# Architecture Improvements

## 1. Module Organization

### Current Issues

- Large modules with multiple responsibilities
- Inactive modules cluttering the codebase
- Inconsistent patterns across similar functionality

### Proposed Structure

```
arklex/
├── core/                    # Core framework components
│   ├── agents/             # Agent implementations
│   ├── workers/            # Worker implementations
│   ├── tools/              # Tool implementations
│   └── orchestrator/       # Orchestration logic
├── integrations/           # External service integrations
│   ├── shopify/           # Shopify integration
│   ├── hubspot/           # HubSpot integration
│   └── google/            # Google services
├── utils/                  # Shared utilities
│   ├── logging/           # Logging utilities
│   ├── exceptions/        # Exception handling
│   └── validators/        # Validation utilities
├── services/              # Business logic services
│   ├── rag/              # RAG services
│   ├── memory/           # Memory management
│   └── evaluation/       # Evaluation services
└── api/                   # API layer
    ├── routes/           # API routes
    ├── middleware/       # API middleware
    └── schemas/          # API schemas
```

## 2. Dependency Management

### Current Issues

- Tight coupling between modules
- Circular dependencies
- Hard to test individual components

### Proposed Solutions

#### Dependency Injection

```python
# arklex/core/container.py
from dependency_injector import containers, providers
from arklex.core.agents import AgentFactory
from arklex.core.workers import WorkerFactory

class Container(containers.DeclarativeContainer):
    config = providers.Configuration()
    
    # Factories
    agent_factory = providers.Factory(AgentFactory)
    worker_factory = providers.Factory(WorkerFactory)
    
    # Services
    logging_service = providers.Singleton(LoggingService)
    memory_service = providers.Singleton(MemoryService)
```

#### Interface Segregation

```python
# arklex/core/interfaces.py
from abc import ABC, abstractmethod
from typing import Protocol

class AgentProtocol(Protocol):
    @abstractmethod
    def execute(self, state: MessageState) -> MessageState:
        pass

class WorkerProtocol(Protocol):
    @abstractmethod
    def process(self, state: MessageState) -> MessageState:
        pass

class ToolProtocol(Protocol):
    @abstractmethod
    def execute(self, state: MessageState) -> MessageState:
        pass
```

## 3. Configuration Management

### Current Issues

- Configuration scattered across multiple files
- Environment-specific settings mixed with code
- Hard to manage different deployment environments

### Proposed Solution

```python
# arklex/config/settings.py
from pydantic_settings import BaseSettings
from typing import Optional

class Settings(BaseSettings):
    # Database
    database_url: str
    database_pool_size: int = 10
    
    # LLM Providers
    openai_api_key: Optional[str] = None
    anthropic_api_key: Optional[str] = None
    google_api_key: Optional[str] = None
    
    # Logging
    log_level: str = "INFO"
    log_format: str = "json"
    
    # Performance
    max_concurrent_requests: int = 100
    request_timeout: int = 30
    
    class Config:
        env_file = ".env"
        env_file_encoding = "utf-8"

settings = Settings()
```

## 4. Error Handling Strategy

### Current Issues

- Inconsistent error handling patterns
- Errors not properly logged or tracked
- No centralized error management

### Proposed Solution

```python
# arklex/core/error_handler.py
from typing import Callable, TypeVar, ParamSpec
from functools import wraps
import logging

T = TypeVar('T')
P = ParamSpec('P')

class ErrorHandler:
    def __init__(self, logger: logging.Logger):
        self.logger = logger
    
    def handle_errors(self, error_types: tuple = (Exception,)):
        def decorator(func: Callable[P, T]) -> Callable[P, T]:
            @wraps(func)
            def wrapper(*args: P.args, **kwargs: P.kwargs) -> T:
                try:
                    return func(*args, **kwargs)
                except error_types as e:
                    self.logger.error(
                        f"Error in {func.__name__}: {str(e)}",
                        exc_info=True
                    )
                    raise
            return wrapper
        return decorator

error_handler = ErrorHandler(logging.getLogger(__name__))
```

## 5. Performance Optimization

### Current Issues

- No connection pooling for databases
- Synchronous operations blocking the event loop
- No caching strategy

### Proposed Solutions

#### Database Connection Pooling

```python
# arklex/core/database.py
import asyncio
from typing import AsyncGenerator
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker

class DatabaseManager:
    def __init__(self, database_url: str):
        self.engine = create_async_engine(
            database_url,
            pool_size=20,
            max_overflow=30,
            pool_pre_ping=True
        )
        self.session_factory = sessionmaker(
            self.engine, class_=AsyncSession, expire_on_commit=False
        )
    
    async def get_session(self) -> AsyncGenerator[AsyncSession, None]:
        async with self.session_factory() as session:
            yield session
```

