AI Tools for Academic Libraries: AI Programming and Coding Tools

Evaluating AI coding tools from a developer's perspective

Posted on October 6, 2025 in OCUL's AI Tools for Academic Libraries

AI Tools for Academic Libraries is a bimonthly series from the Ontario Council of University Libraries (OCUL) and Choice that explores specific categories of AI tools through concise, practical evaluations. Authored by members of OCUL’s Artificial Intelligence and Machine Learning (AIML) Team, the series helps academic library staff understand, assess, and thoughtfully integrate AI into their workflows. Learn more about this series.

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Many academic libraries offer research services that focus on digital scholarship by providing access to advanced technologies, expert guidance, and responsible knowledge creation. Beyond user-facing services, supporting web development and the digital research infrastructure is a critical dimension of work across academic libraries. Artificial intelligence (AI) tools focused on coding are becoming vital to this work.

Drawing on over two years of AI application coding experience from OCUL’s AIML Technical Manager, Pieter Botha, this article explores the current landscape of AI coding tools. From open-source models to proprietary platforms, the AI coding ecosystem is rapidly evolving. Selecting a best-fit AI coding tool depends on factors such as the development task (planning, debugging, refactoring, or creating new code), the programming language, and the application. Ethical considerations, such as privacy and security, may also be of importance, particularly for research support use cases where data or intellectual property must be protected.

AI Chat Models for Programming and Coding

AI chat models for coding are conversational artificial intelligence systems specifically optimized to assist with programming tasks. These models can help write, debug, explain, and refactor code across various programming languages and frameworks. They are trained on large datasets of code and technical documentation, enabling them to understand and respond to coding-related queries. They also support a “vibe coding” approach, which is a freestyle approach to code development using LLM prompting to generate code. It involves less planning, less structure, and going with the flow and feel of the work rather than using intensive coding or programming knowledge. Though useful for all, vibe coding may be of particular interest for individuals with limited or no coding skills. Most models also offer additional integrated features, such as the Gemini Canvas mode described later, that can support coding and programming tasks.

Commercial AI providers are listed below in order of preference. They allow free access with limited use and paid options that support advanced features and heavy usage. All the providers also offer users to opt out of data sharing and a private mode, where user data will only be kept to satisfy local legal requirements and will not be used for training data.

ChatGPT 5(OpenAI, USA)

A well-rounded model that is state-of-the-art for most tasks and queries. Good at coding, reasoning, lookups, research, and summarizing—almost anything you throw at it.

Claude Sonnet 4 (Anthropic, USA)

Currently, the best coding model. It is also very good with general support. It lags slightly behind ChatGPT 5 in debugging tasks and technical understanding of complex contexts. The Claude app is also very convenient and works well from the desktop.

Gemini Pro 2.5 (Google, USA)

One of the best models for debugging and vibe coding via the Canvas mode. It has the ability to handle up to 1 million tokens, making it ideal for large, complex projects. It can be especially useful in research services applications with extensive coding tasks. It is a good all-rounder too.

Kimi K2 (Moonshot AI, China)

It is exceptional at technical tasks and coding, though it’s limited by slow response times in its free version. It is open source, but too large for local deployment.

Grok 4 (xAI, USA)

Grok is especially fast and has a very generous free usage allocation. It handles many technical problems, such as configuration issues, better than many other models. The research mode is very useful, and another great feature is its customizability to respond with concise and exact answers, if that is the user’s preference. The other models listed here are better at general coding tasks, though.

Locally Hosted Coding Tools

Locally hosted AI coding tools are artificial intelligence models and platforms that run entirely on a user’s own hardware or institutional servers, rather than relying on third-party, cloud-based services. These tools offer greater control over data privacy, customization, and performance, which is especially valuable in academic and research settings.

Devstral-small 24b (Mistral AI, France)

This is nearly on par with proprietary models for classic coding tasks. It is exceptional for its size and has tool calling, making it very good for agentic applications. It lacks the knowledge, diversity, and complexity of the commercial models, but is more than adequate for standard coding tasks—especially in Python.

Qwen3-coder 30b (Alibaba, China)

This is the smaller version of the 480b flagship model in the Qwen series. While it is slightly less capable than Devstral, it runs at more than double the speed—something that is critical to consider when working with larger contexts.

Larger, more capable open-source models are available, but they require much more in terms of hardware to host locally. The above two models can be run on consumer-level hardware.

Ollama and LMStudio are currently the most popular tools to host models locally. OpenWebUI is a popular project that exposes the hosted model in a web user interface similar to the online tools that we get from the proprietary AI providers.

