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

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

Character Creation Process

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

📝 Character Templates

YAML Structure

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

Query Analysis: Understand what user is asking
Memory Retrieval: Find relevant memories using smart scoring
Context Assembly: Combine character info + relevant memories
Prompt Construction: Use template system for consistency
LLM Generation: Natural language response in character
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:

New Experiences: Each interaction creates memories
Trait Analysis: System evaluates personality impact
Trait Updates: Strengths/weaknesses adjust over time
Reflection Generation: Insights emerge from patterns
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.

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