Top 12 Open Source AI Projects to Add to Your Tech Stack

Since the mid-1980s, open source software has continued to grow in development and applications as an alternative to expensive, proprietary software. The communities that support open source development consistently look for new ways to make infrastructure more interoperable and scalable, and support new technologies, and AI is no exception. As more organizations increase their investment in AI, more developers are looking to see how they can integrate open source AI as part of their tech stack.

Many developers now prefer open-source AI frameworks over proprietary APIs and software. According to the Linux Foundation’s report, The Economic and Workforce Impacts of Open Source AI, 89% of organizations that have adopted AI use open-source AI in some form for their infrastructure.

In this article, we explore the widespread adoption of open source AI among developers and researchers, driven by significant investments from tech giants. We also delve into how this adoption promises transformative technologies for organizations.

What is open source AI?

Open source AI is an AI system, model, or algorithm with freely accessible source code that you can use, modify, study, and share for your AI projects. It exists under free and open source software licenses, such as the Apache, MIT, BSD-3, and GNU General Public licenses.

This openness encourages the creation of creative AI applications as a community of enthusiasts collaborates, expediting the development of practical solutions.

These projects, available on platforms like GitHub, enable innovation across sectors such as healthcare, finance, and education. The availability of AI frameworks on diverse operating systems like Microsoft Windows, Linux, iOS, and Android empowers developers to address complex challenges efficiently. The more popular applications of open source AI include large language models, machine translation tools, and chatbots.

Open source AI democratizes access to cutting-edge technologies and accelerates the development of impactful applications for a range of enterprise use cases.

When to use open source AI

Using open source AI can be beneficial in certain use cases, depending on your goals for using AI, as well as your organization’s resources. This is because open source AI has minimal (if any) overhead costs, can be adjusted to your organization’s specific data sets, and offers customizable features compared to closed source or proprietary AI models. With these characteristics, open source AI is ideal if you have the time to experiment with custom data sets, have specific parameters around how you can use company data with third-party software, or want to try out a specialized AI use case.

You should use open source AI specifically if you:

• Have custom organizational data you want to use for model training and deployment.

• Want to train the AI model for a specific industry or use case, and have it understand the context and terminology of your application.

• Require specific functions or features that aren’t available in out-of-the-box AI models or platforms.

• Need your AI model to match the company style, workflow, and formats with its specific outputs.

Training data for open source AI

Open source AI models require data to effectively produce accurate and relevant outcomes. Yet how do you gather data for a technology that is based on community support and available to both enthusiasts and professional developers? You might already be using these open source models with your company data, but if you don’t have readily available data, you must find it online and make sure it is also open source and is properly licensed so you can use it without pushback. This has led to several open source large-language model training sets being curated and available within the open source community:

• Common Crawl

• Starcoder Data

• Red Pajama

• ROOTS

When using data for your open source AI model, you want to make sure that the data is effectively labeled, clear of any duplicate entries, has enough data for the model to learn new information, and does not contain any sensitive or proprietary information.

Advantages of open source AI

Open-source AI has emerged as a powerful force in driving innovation and accessibility across various fields. Its unique characteristics offer significant advantages for developers, researchers, and organizations alike. The key benefits include:

• Diverse use cases: Open source AI platform offers a wide array of practical applications, such as real-time fraud detection, medical image analysis, personalized recommendations, and tailored learning experiences.

• Accessibility: Open source AI projects and models are readily accessible to developers, researchers, and organizations, facilitating their widespread adoption and use. Because it is freely available, it also has a low overhead cost.

• Community engagement: Using open source AI provides organizations with access to a diverse community of developers who continuously contribute to the enhancement and advancement of AI tools.

• Transparency and iterative improvement: Open source AI’s collaborative nature fosters transparency and facilitates ongoing improvement, resulting in the development of feature-rich, dependable, and modular tools.

• Vendor neutrality: Open source AI solutions ensure organizations are not bound to any specific vendor, which allows you to build out your tech stack with flexibility and helps you achieve more interoperability between software tools.

Challenges and considerations of open source AI

While open-source AI does provide flexibility and it’s crucial to acknowledge and mitigate its inherent challenges:

• Risk of misalignment and failure: Embarking on custom AI development without clear objectives can result in misaligned outcomes, resource waste, and project failure. These projects also usually have a higher learning curve and require specific programming and data analysis skill sets.

