
    print(f"   ⚠️ Risk score: {risk_report['overall_risk_score']}/10")
    
    if risk_report['risk_alerts']:
        print(f"   🚨 Risk alerts:")
        for alert in risk_report['risk_alerts']:
            print(f"      • {alert['type']}: {alert['message']}")
    
    print(f"   📊 Recommendations:")
    for rec in risk_report['recommendations']:
        print(f"      • {rec['action']}: {rec['reason']}")
else:
    error = risk_result.unwrap_err()
    print(f"❌ Risk analysis failed: {error['message']}")

🔍 Fraud Detection Systems

# Multi-Agent Fraud Detection System
fraud_coordinator = Agent("fraud_detection_coordinator")

# Real-time transaction monitoring
transaction_analysis = Message(
    message_type="FRAUD_DETECTION_ANALYSIS",
    priority=Priority.HIGH,
    payload={
        "transaction": {
            "transaction_id": "TXN_2024_1225_001",
            "amount": 15000.00,
            "currency": "USD",
            "transaction_type": "wire_transfer",
            "timestamp": "2024-12-25T18:00:00Z",
            "source_account": {
                "account_id": "ACC_123456",
                "customer_id": "CUST_789012",
                "account_type": "checking",
                "balance": 45000.00
            },
            "destination_account": {
                "account_id": "ACC_654321",
                "bank": "International_Bank_XYZ",
                "country": "Switzerland",
                "swift_code": "IBKXCH22"
            }
        },
        "customer_profile": {
            "customer_id": "CUST_789012",
            "risk_score": 3.2,
            "account_age": "5_years",
            "transaction_history": {
                "avg_monthly_volume": 25000.00,
                "max_single_transaction": 8500.00,
                "international_transfers": 12
            },
            "behavioral_patterns": {
                "typical_transaction_times": ["09:00-17:00"],
                "frequent_destinations": ["domestic_banks"],
                "seasonal_patterns": ["holiday_spending"]
            }
        },
        "detection_agents": {
            "pattern_analysis_agent": {
                "capabilities": ["anomaly_detection", "behavioral_analysis"],
                "algorithms": ["isolation_forest", "lstm_autoencoder"]
            },
            "rule_engine_agent": {
                "capabilities": ["compliance_rules", "threshold_checks"],
                "rule_sets": ["aml_rules", "kyc_rules", "sanctions_screening"]
            },
            "network_analysis_agent": {
                "capabilities": ["graph_analysis", "entity_linking"],
                "algorithms": ["community_detection", "centrality_analysis"]
            }
        },
        "external_data": {
            "sanctions_lists": ["ofac", "eu_sanctions", "un_sanctions"],
            "pep_lists": ["world_check", "dow_jones"],
            "adverse_media": {"sources": ["news_feeds", "regulatory_actions"]}
        },
        "analysis_requirements": {
            "response_time": "<2s",
            "confidence_threshold": 0.80,
            "false_positive_tolerance": 0.05
        }
    }
)

# Execute real-time fraud detection
fraud_result = fraud_coordinator.analyze_transaction(transaction_analysis)
if fraud_result.is_ok():
    analysis = fraud_result.unwrap()
    print(f"✅ Fraud detection analysis complete")
    print(f"   📏 Risk score: {analysis['fraud_score']:.2f}/10")
    print(f"   🏁 Confidence: {analysis['confidence']:.1%}")
    print(f"   ⏱️ Analysis time: {analysis['processing_time_ms']}ms")
    
    if analysis['fraud_indicators']:
        print(f"   ⚠️ Fraud indicators detected:")
        for indicator in analysis['fraud_indicators']:
            print(f"      • {indicator['type']}: {indicator['description']} (Score: {indicator['score']:.1f})")
    
    print(f"   📌 Recommended action: {analysis['recommended_action']}")
    
    if analysis['recommended_action'] == 'BLOCK':
        print(f"   🚨 Transaction blocked for manual review")
        print(f"   📞 Alert sent to fraud investigation team")
else:
    error = fraud_result.unwrap_err()
    print(f"❌ Fraud detection failed: {error['message']}")

📊 Algorithmic Trading

# Multi-Strategy Algorithmic Trading
trading_orchestrator = Agent("algo_trading_orchestrator")

# Coordinate multiple trading strategies
strategy_coordination = Message(
    message_type="MULTI_STRATEGY_COORDINATION",
    payload={
        "trading_session": {
            "session_id": "SESSION_2024_1225",
            "market_hours": "09:30-16:00_EST",
            "available_capital": 10000000,
            "risk_budget": 200000
        },
        "active_strategies": {
            "momentum_strategy": {
                "agent_id": "momentum_trader_001",
                "capital_allocation": 3000000,
                "target_instruments": ["large_cap_stocks", "etfs"],
                "parameters": {
                    "lookback_period": "20_days",
                    "momentum_threshold": 0.02,
                    "stop_loss": 0.03
                }
            },
            "mean_reversion_strategy": {
                "agent_id": "mean_reversion_001",
                "capital_allocation": 2500000,
                "target_instruments": ["currency_pairs", "commodities"],
                "parameters": {
                    "lookback_period": "5_days",
                    "reversion_threshold": 2.0,
                    "holding_period": "1_day"
                }
            },
            "arbitrage_strategy": {
                "agent_id": "arbitrage_hunter_001",
                "capital_allocation": 4500000,
                "target_instruments": ["cross_listed_stocks", "etf_arbitrage"],
                "parameters": {
                    "min_spread": 0.005,
                    "execution_timeout": "500ms",
                    "max_position_size": 100000
                }
            }
        },
        "coordination_rules": {
            "position_limits": {
                "single_instrument": 0.05,
                "sector_concentration": 0.25,
                "correlation_limit": 0.70
            },
            "risk_management": {
                "daily_loss_limit": 150000,
                "var_limit": 300000,
                "leverage_limit": 2.0
            },
            "execution_priority": {
                "arbitrage": 1,
                "momentum": 2,
                "mean_reversion": 3
            }
        },
        "market_conditions": {
            "volatility_regime": "normal",
            "market_sentiment": "neutral",
            "liquidity_conditions": "good"
        }
    }
)

