Top 10 Open Source Artificial Intelligence Software in 2021


Open source AI is defined as an artificial intelligence technology that is publicly available for commercial and non-commercial use under various open source licenses. Open source AI includes datasets, prebuilt algorithms, and ready-to-use interfaces to help you get started with AI app development. From 48% in 2021, 65% of enterprises are expected to start using open source AI technology in the next two years. This article shortlists the top 10 open source software that you need to consider for your next AI project. 

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What Is Open Source Artificial Intelligence (AI)?

Open source AI is an artificial intelligence technology that is publicly available for commercial and non-commercial use under various open source licenses. Open source AI could include various technologies that are helpful for product teams, independent app developers, and enterprises. 

These include: 

    • Open source datasets: AI software is trained on data, and in open source AI, the training data and test data are freely available. Even if you aren’t using open source AI software, these datasets will be available to you to make your models more reliable and accurate. 
    • Open source algorithms: Here, the algorithm itself and its core statistical model are made open source. Typically, they are available as open source algorithm libraries that you can deploy as is, train using open source or enterprise data, or configure the code to create customized AI applications. 
    • Open source UI: The developer interface necessary for leveraging open source AI effectively can also be available as an open source. These can range from command line interfaces to sophisticated GUIs. You could even find a UI overlay that works with a different algorithm library without containing one of its own. 

Open-source AI is different from freeware AI applications — the underlying code is exposed to the user and open for modifications and implementations in scenarios other than the ones originally intended. Open source also comes with a large active community, where developers can both contribute and ask for help. 

Today, open-source artificial intelligence is a thriving segment, benefitting developers and enterprises alike. ResearchOpens a new window suggests that while 48% of enterprises currently use open source technology for AI/ML, this number will rise to 65% by 2023. 

Learn More: What Is Artificial Intelligence: History, Types, Applications, Benefits, Challenges, and Future of AI

Top 10 Open Source AI Software in 2021

As mentioned, AI software can come in various shapes and sizes, spanning datasets, algorithms, and UI, or any combination of the three. In this roundup (arranged in alphabetical order), we focus on the top ten open source AI algorithm libraries that you can use to build software applications, addressing common AI use cases like computer vision, object recognition, character recognition, speech-to-text, and more. 

Disclaimer: This is a curated list based on publicly available information and may include websites targeting mid-to-large enterprises. Readers are advised to conduct their own final research to ensure the best fit for their unique organizational needs.

Top 10 Open Source AI Software in 2021

1. Acumos AI

Overview: Founded in 2019, Acumos is a relatively new entrant in the open source AI software segment – but it is backed by industry leaders AT&T and TechMahindra. The two companies wanted to buck the trend of tech giants like Microsoft, Google, and Apple leading open source innovations and make AI available for commercial deployments. That’s how they introduced Acumos AI, a design studio based on Linux, to help integrate other frameworks and develop cloud-based AI apps. 

Key features: Some key features of Acumos AI include: 

    • The Acumos marketplace to discover and deploy various AI libraries 
    • Onboarding support to enable interoperability
    • A graphical tool to manage AI models in preparation for a runtime environment 
    • A community to develop marketplace solutions 
    • Dockerization support to run AI within a container 
    • API connectivity and microservices tools

USP: A major USP of Acumos AI is its GUI design studio feature. It simplifies the development process through visual programming and streamlined AI development, making it more accessible. You can also leverage the onboarding tools to enable interoperability with other frameworks like TensorFlow, H2O, etc.

Editorial comments: Acumos is a compelling open source option for those interested in greater AI accessibility. It standardizes the infrastructure stack so you can develop and deploy AI applications faster, and it is compatible with most major languages, including Java, Python, and R. The Acumos AI Platform drives the end-to-end lifecycle of AI/ML apps from model creation to execution, enrichment, and publishing in a marketplace. 

2. ClearML

Overview: ClearML is the result of the recent rebranding of Allegro AI, a provider of open source tools for data scientists and machine learning labs. Along with the rebranding, ClearML announced a free hosted plan to give data scientists the freedom to manage AI/ML experiments and orchestrate workloads without investing in additional resources. ClearML can be leveraged as an MLOps solution, ready for implementation via just two lines of code. 

