Agent RED
An autonomous multi-agent AI system that plays Pokemon Red using Claude AI models and a Game Boy emulator.
View on GitHub- Role
- AI Architect
- Year
- 2024
- Type
- Independent
- Category
- AI/ML
- Stack
- Claude AI · Claude Code · Python · FastAPI · React · TypeScript · PyBoy · Multi-Agent Systems
Tools across agents
Specialized agents
Maps navigated
01
Overview
Agent RED is an autonomous AI system that plays Pokemon Red by combining Claude AI models with a PyBoy Game Boy emulator. The system features a hierarchical multi-agent architecture where specialized agents handle different gameplay domains (navigation, battles, and menus) coordinated by an orchestrator that routes tasks to the appropriate agent.
02
The Challenge
Playing Pokemon Red autonomously requires solving multiple complex problems: understanding game state from raw memory, navigating a world with 223 interconnected maps, making strategic battle decisions against 391 different trainer teams, and managing inventory and party composition, all while maintaining context across hours of gameplay.
03
The Solution
Multi-agent architecture
I designed a hierarchical multi-agent system with four specialized agents: an Orchestrator (Sonnet) for task routing, Navigation (Haiku) with A* pathfinding, Battle (Sonnet/Opus) with automatic escalation for boss fights, and Menu (Haiku) for inventory management.
Game knowledge base
The system extracts comprehensive game state from emulator memory and leverages a knowledge base parsed from the Pokemon Red disassembly, including all 223 maps, 151 Pokemon stats, and type effectiveness matrices.
Real-time dashboard
A real-time React dashboard provides monitoring and control via WebSocket streaming.
04
Results & Impact
- 38 tools distributed across 4 specialized AI agents
- 223 maps with collision data and trainer positions parsed
- 151 Pokemon with stats, types, and learnsets integrated
- Real-time monitoring dashboard with WebSocket streaming