# Execute coordinated trading strategies
trading_result = trading_orchestrator.coordinate_strategies(strategy_coordination)
if trading_result.is_ok():
    coordination = trading_result.unwrap()
    print(f"✅ Strategy coordination active")
    print(f"   💰 Total capital deployed: ${coordination['capital_deployed']:,.2f}")
    print(f"   📈 Active positions: {coordination['total_positions']}")
    print(f"   ⚡ Execution efficiency: {coordination['execution_efficiency']:.1%}")
    
    print(f"   📋 Strategy performance:")
    for strategy, performance in coordination['strategy_performance'].items():
        print(f"      • {strategy}: PnL ${performance['pnl']:,.2f}, Sharpe {performance['sharpe']:.2f}")
else:
    error = trading_result.unwrap_err()
    print(f"❌ Strategy coordination failed: {error['message']}")

🏙️ Smart Cities

Build intelligent cities with coordinated traffic management, energy optimization, and emergency response using MAPLE's revolutionary multi-agent coordination.

🚗 Intelligent Traffic Management

# Smart City Traffic Management System
from maple import Agent, Message, Priority

# City traffic coordinator
traffic_coordinator = Agent("city_traffic_coordinator")

# City-wide traffic optimization
traffic_optimization = Message(
    message_type="TRAFFIC_FLOW_OPTIMIZATION",
    priority=Priority.HIGH,
    payload={
        "city_zone": "downtown_district",
        "traffic_data": {
            "intersections": {
                "intersection_001": {
                    "location": {"lat": 40.7589, "lon": -73.9851},
                    "current_flow": {
                        "north_south": {"vehicles_per_hour": 1200, "avg_speed": 25},
                        "east_west": {"vehicles_per_hour": 800, "avg_speed": 30}
                    },
                    "signal_timing": {
                        "current_cycle": 120,
                        "green_north_south": 60,
                        "green_east_west": 45
                    },
                    "congestion_level": 0.75
                },
                "intersection_002": {
                    "location": {"lat": 40.7614, "lon": -73.9776},
                    "current_flow": {
                        "north_south": {"vehicles_per_hour": 950, "avg_speed": 35},
                        "east_west": {"vehicles_per_hour": 1100, "avg_speed": 22}
                    },
                    "congestion_level": 0.68
                }
            },
            "road_segments": {
                "broadway_segment_a": {
                    "length": 800,
                    "current_density": 45,
                    "speed_limit": 35,
                    "average_speed": 28
                }
            }
        },
        "optimization_goals": {
            "minimize_travel_time": True,
            "reduce_emissions": True,
            "improve_safety": True,
            "maximize_throughput": True
        },
        "constraints": {
            "emergency_vehicle_priority": True,
            "pedestrian_crossing_time": {"min": 15, "max": 30},
            "noise_pollution_limits": True
        },
        "coordination_agents": {
            "signal_controller_001": {
                "capabilities": ["adaptive_timing", "emergency_preemption"],
                "coverage_area": "downtown_grid"
            },
            "route_optimizer_001": {
                "capabilities": ["dynamic_routing", "congestion_prediction"],
                "data_sources": ["gps_tracking", "mobile_apps"]
            },
            "incident_detector_001": {
                "capabilities": ["accident_detection", "road_closure_monitoring"],
                "sensors": ["traffic_cameras", "roadside_sensors"]
            }
        },
        "resources": {
            "real_time_processing": {"latency": "<1s"},
            "traffic_simulation": {"compute": "high_performance"},
            "data_storage": {"historical": "5_years", "real_time": "24_hours"}
        }
    }
)

# Execute traffic optimization
optimization_result = traffic_coordinator.optimize_traffic_flow(traffic_optimization)
if optimization_result.is_ok():
    optimization = optimization_result.unwrap()
    print(f"✅ Traffic optimization implemented")
    print(f"   🚗 Average speed improvement: {optimization['speed_improvement']:.1%}")
    print(f"   ⏱️ Travel time reduction: {optimization['time_savings']} minutes")
    print(f"   🌍 CO2 reduction: {optimization['emission_reduction']:.1%}")
    print(f"   📈 Throughput increase: {optimization['throughput_improvement']:.1%}")
    
    if optimization['signal_adjustments']:
        print(f"   🚦 Signal timing adjustments:")
        for intersection, adjustment in optimization['signal_adjustments'].items():
            print(f"• {intersection}: Cycle {adjustment['new_cycle']}s, 
                Green ratio {adjustment['green_ratio']:.1%}")
else:
    error = optimization_result.unwrap_err()
    print(f"❌ Traffic optimization failed: {error['message']}")

# Emergency Vehicle Priority System
emergency_coordinator = Agent("emergency_vehicle_coordinator")

# Emergency vehicle preemption
emergency_preemption = Message(
    message_type="EMERGENCY_VEHICLE_PREEMPTION",
    priority=Priority.CRITICAL,
    payload={
        "emergency_vehicle": {
            "vehicle_id": "AMB_001",
            "type": "ambulance",
            "priority_level": "CRITICAL",
            "current_location": {"lat": 40.7505, "lon": -73.9934},
            "destination": {"lat": 40.7614, "lon": -73.9776, "name": "Mount_Sinai_Hospital"},
            "estimated_arrival": "8_minutes",
            "patient_condition": "cardiac_emergency"
        },
        "route_optimization": {
            "preferred_route": ["broadway", "42nd_street", "park_avenue"],
            "alternative_routes": [
                ["7th_avenue", "34th_street", "lexington_avenue"],
                ["6th_avenue", "42nd_street", "madison_avenue"]
            ],
            "optimization_criteria": {
                "minimize_time": True,
                "avoid_construction": True,
                "clear_path": True
            }
        },
        "traffic_control": {
            "preemption_radius": "500m",
            "signal_override": True,
            "traffic_holding": True,
            "lane_clearing": "dynamic"
        },
        "coordination_requirements": {
            "affected_intersections": 12,
            "estimated_disruption": "3_minutes",
            "traffic_recovery_time": "5_minutes",
            "public_notification": True
        },
        "communication": {
            "police_dispatch": True,
            "traffic_management_center": True,
            "connected_vehicles": True,
            "mobile_apps": ["waze", "google_maps"]
        }
    }
)

