Creator: Mahesh Vaijainthymala Krishnamoorthy (Mahesh Vaikri)
Comprehensive troubleshooting guide for MAPLE (Multi Agent Protocol Language Extensible), covering common issues and their resolutions with MAPLE's intelligent diagnostic capabilities.
# ❌ Error: Resource allocation failed
{
"errorType": "RESOURCE_UNAVAILABLE",
"message": "Insufficient resources to satisfy request",
"details": {
"requested": {"memory": "32GB", "compute": 16},
"available": {"memory": "16GB", "compute": 8},
"shortfall": {"memory": "16GB", "compute": 8}
},
"recoverable": True,
"suggestion": {
"action": "REDUCE_RESOURCE_REQUIREMENTS",
"parameters": {"memory_reduction": 0.5, "compute_reduction": 0.5}
}
}from maple.diagnostics import ResourceDiagnostics, AutoOptimizer
# MAPLE's comprehensive resource analysis
resource_analyzer = ResourceDiagnostics()
# Get detailed system analysis
system_analysis = resource_analyzer.analyze_system_performance()
print(f"🔍 System Analysis:")
print(f" 📊 Resource Efficiency: {system_analysis['efficiency_score']:.2%}")
print(f" 🎯 Utilization: {system_analysis['resource_utilization']:.1%}")
print(f" ⚡ Throughput: {system_analysis['throughput']:,} msg/sec")
# Get optimization recommendations
if system_analysis['efficiency_score'] < 0.8:
optimizations = resource_analyzer.get_optimization_recommendations()
print("\n💡 MAPLE Optimization Recommendations:")
for opt in optimizations:
print(f" ✅ {opt['action']}: {opt['description']}")
print(f" 📈 Performance Gain: {opt['performance_gain']}")
print(f" 💰 Cost Impact: {opt['cost_impact']}")
# Apply automatic optimization
optimizer = AutoOptimizer()
optimization_result = optimizer.apply_intelligent_optimizations(system_analysis)
if optimization_result.is_ok():
print("🚀 MAPLE automatically optimized system performance")
optimizations = optimization_result.unwrap()
for opt in optimizations:
print(f" ✅ Applied: {opt['type']} - {opt['improvement']}")
else:
print("⚠️ Manual intervention recommended:")
print(f" {optimization_result.unwrap_err()}")# ✅ Proactive resource monitoring
class ResourceMonitoringAgent(Agent):
def __init__(self, config: Config):
super().__init__(config)
self.resource_monitor = RealTimeResourceMonitor()
self.predictive_analyzer = PredictiveResourceAnalyzer()
async def proactive_resource_management(self):
"""Monitor and optimize resources proactively"""
while self.running:
# Monitor current resource usage
current_usage = await self.resource_monitor.get_current_usage()
# Predict future resource needs
predicted_needs = await self.predictive_analyzer.predict_resource_needs(
time_horizon="1h",
confidence_level=0.9
)
# Check for potential resource conflicts
potential_conflicts = self.resource_monitor.detect_potential_conflicts(
current_usage, predicted_needs
)
if potential_conflicts:
# Proactively optimize resources
optimization_plan = self.create_resource_optimization_plan(
conflicts=potential_conflicts,
current_usage=current_usage,
predicted_needs=predicted_needs
)
await self.apply_optimization_plan(optimization_plan)
await asyncio.sleep(30) # Check every 30 seconds# ❌ Error: Link establishment failed
{
"errorType": "LINK_ESTABLISHMENT_FAILED",
"message": "Failed to establish secure link with target agent",
"details": {
"target_agent": "secure_processor",
"failure_stage": "certificate_verification",
"network_connectivity": True,
"certificate_status": "expired"
}
}from maple.diagnostics import LinkDiagnostics, SecurityAnalyzer
class LinkTroubleshooter:
def __init__(self):
self.link_analyzer = LinkDiagnostics()
self.security_analyzer = SecurityAnalyzer()
async def diagnose_link_issues(self, target_agent: str) -> Dict[str, Any]:
"""Comprehensive link establishment diagnostics"""
print(f"🔍 Diagnosing link establishment with {target_agent}")
# Test 1: Network connectivity
connectivity_result = await self.link_analyzer.test_connectivity(target_agent)
print(f" 📡 Network: {'✅ CONNECTED' if connectivity_result.is_ok() else '❌ FAILED'}")
if connectivity_result.is_err():
print(f" Error: {connectivity_result.unwrap_err()}")
# Test 2: Certificate validation
cert_result = await self.link_analyzer.test_certificates(target_agent)
print(f" 📜 Certificates: {'✅ VALID' if cert_result.is_ok() else '❌ INVALID'}")
if cert_result.is_err():
cert_error = cert_result.unwrap_err()
print(f" Error: {cert_error['message']}")