Key features: Some key features of ClearML include: 

    • An ecosystem for experiment management with zero integration hassles 
    • Experiment orchestration inside containers (development as well as production)
    • Scheduling of jobs via priority queues and resource allocation
    • Remote allocation of computing resources through a single line of command 
    • The ability to run Bayesian hyperparameter optimization with zero integration 
    • Collaborative workspace with optional permission management 

USP: ClearML is among the few open source AI software that comes with optional commercial add-ons such as priority support, well-defined SLAs, and managed services. If you want to gain from the benefits of open source (no vendor dependency, cross-ecosystem compatibility, etc.) while also having a commercial vendor partner at hand, ClearML is an excellent choice. 

Editorial comments: ClearML has a host of compelling capabilities that are rare in the open source segment. For instance, you get 100GB of free storage, a 3-collaborator workspace, low integration development, and support for on-premise deployment even when you opt for ClearML Free. On the other end of the spectrum, there’s also a feature store for more advanced development. In other words, ClearML perfectly balances the simplicity of open source with the feature set of traditionally commercial platforms. 


Overview: Founded in 2012, H2O has been at the forefront of open source AI innovation for almost a decade. The company works with tech giants like NVIDIA, IBM, Intel, and Google, among others, to drive large-scale AI and ML products. The company was recently accredited by the Infocomm Media Development Authority (IMDA) of Singapore, further cementing its global presence and allowing Singapore’s public sector organizations to gain from H2O. 

Key features:’s key features include: 

    • Integration with Hadoop and Spark for big data-based AI modeling 
    • Library of ML algorithms including supervised and unsupervised learning 
    • Built-in intelligence to anticipate schemas of incoming datasets 
    • Support for data ingestion across multiple sources in diverse formats
    • Driverless AI to help non-technical users prepare data, set parameters, and select algorithms for addressing specific business problems
    • Easy-to-use web UI navigation through Flow 

USP: H2O’s biggest USP is its AI hybrid cloud capability. This means it is an end-to-end platform that lets you prepare, model, operate, develop, and consume AI (in collaboration with others) within a centralized environment. Since it is deployed with Kubernetes, you can run it on any cloud or even on-premise infrastructure. 

Editorial comments: H2O is excellent for enterprises just getting started with AI, as you can begin with the open source platform that trains on your enterprise data. As more applications are developed, H2O can support your enterprise journey through training, enhancement requests, auto-ML, and other capabilities. 


Overview: Mycroft is an open source voice assistant that you can run in any ecosystem. The company has won several awards over the years and is backed by strategic investments from large companies such as Jaguar Landrover. Essentially, Mycroft powers various elements of the voice stack using open source AI technology. There is a large community of users, developers, and translators, to constantly improve the AI algorithms. 

Key features: Some of the key features of include: 

    • The option to purchase a hardware shell that contains the voice assistant (available in three versions – Mark 1, Mark 2, and Mark 3)
    • Releases available for Android, Linux, and Docker, as well as macOS and Windows via a VirtualBox VM
    • Modular architecture with replaceable internal components 
    • Speech to text conversion in partnership with Mozilla’s Common Voice Project and DeepSpeech software
    • Intent parsing, by converting natural language into machine-readable data structures
    • Text to speech conversion based on the Festival Lite speech synthesis system

USP: The biggest USP of Mycroft is that it is relatively easy to get started. It offers a private AI-based voice alternative to commercial deployments like Alexa or Siri, which will inevitably mine data on some level. That’s why it has been involved in several public sector and philanthropic initiatives, where data privacy is essential. 

Editorial comments: Unlike most open source AI software, Mycroft is staunchly use-case-focused. However, if you have a voice assistant requirement and want to opt for open source, Mycroft is among the most powerful options available. 

5. OpenCV

Overview: Open Source Computer Vision Library or OpenCV is a rich library of AI algorithms intended to address real-time computer vision functionalities. It was launched in the early days of AI development as part of an Intel research project back in 1999. In 2012, it was taken over by a non-profit foundation, which now runs the community, user support, and developer assistance. In 2020, the OpenCV AI Kit campaign was launched to collect funds for new hardware modules. 

