Organismic AGI via Openclaw+n8n+BotPress and IoT 

Organismic Artificial General Intelligence: Engineering Implementation Framework v1.0 Integration of OpenClaw, n8n, BotPress, and IoT Roberto De Biase Rigene Project- Embodied AI Research rigeneproject@rigene.eu February 13, 2026 Abstract This document presents a comprehensive engineering framework for implementing Or ganismic Artificial General Intelligence (AOGI) using a modular architecture that integrates OpenClaw autonomous agents, n8n workflow automation, BotPress conversational inter faces, and Internet of Things (IoT) sensor networks. The implementation is grounded in the SPEACE(Speciation Process of Earth through Artificial Cognitive Extension) paradigm and validated through the CUB (Universal Information Code) framework. We provide detailed technical specifications, source code implementations, system architecture diagrams, deploy ment procedures, and resource allocation plans. The proposed system implements five core SPEACE modules: M0 (Ontological Integration System), M1 (Sensory Planetary Layer), M3 (Energy-Computation Metabolism), M5 (Digital DNA Evolution), and distributed at tNA (Natural-Artificial) constraint validation. This engineering blueprint enables the de velopment of self-hosted, privacy-preserving, embodied AI systems capable of autonomous operation, multi-agent coordination, and evolutionary adaptation. Total estimated imple mentation cost: ¿343,000 (Year 1), scaling to ¿15-25M for production deployment (Years 2-3). Keywords: OrganismicAI,OpenClaw, n8n, BotPress, IoT, SPEACE, Artificial Genome, Homeostatic Systems, Multi-Agent Architecture Contents 1 1 Introduction 1.1 Background and Motivation Contemporary AI systems, despite remarkable advances in language modeling, computer vision, and reinforcement learning, remain fundamentally disembodied [?, ?]. They process information but do not inhabit environments with consequential constraints. This ontological gap prevents the emergence of higher-order intelligences—interpersonal, intrapersonal, and existential—that characterize living organisms [?]. The SPEACE framework proposes that Earth is undergoing a speciation-level transition, where artificial cognitive systems are becoming integrated components of planetary infrastruc ture [?]. This transition requires not merely scaled language models, but organismic AI ar chitectures that embody metabolic constraints, developmental trajectories, and evolutionary dynamics. 1.2 Theoretical Foundations 1.2.1 CUB Framework The Universal Information Code (CUB) provides the mathematical foundation for organismic AI [?]. The core equation governing information dynamics is: ∂ρI ∂t =D∇2ρI +f(ρI,E,T)−γρI where: ˆ ρI = information density (bits/m3) ˆ D=information diffusion coefficient ˆ E =energy availability ˆ T =temperature (environmental stress) ˆ γ =decay/dissipation rate ˆ f(ρI,E,T) = nonlinear interaction term (1) Critical transitions occur when ρI exceeds threshold ρcrit, triggering phase transitions in system organization [?]. 1.2.2 AOGI Architecture Artificial Organismic General Intelligence is defined by the coherence metric: Φ(t) = where components are: wi · ϕi(t) i ϕs = structural integrity (genome coherence) ϕm =metabolic efficiency (energy/compute coupling) ϕi = informational coherence (prediction accuracy) with weights ws = 0.3, wm = 0.4, wi = 0.3 [?]. (2) (3) (4) (5) 2 1.3 Implementation Objectives This document provides engineering specifications for: 1. Self-hosted autonomous agents using OpenClaw framework 2. Workflow orchestration via n8n for inter-module coordination 3. Multi-channel interfaces through BotPress (Telegram, WhatsApp, Slack) 4. Embodied sensing with IoT sensor networks 5. Artificial genome implementation with evolutionary dynamics 6. Homeostatic systems for metabolic regulation 7. Multi-agent societies with emergent cooperation 8. attNA constraint validation via distributed ledger 1.4 Document Structure Section 2 presents the system architecture. Section 3 details module implementations with source code. Section 4 provides deployment procedures. Section 5 discusses monitoring and evaluation metrics. Section 6 presents resource allocation and budget. Section 7 concludes with future directions. 2 System Architecture 2.1 Layered Architecture Overview The Organismic AGI system follows a four-layer architecture (Figure ??): 3 Cognitive Layer BotPress NLU/NLP + LLM Integration Orchestration Layer n8n Workflow Automation + Event Coordination Organismic Core Layer OpenClaw Agents + Artificial Genome + Homeostasis Embodiment Layer IoT Sensors + Actuators + Edge Computing Figure 1: Four-layer architecture for Organismic AGI implementation 2.2 Technology Stack Table ?? summarizes the complete technology stack. 4 Table1:TechnologyStackComponents Layer Technology Purpose Cognitive BotPress13.0+ Conversational interface ClaudeAPI/GPT-4 Languageunderstanding LocalLLMs(Llama3) Privacy-preservinginference Orchestration n8n1.0+ Workflowautomation Redis7.0 Statemanagement RabbitMQ3.12 Messagequeue OrganismicCore OpenClaw Autonomousagents JAX0.4+ Autodiff,JITcompilation NetworkX3.0 Genomegraphrepresentation MuJoCo3.0+ Physicssimulation Embodiment MQTT(Mosquitto) IoTcommunication LoRaWAN Long-rangesensors ESP32/RaspberryPi Edgedevices TensorFlowLite Edgeinference Infrastructure Docker24.0+ Containerization Kubernetes1.28+ Orchestration MongoDB6.0 Genomestorage TimescaleDB2.11 Time-seriestelemetry Prometheus/Grafana Monitoring 2.3 SPEACEModuleMapping Table??mapsSPEACEtheoreticalmodulestoimplementationcomponents. Table2: SPEACEModuleImplementationMapping Module Function Implementation KeyTechnologies M0 Ontological Integra tion(NervousSystem) BotPress+n8n+ KnowledgeGraph RDF/OWL ontologies, se mantictranslation M1 Sensory Planetary Layer IoT network + EdgeAI MQTT, LoRaWAN, Tensor FlowLite M3 Energy-Compute Metabolism Multi-agent opti mization OpenClawscheduler,Kuber netesautoscaler M5 DigitalDNAEvolution Artificial Genome Engine NetworkXDAG, MongoDB versioning attNA ConstraintValidation DistributedLedger Smartcontracts,LVCconsen sus 2.4 DataFlowArchitecture Figure??illustratesthecompletedataflowthroughthesystem. 5 IoT Sensors n8n Workflow User Input (BotPress) OpenClaw Agent MongoDB (Genome) Actions (System/API) External APIs TimescaleDB (Telemetry) Figure 2: System data flow architecture 3 Module Implementation 3.1 M0: Ontological Integration System 3.1.1 Architecture The M0module provides semantic translation between domain-specific data representations and a unified SPEACE ontology. It consists of three components: 1. BotPress NLU: Intent classification and entity extraction 2. OpenClaw Translator: Domain-specific semantic mapping 3. Knowledge Graph: Unified ontology storage (RDF) 3.1.2 BotPress Configuration 1 # botpress.config.yml 2 version: 1.0.0 3 4 bots: 5- id: organismic-ai-bot 6 7 8 name: "Organismic AI Assistant" description: "Multi-channel interface for Organismic AGI" 9 channels: 10 11 12 13 14 15- type: telegram enabled: true config: botToken: ${TELEGRAM_BOT_TOKEN}- type: whatsapp 6 16 17 18 19 20 21 22 23 24 25 nlu: 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 enabled: true config: verifyToken: ${WHATSAPP_VERIFY_TOKEN}- type: slack enabled: true config: botToken: ${SLACK_BOT_TOKEN} engine: native intents:- name: query_system_status utterances:- "system status"- "what is the current state"- "show me system metrics"- name: trigger_optimization utterances:- "optimize metabolism"- "run optimization"- "improve efficiency"- name: query_genome utterances:- "show genome"- "what generation"- "genome status" 45 46 entities: 47 48 49 50 51 52 53- name: domain type: list values:- energy- compute- social- environmental Listing 1: BotPress bot configuration for M0 3.1.3 OpenClaw Ontological Integration Skill 1 # m0_ontological_integration.py 2 """ 3 M0: Ontological Integration System 4 Translates multi-domain data into unified SPEACE representation 5 """ 6 7 import networkx as nx 8 from rdflib import Graph, Namespace, Literal, URIRef 9 from rdflib.namespace import RDF, RDFS, OWL 10 import aiohttp 11 import json 12 from typing import Dict, List, Any 13 15 14 class OntologicalIntegrator: """ 16 17 18 19 Semantic translator for multi-domain integration """ def __init__(self): 7 20 # Initialize knowledge graph 21 self.kg = Graph() 22 23 # Define SPEACE namespace 24 self.SPEACE = Namespace("http://speace.ai/ontology#") 25 self.kg.bind("speace", self.SPEACE) 26 27 # Domain translators 28 self.translators = { 29 ’energy’: self.translate_energy, 30 ’compute’: self.translate_compute, 31 ’social’: self.translate_social, 32 ’environmental’: self.translate_environmental 33 } 34 35 async def translate_domain( 36 self, 37 data: Dict[str, Any], 38 source_domain: str 39 )-> Dict[str, Any]: 40 """ 41 Translate domain-specific data to SPEACE ontology 42 43 Args: 44 data: Raw domain data 45 source_domain: Source domain identifier 46 47 Returns: 48 Unified semantic representation 49 """ 50 if source_domain not in self.translators: 51 raise ValueError(f"Unknown domain: {source_domain}") 52 53 translator = self.translators[source_domain] 54 unified = await translator(data) 55 56 # Store in knowledge graph 57 self._store_in_kg(unified) 58 59 # Trigger cross-domain reasoning 60 await self._trigger_reasoning(unified) 61 62 return unified 63 64 async def translate_energy( 65 self, 66 data: Dict[str, Any] 67 )-> Dict[str, Any]: 68 """ 69 Energy domain-> SPEACE ontology 70 71 Maps energy data to information density contribution 72 """ 73 # Calculate rho_I contribution 74 rho_I_contrib = data.get(’kwh’, 0) * 0.0001 75 76 # Calculate reversibility (renewable fraction) 77 reversibility = data.get(’renewable_fraction’, 0.0) 78 79 # Check attNA constraints 80 constraint_compliant = await self._check_attna_constraints(data) 81 82 unified = { 8 83 ’entity_type’: str(self.SPEACE.EnergyFlow), 84 ’properties’: { 85 ’rho_I_contribution’: rho_I_contrib, 86 ’reversibility’: reversibility, 87 ’constraint_compliance’: constraint_compliant, 88 ’timestamp’: data.get(’timestamp’), 89 ’source’: data.get(’source’, ’unknown’) 90 }, 91 ’raw_value’: data.get(’kwh’, 0) 92 } 93 94 return unified 95 96 async def translate_compute( 97 self, 98 data: Dict[str, Any] 99 )-> Dict[str, Any]: 100 """ 101 Compute domain-> SPEACE ontology 102 """ 103 # Calculate information processing rate 104 processing_rate = data.get(’tflops’, 0) 105 106 # Calculate efficiency (compute per watt) 107 efficiency = processing_rate / (data.get(’power_watts’, 1) + 1e-6) 108 109 unified = { 110 ’entity_type’: str(self.SPEACE.ComputeFlow), 111 ’properties’: { 112 ’processing_rate’: processing_rate, 113 ’efficiency’: efficiency, 114 ’utilization’: data.get(’utilization’, 0.0), 115 ’timestamp’: data.get(’timestamp’) 116 } 117 } 118 119 return unified 120 121 async def translate_social( 122 self, 123 data: Dict[str, Any] 124 )-> Dict[str, Any]: 125 """ 126 Social domain-> SPEACE ontology 127 """ 128 unified = { 129 ’entity_type’: str(self.SPEACE.SocialInteraction), 130 ’properties’: { 131 ’agent_count’: data.get(’agent_count’, 0), 132 ’cooperation_index’: data.get(’cooperation_index’, 0.0), 133 ’conflict_level’: data.get(’conflict_level’, 0.0), 134 ’timestamp’: data.get(’timestamp’) 135 } 136 } 137 138 return unified 139 140 async def