Autocomplete Tools for Visual Studio Code

Autocomplete tools for Visual Studio Code are AI-powered extensions that help developers write code faster and more accurately by predicting and completing code snippets in real time. These tools analyze the context of your code and suggest relevant completions, often supporting advanced features like fill-in-the-middle (FIM), multiline suggestions, and integration with large language models.

Continue.dev with Qwen2.5-coder 14b/7b 

The Continue.dev plugin is free for individuals, with paid options for teams and organizations. It supports FIM and handles context in the codebase well (the entire codebase can be encoded into a vector database for fast querying). Even the 7b version performs well for autocompletion and runs fast on modest hardware. It can be combined with other LLM models.

GitHub Copilot (Microsoft, USA)

GitHub Copilot defaults to a ChatGPT plugin, but other models can be selected, such as  Claude or Gemini. A recent update also allows integration with open-source AI tools. This is a very robust all-in-one solution for chat, autocomplete, and agentic development. It has free access for students and teachers, with individual subscriptions costing $10 (USD)/month. Free tokens are limited, so a subscription is recommended for heavy use. It is very well integrated into VSCode, making it ideal for those with limited coding or programming experience.

Agentic Coding Tools

Agentic AI coding tools are systems that go beyond simple code generation. They can autonomously plan, execute, and debug software with minimal human input. These tools often integrate with development environments and use AI models to perform multistep operations like debugging, refactoring, or even building entire applications.

Claude Code (Anthropic, USA)

This is the industry leader for agentic workloads. You won’t be able to get much done on the $24 plan, so it’s best to upgrade to the Max plan at $140/month. Claude Code is a stand-alone console app and doesn’t integrate with an Integrated Development Environment (IDE). It is exceptional at vibe coding and working with complex code bases.

Roo Code, VSCode Plugin (Roo Code Inc., Community Developed)

Roo Code is capable of integrating most AI models (e.g., OpenAI, Anthropic, locally-created LLMs). The best free option uses Gemini Flash 2.5 with a 15-requests-per-minute limit. It is only as capable as the AI model it is connected to. It does work with the local models listed above, but it won’t be nearly as capable compared to when using Gemini 2.5 Flash/Pro or Sonnet 4.

GitHub Copilot (Microsoft, USA)

GitHub Copilot defaults to a ChatGPT plugin, but other models can be selected, such as Claude or Gemini. A recent update also allows integration with open-source AI tools. Subscriptions are recommended for agentic tasks.

As the most polished all-in-one coding tool, GitHub Copilot has a variety of use cases. The Agent mode of the VSCode plugin basically requires a subscription, as you will likely run out of free tokens with your first task. Sonnet 4 from Anthropic is the best model to use, but the Gemini and ChatGPT models work well too. It offers a more polished and integrated experience compared to Roo Code, but it is subjectively slightly less capable when using the same model.

A critical feature for successful agentic workflows is the integration of Model Context Protocol (MCP) servers. MCP servers allow AI models to interact with the outside world and augment their internal knowledge. One must-have MCP server that this developer can’t work without is Context7, which allows AI models to look up the latest documentation and examples for the most popular libraries and packages.

Despite the rise of agentic automation, many developers still find manual planning and prompting more productive.

Considerations

While the AI coding landscape is maturing and continuously evolving, academic libraries have a unique opportunity to serve as both facilitators and educators in this space. Whether through offering access to powerful local models or integrating AI into digital scholarship services, libraries can foster innovation and empower users to engage with AI in meaningful, ethical ways.

Everyone in academic libraries should be aware that while this article discusses particular models, the major companies developing AI tools provide a variety of models under their service umbrella. New models and updates are released frequently, which indicates that continued monitoring of development in this area is prudent.

From a librarian workflow perspective, individuals with a limited coding background may be better empowered to explore research and professional applications using coding and programming due to the availability of these tools. For simple applications, like coding materials on a research guide or LibGuide or experimenting with Python scripting with a graduate student, these tools expand possibilities without the need to engage in expensive or time-consuming training while helping to support the work of already limited developers in academic library settings.

As with all GenAI tools, outputs should be reviewed, and implemented code or programs found to be nonfunctional are likely the result of poorly generated code. Finally, all the models work well for coding and programming tasks, and are constantly improving. A feature that distinguishes one today is likely to be widespread in a few months’ time. Selecting one and building familiarity would serve most library professionals well as these tools continue to improve.

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