Technical Documentation

Table of Contents

Architectural Overview

Nara implements a multi-layered architecture designed for scalable, intelligent agent behavior. The system follows domain-driven design principles with clear separation of concerns across cognitive, operational, and persistence layers.

System Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Application Layer                        │
├─────────────────────────────────────────────────────────────┤
│  Agent Controller │  API Routes  │  Configuration Manager   │
├─────────────────────────────────────────────────────────────┤
│                     Domain Layer                            │
├─────────────────────────────────────────────────────────────┤
│  Memory System   │  A/B Testing  │  Performance Predictor   │
│  Tool Registry   │  Personality  │  Content Pipeline        │
├─────────────────────────────────────────────────────────────┤
│                  Infrastructure Layer                       │
├─────────────────────────────────────────────────────────────┤
│  AI Service      │  Platform APIs │  Storage Adapters       │
│  Logging         │  Metrics       │  External Integrations  │
└─────────────────────────────────────────────────────────────┘

Design Principles

Cognitive Architecture: Each agent operates with distinct cognitive capabilities including perception (input processing), reasoning (decision making), memory (experience storage), and action (output generation).

Compositional Intelligence: Complex behaviors emerge from the interaction of simpler, well-defined components rather than monolithic intelligence implementations.

Adaptive Learning: All system components implement feedback loops enabling continuous improvement based on performance metrics and environmental changes.

Extensible Tool System: Function calling architecture allows agents to extend their capabilities dynamically without core system modifications.

Core Systems

Agent Lifecycle Management

interface AgentLifecycle {
  initialization: {
    configurationValidation: boolean;
    dependencyInjection: boolean;
    toolRegistration: boolean;
    memorySystemBootstrap: boolean;
  };
  runtime: {
    contextualDecisionMaking: boolean;
    toolExecution: boolean;
    memoryConsolidation: boolean;
    performanceTracking: boolean;
  };
  shutdown: {
    gracefulTermination: boolean;
    statePresistence: boolean;
    resourceCleanup: boolean;
  };
}

Initialization Phase: Validates configuration schemas, bootstraps memory systems, registers available tools, and establishes platform connections. The system performs dependency injection to ensure loose coupling between components.

Runtime Phase: Continuously processes inputs, makes contextual decisions using available tools, updates memory with experiences, and tracks performance metrics. The agent maintains state consistency across all cognitive operations.

Shutdown Phase: Persists critical state information, cleanly terminates external connections, and ensures no data loss during system termination.

Event-Driven Architecture

Nara implements event-driven patterns for loose coupling and system responsiveness:

interface SystemEvents {
  'memory.stored': (entry: MemoryEntry) => void;
  'abtest.variant_selected': (testId: string, variantId: string) => void;
  'prediction.completed': (content: string, score: number) => void;
  'tool.executed': (toolName: string, result: any) => void;
  'agent.state_changed': (oldState: AgentState, newState: AgentState) => void;
}

This architecture enables real-time monitoring, debugging, and system extension without core modifications.

Memory Architecture

Theoretical Foundation

Nara's memory system implements a simplified version of human memory models with distinct storage types and retrieval mechanisms:

Episodic Memory: Specific experiences with temporal and contextual information Semantic Memory: General knowledge extracted from patterns across experiences Working Memory: Current context and active reasoning processes Procedural Memory: Learned behavioral patterns and successful strategies

Implementation Details

interface MemoryEntry {
  id: string;                    // Unique identifier
  type: MemoryType;             // Classification for retrieval optimization
  data: any;                    // Flexible data storage
  timestamp: Date;              // Temporal ordering
  importance: number;           // Retention priority (0-1)
  tags: string[];              // Semantic indexing
  embedding?: number[];         // Vector representation for similarity search
  accessCount: number;          // Usage frequency tracking
  lastAccessed: Date;          // Recency tracking
}

Pattern Extraction Algorithm

The memory system employs sophisticated pattern recognition to identify successful content strategies:

interface LearningPattern {
  id: string;
  type: 'temporal' | 'content' | 'audience' | 'engagement';
  pattern: {
    conditions: Record<string, any>;    // Pattern triggers
    outcomes: Record<string, number>;   // Success metrics
    confidence: number;                 // Statistical confidence
    sampleSize: number;                // Data points used
  };
  lastUpdated: Date;
  applications: number;                // Times pattern was applied
}

Temporal Patterns: Identify optimal posting times based on historical engagement data. The system tracks performance across different time slots and builds predictive models for content scheduling.

