nobodywho
NobodyWho is a plugin that lets you interact with local LLMs in the Godot game engine, with more plugins in development.
Stars: 266
NobodyWho is a plugin for the Godot game engine that enables interaction with local LLMs for interactive storytelling. Users can install it from Godot editor or GitHub releases page, providing their own LLM in GGUF format. The plugin consists of `NobodyWhoModel` node for model file, `NobodyWhoChat` node for chat interaction, and `NobodyWhoEmbedding` node for generating embeddings. It offers a programming interface for sending text to LLM, receiving responses, and starting the LLM worker.
README:
NobodyWho is a plugin that lets you interact with local LLMs in the Godot game engine, with more plugins in development.
- 🏃 Run LLM-driven characters locally without internet
- ⚡ Super fast inference on GPU powered by Vulkan or Metal
- 🔧 Easy setup - just two nodes to get started
- 🎯 Perfect for games, interactive stories, and NPCs
- 💻 Cross-platform: Windows, Linux, macOS
- 🦙 Powered by the wonderful llama.cpp
Small demo of a use-case. This video was recorded in real time on a laptop, to give you an idea of performance.
The code for this showcase is in the demo-game folder of this repo. It amounts to about 100 lines of code in a single file, most of it being UI stuff.
You can install it from inside the Godot editor: In Godot 4.3+, go to AssetLib and search for "NobodyWho".
...or you can grab a specific version from our github releases page. You can install these zip files by going to the "AssetLib" tab in Godot and selecting "Import".
Make sure that the ignore asset root option is set in the import dialogue.
- ⭐ Star the repo and spread the word about NobodyWho!
- Join our Discord or Matrix communities
- Found a bug? Open an issue!
- Submit your own PR - contributions welcome
- 💝 Become a sponsor to support development
- Help improve docs or write tutorials
The plugin does not include a large language model (LLM). You need to provide an LLM in the GGUF file format. A good place to start is something like Gemma 2 2B
Once you have a GGUF model file, you can add a NobodyWhoModel node to your Godot scene. On this node, set the model file to the GGUF model you just downloaded.
NobodyWhoModel contains the weights of the model. The model takes up a lot of RAM, and can take a little while to initialize, so if you plan on having several characters/conversations, it's a big advantage to point to the same NobodyWhoModel node.
Now you can add a NobodyWhoChat node to your scene. From the node inspector, set the "Model Node" field, to show this chat node where to find the NobodyWhoModel.
Also in the inspector, you can provide a prompt, which gives the LLM instructions on how to carry out the chat.
Now you can add a script to the NobodyWhoChat node, to provide your chat interaction.
NobodyWhoChat uses this programming interface:
-
say(text: String): a function that can be used to send text from the user to the LLM. -
response_updated(token: String): a signal that is emitted every time the LLM produces more text. Contains roughly one word per invocation. -
response_finished(response: String): a signal which indicates that the LLM is done speaking. -
start_worker(): a function that starts the LLM worker. The LLM needs a few seconds to get ready before chatting, so you may want to call this ahead of time.
extends NobodyWhoChat
func _ready():
# configure node
model_node = get_node("../ChatModel")
system_prompt = "You are an evil wizard. Always try to curse anyone who talks to you."
# say something
say("Hi there! Who are you?")
# wait for the response
var response = await response_finished
print("Got response: " + response)
# in this example we just use the `response_finished` signal to get the complete response
# in real-world-use you definitely want to connect `response_updated`, which gives one word at a time
# the whole interaction feels *much* smoother if you stream the response out word-by-word.extends NobodyWhoEmbedding
func _ready():
# configure node
self.model_node = get_node("../EmbeddingModel")
# generate some embeddings
embed("The dragon is on the hill.")
var dragon_hill_embd = await self.embedding_finished
embed("The dragon is hungry for humans.")
var dragon_hungry_embd = await self.embedding_finished
embed("This doesn't matter.")
var irrelevant_embd = await self.embedding_finished
# test similarity,
# here we show that two embeddings will have high similarity, if they mean similar things
var low_similarity = cosine_similarity(irrelevant_embd, dragon_hill_embd)
var high_similarity = cosine_similarity(dragon_hill_embd, dragon_hungry_embd)
assert(low_similarity < high_similarity)Exports are a bit weird for now: Llama.cpp expects a path to a GGUF file on your filesystem, while Godot really wants to package everything in one big .pck file.
