# Nijika AI Agent Framework - Architecture Overview

## 🏗️ System Architecture

The Nijika framework follows a modular, scalable architecture designed for enterprise-grade AI agent deployment across multiple industries.

### Core Components

```
┌─────────────────────────────────────────────────────────────────┐
│                        Nijika Framework                          │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │
│  │      Agent      │  │   Workflow      │  │   Planning      │  │
│  │   Management    │  │    Engine       │  │    Engine       │  │
│  └─────────────────┘  └─────────────────┘  └─────────────────┘  │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │
│  │   Provider      │  │      Tool       │  │      RAG        │  │
│  │   Manager       │  │    Registry     │  │    System       │  │
│  └─────────────────┘  └─────────────────┘  └─────────────────┘  │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │
│  │     Memory      │  │  Configuration  │  │      Core       │  │
│  │   Management    │  │   Management    │  │   Foundation    │  │
│  └─────────────────┘  └─────────────────┘  └─────────────────┘  │
└─────────────────────────────────────────────────────────────────┘
```

## 🧩 Component Details

### 1. Agent Management (`nijika/core/agent.py`)
- **Purpose**: Orchestrates all framework components
- **Key Features**:
  - Multi-provider AI integration
  - Execution lifecycle management
  - Context-aware processing
  - Error handling and recovery

### 2. Provider System (`nijika/core/providers.py`)
- **Purpose**: Abstracts multiple AI providers
- **Supported Providers**:
  - OpenAI (GPT-3.5, GPT-4)
  - Anthropic (Claude)
  - Google (Gemini)
  - Azure OpenAI
  - Custom providers
- **Features**:
  - Load balancing
  - Failover support
  - Rate limiting
  - Cost optimization

### 3. Workflow Engine (`nijika/workflows/engine.py`)
- **Purpose**: Manages complex task execution
- **Capabilities**:
  - Sequential execution
  - Parallel processing
  - Conditional branching
  - Error handling
  - Retry mechanisms

### 4. Tool Registry (`nijika/tools/registry.py`)
- **Purpose**: Manages and executes tools
- **Built-in Tools**:
  - HTTP requests
  - File operations
  - Calculations
  - Data processing
- **Features**:
  - Plugin architecture
  - Sandboxing
  - Parameter validation
  - Auto-discovery

### 5. RAG System (`nijika/rag/system.py`)
- **Purpose**: Retrieval-Augmented Generation
- **Components**:
  - Document processing
  - Vector storage
  - Semantic search
  - Context generation
- **Features**:
  - Multi-format support
  - Chunking strategies
  - Hybrid search

### 6. Planning Engine (`nijika/planning/planner.py`)
- **Purpose**: Task decomposition and planning
- **Strategies**:
  - Sequential planning
  - Hierarchical decomposition
  - Parallel execution
  - Reactive planning
  - Goal-oriented planning

### 7. Memory Management (`nijika/core/memory.py`)
- **Purpose**: Conversation and execution history
- **Types**:
  - Conversation memory
  - Execution memory
  - Knowledge storage
  - Context management
- **Features**:
  - SQLite backend
  - TTL management
  - Search capabilities

### 8. Configuration System (`nijika/core/config.py`)
- **Purpose**: Centralized configuration management
- **Sources**:
  - File-based (YAML/JSON)
  - Environment variables
  - Runtime configuration
- **Features**:
  - Validation
  - Type checking
  - Nested configuration

## 🔄 Data Flow

```
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│    User     │───▶│   Agent     │───▶│  Planning   │
│   Query     │    │  Manager    │    │   Engine    │
└─────────────┘    └─────────────┘    └─────────────┘
                          │                   │
                          ▼                   ▼
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│    RAG      │◀───│  Workflow   │◀───│    Plan     │
│   System    │    │   Engine    │    │ Generation  │
└─────────────┘    └─────────────┘    └─────────────┘
       │                   │                   
       ▼                   ▼                   
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│  Context    │    │    Tool     │    │  Provider   │
│ Retrieval   │    │ Execution   │    │  Manager    │
└─────────────┘    └─────────────┘    └─────────────┘
       │                   │                   │
       └─────────────┐     │     ┌─────────────┘
                     ▼     ▼     ▼
              ┌─────────────────────────┐
              │      Memory &           │
              │   Result Storage        │
              └─────────────────────────┘
```

