# LlamaAgent Architecture Overview

This document provides a comprehensive overview of LlamaAgent's architecture, design principles, and core components.

## Table of Contents

1. [High-Level Architecture](#high-level-architecture)
2. [Core Components](#core-components)
3. [Design Principles](#design-principles)
4. [Data Flow](#data-flow)
5. [Extension Points](#extension-points)
6. [Performance Considerations](#performance-considerations)

## High-Level Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                          LlamaAgent API                          │
├─────────────────────────────────────────────────────────────────┤
│                        Orchestration Layer                       │
│  ┌─────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
│  │   Agents    │  │   Workflow   │  │  Execution Engine    │  │
│  │             │  │   Manager    │  │                      │  │
│  └─────────────┘  └──────────────┘  └──────────────────────┘  │
├─────────────────────────────────────────────────────────────────┤
│                         Core Services                            │
│  ┌─────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
│  │     LLM     │  │   Memory     │  │      Tools           │  │
│  │  Providers  │  │   Systems    │  │    Registry          │  │
│  └─────────────┘  └──────────────┘  └──────────────────────┘  │
├─────────────────────────────────────────────────────────────────┤
│                      Infrastructure Layer                        │
│  ┌─────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
│  │   Caching   │  │  Monitoring  │  │     Security         │  │
│  │             │  │              │  │                      │  │
│  └─────────────┘  └──────────────┘  └──────────────────────┘  │
├─────────────────────────────────────────────────────────────────┤
│                         Storage Layer                            │
│  ┌─────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
│  │ PostgreSQL  │  │    Redis     │  │   Vector Store       │  │
│  │             │  │              │  │    (Qdrant)          │  │
│  └─────────────┘  └──────────────┘  └──────────────────────┘  │
└─────────────────────────────────────────────────────────────────┘
```

## Core Components

### 1. Agent System

The Agent is the fundamental unit of intelligence in LlamaAgent:

```python
class Agent:
    """Core agent implementation"""
    def __init__(
        self,
        name: str,
        llm: BaseLLMProvider,
        tools: List[Tool],
        memory: Optional[BaseMemory] = None,
        prompting_strategy: Optional[PromptingStrategy] = None
    ):
        self.name = name
        self.llm = llm
        self.tools = ToolRegistry(tools)
        self.memory = memory or InMemoryStorage()
        self.prompting = prompting_strategy or DefaultStrategy()
```

**Key Features:**
- **Modular Design**: Each component is pluggable and replaceable
- **Tool Integration**: Seamless integration with external tools
- **Memory Management**: Short and long-term memory systems
- **Prompting Strategies**: Flexible prompt engineering

### 2. LLM Provider System

Unified interface for 100+ language models:

```python
class BaseLLMProvider(ABC):
    """Abstract base for LLM providers"""
    
    @abstractmethod
    async def complete(
        self,
        messages: List[LLMMessage],
        **kwargs
    ) -> LLMResponse:
        pass
    
    @abstractmethod
    async def stream(
        self,
        messages: List[LLMMessage],
        **kwargs
    ) -> AsyncIterator[LLMResponse]:
        pass
```

**Implementations:**
- `LiteLLMProvider`: Universal provider for 100+ models
- `OpenAIProvider`: Optimized for OpenAI models
- `AnthropicProvider`: Claude models
- `LocalProvider`: Local model support (Ollama, MLX)

### 3. Memory Systems

Hierarchical memory architecture:

```python
class MemoryHierarchy:
    """Multi-level memory system"""
    
    def __init__(self):
        self.working_memory = WorkingMemory(capacity=10)
        self.short_term = ShortTermMemory(ttl=3600)
        self.long_term = LongTermMemory(
            vector_store=QdrantStore(),
            embedding_model="all-MiniLM-L6-v2"
        )
```

**Memory Types:**
- **Working Memory**: Current context (10-20 items)
- **Short-term Memory**: Recent interactions (1 hour TTL)
- **Long-term Memory**: Persistent vectorized storage
- **Episodic Memory**: Specific experiences and outcomes

### 4. Tool System

Flexible tool integration framework:

