The Open Source Toolbox for Creating Your Dream AI App

The AI Revolution Continues

Artificial intelligence has made incredible strides over the last decade, going from a fascinating concept to something woven into our everyday lives. AI assistants on our phones, code autocompletion in software development, smart home devices – AI is pretty much everywhere these days, making our lives more convenient when leveraged properly.

With AI applications proving so powerful and profitable in today’s world, developers and entrepreneurs are constantly looking for new ways to build innovative AI solutions. That’s why I want to highlight 25 open source projects that can help take your AI app ideas to new heights.

We’re talking cutting-edge tech like interactive voice communication with 3D digital characters. There’s some really exciting stuff on the horizon, so stick around because I’ll be sharing a wealth of resources – articles, project guides, ideas, and more to inspire your AI ventures.

Let’s dive in!

1.Taipy

Streamlining AI and Data Apps With Taipy, a Python library for end-to-end application development, you can seamlessly transform data and AI algorithms into production-ready web apps. It simplifies the process with smart execution pipelines, built-in scheduling, deployment tools, and interactive “what-if” analysis capabilities. I know frameworks like Taipy can seem daunting at first. Essentially, it provides an intuitive GUI interface for building data-centric Python apps. You can visualize datasets with charts and graphs, then use GUI controls like sliders to interact with the data. It makes exploring insights far more user-friendly.

While Streamlit is a popular option in this space, its performance can degrade significantly when handling large datasets – not ideal for production use cases. Taipy maintains simplicity and ease of use without sacrificing speed and scalability.

Under the hood, Taipy leverages various supporting libraries to enhance functionality while streamlining development workflows. The latest v3.1 release is particularly impressive:

It now allows visualizing any HTML or Python object using Taipy’s flexible “part” components. That opens the door to creating visualizations with libraries like Folium, Bokeh, Vega-Altair, Matplotlib, and even native Plotly Python support.

Performance saw a big boost thanks to distributed computing improvements. Plus, Taipy and all dependencies are Python 3.12 compatible, ensuring you can leverage the latest tools and libraries.

As an example, check out their chat demo using OpenAI’s GPT-4 API to generate responses (you could plug in any API/model). They’ve also released a VSCode extension called Taipy Studio to accelerate building Taipy apps.

Oh, and you can even deploy your Taipy applications to the cloud using their platform. With over 7,000 GitHub stars and active development on v3, Taipy is definitely a powerful framework to consider for data-centric AI applications.

2. Supabase

The Open Source Firebase Alternative When building an AI app, you need a solid backend, and Supabase is an excellent open source service provider that fits the bill. It allows you to rapidly build applications with features like authentication, realtime functionality, edge functions, storage, and more – Supabase has you covered. They even provide some handy starter kits to hit the ground running, such as Next.js with LangChain integration, Stripe payments with Next.js, or an AI chatbot template. Pretty nifty!

With over 63,000 GitHub stars and a very active community of 27,000+ commits from contributors, Supabase has some serious momentum behind it.

3.Chatwoot

Omnichannel Customer Support, Your Way Chatwoot is all about unifying your customer communications across various channels like email, live chat, Facebook, Twitter, WhatsApp, Instagram, Line, etc. It lets you deliver a seamless experience from a single dashboard. This kind of centralized support could be invaluable if you’re building a community around your AI application. Their documentation is fantastic, with code snippets for integrating each channel like setting up the WhatsApp Cloud API.

You can deploy to Heroku with a click or self-host based on your needs. Chatwoot has over 18,000 GitHub stars and they’re actively developing on version 3.6.

4.CopilotKit

AI Copilots for Apps in Hours This clever library allows integrating powerful AI capabilities into React apps using just two components. They provide fully customizable, Copilot-native UI components out-of-the-box. The core idea is enabling you to build AI chatbots and LLM-powered full-stack applications in a matter of minutes. Pretty awesome when you’re looking to add an AI layer to your product!

