UnrealGenAISupport

UnrealGenAISupport

An Unreal Engine plugin for LLM/GenAI models & MCP UE5 server. Supports Claude Desktop App, Windsurf & Cursor, also includes OpenAI's GPT 5, Deepseek V3.1, Claude Sonnet 4 APIs and Grok 4, with plans to add Gemini, audio & realtime APIs soon. UnrealMCP is also here!! Automatic blueprint and scene generation from AI!!

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The Unreal Engine Generative AI Support Plugin is a tool designed to integrate various cutting-edge LLM/GenAI models into Unreal Engine for game development. It aims to simplify the process of using AI models for game development tasks, such as controlling scene objects, generating blueprints, running Python scripts, and more. The plugin currently supports models from organizations like OpenAI, Anthropic, XAI, Google Gemini, Meta AI, Deepseek, and Baidu. It provides features like API support, model control, generative AI capabilities, UI generation, project file management, and more. The plugin is still under development but offers a promising solution for integrating AI models into game development workflows.

README:

Unreal Engine Generative AI Support Plugin

Discord License: MIT

Usage Examples:

MCP Example:

Claude spawning scene objects and controlling their transformations and materials, generating blueprints, functions, variables, adding components, running python scripts etc.

API Example:

A project called become human, where NPCs are OpenAI agentic instances. Built using this plugin. Become Human

Every month, hundreds of new AI models are released by various organizations, making it hard to keep up with the latest advancements.

The Unreal Engine Generative AI Support Plugin allows you to focus on game development without worrying about the LLM/GenAI integration layer.

Currently integrating Model Control Protocol (MCP) with Unreal Engine 5.5.

This project aims to build a long-term support (LTS) plugin for various cutting-edge LLM/GenAI models and foster a community around it. It currently includes OpenAI's GPT-4o, Deepseek R1, Claude Sonnet 4, Claude Opus 4, and GPT-4o-mini for Unreal Engine 5.1 or higher, with plans to add , real-time APIs, Gemini, MCP, and Grok 3 APIs soon. The plugin will focus exclusively on APIs useful for game development, evals and interactive experiences. All suggestions and contributions are welcome. The plugin can also be used for setting up new evals and ways to compare models in game battlefields.

Current Progress:

[!WARNING]
This plugin is still under rapid development.

  1. Do not use it in production environments. ⚠️
  2. Do not use it without version control. ⚠️

I will continue to keep this repo updated with the latest features and models as they become available. Contributions are welcome. If you want a production ready plugin, with more features and guaranteed stability please checkout the Gen AI Pro plugin, as it costs a lot of API credits for me to test different models per feature and per engine version to make sure everything works well and compatible. Otherwise, I feel this plugin is good enough for many use cases (including the examples shown in the beginning) and you can use it for free, forever.

Currently working on fixing the issues with MCP (especially the node generation) and adding more features to it.

LLM/GenAI API Support:

  • OpenAI API Support:
  • Anthropic Claude API Support:
    • Claude Chat API ✅
      • claude-4-latest, claude-3-7-sonnet-latest, claude-3-5-sonnet, claude-3-5-haiku-latest, claude-3-opus-latest Model ✅
    • Multimodal/Vision API Support (available in pro) ☑️
  • XAI (Grok 3) API Support:
    • XAI Chat Completions API ✅
      • grok-3-latest, grok-3-mini-beta Model ✅
      • grok-3, grok-3-mini, grok-3-fast, grok-3-mini-fast, grok-2-vision-1212, grok-2-1212.
      • grok-4 Reasoning API (available in pro) ☑️
    • XAI Image API 🚧
    • Text Streaming API (available in pro) ☑️
    • Multimodal API Support (available in pro) ☑️
  • Google Gemini API Support:
  • Meta AI API Support:
    • Llama 4 herd: ❌
      • Llama 4 Behemoth, Llama 4 Maverick, Llama 4 Scout ❌
      • llama3.3-70b, llama3.1-8b Model❌
    • Local Llama API 🚧🤝
  • Deepseek API Support:
    • Deepseek Chat API ✅
      • deepseek-chat (DeepSeek-V3.1) Model ✅
    • Deepseek Reasoning API, R1 ✅
      • deepseek-reasoning-r1 Model ✅
      • deepseek-reasoning-r1 CoT Streaming ❌
    • Independently Hosted Deepseek Models ❌
  • Baidu API Support:
    • Baidu Chat API ❌
      • baidu-chat Model ❌
  • 3D generative model APIs:
    • TripoSR by StabilityAI 🚧
  • Plugin Documentation 🛠️🤝
  • Plugin Example Project 🛠️ here
  • Version Control Support
    • Perforce Support 🛠️
    • Git Submodule Support ✅
  • LTS Branching 🚧
    • Stable Branch with Bug Fixes 🚧
    • Dedicated Contributor for LTS 🚧
  • Lightweight Plugin (In Builds)
    • No External Dependencies ✅
    • Build Flags to enable/disable APIs 🚧
    • Submodules per API Organization 🚧
    • Exclude MCP from build 🚧
  • Testing