Content Patterns: Analyze successful content characteristics including length, sentiment, topic distribution, and linguistic features. These patterns inform content generation strategies.

Audience Patterns: Model audience preferences and behavior patterns to optimize content targeting and engagement strategies.

Engagement Patterns: Identify correlation between content features and engagement metrics to guide content optimization decisions.

Memory Consolidation Process

Memory consolidation runs as a background process that:

  1. Relevance Scoring: Calculates memory importance based on access frequency, recency, and outcome success

  2. Pattern Extraction: Identifies recurring patterns in high-importance memories

  3. Memory Compression: Consolidates similar experiences into generalized patterns

  4. Garbage Collection: Removes low-importance memories when capacity limits are reached

The consolidation process uses a weighted scoring algorithm:

importance_score = (base_importance * 0.4) + 
                  (access_frequency * 0.3) + 
                  (recency_factor * 0.2) + 
                  (outcome_success * 0.1)

A/B Testing Engine

Statistical Foundation

The A/B testing system implements rigorous statistical analysis to ensure reliable results:

Sample Size Calculation: Uses power analysis to determine minimum sample sizes for detecting meaningful differences with specified confidence levels.

Statistical Significance Testing: Implements chi-square tests for categorical outcomes and t-tests for continuous metrics.

Multiple Comparison Correction: Applies Bonferroni correction when running multiple simultaneous tests to control family-wise error rates.

Test Configuration

interface ABTestConfig {
  name: string;
  hypothesis: string;
  variants: ABVariant[];
  targetMetric: 'engagement' | 'clicks' | 'conversions' | 'sentiment';
  minimumSampleSize: number;
  confidenceLevel: number;        // Default: 0.95
  minimumDetectableEffect: number; // Minimum effect size to detect
  maxDuration: number;            // Maximum test duration in hours
  trafficAllocation: number[];    // Traffic split across variants
}

interface ABVariant {
  id: string;
  name: string;
  config: Record<string, any>;   // Variant-specific configuration
  traffic: number;               // Proportion of traffic (0-1)
  metrics: PerformanceMetrics;   // Accumulated performance data
}

Performance Tracking

The system tracks comprehensive metrics for each variant:

interface PerformanceMetrics {
  impressions: number;           // Total exposures
  engagements: number;          // User interactions
  clicks: number;               // Click-through events
  conversions: number;          // Goal completions
  engagementRate: number;       // Calculated ratio
  conversionRate: number;       // Calculated ratio
  averageScore: number;         // Average performance score
  confidenceInterval: [number, number]; // 95% confidence bounds
}

Statistical Analysis Implementation

class StatisticalAnalyzer {
  calculateChiSquare(observed: number[], expected: number[]): {
    statistic: number;
    pValue: number;
    degreesOfFreedom: number;
  }
  
  calculateZScore(sample1: Sample, sample2: Sample): {
    zScore: number;
    pValue: number;
    standardError: number;
  }
  
  calculateConfidenceInterval(
    proportion: number, 
    sampleSize: number, 
    confidenceLevel: number
  ): [number, number]
}

The engine automatically determines when tests have reached statistical significance and can provide reliable results for decision making.

