EQ+ System Overview

The EQ+ system is KAIKO's advanced emotional intelligence framework that enhances ElizaOS agents with sophisticated emotional understanding and adaptive behavior.

Core Components

1. Emotion Classification

The EQ+ system uses a custom DistilBERT checkpoint for efficient and accurate emotion detection:

• Real-time emotion classification

• Complex emotion derivation

• Contextual understanding

• Fallback mechanisms

Example

import { EmotionClassifier } from '@kaiko/core';

const classifier = new EmotionClassifier({
  huggingFaceEnabled: true,
  useRuleBasedFallback: true
});

const emotions = await classifier.classifyText(
  "I'm feeling excited but also nervous about this project"
);
// Returns: { joy: 0.8, anxiety: 0.6, anticipation: 0.7 }

2. Emotional Context Management

Maintains and tracks emotional states throughout interactions:

• Current emotional state

• Historical patterns

• Emotional memory integration

• Context-aware responses

Example

import { EmotionalContextService } from '@kaiko/core';

const contextService = new EmotionalContextService();

// Update emotional state
await contextService.updateState({
  joy: 0.8,
  trust: 0.6
});

// Get current state for response generation
const currentState = await contextService.getCurrentState();

3. Adaptive TDL System

Trait-Directed Learning with emotional awareness:

• Emotion-to-trait mapping

• Adaptive response strategies

• Learning from emotional patterns

• Behavioral adjustments

graph LR
    A[Emotions] -->|Influence| B[Traits]
    B -->|Shape| C[Behavior]
    C -->|Generate| D[Response]
    D -->|Impact| A

4. Neo4j Memory System

Graph-based emotional memory storage:

• Emotional context persistence

• Relationship tracking

• Temporal decay

• Efficient retrieval

Example

import { EmotionalMemoryManager } from '@kaiko/core';

const memoryManager = new EmotionalMemoryManager({
  neo4j: {
    uri: process.env.NEO4J_URI,
    // ... other config
  }
});

// Store emotional memory
await memoryManager.storeMemory({
  text: "User expressed excitement about the project",
  emotions: { joy: 0.8, anticipation: 0.7 },
  timestamp: Date.now()
});

Integration with Agent Runtime

The EQ+ system integrates with ElizaOS agents through:

1. Emotional State Management

   runtime.eqplus.contextService.updateState(emotions);

2. Memory Integration

   runtime.eqplus.memoryManager.storeMemory(memory);

3. Response Generation

   const response = await runtime.eqplus.generateResponse(prompt, emotions);

4. Trait Evolution

   runtime.eqplus.adaptiveTDL.evolveTraits(emotions);

Emotional Processing Pipeline

sequenceDiagram
    participant U as User
    participant C as Classifier
    participant M as Memory
    participant T as TDL
    participant A as Agent

    U->>C: Input Text
    C->>M: Classified Emotions
    M->>T: Update Context
    T->>A: Adapt Behavior
    A->>U: Emotional Response

Configuration

Configure the EQ+ system in your runtime:

const runtime = await createKaikoRuntime({
  eqplus: {
    enabled: true,
    emotionalAnalysis: {
      huggingFaceEnabled: true,
      useRuleBasedFallback: true,
      complexEmotions: true
    },
    memory: {
      neo4j: {
        uri: process.env.NEO4J_URI,
        username: process.env.NEO4J_USERNAME,
        password: process.env.NEO4J_PASSWORD
      },
      decayRate: 0.1,
      maxHistorySize: 1000
    },
    adaptiveTDL: {
      enabled: true,
      learningRate: 0.1,
      maxTraitValue: 1.0
    }
  }
});

Performance Considerations

The EQ+ system is designed for efficiency, but consider these factors for optimal performance.

1. Memory Management

   • Use appropriate retention periods

   • Implement cleanup strategies

   • Monitor graph size

2. Emotion Classification

   • Cache frequent classifications

   • Use batch processing when possible

   • Configure fallback thresholds

3. Response Generation

   • Balance emotional depth with latency

   • Cache common responses

   • Monitor token usage

Best Practices

1. Emotional Analysis

   • Validate emotion classifications

   • Handle edge cases

   • Use appropriate thresholds

2. Memory Usage

   • Implement retention policies

   • Clean up old data

   • Monitor storage usage

3. Integration

   • Handle errors gracefully

   • Validate emotional states

   • Test edge cases

Monitoring

Monitor EQ+ system performance:

const metrics = await runtime.eqplus.getMetrics();
console.log({
  emotionClassifications: metrics.classificationCount,
  averageLatency: metrics.averageLatency,
  memoryUsage: metrics.memoryUsage,
  errorRate: metrics.errorRate
});

Next Steps

• Learn about Emotional Memory Management

• Explore Adaptive TDL

• Understand Integration Patterns

With the EQ+ system, your agents can understand and respond to emotions effectively. Continue to the implementation guides for detailed integration steps.