Open Source Swift Artificial Intelligence Software
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Swift Artificial Intelligence Software
View 13810 business solutionsBrowse free open source Swift Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Swift Artificial Intelligence Software by OS, license, language, programming language, and project status.
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Enchanted
Enchanted is iOS and macOS app for chatting with language models
Enchanted is an open-source, cross-platform application built to let users chat with privately hosted large language models from Apple devices, including macOS, iOS, and visionOS. Designed to work seamlessly with servers like Ollama, it provides a privacy-focused alternative to traditional cloud AI UIs by connecting directly to your own LLM endpoints such as Llama, Mistral, Vicuna, and more. The interface resembles familiar commercial chat apps but emphasizes local control, offline capabilities, and multimodal support, making it ideal for users who want rich AI interaction without exposing sensitive prompts or conversations to third-party services. Enchanted enables features like voice prompts, image attachments, and markdown formatting within chats, giving flexibility for both casual and professional use. Built with attention to design and native Apple UI conventions, it aims to deliver consistent performance across devices while preserving powerful AI access. -
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Mochi Diffusion
Run Stable Diffusion on Mac natively
Run Stable Diffusion on Mac natively. This app uses Apple's Core ML Stable Diffusion implementation to achieve maximum performance and speed on Apple Silicon based Macs while reducing memory requirements. Extremely fast and memory efficient (~150MB with Neural Engine) Runs well on all Apple Silicon Macs by fully utilizing Neural Engine. Generate images locally and completely offline. Generate images based on an existing image (commonly known as Image2Image) Generated images are saved with prompt info inside EXIF metadata (view in Finder's Get Info window) Convert generated images to high resolution (using RealESRGAN) Autosave & restore images. Use custom Stable Diffusion Core ML models. No worries about pickled models. macOS native app using SwiftUI. -
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Clicky
AI teacher that lives as a buddy next to your cursor
Clicky is an experimental AI-powered desktop companion designed to act as an interactive, real-time teaching assistant that lives directly alongside the user’s cursor on macOS. It functions as a menu bar application that can observe the user’s screen, interpret context, and provide guidance through both voice and visual cues, effectively simulating the experience of having a human tutor sitting next to you. The system captures screenshots and combines them with voice input to send contextual queries to AI models, which then respond with both spoken explanations and on-screen visual pointers. One of its defining features is the ability to physically “point” at UI elements across multiple monitors using a cursor overlay, helping users navigate complex software step by step. The architecture includes integrations for speech-to-text, text-to-speech, and AI reasoning models, all routed securely through a proxy to protect API keys. -
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OpenAI
Swift community driven package for OpenAI public API
MacPaw OpenAI is a community-driven Swift SDK that provides developers with a structured and type-safe way to interact with the OpenAI API and compatible providers within Apple ecosystem applications. It simplifies the integration of AI capabilities into iOS, macOS, and other Swift-based applications by offering a clean abstraction over the underlying REST API, enabling developers to focus on functionality rather than low-level implementation details. The SDK supports a wide range of features including chat completions, embeddings, image generation, audio processing, and structured outputs, making it a comprehensive toolkit for building AI-powered applications. It also includes support for advanced features such as function calling, assistants, and tool integration through protocols like Model Context Protocol, enabling more complex and interactive AI workflows. -
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CodexBar
Show usage stats for OpenAI Codex and Claude Code
CodexBar is a lightweight macOS utility that displays real-time usage statistics for AI coding tools such as OpenAI Codex and Claude Code directly from the system menu bar. The application is designed to give developers quick visibility into token consumption and activity without requiring them to open web dashboards or log into provider portals. Built in Swift with a native macOS interface, it integrates seamlessly into the desktop environment and emphasizes minimal overhead. The tool is particularly useful for monitoring usage limits, managing costs, and keeping track of AI-assisted development sessions. CodexBar focuses on simplicity and fast feedback, presenting key metrics in an always-accessible format. Overall, it functions as a convenient observability companion for developers who rely heavily on AI coding assistants. -
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LLM.swift
LLM.swift is a simple and readable library
LLM.swift is a Swift package that enables developers to run Large Language Models (LLMs) directly on Apple devices, including iOS, macOS, and watchOS. By leveraging Apple's hardware and software optimizations, LLM.swift facilitates on-device natural language processing tasks, ensuring user privacy and reducing latency associated with cloud-based solutions. -
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NSFWDetector
A NSFW detector with CoreML