Performance Prediction

Machine Learning Architecture

The performance predictor implements a custom neural network optimized for content engagement forecasting:

interface PredictionModel {
  weights: number[][];           // Layer weights
  biases: number[];             // Layer biases
  learningRate: number;         // Gradient descent parameter
  momentum: number;             // Momentum coefficient
  regularization: number;       // L2 regularization strength
  trainingHistory: TrainingEpoch[];
}

interface TrainingEpoch {
  epoch: number;
  loss: number;
  accuracy: number;
  validationLoss: number;
  validationAccuracy: number;
}

Feature Engineering

The system extracts comprehensive features from content for prediction:

Linguistic Features:

  • Content length and readability scores

  • Sentiment polarity and intensity

  • Lexical diversity and complexity

  • Part-of-speech distribution

Structural Features:

  • Hashtag count and distribution

  • Emoji usage patterns

  • Link presence and type

  • Mention and reply patterns

Temporal Features:

  • Posting time and day of week

  • Time since last post

  • Seasonal and trending topic alignment

Contextual Features:

  • Platform-specific characteristics

  • Audience demographics alignment

  • Historical performance correlation

Training Algorithm

class PerformancePredictionModel {
  async trainModel(trainingData: ContentPerformance[]): Promise<void> {
    // Feature normalization
    const normalizedFeatures = this.normalizeFeatures(trainingData);
    
    // Split into training/validation sets
    const { training, validation } = this.splitData(normalizedFeatures, 0.8);
    
    // Training loop with gradient descent
    for (let epoch = 0; epoch < this.maxEpochs; epoch++) {
      // Forward pass
      const predictions = this.forwardPass(training.features);
      
      // Calculate loss
      const loss = this.calculateLoss(predictions, training.targets);
      
      // Backward pass
      const gradients = this.backwardPass(predictions, training.targets);
      
      // Update weights
      this.updateWeights(gradients);
      
      // Validation
      const validationMetrics = this.validate(validation);
      
      // Early stopping if no improvement
      if (this.shouldStop(validationMetrics)) break;
    }
  }
}

Prediction Confidence Calculation

The system provides confidence intervals for predictions using bootstrap sampling:

interface PredictionResult {
  score: number;                 // Expected engagement score (0-100)
  confidence: number;            // Prediction confidence (0-1)
  factors: FeatureContributions; // Individual feature impacts
  recommendations: string[];      // Optimization suggestions
  confidenceInterval: [number, number]; // Score bounds
}

Tool System

Architecture Design

The tool system implements a plugin architecture enabling dynamic capability extension:

interface ToolRegistry {
  register(tool: Tool): void;
  unregister(toolName: string): void;
  execute(toolName: string, parameters: any): Promise<ToolResult>;
  listAvailable(): ToolConfig[];
  validateParameters(toolName: string, parameters: any): boolean;
}

interface Tool {
  getConfig(): ToolConfig;
  validate(parameters: any): boolean;
  execute(parameters: any): Promise<any>;
  transform?(result: any): any;     // Optional result transformation
  onError?(error: Error): void;     // Optional error handling
}

Sentiment Analysis Implementation

The sentiment analysis tool combines multiple approaches for robust emotion detection:

Lexicon-Based Analysis: Uses pre-trained sentiment dictionaries with contextual weighting Rule-Based Processing: Applies linguistic rules for negation, intensification, and context Statistical Classification: Employs machine learning models for nuanced sentiment detection

interface SentimentResult {
  sentiment: 'very positive' | 'positive' | 'neutral' | 'negative' | 'very negative';
  score: number;              // Numeric sentiment score (-1 to 1)
  confidence: number;         // Analysis confidence (0-1)
  polarity: 'positive' | 'neutral' | 'negative';
  emotions?: {
    joy: number;
    anger: number;
    fear: number;
    sadness: number;
    surprise: number;
    disgust: number;
  };
  toxicity?: number;          // Toxicity score (0-1)
  subjectivity?: number;      // Subjectivity score (0-1)
}

Content Enhancement Engine

The content enhancement tool optimizes content across multiple dimensions:

Platform Optimization: Tailors content format, length, and style for specific platforms Audience Targeting: Adapts language, tone, and topics for target demographics Engagement Enhancement: Adds elements proven to increase engagement rates SEO Integration: Incorporates relevant keywords and hashtags for discoverability

interface EnhancementResult {
  original: string;
  enhanced: string;
  enhancements: string[];        // List of applied improvements
  metrics: {
    originalLength: number;
    enhancedLength: number;
    lengthIncrease: number;
    readabilityScore: number;
    engagementScore: number;
    hashtagCount: number;
    emojiCount: number;
    hasLinks: boolean;
    hasMentions: boolean;
    hasQuestions: boolean;
  };
  suggestions: string[];         // Additional recommendations
}

