# Architecture Overview

This document provides a comprehensive overview of the Nijika AI Agent Framework architecture.

## 🏗️ System Architecture

### High-Level Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                        Application Layer                        │
├─────────────────────────────────────────────────────────────────┤
│                         Agent Layer                            │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │   Agent     │  │  Workflow   │  │  Planning   │            │
│  │  Manager    │  │   Engine    │  │   Engine    │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
├─────────────────────────────────────────────────────────────────┤
│                        Core Services                           │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │   Memory    │  │     RAG     │  │    Tool     │            │
│  │  Manager    │  │   System    │  │  Registry   │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
├─────────────────────────────────────────────────────────────────┤
│                       Provider Layer                          │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │   OpenAI    │  │  Anthropic  │  │   Google    │            │
│  │  Provider   │  │  Provider   │  │  Provider   │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
├─────────────────────────────────────────────────────────────────┤
│                      Infrastructure                           │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │  Database   │  │   Vector    │  │   Cache     │            │
│  │   Layer     │  │   Store     │  │   Layer     │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
└─────────────────────────────────────────────────────────────────┘
```

## 🧠 Core Components

### 1. Agent Manager

The central component that orchestrates all agent operations.

```python
class Agent:
    """
    Main agent class that coordinates all components
    """
    def __init__(self, config: AgentConfig):
        self.config = config
        self.provider_manager = ProviderManager(config.providers)
        self.memory_manager = MemoryManager(config.memory_config)
        self.tool_registry = ToolRegistry(config.tools)
        self.workflow_engine = WorkflowEngine(config.workflow_config)
        self.planning_engine = PlanningEngine(config.planning_config)
        self.rag_system = RAGSystem(config.rag_config)
    
    async def execute(self, query: str) -> Dict[str, Any]:
        """Main execution pipeline"""
        # 1. Create execution context
        # 2. Retrieve relevant memory/RAG context
        # 3. Generate execution plan
        # 4. Execute plan using tools/workflows
        # 5. Store results in memory
        # 6. Return response
```

**Key Responsibilities:**
- Coordinate between all subsystems
- Manage execution lifecycle
- Handle error recovery
- Maintain agent state

### 2. Provider Manager

Manages multiple AI providers with load balancing and failover.

```python
class ProviderManager:
    """
    Manages multiple AI providers with load balancing
    """
    def __init__(self, provider_configs: List[Dict[str, Any]]):
        self.providers = {}
        self.load_balancer = LoadBalancer()
        self.circuit_breaker = CircuitBreaker()
    
    async def complete(self, prompt: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Route request to appropriate provider"""
        provider = self.load_balancer.select_provider()
        return await provider.complete(prompt, context)
```

**Features:**
- Multi-provider support (OpenAI, Anthropic, Google, Azure)
- Load balancing algorithms
- Circuit breaker pattern
- Automatic failover
- Rate limiting
- Cost optimization

### 3. Memory Manager

Persistent memory system for conversations, executions, and knowledge.

```python
class MemoryManager:
    """
    Manages persistent memory across agent sessions
    """
    def __init__(self, config: Dict[str, Any]):
        self.backend = self._create_backend(config)
        self.cleanup_scheduler = CleanupScheduler()
    
    async def store_execution(self, execution_id: str, context: Dict, result: Dict):
        """Store execution results"""
        entry = MemoryEntry(
            memory_type=MemoryType.EXECUTION,
            content={"context": context, "result": result},
            timestamp=datetime.now()
        )
        await self.backend.store(entry)
```

**Memory Types:**
- **Conversation**: Chat history and context
- **Execution**: Task execution results
- **Knowledge**: Learned information
- **Episodic**: Temporal event sequences
- **Semantic**: Factual knowledge

### 4. RAG System

Retrieval-Augmented Generation for document processing and context.

```python
class RAGSystem:
    """
    Retrieval-Augmented Generation system
    """
    def __init__(self, config: Dict[str, Any]):
        self.document_processor = DocumentProcessor()
        self.vector_store = VectorStore(config.get("vector_store", {}))
        self.retriever = Retriever(config)
    
    async def retrieve_context(self, query: str) -> List[RetrievalResult]:
        """Retrieve relevant context for query"""
        embeddings = await self.embed_query(query)
        return await self.vector_store.similarity_search(embeddings)
```

**Components:**
- **Document Processor**: Text chunking and preprocessing
- **Vector Store**: Embedding storage and retrieval
- **Retriever**: Semantic search and ranking
- **Context Generator**: Relevant context assembly

### 5. Planning Engine

Intelligent task decomposition and execution planning.