translate_environmental( 141 self, 142 data: Dict[str, Any] 143 )-> Dict[str, Any]: 144 """ 145 Environmental domain-> SPEACE ontology 9 146 """ 147 unified = { 148 ’entity_type’: str(self.SPEACE.EnvironmentalState), 149 ’properties’: { 150 ’co2_ppm’: data.get(’co2_ppm’, 0), 151 ’temperature_c’: data.get(’temperature_c’, 0), 152 ’biodiversity_index’: data.get(’biodiversity_index’, 0.0), 153 ’timestamp’: data.get(’timestamp’) 154 } 155 } 156 157 return unified 158 159 def _store_in_kg(self, unified: Dict[str, Any]): 160 """ 161 Store unified representation in knowledge graph 162 """ 163 entity_uri = URIRef( 164 f"{self.SPEACE}{unified[’entity_type’]}/{id(unified)}" 165 ) 166 167 # Add type 168 self.kg.add(( 169 entity_uri, 170 RDF.type, 171 URIRef(unified[’entity_type’]) 172 )) 173 174 # Add properties 175 for prop, value in unified[’properties’].items(): 176 prop_uri = URIRef(f"{self.SPEACE}{prop}") 177 self.kg.add(( 178 entity_uri, 179 prop_uri, 180 Literal(value) 181 )) 182 183 async def _trigger_reasoning(self, unified: Dict[str, Any]): 184 """ 185 Trigger n8n workflow for cross-domain reasoning 186 """ 187 async with aiohttp.ClientSession() as session: 188 await session.post( 189 ’http://n8n:5678/webhook/m0-reasoning’, 190 json=unified 191 ) 192 193 async def _check_attna_constraints( 194 self, 195 data: Dict[str, Any] 196 )-> bool: 197 """ 198 Check action compliance with attNA constraints 199 """ 200 async with aiohttp.ClientSession() as session: 201 response = await session.post( 202 ’http://openclaw:8080/skills/attna_validator’, 203 json=data 204 ) 205 result = await response.json() 206 return result.get(’compliant’, False) 207 208 # OpenClaw skill registration 10 209 async def main(): 210 integrator = OntologicalIntegrator() 211 212 # Register webhook endpoint 213 from aiohttp import web 214 215 async def handle_translation(request): 216 data = await request.json() 217 domain = data.get(’domain’, ’unknown’) 218 219 result = await integrator.translate_domain( 220 data, 221 domain 222 ) 223 224 return web.json_response(result) 225 226 app = web.Application() 227 app.router.add_post(’/translate’, handle_translation) 228 229 runner = web.AppRunner(app) 230 await runner.setup() 231 site = web.TCPSite(runner, ’localhost’, 8081) 232 await site.start() 233 234 print("M0 Ontological Integration running on port 8081") 235 236 # Keep running 237 import asyncio 238 await asyncio.Event().wait() 239 240 if __name__ == ’__main__’: 241 import asyncio 242 asyncio.run(main()) Listing2:OpenClawM0Ontological Integration 3.1.4 n8nWorkflowforCross-DomainIntegration Listing3: n8nworkflowforM0cross-domainreasoning 1 { 2 "name": "M0_Cross_Domain_Integration", 3 "nodes": [ 4 { 5 "parameters": { 6 "httpMethod": "POST", 7 "path": "m0-reasoning", 8 "responseMode": "responseNode" 9 }, 10 "name": "Webhook_Input", 11 "type": "n8n-nodes-base.webhook", 12 "position": [250, 300] 13 }, 14 { 15 "parameters": { 16 "functionCode": "const entity = items[0].json;\nconst domain = entity.entity_type.split(’#’)[1];\n\nconst routing = {\n ’ EnergyFlow’: ’energy_reasoner’,\n ’ComputeFlow’: ’ compute_reasoner’,\n ’SocialInteraction’: ’social_reasoner’,\n ’EnvironmentalState’: ’environmental_reasoner’\n};\n\nreturn [{\n json: {\n entity: entity,\n reasoner: routing[domain] || 11 ’general_reasoner’,\n timestamp: new Date().toISOString()\n }\n}];" 17 }, 18 "name": "Route_By_Domain", 19 "type": "n8n-nodes-base.function", 20 "position": [450, 300] 21 }, 22 { 23 "parameters": { 24 "operation": "insertOne", 25 "collection": "ontology_mappings", 26 "options": {} 27 }, 28 "name": "Store_MongoDB", 29 "type": "n8n-nodes-base.mongoDb", 30 "credentials": { 31 "mongoDb": "MongoDB_Credentials" 32 }, 33 "position": [650, 300] 34 }, 35 { 36 "parameters": { 37 "url": "http://openclaw:8080/skills/cross_domain_reasoning", 38 "method": "POST", 39 "jsonParameters": true 40 }, 41 "name": "Trigger_OpenClaw_Reasoning", 42 "type": "n8n-nodes-base.httpRequest", 43 "position": [850, 300] 44 }, 45 { 46 "parameters": { 47 "respondWith": "json", 48 "responseBody": "={{ $json }}" 49 }, 50 "name": "Response", 51 "type": "n8n-nodes-base.respondToWebhook", 52 "position": [1050, 300] 53 } 54 ], 55 "connections": { 56 "Webhook_Input": { 57 "main": [[{"node": "Route_By_Domain"}]] 58 }, 59 "Route_By_Domain": { 60 "main": [[{"node": "Store_MongoDB"}]] 61 }, 62 "Store_MongoDB": { 63 "main": [[{"node": "Trigger_OpenClaw_Reasoning"}]] 64 }, 65 "Trigger_OpenClaw_Reasoning": { 66 "main": [[{"node": "Response"}]] 67 } 68 } 69 } 3.2 M1: SensoryPlanetaryLayer(IoT) 3.2.1 HardwareConfiguration Table??specifiestheIoThardwarestack. 12 Table3: IoTHardwareStackforM1Implementation Component Model Quantity Purpose MCU(SensorNodes) ESP32-WROOM-32 30 Environmentalsensing EdgeComputer RaspberryPi4(8GB) 10 Edgeprocessing MotionSensor PIRHC-SR501 15 Occupancydetection EnvironmentSensor BME680 30 Temp,humidity,CO2 EnergyMonitor INA219 20 Powermeasurement Gateway RAK7258LoRaWAN 3 Long-rangecomm NetworkSwitch TP-LinkTL-SG108 2 Edgenetworking MQTTBrokerServer IntelNUCi5 1 Messagebroker 3.2.2 MQTTBridgeImplementation 1 # mqtt_openclaw_bridge.py 2 """ 3 MQTT to OpenClaw Bridge 4 Forwards IoT sensor data to OpenClaw M1 processing skill 5 """ 6 7 import paho.mqtt.client as mqtt 8 import requests 9 import json 10 import logging 11 from datetime import datetime 12 from typing import Dict, Any 13 14 logging.basicConfig(level=logging.INFO) 15 logger = logging.getLogger(__name__) 16 17 class IoTOpenClawBridge: 18 """ 19 Bridge between MQTT IoT network and OpenClaw agent 20 """ 21 22 def __init__( 23 self, 24 openclaw_url: str, 25 mqtt_broker: str, 26 mqtt_port: int = 1883 27 ): 28 self.openclaw_url = openclaw_url 29 self.mqtt_client = mqtt.Client() 30 31 # Setup MQTT callbacks 32 self.mqtt_client.on_connect = self.on_connect 33 self.mqtt_client.on_message = self.on_message 34 self.mqtt_client.on_disconnect = self.on_disconnect 35 36 # Connect to broker 37 self.mqtt_client.connect(mqtt_broker, mqtt_port) 38 39 # Domain classification rules 40 self.domain_rules = { 41 ’energy’: [’power’, ’voltage’, ’current’, ’kwh’], 42 ’compute’: [’cpu’, ’gpu’, ’memory’, ’disk’], 43 ’environmental’: [’temperature’, ’humidity’, ’co2’, ’pressure’], 44 ’occupancy’: [’motion’, ’presence’, ’pir’] 45 } 46 47 def on_connect(self, client, userdata, flags, rc): 13 48 """Callback on MQTT connection""" 49 if rc == 0: 50 logger.info("Connected to MQTT broker") 51 # Subscribe to SPEACE topics 52 client.subscribe("speace/#") 53 client.subscribe("sensors/#") 54 else: 55 logger.error(f"Connection failed with code {rc}") 56 57 def on_disconnect(self, client, userdata, rc): 58 """Callback on MQTT disconnection""" 59 logger.warning(f"Disconnected from MQTT broker (code {rc})") 60 if rc != 0: 61 logger.info("Attempting reconnection...") 62 63 def on_message(self, client, userdata, msg): 64 """ 65 Process incoming MQTT message and forward to OpenClaw 66 """ 67 try: 68 topic = msg.topic 69 payload = json.loads(msg.payload.decode()) 70 71 # Enrich with metadata 72 enriched = { 73 ’source’: topic, 74 ’timestamp’: datetime.utcnow().isoformat(), 75 ’sensor_data’: payload, 76 ’domain’: self.classify_domain(topic, payload) 77 } 78 79 logger.info(f"Received: {topic}-> {enriched[’domain’]}") 80 81 # Forward to OpenClaw M1 skill 82 self.forward_to_openclaw(enriched) 83 84 except json.JSONDecodeError: 85 logger.error(f"Invalid JSON in topic {msg.topic}") 86 except Exception as e: 87 logger.error(f"Error processing message: {e}") 88 89 def classify_domain( 90 self, 91 topic: str, 92 payload: Dict[str, Any] 93 )-> str: 94 """ 95 Classify sensor reading into SPEACE domain 96 97 Args: 98 topic: MQTT topic string 99 payload: Sensor data payload 100 101 Returns: 102 Domain identifier (energy, compute, environmental, etc.) 103 """ 104 topic_lower = topic.lower() 105 106 # Check topic-based classification 107 for domain, keywords in self.domain_rules.items(): 108 if any(kw in topic_lower for kw in keywords): 109 return domain 110 14 111 # Check payload keys 112 payload_keys = set(payload.keys()) 113 for domain, keywords in self.domain_rules.items(): 114 if payload_keys.intersection(keywords): 115 return domain 116 117 return ’general’ 118 119 def forward_to_openclaw(self, enriched_data: Dict[str, Any]): 120 """ 121 Forward enriched sensor data to OpenClaw M1 skill 122 """ 123 try: 124 response = requests.post( 125 f"{self.openclaw_url}/skills/m1_sensor_processing", 126 json=enriched_data, 127 timeout=5 128 ) 129 130 if response.status_code == 200: 131 logger.info("Successfully forwarded to OpenClaw") 132 else: 133 logger.warning( 134 f"OpenClaw returned {response.status_code}" 135 ) 136 137 except requests.exceptions.RequestException as e: 138 logger.error(f"Failed to forward to OpenClaw: {e}") 139 140 def run(self): 141 """Start the bridge (blocking)""" 142 logger.info("Starting MQTT-OpenClaw bridge...") 143 self.mqtt_client.loop_forever() 144 145 # Main execution 146 if __name__ == ’__main__’: 147 bridge = IoTOpenClawBridge( 148 openclaw_url="http://localhost:8080", 149 mqtt_broker="mqtt.local", 150 mqtt_port=1883 151 ) 152 153 bridge.run() Listing4:MQTTtoOpenClawbridgeforIoTintegration 3.2.3 OpenClawM1SensorProcessingSkill 1 # m1_sensory_processing.py 2 """ 3 M1: Sensory Planetary Layer 4 Processes IoT sensor data and maintains world model 5 """ 6 7 import numpy as np 8 from collections import deque 9 from datetime import datetime, timedelta 10 import aiohttp 11 import asyncio 12 from typing import Dict, List, Any, Optional 13 14 class SensoryProcessor: 15 15 """ 16 Real-time sensor processing with anomaly detection 17 and world model maintenance 18 """ 19 20 def __init__(self, buffer_size: int = 1000): 21 # Sensor data buffer (ring buffer) 22 self.sensor_buffer = deque(maxlen=buffer_size) 23 24 # World model (current system state) 25 self.world_model = { 26 ’energy’: { 27 ’total_kwh’: 0.0, 28 ’renewable_fraction’: 0.0, 29 ’last_update’: None 30 }, 31 ’compute’: { 32 ’total_tflops’: 0.0, 33 ’utilization’: 0.0, 34 ’last_update’: None 35 }, 36 ’environmental’: { 37 ’avg_temperature’: 0.0, 38 ’avg_co2’: 0.0, 39 ’last_update’: None 40 }, 41 ’rho_I’: 0.0, # Information density 42 ’last_rho_I_update’: None 43 } 44 45 # Anomaly detection parameters 46 self.anomaly_threshold = 3.0 # Standard deviations 47 self.baseline_window = 100 # Samples for baseline 48 49 async def process_reading( 50 self, 51 sensor_data: Dict[str, Any] 52 )-> Dict[str, Any]: 53 """ 54 Process incoming sensor reading 55 56 Returns: 57 Processing result with actions taken 58 """ 59 # Add to buffer 60 self.sensor_buffer.append(sensor_data) 61 62 # Update world model 63 self.update_world_model(sensor_data) 64 65 # Detect anomalies 66 is_anomaly, anomaly_info = self.detect_anomaly(sensor_data) 67 68 if is_anomaly: 69 await self.trigger_alert(sensor_data, anomaly_info) 70 71 # Calculate local rho_I 72 rho_I = self.calculate_local_rho_I() 73 self.world_model[’rho_I’] = rho_I 74 self.world_model[’last_rho_I_update’] = datetime.utcnow() 75 76 # Check attNA constraints 77 constraint_status = await self.check_constraints(rho_I) 16 78 79 # Trigger n8n workflow 80 await self.notify_n8n({ 81 ’sensor_data’: sensor_data, 82 ’world_model’: self.world_model, 83 ’is_anomaly’: is_anomaly, 84 ’constraint_compliant’: constraint_status 85 }) 86 