• Bias in algorithms: Biased algorithms have the potential to generate flawed results and perpetuate harmful assumptions, undermining the reliability and usefulness of AI solutions.

• Security concerns: The accessibility of open source AI raises security concerns, as malicious actors could exploit these tools to manipulate outcomes or create harmful content.

• Data-related issues: Biased training data can lead to discriminatory outcomes, while data drift and labeling errors can render AI models ineffective and unreliable. There are also concerns around how to effectively gather open source data for AI models.

• Outsourced technology risks: Enterprises using open-source AI solutions from external sources may expose their stakeholders to risks, emphasizing the importance of cautious consideration and responsible implementation.

Top 10 open source AI platforms for innovation

Interest in open source AI continues to grow, and there are a handful of available models that you can use to build out programs and applications for a variety of use cases. These 12 open source AI tools and platforms can be used for machine learning, chatbots, GPU-accelerated AI, deep learning, and data analysis.

1. TensorFlow

TensorFlow is a versatile learning framework compatible with Python and JavaScript programming languages. It empowers programmers to create and deploy machine learning models across web, mobile, edge devices, and production environments. TensorFlow provides APIs, model libraries, tutorials, experimentation tools, and a large community that enables both novices and seasoned practitioners to innovate and experiment with AI effectively.

Focus: Numerical computation and large-scale machine learning.

Strengths:

• Flexible computational graph for diverse architectures.

• Extensive community and ecosystem.

• Production-ready scalability and performance.

Weaknesses:

• It can be complex for beginners due to its more complex API.

• Primarily focused on numerical data, less suited for symbolic reasoning.

2. PyTorch

PyTorch offers an intuitive interface that facilitates easier debugging and dynamic computation graphs to construct deep learning models. Its integration with Python libraries and support for GPU acceleration ensures efficient model training and experimentation. PyTorch is favored by researchers and developers for its capability in rapid prototyping for software development and deep learning research. It also has a well-developed documentation library and large community that can help you with questions, tutorials, and tasks.

Focus: Deep learning, especially computer vision and natural language processing.

Strengths:

• Dynamic computation graphs enable rapid experimentation.

• Pythonic API for ease of use and readability.

• Large community and active development.

Weaknesses:

• Can be less performant than TensorFlow for very large models.

• Primarily focused on deep learning, less versatile for broader AI tasks.

3. Keras

Keras, a Python-based neural networks library, is renowned for its user-friendly interface and modular design that facilitates swift prototyping of deep learning models. The library supports deployments across servers, mobile devices, and browsers. Its notable feature is its high-level API, which is both intuitive for beginners and extensive for advanced users, making it a preferred choice to learn about open source AI, machine learning development, and intricate deep learning tasks. It also has an active community and widely available documentation to help your development and testing workflows.

Focus: High-level API for building and training deep learning models.

Strengths:

• User-friendly and approachable API, especially for beginners.

• Runs on top of various backends like TensorFlow, PyTorch, and JAX, offering flexibility.

• Efficient implementation with XLA compilation for faster training and inference.

Weaknesses:

• Lower-level control compared to directly using a backend library.

• Less performant for highly customized or complex architectures.

• Primarily focused on deep learning, less suitable for classical machine learning tasks.

4. Giskard

Giskard is an open source AI platform that helps you evaluate and test large language model data for quality, accuracy, and security. It is designed to ensure data compliance, reduce algorithm hallucinations, and increase overall code and model security. You can generate instant domain-specific tests to scan for vulnerabilities, integrate and automate AI model testing into CI/CD pipelines, and scan for performance bias, prompt injection, overconfidence, and data leakage.

Focus: Testing platform for AI models for compliance, security, and data quality.

Strengths

• Designed to detect hallucinations and bias. Also evaluates models for explainability and robustness.

• Automates vulnerability detection and easily integrates into CI/CD pipelines.

• Helps increase overall transparency and data quality in AI systems.

Weaknesses

• Limited to AI data quality and testing use cases.

• May require custom configurations or dedicated support for more complicated setups or specific features.

5. Rasa

Rasa is an open-source conversational AI platform designed specifically for chatbots and virtual assistant creation. Leveraging machine learning technology, it facilitates natural language processing and generation for conversational AI applications. Noted for its flexibility, Rasa empowers developers to customize and deploy conversational agents across different industries. It does also offer Rasa Pro and Rasa Enterprise, which are paid versions that come with dedicated support.

