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Build an AI Game Design Agent Team
Fully functional AI agent app with step-by-step instructions (100% opensource)
Game development demands handling a daunting array of specialized skills - a compelling narrative and storylines, intricate mechanics, visual aesthetics, technical architecture, and more. It’s a struggle synchronizing these - scope creep, misaligned creative visions, and technical bottlenecks. Early-stage indie developers particularly face an uphill battle, needing to wear multiple hats while lacking the deep expertise in each domain that AAA studios can access.
In this tutorial, we'll build an AI Game Design Agent Team that coordinates multiple specialized AI agents - each focusing on their domain expertise - to generate cohesive game concepts where narrative, gameplay, visuals, and technical specifications work in harmony. The entire process is automated so developers can quickly iterate on ideas and ensure all crucial aspects of game design are considered.
We’re using Microsoft’s Autogen, an open-source framework specifically designed to build AI agents that can collaborate. It provides robust tools for agent communication, workflow management, and task coordination - perfect for complex creative tasks.
What We’re Building
This Streamlit application implements a collaborative game design system where multiple AI agents work together to generate comprehensive game concepts.
Meet our AI agents team:
🎭 Story Agent: Specializes in narrative design and world-building, including character development, plot arcs, dialogue writing, and lore creation
🎮 Gameplay Agent: Focuses on game mechanics and systems design, including player progression, combat systems, resource management, and balancing
🎨 Visuals Agent: Handles art direction and audio design, covering UI/UX, character/environment art style, sound effects, and music composition
⚙️ Tech Agent: Provides technical architecture and implementation guidance, including engine selection, optimization strategies, networking requirements, and development roadmap
🎯 Task Agent: Coordinates between all specialized agents and ensures cohesive integration of different game aspects
Features:
Comprehensive Game Design Outputs:
Detailed narrative and world-building elements
Core gameplay mechanics and systems
Visual and audio direction
Technical specifications and requirements
Development timeline and budget considerations
Customizable Input Parameters:
Game type and target audience
Art style and visual preferences
Platform requirements
Development constraints (time, budget)
Core mechanics and gameplay features
Interactive Results: Results are presented in expandable sections for easy navigation and reference
Prerequisites
Before we begin, make sure you have the following:
Python installed on your machine (version 3.10 or higher is recommended)
Your OpenAI API key
A code editor of your choice (we recommend VS Code or PyCharm for their excellent Python support)
Basic familiarity with Python programming
Step-by-Step Instructions
Setting Up the Environment
First, let's get our development environment ready:
Clone the GitHub repository:
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
Go to the ai_game_design_agent_team folder:
cd ai_agent_tutorials/ai_game_design_agent_team
Install the required dependencies:
pip install -r requirements.txt
Get your API Key: You'll need to obtain OpenAI API key - Create a new account > Go to Dashboard > Navigate to API Keys > Generate a new key
Creating the Streamlit App
Let’s create our app. Create a new file game_design_agent_team.py
and add the following code:
Let's set up our imports and initialize session state:
import streamlit as st
import autogen
from autogen.agentchat import GroupChat, GroupChatManager
if 'output' not in st.session_state:
st.session_state.output = {
'story': '', 'gameplay': '',
'visuals': '', 'tech': ''
}
Create the user interface for game details:
st.title("AI Game Design Agent Team")
col1, col2 = st.columns(2)
with col1:
background_vibe = st.text_input("Background Vibe")
game_type = st.selectbox("Game Type", ["RPG", "Action", "Adventure"])
target_audience = st.selectbox("Target Audience", ["Kids", "Teens", "Adults"])
with col2:
game_goal = st.text_input("Game Goal")
art_style = st.selectbox("Art Style", ["Realistic", "Cartoon", "Pixel Art"])
platform = st.multiselect("Target Platforms", ["PC", "Mobile", "Console"])
Add detailed preferences:
st.subheader("Detailed Preferences")
core_mechanics = st.multiselect(
"Core Gameplay Mechanics",
["Combat", "Exploration", "Puzzle Solving"]
)
mood = st.multiselect(
"Game Mood/Atmosphere",
["Epic", "Mysterious", "Peaceful"]
)
inspiration = st.text_area("Games for Inspiration")
Configure LLM settings:
llm_config = {
"timeout": 600,
"cache_seed": 44,
"config_list": [{
"model": "gpt-4o-mini",
"api_key": api_key,
}],
"temperature": 0,
}
Create the Task Agent:
task_agent = autogen.AssistantAgent(
name="task_agent",
llm_config=llm_config,
system_message="You are a task provider. Your only job is to provide the task details to the other agents.",
)
Create the Story Agent:
story_agent = autogen.AssistantAgent(
name="story_agent",
llm_config=llm_config,
system_message="""
You are a game story designer specializing in:
1. Creating compelling narratives
2. Designing memorable characters
3. Developing game worlds
4. Planning story progression
"""
)
Create the Gameplay Agent:
gameplay_agent = autogen.AssistantAgent(
name="gameplay_agent",
llm_config=llm_config,
system_message="""
You are a game mechanics designer focusing on:
1. Core gameplay loops
2. Progression systems
3. Player interactions
4. Game balance
"""
)
Create the Visuals Agent:
visuals_agent = autogen.AssistantAgent(
name="visuals_agent",
llm_config=llm_config,
system_message="""
You are an art director responsible for:
1. Visual style guides
2. Character aesthetics
3. Environmental design
4. Audio direction
"""
)
Create the Tech Agent:
tech_agent = autogen.AssistantAgent(
name="tech_agent",
llm_config=llm_config,
system_message="""
You are a technical director handling:
1. Game engine selection
2. Technical requirements
3. Development pipeline
4. Performance optimization
"""
)
Implement sequential agent execution:
def run_agents_sequentially(task):
task_agent.initiate_chat(story_agent, task)
story_response = story_agent.last_message()["content"]
task_agent.initiate_chat(gameplay_agent, task)
gameplay_response = gameplay_agent.last_message()["content"]
# Similar for visuals and tech agents
return {
"story": story_response,
"gameplay": gameplay_response,
"visuals": visuals_response,
"tech": tech_response,
}
Set up group chat collaboration:
groupchat = GroupChat(
agents=[task_agent, story_agent, gameplay_agent,
visuals_agent, tech_agent],
speaker_selection_method="round_robin",
allow_repeat_speaker=False,
max_round=5
)
manager = GroupChatManager(
groupchat=groupchat,
llm_config=llm_config
)
Create result display:
with st.expander("Story Design"):
st.markdown(st.session_state.output['story'])
with st.expander("Gameplay Mechanics"):
st.markdown(st.session_state.output['gameplay'])
Running the App
With our code in place, it's time to launch the app.
In your terminal, navigate to the project folder, and run the following command
streamlit run game_design_agent_team.py
Streamlit will provide a local URL (typically http://localhost:8501).
Working Application Demo
Conclusion
You've now built a sophisticated AI Game Design Agent Team that can generate comprehensive game concepts through multi-agent collaboration. Want to make it even better? Here are some fun ideas:
Add image generation capabilities for concept art
Implement save/load functionality for game concepts
Add version control for iterative concept development
Expand the system to generate design documents
Integrate with project management tools
Keep experimenting and have fun watching these AI agents work together as a team to develop your game plan!
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