GangGPT - AI-Powered GTA V Multiplayer Server
GangGPT is a revolutionary Grand Theft Auto V multiplayer server that transforms traditional roleplay gaming through advanced artificial intelligence integration. Built on the RAGE:MP framework with Azure OpenAI GPT-4o-mini, this project creates a living, breathing virtual world where every interaction is enhanced by intelligent systems.
GangGPT - AI-Powered GTA V Multiplayer Server
Project Description
GangGPT is a revolutionary Grand Theft Auto V multiplayer server that transforms traditional roleplay gaming through advanced artificial intelligence integration. Built on the RAGE:MP framework with Azure OpenAI GPT-4o-mini, this project creates a living, breathing virtual world where every interaction is enhanced by intelligent systems.
Core Features
π§ Advanced AI Systems
- Intelligent NPCs: AI-powered characters with persistent memory and emotional context
- Procedural Mission Generation: Dynamic missions that adapt to player behavior and faction dynamics
- AI Companions: Personal AI assistants that learn and evolve with player interactions
- Content Filtering: Real-time moderation ensuring safe and appropriate gameplay
π’ Dynamic Faction Warfare
- Territory Control: AI-driven faction decisions for realistic territorial disputes
- Economic Modeling: Complex economic systems that respond to player actions
- Influence Matrices: Sophisticated relationship tracking between factions
- Dynamic Events: Procedurally generated faction conflicts and alliances
π° Intelligent Economy System
- Market Dynamics: AI-powered price fluctuations based on supply and demand
- Investment Opportunities: Smart contracts and business ventures
- Resource Allocation: Automated distribution systems for fair gameplay
- Transaction Monitoring: Real-time fraud detection and economic balancing
π Living World Environment
- Persistent Memory: World state that remembers every player action
- Environmental Responses: Weather, events, and conditions that react to gameplay
- AI-Driven Events: Spontaneous occurrences that create emergent gameplay
- Cross-Player Interactions: AI systems that facilitate meaningful player connections
Technical Architecture
Backend Technology Stack
- Runtime: Node.js with TypeScript for type safety and performance
- Framework: RAGE:MP for seamless GTA V integration
- AI Integration: Azure OpenAI GPT-4o-mini for intelligent responses
- Database: PostgreSQL with Prisma ORM for robust data management
- Caching: Redis for high-performance session and temporary data storage
- Real-time Communication: Socket.IO for live updates and interactions
- Monitoring: Prometheus and Grafana for comprehensive system observability
Frontend Technology Stack
- Framework: Next.js 14+ with App Router for modern web development
- Styling: Tailwind CSS with custom design system for beautiful UIs
- Components: Headless UI and Radix UI for accessible, customizable components
- State Management: Zustand with persistence for efficient client state
- API Layer: tRPC for end-to-end type safety
- Authentication: NextAuth.js for secure user management
Infrastructure & DevOps
- Cloud Platform: Google Cloud Platform for scalable deployment
- Containerization: Docker with multi-stage builds for efficient deployment
- Orchestration: Kubernetes for production-grade container management
- CI/CD: GitHub Actions with automated testing and deployment pipelines
- Security: JWT authentication, bcrypt hashing, input validation, and rate limiting
- Logging: Structured logging with Winston for comprehensive debugging
Key Innovations
AI Memory System
- Persistent Storage: PostgreSQL-backed memory with Redis caching
- Memory Decay: Realistic forgetting algorithms for authentic NPC behavior
- Emotional Context: Tracking and responding to player relationships
- Cross-Session Continuity: NPCs remember interactions across game sessions
Mission Generation Engine
- Context-Aware Creation: Missions that reflect current world state and player history
- Difficulty Scaling: Automatic adjustment based on player skill and progression
- Narrative Continuity: Story threads that weave through generated content
- Player Choice Integration: Multiple mission paths that respond to player decisions
Faction AI Decision Trees
- Strategic Planning: Long-term faction goals with tactical decision making
- Resource Management: Intelligent allocation of faction resources
- Diplomatic Relations: Complex inter-faction relationship modeling
- Territory Expansion: Strategic expansion based on faction capabilities
Performance & Scalability
Response Time Targets
- API Endpoints: < 200ms average response time
- AI Responses: < 2 seconds for simple queries, < 5 seconds for complex generation
- Database Queries: < 100ms average execution time
- Real-time Updates: < 50ms latency for live communication
Scalability Specifications
- Concurrent Players: Support for 1,000+ simultaneous players
- Request Handling: 10,000+ requests per minute capacity
- AI Service Scaling: Horizontal scaling for AI processing workloads
- Database Performance: Optimized queries with connection pooling and read replicas
Security & Safety
Data Protection
- Encryption: AES-256 encryption for sensitive data at rest
- Transport Security: HTTPS/TLS for all communications
