Technical Architecture

Technology Stack

Core Technologies

Component	Technology	Purpose
Primary Language	Python 3.10+	Main implementation language
Web Framework	FastAPI	API and WebSocket endpoints
Graph Database	Neo4j	Entity storage and relationship mapping
Event Stream	Kafka	Reliable event processing
Cache/State	Redis	In-memory data and session management
Object Storage	MinIO	File and artifact management
ML Framework	PyTorch Geometric	GNN implementation
Container Runtime	Docker	Component isolation and deployment
Orchestration	Kubernetes	Scaling and service management

Development Tools

Tool	Purpose
Pydantic	Data validation and schema definition
Protocol Classes	Service interface contracts
pytest	Testing framework with coverage metrics
mypy	Static type checking
black & isort	Code formatting

Architecture Patterns

Service Registry Pattern

All services in Agent Party are registered through a centralized Service Registry, which:

• Provides dependency injection for components
• Manages service lifecycle and initialization
• Enables mocking and testing of service dependencies
• Controls service scoping (singleton, transient, scoped)

class ServiceRegistry:
    """Central registry for all system services."""
    
    def __init__(self):
        self._services = {}
        
    def register(self, service_type: Type, implementation: Any, scope: str = "singleton"):
        """Register a service implementation for a given interface type."""
        self._services[service_type] = {
            "implementation": implementation,
            "scope": scope,
            "instance": None if scope != "singleton" else implementation
        }
        
    def get_service(self, service_type: Type) -> Any:
        """Retrieve a service of the specified type."""
        if service_type not in self._services:
            raise ServiceNotFound(f"Service {service_type.__name__} not registered")
            
        service = self._services[service_type]
        
        if service["scope"] == "singleton":
            return service["instance"]
        elif service["scope"] == "transient":
            return service["implementation"]()
        # Handle other scopes as needed

Repository Pattern

Data access is implemented through repositories, which:

• Abstract database operations from business logic
• Provide strongly-typed access methods
• Implement proper transaction management
• Optimize Neo4j queries with appropriate indexing

class AgentRepository(Protocol):
    """Interface for agent data access."""
    
    async def get_agent_by_id(self, agent_id: str) -> Agent:
        """Retrieve an agent by ID."""
        ...
        
    async def create_agent(self, agent: Agent) -> str:
        """Create a new agent record."""
        ...
        
    async def update_agent_state(self, agent_id: str, state: str) -> None:
        """Update an agent's state."""
        ...

Event-Driven Architecture

The system uses event-driven communication with:

• Strongly-typed event definitions using Pydantic
• Reliable event delivery through Kafka
• Idempotent event handlers
• Clear event ownership boundaries

class AgentStateChangedEvent(BaseModel):
    """Event triggered when an agent changes state."""
    
    agent_id: str
    previous_state: str
    new_state: str
    timestamp: datetime
    transition_approver: str | None
    transition_reason: str
    metrics: dict[str, Any]

Component Architecture

Service Layer Components

┌───────────────────────────────────────────────────────────────┐
│                     API Layer (FastAPI)                       │
└───────────────▲───────────────────────────────▲───────────────┘
                │                               │
                │                               │
┌───────────────▼───────────┐   ┌───────────────▼───────────────┐
│  Service Layer            │   │  WebSocket Manager            │
│                           │   │                               │
│  - AgentService           │   │  - ConnectionManager          │
│  - TeamService            │   │  - MessageHandler             │
│  - TaskService            │   │  - VisualizationPublisher     │
│  - TemplateService        │   │  - EventSubscriber            │
└───────────────▲───────────┘   └───────────────────────────────┘
                │
                │
┌───────────────▼───────────────────────────────────────────────┐
│  Domain Services                                              │
│                                                               │
│  - AgentLifecycleManager                                      │
│  - TeamAssemblyService                                        │
│  - GNNRecommendationService                                   │
│  - TaskAnalysisService                                        │
└───────────────▲───────────────────────────────▲───────────────┘
                │                               │
                │                               │
┌───────────────▼───────────┐   ┌───────────────▼───────────────┐
│  Repository Layer         │   │  Infrastructure Services      │
│                           │   │                               │
│  - AgentRepository        │   │  - KafkaEventPublisher        │
│  - TeamRepository         │   │  - MinIOStorageService        │
│  - TaskRepository         │   │  - RedisStateManager          │
│  - TemplateRepository     │   │  - ModelProviderService       │
└───────────────▲───────────┘   └───────────────▲───────────────┘
                │                               │
                │                               │
┌───────────────▼───────────┐   ┌───────────────▼───────────────┐
│  Neo4j Database           │   │  External Services            │
└───────────────────────────┘   └───────────────────────────────┘

