DegentCivil
A new AI Game Paradigm in Autonomous world. it includes configurations for agents, functional buildings, and equipment, as well as the logic for agents daily behavior and interactions with buildings, equipments...etc.
Install / Use
/learn @KingJiongEN/DegentCivilREADME
🏘️ Degent Civil
<div align="center">A sophisticated AI-driven town simulation system with autonomous characters, dynamic interactions, and realistic behaviors.
📚 Documentation | 🚀 Quick Start | 💡 Examples | 🤝 Contributing
</div>✨ Features
- 🤖 AI-Driven Characters: Autonomous NPCs with realistic behaviors and decision-making capabilities
- 🔄 Dynamic State Management: Sophisticated state system for character behaviors and interactions
- ⚡ Real-time Simulation: Live updates and interactions within the town environment
- 🧠 Memory System: Advanced memory management using vector embeddings and semantic search
- 🏢 Building System: Flexible building management with dynamic interactions
- 🔗 LLM Integration: Seamless integration with Large Language Models for natural interactions
📋 Prerequisites
- Python 3.8 or higher
- Docker and Docker Compose
- OpenAI API access (or compatible LLM service)
🚀 Quick Start
- Clone the Repository
git clone https://github.com/KingJiongEN/DegentCivil.git
cd DegentCivil
- Set Up Environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
- Start Required Services
# Start Milvus
mkdir -p milvus/{db,minio}
docker-compose -f docker-compose_milvus.yml up -d
# Start Redis
docker run --name my-redis -p 6379:6379 -d redis
- Run the Service
DEBUG=1 Milvus=1 python main.py
📚 Documentation
Visit our comprehensive documentation for detailed information:
🛠️ Development Tools
Milvus Visualization
- URL:
localhost:18000 - Username:
minioadmin - Password:
minioadmin
Memory Demo
export PYTHONPATH="{project_path}:$PYTHONPATH"
export OPENAI_API_KEY=your_api_key_here
python -m app.models.memory
📁 Project Structure
DegentCivil/
├── app/ # Main application directory
│ ├── main.py # Application entry point
│ ├── models/ # Data models
│ ├── services/ # Business logic
│ ├── llm/ # LLM integration
│ └── utils/ # Utility functions
├── config/ # Configuration files
├── docs/ # Documentation
└── tests/ # Test suite
🤝 Contributing
We welcome contributions! Please see our Contributing Guide for details.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🌟 Acknowledgments
- OpenAI for LLM capabilities
- Milvus for vector database
- Redis for caching
- All our contributors
<div align="center"> Made with ❤️ by the Town Simulation Team </div>
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