The solution (for now) is to manually copy your chosen GGUF file into the export directory (the folder with your exported game executable).
We're looking into solutions for including this file automatically.
New language models are coming out at a breakneck pace. If you search the web for "best language models for roleplay" or something similar, you'll probably find results that are several months or years old. You want to use something newer.
We recommend checking leaderboards like The GPU-Poor LLM Gladiator Arena, or OpenRouter's Roleplay Rankings. Once you select a model, remember that you need a quantization of it in the GGUF format. The huggingface user bartowski regularly uploads GGUF quantizations for a lot of new models.
Selecting the best model for your usecase is mostly about finding the right tradeoff between speed, memory usage and quality of the responses. Using bigger models general will yield better responses, but raise minimum system requirements and slow down generation speed.
Currently only Linux, MacOS, and Windows are supported platforms.
Mobile exports seem very feasible. See issues #114, #66, and #67 for progress.
Web exports will be a bit trickier to get right. See issue #111.
There has been some confusion about the licensing terms of this plugin. To clarify:
You are allowed to use this plugin in proprietary and commercial projects, free of charge.
If you distribute modified versions of the code in this repo, you must open source those changes.
Feel free to make proprietary games using NobodyWho, but don't make a proprietary fork of NobodyWho.
-
Neophyte
- Describe your actions and use the items you buy with your words to finish off the enemies.
-
The Merchant's Road
- An LLM-driven text adventure where you guard a merchant in his travels.
-
Who Farted in the Eleveator?
- LLM game where you talk to NPCs about farting in the elevator.
-
Procedural
- A procedurally generated 2D shooter that takes place in an infinite dungeon.
-
The Black Box
- Half Life inspired short story with a mysterious Black Box.
-
Ai rgument
- A party game where you argue a position before an AI judge.
-
The World Will End in 60 Seconds!
- What will you do before it’s all over?
-
Stonecot Prototype
- Stonecot Prototype is a stripped-down, experimental build of Mythara Chronicles, showcasing AI-driven party interactions and a prototype main quest.
-
The Asteroid
- A game where you can chat with the crew of a spacestation to figure out what happened in the accident.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for nobodywho
Similar Open Source Tools
nobodywho
NobodyWho is a plugin for the Godot game engine that enables interaction with local LLMs for interactive storytelling. Users can install it from Godot editor or GitHub releases page, providing their own LLM in GGUF format. The plugin consists of `NobodyWhoModel` node for model file, `NobodyWhoChat` node for chat interaction, and `NobodyWhoEmbedding` node for generating embeddings. It offers a programming interface for sending text to LLM, receiving responses, and starting the LLM worker.
lovelaice
Lovelaice is an AI-powered assistant for your terminal and editor. It can run bash commands, search the Internet, answer general and technical questions, complete text files, chat casually, execute code in various languages, and more. Lovelaice is configurable with API keys and LLM models, and can be used for a wide range of tasks requiring bash commands or coding assistance. It is designed to be versatile, interactive, and helpful for daily tasks and projects.
LLM_Web_search
LLM_Web_search project gives local LLMs the ability to search the web by outputting a specific command. It uses regular expressions to extract search queries from model output and then utilizes duckduckgo-search to search the web. LangChain's Contextual compression and Okapi BM25 or SPLADE are used to extract relevant parts of web pages in search results. The extracted results are appended to the model's output.
kobold_assistant
Kobold-Assistant is a fully offline voice assistant interface to KoboldAI's large language model API. It can work online with the KoboldAI horde and online speech-to-text and text-to-speech models. The assistant, called Jenny by default, uses the latest coqui 'jenny' text to speech model and openAI's whisper speech recognition. Users can customize the assistant name, speech-to-text model, text-to-speech model, and prompts through configuration. The tool requires system packages like GCC, portaudio development libraries, and ffmpeg, along with Python >=3.7, <3.11, and runs on Ubuntu/Debian systems. Users can interact with the assistant through commands like 'serve' and 'list-mics'.
ezkl
EZKL is a library and command-line tool for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). It enables the following workflow: 1. Define a computational graph, for instance a neural network (but really any arbitrary set of operations), as you would normally in pytorch or tensorflow. 2. Export the final graph of operations as an .onnx file and some sample inputs to a .json file. 3. Point ezkl to the .onnx and .json files to generate a ZK-SNARK circuit with which you can prove statements such as: > "I ran this publicly available neural network on some private data and it produced this output" > "I ran my private neural network on some public data and it produced this output" > "I correctly ran this publicly available neural network on some public data and it produced this output" In the backend we use the collaboratively-developed Halo2 as a proof system. The generated proofs can then be verified with much less computational resources, including on-chain (with the Ethereum Virtual Machine), in a browser, or on a device.