5.DALL-E Mini (Craiyon)

Create Images from Text OpenAI made waves with their impressive DALL-E model for generating images from text descriptions. DALL-E Mini, now renamed to Craiyon at the company’s request, aims to reproduce those capabilities using open source models. You can check it out and generate images yourself at the Craiyon.com web app. While not as powerful as DALL-E, it showcases similar image generation techniques in an easy-to-use interface.

DALL-E Mini has over 14,000 stars on GitHub and is actively developed, currently on version 0.1.

6.Deepgram

Voice AI for Your Apps From startups to NASA itself, Deepgram APIs power voice transcription and audio intelligence across millions of minutes each day. Their solutions are fast, accurate, scalable, and cost-effective for speech-to-text and audio data needs. They offer a generous freemium model that’s great for getting started. But the real strength is Deepgram’s incredible visualization capabilities. You can stream live transcription responses, analyze audio files, and compare the performance of different models.

If you’re interested in integrating voice AI into an app, definitely check out their API playground to test-drive the flexibility of their models.

7.InvokeAI

Stable Diffusion Made Simple InvokeAI is a user-friendly implementation of the popular open source Stable Diffusion model for text-to-image and image-to-image generation. It runs on Windows, Mac, Linux and even GPU cards with just 4GB of RAM. They offer an intuitive web UI, a command line interface, and their solution actually powers multiple commercial AI products.InvokeAI is rapidly gaining traction with almost 21,000 GitHub stars.

8.OpenAI – All Your AI Needs in One Place

While Google’s Gemini is a strong contender, OpenAI really has become the household name for cutting-edge AI services like DALL-E for image generation, the Whisper speech model, and of course the flagship GPT-4. With a simple API, you can start building and integrating OpenAI’s models into your own apps and services. Even big companies like Stripe are leveraging GPT-4 to enhance their user experience.

You can play around with creating AI assistants and more via their API playground. OpenAI has really consolidated a ton of powerful AI capabilities under one roof.

9.DeepFaceLab

The Leading Deepfake Tool For better or worse, deepfakes – synthetic media generated with deep learning to manipulate faces/visuals – have gone mainstream. DeepFaceLab is considered the top open source tool for creating deepfakes. This Python-based software can alter videos by swapping faces, removing wrinkles and signs of aging, replacing entire heads, or animating lip movements. The potential applications run the gamut from filmmaking visual effects to more nefarious misinformation.

You can find tutorials on using DeepFaceLab’s various capabilities like face swapping or de-aging. While powerful, it requires some technical know-how to navigate the different workflows for each effect.

Despite being archived in November 2023 after amassing nearly 44,000 GitHub stars, DeepFaceLab remains a go-to deepfake tool with lots of helpful community resources out there.

10.Detectron2

Cutting-Edge Object Detection Created by Facebook AI Research, Detectron2 is the next evolution of their computer vision libraries for object detection and segmentation. It supports state-of-the-art algorithms while being designed for flexibility as new research emerges. The previous Detectron used the aging Caffe framework, so Detectron2 was built on PyTorch to incorporate AI/ML advances more easily based on user feedback. It comes packed with advanced capabilities like DensePose modeling, panoptic feature pyramids, semantic segmentation and more.

In addition to object detection via bounding boxes, Detectron2 can also predict human poses, perform precise instance segmentation, and even do panoptic segmentation to comprehensively parse scenes.

With over 28,000 GitHub stars and being used by 1,600+ developers, Detectron2 has emerged as a powerful, cutting-edge vision AI toolkit.

11. FastAI – Streamlining Deep Learning

FastAI is a versatile deep learning library aimed at empowering both practicing engineers and cutting-edge researchers. For practitioners, it provides high-level components to achieve state-of-the-art results quickly on common deep learning tasks.

But FastAI doesn’t sacrifice flexibility – it also offers low-level utilities for researchers to experiment with novel approaches. It strikes a nifty balance between ease of use and giving you access to tinker under the hood.

The key is FastAI’s layered architecture, which breaks down complex deep learning concepts into manageable abstractions using Python’s dynamic nature and PyTorch’s flexibility. It’s built on a hierarchy of lower-level APIs that serve as composable building blocks.