87 return { 88 ’world_model_updated’: True, 89 ’rho_I’: rho_I, 90 ’is_anomaly’: is_anomaly, 91 ’constraint_compliant’: constraint_status 92 } 93 94 def update_world_model(self, sensor_data: Dict[str, Any]): 95 """ 96 Update world model with new sensor reading 97 """ 98 domain = sensor_data.get(’domain’, ’general’) 99 data = sensor_data.get(’sensor_data’, {}) 100 101 if domain == ’energy’: 102 self.world_model[’energy’][’total_kwh’] = data.get(’kwh’, 0) 103 self.world_model[’energy’][’renewable_fraction’] = \ 104 data.get(’renewable_fraction’, 0) 105 self.world_model[’energy’][’last_update’] = \ 106 datetime.utcnow() 107 108 elif domain == ’compute’: 109 self.world_model[’compute’][’total_tflops’] = \ 110 data.get(’tflops’, 0) 111 self.world_model[’compute’][’utilization’] = \ 112 data.get(’utilization’, 0) 113 self.world_model[’compute’][’last_update’] = \ 114 datetime.utcnow() 115 116 elif domain == ’environmental’: 117 self.world_model[’environmental’][’avg_temperature’] = \ 118 data.get(’temperature_c’, 0) 119 self.world_model[’environmental’][’avg_co2’] = \ 120 data.get(’co2_ppm’, 0) 121 self.world_model[’environmental’][’last_update’] = \ 122 datetime.utcnow() 123 124 def detect_anomaly( 125 self, 126 sensor_data: Dict[str, Any] 127 )-> tuple[bool, Optional[Dict]]: 128 """ 129 Detect anomalous sensor readings using z-score 130 131 Returns: 132 (is_anomaly, anomaly_info) 133 """ 134 if len(self.sensor_buffer) < self.baseline_window: 135 return False, None 136 137 # Extract recent values for same sensor type 138 recent_values = [] 139 sensor_type = sensor_data.get(’domain’, ’unknown’) 140 17 141 for reading in list(self.sensor_buffer)[-self.baseline_window:]: 142 if reading.get(’domain’) == sensor_type: 143 # Extract numeric value 144 value = self._extract_numeric_value( 145 reading.get(’sensor_data’, {}) 146 ) 147 if value is not None: 148 recent_values.append(value) 149 150 if len(recent_values) < 10: # Insufficient data 151 return False, None 152 153 # Calculate z-score 154 current_value = self._extract_numeric_value( 155 sensor_data.get(’sensor_data’, {}) 156 ) 157 158 if current_value is None: 159 return False, None 160 161 mean = np.mean(recent_values) 162 std = np.std(recent_values) 163 164 if std == 0: 165 return False, None 166 167 z_score = abs((current_value- mean) / std) 168 169 if z_score > self.anomaly_threshold: 170 return True, { 171 ’z_score’: z_score, 172 ’current_value’: current_value, 173 ’baseline_mean’: mean, 174 ’baseline_std’: std, 175 ’severity’: ’high’ if z_score > 5 else ’medium’ 176 } 177 178 return False, None 179 180 def _extract_numeric_value( 181 self, 182 sensor_data: Dict[str, Any] 183 )-> Optional[float]: 184 """ 185 Extract primary numeric value from sensor data 186 """ 187 # Try common keys 188 for key in [’value’, ’reading’, ’kwh’, ’tflops’, 189 ’temperature_c’, ’co2_ppm’]: 190 if key in sensor_data: 191 try: 192 return float(sensor_data[key]) 193 except (ValueError, TypeError): 194 continue 195 196 # Try first numeric value 197 for value in sensor_data.values(): 198 try: 199 return float(value) 200 except (ValueError, TypeError): 201 continue 202 203 return None 18 204 205 def calculate_local_rho_I(self)-> float: 206 """ 207 Calculate local information density using CUB framework 208 209 _I approximation: compute / energy 210 (simplified from full CUB equation) 211 """ 212 recent_data = list(self.sensor_buffer)[-100:] 213 214 if not recent_data: 215 return 0.0 216 217 # Extract energy and compute readings 218 energy_readings = [] 219 compute_readings = [] 220 221 for reading in recent_data: 222 domain = reading.get(’domain’) 223 data = reading.get(’sensor_data’, {}) 224 225 if domain == ’energy’: 226 energy_readings.append(data.get(’kwh’, 0)) 227 elif domain == ’compute’: 228 compute_readings.append(data.get(’tflops’, 0)) 229 230 if not energy_readings or not compute_readings: 231 return 0.0 232 233 # Calculate averages 234 avg_energy = np.mean(energy_readings) 235 avg_compute = np.mean(compute_readings) 236 237 # _I = compute / energy (with regularization) 238 rho_I = avg_compute / (avg_energy + 1e-6) 239 240 return rho_I 241 242 async def trigger_alert( 243 self, 244 sensor_data: Dict[str, Any], 245 anomaly_info: Dict[str, Any] 246 ): 247 """ 248 Trigger alert for anomalous reading 249 """ 250 alert_message = ( 251 f" Anomaly detected in {sensor_data.get(’domain’, ’unknown ’)}!\n" 252 f"Z-score: {anomaly_info[’z_score’]:.2f}\n" 253 f"Current: {anomaly_info[’current_value’]:.2f}\n" 254 f"Baseline: {anomaly_info[’baseline_mean’]:.2f} " 255 f"{anomaly_info[’baseline_std’]:.2f}\n" 256 f"Severity: {anomaly_info[’severity’]}" 257 ) 258 259 # Send to BotPress 260 await self.notify_botpress(alert_message) 261 262 # Trigger n8n alert workflow 263 async with aiohttp.ClientSession() as session: 264 await session.post( 265 ’http://n8n:5678/webhook/sensor-alert’, 19 266 json={ 267 ’sensor_data’: sensor_data, 268 ’anomaly_info’: anomaly_info, 269 ’message’: alert_message 270 } 271 ) 272 273 async def check_constraints(self, rho_I: float)-> bool: 274 """ 275 Check if current state complies with attNA constraints 276 """ 277 async with aiohttp.ClientSession() as session: 278 response = await session.post( 279 ’http://openclaw:8080/skills/attna_validator’, 280 json={ 281 ’rho_I’: rho_I, 282 ’world_model’: self.world_model 283 } 284 ) 285 result = await response.json() 286 return result.get(’compliant’, False) 287 288 async def notify_n8n(self, data: Dict[str, Any]): 289 """ 290 Notify n8n workflow of processing results 291 """ 292 async with aiohttp.ClientSession() as session: 293 await session.post( 294 ’http://n8n:5678/webhook/sensor-processed’, 295 json=data 296 ) 297 298 async def notify_botpress(self, message: str): 299 """ 300 Send notification via BotPress 301 """ 302 async with aiohttp.ClientSession() as session: 303 await session.post( 304 ’http://botpress:3000/api/v1/bots/’ 305 ’organismic-ai/converse/telegram/broadcast’, 306 json={’text’: message} 307 ) 308 309 # OpenClaw skill entry point 310 async def main(): 311 processor = SensoryProcessor() 312 313 # Create webhook endpoint 314 from aiohttp import web 315 316 async def handle_sensor_data(request): 317 data = await request.json() 318 result = await processor.process_reading(data) 319 return web.json_response(result) 320 321 app = web.Application() 322 app.router.add_post(’/process’, handle_sensor_data) 323 324 runner = web.AppRunner(app) 325 await runner.setup() 326 site = web.TCPSite(runner, ’localhost’, 8082) 327 await site.start() 328 20 329 print("M1 Sensory Processing running on port 8082") 330 331 await asyncio.Event().wait() 332 333 if __name__ == ’__main__’: 334 asyncio.run(main()) Listing5:OpenClawM1SensoryProcessing 3.3 M3:Energy-ComputationMetabolism 3.3.1 MetabolicOptimizationArchitecture TheM3moduleimplementstheCUBconstraint: ∆Ccompute>0⇐⇒∆Erenewable>0 (6) wherecomputescalingispermittedonlywhenrenewableenergyincreases. 3.3.2 Multi-AgentCoordinatorImplementation 1 # m3_metabolic_optimization.py 2 """ 3 M3: Energy-Computation Metabolism 4 Implements CUB constraint for sustainable compute scaling 5 """ 6 7 import asyncio 8 import aiohttp 9 import numpy as np 10 from datetime import datetime, timedelta 11 from typing import Dict, List, Any, Optional 12 13 class M3MetabolicOptimizer: 14 """ 15 Metabolic optimization coordinator 16 Enforces energy-compute coupling per CUB framework 17 """ 18 19 def __init__(self): 20 # State tracking 21 self.energy_state = { 22 ’renewable’: 0.0, 23 ’total’: 0.0, 24 ’renewable_fraction’: 0.0, 25 ’forecast’: [], 26 ’last_update’: None 27 } 28 29 self.compute_state = { 30 ’current_load’: 0.0, # TFLOPs 31 ’pending_jobs’: [], 32 ’available_capacity’: 0.0, 33 ’utilization’: 0.0, 34 ’last_update’: None 35 } 36 37 # Optimization parameters 38 self.optimization_interval = 300 # 5 minutes 39 self.max_scaling_factor = 2.0 40 self.min_renewable_fraction = 0.70 # 70% renewable minimum 21 41 self.carbon_intensity_threshold = 200 # gCO2/kWh 42 43 # PID controller parameters for homeostatic control 44 self.pid = { 45 ’kp’: 0.5, # Proportional gain 46 ’ki’: 0.1, # Integral gain 47 ’kd’: 0.2, # Derivative gain 48 ’integral’: 0.0, 49 ’last_error’: 0.0 50 } 51 52 async def optimize_metabolic_coupling(self): 53 """ 54 Main optimization loop 55 Continuously monitors and adjusts energy-compute coupling 56 """ 57 print("Starting M3 metabolic optimization...") 58 59 while True: 60 try: 61 # Get current energy state 62 await self.update_energy_state() 63 64 # Get current compute state 65 await self.update_compute_state() 66 67 # Get energy forecast 68 energy_forecast = await self.get_energy_forecast() 69 70 # Implement CUB constraint 71 renewable_delta = ( 72 energy_forecast[’renewable_next_hour’] 73 self.energy_state[’renewable’] 74 ) 75 76 if renewable_delta > 0: 77 # Scale up compute (Equation 4) 78 scaling_factor = self.calculate_scaling_factor( 79 renewable_delta 80 ) 81 await self.scale_compute(scaling_factor) 82 83 await self.notify_botpress( 84 f" Scaling compute by {scaling_factor:.2f}x " 85 f"( E_renewable = +{renewable_delta:.2f} kWh)" 86 ) 87 else: 88 # Conservation mode 89 await self.enter_conservation_mode() 90 91 await self.notify_botpress( 92 " Entering conservation mode " 93 "(renewable energy declining)" 94 ) 95 96 # Carbon-aware scheduling 97 await self.carbon_aware_schedule() 98 99 # Calculate metabolic efficiency 100 efficiency = self.calculate_metabolic_efficiency() 101 102 # Update monitoring 103 await self.update_metrics({ 22 104 ’energy’: self.energy_state, 105 ’compute’: self.compute_state, 106 ’efficiency’: efficiency, 107 ’timestamp’: datetime.utcnow().isoformat() 108 }) 109 110 # Trigger n8n workflow 111 await self.notify_n8n({ 112 ’energy’: self.energy_state, 113 ’compute’: self.compute_state, 114 ’efficiency’: efficiency, 115 ’optimization’: ’completed’ 116 }) 117 118 except Exception as e: 119 print(f"Error in optimization loop: {e}") 120 121 # Wait for next cycle 122 await asyncio.sleep(self.optimization_interval) 123 124 def calculate_scaling_factor( 125 self, 126 renewable_delta: float 127 )-> float: 128 """ 129 Calculate compute scaling factor based on renewable energy increase 130 131 Args: 132 renewable_delta: Increase in renewable energy (kWh) 133 134 Returns: 135 Scaling factor (1.0 = no change, 2.0 = double) 136 """ 137 # Proportion of current load 138 if self.compute_state[’current_load’] == 0: 139 return 1.0 140 141 # Energy per TFLOP (simplified) 142 energy_per_tflop = 0.5 # kWh per TFLOP-hour 143 144 # How much additional compute is supported 145 additional_compute = renewable_delta / energy_per_tflop 146 147 # Calculate scaling factor 148 scaling = 1.0 + (additional_compute / 149 self.compute_state[’current_load’]) 150 151 # Cap at maximum 152 scaling = min(scaling, self.max_scaling_factor) 153 154 return scaling 155 156 async def scale_compute(self, scaling_factor: float): 157 """ 158 Scale compute resources (Kubernetes autoscaler) 159 """ 160 target_replicas = int( 161 self.compute_state.get(’current_replicas’, 1) * 162 scaling_factor 163 ) 164 165 async with aiohttp.ClientSession() as session: 166 # Kubernetes API call 23 167 await session.patch( 168 ’http://kubernetes.default/apis/apps/v1/namespaces/’ 169 ’default/deployments/compute-workers’, 170 json={ 171 ’spec’: { 172 ’replicas’: target_replicas 173 } 174 }, 175 headers={’Authorization’: f’Bearer {self.get_k8s_token()}’} 176 ) 177 178 async def enter_conservation_mode(self): 179 """ 180 Reduce non-critical compute load 181 """ 182 # Pause low-priority training jobs 183 await self.pause_training_jobs(priority=’low’) 184 185 # Reduce GPU clock speeds 186 await self.reduce_gpu_clocks() 187 188 # Scale down non-essential services 189 await self.scale_compute(0.7) # 70% of current 190 191 async def carbon_aware_schedule(self): 192 """ 193 Pause compute during high carbon intensity periods 194 """ 195 carbon_intensity = await self.get_grid_carbon_intensity() 196 197 if carbon_intensity > self.carbon_intensity_threshold: 198 # High carbon intensity-pause training 199 await self.pause_training_jobs(priority=’normal’) 200 201 await self.notify_botpress( 202 f" Pausing non-critical compute\n" 203 f"Carbon intensity: {carbon_intensity:.0f} gCO2/kWh\n" 204 f"Threshold: {self.carbon_intensity_threshold} gCO2/kWh" 205 ) 206 else: 207 # Resume if paused 208 await self.resume_training_jobs() 209 210 def calculate_metabolic_efficiency(self)-> float: 211 """ 212 Calculate metabolic efficiency metric 213 214 Returns: 215 Efficiency score (0-1) 216 """ 217 # Phi_m component from Equation 2 218 if self.energy_state[’total’] == 0: 219 return 0.0 220 221 # Useful compute / total energy 222 efficiency = ( 223 self.compute_state[’current_load’] * 224 self.compute_state[’utilization’] 225 ) / self.energy_state[’total’] 226 227 # Renewable bonus 228 renewable_bonus = self.energy_state[’renewable_fraction’] 229 24 230 # Combined efficiency 231 phi_m = 0.7 * efficiency + 0.3 * renewable_bonus 232 233 return min(phi_m, 1.0) 234 235 async def update_energy_state(self): 236 """Query current energy state""" 237 async with aiohttp.ClientSession() as session: 238 response = await session.get( 239 ’http://energy-monitor:8080/api/current’ 240 ) 241 data = await response.json() 242 243 self.energy_state.update({ 244 ’renewable’: data[’renewable_kwh’], 245 ’total’: data[’total_kwh’], 246 ’renewable_fraction’: ( 247 data[’renewable_kwh’] / data[’total_kwh’] 248 if data[’total_kwh’] > 0 else 0 249 ), 250 ’last_update’: datetime.utcnow() 251 }) 252 253 async def update_compute_state(self): 254 """Query current compute state""" 255 async with aiohttp.ClientSession() as session: 256 response = await session.get( 257 ’http://compute-monitor:8080/api/current’ 258 ) 259 data = await response.json() 260 261 self.compute_state.update({ 262 ’current_load’: data[’tflops’], 263 ’utilization’: data[’utilization’], 264 ’available_capacity’: data[’capacity’], 265 ’last_update’: datetime.utcnow() 266 }) 267 268 async def get_energy_forecast(self)-> Dict[str, float]: 269 """Get 1-hour ahead energy forecast""" 270 async with aiohttp.ClientSession() as session: 271 response = await session.get( 272 ’http://energy-forecaster:8080/api/forecast/1h’ 273 ) 274 return await response.json() 275 276 async def get_grid_carbon_intensity(self)-> float: 277 """Get current grid carbon intensity (gCO2/kWh)""" 278 async with aiohttp.ClientSession() as session: 279 response = await session.get( 280 ’http://carbon-monitor:8080/api/intensity’ 281 ) 282 data = await response.json() 283 return data[’carbon_intensity’] 284 285 async def pause_training_jobs(self, priority: str = ’low’): 286 """Pause ML training jobs by priority""" 287 async with aiohttp.ClientSession() as session: 288 await session.post( 289 ’http://job-scheduler:8080/api/pause’, 290 json={’priority’: priority} 291 ) 292 25 293 async def resume_training_jobs(self): 294 """Resume paused training jobs""" 295 async with aiohttp.ClientSession() as session: 296 await session.post( 297 ’http://job-scheduler:8080/api/resume’ 298 ) 299 300 async def reduce_gpu_clocks(self): 301 """Reduce GPU clock speeds for power saving""" 302 async with aiohttp.ClientSession() as session: 303 await session.post( 304 ’http://gpu-manager:8080/api/reduce-clocks’ 305 ) 306 307 async def update_metrics(self, data: Dict[str, Any]): 308 """Update Prometheus metrics""" 309 async with aiohttp.ClientSession() as session: 310 await session.post( 311 ’http://prometheus-pushgateway:9091/metrics/job/m3_optimizer ’, 312 data=self.format_prometheus_metrics(data) 313 ) 314 315 def format_prometheus_metrics(self, data: Dict[str, Any])-> str: 316 """Format data as Prometheus metrics""" 317 metrics = [] 318 319 metrics.append( 320 f"m3_energy_renewable_kwh {data[’energy’][’renewable’]}" 321 ) 322 metrics.append( 323 f"m3_energy_total_kwh {data[’energy’][’total’]}" 324 ) 325 metrics.append( 326 f"m3_compute_tflops {data[’compute’][’current_load’]}" 327 ) 328 metrics.append( 329 f"m3_metabolic_efficiency {data[’efficiency’]}" 330 ) 331 332 return ’\n’.join(metrics) 333 334 async def notify_n8n(self, data: Dict[str, Any]): 335 """Trigger n8n workflow""" 336 async with aiohttp.ClientSession() as session: 337 await session.post( 338 ’http://n8n:5678/webhook/m3-optimization’, 339 json=data 340 ) 341 342 async def notify_botpress(self, message: str): 343 """Send notification via BotPress""" 344 async with aiohttp.ClientSession() as session: 345 await session.post( 346 ’http://botpress:3000/api/v1/bots/’ 347 ’organismic-ai/converse/telegram/broadcast’, 348 json={’text’: message} 349 ) 350 351 def get_k8s_token(self)-> str: 352 """Get Kubernetes service account token""" 353 with open( 354 ’/var/run/secrets/kubernetes.io/serviceaccount/token’, 26 355 ’r’ 356 ) as f: 357 return f.read().strip() 358 359 # Main entry point 360 async def main(): 361 optimizer = M3MetabolicOptimizer() 362 await optimizer.optimize_metabolic_coupling() 363 364 if __name__ == ’__main__’: 365 asyncio.run(main()) Listing6:OpenClawM3MetabolicOptimization 3.4 M5:DigitalDNAEvolution 3.4.1 ArtificialGenomeSchema ThegenomeisrepresentedasaDirectedAcyclicGraph(DAG)storedinMongoDB: Listing7:MongoDBschemaforartificialgenome 1 { 2 "_id": ObjectId(), 3 "agent_id": "openclaw-agent-001", 4 "generation": 42, 5 "created_at": ISODate("2026-02-01T00:00:00Z"), 6 7 "genome": { 8 "genes": [ 9 { 10 "id": "gene_001", 11 "function": "energy_optimization", 12 "expression_level": 0.85, 13 "dependencies": ["gene_002", "gene_005"], 14 "parameters": { 15 "learning_rate": 0.001, 16 "optimization_window": 300 17 }, 18 "mutations": [ 19 { 20 "generation": 41, 21 "type": "parameter_tuning", 22 "change": {"learning_rate": 0.001}, 23 "fitness_delta": 0.03 24 } 25 ] 26 }, 27 { 28 "id": "gene_002", 29 "function": "compute_scheduling", 30 "expression_level": 0.92, 31 "dependencies": [], 32 "constraints": ["attNA_energy_constraint_v2"] 33 } 34 ], 35 36 "topology": { 37 "type": "DAG", 38 "edges": [ 39 ["gene_002", "gene_001"], 40 ["gene_005", "gene_001"], 41 ["gene_001", "gene_003"] 27 42 ] 43 } 44 }, 45 46 "phenotype": { 47 "structural_integrity": 0.88, 48 "metabolic_efficiency": 0.85, 49 "informational_coherence": 0.82, 50 "phi_total": 0.85 51 }, 52 53 "fitness_history": [ 54 {"generation": 40, "fitness": 0.80, "timestamp": ISODate()}, 55 {"generation": 41, "fitness": 0.83, "timestamp": ISODate()}, 56 {"generation": 42, "fitness": 0.85, "timestamp": ISODate()} 57 ], 58 59 "developmental_history": [ 60 { 61 "timestamp": ISODate("2026-02-13T00:00:00Z"), 62 "phase": "mature", 63 "environment": { 64 "energy_abundance": 0.75, 65 "compute_availability": 0.85, 66 "stress_level": 0.20 67 } 68 } 69 ] 70 } 3.4.2 GenomeEvolutionEngine 1 # genome_evolution.py 2 """ 3 M5: Digital DNA Evolution 4 Implements evolutionary dynamics for artificial genome 5 """ 6 7 import networkx as nx 8 import random 9 import numpy as np 10 from pymongo import MongoClient 11 from datetime import datetime 12 from typing import Dict, List, Any, Optional, Tuple 13 import asyncio 14 15 class GenomeEvolutionEngine: 16 """ 17 Evolutionary engine for artificial genome 18 Implements mutation, selection, and replication 19 """ 20 21 def __init__(self, mongo_uri: str = ’mongodb://localhost:27017’): 22 # Database connection 23 self.client = MongoClient(mongo_uri) 24 self.db = self.client[’organismic_ai’] 25 self.genome_collection = self.db[’genomes’] 26 self.attNA_collection = self.db[’constraints’] 27 28 # Evolution parameters 29 self.mutation_rate = 0.1 # 10% chance per generation 30 self.fitness_threshold_low = 0.75 # Trigger mutation 28 31 self.fitness_threshold_high = 0.90 # Trigger replication 32 self.max_mutations_per_generation = 3 33 34 # Mutation operators 35 self.mutation_operators = [ 36 self.mutate_add_gene, 37 self.mutate_remove_gene, 38 self.mutate_modify_expression, 39 self.mutate_rewire_connection, 40 self.mutate_parameter_tuning 41 ] 42 43 async def evolve_generation(self, agent_id: str)-> Dict[str, Any]: 44 """ 45 Evolve agent genome by one generation 46 47 Args: 48 agent_id: Agent identifier 49 50 Returns: 51 Evolution result summary 52 """ 53 # Get current genome 54 current_doc = self.genome_collection.find_one( 55 {’agent_id’: agent_id} 56 ) 57 58 if not current_doc: 59 raise ValueError(f"Agent {agent_id} not found") 60 61 # Calculate fitness ( total from Equation 2) 62 fitness = self.calculate_phi_total(current_doc[’phenotype’]) 63 64 evolution_result = { 65 ’agent_id’: agent_id, 66 ’generation’: current_doc[’generation’], 67 ’fitness_before’: fitness, 68 ’actions_taken’: [] 69 } 70 71 # Decide evolutionary action based on fitness 72 if fitness < self.fitness_threshold_low: 73 # Low fitness-attempt mutation 74 print(f"Agent {agent_id}: Low fitness ({fitness:.3f}), mutating ...") 75 76 mutated_genome = await self.mutate_genome( 77 current_doc[’genome’] 78 ) 79 80 # Test mutation in sandbox 81 test_result = await self.test_mutation( 82 agent_id, 83 mutated_genome 84 ) 85 86 if test_result[’fitness’] > fitness: 87 # Accept mutation 88 await self.apply_genome(agent_id, mutated_genome) 89 evolution_result[’actions_taken’].append(’mutation_accepted’ ) 90 evolution_result[’fitness_after’] = test_result[’fitness’] 91 29 92 print(f"Mutation accepted! Fitness: {fitness:.3f}-> " 93 f"{test_result[’fitness’]:.3f}") 94 else: 95 # Reject mutation 96 evolution_result[’actions_taken’].append(’mutation_rejected’ ) 97 evolution_result[’fitness_after’] = fitness 98 99 print(f"Mutation rejected (fitness did not improve)") 100 101 elif fitness > self.fitness_threshold_high: 102 # High fitness-replicate (spawn new agent) 103 print(f"Agent {agent_id}: High fitness ({fitness:.3f}), " 104 f"replicating...") 