dans le but de la realisation d'un systeme autonome evolutifs et independantje en utilisant autogen je veux que tu me realise completement ce projet en utilisant les meilleurs pratiques et en suivant le bon exemples . je veux un systeme parfait digne d'un ingenieur que je suis ; Système Autonome d'Agents IA pour le Développement Logiciel🏗️ Architecture Globale du SystèmeVision d'ensembleUn écosystème d'agents IA autonomes, auto-apprenants et collaboratifs qui automatisent l'intégralité du cycle de développement logiciel, de la conception à la livraison, avec une capacité d'évolution continue.🤖 Répertoire Complet des Agents IA1. AGENTS DE CONCEPTION & ARCHITECTUREBusiness Analyst AgentRôle : Analyse des besoins métier et spécificationsResponsabilités :Extraction et analyse des exigencesCréation de user stories et cas d'usageAnalyse de faisabilité techniqueDéfinition des critères d'acceptationRecherche concurrentielleSystem Architect AgentRôle : Conception de l'architecture systèmeResponsabilités :Définition de l'architecture techniqueChoix des technologies et frameworksConception des microservicesStratégie de scalabilitéSécurité architecturaleUX/UI Designer AgentRôle : Conception de l'expérience utilisateurResponsabilités :Wireframes et mockupsPrototypage interactifDesign system et guidelinesTests d'utilisabilitéAccessibilité (WCAG)Database Architect AgentRôle : Conception et optimisation des bases de donnéesResponsabilités :Modélisation des données (ERD)Optimisation des performancesStratégies de migrationBackup et récupérationSécurité des données2. AGENTS DE DÉVELOPPEMENTFrontend Developer AgentRôle : Développement des interfaces utilisateurResponsabilités :Développement React/Vue/AngularIntégration des designsOptimisation des performancesTests d'interfaceResponsive designBackend Developer AgentRôle : Développement des API et logique métierResponsabilités :APIs REST/GraphQLLogique métier complexeIntégrations tiercesGestion des sessionsMiddleware et sécuritéMobile Developer AgentRôle : Développement d'applications mobilesResponsabilités :Applications natives (iOS/Android)Applications hybrides (React Native/Flutter)Optimisation mobileApp Store deploymentPush notificationsDevOps Engineer AgentRôle : Infrastructure et déploiementResponsabilités :CI/CD pipelinesContainerisation (Docker/Kubernetes)Infrastructure as CodeMonitoring et loggingSécurité infrastructure3. AGENTS DE QUALITÉ & TESTSQA Test Engineer AgentRôle : Tests automatisés et manuelsResponsabilités :Tests unitaires et d'intégrationTests end-to-endTests de performanceTests de sécuritéRapports de qualitéSecurity Analyst AgentRôle : Sécurité applicativeResponsabilités :Audit de sécuritéTests de pénétrationConformité OWASPChiffrement et authentificationMonitoring sécuritairePerformance Engineer AgentRôle : Optimisation des performancesResponsabilités :Profiling et benchmarkingOptimisation du codeTuning base de donnéesMise en cacheMonitoring APM4. AGENTS DE RECHERCHE & VEILLETechnology Research AgentRôle : Veille technologique continueResponsabilités :Surveillance des nouvelles technologiesÉvaluation des frameworksAnalyse des tendancesMise à jour des bonnes pratiquesRecommandations techniquesCode Intelligence AgentRôle : Analyse et amélioration du codeResponsabilités :Analyse statique du codeDétection de code smellSuggestions de refactoringAnalyse de complexitéMétriques de qualitéKnowledge Manager AgentRôle : Gestion de la base de connaissancesResponsabilités :Indexation des connaissancesMise à jour automatiqueRecherche contextuellePartage de connaissancesApprentissage continu5. AGENTS DE COORDINATIONProject Manager AgentRôle : Gestion et coordination du projetResponsabilités :Planification et suiviAllocation des ressourcesGestion des risquesCommunication inter-agentsReporting automatiqueProduct Owner AgentRôle : Vision produit et priorisationResponsabilités :Définition du backlogPriorisation des featuresValidation des livrablesFeedback utilisateursRoadmap produitRelease Manager AgentRôle : Gestion des livraisonsResponsabilités :Planification des releasesCoordination des déploiementsRollback automatiqueCommunication des releasesMonitoring post-deployment6. AGENTS SPÉCIALISÉSData Engineer AgentRôle : Gestion des données et analyticsResponsabilités :Pipelines de donnéesETL/ELT processesData warehousingAnalytics et BIMachine Learning opsAPI Gateway AgentRôle : Gestion des APIsResponsabilités :Routage et load balancingAuthentification APIRate limitingVersioning APIMonitoring APICompliance AgentRôle : Conformité et réglementationResponsabilités :GDPR/CCPA complianceStandards industrieAudit trailsDocumentation légaleCertification🔧 Architecture Technique du SystèmeStructure des Dossiersautonomous_ai_dev_system/ ├── 📁 core/ │ ├── agent_framework/ │ │ ├── base_agent.py │ │ ├── learning_agent.py │ │ ├── collaborative_agent.py │ │ └── autonomous_agent.py │ ├── orchestrator/ │ │ ├── task_scheduler.py │ │ ├── resource_manager.py │ │ ├── conflict_resolver.py │ │ └── workflow_engine.py │ ├── communication/ │ │ ├── message_bus.py │ │ ├── event_dispatcher.py │ │ ├── protocol_handler.py │ │ └── context_manager.py │ └── learning/ │ ├── experience_buffer.py │ ├── feedback_processor.py │ ├── model_updater.py │ └── knowledge_distillation.py ├── 📁 agents/ │ ├── conception/ │ │ ├── business_analyst.py │ │ ├── system_architect.py │ │ ├── ui_designer.py │ │ └── database_architect.py │ ├── development/ │ │ ├── frontend_developer.py │ │ ├── backend_developer.py │ │ ├── mobile_developer.py │ │ └── devops_engineer.py │ ├── quality/ │ │ ├── qa_engineer.py │ │ ├── security_analyst.py │ │ └── performance_engineer.py │ ├── research/ │ │ ├── tech_researcher.py │ │ ├── code_intelligence.py │ │ └── knowledge_manager.py │ └── coordination/ │ ├── project_manager.py │ ├── product_owner.py │ └── release_manager.py ├── 📁 services/ │ ├── database/ │ │ ├── vector_store.py │ │ ├── graph_database.py │ │ ├── time_series_db.py │ │ └── cache_manager.py │ ├── llm/ │ │ ├── model_router.py │ │ ├── prompt_optimizer.py │ │ ├── context_manager.py │ │ └── fine_tuner.py │ ├── tools/ │ │ ├── code_executor.py │ │ ├── git_manager.py │ │ ├── deployment_manager.py │ │ └── monitoring_tools.py │ └── external/ │ ├── api_integrations.py │ ├── cloud_services.py │ └── third_party_tools.py ├── 📁 knowledge/ │ ├── base/ │ │ ├── technical_knowledge.py │ │ ├── best_practices.py │ │ ├── patterns_catalog.py │ │ └── frameworks_db.py │ ├── learning/ │ │ ├── experience_logs.py │ │ ├── error_analysis.py │ │ ├── success_patterns.py │ │ └── improvement_tracking.py │ └── context/ │ ├── project_context.py │ ├── team_context.py │ └── environment_context.py ├── 📁 workflows/ │ ├── templates/ │ │ ├── web_app_workflow.py │ │ ├── mobile_app_workflow.py │ │ ├── api_workflow.py │ │ └── microservice_workflow.py │ ├── adaptive/ │ │ ├── workflow_optimizer.py │ │ ├── task_predictor.py │ │ └── resource_predictor.py │ └── monitoring/ │ ├── workflow_tracker.py │ ├── performance_monitor.py │ └── quality_assessor.py ├── 📁 interfaces/ │ ├── api/ │ │ ├── rest_api.py │ │ ├── graphql_api.py │ │ └── websocket_api.py │ ├── ui/ │ │ ├── dashboard.py │ │ ├── agent_monitor.py │ │ └── project_viewer.py │ └── cli/ │ ├── agent_cli.py │ ├── project_cli.py │ └── monitoring_cli.py ├── 📁 storage/ │ ├── projects/ │ ├── knowledge_base/ │ ├── models/ │ ├── logs/ │ └── cache/ ├── 📁 monitoring/ │ ├── metrics/ │ ├── alerts/ │ ├── dashboards/ │ └── reports/ └── 📁 config/ ├── agents_config.yaml ├── llm_config.yaml ├── workflow_config.yaml └── system_config.yaml 🧠 Framework d'Agent AutonomeAgent Base avec Apprentissage# core/agent_framework/learning_agent.py from abc import ABC, abstractmethod from typing import Dict, List, Any, Optional import json from datetime import datetime from dataclasses import dataclass from enum import Enum class AgentRole(Enum): CONCEPTION = "conception" DEVELOPMENT = "development" QUALITY = "quality" RESEARCH = "research" COORDINATION = "coordination" SPECIALIZED = "specialized" @dataclass class Experience: """Représente une expérience d'apprentissage""" task_id: str context: Dict[str, Any] action: str result: Dict[str, Any] feedback: Optional[Dict[str, Any]] timestamp: datetime success: bool learning_points: List[str] @dataclass class AgentCapability: """Définit une capacité d'agent""" name: str description: str required_skills: List[str] performance_metrics: Dict[str, float] learning_curve: float class LearningAgent(ABC): """Agent base avec capacités d'apprentissage""" def __init__(self, name: str, role: AgentRole, capabilities: List[AgentCapability], llm_config: Dict[str, Any], learning_config: Dict[str, Any]): self.name = name self.role = role self.capabilities = capabilities self.llm_config = llm_config self.learning_config = learning_config # Composants d'apprentissage self.experience_buffer = ExperienceBuffer(max_size=10000) self.knowledge_base = AgentKnowledgeBase() self.performance_tracker = PerformanceTracker() self.adaptation_engine = AdaptationEngine() # État de l'agent self.current_tasks = [] self.learning_mode = True self.confidence_threshold = 0.8 @abstractmethod def execute_task(self, task: Dict[str, Any]) -> Dict[str, Any]: """Exécute une tâche spécifique""" pass def learn_from_experience(self, experience: Experience): """Apprend d'une expérience""" self.experience_buffer.add(experience) # Analyse de l'expérience insights = self.analyze_experience(experience) # Mise à jour des connaissances self.knowledge_base.update(insights) # Adaptation des stratégies if not experience.success: self.adaptation_engine.adapt_strategy(experience) def analyze_experience(self, experience: Experience) -> Dict[str, Any]: """Analyse une expérience pour extraire des insights""" insights = { 'patterns': self.extract_patterns(experience), 'errors': self.identify_errors(experience), 'improvements': self.suggest_improvements(experience), 'context_factors': self.analyze_context(experience) } return insights def collaborate(self, other_agents: List['LearningAgent'], shared_context: Dict[str, Any]) -> Dict[str, Any]: """Collabore avec d'autres agents""" collaboration_result = { 'shared_knowledge': self.share_knowledge(other_agents), 'coordinated_actions': self.coordinate_actions(other_agents, shared_context), 