```python
@dataclass
class Tool:
    """Tool definition"""
    name: str
    description: str
    function: Callable
    parameters: Dict[str, ToolParameter]
    
    async def execute(self, **kwargs) -> Any:
        """Execute tool with validation"""
        validated_params = self.validate_parameters(kwargs)
        if asyncio.iscoroutinefunction(self.function):
            return await self.function(**validated_params)
        return self.function(**validated_params)
```

**Built-in Tools:**
- Web search and browsing
- Code execution (Python, JavaScript)
- File system operations
- Database queries
- API integrations

### 5. Orchestration System

Multi-agent coordination:

```python
class Orchestrator:
    """Multi-agent orchestration"""
    
    def __init__(
        self,
        agents: List[Agent],
        workflow: WorkflowType,
        communication: CommunicationType = "direct"
    ):
        self.agents = {agent.name: agent for agent in agents}
        self.workflow = WorkflowFactory.create(workflow)
        self.communication = communication
```

**Workflow Types:**
- **Sequential**: Agents work in order
- **Parallel**: Concurrent execution
- **Hierarchical**: Manager-worker pattern
- **Collaborative**: Agents work together
- **Competitive**: Best solution wins

### 6. Caching System

Multi-level caching architecture:

```python
class CacheHierarchy:
    """Hierarchical cache system"""
    
    def __init__(self):
        self.l1_cache = MemoryCache(size=1000)  # In-memory
        self.l2_cache = DiskCache(size_mb=1024)  # Local disk
        self.l3_cache = RedisCache()  # Distributed
```

**Cache Features:**
- **Semantic Caching**: Similar prompts return cached results
- **Result Caching**: Function and API call results
- **Intelligent Eviction**: LRU with semantic importance
- **Compression**: Automatic for large values

### 7. Monitoring & Observability

Comprehensive monitoring stack:

```python
class MonitoringSystem:
    """Integrated monitoring"""
    
    def __init__(self):
        self.metrics = PrometheusMetrics()
        self.tracing = JaegerTracing()
        self.logging = StructuredLogger()
        self.profiling = PerformanceProfiler()
```

**Metrics Collected:**
- Request latency and throughput
- Token usage and costs
- Cache hit rates
- Memory usage
- Error rates and types

## Design Principles

### 1. Modularity

Every component is designed to be:
- **Pluggable**: Easy to swap implementations
- **Extensible**: Simple to add new features
- **Testable**: Isolated units with clear interfaces

### 2. Performance First

- **Async by Default**: All I/O operations are async
- **Intelligent Caching**: Multi-level cache hierarchy
- **Resource Pooling**: Connection and object pools
- **Lazy Loading**: Components loaded on demand

### 3. Production Ready

- **Error Handling**: Graceful degradation
- **Monitoring**: Built-in observability
- **Security**: Input validation, rate limiting
- **Scalability**: Horizontal scaling support

### 4. Developer Experience

- **Type Safety**: Full type annotations
- **Clear APIs**: Intuitive interfaces
- **Rich Documentation**: Comprehensive guides
- **Debugging Tools**: Built-in debugging support

## Data Flow

### 1. Request Processing

```
User Request
    ↓
Input Validation
    ↓
Rate Limiting
    ↓
Cache Check ←─────┐
    ↓             │
Agent Selection   │
    ↓             │
Prompt Building   │
    ↓             │
LLM Call          │
    ↓             │
Tool Execution    │
    ↓             │
Response Building │
    ↓             │
Cache Update ─────┘
    ↓
Response
```

### 2. Memory Flow

```
Experience
    ↓
Working Memory (immediate)
    ↓
Short-term Memory (recent)
    ↓
Embedding Generation
    ↓
Vector Storage (persistent)
    ↓
Retrieval via Similarity
```

### 3. Multi-Agent Flow