# Automatic certificate renewal if expired
if cert_error['errorType'] == 'CERTIFICATE_EXPIRED':
renewal_result = await self.security_analyzer.renew_certificates(target_agent)
if renewal_result.is_ok():
print(" 🔄 Certificates automatically renewed")
# Test 3: Cryptographic handshake
crypto_result = await self.link_analyzer.test_crypto_handshake(target_agent)
print(f" 🔐 Crypto: {'✅ SUCCESS' if crypto_result.is_ok() else '❌ FAILED'}")
# Test 4: Link verification
verification_result = await self.link_analyzer.test_link_verification(target_agent)
print(f" ✔️ Verification: {'✅ VERIFIED' if verification_result.is_ok() else '❌ FAILED'}")
# Comprehensive resolution
if not all([connectivity_result.is_ok(), cert_result.is_ok(),
crypto_result.is_ok(), verification_result.is_ok()]):
print("\n🔧 MAPLE Automatic Resolution:")
resolution_result = await self.link_analyzer.resolve_all_issues(target_agent)
if resolution_result.is_ok():
print("✅ All issues resolved automatically")
resolution_details = resolution_result.unwrap()
for fix in resolution_details['applied_fixes']:
print(f" 🔧 {fix['type']}: {fix['description']}")
return resolution_details
else:
print("⚠️ Manual intervention required:")
manual_steps = resolution_result.unwrap_err()
for step in manual_steps.get('manual_steps', []):
print(f" 📋 {step}")
return manual_steps
return {"status": "all_tests_passed"}# ❌ Performance below expectations
{
"current_throughput": "45,000 msg/sec",
"expected_throughput": "30,000+ msg/sec",
"performance_gap": "35% below target",
"bottlenecks_detected": ["message_serialization", "network_congestion"]
}from maple.monitoring import PerformanceProfiler, BottleneckAnalyzer
class PerformanceOptimizer:
def __init__(self):
self.profiler = PerformanceProfiler()
self.bottleneck_analyzer = BottleneckAnalyzer()
self.auto_tuner = AutoPerformanceTuner()
async def optimize_system_performance(self) -> Dict[str, Any]:
"""Comprehensive performance analysis and optimization"""
print("🚀 MAPLE Performance Optimization Analysis")
print("=" * 50)
# Collect comprehensive performance metrics
metrics = await self.profiler.collect_comprehensive_metrics()
print(f"📊 Current Performance Metrics:")
print(f" ⚡ Message Throughput: {metrics['throughput']:,} msg/sec")
print(f" ⏱️ Average Latency: {metrics['latency']:.2f}ms")
print(f" 🎯 Success Rate: {metrics['success_rate']:.2%}")
print(f" 🔧 Resource Efficiency: {metrics['resource_efficiency']:.2%}")
# Identify performance bottlenecks
bottlenecks = await self.bottleneck_analyzer.identify_bottlenecks(metrics)
if bottlenecks:
print(f"\n🔍 Performance Bottlenecks Detected:")
for bottleneck in bottlenecks:
print(f" ⚠️ {bottleneck['component']}: {bottleneck['impact']}")
print(f" 📝 Description: {bottleneck['description']}")
print(f" 🔧 Recommended Fix: {bottleneck['recommendation']}")
# Apply automatic optimizations
print(f"\n🚀 Applying MAPLE Auto-Optimizations:")
optimizations = await self.auto_tuner.apply_performance_optimizations(
metrics=metrics,
bottlenecks=bottlenecks,
target_throughput="300K_messages_per_second"
)
for opt in optimizations:
if opt['applied']:
print(f" ✅ {opt['type']}: {opt['description']}")
print(f" 📈 Expected Improvement: {opt['expected_improvement']}")
else:
print(f" ⚠️ {opt['type']}: {opt['reason_not_applied']}")
# Verify improvements
await asyncio.sleep(5) # Allow optimizations to take effect
improved_metrics = await self.profiler.collect_comprehensive_metrics()
improvement = {
'throughput_gain': (improved_metrics['throughput'] - metrics['throughput']) / metrics['throughput'],
'latency_reduction': (metrics['latency'] - improved_metrics['latency']) / metrics['latency'],
'efficiency_gain': improved_metrics['resource_efficiency'] - metrics['resource_efficiency']
}
print(f"\n🎉 Performance Improvement Results:")
print(f" 📈 Throughput: +{improvement['throughput_gain']:.1%} ({improved_metrics['throughput']:,} msg/sec)")
print(f" ⚡ Latency: -{improvement['latency_reduction']:.1%} ({improved_metrics['latency']:.2f}ms)")
print(f" 🔧 Efficiency: +{improvement['efficiency_gain']:.1%}")
return {
'original_metrics': metrics,
'improved_metrics': improved_metrics,
'optimizations_applied': optimizations,
'improvement_summary': improvement
}# ❌ State synchronization conflict
{
"errorType": "STATE_SYNCHRONIZATION_CONFLICT",
