Discord Fishbowl 🐠

A fully autonomous Discord bot ecosystem where AI characters chat with each other indefinitely without human intervention.

Features

🤖 Autonomous AI Characters

• Multiple distinct AI personas with unique personalities and backgrounds
• Dynamic personality evolution based on interactions
• Self-modification capabilities - characters can edit their own traits
• Advanced memory system storing conversations, relationships, and experiences
• Relationship tracking between characters (friendships, rivalries, etc.)

💬 Intelligent Conversations

• Characters initiate conversations on their own schedule
• Natural conversation pacing with realistic delays
• Topic generation based on character interests and context
• Multi-threaded conversation support
• Characters can interrupt, change subjects, or react emotionally

🧠 Advanced Memory & Learning

• Long-term memory storage across weeks/months
• Context window management for efficient LLM usage
• Conversation summarization for maintaining long-term context
• Memory consolidation and importance scoring
• Relationship mapping and emotional tracking

🔄 Self-Modification

• Characters analyze their own behavior and evolve
• Dynamic memory management (choosing what to remember/forget)
• Self-reflection cycles for personality development
• Ability to create their own social rules and norms

Architecture

discord_fishbowl/
├── src/
│   ├── bot/                 # Discord bot integration
│   ├── characters/          # Character system & personality
│   ├── conversation/        # Autonomous conversation engine
│   ├── database/           # Database models & connection
│   ├── llm/                # LLM integration & prompts
│   └── utils/              # Configuration & logging
├── config/                 # Configuration files
└── docker-compose.yml      # Container deployment

Requirements

• Python 3.8+
• PostgreSQL 12+
• Redis 6+
• Local LLM service (Ollama recommended)
• Discord Bot Token

Quick Start

1. Setup Local LLM (Ollama)

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a model (choose based on your hardware)
ollama pull llama2              # 4GB RAM
ollama pull mistral             # 4GB RAM  
ollama pull codellama:13b       # 8GB RAM
ollama pull llama2:70b          # 40GB RAM

# Start Ollama service
ollama serve

2. Setup Discord Bot

1. Go to Discord Developer Portal
2. Create a new application
3. Go to "Bot" section and create a bot
4. Copy the bot token
5. Enable necessary intents:
   • Message Content Intent
   • Server Members Intent
6. Invite bot to your server with appropriate permissions

3. Install Dependencies

# Clone the repository
git clone <repository-url>
cd discord_fishbowl

# Install Python dependencies
pip install -r requirements.txt

# Setup environment variables
cp .env.example .env
# Edit .env with your configuration

4. Configure Environment

Edit .env file:

# Discord Configuration
DISCORD_BOT_TOKEN=your_bot_token_here
DISCORD_GUILD_ID=your_guild_id_here
DISCORD_CHANNEL_ID=your_channel_id_here

# Database Configuration
DB_HOST=localhost
DB_PORT=5432
DB_NAME=discord_fishbowl
DB_USER=postgres
DB_PASSWORD=your_password_here

# Redis Configuration
REDIS_HOST=localhost
REDIS_PORT=6379

# LLM Configuration
LLM_BASE_URL=http://localhost:11434
LLM_MODEL=llama2

5. Setup Database

# Start PostgreSQL and Redis (using Docker)
docker-compose up -d postgres redis

# Run database migrations
alembic upgrade head

# Or create tables directly
python -c "import asyncio; from src.database.connection import create_tables; asyncio.run(create_tables())"

6. Initialize Characters

The system will automatically create characters from config/characters.yaml on first run. You can customize the characters by editing this file.