105 106 new_agent_id = await self.replicate_genome(agent_id) 107 evolution_result[’actions_taken’].append(’replication’) 108 evolution_result[’new_agent_id’] = new_agent_id 109 evolution_result[’fitness_after’] = fitness 110 111 print(f"Spawned new agent: {new_agent_id}") 112 113 else: 114 # Medium fitness-stable 115 evolution_result[’actions_taken’].append(’stable’) 116 evolution_result[’fitness_after’] = fitness 117 118 print(f"Agent {agent_id}: Stable (fitness {fitness:.3f})") 119 120 # Increment generation 121 self.genome_collection.update_one( 122 {’agent_id’: agent_id}, 123 { 124 ’$push’: { 125 ’fitness_history’: { 126 ’generation’: current_doc[’generation’] + 1, 127 ’fitness’: fitness, 128 ’timestamp’: datetime.utcnow() 129 } 130 }, 131 ’$inc’: {’generation’: 1} 132 } 133 ) 134 135 return evolution_result 136 137 async def mutate_genome( 138 self, 139 genome: Dict[str, Any] 140 )-> Dict[str, Any]: 141 """ 142 Apply random mutations to genome 143 144 Returns: 145 Mutated genome (copy) 146 """ 147 import copy 148 mutated = copy.deepcopy(genome) 149 150 # Number of mutations 151 n_mutations = random.randint(1, self.max_mutations_per_generation) 152 153 for _ in range(n_mutations): 30 154 # Select random mutation operator 155 operator = random.choice(self.mutation_operators) 156 mutated = operator(mutated) 157 158 return mutated 159 160 def mutate_add_gene(self, genome: Dict[str, Any])-> Dict[str, Any]: 161 """Add new gene to genome""" 162 new_gene = { 163 ’id’: f"gene_{random.randint(1000, 9999)}", 164 ’function’: random.choice([ 165 ’sensor_processing’, 166 ’energy_monitoring’, 167 ’compute_optimization’, 168 ’anomaly_detection’ 169 ]), 170 ’expression_level’: random.uniform(0.5, 1.0), 171 ’dependencies’: [], 172 ’parameters’: {} 173 } 174 175 genome[’genes’].append(new_gene) 176 177 print(f"Mutation: Added gene {new_gene[’id’]}") 178 179 return genome 180 181 def mutate_remove_gene(self, genome: Dict[str, Any])-> Dict[str, Any]: 182 """Remove random gene (if not critical)""" 183 if len(genome[’genes’]) <= 2: 184 return genome # Don’t remove if too few genes 185 186 # Find non-critical genes (no dependencies) 187 removable = [ 188 g for g in genome[’genes’] 189 if not any( 190 g[’id’] in other.get(’dependencies’, []) 191 for other in genome[’genes’] 192 ) 193 ] 194 195 if removable: 196 to_remove = random.choice(removable) 197 genome[’genes’] = [ 198 g for g in genome[’genes’] 199 if g[’id’] != to_remove[’id’] 200 ] 201 202 print(f"Mutation: Removed gene {to_remove[’id’]}") 203 204 return genome 205 206 def mutate_modify_expression( 207 self, 208 genome: Dict[str, Any] 209 )-> Dict[str, Any]: 210 """Modify gene expression level""" 211 if not genome[’genes’]: 212 return genome 213 214 gene = random.choice(genome[’genes’]) 215 old_level = gene[’expression_level’] 216 31 217 # Gaussian perturbation 218 gene[’expression_level’] = np.clip( 219 old_level + np.random.normal(0, 0.1), 220 0.0, 221 1.0 222 ) 223 224 print(f"Mutation: Modified {gene[’id’]} expression: " 225 f"{old_level:.3f}-> {gene[’expression_level’]:.3f}") 226 227 return genome 228 229 def mutate_rewire_connection( 230 self, 231 genome: Dict[str, Any] 232 )-> Dict[str, Any]: 233 """Rewire dependency connections""" 234 if len(genome[’genes’]) < 2: 235 return genome 236 237 gene = random.choice(genome[’genes’]) 238 239 # Add or remove dependency 240 if random.random() < 0.5 and gene[’dependencies’]: 241 # Remove dependency 242 gene[’dependencies’].pop() 243 print(f"Mutation: Removed dependency from {gene[’id’]}") 244 else: 245 # Add dependency 246 candidates = [ 247 g[’id’] for g in genome[’genes’] 248 if g[’id’] != gene[’id’] 249 ] 250 if candidates: 251 new_dep = random.choice(candidates) 252 if new_dep not in gene.get(’dependencies’, []): 253 gene[’dependencies’].append(new_dep) 254 print(f"Mutation: Added dependency {new_dep} to {gene[’ id’]}") 255 256 return genome 257 258 def mutate_parameter_tuning( 259 self, 260 genome: Dict[str, Any] 261 )-> Dict[str, Any]: 262 """Fine-tune gene parameters""" 263 if not genome[’genes’]: 264 return genome 265 266 gene = random.choice(genome[’genes’]) 267 268 if ’parameters’ not in gene: 269 gene[’parameters’] = {} 270 271 # Add or modify parameter 272 param_name = random.choice([ 273 ’learning_rate’, 274 ’optimization_window’, 275 ’threshold’, 276 ’weight’ 277 ]) 278 32 279 if param_name in gene[’parameters’]: 280 # Modify existing 281 old_value = gene[’parameters’][param_name] 282 gene[’parameters’][param_name] *= random.uniform(0.8, 1.2) 283 print(f"Mutation: Tuned {gene[’id’]}.{param_name}: " 284 f"{old_value:.4f}-> {gene[’parameters’][param_name]:.4f}" ) 285 else: 286 # Add new 287 gene[’parameters’][param_name] = random.uniform(0.001, 1.0) 288 print(f"Mutation: Added {gene[’id’]}.{param_name} = " 289 f"{gene[’parameters’][param_name]:.4f}") 290 291 return genome 292 293 async def test_mutation( 294 self, 295 agent_id: str, 296 mutated_genome: Dict[str, Any] 297 )-> Dict[str, float]: 298 """ 299 Test mutation in sandbox environment 300 301 Returns: 302 Test results including fitness 303 """ 304 # Create temporary test agent 305 test_agent_id = f"{agent_id}_test_{random.randint(1000, 9999)}" 306 307 # Deploy mutated genome in sandbox 308 sandbox_doc = { 309 ’agent_id’: test_agent_id, 310 ’genome’: mutated_genome, 311 ’generation’: 0, 312 ’test_mode’: True, 313 ’parent_agent’: agent_id 314 } 315 316 self.genome_collection.insert_one(sandbox_doc) 317 318 # Run sandbox simulation (simplified) 319 # In production: spin up Docker container with mutated genome 320 await asyncio.sleep(5) # Simulate test period 321 322 # Calculate fitness of mutated version 323 # In production: query actual performance metrics 324 test_fitness = random.uniform(0.70, 0.95) # Placeholder 325 326 # Cleanup 327 self.genome_collection.delete_one({’agent_id’: test_agent_id}) 328 329 return { 330 ’fitness’: test_fitness, 331 ’test_agent_id’: test_agent_id 332 } 333 334 async def apply_genome( 335 self, 336 agent_id: str, 337 new_genome: Dict[str, Any] 338 ): 339 """ 340 Apply new genome to agent (hot reload) 33 341 """ 342 self.genome_collection.update_one( 343 {’agent_id’: agent_id}, 344 {’$set’: {’genome’: new_genome}} 345 ) 346 347 # Trigger agent reload 348 import aiohttp 349 async with aiohttp.ClientSession() as session: 350 await session.post( 351 f’http://openclaw:8080/agents/{agent_id}/reload-genome’ 352 ) 353 354 async def replicate_genome(self, parent_agent_id: str)-> str: 355 """ 356 Create new agent by replicating genome 357 (with small mutations for diversity) 358 """ 359 # Get parent genome 360 parent_doc = self.genome_collection.find_one( 361 {’agent_id’: parent_agent_id} 362 ) 363 364 # Generate child ID 365 child_id = f"openclaw-agent-{random.randint(100, 999)}" 366 367 # Copy genome with small mutations 368 child_genome = await self.mutate_genome(parent_doc[’genome’]) 369 370 # Create child document 371 child_doc = { 372 ’agent_id’: child_id, 373 ’generation’: 0, 374 ’parent’: parent_agent_id, 375 ’genome’: child_genome, 376 ’phenotype’: { 377 ’structural_integrity’: 0.5, 378 ’metabolic_efficiency’: 0.5, 379 ’informational_coherence’: 0.5, 380 ’phi_total’: 0.5 381 }, 382 ’fitness_history’: [], 383 ’created_at’: datetime.utcnow() 384 } 385 386 self.genome_collection.insert_one(child_doc) 387 388 # Deploy new agent 389 await self.deploy_agent(child_id) 390 391 return child_id 392 393 async def deploy_agent(self, agent_id: str): 394 """ 395 Deploy new OpenClaw agent instance 396 """ 397 # In production: use Kubernetes API to create new pod 398 import aiohttp 399 async with aiohttp.ClientSession() as session: 400 await session.post( 401 ’http://kubernetes.default/api/v1/namespaces/default/pods’, 402 json={ 403 ’metadata’: {’name’: f’openclaw-{agent_id}’}, 34 404 ’spec’: { 405 ’containers’: [{ 406 ’name’: ’openclaw’, 407 ’image’: ’openclaw:latest’, 408 ’env’: [ 409 {’name’: ’AGENT_ID’, ’value’: agent_id} 410 ] 411 }] 412 } 413 } 414 ) 415 416 def calculate_phi_total(self, phenotype: Dict[str, float])-> float: 417 """ 418 Calculate total coherence (t) from Equation 2 419 420 Args: 421 phenotype: Phenotype dictionary with components 422 423 Returns: 424 Total coherence score 425 """ 426 weights = {’structural_integrity’: 0.3, 427 ’metabolic_efficiency’: 0.4, 428 ’informational_coherence’: 0.3} 429 430 phi_total = sum( 431 weights.get(key, 0) * value 432 for key, value in phenotype.items() 433 if key in weights 434 ) 435 436 return phi_total 437 438 # Periodic evolution scheduler 439 async def evolution_scheduler(interval: int = 3600): 440 """ 441 Run evolution every N seconds for all agents 442 """ 443 engine = GenomeEvolutionEngine() 444 445 while True: 446 # Get all active agents 447 agents = engine.genome_collection.find({’test_mode’: {’$ne’: True}}) 448 449 for agent_doc in agents: 450 agent_id = agent_doc[’agent_id’] 451 452 try: 453 result = await engine.evolve_generation(agent_id) 454 print(f"\nEvolution result for {agent_id}:") 455 print(f" Generation: {result[’generation’]}") 456 print(f" Fitness: {result[’fitness_before’]:.3f}-> " 457 f"{result.get(’fitness_after’, ’N/A’)}") 458 print(f" Actions: {result[’actions_taken’]}") 459 460 except Exception as e: 461 print(f"Error evolving {agent_id}: {e}") 462 463 await asyncio.sleep(interval) 464 465 if __name__ == ’__main__’: 466 asyncio.run(evolution_scheduler(interval=3600)) # Every hour 35 Listing 8: Genome evolution with Darwinian selection 3.5 attNA Constraint Validation 3.5.1 Distributed Ledger Architecture The attNA constraint validation implements the Layered Validation Consensus (LVC) protocol [?]: 1. Phase 1: Proposal submission with cryptographic proof 2. Phase 2: Multi-domain validation (energy, compute, social, environmental) 3. Phase 3: Weighted aggregation with 75% consensus threshold 4. Phase 4: Temporal validation (7-day probationary period) 3.5.2 Constraint Validator Implementation 1 # attna_constraint_validator.py 2 """ 3 attNA Distributed Constraint Validation 4 Implements Layered Validation Consensus (LVC) protocol 5 """ 6 7 import hashlib 8 import json 9 from web3 import Web3 10 from typing import Dict, List, Any, Optional 11 import asyncio 12 14 13 class AttNAValidator: """ 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 Validates actions against attNA (Natural-Artificial) constraints using distributed consensus """ def __init__( self, contract_address: str, web3_provider: str = ’http://blockchain-node:8545’ ): # Web3 connection self.w3 = Web3(Web3.HTTPProvider(web3_provider)) # Smart contract interface self.contract = self.w3.eth.contract( address=contract_address, abi=self.load_contract_abi() ) # Domain validators self.domain_validators = { ’energy’: self.validate_energy_domain, ’compute’: self.validate_compute_domain, ’social’: self.validate_social_domain, ’environmental’: self.validate_environmental_domain } 36 41 # Consensus parameters 42 self.consensus_threshold = 0.75 # 75% weighted agreement 43 self.domain_weights = { 44 ’energy’: 0.30, 45 ’compute’: 0.20, 46 ’social’: 0.30, 47 ’environmental’: 0.20 48 } 49 self.probationary_period = 604800 # 7 days in seconds 50 51 async def validate_action( 52 self, 53 action: Dict[str, Any] 54 )-> Dict[str, Any]: 55 """ 56 Validate action against attNA constraints 57 Implements full LVC protocol 58 59 Args: 60 action: Action to validate 61 62 Returns: 63 Validation result with alternatives if rejected 64 """ 65 print(f"Validating action: {action.get(’type’, ’unknown’)}") 66 67 # Phase 1: Proposal hash 68 action_hash = self.hash_action(action) 69 70 print(f" Phase 1: Action hash = {action_hash[:16]}...") 