NSFWDetector is a small (17 kB) CoreML Model to scan images for nudity. It was trained using CreateML to distinguish between porn/nudity and appropriate pictures. With the main focus on distinguishing between Instagram model-like pictures and porn. -
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apfel
Apple Intelligence from the command line
apfel is a lightweight and likely experimental development project focused on building efficient and minimal tools or frameworks, typically emphasizing simplicity, performance, and clean abstractions. The project appears to follow a philosophy of reducing unnecessary complexity while still enabling practical functionality for developers who prefer lean systems over heavy frameworks. It is designed to be adaptable, allowing developers to extend or modify its behavior depending on their specific use case. Apfel may include utilities or structural patterns that streamline development workflows, particularly in environments where speed and clarity are more important than feature richness. Its architecture likely avoids over-engineering, making it suitable for small projects, prototypes, or educational purposes. The project encourages direct interaction with code rather than relying on extensive abstraction layers, giving developers more control over implementation details. -
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GitHub Copilot for Xcode
AI coding assistant for Xcode
Copilot for Xcode brings GitHub Copilot’s AI-driven code suggestions directly into Apple’s Xcode IDE, giving iOS and macOS developers predictive completions, context-aware recommendations, and natural language assistance while they write Swift, Objective-C, and related code. It embeds seamlessly into the Xcode editor UI, offering completions as you type, including full lines or blocks of code derived from surrounding context and doc comments. Because the integration understands the structure of Xcode-based projects and Apple’s frameworks, suggestions are often tailored to platform idioms, APIs, and patterns used in Cocoa, UIKit, SwiftUI, and more. It also supports natural language prompts, letting developers ask for example code or explanations inline without leaving the IDE. The extension is designed to respect privacy and project scope, giving users control over when Copilot suggestions are enabled and how telemetry is shared. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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ScreenTranslate
Translate any text on your Mac screen — capture or select,instantly.
ScreenTranslate lets you translate any text on your Mac screen without switching tabs or copy-pasting. Screen Capture Translation: Press Cmd+Shift+T, drag over any text on screen, and get an instant translation popup. Works with images, PDFs, and subtitles using OCR (Apple Vision). Text Selection Translation: Select text in any app and press Cmd+Option+Z to translate directly. No OCR needed. - Free and open-source (GPL-3.0) - On-device translation using Apple Translation - Works offline with downloaded language packs - 20 languages with auto-detect - Optional cloud engines (DeepL, Google, Azure) with your own API key - Auto-copy to clipboard - Translation history with search - Lightweight menu bar app - Apple Silicon and Intel Mac supported - macOS 15 (Sequoia) or later required -
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LlamaChat
Chat with your favourite LLaMA models in a native macOS app
Chat with your favourite LLaMA models, right on your Mac. LlamaChat is a macOS app that allows you to chat with LLaMA, Alpaca, and GPT4All models all running locally on your Mac. -
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Ollamac
Mac app for Ollama
Ollamac is an open-source native macOS application that provides a graphical interface for interacting with local large language models running through the Ollama inference framework. The project was created to simplify the process of using local AI models, which typically require command-line interaction, by offering a clean and intuitive desktop interface. Through this interface, users can run and chat with a variety of LLM models installed through Ollama directly on their own machines. The application focuses on delivering a lightweight and responsive experience that integrates seamlessly with the macOS ecosystem. Because the models run locally, the system enables private AI workflows without sending data to external APIs or cloud services. Ollamac supports different Ollama models and provides features designed to improve usability such as syntax highlighting and configurable settings. -
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Osaurus
AI edge infrastructure for macOS. Run local or cloud models
Osaurus is an open-source AI edge infrastructure platform designed specifically for macOS environments to run and manage AI models locally. The project provides a native runtime that allows applications to access large language models and AI tools directly on the user’s machine without relying entirely on cloud services. Osaurus supports running both local and remote models, enabling developers to build AI-powered applications that can operate offline or leverage external APIs when needed. The platform acts as an always-on runtime that coordinates AI tasks, tools, and workflows while enabling applications to communicate with models through standardized interfaces. Developers can extend the system through plugins that expose additional capabilities, tools, or services to the runtime using a structured plugin architecture. Osaurus also supports the Model Context Protocol, allowing tools and AI services to share context and interact with multiple applications simultaneously. -
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WhisperKit
On-device Speech Recognition for Apple Silicon