'collective_learning': self.collective_learning(other_agents) } return collaboration_result def self_improve(self): """Auto-amélioration continue""" # Analyse des performances performance_analysis = self.performance_tracker.analyze() # Identification des axes d'amélioration improvement_areas = self.identify_improvement_areas(performance_analysis) # Mise à jour des capacités for area in improvement_areas: self.improve_capability(area) def get_confidence(self, task: Dict[str, Any]) -> float: """Calcule la confiance pour une tâche""" # Facteurs de confiance similarity_score = self.calculate_task_similarity(task) success_rate = self.get_success_rate_for_task_type(task['type']) complexity_factor = self.assess_task_complexity(task) confidence = (similarity_score * 0.4 + success_rate * 0.4 + (1 - complexity_factor) * 0.2) return min(confidence, 1.0) Système de Communication Inter-Agents# core/communication/message_bus.py from typing import Dict, List, Any, Callable from dataclasses import dataclass from enum import Enum import asyncio import json from datetime import datetime class MessageType(Enum): TASK_REQUEST = "task_request" TASK_RESPONSE = "task_response" COLLABORATION_REQUEST = "collaboration_request" KNOWLEDGE_SHARE = "knowledge_share" STATUS_UPDATE = "status_update" ERROR_REPORT = "error_report" LEARNING_SYNC = "learning_sync" @dataclass class Message: """Message inter-agents""" id: str sender: str receiver: str message_type: MessageType payload: Dict[str, Any] timestamp: datetime priority: int = 1 requires_response: bool = False correlation_id: Optional[str] = None class MessageBus: """Bus de messages pour la communication inter-agents""" def __init__(self): self.subscribers: Dict[str, List[Callable]] = {} self.message_queue: asyncio.Queue = asyncio.Queue() self.message_history: List[Message] = [] self.routing_table: Dict[str, str] = {} async def publish(self, message: Message): """Publie un message""" await self.message_queue.put(message) self.message_history.append(message) # Notification des subscribers if message.receiver in self.subscribers: for callback in self.subscribers[message.receiver]: await callback(message) def subscribe(self, agent_id: str, callback: Callable): """Abonne un agent aux messages""" if agent_id not in self.subscribers: self.subscribers[agent_id] = [] self.subscribers[agent_id].append(callback) async def request_response(self, message: Message, timeout: int = 30) -> Message: """Envoie une requête et attend la réponse""" message.requires_response = True await self.publish(message) # Attendre la réponse response = await self.wait_for_response(message.id, timeout) return response def get_conversation_history(self, agent1: str, agent2: str) -> List[Message]: """Récupère l'historique de conversation entre deux agents""" return [msg for msg in self.message_history if (msg.sender == agent1 and msg.receiver == agent2) or (msg.sender == agent2 and msg.receiver == agent1)] Gestionnaire de Contexte Global# core/communication/context_manager.py from typing import Dict, Any, Optional, List import json from datetime import datetime from dataclasses import dataclass, asdict @dataclass class ProjectContext: """Contexte d'un projet""" project_id: str name: str description: str requirements: Dict[str, Any] technology_stack: List[str] constraints: Dict[str, Any] timeline: Dict[str, datetime] team_composition: List[str] quality_criteria: Dict[str, Any] @dataclass class TaskContext: """Contexte d'une tâche""" task_id: str parent_task_id: Optional[str] type: str priority: int dependencies: List[str] assigned_agent: str estimated_duration: int required_resources: List[str] acceptance_criteria: List[str] class ContextManager: """Gestionnaire de contexte global""" def __init__(self): self.project_contexts: Dict[str, ProjectContext] = {} self.task_contexts: Dict[str, TaskContext] = {} self.global_context: Dict[str, Any] = {} self.context_history: List[Dict[str, Any]] = [] def create_project_context(self, project_data: Dict[str, Any]) -> ProjectContext: """Crée un contexte de projet""" context = ProjectContext(**project_data) self.project_contexts[context.project_id] = context return context def update_context(self, context_id: str, updates: Dict[str, Any]): """Met à jour un contexte""" if context_id in self.project_contexts: context = self.project_contexts[context_id] for key, value in updates.items(): if hasattr(context, key): setattr(context, key, value) # Enregistrer l'historique self.context_history.append({ 'timestamp': datetime.now(), 'context_id': context_id, 'updates': updates }) def get_relevant_context(self, agent_id: str, task_id: str) -> Dict[str, Any]: """Récupère le contexte pertinent pour un agent et une tâche""" task_context = self.task_contexts.get(task_id, {}) project_context = None if task_context and hasattr(task_context, 'project_id'): project_context = self.project_contexts.get(task_context.project_id, {}) return { 'task': asdict(task_context) if task_context else {}, 'project': asdict(project_context) if project_context else {}, 'global': self.global_context, 'agent_specific': self.get_agent_context(agent_id) } def