```
Task
 ├─→ Agent A ──┐
 ├─→ Agent B ──┼─→ Aggregator ─→ Result
 └─→ Agent C ──┘
```

## Extension Points

### 1. Custom LLM Providers

```python
class CustomProvider(BaseLLMProvider):
    """Custom LLM implementation"""
    
    async def complete(self, messages, **kwargs):
        # Your implementation
        pass
```

### 2. Custom Tools

```python
@Tool.create
def custom_tool(param1: str, param2: int) -> dict:
    """Your custom tool"""
    # Implementation
    return result
```

### 3. Custom Memory Backends

```python
class CustomMemory(BaseMemory):
    """Custom memory implementation"""
    
    async def store(self, key: str, value: Any):
        # Your storage logic
        pass
    
    async def retrieve(self, key: str) -> Any:
        # Your retrieval logic
        pass
```

### 4. Custom Prompting Strategies

```python
class CustomStrategy(PromptingStrategy):
    """Custom prompting approach"""
    
    def format_prompt(self, task: str, context: dict) -> str:
        # Your prompt engineering
        return formatted_prompt
```

## Performance Considerations

### 1. Optimization Techniques

- **Batch Processing**: Group similar requests
- **Streaming Responses**: Return data as available
- **Parallel Execution**: Concurrent tool calls
- **Smart Caching**: Semantic and result caching

### 2. Resource Management

- **Connection Pooling**: Database and API connections
- **Memory Pooling**: Reuse object allocations
- **Thread Pooling**: CPU-bound operations
- **Async I/O**: Non-blocking operations

### 3. Scaling Strategies

- **Horizontal Scaling**: Multiple instances
- **Load Balancing**: Distribute requests
- **Caching Layers**: Redis for shared cache
- **Database Sharding**: Partition data

### 4. Monitoring & Optimization

- **Performance Profiling**: Identify bottlenecks
- **Query Optimization**: Efficient database queries
- **Cache Warming**: Preload common queries
- **Resource Limits**: Prevent resource exhaustion

## Security Architecture

### 1. Input Validation

- Schema validation for all inputs
- SQL injection prevention
- XSS protection
- Command injection prevention

### 2. Authentication & Authorization

- JWT-based authentication
- Role-based access control
- API key management
- OAuth2 integration

### 3. Data Protection

- Encryption at rest
- TLS for data in transit
- Secure key management
- PII handling compliance

### 4. Rate Limiting & DDoS Protection

- Token bucket algorithm
- Distributed rate limiting
- Automatic scaling
- Circuit breakers

## Deployment Architecture

### 1. Container Architecture

```yaml
services:
  api:
    image: llamaagent/api
    replicas: 3
    
  worker:
    image: llamaagent/worker
    replicas: 5
    
  cache:
    image: redis:7
    
  database:
    image: pgvector/pgvector
    
  vectorstore:
    image: qdrant/qdrant
```

### 2. Kubernetes Deployment

- Auto-scaling based on load
- Health checks and readiness probes
- Resource limits and requests
- Network policies

### 3. Monitoring Stack

- Prometheus for metrics
- Grafana for visualization
- Jaeger for distributed tracing
- ELK stack for logging

## Future Roadmap

1. **Advanced Features**
   - Multi-modal support (vision, audio)
   - Advanced reasoning chains
   - Federated learning
   - Edge deployment

2. **Performance**
   - GPU acceleration
   - Model quantization
   - Improved caching
   - Better parallelization

3. **Ecosystem**
   - Plugin marketplace
   - Visual workflow builder
   - Model fine-tuning
   - Enterprise features

---

For more details on specific components, see:
- [LLM Providers Guide](../guides/llm-providers.md)
- [Memory Systems Guide](../guides/memory-systems.md)
- [Tool Development Guide](../guides/tools.md)
- [Deployment Guide](../guides/deployment.md)