Architecture Overview

Understanding ARKOS’s modular architecture and design principles

ARKOS Architecture Overview

ARKOS employs a sophisticated modular architecture designed for extensibility, scalability, and cognitive modeling. This document provides a comprehensive overview of the system design and component interactions.

Core Design Principles

Modularity

Each component is self-contained with well-defined interfaces, enabling independent development and testing

Cognitive Modeling

Memory and state systems inspired by cognitive science principles for human-like reasoning

Extensibility

Plugin-based architecture allows easy addition of new capabilities without core modifications

Fault Tolerance

Graceful degradation with fallback mechanisms ensures system reliability

System Architecture

Module Overview

Base Module

The foundation layer that orchestrates all other modules and provides the main interface.

Key Responsibilities:

• Request routing and orchestration
• Module initialization and lifecycle management
• Configuration loading and validation
• Error handling and recovery

Agent Module

Coming soon! Will implement intelligent agent logic with context awareness and decision-making capabilities.

Planned Features:

• Multi-turn conversation management
• Context building and maintenance
• Decision tree navigation
• Response generation coordination

State Module

Coming soon! Will manage conversation flow using a sophisticated state machine architecture.

Planned Components:

• State Registry: Dynamic state type registration
• State Handler: State transition management
• State Graph: Visual representation and analysis
• Transition Rules: Conditional state changes

Model Module

Provides unified interface to multiple LLM providers with intelligent routing.

Capabilities:

• Multi-provider support (OpenAI, Anthropic, SGLANG)
• Automatic load balancing and failover
• Response caching and optimization
• Token counting and cost tracking

Memory Module

Currently implements basic CSV-based memory storage. Advanced cognitive-inspired memory system Coming soon!

Current Implementation:

• Basic CSV file storage
• Simple memory persistence

Planned Memory Types (Coming soon!):

• Working Memory: Active context (7±2 items)
• Short-term Memory: Recent interactions
• Long-term Memory: Persistent knowledge base
• Episodic Memory: Event sequences
• Semantic Memory: Facts and concepts

Tool Module

Coming soon! Will enable integration with external services and APIs through MCP protocol.

Planned Tools:

• Calendar management
• Weather information
• Web search
• Custom tool development

Data Flow

Request Processing Pipeline

Component Communication

Inter-Module Messaging

Modules communicate through well-defined interfaces using typed messages:

@dataclass
class ModuleMessage:
    sender: str
    receiver: str
    message_type: MessageType
    payload: Any
    timestamp: datetime
    correlation_id: str

Event System

ARKOS uses an event-driven architecture for loose coupling:

class EventBus:
    def publish(event: Event) -> None
    def subscribe(event_type: str, handler: Callable) -> None
    def unsubscribe(subscription_id: str) -> None

Scalability Considerations

Coming soon! Advanced scalability features are planned for future releases.

Planned Horizontal Scaling Features:

• Stateless Design: Most modules will be stateless, enabling horizontal scaling
• Load Balancing: Built-in request distribution across multiple instances
• Distributed Memory: Redis backend will support distributed memory storage

Planned Performance Optimization Features:

• Caching: Multi-level caching for responses and computations
• Batch Processing: Efficient handling of multiple requests
• Async Operations: Non-blocking I/O for improved throughput
• Connection Pooling: Reusable connections to external services

Security Architecture

Coming soon! Comprehensive security features are planned for future releases.

Planned Authentication & Authorization Features:

• API Key Management: Secure storage and rotation
• Role-Based Access Control: Fine-grained permissions
• Session Management: Secure session handling

Planned Data Protection Features:

• Encryption at Rest: Sensitive data encrypted in storage
• Encryption in Transit: TLS for all external communications
• Input Validation: Comprehensive input sanitization
• Rate Limiting: Protection against abuse

Deployment Architecture

Coming soon! Container-based deployment and orchestration features are planned.

Planned Container-Based Deployment:

# Future docker-compose.yml (Coming soon!)
services:
  arkos-core:
    image: arkos:latest
    ports:
      - "8080:8080"
    environment:
      - MEMORY_BACKEND=redis
      - MODEL_PROVIDER=openai

  redis:
    image: redis:alpine
    volumes:
      - redis_data:/data

  sglang:
    image: sglang:latest
    deploy:
      resources:
        reservations:
          devices:
            - capabilities: [gpu]

Cloud Deployment Options

AWS

ECS/EKS with auto-scaling groups

Google Cloud

GKE with Cloud Run integration

Azure

AKS with Azure Functions

Self-Hosted

Docker Compose or Kubernetes

Monitoring & Observability

Coming soon! Comprehensive monitoring and observability features are planned.

Planned Metrics Collection:

• Prometheus: System and application metrics
• Grafana: Visualization and dashboards
• Custom Metrics: Module-specific performance indicators

Planned Logging Strategy:

# Future structured logging example (Coming soon!)
logger.info(
    "model_response_generated",
    extra={
        "module": "model",
        "provider": "openai",
        "latency_ms": 1250,
        "tokens": 500,
        "cost_usd": 0.015
    }
)

Planned Distributed Tracing:

• OpenTelemetry: End-to-end request tracing
• Correlation IDs: Request tracking across modules
• Performance Profiling: Bottleneck identification

Development Workflow

Module Development

1. Interface Definition: Define module interface and contracts
2. Implementation: Develop module following SOLID principles
3. Unit Testing: Comprehensive test coverage
4. Integration: Test with other modules
5. Documentation: Update architecture docs

Testing Strategy

Currently limited to basic model tests. Comprehensive testing Coming soon!

Current Testing:

• Basic model functionality tests

Planned Testing (Coming soon!):

• Unit Tests: Individual module testing
• Integration Tests: Module interaction testing
• System Tests: End-to-end scenarios
• Performance Tests: Load and stress testing
• Chaos Engineering: Failure scenario testing

Future Architecture Considerations

Planned Enhancements

These are architectural improvements planned for future releases:

1. Microservices Migration: Breaking modules into separate services
2. GraphQL API: More flexible API layer
3. Event Sourcing: Complete audit trail of all events
4. CQRS Pattern: Separate read/write models
5. Service Mesh: Istio/Linkerd for service communication

Research Areas

• Neuromorphic Computing: Brain-inspired processing
• Quantum Integration: Quantum computing for optimization
• Federated Learning: Distributed model training
• Edge Deployment: Running on edge devices

Best Practices

Code Organization

module_name/
├── __init__.py       # Module exports
├── core.py           # Core logic
├── models.py         # Data models
├── handlers.py       # Request handlers
├── utils.py          # Utility functions
├── config.py         # Configuration
└── tests/            # Test files

Design Patterns

• Factory Pattern: Dynamic object creation
• Strategy Pattern: Algorithm selection
• Observer Pattern: Event handling
• Singleton Pattern: Resource management
• Chain of Responsibility: Request processing

Conclusion

ARKOS’s architecture is designed to be:

• Modular: Easy to understand and extend
• Scalable: Grows with your needs
• Reliable: Fault-tolerant with fallbacks
• Intelligent: Cognitive-inspired design

This architecture enables building sophisticated AI agents that can learn, remember, and adapt to user needs while maintaining high performance and reliability.