Agent Configuration

Personality Modeling

Agent personalities are modeled using multidimensional emotional and behavioral vectors:

interface PersonalityConfig {
  name: string;
  systemPrompt: string;
  emotionalRange: {
    creativity: number;          // Innovation and originality (0-1)
    analytical: number;          // Logical reasoning capability (0-1)  
    empathy: number;            // Emotional understanding (0-1)
    humor: number;              // Comedic expression (0-1)
    enthusiasm: number;         // Energy and excitement (0-1)
    skepticism?: number;        // Critical thinking (0-1)
    curiosity?: number;         // Information seeking (0-1)
  };
  communicationStyle: {
    temperature: number;         // Response randomness (0-2)
    maxTokens: number;          // Response length limit
    adaptivePersonality: boolean; // Dynamic personality adjustment
    learningRate: number;       // Adaptation speed (0-1)
    contextWindow: number;      // Memory context size
  };
  tools: ToolConfiguration[];
  capabilities: AgentCapabilities;
  learningParameters: LearningConfig;
}

Dynamic Personality Adaptation

Agents can modify their personality parameters based on performance feedback:

interface PersonalityAdapter {
  adaptToFeedback(
    feedback: PerformanceMetrics, 
    currentPersonality: PersonalityConfig
  ): PersonalityConfig;
  
  calculateAdaptationDirection(
    targetMetric: string,
    currentPerformance: number,
    targetPerformance: number
  ): PersonalityAdjustment;
  
  validatePersonalityBounds(
    personality: PersonalityConfig
  ): ValidationResult;
}

Tool Configuration Management

Tools can be dynamically enabled, configured, and customized per agent:

interface ToolConfiguration {
  name: string;
  enabled: boolean;
  config?: {
    platform?: string;           // Platform-specific settings
    targetAudience?: string;     // Audience targeting
    enhancementType?: string;    // Enhancement focus
    confidenceThreshold?: number; // Minimum confidence for execution
    rateLimiting?: {
      maxCalls: number;
      timeWindow: number;        // Time window in seconds
    };
  };
  permissions: ToolPermissions;
  fallbackBehavior: 'skip' | 'default' | 'error';
}

Implementation Patterns

Dependency Injection

Nara uses constructor-based dependency injection for loose coupling:

class Agent {
  constructor(
    private config: AgentConfig,
    private memorySystem: MemorySystem,
    private abTestingEngine: ABTestingEngine,
    private performancePredictor: PerformancePredictor,
    private toolRegistry: ToolRegistry,
    private logger: Logger
  ) {}
}

// Container configuration
const container = new Container();
container.bind<MemorySystem>(TYPES.MemorySystem).to(MemorySystem);
container.bind<ABTestingEngine>(TYPES.ABTestingEngine).to(ABTestingEngine);
// ... other bindings

Error Handling Strategy

Comprehensive error handling with recovery mechanisms:

interface ErrorHandler {
  handleToolError(error: ToolError, context: ExecutionContext): Promise<void>;
  handleMemoryError(error: MemoryError, operation: MemoryOperation): Promise<void>;
  handlePredictionError(error: PredictionError, content: string): Promise<void>;
  
  // Circuit breaker pattern for external services
  executeWithCircuitBreaker<T>(
    operation: () => Promise<T>,
    fallback?: () => Promise<T>
  ): Promise<T>;
}

Asynchronous Processing

Non-blocking operations for responsive agent behavior:

class AsyncAgentRunner {
  async processContentGeneration(): Promise<void> {
    const tasks = [
      this.generateContent(),
      this.analyzePerformancePrediction(),
      this.updateMemoryPatterns(),
      this.checkABTestResults()
    ];
    