Agent System Components

┌───────────────────────────┐
│  Template Registry         │
│                           │
│  - Template management    │
│  - Capability registry    │
│  - Version control        │
└───────────────▲───────────┘
                │
                │
┌───────────────▼───────────┐   ┌───────────────────────────────┐
│  Agent Factory            │   │  Talent Scout Agent           │
│                           │   │                               │
│  - Agent instantiation    │◄──┤  - Template creation          │
│  - Parameter validation   │   │  - Capability analysis        │
│  - Resource allocation    │   │  - Template optimization      │
└───────────────▲───────────┘   └───────────────────────────────┘
                │
                │
┌───────────────▼───────────┐
│  Lifecycle Manager        │
│                           │
│  - State transitions      │
│  - Approval workflows     │
│  - Event generation       │
│  - Resource tracking      │
└───────────────▲───────────┘
                │
                │
┌───────────────▼───────────┐   ┌───────────────────────────────┐
│  Agent Runtime            │   │  Manager Agents               │
│                           │   │                               │
│  - Execution environment  │◄──┤  - Transition approval        │
│  - Context management     │   │  - Policy enforcement         │
│  - Output handling        │   │  - Escalation handling        │
└───────────────────────────┘   └───────────────────────────────┘

Team Formation Components

┌───────────────────────────┐   ┌───────────────────────────────┐
│  Task Analysis Service    │   │  DJ (GNN Engine)              │
│                           │   │                               │
│  - Requirement extraction │   │  - Graph representation       │
│  - Capability mapping     │──►│  - Embeddings generation      │
│  - Priority assignment    │   │  - Collaboration scoring      │
└───────────────▲───────────┘   └───────────────▲───────────────┘
                │                               │
                │                               │
┌───────────────▼───────────────────────────────▼───────────────┐
│  Bartender (Team Assembly Service)                           │
│                                                               │
│  - Candidate selection                                        │
│  - Role assignment                                            │
│  - Team optimization                                          │
│  - Resource balancing                                         │
└───────────────▲───────────────────────────────▲───────────────┘
                │                               │
                │                               │
┌───────────────▼───────────┐   ┌───────────────▼───────────────┐
│  Team Manager             │   │  Collaboration Analyzer       │
│                           │   │                               │
│  - Team state management  │   │  - Historical performance     │
│  - Progress tracking      │   │  - Success pattern detection  │
│  - Resource allocation    │   │  - Feedback integration       │
└───────────────────────────┘   └───────────────────────────────┘

Neo4j Database Design

Core Schema

// Core node types
CREATE CONSTRAINT agent_id IF NOT EXISTS ON (a:Agent) ASSERT a.id IS UNIQUE;
CREATE CONSTRAINT template_id IF NOT EXISTS ON (t:Template) ASSERT t.id IS UNIQUE;
CREATE CONSTRAINT template_version_id IF NOT EXISTS ON (tv:TemplateVersion) ASSERT tv.id IS UNIQUE;
CREATE CONSTRAINT team_id IF NOT EXISTS ON (t:Team) ASSERT t.id IS UNIQUE;
CREATE CONSTRAINT task_id IF NOT EXISTS ON (t:Task) ASSERT t.id IS UNIQUE;
CREATE CONSTRAINT capability_id IF NOT EXISTS ON (c:Capability) ASSERT c.id IS UNIQUE;
CREATE CONSTRAINT user_id IF NOT EXISTS ON (u:User) ASSERT u.id IS UNIQUE;