# Execute emergency preemption
preemption_result = emergency_coordinator.activate_preemption(emergency_preemption)
if preemption_result.is_ok():
    preemption = preemption_result.unwrap()
    print(f"✅ Emergency preemption activated")
    print(f"   🚑 Vehicle: {preemption['vehicle_id']} - Route cleared")
    print(f"   🚦 Signals overridden: {preemption['signals_controlled']} intersections")
    print(f"   ⏱️ Time saved: {preemption['time_saved']} minutes")
    print(f"   🚗 Traffic impact: {preemption['traffic_impact']} vehicles delayed")
    print(f"   📱 Public notifications sent: {preemption['notifications_sent']}")
else:
    error = preemption_result.unwrap_err()
    print(f"❌ Emergency preemption failed: {error['message']}")

⚡ Smart Energy Management

# Smart City Energy Management
energy_coordinator = Agent("city_energy_coordinator")

# City-wide energy optimization
energy_optimization = Message(
    message_type="CITY_ENERGY_OPTIMIZATION",
    payload={
        "city_grid": {
            "total_demand": "850MW",
            "peak_capacity": "1200MW",
            "renewable_generation": {
                "solar_farms": {"current": "180MW", "forecast": "220MW"},
                "wind_farms": {"current": "95MW", "forecast": "130MW"},
                "hydroelectric": {"current": "75MW", "capacity": "75MW"}
            },
            "conventional_generation": {
                "natural_gas": {"current": "300MW", "capacity": "500MW"},
                "nuclear": {"current": "200MW", "capacity": "400MW"}
            },
            "energy_storage": {
                "battery_systems": {"stored": "50MWh", "capacity": "100MWh"},
                "pumped_hydro": {"stored": "200MWh", "capacity": "300MWh"}
            }
        },
        "district_consumption": {
            "residential": {"demand": "320MW", "efficiency_potential": 0.15},
            "commercial": {"demand": "280MW", "demand_response_capacity": "45MW"},
            "industrial": {"demand": "200MW", "load_shifting_capacity": "30MW"},
            "transportation": {"ev_charging": "50MW", "smart_charging_enabled": True}
        },
        "optimization_goals": {
            "minimize_cost": True,
            "maximize_renewable": True,
            "reduce_emissions": True,
            "maintain_reliability": True
        },
        "smart_infrastructure": {
            "smart_meters": {"deployment": 0.85, "real_time_data": True},
            "smart_streetlights": {"adaptive_lighting": True, "energy_savings": 0.40},
            "building_automation": {"hvac_optimization": True, "demand_response": True}
        },
        "weather_forecast": {
            "solar_irradiance": {"next_6_hours": "high", "next_24_hours": "variable"},
            "wind_speed": {"current": "12_mph", "forecast": "15_mph"},
            "temperature": {"current": "75F", "peak_today": "82F"}
        },
        "optimization_agents": {
            "renewable_forecaster": {
                "capabilities": ["solar_prediction", "wind_prediction"],
                "accuracy": 0.92
            },
            "demand_predictor": {
                "capabilities": ["load_forecasting", "demand_response"],
                "prediction_horizon": "24_hours"
            },
            "grid_optimizer": {
                "capabilities": ["economic_dispatch", "unit_commitment"],
                "optimization_algorithm": "mixed_integer_programming"
            }
        }
    }
)

# Execute energy optimization
energy_result = energy_coordinator.optimize_city_energy(energy_optimization)
if energy_result.is_ok():
    optimization = energy_result.unwrap()
    print(f"✅ City energy optimization active")
    print(f"   🌍 Renewable utilization: {optimization['renewable_percentage']:.1%}")
    print(f"   💰 Cost savings: ${optimization['cost_savings']:,.2f}/hour")
    print(f"   🌍 CO2 reduction: {optimization['emission_reduction']:.1%}")
    print(f"   ⚡ Grid efficiency: {optimization['grid_efficiency']:.1%}")
    
    if optimization['demand_response_activated']:
        print(f"   📱 Demand response: {optimization['demand_reduction']}MW load reduced")
    
    print(f"   🔋 Energy storage strategy: {optimization['storage_strategy']}")
else:
    error = energy_result.unwrap_err()
    print(f"❌ Energy optimization failed: {error['message']}")