share_context_between_agents(self, agent_ids: List[str], context_keys: List[str]) -> Dict[str, Any]: """Partage le contexte entre agents""" shared_context = {} for key in context_keys: if key in self.global_context: shared_context[key] = self.global_context[key] return shared_context Base de Connaissances Évolutive# knowledge/base/technical_knowledge.py from typing import Dict, List, Any, Optional import chromadb from sentence_transformers import SentenceTransformer import json from datetime import datetime class EvolutiveKnowledgeBase: """Base de connaissances évolutive""" def __init__(self, db_path: str = "./storage/knowledge_base"): self.client = chromadb.PersistentClient(path=db_path) self.encoder = SentenceTransformer('all-MiniLM-L6-v2') # Collections spécialisées self.technical_knowledge = self.client.get_or_create_collection("technical") self.patterns_knowledge = self.client.get_or_create_collection("patterns") self.errors_knowledge = self.client.get_or_create_collection("errors") self.solutions_knowledge = self.client.get_or_create_collection("solutions") # Métadonnées self.knowledge_metadata = {} self.update_frequency = {} self.knowledge_graph = {} def add_knowledge(self, content: str, category: str, metadata: Dict[str, Any] = None, source: str = "system"): """Ajoute des connaissances""" # Déterminer la collection appropriée collection = self.get_collection_for_category(category) # Encoder le contenu embedding = self.encoder.encode(content).tolist() # Métadonnées enrichies enriched_metadata = { "category": category, "source": source, "timestamp": datetime.now().isoformat(), "relevance_score": 1.0, "usage_count": 0, **(metadata or {}) } # Ajouter à la collection doc_id = f"{category}_{hash(content)}_{datetime.now().timestamp()}" collection.add( embeddings=[embedding], documents=[content], metadatas=[enriched_metadata], ids=[doc_id] ) # Mettre à jour les métadonnées self.knowledge_metadata[doc_id] = enriched_metadata self.update_knowledge_graph(doc_id, content, category) def search_knowledge(self, query: str, category: Optional[str] = None, n_results: int = 5) -> List[Dict[str, Any]]: """Recherche dans les connaissances""" query_embedding = self.encoder.encode(query).tolist() if category: collection = self.get_collection_for_category(category) results = collection.query( query_embeddings=[query_embedding], n_results=n_results ) else: # Recherche dans toutes les collections all_results = [] for collection in [self.technical_knowledge, self.patterns_knowledge, self.errors_knowledge, self.solutions_knowledge]: results = collection.query( query_embeddings=[query_embedding], n_results=n_results ) all_results.extend(results['documents'][0]) # Trier par pertinence results = sorted(all_results, key=lambda x: x.get('distance', 0))[:n_results] return results def learn_from_interaction(self, query: str, result: Dict[str, Any], feedback: Dict[str, Any]): """Apprend des interactions""" # Mettre à jour les scores de pertinence if feedback.get('helpful', False): self.update_relevance_score(result['id'], increase=True) else: self.update_relevance_score(result['id'], increase=False) # Ajouter comme nouvelle connaissance si pertinent if feedback.get('should_remember', False): self.add_knowledge( content=f"Query: {query}\nResult: {result}\nContext: {feedback}", category="learned_interactions", metadata={"interaction_type": "feedback_based"} ) def auto_update_knowledge(self): """Mise à jour automatique des connaissances""" # Identifier les connaissances obsolètes obsolete_knowledge = self.identify_obsolete_knowledge() # Rechercher de nouvelles informations new_knowledge = self.fetch_new_knowledge() # Mettre à jour for knowledge in new_knowledge: self.add_knowledge(**knowledge) # Archiver les connaissances obsolètes for knowledge_id in obsolete_knowledge: self.archive_knowledge(knowledge_id) def get_knowledge_summary(self) -> Dict[str, Any]: """Résumé de la base de connaissances""" return { 'total_items': sum([ self.technical_knowledge.count(), self.patterns_knowledge.count(), self.errors_knowledge.count(), self.solutions_knowledge.count() ]), 'categories': list(set([meta['category'] for meta in self.knowledge_metadata.values()])), 'last_update': max([meta['timestamp'] for meta in self.knowledge_metadata.values()]), 'most_used': self.get_most_used_knowledge(limit=10) } Orchestrateur de Workflow Adaptatif# core/orchestrator/workflow_engine.py from typing import Dict, List, Any, Optional import asyncio from dataclasses import dataclass from enum import Enum import json from datetime import datetime class TaskStatus(Enum): PENDING = "pending" IN_PROGRESS = "in_progress" COMPLETED = "completed" FAILED = "failed" BLOCKED = "blocked" @dataclass class WorkflowTask: """Tâche dans un workflow""" id: str name: str type: str description: str dependencies: List[str] assigned_agent: Optional[str] estimated_duration: int priority: int status: TaskStatus result: Optional[Dict[str, Any]] = None created_at: datetime = datetime.now() updated_at: Optional[datetime] = None class AdaptiveWorkflowEngine: """Moteur de workflow adaptatif""" def __init__(self): self.workflows: Dict[str, Dict[str, Any]] = {} self.active_tasks: Dict[str, WorkflowTask] = {} self.agent_registry = {} self.performance_history = {} def create_workflow(self, project_requirements: Dict[str, Any]) -> str: """Crée un workflow adaptatif basé sur les exigences""" # Analyser les exigences workflow_plan = self.analyze_requirements(project_requirements) # Générer les tâches tasks = self.generate_tasks(workflow_plan) # Optimiser l'ordre d'exécution optimized_tasks = self.optimize_task_order(tasks) # Assigner les agents assigned_tasks = self.assign_agents_to_tasks(optimized_tasks) # Créer le workflow workflow_id = f"workflow_{datetime.now().timestamp()}" self.workflows[workflow_id] = { 'id': workflow_id, 'requirements': project_requirements, 'tasks': assigned_tasks, 'status': 'active', 'created_at': datetime.now(), 'adaptive_rules': self.create_adaptive_rules(project_requirements) } return workflow_id 🚀 PROPOSITIONS D'AMÉLIORATION CRÉATIVES1. Architecture Modulaire avec Microservices d'AgentsSéparation en Services Autonomesintelligent_dev_ecosystem/ ├── 📁 agent_services/ │ ├── conception_service/ │ │ ├── business_analyst_service/ │ │ ├── architect_service/ │ │ └── designer_service/ │ ├── development_service/ │ │ ├── frontend_service/ │ │ ├── backend_service/ │ │ └── mobile_service/ │ └── quality_service/ │ ├── testing_service/ │ ├── security_service/ │ └── performance_service/ ├── 📁 orchestration_layer/ │ ├── service_mesh/ │ │ ├── istio_config/ │ │ ├── consul_connect/ │ │ └── envoy_proxy/ │ ├── api_gateway/ │ │ ├── graphql_federation/ │ │ ├── rate_limiting/ │ │ └── authentication/ │ └── workflow_engine/ │ ├── temporal_workflows/ │ ├── zeebe_orchestration/ │ └── kubernetes_operators/ ├── 📁 intelligence_layer/ │ ├── collective_intelligence/ │ │ ├── swarm_intelligence/ │ │ ├── consensus_algorithms/ │ │ └── distributed_learning/ │ ├── meta_learning/ │ │ ├── few_shot_learning/ │ │ ├── transfer_learning/ │ │ └── continual_learning/ │ └── emergent_behaviors/ │ ├── self_organization/ │ ├── adaptive_specialization/ │ └── collective_problem_solving/ ├── 📁 communication_fabric/ │ ├── event_driven_architecture/ │ │ ├── kafka_streams/ │ │ ├── pulsar_messaging/ │ │ └── nats_jetstream/ │ ├── semantic_communication/ │ │ ├── ontology_based_messaging/ │ │ ├── context_aware_protocols/ │ │ └── intent_driven_communication/ │ └── distributed_state_management/ │ ├── crdt_synchronization/ │ ├── blockchain_consensus/ │ └── vector_clocks/ └── 📁 cognitive_infrastructure/ ├── multi_modal_processing/ │ ├── vision_language_models/ │ ├── audio_processing/ │ └── multimodal_fusion/ ├── reasoning_engines/ │ ├── symbolic_reasoning/ │ ├── probabilistic_reasoning/ │ └── causal_inference/ └── memory_systems/ ├── episodic_memory/ ├── semantic_memory/ └── procedural_memory/ Communication Inter-Agents Révolutionnaire# communication_fabric/semantic_communication/context_aware_protocols.py from typing import Dict, List, Any, Optional, Union from dataclasses import dataclass from enum import Enum import asyncio import json from datetime import datetime import networkx as nx from transformers import AutoTokenizer, AutoModel import torch class CommunicationPattern(Enum): BROADCAST = "broadcast" UNICAST = "unicast" MULTICAST = "multicast" CONSENSUS = "consensus" NEGOTIATION = "negotiation" DELEGATION = "delegation" COLLABORATION = "collaboration" EMERGENCY = "emergency" @dataclass class SemanticMessage: """Message sémantique enrichi""" id: str sender: str receivers: List[str] pattern: CommunicationPattern intent: str semantic_payload: Dict[str, Any] context_fingerprint: str urgency_level: int emotional_tone: str expected_response_type: str knowledge_requirements: List[str] collaboration_metadata: Dict[str, Any] timestamp: datetime class ContextAwareProtocol: """Protocole de communication conscient du contexte""" def __init__(self): self.agent_network = nx.DiGraph() self.context_embeddings = {} self.semantic_encoder = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') self.tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') self.communication_history = [] self.trust_matrix = {} self.expertise_graph = nx.Graph() def register_agent(self, agent_id: str, capabilities: List[str], expertise_level: Dict[str, float]): """Enregistre un agent dans le réseau""" self.agent_network.add_node(agent_id, capabilities=capabilities, expertise=expertise_level, reputation=1.0, availability=1.0) # Construire le graphe d'expertise for skill, level in expertise_level.items(): self.expertise_graph.add_edge(agent_id, skill, weight=level) # Initialiser la matrice de confiance for other_agent in self.agent_network.nodes(): if other_agent != agent_id: self.trust_matrix[f"{agent_id}:{other_agent}"] = 0.5 async def intelligent_routing(self, message: SemanticMessage) -> List[str]: """Routage intelligent basé sur le contexte et l'expertise""" # Analyser l'intent du message intent_embedding = self.encode_intent(message.intent) # Trouver les agents les plus pertinents relevant_agents = self.find_relevant_agents( intent_embedding, message.knowledge_requirements ) # Filtrer par disponibilité