```python
class PlanningEngine:
    """
    Intelligent task planning and decomposition
    """
    def __init__(self, config: Dict[str, Any]):
        self.strategy = config.get("strategy", "sequential")
        self.planners = {
            "sequential": SequentialPlanner(),
            "hierarchical": HierarchicalPlanner(),
            "parallel": ParallelPlanner(),
            "reactive": ReactivePlanner(),
            "goal_oriented": GoalOrientedPlanner()
        }
    
    async def create_plan(self, task: str, context: Dict) -> Plan:
        """Create execution plan for task"""
        planner = self.planners[self.strategy]
        return await planner.plan(task, context)
```

**Planning Strategies:**
- **Sequential**: Step-by-step execution
- **Hierarchical**: Nested task decomposition
- **Parallel**: Concurrent execution
- **Reactive**: Dynamic adaptation
- **Goal-oriented**: Objective-driven planning

### 6. Workflow Engine

Complex task orchestration and execution management.

```python
class WorkflowEngine:
    """
    Workflow execution and orchestration
    """
    def __init__(self, config: Dict[str, Any]):
        self.executors = {
            "sequential": SequentialExecutor(),
            "parallel": ParallelExecutor(),
            "conditional": ConditionalExecutor()
        }
        self.checkpoint_manager = CheckpointManager()
    
    async def execute_workflow(self, workflow: Workflow) -> WorkflowResult:
        """Execute workflow with proper orchestration"""
        executor = self.executors[workflow.execution_type]
        return await executor.execute(workflow)
```

**Execution Types:**
- **Sequential**: One step at a time
- **Parallel**: Multiple steps concurrently
- **Conditional**: Branch-based execution
- **Loop**: Iterative execution
- **Retry**: Error recovery patterns

### 7. Tool Registry

Extensible tool system for agent capabilities.

```python
class ToolRegistry:
    """
    Registry for agent tools and capabilities
    """
    def __init__(self, tool_configs: List[str]):
        self.tools = {}
        self.sandbox = ToolSandbox()
        self._register_builtin_tools()
        self._register_custom_tools(tool_configs)
    
    async def execute_tool(self, name: str, params: Dict) -> Dict[str, Any]:
        """Execute tool in sandboxed environment"""
        tool = self.tools[name]
        return await self.sandbox.execute(tool, params)
```

**Built-in Tools:**
- **Echo**: Message echoing
- **Calculator**: Mathematical operations
- **HTTP Request**: API calls
- **File Operations**: File system access
- **Data Processing**: Data manipulation

## 🔄 Data Flow

### Request Processing Pipeline

```
1. User Query
   ↓
2. Context Retrieval (Memory + RAG)
   ↓
3. Plan Generation
   ↓
4. Plan Execution
   ├─ Tool Execution
   ├─ Workflow Orchestration
   └─ Provider Calls
   ↓
5. Result Assembly
   ↓
6. Memory Storage
   ↓
7. Response Generation
```

### Memory Flow

```
Input → Processing → Storage → Retrieval → Context
  ↓         ↓          ↓         ↓         ↓
Query → Analysis → Database → Search → Response
```

### RAG Flow

```
Documents → Chunking → Embedding → Vector Store
                                      ↓
Query → Embedding → Similarity Search → Context
```

## 🏛️ Design Patterns

### 1. Strategy Pattern

Used for pluggable algorithms (planning strategies, execution modes).

```python
class PlanningStrategy(ABC):
    @abstractmethod
    async def plan(self, task: str, context: Dict) -> Plan:
        pass

class SequentialPlanner(PlanningStrategy):
    async def plan(self, task: str, context: Dict) -> Plan:
        # Sequential planning logic
        pass
```

### 2. Observer Pattern

Used for event handling and monitoring.

```python
class EventEmitter:
    def __init__(self):
        self.listeners = defaultdict(list)
    
    def on(self, event: str, callback: Callable):
        self.listeners[event].append(callback)
    
    async def emit(self, event: str, data: Any):
        for callback in self.listeners[event]:
            await callback(data)
```

### 3. Factory Pattern

Used for creating providers and tools.

```python
class ProviderFactory:
    @staticmethod
    def create_provider(config: Dict[str, Any]) -> BaseProvider:
        provider_type = config["provider_type"]
        if provider_type == ProviderType.OPENAI:
            return OpenAIProvider(config)
        elif provider_type == ProviderType.ANTHROPIC:
            return AnthropicProvider(config)
        # ... other providers
```

### 4. Circuit Breaker Pattern

Used for fault tolerance in provider calls.

```python
class CircuitBreaker:
    def __init__(self, failure_threshold: int = 5, timeout: int = 60):
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.failure_count = 0
        self.last_failure_time = None
        self.state = "CLOSED"  # CLOSED, OPEN, HALF_OPEN
    
    async def call(self, func: Callable, *args, **kwargs):
        if self.state == "OPEN":
            if time.time() - self.last_failure_time > self.timeout:
                self.state = "HALF_OPEN"
            else:
                raise CircuitBreakerOpenError()
        
        try:
            result = await func(*args, **kwargs)
            self.on_success()
            return result
        except Exception as e:
            self.on_failure()
            raise
```

## 🔧 Component Interactions

### Agent Initialization

```python
async def initialize_agent(config: AgentConfig) -> Agent:
    # 1. Initialize core components
    memory_manager = MemoryManager(config.memory_config)
    await memory_manager.initialize()
    
    # 2. Initialize providers
    provider_manager = ProviderManager(config.providers)
    await provider_manager.initialize()
    
