Open-source artificial intelligence

Open-source artificial intelligence, as defined by the Open Source Initiative, is an AI system that is freely available to use, study, modify, and share. This includes datasets used to train the model, its code, and model parameters, promoting a collaborative and transparent approach to AI development so someone could create a substantially similar result.
The debate over what should count as ‘open-source’ given a range of openness among AI projects has been significant. Some large language models touted as open-sourced that only release model-weights have been criticized as "openwashing" systems that are mostly closed.
Popular open-source artificial intelligence project categories include large language models, machine translation tools, and chatbots. Debate over the benefits and risks of open-sourced AI involve a range of factors like security, privacy and technological advancement.

History

The history of open-source artificial intelligence is intertwined with both the development of AI technologies and the growth of the open-source software movement.

1990s: Early development of AI and open-source software

The concept of AI dates back to the mid-20th century, when computer scientists like Alan Turing and John McCarthy laid the groundwork for modern AI theories and algorithms. An early form of AI, the natural language processing "doctor" ELIZA, was re-implemented and shared in 1977 by Jeff Shrager as a BASIC program, and soon translated to many other languages. Early AI research focused on developing symbolic reasoning systems and rule-based expert systems.
During this period, the idea of open-source software was beginning to take shape, with pioneers like Richard Stallman advocating for free software as a means to promote collaboration and innovation in programming. The Free Software Foundation, founded in 1985 by Stallman, was one of the first major organizations to promote the idea of software that could be freely used, modified, and distributed. The ideas from this movement eventually influenced the development of open-source AI, as more developers began to see the potential benefits of open collaboration in software creation, including AI models and algorithms.
In the 1990s, open-source software began to gain more traction, the rise of machine learning and statistical methods also led to the development of more practical AI tools. In 1993, the CMU Artificial Intelligence Repository was initiated, with a variety of openly shared software.

2000s: Emergence of open-source AI

In the early 2000s open-source AI began to take off, with the release of more user-friendly foundational libraries and frameworks that were available for anyone to use and contribute to.
OpenCV was released in 2000 with a variety of traditional AI algorithms like decision trees, k-Nearest Neighbors, Naive Bayes and Support Vector Machines.

2010s: Rise of open-source AI frameworks

Open-source deep learning framework as Torch was released in 2002 and made open-source with Torch7 in 2011, and was later augmented by PyTorch, and TensorFlow.
AlexNet was released in 2012.
OpenAI was founded in 2015 with a mission to create open-source artificial intelligence that benefited humanity, at least in part to help with recruitment in the early phases of the organization. GPT-1 was released in 2018.

2020s: Open-weight and open-source generative AI

With the announcement of GPT-2 in 2019, OpenAI originally planned to keep the source code of their models private citing concerns about malicious applications. After OpenAI faced public backlash, however, it released the source code for GPT-2 to GitHub three months after its release. OpenAI did not publicly release the source code or pretrained weights for the GPT-3 model. At the time of GPT-3's release GPT-2 was still the most powerful open source language model in the world. 2022 also saw the rise of larger and more powerful models under licenses of varying openness including Meta's OPT.
The Open Source Initiative consulted experts over two years to create a definition of "open-source" that would fit the needs of AI software and models. The most controversial aspect relates to data access, since some models are trained on sensitive data which can't be released. In 2024, they published the Open Source AI Definition 1.0. It requires full release of the software for processing the data, training the model and making inferences from the model. For the data, it only requires access to details about the data used to train the AI so others can understand and re-create it.
In 2023, Llama 1 and 2 and Mistral AI's Mistral and Mixtral open-weight models were first released, along with MosaicML's smaller open-source models. The release of the Llama models was a milestone in generating interest in open-weight and open-source models.
In 2024, Meta released a collection of large AI models, including Llama 3.1 405B, which was competitive with less open models. Meta's description of Llama as open-source has been controversial due to various prohibitions from its software license prohibiting it from being used for some purposes to not knowing what data was used to train the models.
DeepSeek released their V3 LLM in December 2024, and their R1 reasoning model on 20 January 2025, both as open-weights models under the MIT license. This release made widely known how China had been embracing using and building more open AI systems as a way to reduce reliance on western software and gatekeeping as well as to help give its industries access to higher-powered AI more quickly. Projects based in China have since become more widely used around the world as well as they have closed at least some of the gap with leading proprietary American models.
Since the release of OpenAI's proprietary ChatGPT model in late 2022, there have been only a few fully open large language models released. In September 2025, a Swiss consortium added to this short list by releasing a fully open model named Apertus.
In December 2025, the Linux Foundation created the Agentic AI Foundation, which assumed control of some open-source agentic AI protocols and other technologies created by OpenAI, Anthropic and Block.

Significance

The label ‘open-source’ can provide real benefits to companies looking to hire top talent or attract customers. The debate around "openwashing” has big implications for the success of various projects within the industry.
Open-source artificial intelligence tends to get more support and adoption in countries and companies that do not have their own leading AI model. These open-source projects can help to undercut the position of business and geopolitical rivals with the strongest proprietary models. Europe is a region pursuing openness as a digital sovereignty strategy to try and reduce the leverage that countries like the United States can use in negotiations on various topics like trade.

Licenses

As of 2025, the license used by the most number of models was Apache 2.0, though a number of the licenses of larger models like Llama 3 have limitations for commercial use among other activities. These partially open-sourced code that contains legal restrictions has scared off some companies from using those models for fear of a future lawsuit or of a change in the terms and conditions. Some of the same fears also exist in the large number of smaller models that do not specify a license.

Applications

Healthcare

In the healthcare industry, open-source AI has been used in diagnostics, patient care, and personalized treatment options. Open-source libraries have been used for medical imaging for tasks such as tumor detection, improving the speed and accuracy of diagnostic processes. Additionally, OpenChem, an open-source library specifically geared toward chemistry and biology applications, enables the development of predictive models for drug discovery, helping researchers identify potential compounds for treatment.

Military

Meta's Llama models, which have been described as open-source by Meta, were adopted by U.S. defense contractors like Lockheed Martin and Oracle after unauthorized adaptations by Chinese researchers affiliated with the People's Liberation Army came to light. The Open Source Initiative and others have contested Meta's use of the term open-source to describe Llama, due to Llama's license containing an acceptable use policy that prohibits use cases including non-U.S. military use. Chinese researchers used an earlier version of Llama to develop tools like ChatBIT, optimized for military intelligence and decision-making, prompting Meta to expand its partnerships with U.S. contractors to ensure the technology could be used strategically for national security. These applications now include logistics, maintenance, and cybersecurity enhancements.

Benefits

Privacy and independence

A Nature editorial suggests medical care could become dependent on AI models that could be taken down at any time, are difficult to evaluate, and may threaten patient privacy. Its authors propose that health-care institutions, academic researchers, clinicians, patients and technology companies worldwide should collaborate to build open-source models for health care of which the underlying code and base models are easily accessible and can be fine-tuned freely with own data sets.

Free speech

Open-source models are harder to censor than close-sourced ones.

Collaboration and faster advancements

Large-scale collaborations, such as those seen in the development of open-source frameworks like TensorFlow and PyTorch, have accelerated advancements in machine learning and deep learning. The open-source nature of these platforms also facilitates rapid iteration and improvement, as contributors from across the globe can propose modifications and enhancements to existing tools.