Multi-Agent AI Assistant for Retro Game Collectors

Building a Multi-Agent AI Assistant for Retro Game Collectors

If you're into collecting old games or emulating classics from your childhood, you know how much information is out there—and how scattered it can be. I'm working on a project that brings all of that together using a multi-agent AI system specifically tailored for retro game enthusiasts.

I've been studying multi-agent AI systems over the past several months as I can see a lot of utility even if the larger AI bubble "bursts". This bot is a way of getting some experience with building something from the ground up that will allow me to leverage this architecture for future work projects.

🎮 What It Does

The assistant helps users with common tasks like:

Looking up the history and background of a game
Estimating market value for cartridges, discs, and complete-in-box (CIB) sets
Finding out how to emulate games on modern systems
Offering restoration tips for old consoles and cartridges
Recommending similar games based on your favorites
Fetching known high scores and speedrun records

Whether you're a seasoned collector, a casual gamer, or someone just getting into ROM hacks and EverDrives, this tool is built to assist.

🧠 Why Multi-Agent AI?

Multi-agent systems are exactly what they sound like: multiple specialized AI agents, each with a focused area of expertise. Instead of using one big general-purpose model like ChatGPT, Claude or Gemini this approach lets me create purpose-built agents like:

A GameHistorianAgent for deep background
A MarketPriceAgent for price estimates
An EmulationAgent to guide you through setting up games
A RestorationAgent for hardware help
And even a RecommendationAgent to suggest what to play next

These agents are orchestrated by a central AI that interprets user queries, figures out which agents are needed, and combines their responses into a final answer. This pattern allows for better scalability, clearer architecture, and more control over the knowledge domains.

💬 Conversational and Context-Aware

The assistant isn't just a search engine. It can:

Ask follow-up questions when a query is unclear (e.g., "Which version of Aladdin—SNES or Genesis?")
Handle small talk or off-topic questions gracefully
Adjust recommendations based on the user's stated interests

⚙️ Under the Hood

Built using Pydantic AI for structured task planning and validation
Uses a combination of OpenAI, Mistral, and Claude depending on the agent role
Future support for RAG (Retrieval-Augmented Generation) to index community wikis, manuals, and pricing data
Architecture is modular, so agents can be updated or swapped as needed

📚 Why It Matters

This project isn't just about retro games—it's about learning how to build AI systems that collaborate, specialize, and reason together. Multi-agent systems mirror how real teams work: you don't ask one person to do everything—you bring in the right experts for the job.

That's the magic of agentic AI: it's modular, extensible, and far more capable than a single generalist model acting alone.

If you're a fellow retro nerd, AI builder, or just curious about what this looks like in action—stay tuned! More updates soon! I hope to have an initial build by May 2025.