Unreal MCP (Model Control Protocol):

  • Clients Support ✅
    • Claude Desktop App Support ✅
    • Cursor IDE Support ✅
    • OpenAI Operator API Support 🚧
  • Blueprints Auto Generation 🛠️
    • Creating new blueprint of types ✅
    • Adding new functions, function/blueprint variables ✅
    • Adding nodes and connections 🛠️ (buggy, issues open)
    • Advanced Blueprints Generation 🛠️
  • Level/Scene Control for LLMs 🛠️
    • Spawning Objects and Shapes ✅
    • Moving, rotating and scaling objects ✅
    • Changing materials and color ✅
    • Advanced scene features 🛠️
  • Generative AI:
    • Prompt to 3D model fetch and spawn 🛠️
  • Control:
    • Ability to run Python scripts ✅
    • Ability to run Console Commands ✅
  • UI:
    • Widgets generation 🛠️
    • UI Blueprint generation 🛠️
  • Project Files:
    • Create/Edit project files/folders ️✅
    • Delete existing project files ❌
  • Others:
    • Project Cleanup 🛠️

Where,

  • ✅ - Completed
  • ☑️ - (available in pro)
  • 🛠️ - In Progress
  • 🚧 - Planned
  • 🤝 - Need Contributors
  • ❌ - Won't Support For Now

Table of Contents

Setting API Keys:

[!NOTE]
There is no need to set the API key for testing the MCP features in Claude app. Anthropic key only needed for Claude API.

For Editor:

Set the environment variable PS_<ORGNAME> to your API key.

For Windows:

setx PS_<ORGNAME> "your api key"

For Linux/MacOS:

  1. Run the following command in your terminal, replacing yourkey with your API key.

    echo "export PS_<ORGNAME>='yourkey'" >> ~/.zshrc

  2. Update the shell with the new variable:

    source ~/.zshrc

PS: Don't forget to restart the Editor and ALSO the connected IDE after setting the environment variable.

Where <ORGNAME> can be: PS_OPENAIAPIKEY, PS_DEEPSEEKAPIKEY, PS_ANTHROPICAPIKEY, PS_METAAPIKEY, PS_GOOGLEAPIKEY etc.

For Packaged Builds:

Storing API keys in packaged builds is a security risk. This is what the OpenAI API documentation says about it:

"Exposing your OpenAI API key in client-side environments like browsers or mobile apps allows malicious users to take that key and make requests on your behalf – which may lead to unexpected charges or compromise of certain account data. Requests should always be routed through your own backend server where you can keep your API key secure."

Read more about it here.

For test builds you can call the GenSecureKey::SetGenAIApiKeyRuntime either in c++ or blueprints function with your API key in the packaged build.

Setting up MCP:

[!NOTE]
If your project only uses the LLM APIs and not the MCP, you can skip this section.

[!CAUTION]
Discalimer: If you are using the MCP feature of the plugin, it will directly let the Claude Desktop App control your Unreal Engine project. Make sure you are aware of the security risks and only use it in a controlled environment.

Please backup your project before using the MCP feature and use version control to track changes.