🚑 Emergency Response Coordination

# City Emergency Response System
emergency_coordinator = Agent("city_emergency_coordinator")

# Multi-agency emergency response
emergency_response = Message(
    message_type="CITY_EMERGENCY_RESPONSE",
    priority=Priority.CRITICAL,
    payload={
        "incident": {
            "incident_id": "INCIDENT_2024_1225_001",
            "type": "building_fire",
            "severity": "3_alarm",
            "location": {"lat": 40.7549, "lon": -73.9840, "address": "350_5th_Avenue"},
            "reported_time": "2024-12-25T18:00:00Z",
            "estimated_affected": {"building_occupants": 200, "evacuation_radius": "2_blocks"}
        },
        "response_agencies": {
            "fire_department": {
                "available_units": {
                    "engines": ["ENG_01", "ENG_07", "ENG_14"],
                    "ladders": ["LAD_03", "LAD_09"],
                    "rescue_squads": ["RESCUE_01"]
                },
                "response_time": {"first_unit": "4_minutes", "full_response": "8_minutes"}
            },
            "police_department": {
                "available_units": {
                    "patrol_cars": ["UNIT_12", "UNIT_28", "UNIT_41"],
                    "emergency_services": ["ESU_01"]
                },
                "responsibilities": ["traffic_control", "evacuation", "perimeter_security"]
            },
            "emergency_medical": {
                "available_units": {
                    "ambulances": ["AMB_05", "AMB_12"],
                    "mass_casualty_unit": ["MCU_01"]
                },
                "medical_facilities": {
                    "nearest_hospital": "Bellevue_Hospital",
                    "trauma_centers": ["NYU_Langone", "Mount_Sinai"]
                }
            }
        },
        "resource_coordination": {
            "incident_command": {
                "location": "intersection_5th_34th",
                "commanding_officer": "CHIEF_SMITH",
                "communication_frequency": "FIRE_TAC_1"
            },
            "staging_areas": {
                "equipment_staging": "madison_square_park",
                "medical_triage": "sidewalk_5th_avenue",
                "media_staging": "broadway_34th"
            },
            "traffic_management": {
                "road_closures": ["5th_avenue_33rd_36th", "broadway_32nd_37th"],
                "detour_routes": ["6th_avenue", "madison_avenue"],
                "estimated_impact": "moderate_congestion"
            }
        },
        "communication_plan": {
            "inter_agency_channels": ["CITYWIDE_1", "FIRE_TAC_1", "POLICE_SOD"],
            "public_notifications": {
                "emergency_alert_system": True,
                "social_media": ["twitter", "facebook"],
                "mobile_apps": ["notify_nyc"],
                "local_media": True
            },
            "hospital_notifications": {
                "mass_casualty_alert": True,
                "bed_availability_check": True,
                "specialist_teams": ["burn_unit", "trauma_surgery"]
            }
        }
    }
)

# Execute emergency response coordination
response_result = emergency_coordinator.coordinate_emergency_response(emergency_response)
if response_result.is_ok():
    response = response_result.unwrap()
    print(f"✅ Emergency response coordinated")
    print(f"   🚑 First responders: {response['units_dispatched']} units en route")
    print(f"   ⏱️ Response time: {response['estimated_response_time']} minutes")
    print(f"   🚦 Traffic management: {len(response['road_closures'])} roads closed")
    print(f"   🏥 Medical resources: {response['medical_units']} units available")
    print(f"   📱 Public alerts sent: {response['public_notifications']} notifications")
    
    if response['evacuation_initiated']:
        print(f"   🏃 Evacuation: {response['evacuation_radius']} area evacuated")
else:
    error = response_result.unwrap_err()
    print(f"❌ Emergency response failed: {error['message']}")

🚚 Logistics & Supply Chain

Revolutionize logistics with intelligent route optimization, inventory management, and demand forecasting using MAPLE's advanced multi-agent coordination capabilities.

🚚 Fleet Management & Route Optimization

# Fleet Management and Route Optimization
from maple import Agent, Message, Priority

# Fleet coordination center
fleet_coordinator = Agent("fleet_optimization_coordinator")

# Dynamic route optimization for delivery fleet
route_optimization = Message(
    message_type="FLEET_ROUTE_OPTIMIZATION",
    priority=Priority.HIGH,
    payload={
        "fleet_status": {
            "active_vehicles": 25,
            "total_capacity": "50000kg",
            "current_utilization": 0.78,
            "average_fuel_efficiency": "8.5mpg"
        },
        "delivery_manifest": {
            "pending_deliveries": 147,
            "total_weight": "38500kg",
            "delivery_windows": {
                "morning": {"count": 62, "deadline": "12:00"},
                "afternoon": {"count": 53, "deadline": "17:00"},
                "evening": {"count": 32, "deadline": "20:00"}
            },
            "priority_deliveries": {
                "same_day": 23,
                "next_day": 89,
                "standard": 35
            }
        },
        "vehicles": {
            "truck_001": {
                "current_location": {"lat": 40.7505, "lon": -73.9934},
                "capacity": {"weight": "3000kg", "volume": "15m3"},
                "fuel_level": 0.75,
                "driver_hours": {"worked": 4.5, "max_daily": 10},
                "maintenance_status": "good",
                "current_load": "1800kg"
            },
            "van_012": {
                "current_location": {"lat": 40.7614, "lon": -73.9776},
                "capacity": {"weight": "1500kg", "volume": "8m3"},
                "fuel_level": 0.45,
                "driver_hours": {"worked": 6.2, "max_daily": 10},
                "current_load": "950kg"
            }
        },
        "optimization_constraints": {
            "traffic_conditions": {
                "current_congestion": "moderate",
                "rush_hour_periods": ["08:00-10:00", "17:00-19:00"],
                "construction_zones": ["brooklyn_bridge", "fdr_drive_south"]
            },
            "delivery_requirements": {
                "signature_required": 45,
                "refrigerated_transport": 12,
                "fragile_handling": 28,
                "apartment_deliveries": 67
            },
            "driver_regulations": {
                "mandatory_breaks": "30min_every_6h",
                "maximum_driving_time": "10h_daily",
                "rest_period_required": "11h_between_shifts"
            }
        },
        "optimization_goals": {
            "minimize_total_distance": True,
            "reduce_fuel_consumption": True,
            "maximize_on_time_delivery": True,
            "optimize_vehicle_utilization": True,
            "minimize_driver_overtime": True
        },
        "real_time_factors": {
            "weather_conditions": "clear",
            "road_incidents": [],
            "fuel_prices": {"diesel": 3.45, "gas": 3.12},
            "parking_availability": "limited_downtown"
        },
        "coordination_agents": {
            "route_planner": {
                "algorithm": "genetic_algorithm_with_constraints",
                "capabilities": ["multi_objective_optimization", "real_time_updates"]
            },
            "traffic_analyzer": {
                "data_sources": ["google_maps", "waze", "city_traffic_sensors"],
                "prediction_accuracy": 0.89
            },
            "fuel_optimizer": {
                "capabilities": ["eco_routing", "fuel_station_planning"],
                "savings_potential": 0.15
            }
        }
    }
)