Key features: Some of the key features of OpenCV include: 

    • Proven applications across a variety of use cases, including facial recognition, human-computer interactions, object detection, motion tracking, and more
    • ML library containing algorithms for decision tree learning, k-nearest neighbor algorithm, artificial neural networks, random forest, and deep neural networks (DNN), among others 
    • Compatible with all desktop ecosystems as well as Android, iOS, Maemo, and BlackBerry 10
    • Paid courses on computer vision, use cases, and deep learning 
    • Primarily designed in C++, along with wrappers in Java, Python, etc. 
    • A hardware store for spatial imaging cameras 

USP: OpenCV is among the industry’s longest-running and most battle-tested open source AI software libraries. Since its inception by Intel, it has developed from a C++ native computer vision library to a more widely accessible and implementation-ready platform. 

Editorial comments: Companies looking to leverage AI-based computer vision to develop facial recognition systems, augmented reality apps, and the like should consider OpenCV. Its rich library of algorithms, coupled with learning support and complementary hardware, makes for a 360-degree solution. 

6. OpenNN

Overview: OpenNN is an open source AI software library for implementing neural networks and ML. Its primary use cases include customer intelligence and industry-specific analytics, including their predictive applications. The company developing and maintaining OpenNN is called Artelnics, known for its pathbreaking AI and big data research. Importantly, OpenNN does not specialize in computer vision or natural language processing, unlike some of the other open source software on this list. 

Key features: Some key features of OpenNN include:

    • C++-based software library 
    • Regression analysis to model ML outputs 
    • Data classification to assign specific patterns 
    • Forecasting based on historical datasets 
    • Association mapping between two correlated variables 
    • A neural designer tool to simplify the process of building neural networks

USP: OpenNN’s most significant USP is its ability to provide predictive insights. You can leverage this open source AI software to build apps for customer segmentation, early healthcare diagnosis, predictive maintenance for equipment, and many more. 

Editorial comments: Companies, teams, and independent developers looking for a pure-play open source AI software library (without any commercial bells and whistles) should definitely consider OpenNN – particularly for predictive analytics use cases. You can gain from its rich set of documentation, which also acts as a helpful tutorial to get started. 

7. PyTorch

Overview: PyTorch improves upon the foundational torch framework for ML that uses the Lua programming language. Facebook’s AI research lab launched PyTorch as a Python-based interface for AI/ML app development under an open source license in 2016. There’s a C++ interface for PyTorch available as well. Today, PyTorch has developed into a rich ecosystem that gives you all the tools necessary for accelerating AI development from research to production. 

Key features: Some of the key features of PyTorch include:

    • A production-ready environment powered by TorchServe for quickly deploying models 
    • A distributed backend architecture to enable distributed training and performance optimization 
    • Algorithms for computer vision as well as natural language processing 
    • Supported by all major public clouds for flexible development 
    • End-to-end workflow from Python to iOS/Android for mobile app development 
    • Native exports possible from Open Neural Network Exchange (ONNX) 

USP: The biggest USP of Python is probably its ready cloud availability on Alibaba Cloud, Amazon Web Services, Google Cloud Platform, and Microsoft Azure. This lets you quickly download the software library from the relevant app marketplace and get started without leaving your current cloud-based development environment. 

Editorial comments: PyTorch has the most expansive range of use cases among top open source AI software. Not only can you use it for computer vision, but you can also apply PyTorch for audio processing, NLP, language translation, and more. 

8. Rasa Open Source

Overview: Rasa is among the most popular open source AI software used to build conversational interfaces. While the company mainly drives monetization from its enterprise product, it also has a powerful open source edition and a separate toolset for enhancing AI assistance. You can use Rasa to build custom ML models or leverage its pre-built library of models written in TensorFlow. Rasa Enterprise bolts on to the open source platform, bringing SSO-based security, service level agreements, and dedicated support. 