1. Install any one of the below clients:
  • Claude Desktop App from here.
  • Cursor IDE from here.
2. Setup the mcp config json:
For Claude Desktop App:

claude_desktop_config.json file in Claude Desktop App's installation directory. (might ask claude where its located for your platform!) The file will look something like this:

{
    "mcpServers": {
      "unreal-handshake": {
        "command": "python",
        "args": ["<your_project_directoy_path>/Plugins/GenerativeAISupport/Content/Python/mcp_server.py"],
        "env": {
          "UNREAL_HOST": "localhost",
          "UNREAL_PORT": "9877" 
        }
      }
    }
}
For Cursor IDE:

.cursor/mcp.json file in your project directory. The file will look something like this:

{
    "mcpServers": {
      "unreal-handshake": {
        "command": "python",
        "args": ["<your_project_directoy_path>/Plugins/GenerativeAISupport/Content/Python/mcp_server.py"],
        "env": {
          "UNREAL_HOST": "localhost",
          "UNREAL_PORT": "9877" 
        }
      }
    }
}
3. Install MCP[CLI] from with either pip or cv.
pip install mcp[cli]
4. Enable python plugin in Unreal Engine. (Edit -> Plugins -> Python Editor Script Plugin)
5. [OPTIONAL] Enable AutoStart MCP server on editor open

Adding the plugin to your project:

With Git:

  1. Add the Plugin Repository as a Submodule in your project's repository.

    git submodule add https://github.com/prajwalshettydev/UnrealGenAISupport Plugins/GenerativeAISupport

  2. Regenerate Project Files: Right-click your .uproject file and select Generate Visual Studio project files.

  3. Enable the Plugin in Unreal Editor: Open your project in Unreal Editor. Go to Edit > Plugins. Search for the Plugin in the list and enable it.

  4. For Unreal C++ Projects, include the Plugin's module in your project's Build.cs file:

    PrivateDependencyModuleNames.AddRange(new string[] { "GenerativeAISupport" });

With Perforce:

Still in development..

With Unreal Marketplace:

Coming soon, for free, in the Unreal Engine Marketplace.

Fetching the Latest Plugin Changes:

With Git:

you can pull the latest changes with:

cd Plugins/GenerativeAISupport
git pull origin main

Or update all submodules in the project:

git submodule update --recursive --remote

With Perforce:

Still in development..

Usage:

There is a example Unreal project that already implements the plugin. You can find it here.

OpenAI:

Currently the plugin supports Chat and Structured Outputs from OpenAI API. Both for C++ and Blueprints. Tested models are gpt-4o, gpt-4o-mini, gpt-4.5, o1-mini, o1, o3-mini-high.

1. Chat:

C++ Example:
    void SomeDebugSubsystem::CallGPT(const FString& Prompt, 
        const TFunction<void(const FString&, const FString&, bool)>& Callback)
    {
        FGenChatSettings ChatSettings;
        ChatSettings.Model = TEXT("gpt-4o-mini");
        ChatSettings.MaxTokens = 500;
        ChatSettings.Messages.Add(FGenChatMessage{ TEXT("system"), Prompt });
    
        FOnChatCompletionResponse OnComplete = FOnChatCompletionResponse::CreateLambda(
            [Callback](const FString& Response, const FString& ErrorMessage, bool bSuccess)
        {
            Callback(Response, ErrorMessage, bSuccess);
        });
    
        UGenOAIChat::SendChatRequest(ChatSettings, OnComplete);
    }
Blueprint Example:

2. Structured Outputs:

C++ Example 1:

Sending a custom schema json directly to function call

FString MySchemaJson = R"({
"type": "object",
"properties": {
    "count": {
        "type": "integer",
        "description": "The total number of users."
    },
    "users": {
        "type": "array",
        "items": {
            "type": "object",
            "properties": {
                "name": { "type": "string", "description": "The user's name." },
                "heading_to": { "type": "string", "description": "The user's destination." }
            },
            "required": ["name", "role", "age", "heading_to"]
        }
    }
},
"required": ["count", "users"]
})";

UGenAISchemaService::RequestStructuredOutput(
    TEXT("Generate a list of users and their details"),
    MySchemaJson,
    [](const FString& Response, const FString& Error, bool Success) {
       if (Success)
       {
           UE_LOG(LogTemp, Log, TEXT("Structured Output: %s"), *Response);
       }
       else
       {
           UE_LOG(LogTemp, Error, TEXT("Error: %s"), *Error);
       }
    }
);
C++ Example 2:

Sending a custom schema json from a file

#include "Misc/FileHelper.h"
#include "Misc/Paths.h"
FString SchemaFilePath = FPaths::Combine(
    FPaths::ProjectDir(),
    TEXT("Source/:ProjectName/Public/AIPrompts/SomeSchema.json")
);

FString MySchemaJson;
if (FFileHelper::LoadFileToString(MySchemaJson, *SchemaFilePath))
{
    UGenAISchemaService::RequestStructuredOutput(
        TEXT("Generate a list of users and their details"),
        MySchemaJson,
        [](const FString& Response, const FString& Error, bool Success) {
           if (Success)
           {
               UE_LOG(LogTemp, Log, TEXT("Structured Output: %s"), *Response);
           }
           else
           {
               UE_LOG(LogTemp, Error, TEXT("Error: %s"), *Error);
           }
        }
    );
}
Blueprint Example:

DeepSeek API:

Currently the plugin supports Chat and Reasoning from DeepSeek API. Both for C++ and Blueprints. Points to note:

  • System messages are currently mandatory for the reasoning model. API otherwise seems to return null
  • Also, from the documentation: "Please note that if the reasoning_content field is included in the sequence of input messages, the API will return a 400 error. Read more about it here"

[!WARNING]
While using the R1 reasoning model, make sure the Unreal's HTTP timeouts are not the default values at 30 seconds. As these API calls can take longer than 30 seconds to respond. Simply setting the HttpRequest->SetTimeout(<N Seconds>); is not enough So the following lines need to be added to your project's DefaultEngine.ini file:

[HTTP]
HttpConnectionTimeout=180
HttpReceiveTimeout=180

1. Chat and Reasoning:

C++ Example:
 FGenDSeekChatSettings ReasoningSettings;
 ReasoningSettings.Model = EDeepSeekModels::Reasoner; // or EDeepSeekModels::Chat for Chat API
 ReasoningSettings.MaxTokens = 100;
 ReasoningSettings.Messages.Add(FGenChatMessage{TEXT("system"), TEXT("You are a helpful assistant.")});
 ReasoningSettings.Messages.Add(FGenChatMessage{TEXT("user"), TEXT("9.11 and 9.8, which is greater?")});
 ReasoningSettings.bStreamResponse = false;
 UGenDSeekChat::SendChatRequest(
     ReasoningSettings,
     FOnDSeekChatCompletionResponse::CreateLambda(
         [this](const FString& Response, const FString& ErrorMessage, bool bSuccess)
         {
             if (!UTHelper::IsContextStillValid(this))
             {
                 return;
             }

             // Log response details regardless of success
             UE_LOG(LogTemp, Warning, TEXT("DeepSeek Reasoning Response Received - Success: %d"), bSuccess);
             UE_LOG(LogTemp, Warning, TEXT("Response: %s"), *Response);
             if (!ErrorMessage.IsEmpty())
             {
                 UE_LOG(LogTemp, Error, TEXT("Error Message: %s"), *ErrorMessage);
             }
         })
 );
Blueprint Example:

Anthropic API:

Currently the plugin supports Chat from Anthropic API. Both for C++ and Blueprints. Tested models are claude-sonnet-4-20250514, claude-opus-4-20250514, claude-3-7-sonnet-latest, claude-3-5-sonnet, claude-3-5-haiku-latest, claude-3-opus-latest.

1. Chat:

C++ Example:
    // ---- Claude Chat Test ----
    FGenClaudeChatSettings ChatSettings;
    ChatSettings.Model = EClaudeModels::Claude_3_7_Sonnet; // Use Claude 3.7 Sonnet model
    ChatSettings.MaxTokens = 4096;
    ChatSettings.Temperature = 0.7f;
    ChatSettings.Messages.Add(FGenChatMessage{TEXT("system"), TEXT("You are a helpful assistant.")});
    ChatSettings.Messages.Add(FGenChatMessage{TEXT("user"), TEXT("What is the capital of France?")});
    
    UGenClaudeChat::SendChatRequest(
        ChatSettings,
        FOnClaudeChatCompletionResponse::CreateLambda(
            [this](const FString& Response, const FString& ErrorMessage, bool bSuccess)
            {
                if (!UTHelper::IsContextStillValid(this))
                {
                    return;
                }
    
                if (bSuccess)
                {
                    UE_LOG(LogTemp, Warning, TEXT("Claude Chat Response: %s"), *Response);
                }
                else
                {
                    UE_LOG(LogTemp, Error, TEXT("Claude Chat Error: %s"), *ErrorMessage);
                }
            })
    );
Blueprint Example:

XAI's Grok 3 API:

Currently the plugin supports Chat from XAI's Grok 3 API. Both for C++ and Blueprints.