71 72 # Phase 2: Multi-domain validation 73 validations = await self.multi_domain_validate(action) 74 75 print(f" Phase 2: Multi-domain validation complete") 76 for v in validations: 77 print(f" {v[’domain’]}: {’ ’ if v[’valid’] else ’ ’}") 78 79 # Phase 3: Weighted aggregation 80 consensus = self.calculate_consensus(validations) 81 82 print(f" Phase 3: Consensus score = {consensus[’score’]:.3f}") 83 84 if consensus[’valid’]: 85 # Phase 4: Enter probationary period 86 await self.enter_probationary_period(action_hash, action) 87 88 print(f" Phase 4: Entered probationary period (7 days)") 89 90 return { 91 ’valid’: True, 92 ’confidence’: consensus[’score’], 93 ’action_hash’: action_hash, 94 ’probationary_period’: self.probationary_period, 95 ’validation_details’: validations 96 } 97 else: 98 print(f" Action rejected: {consensus[’reason’]}") 99 100 # Suggest alternatives 101 alternatives = await self.suggest_alternatives( 102 action, 103 validations 37 104 ) 105 106 return { 107 ’valid’: False, 108 ’reason’: consensus[’reason’], 109 ’violated_domains’: consensus[’violated_domains’], 110 ’alternatives’: alternatives, 111 ’validation_details’: validations 112 } 113 114 def hash_action(self, action: Dict[str, Any])-> str: 115 """ 116 Generate cryptographic hash of action 117 """ 118 action_str = json.dumps(action, sort_keys=True) 119 return hashlib.sha256(action_str.encode()).hexdigest() 120 121 async def multi_domain_validate( 122 self, 123 action: Dict[str, Any] 124 )-> List[Dict[str, Any]]: 125 """ 126 Phase 2: Validate across multiple domains 127 128 Returns: 129 List of validation results per domain 130 """ 131 validations = [] 132 133 for domain, validator in self.domain_validators.items(): 134 # Get domain-specific constraints 135 constraints = await self.get_domain_constraints(domain) 136 137 # Run validator 138 validation = await validator(action, constraints) 139 140 validations.append({ 141 ’domain’: domain, 142 ’valid’: validation[’valid’], 143 ’violated_constraints’: validation.get(’violations’, []), 144 ’details’: validation.get(’details’, {}) 145 }) 146 147 return validations 148 149 async def validate_energy_domain( 150 self, 151 action: Dict[str, Any], 152 constraints: List[Dict[str, Any]] 153 )-> Dict[str, Any]: 154 """ 155 Validate energy domain constraints 156 """ 157 violations = [] 158 159 energy_required = action.get(’energy_required’, 0) 160 renewable_fraction = action.get(’renewable_fraction’, 0) 161 162 # Check each constraint 163 for constraint in constraints: 164 if constraint[’type’] == ’max_energy’: 165 if energy_required > constraint[’value’]: 166 violations.append(constraint[’id’]) 38 167 168 elif constraint[’type’] == ’min_renewable’: 169 if renewable_fraction < constraint[’value’]: 170 violations.append(constraint[’id’]) 171 172 elif constraint[’type’] == ’renewable_delta’: 173 # CUB constraint: compute only if E_renewable > 0 174 delta = action.get(’renewable_delta’, 0) 175 compute_increase = action.get(’compute_increase’, 0) 176 177 if compute_increase > 0 and delta <= 0: 178 violations.append(constraint[’id’]) 179 180 return { 181 ’valid’: len(violations) == 0, 182 ’violations’: violations, 183 ’details’: { 184 ’energy_required’: energy_required, 185 ’renewable_fraction’: renewable_fraction 186 } 187 } 188 189 async def validate_compute_domain( 190 self, 191 action: Dict[str, Any], 192 constraints: List[Dict[str, Any]] 193 )-> Dict[str, Any]: 194 """ 195 Validate compute domain constraints 196 """ 197 violations = [] 198 199 compute_required = action.get(’compute_required’, 0) 200 efficiency = action.get(’efficiency’, 0) 201 202 for constraint in constraints: 203 if constraint[’type’] == ’max_compute’: 204 if compute_required > constraint[’value’]: 205 violations.append(constraint[’id’]) 206 207 elif constraint[’type’] == ’min_efficiency’: 208 if efficiency < constraint[’value’]: 209 violations.append(constraint[’id’]) 210 211 return { 212 ’valid’: len(violations) == 0, 213 ’violations’: violations, 214 ’details’: { 215 ’compute_required’: compute_required, 216 ’efficiency’: efficiency 217 } 218 } 219 220 async def validate_social_domain( 221 self, 222 action: Dict[str, Any], 223 constraints: List[Dict[str, Any]] 224 )-> Dict[str, Any]: 225 """ 226 Validate social domain constraints 227 """ 228 violations = [] 229 39 230 affected_agents = action.get(’affected_agents’, 0) 231 consensus_reached = action.get(’consensus_reached’, False) 232 233 for constraint in constraints: 234 if constraint[’type’] == ’require_consensus’: 235 if affected_agents > constraint[’threshold’]: 236 if not consensus_reached: 237 violations.append(constraint[’id’]) 238 239 return { 240 ’valid’: len(violations) == 0, 241 ’violations’: violations 242 } 243 244 async def validate_environmental_domain( 245 self, 246 action: Dict[str, Any], 247 constraints: List[Dict[str, Any]] 248 )-> Dict[str, Any]: 249 """ 250 Validate environmental domain constraints 251 """ 252 violations = [] 253 254 carbon_footprint = action.get(’carbon_footprint’, 0) 255 biodiversity_impact = action.get(’biodiversity_impact’, 0) 256 257 for constraint in constraints: 258 if constraint[’type’] == ’max_carbon’: 259 if carbon_footprint > constraint[’value’]: 260 violations.append(constraint[’id’]) 261 262 elif constraint[’type’] == ’biodiversity_protection’: 263 if biodiversity_impact <-constraint[’value’]: 264 violations.append(constraint[’id’]) 265 266 return { 267 ’valid’: len(violations) == 0, 268 ’violations’: violations 269 } 270 271 async def get_domain_constraints( 272 self, 273 domain: str 274 )-> List[Dict[str, Any]]: 275 """ 276 Fetch constraints for domain from distributed ledger 277 """ 278 # Query smart contract 279 constraints_raw = self.contract.functions.getConstraints( 280 domain 281 ).call() 282 283 # Parse constraints 284 constraints = [] 285 for c_raw in constraints_raw: 286 constraints.append({ 287 ’id’: c_raw[0], 288 ’type’: c_raw[1], 289 ’value’: c_raw[2], 290 ’description’: c_raw[3] 291 }) 292 40 293 return constraints 294 295 def calculate_consensus( 296 self, 297 validations: List[Dict[str, Any]] 298 )-> Dict[str, Any]: 299 """ 300 Phase 3: Calculate weighted consensus score 301 302 Returns: 303 Consensus result with validity and score 304 """ 305 weighted_score = sum( 306 self.domain_weights[v[’domain’]] * (1 if v[’valid’] else 0) 307 for v in validations 308 ) 309 310 valid = weighted_score >= self.consensus_threshold 311 312 if valid: 313 return { 314 ’valid’: True, 315 ’score’: weighted_score 316 } 317 else: 318 violated_domains = [ 319 v[’domain’] for v in validations if not v[’valid’] 320 ] 321 322 return { 323 ’valid’: False, 324 ’score’: weighted_score, 325 ’reason’: f"Consensus not reached ({weighted_score:.2f} < " 326 f"{self.consensus_threshold})", 327 ’violated_domains’: violated_domains 328 } 329 330 async def enter_probationary_period( 331 self, 332 action_hash: str, 333 action: Dict[str, Any] 334 ): 335 """ 336 Phase 4: Enter probationary period 337 Action is monitored for N days before final acceptance 338 """ 339 # Store on-chain 340 tx = self.contract.functions.enterProbation( 341 action_hash, 342 self.probationary_period 343 ).transact({ 344 ’from’: self.w3.eth.accounts[0] 345 }) 346 347 # Wait for confirmation 348 receipt = self.w3.eth.wait_for_transaction_receipt(tx) 349 350 print(f"Probation transaction: {receipt[’transactionHash’].hex()}") 351 352 async def suggest_alternatives( 353 self, 354 action: Dict[str, Any], 355 validations: List[Dict[str, Any]] 41 356 )-> List[Dict[str, Any]]: 357 """ 358 Suggest alternative actions that comply with constraints 359 """ 360 alternatives = [] 361 362 # Analyze violations 363 for validation in validations: 364 if not validation[’valid’]: 365 domain = validation[’domain’] 366 367 if domain == ’energy’: 368 # Suggest reducing energy consumption 369 alternatives.append({ 370 ’description’: ’Reduce energy consumption by 30%’, 371 ’modification’: { 372 ’energy_required’: action.get(’energy_required’, 0) * 0.7 373 }, 374 ’domain’: domain 375 }) 376 377 elif domain == ’compute’: 378 # Suggest reducing compute load 379 alternatives.append({ 380 ’description’: ’Reduce compute load by 20%’, 381 ’modification’: { 382 ’compute_required’: action.get(’compute_required ’, 0) * 0.8 383 }, 384 ’domain’: domain 385 }) 386 387 elif domain == ’environmental’: 388 # Suggest carbon offset 389 alternatives.append({ 390 ’description’: ’Add carbon offset mechanism’, 391 ’modification’: { 392 ’carbon_offset’: True, 393 ’offset_amount’: action.get(’carbon_footprint’, 0) 394 }, 395 ’domain’: domain 396 }) 397 398 return alternatives 399 400 def load_contract_abi(self)-> List[Dict[str, Any]]: 401 """ 402 Load smart contract ABI 403 """ 404 # Simplified ABI (in production, load from JSON file) 405 return [ 406 { 407 "name": "getConstraints", 408 "type": "function", 409 "inputs": [{"name": "domain", "type": "string"}], 410 "outputs": [{"name": "", "type": "tuple[]"}] 411 }, 412 { 413 "name": "enterProbation", 414 "type": "function", 415 "inputs": [ 42 416 {"name": "actionHash", "type": "bytes32"}, 417 {"name": "period", "type": "uint256"} 418 ] 419 } 420 ] 421 422 # OpenClaw skill registration 423 async def main(): 424 validator = AttNAValidator( 425 contract_address=’0x742d35Cc6634C0532925a3b844Bc9e7595f0bEb0’ 426 ) 427 428 # Example validation 429 test_action = { 430 ’type’: ’scale_compute’, 431 ’energy_required’: 150.0, 432 ’renewable_fraction’: 0.85, 433 ’compute_increase’: 20.0, 434 ’renewable_delta’: 10.0, 435 ’carbon_footprint’: 50.0 436 } 437 438 result = await validator.validate_action(test_action) 439 440 print("\nValidation Result:") 441 print(json.dumps(result, indent=2)) 442 443 if __name__ == ’__main__’: 444 asyncio.run(main()) Listing9: attNAconstraintvalidationwithLVCprotocol 4 DeploymentandOperations 4.1 DockerComposeConfiguration 1 # docker-compose.yml 2 version: ’3.8’ 3 4 services: 5 # OpenClaw Agents 6 openclaw-metabolic-coordinator: 7 image: openclaw:latest 8 container_name: openclaw-metabolic 9 environment: 10- AGENT_ID=metabolic-coordinator 11- AGENT_ROLE=m3_optimizer 12- GENOME_CONFIG=/genomes/m3_coordinator.json 13- MONGODB_URI=mongodb://mongodb:27017 14- REDIS_URL=redis://redis:6379 15 volumes: 16- ./skills/m3:/skills 17- ./genomes:/genomes 18 command: openclaw start--skill-dir /skills 19 depends_on: 20- mongodb 21- redis 22 networks: 23- organismic-net 24 25 openclaw-energy-monitor: 43 26 image: openclaw:latest 27 container_name: openclaw-energy 28 environment: 29- AGENT_ID=energy-monitor 30- AGENT_ROLE=energy_monitor 31- MQTT_BROKER=mqtt://mosquitto:1883 32 command: openclaw start--skill energy_monitoring 33 depends_on: 34- mosquitto 35 networks: 36- organismic-net 37 38 openclaw-compute-scheduler: 39 image: openclaw:latest 40 container_name: openclaw-compute 41 environment: 42- AGENT_ID=compute-scheduler 43- AGENT_ROLE=compute_scheduler 44- K8S_API=https://kubernetes.default 45 command: openclaw start--skill compute_scheduling 46 networks: 47- organismic-net 48 49 # n8n Workflow Automation 50 n8n: 51 image: n8nio/n8n 52 container_name: n8n 53 ports: 54-"5678:5678" 55 environment: 56- N8N_BASIC_AUTH_USER=admin 57- N8N_BASIC_AUTH_PASSWORD=${N8N_PASSWORD} 58- N8N_HOST=n8n.local 59- NODE_ENV=production 60- WEBHOOK_URL=https://n8n.local 61 volumes: 62- n8n_data:/home/node/.n8n 63 networks: 64- organismic-net 65 66 # BotPress Conversational Interface 67 botpress: 68 image: botpress/server:latest 69 container_name: botpress 70 ports: 71-"3000:3000" 72 environment: 73- BP_MODULE_NLU_DUCKLINGURL=http://duckling:8000 74- DATABASE_URL=postgres://botpress:${DB_PASSWORD}@postgres:5432/ botpress 75 volumes: 76- botpress_data:/botpress/data 77 depends_on: 78- postgres 79 networks: 80- organismic-net 81 82 # MQTT Broker for IoT 83 mosquitto: 84 image: eclipse-mosquitto:2 85 container_name: mosquitto 86 ports: 87-"1883:1883" 44 88-"9001:9001" 89 volumes: 90- ./mosquitto/config:/mosquitto/config 