Roblox gives its AI assistant the ability to plan, build, and test games on its own

Learn to build a basic AI assistant with planning and procedural generation capabilities similar to Roblox's new agentic AI tools.

Introduction

In this tutorial, you'll learn how to build a basic AI assistant with planning and procedural generation capabilities similar to what Roblox is introducing. This hands-on guide will walk you through creating a simple AI agent that can analyze code, plan actions, and generate 3D models using Python and basic AI libraries. By the end, you'll understand how agentic AI tools work and how they can be applied to game development tasks.

Prerequisites

To follow this tutorial, you'll need:

A computer with Python 3.8 or higher installed
Basic understanding of Python programming
Internet connection for downloading packages
Text editor (VS Code, PyCharm, or any editor)

Step-by-step instructions

1. Setting Up Your Development Environment

1.1 Install Required Packages

First, we need to install the necessary Python packages for our AI assistant. Open your terminal or command prompt and run:

pip install openai numpy pillow

This installs the OpenAI API client, NumPy for mathematical operations, and Pillow for image handling.

1.2 Create Project Directory

Create a new folder for your project and navigate to it:

mkdir roblox_ai_assistant
 cd roblox_ai_assistant

2. Creating the Basic AI Assistant

2.1 Initialize the AI Client

Create a file called ai_assistant.py and start by importing the necessary modules:

import openai
import json
import os

class RobloxAIAssistant:
    def __init__(self, api_key):
        openai.api_key = api_key
        self.conversation_history = []

    def analyze_code(self, code):
        prompt = f"Analyze this game code and suggest improvements:\n{code}"
        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.choices[0].message.content

This creates a basic AI assistant class that can analyze code using OpenAI's GPT-4 model. The assistant stores conversation history for context.

2.2 Add Planning Functionality

Next, add a method to generate action plans based on code analysis:

    def generate_plan(self, code_analysis):
        prompt = f"Based on the following code analysis, create a step-by-step plan for improving the game:\n{code_analysis}"
        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )
        return response.choices[0].message.content

This function takes the code analysis and creates a structured plan for improvement, mimicking Roblox's planning mode.

3. Implementing Procedural 3D Model Generation

3.1 Create Simple 3D Model Generator

For procedural generation, we'll create a simple cube generator using NumPy:

import numpy as np

    def generate_cube(self, size=1):
        # Create a simple cube mesh
        vertices = np.array([
            [0, 0, 0], [size, 0, 0], [size, size, 0], [0, size, 0],
            [0, 0, size], [size, 0, size], [size, size, size], [0, size, size]
        ])
        
        faces = np.array([
            [0, 1, 2, 3], [4, 5, 6, 7], [0, 1, 5, 4],
            [2, 3, 7, 6], [0, 3, 7, 4], [1, 2, 6, 5]
        ])
        
        return {"vertices": vertices.tolist(), "faces": faces.tolist()}

    def generate_model(self, model_type, **kwargs):
        if model_type == "cube":
            return self.generate_cube(kwargs.get("size", 1))
        else:
            return {"error": "Model type not supported"}

This generates a basic cube model that could be expanded for more complex shapes. The model includes vertices and faces for 3D rendering.

3.2 Add Self-Correction Loop

Implement a testing and refinement loop:

    def test_and_refine(self, model, test_criteria):
        # Simple test - check if model has required components
        if "vertices" not in model or "faces" not in model:
            return {"status": "error", "message": "Incomplete model"}
        
        # Simulate refinement based on criteria
        refinement = f"Refined model based on criteria: {test_criteria}"
        return {"status": "success", "refinement": refinement, "model": model}

This function tests the generated model against specified criteria and refines it if needed, similar to Roblox's self-correcting loops.

4. Building the Main Application

4.1 Create Main Execution Script

Create a file called main.py to put everything together:

from ai_assistant import RobloxAIAssistant
import os

# Initialize assistant
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
    raise ValueError("Please set your OPENAI_API_KEY environment variable")

assistant = RobloxAIAssistant(api_key)

# Example code to analyze
sample_code = """
function movePlayer()
    local player = game.Players.LocalPlayer
    local character = player.Character or player.CharacterAdded:Wait()
    local humanoid = character:FindFirstChild("Humanoid")
    
    if humanoid then
        humanoid:Move(Vector3.new(0, 0, 10))
    end
end
"""

# Analyze code
print("Analyzing code...")
code_analysis = assistant.analyze_code(sample_code)
print(code_analysis)

# Generate plan
print("\nGenerating action plan...")
plan = assistant.generate_plan(code_analysis)
print(plan)

# Generate 3D model
print("\nGenerating 3D model...")
cube = assistant.generate_model("cube", size=2)
print("Generated cube model:", json.dumps(cube, indent=2))

# Test and refine
print("\nTesting and refining model...")
test_result = assistant.test_and_refine(cube, "cube must be solid")
print(json.dumps(test_result, indent=2))

This script demonstrates how all components work together - analyzing code, generating plans, creating models, and testing/refining them.

4.2 Set Up Environment Variables

Before running the application, set your OpenAI API key:

export OPENAI_API_KEY='your_api_key_here'

Replace 'your_api_key_here' with your actual OpenAI API key from https://platform.openai.com/

5. Running Your AI Assistant

5.1 Execute the Application

Run your assistant with:

python main.py

You should see output showing code analysis, action plans, generated 3D models, and testing results.

5.2 Understanding the Output

The assistant demonstrates:

Code analysis capabilities
Plan generation for improvements
Procedural 3D model generation
Self-correction and refinement loops

Summary

In this tutorial, you've built a simplified AI assistant that mimics the capabilities described in Roblox's recent AI update. You learned how to analyze game code, generate action plans, create 3D models procedurally, and implement self-correcting loops. While this is a basic implementation, it demonstrates the core concepts of agentic AI tools that can plan, build, and test games autonomously. This foundation can be expanded with more sophisticated AI models, 3D rendering libraries, and integration with game development platforms.