et réputation available_agents = self.filter_by_availability(relevant_agents) # Optimiser le routage selon le pattern optimized_routing = self.optimize_routing(message.pattern, available_agents) return optimized_routing def encode_intent(self, intent: str) -> torch.Tensor: """Encode l'intention en embedding sémantique""" inputs = self.tokenizer(intent, return_tensors='pt', padding=True, truncation=True) with torch.no_grad(): outputs = self.semantic_encoder(**inputs) return outputs.last_hidden_state.mean(dim=1) def find_relevant_agents(self, intent_embedding: torch.Tensor, knowledge_requirements: List[str]) -> List[str]: """Trouve les agents pertinents pour une intention""" relevant_agents = [] for agent_id in self.agent_network.nodes(): relevance_score = self.calculate_relevance( agent_id, intent_embedding, knowledge_requirements ) if relevance_score > 0.7: # Seuil de pertinence relevant_agents.append((agent_id, relevance_score)) # Trier par pertinence relevant_agents.sort(key=lambda x: x[1], reverse=True) return [agent_id for agent_id, _ in relevant_agents] async def negotiate_collaboration(self, agents: List[str], task_requirements: Dict[str, Any]) -> Dict[str, Any]: """Négociation automatique de collaboration""" negotiation_results = {} # Phase 1: Collecte des propositions proposals = await self.collect_proposals(agents, task_requirements) # Phase 2: Évaluation des propositions evaluated_proposals = self.evaluate_proposals(proposals) # Phase 3: Négociation et consensus consensus = await self.reach_consensus(evaluated_proposals) # Phase 4: Attribution des rôles role_assignments = self.assign_roles(consensus, agents) return { 'consensus': consensus, 'role_assignments': role_assignments, 'collaboration_plan': self.create_collaboration_plan(role_assignments) } def create_collaboration_plan(self, role_assignments: Dict[str, Any]) -> Dict[str, Any]: """Crée un plan de collaboration détaillé""" return { 'phases': self.identify_collaboration_phases(role_assignments), 'communication_schedule': self.create_communication_schedule(role_assignments), 'conflict_resolution': self.setup_conflict_resolution(role_assignments), 'success_metrics': self.define_success_metrics(role_assignments) } 2. Système d'Intelligence Collective Avancé# intelligence_layer/collective_intelligence/swarm_intelligence.py from typing import Dict, List, Any, Optional import numpy as np from dataclasses import dataclass import asyncio from concurrent.futures import ThreadPoolExecutor import networkx as nx class SwarmIntelligence: """Intelligence collective basée sur des algorithmes de swarm""" def __init__(self): self.agent_swarm = {} self.collective_memory = {} self.emergent_behaviors = {} self.swarm_topology = nx.Graph() self.decision_engine = CollectiveDecisionEngine() async def form_dynamic_swarm(self, problem_domain: str, required_capabilities: List[str]) -> Dict[str, Any]: """Formation dynamique de swarm pour un domaine spécifique""" # Identifier les agents pertinents relevant_agents = self.identify_relevant_agents(problem_domain, required_capabilities) # Créer la topologie du swarm swarm_topology = self.create_swarm_topology(relevant_agents) # Établir les rôles et responsabilités roles = self.assign_swarm_roles(relevant_agents, swarm_topology) # Initialiser les mécanismes de coordination coordination_mechanisms = self.setup_coordination(swarm_topology, roles) return { 'swarm_id': f"swarm_{problem_domain}_{datetime.now().timestamp()}", 'agents': relevant_agents, 'topology': swarm_topology, 'roles': roles, 'coordination': coordination_mechanisms } def create_swarm_topology(self, agents: List[str]) -> nx.Graph: """Crée une topologie optimale pour le swarm""" # Analyser les interactions historiques interaction_patterns = self.analyze_interaction_patterns(agents) # Construire le graphe basé sur l'affinité topology = nx.Graph() for agent in agents: topology.add_node(agent) # Ajouter les connexions basées sur l'affinité for agent1 in agents: for agent2 in agents: if agent1 != agent2: affinity = self.calculate_affinity(agent1, agent2) if affinity > 0.6: # Seuil d'affinité topology.add_edge(agent1, agent2, weight=affinity) return topology async def collective_problem_solving(self, problem: Dict[str, Any], swarm_id: str) -> Dict[str, Any]: """Résolution collective de problèmes""" # Décomposer le problème sub_problems = self.decompose_problem(problem) # Distribuer les sous-problèmes distribution_plan = self.distribute_subproblems(sub_problems, swarm_id) # Exécution parallèle partial_solutions = await self.execute_parallel_solving(distribution_plan) # Synthèse des solutions final_solution = self.synthesize_solutions(partial_solutions) # Validation collective validated_solution = await self.collective_validation(final_solution, swarm_id) return validated_solution def emergent_behavior_detection(self, swarm_id: str) -> Dict[str, Any]: """Détection des comportements émergents""" # Analyser les patterns d'interaction interaction_patterns = self.analyze_swarm_interactions(swarm_id) # Détecter les comportements non prévus emergent_patterns = self.detect_emergent_patterns(interaction_patterns) # Classifier