WhisperKit is a Swift package that integrates OpenAI's popular Whisper speech recognition model with Apple's CoreML framework for efficient, local inference on Apple devices. Whisper has pulled the future forward when fast, free and virtually error-free translation and transcription will be ubiquitous. It inspired numerous developers to improve and deploy it with minimal friction and maximum performance. We founded Argmax in November 2023 to empower developers and enterprises everywhere to deploy commercial-scale inference workloads on user devices. The fast-growing need for Whisper inference in production convinced us to take it on as our first project. -
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ARKit + CoreLocation
Combines the high accuracy of AR with the scale of GPS data
ARKit uses camera and motion data to map out the local world as you move around. CoreLocation uses wifi and GPS data to determine your global location, with a low degree of accuracy. ARKit + CoreLocation combines the high accuracy of AR with the scale of GPS data. The potential for combining these technologies is huge, with so many potential applications across many different areas. Allow items to be placed within the AR world using real-world coordinates. Dramatically improved location accuracy, using recent location data points combined with knowledge about movement through the AR world. The improved location accuracy is currently in an “experimental” phase, but could be the most important component. The library and demo come with a bunch of additional features for configuration. It’s all fully documented to be sure to have a look around. -
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Bender
Easily craft fast Neural Networks on iOS
Bender allows you to easily define and run neural networks on your iOS apps, it uses Apple’s MetalPerformanceShaders under the hood. Bender provides the ease of use of CoreML with the flexibility of a modern ML framework. Bender allows you to run trained models, you can use Tensorflow, Keras, Caffe, the choice is yours. Either freeze the graph or export the weights to files. You can import a frozen graph directly from supported platforms or re-define the network structure and load the weights. Either way, it just takes a few minutes. Bender suports the most common ML nodes and layers but it is also extensible so you can write your own custom functions. With Core ML, you can integrate trained machine learning models into your app, it supports Caffe and Keras 1.2.2+ at the moment. Apple released conversion tools to create CoreML models which then can be run easily. Finally, there is no easy way to add additional pre or post-processing layers to run on the GPU. -
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Cheetah
AI macOS app for real-time coding interview coaching assistance
Cheetah is an AI-powered macOS application designed to assist users during software engineering interview practice through real-time coaching capabilities. It integrates audio transcription and AI-generated responses to help users navigate technical interview questions as they happen. Cheetah uses a local speech-to-text engine based on Whisper to capture and transcribe conversations in real time, enabling it to understand interviewer prompts. It then leverages language models to generate suggested answers, refinements, or explanations tailored to the ongoing discussion. Cheetah also connects with live coding environments through a browser extension, allowing it to analyze code and logs directly from supported platforms. Cheetah provides simple controls for generating answers, refining responses, and analyzing technical output, making it interactive during mock interviews. -
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ClaudeBar
A macOS menu bar application that monitors AI coding assistant usage
ClaudeBar is a macOS menu bar utility that helps developers and power users monitor their AI coding assistant usage quotas from a lightweight system tray interface. Rather than constantly running CLI commands or navigating web dashboards, users can glance at their quota statistics for services like Claude, Codex, Gemini, GitHub Copilot, and Antigravity directly from the menu bar. The application provides real-time tracking of session, weekly, and model-specific usage percentages, using visual indicators such as color-coded progress bars to communicate when quotas are healthy, nearing limits, or depleted. It includes options to enable or disable monitoring for individual providers, supports multiple visual themes (including dark mode and a festive theme), and refreshes data at configurable intervals so users always have up-to-date information. -
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Dayflow
Automatic AI-powered timeline of your daily work activity logs
Dayflow is an open source macOS application designed to automatically generate a detailed timeline of a user’s daily work activity by analyzing screen recordings. It continuously captures lightweight snapshots of the screen and processes them at intervals using AI to produce contextual summaries of what the user was actually doing. Unlike traditional time trackers that only log application usage, it focuses on understanding the intent behind activities, distinguishing productive work from distractions. It is built as a native SwiftUI application and emphasizes efficiency, using minimal CPU and memory while running in the background. A strong focus is placed on privacy, as all captured data remains local by default and users can choose their preferred AI provider, including local models or external services. The generated timeline includes summaries, distraction highlights, and visual representations of the day, helping users reflect on productivity and workflow patterns. -
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Elucidate
Straightforward OCR for Mac to create searchable PDFs.