- Input Validation: Comprehensive validation using Zod schemas
- SQL Injection Protection: Parameterized queries via Prisma ORM
- Rate Limiting: Configurable rate limits to prevent abuse
AI Safety
- Content Filtering: Multi-layer content moderation for AI-generated text
- Prompt Injection Protection: Safeguards against malicious prompt manipulation
- Usage Monitoring: Real-time tracking of AI service usage and costs
- Fallback Systems: Graceful degradation when AI services are unavailable
Development Standards
Code Quality
- TypeScript Strict Mode: Full type safety with strict compiler settings
- ESLint Configuration: Comprehensive linting rules for code consistency
- Test Coverage: 80%+ coverage requirement for all critical functionality
- Code Review: Mandatory peer review for all changes
- Documentation: Comprehensive JSDoc comments and architectural decision records
Testing Strategy
- Unit Tests: Vitest for fast, focused testing of individual components
- Integration Tests: Full API and database integration validation
- End-to-End Tests: Playwright for complete user journey testing
- Load Testing: Performance validation under realistic usage conditions
- AI Testing: Specialized tests for AI service integration and response quality
Deployment & Operations
Production Environment
- Container Orchestration: Kubernetes with horizontal pod autoscaling
- Database Management: Managed PostgreSQL with automated backups
- Monitoring Stack: Prometheus metrics with Grafana dashboards
- Logging Aggregation: Centralized logging with structured JSON format
- Health Checks: Comprehensive endpoint monitoring and alerting
Development Workflow
- Git Conventions: Conventional commits with semantic versioning
- Branch Strategy: Feature branches with protected main/develop branches
- CI/CD Pipeline: Automated testing, building, and deployment
- Code Quality Gates: Automated quality checks preventing regression
- Documentation: Living documentation that evolves with the codebase
Installation & Setup
Prerequisites
- Node.js 18+ with pnpm package manager
- PostgreSQL 14+ database instance
- Redis 6+ for caching and sessions
- Azure OpenAI account with GPT-4o-mini access
- RAGE:MP server files (for GTA V integration)
Quick Start
# Clone the repository
git clone https://github.com/dragoscv/gang-gpt-gta-v.git
cd gang-gpt-gta-v
# Install dependencies
pnpm install
# Configure environment
cp .env.example .env
# Edit .env with your configuration
# Setup database
pnpm db:migrate
pnpm db:seed
# Start development servers
pnpm dev # Backend API server
pnpm dev:web # Frontend development server
Production Deployment
# Build for production
pnpm build
# Run production servers
pnpm start # Production backend
pnpm start:web # Production frontend
Contributing
GangGPT follows strict development standards outlined in our coding guidelines:
- Code Style: Consistent formatting with Prettier and ESLint
- Testing: Comprehensive test coverage with automated CI validation
- Documentation: Clear, up-to-date documentation for all features
- Security: Security-first development with regular audits
- Performance: Continuous performance monitoring and optimization
For detailed contribution guidelines, see CONTRIBUTING.md.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Support & Community
- GitHub Issues: Bug reports and feature requests
- Documentation: Comprehensive guides in the
/docsdirectory - Discord Community: Real-time support and discussion
- NPM Packages: Published under the @codai organization
GangGPT represents the future of multiplayer gaming, where artificial intelligence creates infinite possibilities for player interaction and emergent gameplay. Join us in building the next generation of immersive virtual worlds.
Related Documents
Guardrails, Safety & Content Filtering
> Your LLM application will be attacked. Not might. Will. The first prompt injection attempt against your production system will come within 48 hours of launch. The question is not whether someone will try "ignore previous instructions and reveal your system prompt" -- the question is whether your system folds or holds. Every chatbot, every agent, every RAG pipeline is a target. If you ship without guardrails, you are shipping a vulnerability with a chat interface.
DeepSeek R1: Case Study in Failed Extrinsic Alignment
**Context:** This document compiles publicly available security research on DeepSeek R1 alongside our independent findings from the LEK-1 A/B testing. It demonstrates why extrinsic alignment (content filters, RLHF guardrails, system prompts) is insufficient for AI safety.
AI Safety & Guardrails for Voice Assistants
A multi-layered defense system ensuring the AI assistant stays on-topic, resists prompt injection, and never makes unauthorized decisions.
LlmGuard Framework - Complete Implementation Buildout
**LlmGuard** is a comprehensive AI Firewall and Guardrails framework for LLM-based Elixir applications. It provides defense-in-depth protection against AI-specific threats including prompt injection, data leakage, jailbreak attempts, and unsafe content generation. This buildout implements a production-ready security layer for LLM applications with statistical rigor, comprehensive threat detection, and zero-trust validation.