Focus: Conversational AI and chatbot development.

Strengths:

• Pre-built components for common chatbot functionalities.

• Flexible architecture for customization and integration.

• Community support and active development.

Weaknesses:

• Primarily focused on chatbots, less versatile for other natural language processing (NLP) tasks.

• Might require additional expertise for complex conversational designs.

6. Amazon SageMaker

Amazon SageMaker, part of Amazon Web Services (AWS), is a cloud-based solution that streamlines the building, training, and deployment of machine learning models at scale. It provides a fully managed platform equipped with tools for data labeling, model training, and deployment, catering to developers, data scientists, and machine learning practitioners. It allows you to create AutoML jobs for model training, batch preprocess data sets, experiment reconstruction, and manage multiple projects.

Focus: Cloud-based platform for building, training, and deploying machine learning models.

Strengths:

• Offers a wide range of pre-built algorithms and tools for various tasks.

• Scalable infrastructure for managing large-scale AI projects.

• Integration with other AWS products.

Weaknesses:

• Vendor lock-in to the Amazon ecosystem.

• Pricing can be complex for resource-intensive projects.

• Less customization compared to purely open-source platforms.

7. GPT4All

Nomic AI’s GPT4All is an open source, private, and local chatbot that you can run on your devices. It can run on CPU—and GPU-based hardware, whether you’re online or offline. It can also connect to your local documents on your devices to inform your responses, and it can support over 1000 open source large language models. It comes with a large community for support and resources, is a fully customizable chatbot, and the code is completely available on GitHub under the MIT license.

Focus: Chatbot designed for local workloads on multiple devices.

Strengths:

• Can run without an internet connection on laptops or mobile devices.

• Support for DeepSeek R1, LLaMa, Mistral, Nous-Herms, and many more open source large language models.

• Keeps private data local and secure on your machines.

Weaknesses:

• Limited use cases, as mainly just designed to function as a chatbot.

• Potentially steep learning curve for non-developer users.

8. Scikit-learn

Scikit-learn is a potent Python library designed for machine learning and predictive data analysis. It offers scalable supervised and unsupervised learning algorithms, playing a crucial role in the AI frameworks for organizations across industries. You can use it for AI classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. With its straightforward setup, reusable components, and vibrant community, scikit-learn proves accessible and effective for data mining and analysis across diverse applications.

Focus: Machine learning library for classical algorithms and data science.

Strengths:

• Wide range of well-tested and documented algorithms for common tasks.

• Easy integration with other Python data science libraries like NumPy and Pandas.

• Active community and extensive learning resources.

Weaknesses:

• Primarily focused on classical algorithms, with limited support for deep learning.

• Less performant for very large datasets compared to specialized libraries.

9. OpenCV

OpenCV, a library designed for computer vision applications, has a comprehensive library, forums, over 2500 algorithms, courses, and a community to support its work and help you build out open source AI applications. It serves as an excellent option for organizations aiming to automate tasks, analyze visual data, and develop innovative solutions. Operating under the Apache 2 License, OpenCV’s extensiveness means you can easily adapt it to your organization’s requirements, regardless of your project size or available resources.

Focus: Real-time computer vision library for image and video processing.

Strengths:

• Extensive functionality for image manipulation, object detection, and video analysis.

• High performance and real-time capabilities.

• Cross-platform support and integration with various programming languages.

Weaknesses:

• Primarily focused on computer vision, not suitable for broader AI tasks.

• It can have a steeper learning curve for more advanced applications.

10. H2O.ai

H2O.ai offers an end-to-end generative AI platform for machine learning model construction and deployment. Its open source h2oGPT is an easy-to-install package with a large language model, embedding model, database for document embeddings, a CLI, and GUI. You can use it with plain text files, Word, PDFs, Markdown, HTML, epub, and email files. Additionally, H2O.ai provides enterprise-grade support and simple integration with widely used data science tools.

Focus: Open source distributed machine learning platform with both paid and free options.

Strengths:

• Scalable infrastructure for building and deploying models on big data.

• Automatic model tuning and hyperparameter optimization.

• User-friendly interface and visual workflows alongside API access.

Weaknesses:

• The free version has limited features and resources.

