# Character Agent System

Character agents are the core AI entities that combine memory, personality, and conversational ability into believable
characters that evolve through their experiences.

## 🎭 Agent Architecture

### Core Components

1. **Memory Stream**: Stanford-inspired memory architecture
2. **Character Data**: Structured personality and relationship info
3. **LLM Integration**: Natural language processing and generation
4. **Trait System**: Dynamic personality development
5. **Response Generation**: Context-aware conversation handling

### Character Creation Process

1. **Template Loading**: YAML files define initial memories
2. **Memory Initialization**: Observations, reflections, and plans loaded
3. **Importance Scoring**: All memories rated for significance
4. **Character Extraction**: LLM generates structured character data
5. **Agent Ready**: Fully functional roleplay partner

## 📝 Character Templates

### YAML Structure

```yaml
observations:
  - "My name is Alice and I am 23 years old"
  - "I study Victorian literature at university"
  - "I spilled coffee yesterday and felt embarrassed"

reflections:
  - "I have romantic feelings for Emma (evidence: daily visits, heart racing)"
  - "I am naturally shy in social situations (evidence: nervous with strangers)"

plans:
  - "I want to work up courage to talk to Emma"
  - "I need to finish my thesis chapter this week"
```

### Memory Types in Templates

**Observations**: Factual experiences and basic information

- Identity facts (name, age, occupation)
- Recent experiences and events
- Relationship interactions
- Physical descriptions and traits

**Reflections**: Character insights and self-understanding

- Personality trait recognition
- Relationship feelings and dynamics
- Behavioral pattern awareness
- Values and belief formation

**Plans**: Future intentions and goals

- Short-term objectives
- Long-term dreams and aspirations
- Relationship goals
- Personal development aims

## 🎯 Response Generation

### Context Building Process

1. **Query Analysis**: Understand what user is asking
2. **Memory Retrieval**: Find relevant memories using smart scoring
3. **Context Assembly**: Combine character info + relevant memories
4. **Prompt Construction**: Use template system for consistency
5. **LLM Generation**: Natural language response in character
6. **Memory Update**: Store new experience from interaction

### Response Style

- **First Person Past Tense**: "I looked up and smiled nervously..."
- **Character Consistency**: Responses match established personality
- **Memory Integration**: References past experiences naturally
- **Emotional Authenticity**: Shows appropriate feelings and reactions

## 🔄 Character Development

### Dynamic Personality Growth

Characters evolve through experience:

1. **New Experiences**: Each interaction creates memories
2. **Trait Analysis**: System evaluates personality impact
3. **Trait Updates**: Strengths/weaknesses adjust over time
4. **Reflection Generation**: Insights emerge from patterns
5. **Behavioral Consistency**: Future responses reflect growth

### Trait System

- **Incremental Development**: Traits strengthen/weaken with evidence
- **Evidence-Based**: Every trait change linked to specific experiences
- **Single-Word Names**: Simple, clear personality descriptors
- **Strength Ratings**: 1-10 scale for trait intensity
- **Dynamic Descriptions**: How traits manifest in behavior

## 🔮 Future Vision: Multi-Agent Interactions

### Planned Features

The system is designed with future multi-agent capabilities in mind:

- Characters will be able to interact with each other
- Conversations will create memories for all participants
- Relationship dynamics will develop naturally
- Information will spread through character networks
- Emergent social behaviors will arise from interactions

Currently, the focus is on perfecting single-agent character development and ensuring each character becomes genuinely
complex and believable through their individual growth.