# Execute fleet route optimization
optimization_result = fleet_coordinator.optimize_fleet_routes(route_optimization)
if optimization_result.is_ok():
    optimization = optimization_result.unwrap()
    print(f"✅ Fleet route optimization complete")
    print(f"   🚚 Vehicles optimized: {optimization['vehicles_optimized']}")
    print(f"   🗺️ Total distance reduction: {optimization['distance_savings']:.1%}")
    print(f"   ⛽ Fuel savings: {optimization['fuel_savings']:.1%} 
        ({optimization['fuel_cost_savings']:,.2f}$)")
    print(f"   ⏱️ On-time delivery improvement: {optimization['delivery_performance']:.1%}")
    print(f"   📋 Optimized routes: {optimization['total_routes']} routes created")
    
    if optimization['driver_optimization']:
        print(f"   👨‍💼 Driver efficiency: {optimization['driver_utilization']:.1%} utilization")
        print(f"   ⏰ Overtime reduction: {optimization['overtime_reduction']} hours saved")
else:
    error = optimization_result.unwrap_err()
    print(f"❌ Route optimization failed: {error['message']}")

# Real-Time Delivery Tracking System
delivery_coordinator = Agent("delivery_tracking_coordinator")

# Real-time delivery coordination
delivery_tracking = Message(
    message_type="REAL_TIME_DELIVERY_COORDINATION",
    payload={
        "active_deliveries": {
            "delivery_001": {
                "tracking_id": "TRK_2024_1225_001",
                "customer": {
                    "name": "John Smith",
                    "address": "123 Main St, New York, NY",
                    "phone": "+1-555-0123",
                    "delivery_preferences": {
                        "time_window": "14:00-18:00",
                        "special_instructions": "Leave with doorman",
                        "notification_method": "sms"
                    }
                },
                "package_details": {
                    "weight": "2.5kg",
                    "dimensions": "30x20x15cm",
                    "value": 299.99,
                    "fragile": True,
                    "signature_required": True
                },
                "delivery_status": {
                    "current_status": "out_for_delivery",
                    "vehicle_id": "VAN_012",
                    "driver_name": "Mike Johnson",
                    "estimated_arrival": "15:30",
                    "stops_before": 2
                }
            }
        },
        "vehicle_tracking": {
            "van_012": {
                "current_location": {"lat": 40.7400, "lon": -73.9900},
                "speed": "25mph",
                "heading": "northeast",
                "traffic_delay": "5_minutes",
                "fuel_level": 0.60,
                "next_delivery_eta": "15:35"
            }
        },
        "coordination_requirements": {
            "customer_notifications": {
                "departure_notification": True,
                "proximity_alert": "15_minutes_before",
                "delivery_confirmation": True,
                "delay_notifications": True
            },
            "delivery_optimization": {
                "dynamic_rerouting": True,
                "traffic_avoidance": True,
                "parking_assistance": True,
                "time_window_compliance": True
            },
            "exception_handling": {
                "customer_not_available": "reschedule_or_neighbor",
                "address_issues": "contact_customer",
                "package_damage": "photo_documentation",
                "delivery_refusal": "return_to_facility"
            }
        },
        "integration_systems": {
            "customer_app": {
                "real_time_map": True,
                "delivery_window_updates": True,
                "driver_contact": True,
                "delivery_photos": True
            },
            "warehouse_management": {
                "inventory_updates": True,
                "return_processing": True,
                "damage_reports": True
            },
            "payment_processing": {
                "cod_handling": True,
                "digital_signatures": True,
                "receipt_generation": True
            }
        }
    }
)

# Execute delivery coordination
delivery_result = delivery_coordinator.coordinate_deliveries(delivery_tracking)
if delivery_result.is_ok():
    coordination = delivery_result.unwrap()
    print(f"✅ Delivery coordination active")
    print(f"   📦 Active deliveries: {coordination['active_deliveries']}")
    print(f"   📱 Customer notifications: {coordination['notifications_sent']}")
    print(f"   ⏱️ On-time performance: {coordination['on_time_rate']:.1%}")
    print(f"   🗺️ Route adjustments: {coordination['dynamic_reroutes']}")
    
    if coordination['delivery_exceptions']:
        print(f"   ⚠️ Exceptions handled: {len(coordination['delivery_exceptions'])}")
        for exception in coordination['delivery_exceptions']:
            print(f"      • {exception['type']}: {exception['resolution']}")
else:
    error = delivery_result.unwrap_err()
    print(f"❌ Delivery coordination failed: {error['message']}")