llama-on-lambda
This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.
boxcars
Boxcars is a Ruby gem that enables users to create new systems with AI composability, incorporating concepts such as LLMs, Search, SQL, Rails Active Record, Vector Search, and more. It allows users to work with Boxcars, Trains, Prompts, Engines, and VectorStores to solve problems and generate text results. The gem is designed to be user-friendly for beginners and can be extended with custom concepts. Boxcars is actively seeking ways to enhance security measures to prevent malicious actions. Users can use Boxcars for tasks like running calculations, performing searches, generating Ruby code for math operations, and interacting with APIs like OpenAI, Anthropic, and Google SERP.
AlwaysReddy
AlwaysReddy is a simple LLM assistant with no UI that you interact with entirely using hotkeys. It can easily read from or write to your clipboard, and voice chat with you via TTS and STT. Here are some of the things you can use AlwaysReddy for: - Explain a new concept to AlwaysReddy and have it save the concept (in roughly your words) into a note. - Ask AlwaysReddy "What is X called?" when you know how to roughly describe something but can't remember what it is called. - Have AlwaysReddy proofread the text in your clipboard before you send it. - Ask AlwaysReddy "From the comments in my clipboard, what do the r/LocalLLaMA users think of X?" - Quickly list what you have done today and get AlwaysReddy to write a journal entry to your clipboard before you shutdown the computer for the day.
whisper_dictation
Whisper Dictation is a fast, offline, privacy-focused tool for voice typing, AI voice chat, voice control, and translation. It allows hands-free operation, launching and controlling apps, and communicating with OpenAI ChatGPT or a local chat server. The tool also offers the option to speak answers out loud and draw pictures. It includes client and server versions, inspired by the Star Trek series, and is designed to keep data off the internet and confidential. The project is optimized for dictation and translation tasks, with voice control capabilities and AI image generation using stable-diffusion API.
ultravox
Ultravox is a fast multimodal Language Model (LLM) that can understand both text and human speech in real-time without the need for a separate Audio Speech Recognition (ASR) stage. By extending Meta's Llama 3 model with a multimodal projector, Ultravox converts audio directly into a high-dimensional space used by Llama 3, enabling quick responses and potential understanding of paralinguistic cues like timing and emotion in human speech. The current version (v0.3) has impressive speed metrics and aims for further enhancements. Ultravox currently converts audio to streaming text and plans to emit speech tokens for direct audio conversion. The tool is open for collaboration to enhance this functionality.
examor
Examor is a website application that allows you to take exams based on your knowledge notes. It helps you to remember what you have learned and written. The application generates a set of questions from the documents you upload, and you can answer them to test your knowledge. Examor also uses GPT to score and validate your answers, and provides you with feedback. The application is still in its early stages of development, but it has the potential to be a valuable tool for learners.
brokk
Brokk is a code assistant designed to understand code semantically, allowing LLMs to work effectively on large codebases. It offers features like agentic search, summarizing related classes, parsing stack traces, adding source for usages, and autonomously fixing errors. Users can interact with Brokk through different panels and commands, enabling them to manipulate context, ask questions, search codebase, run shell commands, and more. Brokk helps with tasks like debugging regressions, exploring codebase, AI-powered refactoring, and working with dependencies. It is particularly useful for making complex, multi-file edits with o1pro.
GLaDOS
GLaDOS Personality Core is a project dedicated to building a real-life version of GLaDOS, an aware, interactive, and embodied AI system. The project aims to train GLaDOS voice generator, create a 'Personality Core,' develop medium- and long-term memory, provide vision capabilities, design 3D-printable parts, and build an animatronics system. The software architecture focuses on low-latency voice interactions and minimal dependencies. The hardware system includes servo- and stepper-motors, 3D printable parts for GLaDOS's body, animations for expression, and a vision system for tracking and interaction. Installation instructions involve setting up a local LLM server, installing drivers, and running GLaDOS on different operating systems.
