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:
Relevance Scoring: Calculates memory importance based on access frequency, recency, and outcome success
Pattern Extraction: Identifies recurring patterns in high-importance memories
Memory Compression: Consolidates similar experiences into generalized patterns
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.