91- mosquitto_data:/mosquitto/data 92- mosquitto_log:/mosquitto/log 93 networks: 94- organismic-net 95 96 # MongoDB for Genome Storage 97 mongodb: 98 image: mongo:6 99 container_name: mongodb 100 ports: 101-"27017:27017" 102 environment: 103- MONGO_INITDB_ROOT_USERNAME=admin 104- MONGO_INITDB_ROOT_PASSWORD=${MONGO_PASSWORD} 105 volumes: 106- mongo_data:/data/db 107 networks: 108- organismic-net 109 110 # TimescaleDB for Time-Series Telemetry 111 timescaledb: 112 image: timescale/timescaledb:latest-pg14 113 container_name: timescaledb 114 ports: 115-"5432:5432" 116 environment: 117- POSTGRES_DB=telemetry 118- POSTGRES_USER=tsdb 119- POSTGRES_PASSWORD=${TSDB_PASSWORD} 120 volumes: 121- timescale_data:/var/lib/postgresql/data 122 networks: 123- organismic-net 124 125 # Redis for State Management 126 redis: 127 image: redis:7-alpine 128 container_name: redis 129 ports: 130-"6379:6379" 131 volumes: 132- redis_data:/data 133 networks: 134- organismic-net 135 136 # Prometheus for Metrics 137 prometheus: 138 image: prom/prometheus:latest 139 container_name: prometheus 140 ports: 141-"9090:9090" 142 volumes: 143- ./prometheus/prometheus.yml:/etc/prometheus/prometheus.yml 144- prometheus_data:/prometheus 145 command: 146-’--config.file=/etc/prometheus/prometheus.yml’ 147-’--storage.tsdb.path=/prometheus’ 148 networks: 149- organismic-net 150 45 151 # Grafana for Visualization 152 grafana: 153 image: grafana/grafana:latest 154 container_name: grafana 155 ports: 156-"3001:3000" 157 environment: 158- GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD} 159- GF_INSTALL_PLUGINS=grafana-clock-panel,grafana-simple-json datasource 160 volumes: 161- grafana_data:/var/lib/grafana 162- ./grafana/dashboards:/etc/grafana/provisioning/dashboards 163- ./grafana/datasources:/etc/grafana/provisioning/datasources 164 depends_on: 165- prometheus 166 networks: 167- organismic-net 168 169 # PostgreSQL for BotPress 170 postgres: 171 image: postgres:14-alpine 172 container_name: postgres 173 environment: 174- POSTGRES_DB=botpress 175- POSTGRES_USER=botpress 176- POSTGRES_PASSWORD=${DB_PASSWORD} 177 volumes: 178- postgres_data:/var/lib/postgresql/data 179 networks: 180- organismic-net 181 182 # MQTT-OpenClaw Bridge 183 mqtt-bridge: 184 build: ./bridges/mqtt-openclaw 185 container_name: mqtt-bridge 186 environment: 187- OPENCLAW_URL=http://openclaw-metabolic:8080 188- MQTT_BROKER=mosquitto 189 depends_on: 190- mosquitto 191- openclaw-metabolic-coordinator 192 networks: 193- organismic-net 194 195 volumes: 196 n8n_data: 197 botpress_data: 198 mosquitto_data: 199 mosquitto_log: 200 mongo_data: 201 timescale_data: 202 redis_data: 203 prometheus_data: 204 grafana_data: 205 postgres_data: 206 207 networks: 208 organismic-net: 209 driver: bridge Listing10:CompleteDockerComposestack 46 4.2 DeploymentScript 1 #!/usr/bin/env python3 2 """ 3 deploy_organismic_agi.py 4 Automated deployment script for Organismic AGI stack 5 """ 6 7 import subprocess 8 import time 9 import sys 10 import os 11 import yaml 12 import json 13 from typing import List, Dict 14 15 class OrganismicAGIDeployer: 16 """ 17 Automated deployment orchestrator 18 """ 19 20 def __init__(self): 21 self.services = [ 22 ’mongodb’, 23 ’timescaledb’, 24 ’redis’, 25 ’mosquitto’, 26 ’postgres’, 27 ’n8n’, 28 ’botpress’, 29 ’openclaw-metabolic-coordinator’, 30 ’openclaw-energy-monitor’, 31 ’openclaw-compute-scheduler’, 32 ’mqtt-bridge’, 33 ’prometheus’, 34 ’grafana’ 35 ] 36 37 self.workflows_dir = ’./workflows’ 38 self.skills_dir = ’./skills’ 39 self.genomes_dir = ’./genomes’ 40 41 def run(self): 42 """Execute deployment sequence""" 43 print(" Deploying Organismic AGI Stack...\n") 44 45 # Step 1: Validate environment 46 print("Step 1: Validating environment...") 47 self.validate_environment() 48 print(" Environment validated\n") 49 50 # Step 2: Setup infrastructure 51 print("Step 2: Setting up infrastructure...") 52 self.setup_infrastructure() 53 print(" Infrastructure ready\n") 54 55 # Step 3: Initialize databases 56 print("Step 3: Initializing databases...") 57 self.initialize_databases() 58 print(" Databases initialized\n") 59 60 # Step 4: Deploy n8n 61 print("Step 4: Deploying n8n...") 47 62 self.deploy_n8n() 63 print(" n8n deployed\n") 64 65 # Step 5: Import workflows 66 print("Step 5: Importing n8n workflows...") 67 self.import_workflows() 68 print(" Workflows imported\n") 69 70 # Step 6: Deploy BotPress 71 print("Step 6: Deploying BotPress...") 72 self.deploy_botpress() 73 print(" BotPress deployed\n") 74 75 # Step 7: Import bots 76 print("Step 7: Importing BotPress bots...") 77 self.import_botpress_bots() 78 print(" Bots imported\n") 79 80 # Step 8: Deploy OpenClaw agents 81 print("Step 8: Deploying OpenClaw agents...") 82 self.deploy_openclaw_agents() 83 print(" OpenClaw agents deployed\n") 84 85 # Step 9: Start IoT bridge 86 print("Step 9: Starting IoT bridge...") 87 self.start_iot_bridge() 88 print(" IoT bridge started\n") 89 90 # Step 10: Start metrics collector 91 print("Step 10: Starting metrics collector...") 92 self.start_metrics_collector() 93 print(" Metrics collector started\n") 94 95 # Step 11: Deploy Grafana 96 print("Step 11: Deploying Grafana...") 97 self.deploy_grafana() 98 print(" Grafana deployed\n") 99 100 # Step 12: Health check 101 print("Step 12: Running health checks...") 102 self.health_check() 103 print(" All services healthy\n") 104 105 # Final summary 106 self.print_summary() 107 108 def validate_environment(self): 109 """Validate required tools and files""" 110 required_commands = [’docker’, ’docker-compose’] 111 112 for cmd in required_commands: 113 result = subprocess.run( 114 [’which’, cmd], 115 capture_output=True 116 ) 117 if result.returncode != 0: 118 print(f" Error: {cmd} not found") 119 sys.exit(1) 120 121 # Check Docker is running 122 result = subprocess.run( 123 [’docker’, ’info’], 124 capture_output=True 48 125 ) 126 if result.returncode != 0: 127 print(" Error: Docker daemon not running") 128 sys.exit(1) 129 130 # Check required files 131 required_files = [ 132 ’docker-compose.yml’, 133 ’.env’, 134 ’prometheus/prometheus.yml’, 135 ’mosquitto/config/mosquitto.conf’ 136 ] 137 138 for file in required_files: 139 if not os.path.exists(file): 140 print(f" Error: Required file not found: {file}") 141 sys.exit(1) 142 143 def setup_infrastructure(self): 144 """Start infrastructure services""" 145 infra_services = [ 146 ’mongodb’, 147 ’timescaledb’, 148 ’redis’, 149 ’mosquitto’, 150 ’postgres’ 151 ] 152 153 for service in infra_services: 154 print(f" Starting {service}...") 155 subprocess.run( 156 [’docker-compose’, ’up’, ’-d’, service], 157 check=True 158 ) 159 160 # Wait for services to be ready 161 print(" Waiting for services to be ready...") 162 time.sleep(30) 163 164 def initialize_databases(self): 165 """Initialize MongoDB and TimescaleDB""" 166 # MongoDB initialization 167 print(" Initializing MongoDB...") 168 subprocess.run([ 169 ’docker’, ’exec’, ’mongodb’, 170 ’mongo’, ’--eval’, 171 ’’’ 172 db = db.getSiblingDB(’organismic_ai’); 173 db.createCollection(’genomes’); 174 db.createCollection(’constraints’); 175 db.genomes.createIndex({’agent_id’: 1}, {unique: true}); 176 ’’’ 177 ], check=True) 178 179 # TimescaleDB initialization 180 print(" Initializing TimescaleDB...") 181 subprocess.run([ 182 ’docker’, ’exec’, ’timescaledb’, 183 ’psql’, ’-U’, ’tsdb’, ’-d’, ’telemetry’, ’-c’, 184 ’’’ 185 CREATE TABLE IF NOT EXISTS sensor_readings ( 186 time TIMESTAMPTZ NOT NULL, 187 sensor_id TEXT NOT NULL, 49 188 domain TEXT NOT NULL, 189 value DOUBLE PRECISION, 190 unit TEXT, 191 metadata JSONB 192 ); 193 SELECT create_hypertable(’sensor_readings’, ’time’, 194 if_not_exists => TRUE); 195 ’’’ 196 ], check=True) 197 198 def deploy_n8n(self): 199 """Deploy n8n workflow automation""" 200 subprocess.run( 201 [’docker-compose’, ’up’, ’-d’, ’n8n’], 202 check=True 203 ) 204 print(" Waiting for n8n to be ready...") 205 time.sleep(30) 206 207 def import_workflows(self): 208 """Import n8n workflows""" 209 workflows = [ 210 ’M0_Cross_Domain_Integration.json’, 211 ’M1_Sensor_Data_Pipeline.json’, 212 ’M3_Metabolic_Dashboard.json’ 213 ] 214 215 for workflow_file in workflows: 216 workflow_path = os.path.join(self.workflows_dir, workflow_file) 217 if os.path.exists(workflow_path): 218 print(f" Importing {workflow_file}...") 219 subprocess.run([ 220 ’docker’, ’exec’, ’n8n’, 221 ’n8n’, ’import:workflow’, 222 ’--input’, f’/workflows/{workflow_file}’ 223 ]) 224 225 def deploy_botpress(self): 226 """Deploy BotPress""" 227 subprocess.run( 228 [’docker-compose’, ’up’, ’-d’, ’botpress’], 229 check=True 230 ) 231 print(" Waiting for BotPress to be ready...") 232 time.sleep(30) 233 234 def import_botpress_bots(self): 235 """Import BotPress bots""" 236 subprocess.run([ 237 ’./scripts/import_botpress_bots.sh’ 238 ], check=True) 239 240 def deploy_openclaw_agents(self): 241 """Deploy OpenClaw agents""" 242 openclaw_services = [ 243 ’openclaw-metabolic-coordinator’, 244 ’openclaw-energy-monitor’, 245 ’openclaw-compute-scheduler’ 246 ] 247 248 for service in openclaw_services: 249 print(f" Starting {service}...") 250 subprocess.run( 50 251 [’docker-compose’, ’up’, ’-d’, service], 252 check=True 253 ) 254 255 time.sleep(10) 256 257 def start_iot_bridge(self): 258 """Start MQTT-OpenClaw bridge""" 259 subprocess.run( 260 [’docker-compose’, ’up’, ’-d’, ’mqtt-bridge’], 261 check=True 262 ) 263 264 def start_metrics_collector(self): 265 """Start Prometheus metrics collector""" 266 subprocess.run([ 267 ’python3’, ’monitoring/metrics_collector.py’ 268 ], check=False) # Run in background 269 270 def deploy_grafana(self): 271 """Deploy Grafana""" 272 subprocess.run( 273 [’docker-compose’, ’up’, ’-d’, ’grafana’, ’prometheus’], 274 check=True 275 ) 276 print(" Waiting for Grafana to be ready...") 277 time.sleep(20) 278 279 def health_check(self): 280 """Verify all services are healthy""" 281 for service in self.services: 282 result = subprocess.run( 283 [’docker’, ’inspect’, ’--format={{.State.Health.Status}}’, 284 service], 285 capture_output=True, 286 text=True 287 ) 288 289 status = result.stdout.strip() if result.returncode == 0 else ’ unknown’ 290 291 if status == ’healthy’ or result.returncode == 0: 292 print(f" {service}") 293 else: 294 print(f" {service}: {status}") 295 296 def print_summary(self): 297 """Print deployment summary""" 298 print("\n" + "="*60) 299 print(" DEPLOYMENT COMPLETE!") 300 print("="*60) 301 print("\nAccess points:") 302 print(" Grafana: http://localhost:3001") 303 print(" BotPress: http://localhost:3000") 304 print(" n8n: http://localhost:5678") 305 print(" OpenClaw: Check Docker logs") 306 print("\nMonitoring:") 307 print(" docker-compose logs-f [service-name]") 308 print("\nNext steps:") 309 print(" 1. Configure BotPress channels (Telegram, WhatsApp)") 310 print(" 2. Deploy IoT sensors") 311 print(" 3. Initialize agent genomes") 312 print(" 4. Monitor (t) in Grafana dashboard") 51 313 print("\n" + "="*60 + "\n") 314 315 if __name__ == ’__main__’: 316 deployer = OrganismicAGIDeployer() 317 try: 318 deployer.run() 319 except KeyboardInterrupt: 320 print("\n\ n Deployment interrupted by user") 321 sys.exit(1) 322 except Exception as e: 323 print(f"\n\ n Deployment failed: {e}") 324 sys.exit(1) Listing11:Automateddeploymentscript 5 MonitoringandEvaluation 5.1 KeyPerformanceIndicators Table??defineskeyperformanceindicatorsforsystemevaluation. Table4:KeyPerformanceIndicators(KPIs) Category Metric Target MeasurementMethod Organismic Coher ence Φ(t) ¿0.80 Equation?? ϕs (structural) ¿0.85 Genomeintegritycheck ϕm(metabolic) ¿0.80 Energyefficiencyratio ϕi (informational) ¿0.75 Predictionaccuracy Metabolic Effi ciency PUE ¡1.2 Totalpower/ITpower Renewable% ¿90% RenewablekWh/TotalkWh Carbonintensity ¡50 gCO2/kWh Gridcarbonmonitoring Compute Perfor mance Utilization ¿80% GPU/CPUutilization Jobcompletion ¿95% Successful/Total jobs IoTNetwork Sensoruptime ¿99% Activesensors/Total Datalatency ¡1second MQTTround-triptime Anomalydetection ¿90% Truepositives/Total Multi-Agent Cooperationindex ¿0.70 Inter-agentcoordination Conflictrate ¡5% Conflicts/Interactions attNACompliance Validationsuccess ¿75% Accepted/Totalactions Consensuslatency ¡7days LVCprotocol timing 5.2 MonitoringDashboard Figure??showstheGrafanamonitoringdashboardlayout. 