📊 Supply Chain Optimization

# Supply Chain Optimization System
supply_chain_coordinator = Agent("supply_chain_coordinator")

# Comprehensive supply chain optimization
supply_chain_optimization = Message(
    message_type="SUPPLY_CHAIN_OPTIMIZATION",
    payload={
        "supply_network": {
            "suppliers": {
                "supplier_001": {
                    "name": "Global Electronics Inc",
                    "location": "Shenzhen, China",
                    "products": ["smartphones", "tablets", "accessories"],
                    "capacity": {"monthly": 50000, "lead_time": "14_days"},
                    "reliability": 0.95,
                    "cost_structure": {"unit_cost": 250, "shipping": 15}
                },
                "supplier_002": {
                    "name": "TechParts USA",
                    "location": "Austin, Texas",
                    "products": ["components", "chargers", "cases"],
                    "capacity": {"monthly": 25000, "lead_time": "7_days"},
                    "reliability": 0.98,
                    "cost_structure": {"unit_cost": 45, "shipping": 8}
                }
            },
            "warehouses": {
                "warehouse_east": {
                    "location": "New Jersey",
                    "capacity": {"storage": "100000_units", "throughput": "5000_daily"},
                    "current_inventory": 75000,
                    "operating_costs": {"storage": 2.50, "handling": 1.20}
                },
                "warehouse_west": {
                    "location": "California",
                    "capacity": {"storage": "80000_units", "throughput": "4000_daily"},
                    "current_inventory": 62000,
                    "operating_costs": {"storage": 2.80, "handling": 1.35}
                }
            },
            "distribution_centers": {
                "dc_northeast": {
                    "coverage_area": ["NY", "NJ", "CT", "MA"],
                    "daily_capacity": 3000,
                    "current_load": 2400,
                    "service_level": 0.96
                },
                "dc_southwest": {
                    "coverage_area": ["CA", "NV", "AZ"],
                    "daily_capacity": 2500,
                    "current_load": 1800,
                    "service_level": 0.94
                }
            }
        },
        "demand_forecasting": {
            "historical_data": {
                "sales_trend": "growing_15_percent_annually",
                "seasonal_patterns": {
                    "q4_spike": 2.5,
                    "summer_dip": 0.8,
                    "back_to_school": 1.3
                },
                "product_lifecycle": {
                    "smartphones": "mature",
                    "accessories": "growth",
                    "tablets": "declining"
                }
            },
            "market_intelligence": {
                "competitor_analysis": {"market_share_change": -0.02},
                "economic_indicators": {"consumer_confidence": 0.68},
                "technology_trends": ["5g_adoption", "wireless_charging"]
            },
            "forecasting_models": {
                "time_series": {"algorithm": "arima", "accuracy": 0.87},
                "machine_learning": {"algorithm": "xgboost", "accuracy": 0.91},
                "ensemble": {"weighted_average": True, "accuracy": 0.93}
            }
        },
        "optimization_objectives": {
            "minimize_total_cost": True,
            "maximize_service_level": True,
            "reduce_inventory_holding": True,
            "optimize_transportation": True,
            "improve_sustainability": True
        },
        "constraints": {
            "service_level_targets": {"fill_rate": 0.95, "delivery_time": "2_days"},
            "inventory_limits": {"max_holding_cost": 500000, "turnover_target": 8},
            "capacity_constraints": {"warehouse_utilization": 0.85, "transport_capacity": True},
            "financial_constraints": {"working_capital": 2000000, "payment_terms": "net_30"}
        },
        "optimization_agents": {
            "demand_planner": {
                "capabilities": ["statistical_forecasting", "collaborative_planning"],
                "integration": ["sales_data", "market_research"]
            },
            "inventory_optimizer": {
                "capabilities": ["safety_stock_optimization", "abc_analysis"],
                "algorithms": ["economic_order_quantity", "dynamic_programming"]
            },
            "transportation_planner": {
                "capabilities": ["route_optimization", "carrier_selection"],
                "modes": ["air", "ocean", "ground", "intermodal"]
            },
            "sustainability_analyzer": {
                "capabilities": ["carbon_footprint", "circular_economy"],
                "metrics": ["co2_emissions", "packaging_waste", "energy_efficiency"]
            }
        }
    }
)

# Execute supply chain optimization
optimization_result = supply_chain_coordinator.optimize_supply_chain(supply_chain_optimization)
if optimization_result.is_ok():
    optimization = optimization_result.unwrap()
    print(f"✅ Supply chain optimization complete")
    print(f"   💰 Total cost reduction: ${optimization['cost_savings']:,.2f} annually")
    print(f"   📈 Service level improvement: {optimization['service_improvement']:.1%}")
    print(f"   📦 Inventory optimization: ${optimization['inventory_reduction']:,.2f} working capital freed")
    print(f"   🚚 Transportation efficiency: {optimization['transport_optimization']:.1%} improvement")
    print(f"   🌍 Sustainability impact: {optimization['carbon_reduction']:.1%} CO2 reduction")
    
    print(f"   📋 Strategic recommendations:")
    for recommendation in optimization['strategic_recommendations']:
        print(f"      • {recommendation['category']}: {recommendation['action']}")
else:
    error = optimization_result.unwrap_err()
    print(f"❌ Supply chain optimization failed: {error['message']}")

📊 Predictive Analytics

# Predictive Analytics for Logistics
analytics_coordinator = Agent("logistics_analytics_coordinator")

# Advanced demand forecasting and inventory intelligence
predictive_analytics = Message(
    message_type="LOGISTICS_PREDICTIVE_ANALYTICS",
    payload={
        "analytics_scope": {
            "time_horizon": "6_months",
            "granularity": "daily",
            "confidence_interval": 0.95,
            "update_frequency": "real_time"
        },
        "data_sources": {
            "internal_data": {
                "sales_history": "5_years",
                "inventory_levels": "real_time",
                "customer_behavior": "clickstream_and_purchases",
                "supplier_performance": "delivery_and_quality_metrics"
            },
            "external_data": {
                "economic_indicators": ["gdp", "consumer_spending", "inflation"],
                "weather_data": ["temperature", "precipitation", "seasonal_patterns"],
                "social_media": ["sentiment_analysis", "trend_detection"],
                "competitor_intelligence": ["pricing", "promotions", "new_products"]
            }
        },
        "forecasting_models": {
            "statistical_models": {
                "arima": {"accuracy": 0.85, "best_for": "stable_trends"},
                "exponential_smoothing": {"accuracy": 0.82, "best_for": "seasonal_data"},
                "var_models": {"accuracy": 0.88, "best_for": "multivariate_analysis"}
            },
            "machine_learning": {
                "random_forest": {"accuracy": 0.89, "best_for": "feature_rich_data"},
                "neural_networks": {"accuracy": 0.92, "best_for": "complex_patterns"},
                "gradient_boosting": {"accuracy": 0.90, "best_for": "mixed_data_types"}
            },
            "deep_learning": {
                "lstm_networks": {"accuracy": 0.94, "best_for": "sequential_data"},
                "transformer_models": {"accuracy": 0.95, "best_for": "attention_mechanisms"}
            }
        },
        "inventory_optimization": {
            "current_metrics": {
                "inventory_turnover": 6.2,
                "stockout_rate": 0.03,
                "carrying_cost_ratio": 0.25,
                "obsolescence_rate": 0.02
            },
            "optimization_targets": {
                "target_turnover": 8.0,
                "max_stockout_rate": 0.02,
                "target_carrying_cost": 0.20,
                "max_obsolescence": 0.01
            },
            "optimization_strategies": {
                "abc_analysis": True,
                "dynamic_safety_stock": True,
                "seasonal_adjustments": True,
                "lifecycle_management": True
            }
        },
        "predictive_scenarios": {
            "base_case": {"probability": 0.60, "description": "normal_market_conditions"},
            "optimistic": {"probability": 0.20, "description": "strong_economic_growth"},
            "pessimistic": {"probability": 0.20, "description": "economic_downturn"}
        },
        "analytics_agents": {
            "forecast_engine": {
                "capabilities": ["ensemble_forecasting", "scenario_analysis"],
                "update_frequency": "hourly",
                "accuracy_monitoring": True
            },
            "anomaly_detector": {
                "capabilities": ["outlier_detection", "pattern_breaks"],
                "algorithms": ["isolation_forest", "statistical_tests"],
                "alert_thresholds": {"demand_spike": 2.5, "unusual_pattern": 3.0}
            },
            "optimization_engine": {
                "capabilities": ["multi_objective_optimization", "constraint_handling"],
                "algorithms": ["genetic_algorithm", "simulated_annealing"]
            }
        }
    }
)