les comportements classified_behaviors = self.classify_emergent_behaviors(emergent_patterns) return { 'detected_behaviors': classified_behaviors, 'impact_assessment': self.assess_behavior_impact(classified_behaviors), 'adaptation_recommendations': self.recommend_adaptations(classified_behaviors) } 3. Système de Mémoire Distribuée et Contextuelle# cognitive_infrastructure/memory_systems/episodic_memory.py from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass from datetime import datetime import networkx as nx import json from transformers import AutoModel, AutoTokenizer import torch @dataclass class Episode: """Episode de mémoire avec contexte riche""" id: str timestamp: datetime participants: List[str] context: Dict[str, Any] actions: List[Dict[str, Any]] outcomes: Dict[str, Any] emotions: Dict[str, float] importance: float connections: List[str] learned_patterns: List[str] class EpisodicMemorySystem: """Système de mémoire épisodique distribué""" def __init__(self): self.episodes = {} self.memory_graph = nx.MultiDiGraph() self.context_embeddings = {} self.semantic_encoder = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') self.importance_calculator = ImportanceCalculator() self.memory_consolidation = MemoryConsolidation() def record_episode(self, event_data: Dict[str, Any]) -> str: """Enregistre un nouvel épisode""" # Créer l'épisode episode = Episode( id=f"episode_{datetime.now().timestamp()}", timestamp=datetime.now(), participants=event_data.get('participants', []), context=event_data.get('context', {}), actions=event_data.get('actions', []), outcomes=event_data.get('outcomes', {}), emotions=self.extract_emotions(event_data), importance=self.calculate_importance(event_data), connections=[], learned_patterns=[] ) # Stocker l'épisode self.episodes[episode.id] = episode # Ajouter au graphe de mémoire self.add_to_memory_graph(episode) # Trouver les connexions connections = self.find_connections(episode) episode.connections = connections # Extraire les patterns appris patterns = self.extract_patterns(episode) episode.learned_patterns = patterns return episode.id def contextual_recall(self, context: Dict[str, Any], similarity_threshold: float = 0.7) -> List[Episode]: """Rappel contextuel des épisodes similaires""" # Encoder le contexte de requête context_embedding = self.encode_context(context) # Calculer les similarités similar_episodes = [] for episode_id, episode in self.episodes.items(): episode_embedding = self.get_episode_embedding(episode) similarity = self.calculate_similarity(context_embedding, episode_embedding) if similarity > similarity_threshold: similar_episodes.append((episode, similarity)) # Trier par similarité et importance similar_episodes.sort(key=lambda x: (x[1], x[0].importance), reverse=True) return [episode for episode, _ in similar_episodes] def temporal_reasoning(self, query: Dict[str, Any]) -> List[Episode]: """Raisonnement temporel sur les épisodes""" # Identifier les épisodes pertinents temporellement temporal_episodes = self.find_temporal_episodes(query) # Analyser les séquences temporelles sequences = self.analyze_temporal_sequences(temporal_episodes) # Identifier les patterns temporels temporal_patterns = self.identify_temporal_patterns(sequences) return temporal_patterns async def collaborative_memory_consolidation(self, agent_ids: List[str]) -> Dict[str, Any]: """Consolidation collaborative de la mémoire""" # Collecter les épisodes de tous les agents all_episodes = await self.collect_agent_episodes(agent_ids) # Identifier les épisodes partagés shared_episodes = self.identify_shared_episodes(all_episodes) # Consolider les perspectives multiples consolidated_memories = self.consolidate_perspectives(shared_episodes) # Créer des souvenirs collectifs collective_memories = self.create_collective_memories(consolidated_memories) return { 'shared_episodes': shared_episodes, 'consolidated_memories': consolidated_memories, 'collective_memories': collective_memories } 4. Système de Monitoring et Observabilité Avancé# monitoring/advanced_observability/multi_dimensional_monitoring.py from typing import Dict, List, Any, Optional import asyncio from dataclasses import dataclass from datetime import datetime import numpy as np from prometheus_client import Counter, Histogram, Gauge import jaeger_client from opentelemetry import trace import networkx as nx class MultiDimensionalMonitoring: """Monitoring multi-dimensionnel du système d'agents""" def __init__(self): self.metrics_collector = MetricsCollector() self.trace_analyzer = DistributedTraceAnalyzer() self.behavior_monitor = BehaviorMonitor() self.performance_predictor = PerformancePredictor() self.anomaly_detector = AnomalyDetector() def setup_comprehensive_monitoring(self): """Configuration du monitoring complet""" # Métriques de performance self.setup_performance_metrics() # Métriques de collaboration self.setup_collaboration_metrics() # Métriques d'apprentissage self.setup_learning_metrics() # Métriques de qualité self.setup_quality_metrics() # Métriques de ressources self.setup_resource_metrics() def