is Nikolay Nikitin, PhD. I’m the Analysis Lead on the AI Institute of ITMO College and an open-source fanatic. I typically see lots of my colleagues failing to seek out the time and vitality to create open repositories for his or her analysis papers and to make sure they’re of correct high quality. On this article, I’ll focus on how we may also help clear up this drawback utilizing OSA, an AI device developed by our staff that helps the repository turn out to be a greater model of itself. If you happen to’re sustaining or contributing to open supply, this submit will prevent effort and time: you’ll find out how OSA can mechanically enhance your repo by including a correct README, producing documentation, organising CI/CD scripts, and even summarizing the important thing strengths and weaknesses of the venture.
There are a lot of totally different documentation enchancment instruments. Nevertheless, they deal with totally different particular person elements of repository documentation. For instance, the Readme-AI device generates the README file, but it surely doesn’t account for added context, which is necessary, for instance, for repositories of scientific articles. One other device, RepoAgent, generates full documentation for the repository code, however not README or CI/CD scripts. In distinction, OSA considers the repository holistically, aiming to make it simpler to know and able to run. The device was initially made for our colleagues in analysis, together with biologists and chemists, who typically lack expertise in software program engineering and trendy improvement practices. The principle intention was to assist them make the repository extra readable and reproducible in just a few clicks. However OSA can be utilized on any repository, not solely scientific ones.
Why is it wanted?
Scientific open supply faces challenges with the reuse of analysis outcomes. Even when code is shared with scientific papers, it’s not often obtainable or full. This code is normally tough to learn; there isn’t a documentation for it, and typically even a primary README is lacking, because the developer meant to jot down it on the final second however didn’t have time. Libraries and frameworks typically lack primary CI/CD settings equivalent to linters, automated exams, and different high quality checks. Subsequently, it’s inconceivable to breed the algorithm described within the article. And it is a massive drawback, as a result of if somebody publishes their analysis, they do it with a need to share it with the group
However this drawback isn’t restricted to science solely. Skilled builders additionally typically delay writing readme and documentation for lengthy durations. And if a venture has dozens of repositories, sustaining and utilizing them could be sophisticated.
Ideally, every repository must be straightforward to run and user-friendly. And sometimes the posted developments typically lack important parts equivalent to a transparent README file or correct docstrings, which could be compiled into full documentation utilizing commonplace instruments like mkdocs.
Primarily based on our expertise and evaluation of the issue, we tried to counsel an answer and implement it because the Open Supply Advisor device – OSA.
What’s the OSA device?
OSA is an open-source Python library that leverages LLM brokers to enhance open-source repositories and make them simpler to reuse.
The device is a package deal that runs by way of a command-line interface (CLI). It may also be deployed regionally utilizing Docker. By specifying an API key to your most popular LLM, you’ll be able to work together with the device by way of the console. It’s also possible to attempt OSA by way of the general public net GUI. There’s brief introduction to important concepts of repository enchancment with OSA:
How does OSA work?
The Open Supply Advisor (OSA) is a multi-agent device that helps enhance the construction and value of scientific repositories in an automatic manner. It addresses frequent points in analysis initiatives by dealing with duties equivalent to producing documentation (README information, code docstrings), creating important information (licenses and necessities), and suggesting sensible enhancements to the repository. Customers merely present a repository hyperlink and may both obtain an mechanically generated Pull Request (PR) with all beneficial adjustments or evaluation the options regionally earlier than making use of them.
OSA can be utilized in two methods: by cloning the repository and working it by means of a command-line interface (CLI), or by way of an internet interface. It additionally provides three working modes: primary, automated, and superior, that are chosen at runtime to suit totally different wants. In primary mode, OSA applies a small set of ordinary enhancements with no further enter: it generates a report, README, group documentation, and an About part, and provides frequent folders like “exams” and “examples” in the event that they’re lacking. Superior mode offers customers full guide management over each step. In automated mode, OSA makes use of an LLM to investigate the repository construction and the prevailing README, then proposes a listing of enhancements for customers to approve or reject. An experimental multi-agent conversational mode can also be being developed, permitting customers to specify desired enhancements in free-form pure language by way of the CLI. OSA interprets this request and applies the corresponding adjustments. This mode is at present underneath energetic improvement.
