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A multi-agent system that helps plan game discussion club meetings by researching game data. Built with CrewAI for agent orchestration.

What it does

This crew of AI agents works together to plan game club meetings. The system:
  • Researches games using a Metacritic dataset via Snow Leopard
  • Analyzes game scores, platforms, and release dates
  • Generates a formatted report with meeting recommendations
The crew consists of two agents that collaborate sequentially:
  1. Researcher - Queries the game database to find relevant titles
  2. Reporting Analyst - Compiles findings into a formatted report

Architecture

The application uses CrewAI’s multi-agent framework:

Agents

  • Researcher agent - Has access to the Snow Leopard tool to query game metadata (titles, platforms, metascores, user scores, release dates)
  • Reporting analyst agent - Takes research findings and creates structured reports

Snow Leopard tool

A custom CrewAI tool that wraps the Snow Leopard client:

Workflow

The crew runs sequentially:
  1. Research task queries game data based on the meeting topic
  2. Reporting task compiles results into report.md

Run the example

Prerequisites

Setup

Clone the repository:
Download and prepare the dataset:
  1. Download the Metacritic dataset from Kaggle (get the .csv version)
  2. Convert it to a PostgreSQL script:
  1. Load metacritic.sql into your PostgreSQL database:
In Snow Leopard Cloud, add the database as a data source to your instance. Then create an API key on the Keys tab and copy the instance ID from the Connection Info tab. Create a .env file:
Run the crew:
The crew will research games and output a report to report.md.

Next steps