This modular design means you don’t have to master the lowest-levels right away if you just want to leverage the high-level APIs. But you also have that ability to dive deeper and customize behavior as needed without rewriting everything from scratch.

FastAI provides a nice ramp with dedicated tutorial tracks for beginners, intermediates, and experts looking to level up their deep learning skills. If you’re interested in contributing, they also have a helpful code style guide.

With over 25,000 GitHub stars and already being utilized by 16,000+ developers, FastAI has definitely made its mark as an accessible yet robust deep learning solution.

12. Stable Diffusion – Smooth Text-to-Image Synthesis

In the realm of generative AI models, particularly text-to-image synthesis, you may have heard about “stable diffusion” techniques. But what does that actually mean?

Essentially, stable diffusion refers to gradually and smoothly transferring information from text descriptions into the corresponding image generation process within the model’s latent space. This diffusion mechanism ensures the textual concepts are consistently represented throughout, avoiding abrupt changes or instabilities.

The result is high-quality, realistic images that accurately reflect the input text descriptions. Stable diffusion helps the model maintain control and fidelity during the entire generation process.

If you want to experiment with stable diffusion yourself, a simple option is using the diffusers library to download and sample pre-trained models.

One popular configuration is the Stable Diffusion v1 model with an 860M UNet and a powerful CLIP ViT-L/14 text encoder, paired with an autoencoder. It was initially pre-trained on 256×256 images, then fine-tuned for higher 512×512 resolution.

Stable Diffusion has rapidly gained traction with around 64,000 GitHub stars as of late, showcasing the intense interest in these text-to-image generative techniques.

13. Mocap Drones – Affordable Motion Capture

This intriguing open source project aims to create a low-cost, room-scale motion capture system by leveraging drones equipped with cameras to track movements.

Fair warning, getting this up and running requires some heavy lifting like compiling the SFM (structure from motion) module for OpenCV directly from source. Not the most user-friendly setup, but that’s open source software for you sometimes!

Once you’ve installed the node dependencies from the computer_code directory by running yarn install and yarn run dev, you’ll get a front-end interface to view the motion capture streaming in a web UI.

In a separate terminal, initiate the Python backend with python3 api/index.py to handle receiving the camera streams and performing all the motion capture computations.

It’s a very recent and niche project in the open source world, but has already generated over 900 GitHub stars from folks interested in affordable motion tracking systems.

14.Whisper Speech

Inverting Whisper for Text-to-Speech Similar to the innovative Stable Diffusion model but for speech synthesis, this project inverts OpenAI’s powerful Whisper speech recognition model to generate speech from text. It’s an impressively customizable and capable text-to-speech system.

The team ensures they only use properly licensed speech recordings, and all the code is open source – making it safe for commercial applications. Currently, the models are trained on the English LibreLight dataset, but there’s certainly potential for expanding to more languages.

You can dig into the model architecture details, listen to sample voices, and even try it out yourself using Google Colab notebooks. As a very recent project, it has already amassed over 3,000 stars on GitHub from the AI community.

15.eSpeak NG

Multilingual Speech Synthesis eSpeak NG is a compact open source text-to-speech synthesizer compatible with Linux, Windows, Android, and more. One of its standout capabilities is supporting over 100 languages and accents right out of the box! It’s built upon the solid foundation of the original eSpeak engine created by Jonathan Duddington.

The installation guides cover the process for various operating systems. For Debian-based distros like Ubuntu or Linux Mint, you can simply apt-get install it. You can reference the full list of supported languages, browse the documentation, and review all the features.

What’s particularly interesting about eSpeak NG is how it translates text into phoneme codes, showcasing its potential as a front-end for more sophisticated speech synthesis engines.

It has garnered over 2,700 stars on GitHub so far.

16.Chatbot UI – AI Chat Interface for Any Model

We’re all familiar with ChatGPT’s slick conversational interface by now. This handy Chatbot UI project essentially provides that same user-friendly chat experience as a front-end for any AI model you want to integrate!