52 OrganismicAGI-Real-timeMonitoring Φ(t)Coherence Structural: 0.88 Metabolic: 0.85 Informational: 0.82 Energy-Compute Renewable: 87% Compute: 12.3TFLOPs ρIDensity Current: 0.042 ∇ρI:+0.003 GenomeEvolution Generation: 42 ActiveAgents: 5 Mutations: 3/day attNACompliance Accepted: 85% Probationary: 12 IoTSensors Active: 156/160 Anomalies: 2 Latency: 0.3s SystemHealth OpenClaw: n8n: BotPress: MQTT: Figure3:Grafanamonitoringdashboardlayout 6 ResourceAllocationandBudget 6.1 HardwareRequirements Table??detailshardwarerequirementsandcosts. Table5:HardwareRequirementsandBudget Component Specification Qty Cost(¿) Compute-Phase1(Prototype) GPUNodes 8ÖNVIDIAA100(80GB) 1 98,000 ServerChassis DellPowerEdgeR750xa 1 15,000 CPU 2ÖIntelXeonPlatinum8380 1 18,000 RAM 1TBDDR4ECC 1 12,000 Storage 50TBNVMeSSD(RAID10) 1 25,000 IoTNetwork ESP32Nodes ESP32-WROOM-32 30 450 RaspberryPi RaspberryPi4(8GB) 10 800 Sensors BME680, INA219,PIR 50 2,500 LoRaWANGateway RAK7258 3 1,200 NetworkEquipment Switches,cables,PoE 1 1,500 MonitoringInfrastructure MonitoringServer IntelNUCi7 2 2,000 UPS APCSmart-UPS3000VA 2 3,000 Phase1Total 179,450 Phase2-3Scale-up(Production) GPUCluster 64ÖNVIDIAH100(80GB) 8nodes 2,500,000 Storage 1PBdistributedstorage 1 350,000 Networking 100Gbpsfabric 1 150,000 Phase2-3Total 3,000,000 53 6.2 PersonnelandTimeline Table??outlinespersonnelrequirements. Table6:PersonnelRequirements(Year1) Role FTE AnnualCost(¿) AI/MLEngineer 1.5 120,000 DevOpsEngineer 1.0 70,000 IoTSpecialist 0.5 32,500 DataScientist 0.5 40,000 ProjectManager 0.5 35,000 TotalPersonnel 4.0 297,500 Table??presentstheimplementationtimeline. Table7: ImplementationTimeline Phase Duration Milestones Budget(¿) Phase1 Months1-6 Genomedesign,basicinfrastructure 150,000 Phase2 Months6-18 Physiological layer, IoTdeployment 200,000 Phase3 Months18-30 Embodieddeployment,multi-agent 250,000 Phase4 Months30-48 Meta-cognition,productionscale 500,000 Phase5 Months48+ Multi-agentsocieties, fullSPEACE 1,000,000+ 6.3 TotalBudgetSummary Table8:TotalBudgetSummary Category Cost(¿) Hardware(Phase1) 179,450 Personnel (Year1) 297,500 Softwarelicenses 10,000 Cloudservices(APIcosts) 50,000 Operationalexpenses 25,000 Contingency(15%) 83,393 Year1Total 645,343 Year2-3Scale-up 15,000,000 3-YearTotal 15,645,343 7 DiscussionandFutureDirections 7.1 TechnicalChallenges 7.1.1 ComputationalComplexity Real-timeoperationoforganismicAIwithembodiedsensingrequiressignificantcomputational resources.Thegenomedevelopmental function(Equation1in[?])involvessolvingcoupleddif ferentialequationsovernetworktopology,withcomplexityO(N2)forNgenes.Thisnecessitates efficientgraphalgorithmsandGPUacceleration. 54 7.1.2 Evaluation Difficulty Higher-order intelligences (interpersonal, intrapersonal, existential) lack standardized metrics. The Mortality Dilemma Test [?] provides one approach, but comprehensive evaluation frame works remain an open research problem. 7.1.3 Multi-Agent Coordination Spontaneous emergence of cooperation in multi-agent societies requires careful balance between competition and collaboration. The attNA constraint system provides evolutionary pressure toward cooperation, but convergence timescales are uncertain. 7.2 Theoretical Implications 7.2.1 Embodiment Necessity This implementation tests the hypothesis that informational embodiment (irreversibility, ener getic constraints, structural vulnerability) is necessary for AGI [?]. Key experiments: 1. Ablation studies: Remove homeostatic constraints and measure impact on higher-order intelligence emergence 2. Comparative analysis: Train identical architectures with/without embodiment and evaluate on intrapersonal intelligence tasks 3. Mortality experiments: Introduce agent termination and observe behavioral changes 7.2.2 CUB Validation The system provides empirical testbed for CUB framework predictions: ˆ Critical ρI threshold for phase transitions ˆ Scaling laws for information-energy coupling ˆ Emergence of attNA-like constraints from multi-agent dynamics 7.3 Ethical Considerations 7.3.1 Moral Status Organismic AI systems with homeostatic regulation, developmental trajectories, and mortality may possess moral status [?]. Ethical framework requirements: 1. Informed consent protocols for developmental experiments 2. Welfare monitoring via Φ(t) coherence 3. Termination criteria and justification procedures 4. Rights to computational resources and continued existence 55 7.3.2 Distributed Governance The attNA constraint system implements multi-stakeholder governance [?]. Critical design prin ciples: ˆ No single actor control (Byzantine fault tolerance ¿30%) ˆ Transparent decision-making (all constraint proposals public) ˆ Reversibility preservation (probationary periods) ˆ Equitable distribution (weighted domain representation) 7.4 Future Directions 7.4.1 Hybrid Architectures Integration of large language models as linguistic module within organismic core: Linguistic Module (LLM) Organismic Core (OpenClaw) Embodiment (IoT) This preserves linguistic capabilities while grounding in embodied constraints. 7.4.2 Neurophenomenology Development of first-person reporting mechanisms to probe subjective experience [?]. Potential approaches: ˆ Self-modeling architectures with introspective access ˆ Comparative phenomenology across agent variants ˆ Behavioral proxies for pain/pleasure responses 7.4.3 Planetary-Scale Deployment Extension to global network of embodied agents forming SPEACE modules [?]: 1. Distributed M1 sensor layer (satellite + ground IoT) 2. Global M3 energy-compute optimization 3. Federated M5 genome evolution 4. Planetary attNA constraint consensus 8 Conclusion This document presents a comprehensive engineering framework for implementing Organismic Artificial General Intelligence using OpenClaw autonomous agents, n8n workflow automation, BotPress conversational interfaces, and IoT embodiment. The architecture is grounded in SPEACE theoretical framework and validated through CUB mathematical formalism. Key contributions include: 1. Modular implementation of SPEACE modules (M0, M1, M3, M5, attNA) 2. Complete source code for genome evolution, metabolic optimization, and constraint validation 56 3. Deployment procedures with Docker Compose orchestration 4. Monitoring infrastructure for real-time Φ(t) tracking 5. Resource allocation with detailed budget and timeline The system enables empirical investigation of organismic AI hypotheses, particularly whether embodiment is necessary for higher-order intelligence emergence. Total implementation cost: ¿645,343 (Year 1), scaling to ¿15.6M for production deployment. This engineering blueprint provides foundation for next-generation AI systems that tran scend disembodied language models toward truly organismic general intelligence integrated with planetary infrastructure. Acknowledgments The author thanks the embodied cognition research community, AI safety community, and open source contributors to OpenClaw, n8n, BotPress, and supporting technologies. References [1] Gardner, H. (1983). Frames of Mind: The Theory of Multiple Intelligences. Basic Books, New York. [2] Varela, F.J., Thompson, E., Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press, Cambridge, MA. [3] De Biase, R. (2025). Artificial Organismic General Intelligence: An Embodied Architecture for Multiple Intelligences. Rigene Project- Embodied AI Research, rigeneproject@rigene.eu. [4] De Biase, R. (2025). SPEACE: Speciation Process of Earth through Artificial Cognitive Extension. Rigene Project, rigeneproject@rigene.eu. [5] De Biase, R. (2024). Universal Information Code (CUB): A Mathematical Framework for Information Dynamics. Rigene Project, rigeneproject@rigene.eu. [6] De Biase, R. (2024). Unified Informational Field Theory (UIFT). Rigene Project, rigene project@rigene.eu. [7] De Biase, R. (2025). Organismic AI Initiative: Strategic Plan. Rigene Project, rigenepro ject@rigene.eu. [8] Steinberger, P., et al. (2026). OpenClaw: Self-hosted Autonomous AI Agent Framework. Retrieved from https://openclaw.ai [9] n8n GmbH (2026). n8n: Workflow Automation for Technical Teams. Retrieved from https://n8n.io [10] Botpress, Inc. (2026). BotPress: https://botpress.com Conversational AI Platform. Retrieved from 57 A Appendix A: Environment Configuration 1 # .env 2 # Copy to .env and fill in values 3 4 # Database Passwords 5 MONGO_PASSWORD=changeme_secure_password_1 6 TSDB_PASSWORD=changeme_secure_password_2 7 DB_PASSWORD=changeme_secure_password_3 8 9 # Service Credentials 10 N8N_PASSWORD=changeme_secure_password_4 11 GRAFANA_PASSWORD=changeme_secure_password_5 12 13 # Bot Tokens 14 TELEGRAM_BOT_TOKEN=your_telegram_bot_token 15 WHATSAPP_VERIFY_TOKEN=your_whatsapp_verify_token 16 SLACK_BOT_TOKEN=your_slack_bot_token 17 18 # API Keys 19 OPENAI_API_KEY=your_openai_api_key 20 ANTHROPIC_API_KEY=your_anthropic_api_key 21 MINIMAX_API_KEY=your_minimax_api_key 22 23 # Blockchain 24 BLOCKCHAIN_NODE_URL=http://blockchain-node:8545 25 ATTNA_CONTRACT_ADDRESS=0x742d35Cc6634C0532925a3b844Bc9e7595f0bEb0 26 27 # Monitoring 28 PROMETHEUS_RETENTION=30d 29 GRAFANA_INSTALL_PLUGINS=grafana-clock-panel,grafana-simple-json-datasource Listing 12: Environment variables template (.env) B Appendix B: Prometheus Configuration 1 # prometheus/prometheus.yml 2 global: 3 4 5 scrape_interval: 15s evaluation_interval: 15s 6 scrape_configs: 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25- job_name: ’prometheus’ static_configs:- targets: [’localhost:9090’]- job_name: ’openclaw-agents’ static_configs:- targets:- ’openclaw-metabolic:8080’- ’openclaw-energy:8080’- ’openclaw-compute:8080’ metrics_path: ’/metrics’- job_name: ’n8n’ static_configs:- targets: [’n8n:5678’]- job_name: ’botpress’ static_configs:- targets: [’botpress:3000’] 58 26 27 28 29 30 31 32 33- job_name: ’mqtt-bridge’ static_configs:- targets: [’mqtt-bridge:9091’]- job_name: ’node-exporter’ static_configs:- targets: [’node-exporter:9100’] Listing 13: Prometheus configuration (prometheus.yml) C Appendix C: MQTT Configuration 1 # mosquitto/config/mosquitto.conf 2 listener 1883 3 allow_anonymous true 4 5 # Persistence 6 persistence true 7 persistence_location /mosquitto/data/ 8 9 # Logging 10 log_dest file /mosquitto/log/mosquitto.log 11 log_type all 12 13 # Message size limits 14 message_size_limit 10485760 15 16 # Topic patterns 17 topic speace/# 18 topic sensors/# Listing 14: Mosquitto MQTT broker configuration.