#### Caching Strategy

```python
# arklex/core/cache.py
import asyncio
from typing import Any, Optional
import aioredis

class CacheManager:
    def __init__(self, redis_url: str):
        self.redis = aioredis.from_url(redis_url)
    
    async def get(self, key: str) -> Optional[Any]:
        return await self.redis.get(key)
    
    async def set(self, key: str, value: Any, ttl: int = 3600):
        await self.redis.set(key, value, ex=ttl)
    
    async def delete(self, key: str):
        await self.redis.delete(key)
```

## 6. Monitoring and Observability

### Current Issues

- Limited visibility into system performance
- No structured logging
- No metrics collection

### Proposed Solution

```python
# arklex/core/monitoring.py
import time
from contextlib import contextmanager
from typing import Dict, Any
import structlog

class MonitoringService:
    def __init__(self):
        self.logger = structlog.get_logger()
        self.metrics = {}
    
    @contextmanager
    def track_operation(self, operation_name: str, **kwargs):
        start_time = time.time()
        try:
            yield
        finally:
            duration = time.time() - start_time
            self.logger.info(
                "Operation completed",
                operation=operation_name,
                duration=duration,
                **kwargs
            )
    
    def record_metric(self, name: str, value: float, labels: Dict[str, str] = None):
        if name not in self.metrics:
            self.metrics[name] = []
        self.metrics[name].append({
            'value': value,
            'labels': labels or {},
            'timestamp': time.time()
        })
```

## 7. Testing Strategy

### Current Issues

- Some integration tests are slow
- No performance testing
- Limited property-based testing

### Proposed Solutions

#### Test Categories

```python
# tests/conftest.py
import pytest

def pytest_configure(config):
    config.addinivalue_line(
        "markers", "slow: marks tests as slow (deselect with '-m \"not slow\"')"
    )
    config.addinivalue_line(
        "markers", "integration: marks tests as integration tests"
    )
    config.addinivalue_line(
        "markers", "performance: marks tests as performance tests"
    )

@pytest.fixture(scope="session")
def test_container():
    """Provide test container with mocked dependencies."""
    container = Container()
    container.config.from_dict({
        'database_url': 'sqlite:///:memory:',
        'log_level': 'DEBUG'
    })
    return container
```

#### Performance Testing

```python
# tests/performance/test_performance.py
import pytest
import time
from arklex.core.orchestrator import AgentOrg

class TestPerformance:
    @pytest.mark.performance
    def test_response_time(self):
        start_time = time.time()
        # Execute operation
        end_time = time.time()
        assert (end_time - start_time) < 5.0
    
    @pytest.mark.performance
    def test_memory_usage(self):
        import psutil
        process = psutil.Process()
        initial_memory = process.memory_info().rss
        # Execute operation
        final_memory = process.memory_info().rss
        assert (final_memory - initial_memory) < 100 * 1024 * 1024  # 100MB
```

## 8. Deployment Strategy

### Current Issues

- No containerization
- No deployment automation
- Limited environment management
- Missing security configurations
- No monitoring and alerting setup

### Proposed Solutions

#### Docker Configuration

```dockerfile
# Dockerfile
FROM python:3.10-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    gcc \
    && rm -rf /var/lib/apt/lists/*

# Install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY arklex/ arklex/
COPY pyproject.toml .

# Run application
CMD ["python", "-m", "arklex.main"]
```

#### Kubernetes Deployment

```yaml
# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: arklex-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: arklex-api
  template:
    metadata:
      labels:
        app: arklex-api
    spec:
      containers:
      - name: arklex-api
        image: arklex/arklex-api:latest
        ports:
        - containerPort: 8000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: arklex-secrets
              key: database-url
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
```

## Implementation Priority

1. **High Priority** (Immediate)
   - Dependency injection implementation
   - Error handling standardization
   - Configuration management
   - Security improvements (authentication, input validation)
   - Test coverage improvements

2. **Medium Priority** (Next 3 months)
   - Module reorganization
   - Performance optimization
   - Monitoring implementation
   - UI component refactoring
   - Inactive module cleanup

3. **Low Priority** (Next 6 months)
   - Advanced testing strategies
   - Deployment automation
   - Documentation improvements
   - Performance testing framework
   - Security testing integration