// Core relationship indices
CREATE INDEX IF NOT EXISTS FOR ()-[r:MEMBER_OF]->() ON (r.role, r.joined_at);
CREATE INDEX IF NOT EXISTS FOR ()-[r:BASED_ON]->() ON (r.version);
CREATE INDEX IF NOT EXISTS FOR ()-[r:WORKED_WITH]->() ON (r.success_score);
CREATE INDEX IF NOT EXISTS FOR ()-[r:HAS_CAPABILITY]->() ON (r.proficiency);

Performance Optimizations

• Use MERGE for nodes with unique constraints
• Apply appropriate indices for relationship properties
• Batch operations for large data imports
• Use parameterized queries to enable caching
• Implement query timeout management
• Model time-series data with appropriate patterns

Integration Patterns

Event-Driven Integration

┌───────────────┐     ┌───────────────┐     ┌───────────────┐
│  Producer     │     │  Kafka        │     │  Consumer     │
│  Service      │────►│  Topic        │────►│  Service      │
└───────────────┘     └───────────────┘     └───────────────┘

• Reliable Delivery: At-least-once delivery guarantees
• Idempotent Consumers: Handle potential duplicate events
• Event Schemas: Strict typing with Pydantic models
• Error Handling: Dead-letter queues for failed processing
• Event Sourcing: Rebuild state from event streams when needed

Synchronous APIs

• RESTful API for external integrations
• GraphQL for flexible queries
• WebSockets for real-time updates
• Rate limiting and backpressure mechanisms
• Comprehensive authentication and authorization

Testing Strategy

Testing Layers

1. Unit Testing
   • Focus on one module at a time
   • Target 100% test coverage with strategic exclusions
   • Use proper mocking for dependencies
   • Follow test-driven development approach
2. Integration Testing
   • Test repository implementations with Neo4j test containers
   • Validate Kafka producer/consumer integration
   • Test WebSocket communication
3. System Testing
   • End-to-end workflows through API endpoints
   • Performance testing with realistic data volumes
   • Fault injection for resilience testing

Neo4j Testing

• Use test fixtures for database setup and teardown
• Create isolated test databases
• Implement cleanup code to remove test artifacts
• Mock Neo4j connections for pure unit tests

Quality Metrics

• Code coverage with pytest-cov
• Performance benchmarks for critical paths
• Type checking compliance with mypy
• Docstring completeness

Deployment Architecture

┌─────────────────────────────────────────────────────────────┐
│                       Kubernetes Cluster                    │
│                                                             │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │ API Pods    │  │ Worker Pods │  │ ML Pods     │         │
│  │             │  │             │  │             │         │
│  │ - FastAPI   │  │ - Consumers │  │ - GNN       │         │
│  │ - WebSockets│  │ - Processors│  │ - Training  │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
│                                                             │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │ Neo4j       │  │ Kafka       │  │ MinIO       │         │
│  │             │  │             │  │             │         │
│  │ - Graph DB  │  │ - Events    │  │ - Storage   │         │
│  │ - Metrics   │  │ - Streaming │  │ - Objects   │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
│                                                             │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │ Redis       │  │ Monitoring  │  │ Logging     │         │
│  │             │  │             │  │             │         │
│  │ - Cache     │  │ - Prometheus│  │ - ELK       │         │
│  │ - State     │  │ - Grafana   │  │ - Tracing   │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
└─────────────────────────────────────────────────────────────┘

Security Architecture

• Authentication: OAuth2 with JWT tokens
• Authorization: Role-based access control
• API Security: Rate limiting, input validation
• Data Encryption: TLS for all communication
• Secrets Management: Kubernetes secrets or Vault
• Audit Logging: Comprehensive activity logging
• Container Security: Minimal base images, regular scanning

Monitoring and Observability

• Logging: Structured JSON logs with correlation IDs
• Metrics: Prometheus for time-series metrics
• Tracing: Distributed tracing with OpenTelemetry
• Dashboards: Grafana for visualization
• Alerts: Proactive notification for anomalies
• Performance: Tracking execution times of critical paths

Scaling Considerations

• Horizontal Scaling: Stateless components scale horizontally
• Neo4j Clustering: Causal clustering for graph database
• Kafka Partitioning: Topic partitioning for parallel processing
• Redis Clustering: Redis cluster for cache availability
• Resource Optimization: Efficient resource utilization strategies
• Backpressure: Handling overload scenarios gracefully