    // Process tasks concurrently with error isolation
    const results = await Promise.allSettled(tasks);
    
    // Handle individual task results
    results.forEach((result, index) => {
      if (result.status === 'rejected') {
        this.logger.error(`Task ${index} failed:`, result.reason);
      }
    });
  }
}

Performance Considerations

Memory Management

Efficient memory usage through strategic caching and garbage collection:

interface MemoryManager {
  // LRU cache for frequently accessed memories
  memoryCache: LRUCache<string, MemoryEntry>;
  
  // Periodic cleanup of low-importance memories
  scheduleGarbageCollection(): void;
  
  // Memory usage monitoring
  getMemoryUsage(): {
    totalEntries: number;
    totalSizeBytes: number;
    cacheHitRate: number;
    averageAccessTime: number;
  };
}

Performance Optimization

Batch Processing: Group similar operations to reduce overhead:

class BatchProcessor {
  async processBatch<T, R>(
    items: T[],
    processor: (item: T) => Promise<R>,
    batchSize: number = 10
  ): Promise<R[]> {
    const results: R[] = [];
    
    for (let i = 0; i < items.length; i += batchSize) {
      const batch = items.slice(i, i + batchSize);
      const batchResults = await Promise.all(
        batch.map(item => processor(item))
      );
      results.push(...batchResults);
    }
    
    return results;
  }
}

Connection Pooling: Reuse database and API connections:

interface ConnectionPool {
  acquire(): Promise<Connection>;
  release(connection: Connection): void;
  getStats(): {
    totalConnections: number;
    activeConnections: number;
    queuedRequests: number;
  };
}

Monitoring and Metrics

Comprehensive performance monitoring:

interface MetricsCollector {
  recordExecutionTime(operation: string, duration: number): void;
  recordMemoryUsage(component: string, bytes: number): void;
  recordErrorRate(component: string, errorCount: number, totalRequests: number): void;
  recordThroughput(operation: string, requestsPerSecond: number): void;
  
  generateReport(): PerformanceReport;
}

Advanced Usage

Custom Tool Development

Creating domain-specific tools:

class CustomAnalyticsTool implements Tool {
  getConfig(): ToolConfig {
    return {
      name: 'custom_analytics',
      description: 'Domain-specific analytics processing',
      parameters: {
        type: 'object',
        properties: {
          data: { type: 'string', description: 'Raw analytics data' },
          analysisType: { type: 'string', enum: ['trend', 'cohort', 'funnel'] }
        },
        required: ['data', 'analysisType']
      },
      enabled: true
    };
  }
  
  validate(parameters: any): boolean {
    return parameters.data && parameters.analysisType;
  }
  
  async execute(parameters: any): Promise<AnalyticsResult> {
    // Custom analytics logic
    const processor = this.getProcessor(parameters.analysisType);
    return await processor.analyze(parameters.data);
  }
}

Multi-Agent Coordination

Implementing agent networks:

interface AgentNetwork {
  registerAgent(agent: Agent): void;
  broadcastMessage(message: AgentMessage): void;
  routeMessage(fromAgent: string, toAgent: string, message: AgentMessage): void;
  
  // Consensus mechanisms
  reachConsensus(proposal: NetworkProposal): Promise<ConsensusResult>;
  
  // Load balancing
  selectAgent(criteria: SelectionCriteria): Agent;
}

Integration Patterns

Webhook integration for external systems:

interface WebhookManager {
  registerWebhook(event: string, url: string, config: WebhookConfig): void;
  
  async triggerWebhook(event: string, payload: any): Promise<void> {
    const webhooks = this.getWebhooksForEvent(event);
    
    await Promise.allSettled(
      webhooks.map(webhook => 
        this.httpClient.post(webhook.url, payload, {
          headers: webhook.headers,
          timeout: webhook.timeout
        })
      )
    );
  }
}

This technical documentation provides comprehensive coverage of Nara's architecture, implementation details, and advanced usage patterns. The framework's modular design enables sophisticated agent behaviors while maintaining extensibility and performance.