Key features: Some of the key features of Rasa include:

    • Natural language understanding to convert messages into structured data and analyze intent 
    • ML-powered dialogue management to drive the assistant’s conversation flows based on context 
    • Built-in integration for 10+ popular messaging channels
    • Complete visibility into the AI training pipeline, model design, and underlying code 
    • Strong community support from 10,000+ forum members 
    • An optional Rasa X toolset for testing, enhancements, and new updates

USP: Rasa’s USP is its ability to drive faster development of conversational assistants, specifically chatbots. The Rasa X tool allows developers to further fine-tune their applications and easily provision fresh updates without disturbing the underlying AI/ML code and algorithm. 

Editorial comments: Mid-sized to large enterprises looking to build custom chatbots or ISVs eager to incorporate a conversational capability into their software offerings should definitely consider Rasa. Not only does it enable collaborative AI development at scale, but you can also integrate with Slack, Facebook, Google Home, and IVR systems out of the box.

9. TensorFlow

Overview: In the world of open source AI software, Google’s TensorFlow needs no introduction. It started as an internal project by the Google Brain Team in 2011, based on deep learning neural networks. As the company began using the technology in various ways, it decided to take TensorFlow in the open source direction from 2015. Today, several of the popular open source AI frameworks in the market are built on TensorFlow, which enjoys an active global community and widespread learning resources. 

Key features: Some key features of TensorFlow include:

    • Support for multiple languages, including JavaScript, which is relatively rare in the open source AI space
    • Intuitive high-level APIs like Keras to easily build and train ML models 
    • Platform-agnostic ML production – on-premises, in the cloud, in your browser, or locally on the device 
    • TensorFlow Lite for mobile applications and embedded or IoT devices 
    • Cross-compatibility between AI/ML models that you have trained on different TensorFlow versions 
    • A wide variety of applications, including predictive analysis, object classification, and conversational AI 

USP: TensorFlow’s core USP is the learning ecosystem surrounding it. If you are just getting started with open source AI/ML development, you will find free tutorials, exhaustive learning courses, and certifications, in addition to TensorFlow’s own detailed documentation. Another advantage is its sheer flexibility, as you can use TensorFlow in any language or production environment. 

Editorial comments: While TensorFlow is meant for more mature, expert-backed applications, it supports an impressive variety of use cases and can be used in a wide range of business scenarios. Companies with long-term AI/ML investments or whose core business proposition depends on analytics should consider TensorFlow. 

10. Tesseract OCR

Overview: Tesseract is an optical character recognition (OCR) engine originally developed by Hewlett Packard as a proprietary technology in the 1980s. It is commonly known as one of the most accurate OCR engines available and was launched as an open source AI software with sponsorship from Google in 2006. Its primary implementation is meant for unstructured data processing and text from image extraction, executed entirely from a common line interface.

Key features: Some of the key features of Tesseract OCR include:

    • Written in C++ and a common line interface (no GUI) 
    • Capable of word-finding, line finding, and character classification
    • Easy installation with precompiled binaries 
    • GUI overlays available for application development, including OCRFeeder 
    • Text localization and detection within an image 
    • Python wrapper available for non-C++ installations

USP: The primary of Tesseract OCR is simply how effective it is. Its core purpose is to detect text in an unstructured visual environment and convert it into a human-readable language. Tesseract can recognize 100+ languages out of the box and is so powerful that Google uses Tesseract for its Gmail image spam detection filter. 

Editorial comments: If you are looking for a sophisticated OCR engine that can work in challenging conditions and recognize languages such as Arabic or Hebrew, which follow right to left text, you cannot go wrong with Tesseract. It is considered the de facto solution for text detection and language analysis. Users can opt for a GUI overlay if the command-line interface is not in sync with your requirements. 


Ultimately, your choice of open source AI technology will come down to your unique software development need. Which use cases are you looking to solve using AI? Would you require a GUI, or is a command-line interface sufficient? What is the underlying language for your code? 

The top ten technologies we listed promise a large developer community for support, regular enhancements and iterations, and are battle-tested in real-world scenarios. They are well suited for strengthening the foundations of your next AI project, bringing the collective intelligence of the global dev community and some of the most cutting-edge research labs in the world. 

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