1. Chat:

	FGenXAIChatSettings ChatSettings;
	ChatSettings.Model = TEXT("grok-3-latest");
		ChatSettings.Messages.Add(FGenXAIMessage{
		TEXT("system"),
		TEXT("You are a helpful AI assistant for a game. Please provide concise responses.")
	});
	ChatSettings.Messages.Add(FGenXAIMessage{TEXT("user"), TEXT("Create a brief description for a forest level in a fantasy game")});
	ChatSettings.MaxTokens = 1000;

	UGenXAIChat::SendChatRequest(
		ChatSettings,
		FOnXAIChatCompletionResponse::CreateLambda(
			[this](const FString& Response, const FString& ErrorMessage, bool bSuccess)
			{
				if (!UTHelper::IsContextStillValid(this))
				{
					return;
				}
				
				UE_LOG(LogTemp, Warning, TEXT("XAI Chat response: %s"), *Response);
				
				if (!bSuccess)
				{
					UE_LOG(LogTemp, Error, TEXT("XAI Chat error: %s"), *ErrorMessage);
				}
			})
	);

Model Control Protocol (MCP):

This is currently work in progress. The plugin supports various clients like Claude Desktop App, Cursor etc.

Usage:

If Autostart MCP server is enabled: (In plugin's settings)

1. Open the Unreal Engine Editor.
2. Open the Claude Desktop App or Cursor IDE or Windsor.

That's it! You can now use the MCP features of the plugin.

If Autostart MCP server is disabled:

1. Run the MCP server from the plugin's python directory.
python <your_project_directoy>/Plugins/GenerativeAISupport/Content/Python/mcp_server.py
2. Run the MCP client by opening or restarting the Claude desktop app or Cursor IDE.
3. Open a new Unreal Engine project and run the below python script from the plugin's python directory.

Tools -> Run Python Script -> Select the Plugins/GenerativeAISupport/Content/Python/unreal_socket_server.py file.

4. Now you should be able to prompt the Claude Desktop App to use Unreal Engine.

Known Issues:

  • Nodes fail to connect properly with MCP
  • No undo redo support for MCP
  • No streaming support for Deepseek reasoning model
  • No complex material generation support for the create material tool
  • Issues with running some llm generated valid python scripts
  • When LLM compiles a blueprint no proper error handling in its response
  • Issues spawning certain nodes, especially with getters and setters
  • Doesn't open the right context window during scene and project files edit.
  • Doesn't dock the window properly in the editor for blueprints.

Config Window:

(Still wip)

Contribution Guidelines:

Setting up for Development:

  1. Install unreal python package and setup the IDE's python interpreter for proper intellisense.
pip install unreal

More details will be added soon.

Project Structure:

More details will be added soon.

References:

Quick Links:

Support This Project

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If you find UnrealGenAISupport helpful, consider sponsoring me to keep the project going! Click the "Sponsor" button above to contribute.

For Tasks:

Click tags to check more tools for each tasks
control scene objects generate blueprints run python scripts manage project files integrate ai models

For Jobs:

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R2R (RAG to Riches) is a fast and efficient framework for serving high-quality Retrieval-Augmented Generation (RAG) to end users. The framework is designed with customizable pipelines and a feature-rich FastAPI implementation, enabling developers to quickly deploy and scale RAG-based applications. R2R was conceived to bridge the gap between local LLM experimentation and scalable production solutions. **R2R is to LangChain/LlamaIndex what NextJS is to React**. A JavaScript client for R2R deployments can be found here. ### Key Features * **🚀 Deploy** : Instantly launch production-ready RAG pipelines with streaming capabilities. * **🧩 Customize** : Tailor your pipeline with intuitive configuration files. * **🔌 Extend** : Enhance your pipeline with custom code integrations. * **⚖️ Autoscale** : Scale your pipeline effortlessly in the cloud using SciPhi. * **🤖 OSS** : Benefit from a framework developed by the open-source community, designed to simplify RAG deployment.