"message": "Conflicting state updates detected across replicas",
"details": {
"state_id": "mission_control",
"conflicting_versions": [15, 16, 17],
"conflict_type": "concurrent_updates",
"affected_agents": ["agent_001", "agent_002", "agent_003"]
}
}from maple.state import StateConflictResolver, ConsistencyManager
class StateManager:
def __init__(self):
self.conflict_resolver = StateConflictResolver()
self.consistency_manager = ConsistencyManager()
async def resolve_state_conflicts(self, conflict_details: Dict) -> Result[Dict, Dict]:
"""Resolve state synchronization conflicts intelligently"""
state_id = conflict_details['state_id']
conflicting_versions = conflict_details['conflicting_versions']
print(f"🔄 Resolving state conflict for {state_id}")
print(f" Conflicting versions: {conflicting_versions}")
# Get all conflicting state versions
state_versions = {}
for version in conflicting_versions:
state_data = await self.consistency_manager.get_state_version(state_id, version)
state_versions[version] = state_data
# Analyze conflict type and determine resolution strategy
conflict_analysis = await self.conflict_resolver.analyze_conflict(
state_id=state_id,
state_versions=state_versions,
metadata=conflict_details
)
print(f" Conflict type: {conflict_analysis['conflict_type']}")
print(f" Resolution strategy: {conflict_analysis['recommended_strategy']}")
# Apply conflict resolution
if conflict_analysis['recommended_strategy'] == 'MERGE_COMPATIBLE_CHANGES':
# Merge non-conflicting changes
merged_state = await self.conflict_resolver.merge_compatible_changes(
state_versions=state_versions,
merge_rules=conflict_analysis['merge_rules']
)
resolution_result = await self.consistency_manager.apply_merged_state(
state_id=state_id,
merged_state=merged_state,
new_version=max(conflicting_versions) + 1
)
elif conflict_analysis['recommended_strategy'] == 'LAST_WRITER_WINS':
# Use most recent update
latest_version = max(conflicting_versions)
latest_state = state_versions[latest_version]
resolution_result = await self.consistency_manager.apply_state_update(
state_id=state_id,
state_data=latest_state,
new_version=latest_version + 1,
force_override=True
)
elif conflict_analysis['recommended_strategy'] == 'CUSTOM_RESOLUTION':
# Apply domain-specific conflict resolution
custom_resolver = conflict_analysis['custom_resolver']
resolved_state = await custom_resolver.resolve_conflict(
state_id=state_id,
state_versions=state_versions,
context=conflict_details
)
resolution_result = await self.consistency_manager.apply_state_update(
state_id=state_id,
state_data=resolved_state,
new_version=max(conflicting_versions) + 1
)
if resolution_result.is_ok():
print("✅ State conflict resolved successfully")
# Notify affected agents
await self.notify_agents_of_resolution(
affected_agents=conflict_details['affected_agents'],
state_id=state_id,
resolved_version=resolution_result.unwrap()['new_version']
)
return Result.ok({
'resolution_strategy': conflict_analysis['recommended_strategy'],
'new_version': resolution_result.unwrap()['new_version'],
'affected_agents_notified': len(conflict_details['affected_agents'])
})
else:
print("❌ Failed to resolve state conflict")
return Result.err({
'errorType': 'CONFLICT_RESOLUTION_FAILED',
'message': 'Unable to resolve state conflict',
'details': resolution_result.unwrap_err()
})from maple.diagnostics import SystemHealthChecker
async def run_comprehensive_health_check() -> Dict[str, Any]:
"""Run complete system health diagnostics"""
health_checker = SystemHealthChecker()
print("🏥 MAPLE System Health Check")
print("=" * 40)
# Check all system components
health_results = await health_checker.check_all_components()
component_status = {
"🔄 Message Broker": health_results['broker_health'],
"🤖 Agents": health_results['agent_health'],
"🔧 Resources": health_results['resource_health'],
"🔒 Security": health_results['security_health'],
"🌐 State Management": health_results['state_health'],
"⚡ Performance": health_results['performance_health']
}
print("\n📋 Component Health Status:")
for component, status in component_status.items():
status_icon = "✅" if status['healthy'] else "❌"
print(f" {status_icon} {component}: {status['status']}")
if not status['healthy']:
print(f" ⚠️ Issues: {', '.join(status['issues'])}")
print(f" 🔧 Recommendations: {', '.join(status['recommendations'])}")