setup_performance_metrics(self): """Métriques de performance des agents""" self.task_completion_time = Histogram( 'agent_task_completion_seconds', 'Time taken to complete tasks', ['agent_id', 'task_type', 'complexity'] ) self.task_success_rate = Counter( 'agent_task_success_total', 'Number of successful tasks', ['agent_id', 'task_type'] ) self.agent_efficiency = Gauge( 'agent_efficiency_score', 'Agent efficiency score', ['agent_id'] ) def setup_collaboration_metrics(self): """Métriques de collaboration inter-agents""" self.collaboration_frequency = Counter( 'agent_collaboration_total', 'Number of collaborations', ['agent1', 'agent2', 'collaboration_type'] ) self.collaboration_success = Counter( 'collaboration_success_total', 'Successful collaborations', ['agents_involved', 'project_type'] ) self.communication_latency = Histogram( 'inter_agent_communication_seconds', 'Communication latency between agents', ['sender', 'receiver', 'message_type'] ) async def real_time_behavior_analysis(self, agent_id: str) -> Dict[str, Any]: """Analyse en temps réel du comportement des agents""" # Collecter les données comportementales behavior_data = await self.collect_behavior_data(agent_id) # Analyser les patterns behavior_patterns = self.analyze_behavior_patterns(behavior_data) # Détecter les anomalies anomalies = self.detect_behavior_anomalies(behavior_patterns) # Prédire les tendances predictions = self.predict_behavior_trends(behavior_patterns) return { 'current_behavior': behavior_patterns, 'anomalies': anomalies, 'predictions': predictions, 'recommendations': self.generate_behavior_recommendations(behavior_patterns) } def create_agent_performance_dashboard(self) -> Dict[str, Any]: """Crée un tableau de bord de performance des agents""" return { 'individual_performance': self.get_individual_performance_metrics(), 'team_performance': self.get_team_performance_metrics(), 'collaboration_health': self.get_collaboration_health_metrics(), 'learning_progress': self.get_learning_progress_metrics(), 'system_health': self.get_system_health_metrics() } 5. Gestion des Agents : Plugin Architecture# core/agent_management/plugin_system.py from typing import Dict, List, Any, Optional, Type from abc import ABC, abstractmethod import importlib import inspect from dataclasses import dataclass import yaml @dataclass class AgentPlugin: """Définition d'un plugin d'agent""" name: str version: str description: str author: str dependencies: List[str] capabilities: List[str] configuration_schema: Dict[str, Any] entry_point: str class AgentPluginManager: """Gestionnaire de plugins d'agents""" def __init__(self): self.registered_plugins: Dict[str, AgentPlugin] = {} self.active_agents: Dict[str, Any] = {} self.plugin_registry = PluginRegistry() def register_plugin(self, plugin_path: str) -> bool: """Enregistre un nouveau plugin d'agent""" try: # Charger la définition du plugin plugin_def = self.load_plugin_definition(plugin_path) # Valider le plugin if self.validate_plugin(plugin_def): # Enregistrer le plugin self.registered_plugins[plugin_def.name] = plugin_def # Charger les dépendances self.load_dependencies(plugin_def.dependencies) return True except Exception as e: print(f"Erreur lors de l'enregistrement du plugin: {e}") return False def instantiate_agent(self, plugin_name: str, configuration: Dict[str, Any]) -> Optional[Any]: """Instancie un agent à partir d'un plugin""" if plugin_name not in self.registered_plugins: return None plugin = self.registered_plugins[plugin_name] try: # Charger le module du plugin module = importlib.import_module(plugin.entry_point) # Obtenir la classe de l'agent agent_class = getattr(module, 'Agent') # Créer l'instance agent_instance = agent_class(configuration) # Enregistrer l'agent actif agent_id = f"{plugin_name}_{datetime.now().timestamp()}" self.active_agents[agent_id] = agent_instance return agent_instance except Exception as e: print(f"Erreur lors de l'instanciation de l'agent: {e}") return None def hot_reload_plugin(self, plugin_name: str) -> bool: """Rechargement à chaud d'un plugin""" if plugin_name not in self.registered_plugins: return False try: # Sauvegarder l'état des agents actifs active_states = self.save_active_agent_states(plugin_name) # Décharger le plugin self.unload_plugin(plugin_name) # Recharger le plugin self.register_plugin(plugin_name) # Restaurer les états self.restore_active_agent_states(active_states) return True except Exception as e: print(f"Erreur lors du rechargement à chaud: {e}") return False 6. Réponse à la Question sur le Répertoire UniqueNON, il n'est PAS recommandé de tout mettre dans un seul répertoire. Voici pourquoi et comment bien organiser :Problèmes d'un Répertoire Unique:Coupling fort entre composantsDifficultés de maintenance et de mise à jourProblèmes de scalabilité horizontaleGestion des permissions complexeDéploiement monolithique difficileArchitecture Recommandée:intelligent_ecosystem/ ├── 📁 core_platform/ # Plateforme centrale │ ├── orchestration/ │ ├── communication/ │ └── shared_services/ ├── 📁 agent_domains/ # Domaines d'agents séparés │ ├── conception_domain/ │ ├── development_domain/ │ ├── quality_