• Primarily focused on business use cases, less suitable for research or experimentation.

• Might require additional resources for maintenance and administration.

11. Hugging Face Transformers

Hugging Face Transformers are open source, pre-trained models for inference and training. You can use them to train models, build applications, and generate text for large language models. They offer APIs and support for PyTorch, TensorFlow, and JAX frameworks and streamline the model-building process by providing all the necessary components for model training or inference. This includes a pipeline to support machine learning tasks, a comprehensive trainer, and functions to help with text generation. There are currently over 63,000 transformers available for a wide variety of AI and machine learning applications.

Focus: Pre-trained models for inference or machine learning application development

Strengths:

• Comprehensive documentation and a regularly active community that can provide assistance, new transformers, and documentation.

• Transformers are available for a wide variety of machine learning tasks, such as answer extraction, object detection, summary creation, audio transcription, and image segmentation.

• User-friendly interface and easy integration with data science tools.

• A community that regularly evolves and produces innovations and integrates updates.

Weaknesses:

• Certain transformers and models may require extensive computing resources.

• Hub includes an extensive array of models, some of which might be out of date or might not receive regular updates.

12. MindsDB

MindsDB is an open source AI offering developers build AI-powered applications and automates machine learning frameworks into data stacks. It has a federated query engine that you can use to handle data across applications, data warehouses, and databases for your extensive data analysis requirements. Its automation features remove the need to build custom automation logic for predictions, frequently retrain model data, or consistently move data. It also offers integration with databases, vector stores, and applications to accelerate real-time AI workload development.

Focus: AI data automation and data connectivity for application development

Strengths:

• Open source server that can be deployed anywhere and connect to over hundreds of data integrations.

• A stable community that provides support and resources for you and your team.

• Can easily scale up to handle large AI and machine learning workloads.

• Also has a managed service offering for more direct support and deployment assistance.

Weaknesses:

• Limited direct integrations. More specific integrations might require custom code or configuration.

• Potentially steep learning curve during program adoption.

The future of open source AI

Open source AI is reshaping enterprise scalability and transformation. Its influence spans industries, driving widespread adoption and deeper AI integration. Advancements in NLP, tools like Hugging Face Transformers, and computer vision libraries like OpenCV promise complex applications such as advanced chatbots, image recognition, and automation. Projects such as OpenAssistant and UnslothAI are showcasing a variety of applications with open source AI and just how you can use the technology across personal and professional applications.

However, adopting open-source AI requires careful navigation and strong partnerships. While accessible, it often requires significant fine-tuning for enterprise effectiveness, trust, and safety. Bespoke AI solutions may be necessary, as open-source tools may fall short and also bring about potential legal and data privacy concerns. Organizations must invest in resources and expertise for effective use.

Resources

• Interacting with the Open Source Model LLaVA 1.5

• Introduction to TensorFlow: Build AI Across Domains

• Devstral: An Open-Source Agentic LLM for Software Engineering

Open source AI FAQs

What is open source AI?

Open source AI is an AI system in which code can be shared, studied, modified, and distributed for commercial use at no cost. It is distributed under free and open source software licenses like Apache, MIT, and GNU General Public licenses.

What is the difference between open source and closed source AI?

While open source AI code is freely available and can be largely customized depending on what features you require, closed source AI keeps its source code private and has vendor-provided features.

When would I use open source AI?

Open source AI is highly customizable and has no overhead cost. You can benefit from using it for applications that have a lot of specific requirements, custom data sets, or want to have a large return on investment.

What is the best open source AI option?

The best open source option will depend on your desired use case. However, you can find code that supports large language, computer vision, machine learning, and chatbots.

Build with DigitalOcean’s GenAI platform

DigitalOcean’s GenAI Platform makes it easier to build and deploy AI agents without managing complex infrastructure. Our fully-managed service gives you access to industry-leading models from Meta, Mistral AI, and Anthropic with must-have features for creating AI/ML applications.

Key features include:

• RAG workflows for building agents that reference your data

• Guardrails to create safer, on-brand agent experiences

• Function calling capabilities for real-time information access

• Agent routing for handling multiple tasks

• Fine-tuning tools to create custom models with your data

Don’t just take our word for it—see for yourself. Get started with AI and machine learning at DigitalOcean to get access to everything you need to build, run, and manage the next big thing.