# Execute predictive analytics
analytics_result = analytics_coordinator.execute_predictive_analytics(predictive_analytics)
if analytics_result.is_ok():
    analytics = analytics_result.unwrap()
    print(f"✅ Predictive analytics complete")
    print(f"   🔮 Forecast accuracy: {analytics['forecast_accuracy']:.1%}")
    print(f"   📈 Demand trend: {analytics['demand_trend']} ({analytics['trend_confidence']:.1%} confidence)")
    print(f"   📦 Inventory optimization: {analytics['inventory_improvement']:.1%} efficiency gain")
    print(f"   💰 Cost impact: ${analytics['cost_optimization']:,.2f} annual savings")
    
    if analytics['anomalies_detected']:
        print(f"   ⚠️ Anomalies detected: {len(analytics['anomalies_detected'])}")
        for anomaly in analytics['anomalies_detected']:
            print(f"      • {anomaly['type']}: {anomaly['description']} (Severity: {anomaly['severity']})")
    
    print(f"   📋 Recommendations:")
    for rec in analytics['recommendations']:
        print(f"      • {rec['category']}: {rec['action']} (Impact: {rec['impact']})")
else:
    error = analytics_result.unwrap_err()
    print(f"❌ Predictive analytics failed: {error['message']}")

🏗️ System Architecture Diagrams

📐 Overall System Architecture

🏭 Manufacturing Architecture

🔄 Communication Flow Diagrams

📊 Complex Data Analysis Communication Flow

🤖 AI Collaboration Flow

🏭 Multi-Agent Protocol Flow

🔧 Enhanced Type System

⚡ Revolutionary Result<T,E> Pattern

The Result<T,E> type is MAPLE's breakthrough innovation that eliminates ALL silent failures in agent communication.

# Every operation returns Result<T,E> - success or structured error
def process_data(data) -> Result[ProcessedData, ProcessingError]:
    if not validate_input(data):
        return Result.err({
            "errorType": "VALIDATION_ERROR",
            "message": "Invalid input format",
            "details": {
                "expected": "JSON with timestamp",
                "received": type(data).__name__,
                "missing_fields": ["timestamp", "agent_id"]
            },
            "severity": "HIGH",
            "recoverable": True,
            "suggestion": {
                "action": "REFORMAT_DATA",
                "parameters": {
                    "add_timestamp": True,
                    "validate_schema": True
                }
            }
        })
    
    try:
        processed = advanced_processing(data)
        return Result.ok({
            "data": processed,
            "confidence": 0.98,
            "processing_time": "1.2s",
            "resource_usage": {
                "cpu": "45%",
                "memory": "2.1GB"
            }
        })
    except Exception as e:
        return Result.err({
            "errorType": "PROCESSING_ERROR", 
            "message": str(e),
            "recoverable": False
        })

🔗 Result Operations Chaining

# Chain operations safely - NO SILENT FAILURES
result = (
    load_data(source)
    .and_then(lambda data: validate_schema(data))
    .map(lambda valid_data: process_ai_analysis(valid_data))
    .and_then(lambda analysis: generate_insights(analysis))
    .map(lambda insights: format_output(insights))
)

if result.is_ok():
    final_output = result.unwrap()
    print(f"Success: {final_output}")
else:
    error = result.unwrap_err()
    print(f"Pipeline failed: {error['message']}")
    
    # Intelligent error recovery
    if error.get('recoverable'):
        recovery_strategy = error.get('suggestion', {})
        apply_recovery_strategy(recovery_strategy)

🏷️ Resource Types (UNIQUE TO MAPLE)

# Define resource requirements with precision
resource_spec = ResourceRequest(
    # Computational resources
    compute=ResourceRange(min=4, preferred=8, max=16),
    memory=ResourceRange(min="8GB", preferred="16GB", max="32GB"),
    gpu_memory=ResourceRange(min="4GB", preferred="8GB", max="24GB"),
    
    # Network resources  
    network_bandwidth=ResourceRange(min="100Mbps", preferred="1Gbps", max="10Gbps"),
    network_latency=ResourceRange(max="10ms", preferred="1ms"),
    
    # Storage resources
    storage=ResourceRange(min="100GB", preferred="1TB", max="10TB"),
    iops=ResourceRange(min=1000, preferred=10000, max=100000),
    
    # Time constraints
    deadline="2024-12-25T18:00:00Z",
    timeout="30s",
    
    # Optimization preferences
    priority="HIGH",
    cost_optimization=False,
    energy_efficiency=True
)

🔧 Type Validation System

from maple.core.types import (
    Boolean, Integer, Float, String, 
    Timestamp, UUID, Byte, Size, Duration
)

# Type validation with detailed error information
try:
    memory_size = Size.validate("16GB")  # Returns bytes
    duration = Duration.validate("30s")  # Returns seconds
    agent_id = UUID.validate("550e8400-e29b-41d4-a716-446655440000")
except ValueError as e:
    print(f"Type validation failed: {e}")

📚 Enhanced API Reference

🤖 Agent Class - Core Methods

Resource-Aware Communication (UNIQUE TO MAPLE)

Secure Communication (UNIQUE TO MAPLE)

Distributed State Management (UNIQUE TO MAPLE)

🔧 Resource Types API

📬 Comprehensive Message Examples

MAPLE messages showcase the protocol's revolutionary features including resource awareness, type safety, security, and error handling capabilities that are impossible with other agent communication protocols.