## 🏭 Industry Applications

### Finance
- **Use Cases**: Fraud detection, risk assessment, trading strategies
- **Tools**: Market data APIs, risk calculators, compliance checkers
- **Workflows**: Multi-step analysis, alert systems, reporting

### Healthcare
- **Use Cases**: Diagnosis assistance, patient care, drug discovery
- **Tools**: Medical databases, imaging analysis, patient records
- **Workflows**: Clinical decision support, research pipelines

### E-commerce
- **Use Cases**: Product recommendations, customer support, inventory
- **Tools**: Product catalogs, payment systems, shipping APIs
- **Workflows**: Order processing, recommendation engines

### Education
- **Use Cases**: Personalized learning, content generation, assessment
- **Tools**: Learning management systems, content APIs, assessment tools
- **Workflows**: Adaptive learning paths, automated grading

## 🚀 Deployment Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                    Production Deployment                         │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────┐  │
│  │    Load     │  │   API       │  │  Agent      │  │ Worker  │  │
│  │  Balancer   │  │ Gateway     │  │ Manager     │  │ Nodes   │  │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────┘  │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────┐  │
│  │  Database   │  │   Vector    │  │   Redis     │  │   S3    │  │
│  │ (PostgreSQL)│  │   Store     │  │   Cache     │  │Storage  │  │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────┘  │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────┐  │
│  │ Monitoring  │  │   Logging   │  │   Metrics   │  │  Alerts │  │
│  │ (Prometheus)│  │  (ELK)      │  │ (Grafana)   │  │ (Slack) │  │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────┘  │
└─────────────────────────────────────────────────────────────────┘
```

## 📊 Performance Characteristics

### Scalability
- **Horizontal**: Multiple agent instances
- **Vertical**: Resource optimization per agent
- **Load Balancing**: Round-robin, least-loaded
- **Caching**: Multi-level caching strategy

### Reliability
- **Fault Tolerance**: Provider failover, retry mechanisms
- **Monitoring**: Health checks, performance metrics
- **Logging**: Structured logging, audit trails
- **Backup**: Data persistence, state recovery

### Security
- **Authentication**: API key management, JWT tokens
- **Authorization**: Role-based access control
- **Encryption**: Data at rest and in transit
- **Sandboxing**: Tool execution isolation

## 🔧 Configuration Management

### Environment-Specific Settings
```yaml
# Development
framework:
  environment: development
  debug: true

# Production
framework:
  environment: production
  debug: false
  
agents:
  max_concurrent_executions: 100
  auto_cleanup: true

providers:
  rate_limiting: true
  fallback_enabled: true
```

### Provider Configuration
```yaml
providers:
  - name: openai
    provider_type: openai
    api_key: ${OPENAI_API_KEY}
    model: gpt-4
    rate_limit: 100
    
  - name: anthropic
    provider_type: anthropic
    api_key: ${ANTHROPIC_API_KEY}
    model: claude-3-sonnet
```

## 📈 Monitoring and Observability

### Key Metrics
- **Agent Performance**: Execution time, success rate
- **Provider Health**: Response time, error rate
- **Memory Usage**: Storage utilization, cleanup efficiency
- **Tool Execution**: Usage patterns, performance

### Alerting
- **Error Thresholds**: Provider failures, tool errors
- **Performance Degradation**: Slow responses, high memory
- **Capacity Planning**: Resource utilization, scaling needs

## 🔮 Future Enhancements

### Planned Features
1. **Advanced Planning**: Multi-agent coordination, learning from execution
2. **Enhanced RAG**: Multi-modal support, advanced retrieval
3. **Real-time Processing**: Streaming workflows, event-driven execution
4. **Advanced Security**: Zero-trust architecture, advanced encryption
5. **Cloud Integration**: Native cloud provider support, serverless deployment

### Extension Points
- **Custom Providers**: Plugin architecture for new AI services
- **Custom Tools**: Easy tool development and integration
- **Workflow Templates**: Industry-specific workflow libraries
- **UI Components**: Dashboard and monitoring interfaces

This architecture provides a solid foundation for building sophisticated AI agents that can adapt to various industry needs while maintaining high performance, reliability, and security. 