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pilottai Screenshot
pilottai

PilottAI is a Python framework for building autonomous multi-agent systems with advanced orchestration capabilities. It provides enterprise-ready features for building scalable AI applications. The framework includes hierarchical agent systems, production-ready features like asynchronous processing and fault tolerance, advanced memory management with semantic storage, and integrations with multiple LLM providers and custom tools. PilottAI offers specialized agents for various tasks such as customer service, document processing, email handling, knowledge acquisition, marketing, research analysis, sales, social media, and web search. The framework also provides documentation, example use cases, and advanced features like memory management, load balancing, and fault tolerance.

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aioshelly Screenshot
aioshelly

Aioshelly is an asynchronous library designed to control Shelly devices. It is currently under development and requires Python version 3.11 or higher, along with dependencies like bluetooth-data-tools, aiohttp, and orjson. The library provides examples for interacting with Gen1 devices using CoAP protocol and Gen2/Gen3 devices using RPC and WebSocket protocols. Users can easily connect to Shelly devices, retrieve status information, and perform various actions through the provided APIs. The repository also includes example scripts for quick testing and usage guidelines for contributors to maintain consistency with the Shelly API.

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The Unreal Engine Generative AI Support Plugin is a tool designed to integrate various cutting-edge LLM/GenAI models into Unreal Engine for game development. It aims to simplify the process of using AI models for game development tasks, such as controlling scene objects, generating blueprints, running Python scripts, and more. The plugin currently supports models from organizations like OpenAI, Anthropic, XAI, Google Gemini, Meta AI, Deepseek, and Baidu. It provides features like API support, model control, generative AI capabilities, UI generation, project file management, and more. The plugin is still under development but offers a promising solution for integrating AI models into game development workflows.

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The Google Generative AI SDK for Dart enables developers to utilize cutting-edge Large Language Models (LLMs) for creating language applications. It provides access to the Gemini API for generating content using state-of-the-art models. Developers can integrate the SDK into their Dart or Flutter applications to leverage powerful AI capabilities. It is recommended to use the SDK for server-side API calls to ensure the security of API keys and protect against potential key exposure in mobile or web apps.

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This repository contains an assistant proposal for the Semantic Kernel, allowing the usage of assistants without relying on OpenAI Assistant APIs. It runs locally planners and plugins for the assistants, providing scenarios like Assistant with Semantic Kernel plugins, Multi-Assistant conversation, and AutoGen conversation. The Semantic Kernel is a lightweight SDK enabling integration of AI Large Language Models with conventional programming languages, offering functions like semantic functions, native functions, and embeddings-based memory. Users can bring their own model for the assistants and host them locally. The repository includes installation instructions, usage examples, and information on creating new conversation threads with the assistant.

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ezlocalai is an artificial intelligence server that simplifies running multimodal AI models locally. It handles model downloading and server configuration based on hardware specs. It offers OpenAI Style endpoints for integration, voice cloning, text-to-speech, voice-to-text, and offline image generation. Users can modify environment variables for customization. Supports NVIDIA GPU and CPU setups. Provides demo UI and workflow visualization for easy usage.

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llmproxy is a reverse proxy for LLM API based on Cloudflare Worker, supporting platforms like OpenAI, Gemini, and Groq. The interface is compatible with the OpenAI API specification and can be directly accessed using the OpenAI SDK. It provides a convenient way to interact with various AI platforms through a unified API endpoint, enabling seamless integration and usage in different applications.

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This repository contains a simple Python Flask App utilizing the Google AI Gemini API to explore multi-modal capabilities. It provides a basic UI and Flask backend for easy integration and testing. The app allows users to interact with the AI model through chat messages, making it a great starting point for developers interested in AI-powered applications.

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KaibanJS is a JavaScript-native framework for building multi-agent AI systems. It enables users to create specialized AI agents with distinct roles and goals, manage tasks, and coordinate teams efficiently. The framework supports role-based agent design, tool integration, multiple LLMs support, robust state management, observability and monitoring features, and a real-time agentic Kanban board for visualizing AI workflows. KaibanJS aims to empower JavaScript developers with a user-friendly AI framework tailored for the JavaScript ecosystem, bridging the gap in the AI race for non-Python developers.

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