117 lines
3.9 KiB
Markdown
117 lines
3.9 KiB
Markdown
# Character Agent System
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Character agents are the core AI entities that combine memory, personality, and conversational ability into believable
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characters that evolve through their experiences.
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## 🎭 Agent Architecture
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### Core Components
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1. **Memory Stream**: Stanford-inspired memory architecture
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2. **Character Data**: Structured personality and relationship info
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3. **LLM Integration**: Natural language processing and generation
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4. **Trait System**: Dynamic personality development
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5. **Response Generation**: Context-aware conversation handling
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### Character Creation Process
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1. **Template Loading**: YAML files define initial memories
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2. **Memory Initialization**: Observations, reflections, and plans loaded
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3. **Importance Scoring**: All memories rated for significance
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4. **Character Extraction**: LLM generates structured character data
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5. **Agent Ready**: Fully functional roleplay partner
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## 📝 Character Templates
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### YAML Structure
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```yaml
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observations:
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- "My name is Alice and I am 23 years old"
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- "I study Victorian literature at university"
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- "I spilled coffee yesterday and felt embarrassed"
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reflections:
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- "I have romantic feelings for Emma (evidence: daily visits, heart racing)"
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- "I am naturally shy in social situations (evidence: nervous with strangers)"
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plans:
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- "I want to work up courage to talk to Emma"
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- "I need to finish my thesis chapter this week"
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```
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### Memory Types in Templates
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**Observations**: Factual experiences and basic information
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- Identity facts (name, age, occupation)
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- Recent experiences and events
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- Relationship interactions
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- Physical descriptions and traits
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**Reflections**: Character insights and self-understanding
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- Personality trait recognition
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- Relationship feelings and dynamics
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- Behavioral pattern awareness
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- Values and belief formation
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**Plans**: Future intentions and goals
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- Short-term objectives
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- Long-term dreams and aspirations
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- Relationship goals
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- Personal development aims
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## 🎯 Response Generation
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### Context Building Process
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1. **Query Analysis**: Understand what user is asking
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2. **Memory Retrieval**: Find relevant memories using smart scoring
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3. **Context Assembly**: Combine character info + relevant memories
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4. **Prompt Construction**: Use template system for consistency
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5. **LLM Generation**: Natural language response in character
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6. **Memory Update**: Store new experience from interaction
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### Response Style
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- **First Person Past Tense**: "I looked up and smiled nervously..."
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- **Character Consistency**: Responses match established personality
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- **Memory Integration**: References past experiences naturally
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- **Emotional Authenticity**: Shows appropriate feelings and reactions
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## 🔄 Character Development
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### Dynamic Personality Growth
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Characters evolve through experience:
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1. **New Experiences**: Each interaction creates memories
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2. **Trait Analysis**: System evaluates personality impact
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3. **Trait Updates**: Strengths/weaknesses adjust over time
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4. **Reflection Generation**: Insights emerge from patterns
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5. **Behavioral Consistency**: Future responses reflect growth
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### Trait System
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- **Incremental Development**: Traits strengthen/weaken with evidence
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- **Evidence-Based**: Every trait change linked to specific experiences
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- **Single-Word Names**: Simple, clear personality descriptors
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- **Strength Ratings**: 1-10 scale for trait intensity
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- **Dynamic Descriptions**: How traits manifest in behavior
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## 🔮 Future Vision: Multi-Agent Interactions
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### Planned Features
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The system is designed with future multi-agent capabilities in mind:
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- Characters will be able to interact with each other
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- Conversations will create memories for all participants
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- Relationship dynamics will develop naturally
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- Information will spread through character networks
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- Emergent social behaviors will arise from interactions
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Currently, the focus is on perfecting single-agent character development and ensuring each character becomes genuinely
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complex and believable through their individual growth. |