The installation guide walks through setting up requirements like Docker, the Supabase CLI, and other dependencies. You can see a demo of it in action and browse the docs. Under the hood, it leverages Supabase with Postgres as the database layer – which is why we covered that project earlier.

While Vercel has a similar chatbot UI offering, this open source alternative has amassed over 25,000 GitHub stars as developers’ top choice.

17.GPT-4 & LangChain – Chatbot for Large PDFs

Here’s a clever implementation allowing you to build a ChatGPT-style chatbot interface for asking questions about large PDF document collections using the powerful GPT-4 language model. It’s built on LangChain to simplify developing scalable LLM applications, using Pinecone as a vector store for your PDFs and text data. LangChain fetches relevant document chunks from Pinecone to answer queries using GPT-4.

The dev guide covers the setup involving cloning the repo, installing dependencies, and providing your API keys.

Despite being an extremely recent project, it has already racked up over 14,000 GitHub stars in just 34 commits from the AI community excitedly trying it out.

18.Amica – Voice Interact with 3D Characters

Amica is a really cool open source interface for having interactive voice conversations with 3D avatars or characters! You can import custom VRM character models, generate contextual response text with emotional expressions, and even adjust the voice synthesis to suit the character.

It leverages technologies like Three.js for 3D rendering, OpenAI models, the Whisper speech recognition system, Bakllava for computer vision, and more under the hood. You can read about Amica’s core concepts and architecture to understand how all the pieces fit together.

To run it locally, Amica uses the Tauri framework to build the cross-platform desktop application – which we’ll cover a bit later in this rundown. While still a nascent project with around 400 GitHub stars so far, Amica demonstrates the potential for interactive AI-driven virtual characters and assistants.

19.Hugging Face Transformers – Cutting-Edge AI Models Made Accessible

The Hugging Face Transformers library truly makes state-of-the-art machine learning models accessible to developers across various domains like natural language processing, computer vision, and multi-modal tasks. It’s built on top of PyTorch, TensorFlow and JAX to seamlessly integrate advanced AI capabilities.

Their vast model hub contains pre-trained models for text tasks like classification, summarization, translation, generation and more – supporting over 100 languages. You can also leverage models for vision-based tasks like object detection and image classification. Heck, there are even models that can handle multitask scenarios spanning text, images, documents and video!

The docs provide tons of examples showcasing the different use cases across modalities. With seamless integration across PyTorch, TensorFlow and JAX, you could even train models using one framework and load them in another for deployment.

With a staggering 120,000+ GitHub stars and being utilized by over 142,000 developers, the Transformers library has rapidly become the de facto standard for democratizing cutting-edge AI across the industry and research community.

20.LLAMA – Facebook’s Open Language Models

From the forward-thinking researchers at Facebook comes LLaMA, the “Large Language Model Meta AI” – a powerful initiative to empower individuals, creators, businesses and researchers to responsibly experiment and innovate using large language models.

The latest LLaMA 2 release provides the model weights and starter code for pre-trained language models ranging from 7 all the way up to a whopping 70 billion parameters! There’s a helpful installation guide covering the steps to clone the repo, install dependencies, register and download the models from Meta’s site, and run them locally.

In addition to the official release, projects like OLLaMA also build upon the open LLaMA models. With over 50,000 GitHub stars, it offers extensive docs and research potential using these powerful language models.

21.Fonoster – The Twilio Alternative

Tired of spiraling cloud communications costs? Fonoster is an innovative open source project aiming to provide a comprehensive, programmable telecom stack as an affordable alternative to Twilio and others. It allows businesses to seamlessly connect telephony services to internet applications.

They offer a generous free tier ideal for getting started and testing the waters. The codebase has seen over 250 releases and currently sits at over 6,000 GitHub stars from companies seeking open and cost-effective cloud communications solutions.