Elucidate is now available on the Mac App Store: https://itunes.apple.com/us/app/elucidate/id1066088407?ls=1&mt=12 -
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Generative AI Swift
This SDK is now deprecated, use the unified Firebase SDK
deprecated-generative-ai-swift is a Swift client and example scaffold for building generative AI apps using the Gemini models. Although marked “deprecated”, the repo demonstrates how to integrate Gemini inference into iOS and macOS apps via Swift APIs, providing boilerplate for prompt dispatching, streaming responses, UI integration, and error handling. It includes a sample app that showcases a chat interface, where users send messages and receive responses streamed in real time, with UI updates as tokens arrive. The code also handles request queuing, cancellation, and retry logic, giving developers a realistic foundation rather than a minimalist “hello world.” Despite its deprecated label, the repo remains valuable for developers who want to see how a native Swift integration might be structured before migrating to newer SDKs. Maintainability is emphasized: modular layers separate networking, prompt handling, and UI logic, making adaptation easier when switching to updated APIs. -
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ImagePicker
Reinventing the way ImagePicker works
ImagePicker is an all-in-one camera solution for your iOS app. It lets your users select images from the library and take pictures at the same time. As a developer you get notified of all the user interactions and get the beautiful UI for free, out of the box, it's just that simple. ImagePicker has been optimized to give a great user experience, it passes around referenced images instead of the image itself which makes it less memory-consuming. This is what makes it smooth as butter. ImagePicker works with referenced images, that is really powerful because it lets you download the asset and choose the size you want. If you want to change the default implementation, just add a variable in your controller. -
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Lumo iPhone App
iOS application for Lumo
Lumo iPhone App is the native iPhone and iPad client for Lumo, Proton’s privacy-centric AI assistant that allows users to ask questions, get summaries, generate content, and leverage AI help while keeping all conversations confidential and encrypted. Built with SwiftUI, the iOS app wraps a secure web-powered interface and communicates with the Lumo service in a way that ensures zero-access encryption, meaning even Proton cannot read or log user chats—only the device holder can decrypt them. It includes native features like on-device voice recording and speech recognition, flexible navigation, and payment integration for subscription plans if users choose expanded capabilities. The app’s architecture uses a combination of WebView and JavaScript bridges to power responsive chat UI while retaining strong data protection principles. -
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MochiDiffusion
Run Stable Diffusion on Mac natively
MochiDiffusion is a native macOS application that allows users to run Stable Diffusion models locally, leveraging Apple Silicon GPU acceleration via Core ML. It offers users GUI controls for prompts and model configuration without needing Python or Docker, enabling offline image generation. -
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OpenAI DALL·E AsyncImage SwiftUI
OpenAI swift async text to image for SwiftUI app using OpenAI
SwiftUI views that asynchronously loads and displays an OpenAI image from open API. You just type in your idea and AI will give you an art solution. DALL-E and DALL-E 2 are deep learning models developed by OpenAI to generate digital images from natural language descriptions, called "prompts". You need to have Xcode 13 installed in order to have access to Documentation Compiler (DocC) OpenAI's text-to-image model DALL-E 2 is a recent example of diffusion models. It uses diffusion models for both the model's prior (which produces an image embedding given a text caption) and the decoder that generates the final image. In machine learning, diffusion models, also known as diffusion probabilistic models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space.