    # 3. Initialize tools
    tool_registry = ToolRegistry(config.tools)
    await tool_registry.initialize()
    
    # 4. Initialize RAG system
    rag_system = RAGSystem(config.rag_config)
    await rag_system.initialize()
    
    # 5. Create agent
    agent = Agent(config, memory_manager, provider_manager, tool_registry, rag_system)
    await agent.initialize()
    
    return agent
```

### Query Execution

```python
async def execute_query(agent: Agent, query: str) -> Dict[str, Any]:
    # 1. Create execution context
    context = {
        "query": query,
        "agent_id": agent.id,
        "timestamp": datetime.now().isoformat()
    }
    
    # 2. Retrieve relevant context
    memory_context = await agent.memory_manager.get_context(agent.id, query)
    rag_context = await agent.rag_system.retrieve_context(query)
    
    context.update({
        "memory_context": memory_context,
        "rag_context": rag_context
    })
    
    # 3. Generate execution plan
    plan = await agent.planning_engine.create_plan(query, context)
    
    # 4. Execute plan
    result = await agent.execute_plan(plan, context)
    
    # 5. Store results
    await agent.memory_manager.store_execution(
        execution_id=str(uuid.uuid4()),
        context=context,
        result=result
    )
    
    return result
```

## 🔐 Security Architecture

### Authentication & Authorization

```python
class SecurityManager:
    def __init__(self, config: Dict[str, Any]):
        self.auth_provider = AuthProvider(config.get("auth", {}))
        self.authorizer = Authorizer(config.get("authz", {}))
    
    async def authenticate(self, token: str) -> User:
        return await self.auth_provider.verify_token(token)
    
    async def authorize(self, user: User, resource: str, action: str) -> bool:
        return await self.authorizer.check_permission(user, resource, action)
```

### Data Protection

- **Encryption**: All sensitive data encrypted at rest and in transit
- **Access Control**: Role-based access control (RBAC)
- **Audit Logging**: Complete audit trail of all operations
- **Secret Management**: Secure API key and credential handling

## 📊 Monitoring & Observability

### Metrics Collection

```python
class MetricsCollector:
    def __init__(self):
        self.metrics = {}
        self.prometheus_client = PrometheusClient()
    
    def increment_counter(self, name: str, labels: Dict[str, str] = None):
        self.prometheus_client.increment(name, labels)
    
    def record_histogram(self, name: str, value: float, labels: Dict[str, str] = None):
        self.prometheus_client.record(name, value, labels)
```

### Logging Architecture

```python
class StructuredLogger:
    def __init__(self, name: str):
        self.logger = logging.getLogger(name)
        self.formatter = JSONFormatter()
    
    def info(self, message: str, **kwargs):
        self.logger.info(message, extra=kwargs)
```

## 🚀 Scalability Considerations

### Horizontal Scaling

- **Stateless Design**: Agents can be replicated across multiple instances
- **Load Balancing**: Distribute requests across agent instances
- **Caching**: Redis-based caching for frequently accessed data
- **Database Sharding**: Partition data across multiple databases

### Vertical Scaling

- **Async Processing**: Non-blocking I/O for better resource utilization
- **Connection Pooling**: Efficient database connection management
- **Memory Optimization**: Efficient memory usage patterns
- **CPU Optimization**: Optimized algorithms and data structures

## 🔄 Extension Points

### Custom Providers

```python
class CustomProvider(BaseProvider):
    async def complete(self, prompt: str, context: Dict) -> Dict[str, Any]:
        # Custom implementation
        pass
```

### Custom Tools

```python
class CustomTool(BaseTool):
    name = "custom_tool"
    description = "Custom tool implementation"
    
    async def execute(self, params: Dict, context: Dict) -> Dict[str, Any]:
        # Custom implementation
        pass
```

### Custom Memory Backends

```python
class CustomMemoryBackend(BaseMemoryBackend):
    async def store(self, entry: MemoryEntry) -> str:
        # Custom storage implementation
        pass
```

## 🎯 Performance Characteristics

### Latency Targets

- **Simple Query**: < 500ms
- **Complex Query**: < 2s
- **Workflow Execution**: < 5s
- **RAG Retrieval**: < 100ms

### Throughput Targets

- **Concurrent Users**: 1000+
- **Requests per Second**: 100+
- **Memory Usage**: < 512MB per agent
- **CPU Usage**: < 50% under normal load

## 🔍 Debugging & Troubleshooting

### Debug Mode

```python
config = AgentConfig(
    debug=True,
    log_level="DEBUG",
    trace_execution=True
)
```

### Health Checks

```python
class HealthChecker:
    async def check_system_health(self) -> Dict[str, Any]:
        return {
            "database": await self.check_database(),
            "providers": await self.check_providers(),
            "memory": await self.check_memory_usage(),
            "disk": await self.check_disk_space()
        }
```

This architecture provides a solid foundation for building scalable, maintainable, and extensible AI agent systems. The modular design allows for easy customization and extension while maintaining performance and reliability. 