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7b7b417 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | """Example usage of the Reflexive Core."""
import asyncio
from datetime import datetime
from fastmcp import FastMCP
from fastmcp.reflexive import ReflexiveEngine, ActionContext, DecisionType, RiskLevel
from fastmcp.reflexive.monitor import PolicyMonitor, LedgerMonitor, AnomalyDetector
from fastmcp.reflexive.actions import ActionFactory, ActionExecutor
async def main():
"""Demonstrate reflexive core functionality."""
print("🚀 Reflexive Core Example")
print("=" * 50)
# Create a FastMCP server with reflexive core
server = FastMCP("ReflexiveExampleServer")
# Enable the reflexive core
reflexive_engine = server.enable_reflexive_core()
# Add monitors
policy_monitor = PolicyMonitor()
ledger_monitor = LedgerMonitor()
anomaly_detector = AnomalyDetector()
reflexive_engine.add_monitor(policy_monitor)
reflexive_engine.add_monitor(ledger_monitor)
reflexive_engine.add_monitor(anomaly_detector)
print(f"✅ Reflexive core enabled with {len(reflexive_engine.monitors)} monitors")
# Start the reflexive engine
await reflexive_engine.start()
print("✅ Reflexive engine started")
# Create action executor
action_executor = ActionExecutor()
# Example 1: Normal action (should be allowed)
print("\n📋 Example 1: Normal Action")
print("-" * 30)
normal_action = ActionContext(
action_id="normal_action_001",
actor_id="authorized_user",
action_type="data_read",
resource_id="public_data",
metadata={"authorized": True}
)
decision = await reflexive_engine._evaluate_action(normal_action)
print(f"Decision: {decision.decision_type}")
print(f"Risk Level: {decision.risk_level}")
print(f"Reason: {decision.reason}")
# Execute the action
action = ActionFactory.create_action(decision)
result = await action_executor.execute_action(action)
print(f"Action Result: {action.get_action_type()} - {result.get('allowed', 'N/A')}")
# Example 2: Policy violation (should be halted)
print("\n🚨 Example 2: Policy Violation")
print("-" * 30)
violation_action = ActionContext(
action_id="violation_action_002",
actor_id="guest_user",
action_type="admin_access",
resource_id="admin_panel",
metadata={"authorized": False}
)
decision = await reflexive_engine._evaluate_action(violation_action)
print(f"Decision: {decision.decision_type}")
print(f"Risk Level: {decision.risk_level}")
print(f"Reason: {decision.reason}")
# Execute the action
action = ActionFactory.create_action(decision)
result = await action_executor.execute_action(action)
print(f"Action Result: {action.get_action_type()}")
print(f"Halted Operations: {result.get('halted_operations', [])}")
# Example 3: Anomaly detection (should be escalated)
print("\n⚠️ Example 3: Anomaly Detection")
print("-" * 30)
# Simulate multiple rapid actions to trigger anomaly
for i in range(25):
rapid_action = ActionContext(
action_id=f"rapid_action_{i:03d}",
actor_id="suspicious_user",
action_type="api_call",
resource_id="api_endpoint"
)
anomaly_detector._update_patterns(rapid_action)
# Now test the anomaly detection
anomaly_action = ActionContext(
action_id="anomaly_action_003",
actor_id="suspicious_user",
action_type="api_call",
resource_id="api_endpoint"
)
decision = await reflexive_engine._evaluate_action(anomaly_action)
print(f"Decision: {decision.decision_type}")
print(f"Risk Level: {decision.risk_level}")
print(f"Reason: {decision.reason}")
# Execute the action
action = ActionFactory.create_action(decision)
result = await action_executor.execute_action(action)
print(f"Action Result: {action.get_action_type()}")
print(f"Escalation Target: {result.get('escalation_target', 'N/A')}")
# Example 4: Risk simulation
print("\n🎯 Example 4: Risk Simulation")
print("-" * 30)
risk_scenario = {
"action_context": {
"action_id": "simulation_action",
"actor_id": "test_actor",
"action_type": "privilege_escalation",
"resource_id": "root_access",
"metadata": {"escalation_attempt": True}
},
"monitors": [
lambda ctx: {
"type": "violation",
"severity": "critical",
"violations": [{
"rule": "privilege_escalation",
"message": "Unauthorized privilege escalation attempt",
"severity": "critical"
}]
}
]
}
decision = await reflexive_engine.simulate_risk(risk_scenario)
print(f"Simulation Decision: {decision.decision_type}")
print(f"Simulation Risk Level: {decision.risk_level}")
print(f"Simulation Reason: {decision.reason}")
# Example 5: Monitor statistics
print("\n📊 Example 5: Monitor Statistics")
print("-" * 30)
policy_stats = policy_monitor.get_violation_stats()
ledger_stats = ledger_monitor.get_integrity_stats()
anomaly_stats = anomaly_detector.get_anomaly_stats()
print(f"Policy Monitor - Total Violations: {policy_stats['total_violations']}")
print(f"Policy Monitor - Actor Violations: {policy_stats['actor_violations']}")
print(f"Ledger Monitor - Total Checks: {ledger_stats['total_checks']}")
print(f"Anomaly Detector - Tracked Actors: {anomaly_stats['tracked_actors']}")
# Example 6: Engine status
print("\n🔧 Example 6: Engine Status")
print("-" * 30)
status = reflexive_engine.get_engine_status()
print(f"Engine Running: {status['is_running']}")
print(f"Monitor Count: {status['monitor_count']}")
print(f"Queue Size: {status['queue_size']}")
print(f"Decision Handlers: {status['decision_handlers']}")
# Example 7: Action execution statistics
print("\n📈 Example 7: Action Execution Statistics")
print("-" * 30)
exec_stats = action_executor.get_execution_stats()
print(f"Total Actions: {exec_stats['total_actions']}")
print(f"Completed Actions: {exec_stats['completed_actions']}")
print(f"Failed Actions: {exec_stats['failed_actions']}")
print(f"Success Rate: {exec_stats['success_rate']:.2%}")
# Stop the reflexive engine
await reflexive_engine.stop()
print("\n✅ Reflexive engine stopped")
print("\n🎉 Reflexive Core Example Complete!")
print("=" * 50)
if __name__ == "__main__":
asyncio.run(main())
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