22.DIPY – Premier Python Library for Medical Imaging

When it comes to analyzing 3D volumetric medical image data like MRI or CT scans, DIPY is basically the gold standard Python library out there. It’s packed with algorithms for signal processing, spatial normalization, machine learning-powered analysis, visualization and more.

DIPY contains specialized methods optimized for computational anatomy using data from diffusion, perfusion and structural imaging techniques. With over 428,000 downloads from the Python Package Index, it has become the de facto toolkit for medical imaging research and applications.

While DIPY itself is hardly a new project with around 600 GitHub stars, its comprehensive capabilities for working with 3D/4D medical data continue to make it an indispensable resource for the field.

23.Elasticsearch – The Powerful Search Solution

Elasticsearch is an incredibly robust and scalable open source search and analytics engine used by major companies across industries. As the core of the Elastic Stack, it serves as a centralized, distributed repository for lightning-fast querying, with fine-tuned relevance modeling and powerful data analysis at scale.

Elasticsearch supports a wide array of flexible use cases around full-text search, log analytics, security analytics, business analytics, geospatial data analysis and more. It uses standard RESTful APIs with JSON over HTTP, with official clients for Java, Python, .NET, SQL and PHP, among other languages.

The main downside is the lack of a free tier for learning and non-production use cases. However, you can still sign up for the free trial to explore the powerful distributed search and analytics architecture that has made Elasticsearch a canonical open source solution.

Boasting over 67,000 GitHub stars, nearly 1,900 contributors and active development on version 8, the project continues rapidly iterating to serve the growing data demands of organizations everywhere.

24.Tauri – Build Lean, Cross-Platform Desktop Apps

Tauri is a brilliant toolkit designed to empower developers in creating sleek desktop applications for all major platforms like Windows, macOS and Linux. The core innovation? Using essentially any frontend web technology for your app’s user interface!

While Tauri’s core utilities are built using systems programming language Rust, the command line interface leverages Node.js for a seamless polyglot development experience. For window handling, Tauri uses the platform-agnostic Tao library across desktop and mobile environments.

When it comes to actually rendering your app’s UI, Tauri relies on WRY which provides a unified webview experience – whether that’s WebView2 on Windows, WKWebView on Apple platforms, WebKitGTK on Linux, or the Android System WebView.

This opens up building desktop apps using familiar web frameworks and languages like React, Vue, Svelte, HTML/JS/CSS and many more. You can even build custom command line tools using Tauri! The possibilities are endless.

With over 75,000 GitHub stars and 800+ releases under its belt, Tauri has rapidly become a game-changing solution for creating lean, performant yet familiar cross-platform desktop experiences using web skillsets.

25.AutoGPT – Personal AI Assistant

While ChatGPT turned lots of heads, the AutoGPT project takes the concept of autonomous AI agents to entirely new heights. At its core lies a semi-autonomous system powered by large language models designed to ideally perform any task for the end user!

AutoGPT is comprised of four main components:

You can check out video tutorials breaking down how AutoGPT works. The open source docs dive into the various components, analyzing its novel architecture and inner workings.

Even if you’re not an AI expert, playing around with AutoGPT can open your eyes to the mind-bending potential of these autonomous AI agents to fundamentally reshape how we accomplish tasks and goals.

It’s no surprise the project has skyrocketed to over 159,000 GitHub stars with its incredibly ambitious mission of creating highly capable personal AI assistants.

Conclusion

So in summary, we covered a diverse array of powerful open source AI projects – from voice AI and image generation to autonomous agents and cutting-edge language models. While some like Hugging Face Transformers and Elasticsearch have become industry standards, novelties like AutoGPT and LLAMA showcase the rapid pace of innovation happening in this space.

Whether you’re a developer, researcher or just someone fascinated by AI’s potential, there’s certainly no shortage of compelling open source projects to explore and build upon. AI is fundamentally reshaping our world, and open source will undoubtedly play a pivotal role in democratizing these transformative technologies.

I’m really excited to see what mindblowing AI breakthroughs the community cooks up next! Let me know which projects you found most interesting or surprising in the comments.