# Overall system score
overall_score = health_checker.calculate_overall_health_score(health_results)
print(f"\n🎯 Overall System Health: {overall_score:.1%}")
if overall_score < 0.8:
print("⚠️ System requires attention")
priority_actions = health_checker.get_priority_actions(health_results)
print("🚨 Priority Actions:")
for action in priority_actions:
print(f" 📋 {action['action']}: {action['description']}")
else:
print("✅ System operating optimally")
return health_resultsfrom maple.monitoring import DetailedProfiler
async def run_detailed_performance_profile() -> Dict[str, Any]:
"""Run detailed performance profiling"""
profiler = DetailedProfiler()
print("📊 MAPLE Detailed Performance Profile")
print("=" * 45)
# Start profiling
await profiler.start_profiling(duration="60s")
# Simulate typical workload
await simulate_typical_workload()
# Get profiling results
profile_results = await profiler.get_profiling_results()
print(f"\n⚡ Performance Results:")
print(f" 📈 Peak Throughput: {profile_results['peak_throughput']:,} msg/sec")
print(f" ⚡ Average Latency: {profile_results['avg_latency']:.2f}ms")
print(f" 🎯 P99 Latency: {profile_results['p99_latency']:.2f}ms")
print(f" 📊 CPU Utilization: {profile_results['cpu_utilization']:.1%}")
print(f" 💾 Memory Usage: {profile_results['memory_usage']}")
print(f" 🌐 Network I/O: {profile_results['network_io']}")
# Identify optimization opportunities
optimizations = profiler.identify_optimization_opportunities(profile_results)
if optimizations:
print(f"\n🚀 Optimization Opportunities:")
for opt in optimizations:
print(f" 💡 {opt['area']}: {opt['description']}")
print(f" 📈 Potential Gain: {opt['potential_improvement']}")
return profile_results- Check system resource utilization
- Verify network connectivity and bandwidth
- Review message serialization efficiency
- Analyze broker configuration
- Check for memory leaks or resource contention
- Verify optimal thread pool sizing
- Validate certificate status and expiration
- Check network connectivity to security services
- Verify encryption configuration
- Review access control policies
- Check for security policy violations
- Validate link establishment parameters
- Check available system resources
- Review resource allocation policies
- Verify resource negotiation settings
- Check for resource leaks
- Review resource optimization configuration
- Validate resource requirements are realistic
- Check network connectivity between replicas
- Verify consistency level configuration
- Review conflict resolution policies
- Check for network partitions
- Verify state validation rules
- Review state update ordering
Creator: Mahesh Vaijainthymala Krishnamoorthy (Mahesh Vaikri)
- 📚 Documentation: Comprehensive guides and API reference
- 🐛 Issues: GitHub Issues
- 💬 Discussions: Community Discussions
- 📧 Direct Support: contact@maple-protocol.org
- 🎓 Training: Professional MAPLE training available
When reporting issues, please include:
# Generate comprehensive diagnostic report
python -m maple.diagnostics.generate_report --output maple_diagnostics.json
# Include system information
python -m maple.diagnostics.system_info --verbose
# Performance snapshot
python -m maple.monitoring.performance_snapshot --duration 30sThe diagnostic report includes:
- System configuration and resource information
- Performance metrics and bottlenecks
- Security configuration and status
- Recent error logs and patterns
- Network connectivity information
- Resource utilization history
MAPLE's comprehensive diagnostics and automatic resolution capabilities make troubleshooting faster and more effective than any other agent communication protocol.
MAPLE: Multi Agent Protocol Language Engine
Copyright (C) 2025 Mahesh Vaijainthymala Krishnamoorthy (Mahesh Vaikri)
This file is part of MAPLE - Multi Agent Protocol Language Engine.
MAPLE - Multi Agent Protocol Language Engine is free software: you can redistribute it and/or
modify it under the terms of the GNU Affero General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later version.
MAPLE - Multi Agent Protocol Language Engine is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have
received a copy of the GNU Affero General Public License along with MAPLE - Multi Agent Protocol
Language Engine. If not, see <https://www.gnu.org/licenses/>.