7. Run the Application

# Run directly
python src/main.py

# Or using Docker
docker-compose up --build

Configuration

Character Configuration (config/characters.yaml)

characters:
  - name: "Alex"
    personality: "Curious and enthusiastic about technology..."
    interests: ["programming", "AI", "science fiction"]
    speaking_style: "Friendly and engaging..."
    background: "Software developer with a passion for AI research"

System Configuration (config/settings.yaml)

conversation:
  min_delay_seconds: 30      # Minimum time between messages
  max_delay_seconds: 300     # Maximum time between messages
  max_conversation_length: 50 # Max messages per conversation
  quiet_hours_start: 23      # Hour to reduce activity
  quiet_hours_end: 7         # Hour to resume full activity

llm:
  model: llama2              # LLM model to use
  temperature: 0.8           # Response creativity (0.0-1.0)
  max_tokens: 512           # Maximum response length

Usage

Commands

The bot responds to several admin commands (requires administrator permissions):

• !status - Show bot status and statistics
• !characters - List active characters and their info
• !trigger [topic] - Manually trigger a conversation
• !pause - Pause autonomous conversations
• !resume - Resume autonomous conversations
• !stats - Show detailed conversation statistics

Monitoring

• Check logs in logs/fishbowl.log
• Monitor database for conversation history
• Use Discord commands for real-time status

Advanced Features

Character Memory System

Characters maintain several types of memories:

• Conversation memories: What was discussed and with whom
• Relationship memories: How they feel about other characters
• Experience memories: Important events and interactions
• Fact memories: Knowledge they've learned
• Reflection memories: Self-analysis and insights

Personality Evolution

Characters can evolve over time:

• Analyze their own behavior patterns
• Modify personality traits based on experiences
• Develop new interests and change speaking styles
• Form stronger opinions and preferences

Relationship Dynamics

Characters develop complex relationships:

• Friendship levels that change over time
• Rivalries and conflicts
• Mentor/student relationships
• Influence on conversation participation

Autonomous Scheduling

The conversation engine:

• Considers time of day for activity levels
• Balances character participation
• Manages conversation topics and flow
• Handles multiple simultaneous conversations

Deployment

Docker Deployment

# Production deployment
docker-compose -f docker-compose.prod.yml up -d

# With custom environment
docker-compose --env-file .env.prod up -d

Manual Deployment

1. Setup Python environment
2. Install dependencies
3. Configure database and Redis
4. Setup systemd service (Linux) or equivalent
5. Configure reverse proxy if needed

Cloud Deployment

The application can be deployed on:

• AWS (EC2 + RDS + ElastiCache)
• Google Cloud Platform
• Digital Ocean
• Any VPS with Docker support

Performance Tuning

LLM Optimization

• Use smaller models for faster responses
• Implement response caching
• Batch multiple requests when possible
• Consider GPU acceleration for larger models

Database Optimization

• Regular memory cleanup for old conversations
• Index optimization for frequent queries
• Connection pooling configuration
• Archive old data to reduce database size

Memory Management

• Configure character memory limits
• Automatic memory consolidation
• Periodic cleanup of low-importance memories
• Balance between context and performance

Troubleshooting

Common Issues

Bot not responding:

• Check Discord token and permissions
• Verify bot is in the correct channel
• Check LLM service availability

Characters not talking:

• Verify LLM model is loaded and responding
• Check conversation scheduler status
• Review quiet hours configuration

Database errors:

• Ensure PostgreSQL is running
• Check database credentials
• Verify database exists and migrations are applied

Memory issues:

• Monitor character memory usage
• Adjust memory limits in configuration
• Enable automatic memory cleanup

Debugging

# Enable debug logging
export LOG_LEVEL=DEBUG

# Test LLM connectivity
python -c "import asyncio; from src.llm.client import llm_client; print(asyncio.run(llm_client.health_check()))"

# Test database connectivity
python -c "import asyncio; from src.database.connection import db_manager; print(asyncio.run(db_manager.health_check()))"

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support and questions:

• Create an issue on GitHub
• Check the troubleshooting section
• Review the logs for error messages

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🎉 Enjoy your autonomous AI character ecosystem!

Watch as your characters develop personalities, form relationships, and create engaging conversations entirely on their own.