🔐 Session Control Messages

{
    "id": "msg_001",
    "type": "SessionControl",
    "timestamp": "2024-12-12T10:00:00Z",
    "sender": "client_agent_123",
    "receiver": "broker_main",
    "priority": "HIGH",
    "payload": {
        "action": "START",
        "credentials": {
            "type": "jwt",
            "value": "eyJhbGciOiJIUzI1NiIs...",
            "expiry": "2024-12-12T11:00:00Z"
        },
        "parameters": {
            "session_timeout": "1h",
            "required_resources": ["compute", "memory"]
        }
    }
}

📊 Analytics Task Assignment

{
    "id": "msg_002",
    "type": "TaskAssignment",
    "timestamp": "2024-12-12T10:00:05Z",
    "sender": "client_agent_123",
    "receiver": "broker_main",
    "priority": "HIGH",
    "payload": {
        "taskId": "task_789",
        "description": "Large Dataset Analysis",
        "parameters": {
            "dataset_size": 1000000,
            "analysis_type": "clustering",
            "algorithms": ["k-means", "dbscan"],
            "output_format": "parquet"
        },
        "constraints": {
            "deadline": "2024-12-12T10:30:00Z",
            "max_memory": "16GB",
            "min_accuracy": 0.95
        }
    },
    "metadata": {
        "retry_count": 0,
        "source_system": "research_pipeline"
    }
}

⚠️ Error Handling Messages

{
    "id": "msg_005",
    "type": "ErrorMessage",
    "timestamp": "2024-12-12T10:20:00Z",
    "sender": "analytics_agent_1",
    "receiver": "broker_main",
    "priority": "HIGH",
    "payload": {
        "code": "RESOURCE_EXCEEDED",
        "description": "Memory usage exceeded allocated limit",
        "severity": "HIGH",
        "context": {
            "taskId": "subtask_001",
            "memory_usage": "15.8GB",
            "memory_limit": "16GB",
            "current_batch": 45
        },
        "recovery_suggestions": [
            {
                "action": "REDUCE_BATCH_SIZE",
                "parameters": {"new_batch_size": 5000}
            },
            {
                "action": "REQUEST_MORE_MEMORY",
                "parameters": {"additional_memory": "8GB"}
            }
        ],
        "impact_assessment": {
            "affected_operations": ["clustering_analysis"],
            "estimated_delay": "5_minutes",
            "data_loss_risk": "none"
        }
    }
}

🏥 Healthcare Emergency Messages

{
    "id": "emergency_001",
    "type": "EMERGENCY_ALERT",
    "timestamp": "2024-12-12T15:30:00Z",
    "sender": "patient_monitor_room_301",
    "receiver": "emergency_coordinator",
    "priority": "LIFE_CRITICAL",
    "payload": {
        "patient_id": "P-2024-789",
        "emergency_type": "cardiac_arrest",
        "location": "room_301_bed_a",
        "vital_signs": {
            "heart_rate": 0,
            "blood_pressure": "undetectable",
            "oxygen_saturation": "65%",
            "consciousness": "unresponsive"
        },
        "required_response": {
            "personnel": {
                "cardiologist": {"count": 1, "eta": "< 2min"},
                "nurses": {"count": 2, "specialty": "critical_care"}
            },
            "equipment": {
                "defibrillator": {"location": "crash_cart_7"},
                "ventilator": {"prep_time": "30s"},
                "medications": ["epinephrine", "atropine"]
            }
        },
        "response_time_target": "< 90_seconds"
    }
}

🏭 Manufacturing Control Messages

{
    "id": "production_control_001",
    "type": "PRODUCTION_OPTIMIZATION",
    "timestamp": "2024-12-12T08:15:00Z",
    "sender": "production_optimizer",
    "receiver": "assembly_line_control",
    "priority": "HIGH",
    "payload": {
        "production_line": "line_A_electronics",
        "optimization_target": "maximize_throughput",
        "current_metrics": {
            "throughput": 850,
            "quality_rate": 0.987,
            "efficiency": 0.92,
            "downtime": "0.5%"
        },
        "recommended_adjustments": {
            "conveyor_speed": {"current": "1.2m/s", "recommended": "1.35m/s"},
            "temperature_zone_3": {"current": "245C", "recommended": "250C"},
            "pressure_station_7": {"current": "15PSI", "recommended": "16PSI"}
        },
        "resource_allocation": {
            "robots": 12,
            "operators": 3,
            "quality_inspectors": 2
        },
        "expected_improvements": {
            "throughput_increase": "8%",
            "quality_maintained": true,
            "energy_efficiency": "+3%"
        }
    }
}

📦 Payload Examples

Detailed payload examples showing MAPLE's rich type system and validation capabilities.

Healthcare Payload Example

{
  "patient_id": "PAT-001",
  "vital_signs": {
    "heart_rate": 72,
    "blood_pressure": "120/80",
    "temperature": 98.6,
    "oxygen_saturation": 98
  },
  "alerts": [],
  "timestamp": "2024-12-25T18:00:00Z"
}

Manufacturing Payload Example

{
  "production_line": "line_A",
  "quality_metrics": {
    "defect_rate": 0.002,
    "throughput": 1000,
    "efficiency": 0.95
  },
  "recommendations": [
    "adjust_temperature",
    "recalibrate_sensors"
  ]
}

📄 Protocol Specification

Complete technical specification of the MAPLE protocol, including message formats, type definitions, and communication patterns.

Message Format Specification

Type System Specification

MAPLE's comprehensive type system ensures message integrity and enables advanced validation.

Primitive Types

• Boolean: true/false values
• Integer: 64-bit signed integers
• Float: 64-bit floating point numbers
• String: UTF-8 encoded strings
• Timestamp: ISO8601 formatted timestamps
• UUID: RFC 4122 compliant UUIDs