langgraph-studio
LangGraph Studio is a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications. It offers visual graphs and state editing to better understand agent workflows and iterate faster. Users can collaborate with teammates using LangSmith to debug failure modes. The tool integrates with LangSmith and requires Docker installed. Users can create and edit threads, configure graph runs, add interrupts, and support human-in-the-loop workflows. LangGraph Studio allows interactive modification of project config and graph code, with live sync to the interactive graph for easier iteration on long-running agents.
lumigator
Lumigator is an open-source platform developed by Mozilla.ai to help users select the most suitable language model for their specific needs. It supports the evaluation of summarization tasks using sequence-to-sequence models such as BART and BERT, as well as causal models like GPT and Mistral. The platform aims to make model selection transparent, efficient, and empowering by providing a framework for comparing LLMs using task-specific metrics to evaluate how well a model fits a project's needs. Lumigator is in the early stages of development and plans to expand support to additional machine learning tasks and use cases in the future.
gpt-subtrans
GPT-Subtrans is an open-source subtitle translator that utilizes large language models (LLMs) as translation services. It supports translation between any language pairs that the language model supports. Note that GPT-Subtrans requires an active internet connection, as subtitles are sent to the provider's servers for translation, and their privacy policy applies.
For similar tasks
nobodywho
NobodyWho is a plugin for the Godot game engine that enables interaction with local LLMs for interactive storytelling. Users can install it from Godot editor or GitHub releases page, providing their own LLM in GGUF format. The plugin consists of `NobodyWhoModel` node for model file, `NobodyWhoChat` node for chat interaction, and `NobodyWhoEmbedding` node for generating embeddings. It offers a programming interface for sending text to LLM, receiving responses, and starting the LLM worker.
blockoli
Blockoli is a high-performance tool for code indexing, embedding generation, and semantic search tool for use with LLMs. It is built in Rust and uses the ASTerisk crate for semantic code parsing. Blockoli allows you to efficiently index, store, and search code blocks and their embeddings using vector similarity. Key features include indexing code blocks from a codebase, generating vector embeddings for code blocks using a pre-trained model, storing code blocks and their embeddings in a SQLite database, performing efficient similarity search on code blocks using vector embeddings, providing a REST API for easy integration with other tools and platforms, and being fast and memory-efficient due to its implementation in Rust.
client-js
The Mistral JavaScript client is a library that allows you to interact with the Mistral AI API. With this client, you can perform various tasks such as listing models, chatting with streaming, chatting without streaming, and generating embeddings. To use the client, you can install it in your project using npm and then set up the client with your API key. Once the client is set up, you can use it to perform the desired tasks. For example, you can use the client to chat with a model by providing a list of messages. The client will then return the response from the model. You can also use the client to generate embeddings for a given input. The embeddings can then be used for various downstream tasks such as clustering or classification.
fastllm
A collection of LLM services you can self host via docker or modal labs to support your applications development. The goal is to provide docker containers or modal labs deployments of common patterns when using LLMs and endpoints to integrate easily with existing codebases using the openai api. It supports GPT4all's embedding api, JSONFormer api for chat completion, Cross Encoders based on sentence transformers, and provides documentation using MkDocs.
openai-kotlin
OpenAI Kotlin API client is a Kotlin client for OpenAI's API with multiplatform and coroutines capabilities. It allows users to interact with OpenAI's API using Kotlin programming language. The client supports various features such as models, chat, images, embeddings, files, fine-tuning, moderations, audio, assistants, threads, messages, and runs. It also provides guides on getting started, chat & function call, file source guide, and assistants. Sample apps are available for reference, and troubleshooting guides are provided for common issues. The project is open-source and licensed under the MIT license, allowing contributions from the community.
azure-search-vector-samples
This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.
llm
LLM is a CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. It allows users to run prompts from the command-line, store results in SQLite, generate embeddings, and more. The tool supports self-hosted language models via plugins and provides access to remote and local models. Users can install plugins to access models by different providers, including models that can be installed and run on their own device. LLM offers various options for running Mistral models in the terminal and enables users to start chat sessions with models. Additionally, users can use a system prompt to provide instructions for processing input to the tool.
GenAI-Showcase
The Generative AI Use Cases Repository showcases a wide range of applications in generative AI, including Retrieval-Augmented Generation (RAG), AI Agents, and industry-specific use cases. It provides practical notebooks and guidance on utilizing frameworks such as LlamaIndex and LangChain, and demonstrates how to integrate models from leading AI research companies like Anthropic and OpenAI.
For similar jobs
sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.
teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.
ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.