One other key energy of OSA is its flexibility with language fashions. It really works with fashionable suppliers like OpenRouter and OpenAI, in addition to native fashions equivalent to Ollama and self-hosted LLMs working by way of FastAPI.
OSA additionally helps a number of repository platforms, together with GitHub and GitLab (each GitLab.com and self-hosted cases). It will possibly alter CI/CD configuration information, arrange documentation deployment workflows, and appropriately configure paths for group documentation.
an experimental multi-agent system (MAS), at present underneath energetic improvement, that serves as the premise for its automated and conversational modes. The system decomposes repository enchancment right into a sequence of reasoning and execution levels, every dealt with by a specialised agent. Brokers talk by way of a shared state and are coordinated by means of a directed state graph, enabling conditional transitions and iterative workflows.
README era
OSA features a README era device that mechanically creates clear and helpful README information in two codecs: a typical README and an article-style README. The device decides which format to make use of by itself, for instance, if the consumer gives a path or URL to a scientific paper by means of the CLI, OSA switches to the article format. To start out, it scans the repository to seek out an important information, specializing in core logic and venture descriptions, and takes under consideration the folder construction and any current README.
For the usual README, OSA analyzes the important thing venture information, repository construction, metadata, and the principle sections of an current README if one is current. It then generates a “Core Options” part that serves as the inspiration for the remainder of the doc. Utilizing this data, OSA writes a transparent venture overview and provides a “Getting Began” part when instance scripts or demo information can be found, serving to customers rapidly perceive easy methods to use the venture.
In article mode, the device creates a abstract of the related scientific paper and extracts related data from the principle code information. These items are mixed into an Overview that explains the venture objectives, a Content material part that describes the principle elements and the way they work collectively, and an Algorithms part that explains how the carried out strategies match into the analysis. This strategy retains the documentation scientifically correct whereas making it simpler to learn and perceive.
Documentation era
The documentation era device produces concise, context-aware documentation for features, strategies, lessons, and code modules. The documentation era course of is as follows:
(1) Reference parsing: Initially, a TreeSitter-driven parser fetches imported modules and resolves paths to them for every specific supply code file, forming an import map that can additional be used to find out technique and performance requires the overseas modules utility. By implementing such an strategy, it’s comparatively straightforward to rectify interconnections between totally different elements of the processed venture and to tell apart between inside aliases. Together with the import maps, the parser additionally preserves basic data such because the processing file, a listing of occurring lessons, and standalone features. Every class comprises its identify, attributes checklist, decorators, docstring, checklist of its strategies, and every technique has its particular particulars that are of the identical construction as standalone features, that’s: technique identify, docstring, return kind, supply code and alias resolved overseas technique calls with a reputation of the imported module, class, technique, and path to it.
(2) Preliminary docstrings era for features, strategies, and lessons: With a parser having a construction fashioned, an preliminary docstrings era stage is ongoing. Solely docstrings that lack lessons, strategies, and features are processed at this stage. Here’s a basic description of what the ‘what’ technique does. The context is usually the strategy’s supply code, since at this level, forming a basic description of the performance is essential. The onward immediate consists of details about the strategy’s arguments and interior decorators, and it trails with the supply code of the referred to as overseas strategies to offer extra context for processing technique utility. A neat second right here is that class docstrings are generated solely in spite of everything their docstring-lacking strategies are generated; then class attributes, their strategies’ names, and docstrings are offered to the mannequin.
(3) Technology of “the principle thought” of the venture utilizing descriptions of elements derived from the earlier stage.
(4) Docstrings replace utilizing generated “important thought”: Therefore, all docstrings for the venture are presumably current, era of the principle thought of the venture could be carried out. Basically, the immediate for the thought consists of docstrings for all lessons and features, together with their significance rating based mostly on the speed of incidence of every part within the import maps talked about earlier than, and their place within the venture hierarchy decided by supply path. The mannequin response is returned in markdown format, summarizing the venture’s elements. As soon as the principle thought is acquired, the second stage of docstring era begins, throughout which the entire venture’s supply code elements are processed. At this second, the important thing focus is on offering the mannequin with an authentic or generated docstring on the preliminary stage docstring with the principle thought to elaborate on ‘why’ this part is required for the venture. The supply code for the strategies can also be being offered, since an expanded venture narrative could immediate the mannequin to right some factors within the authentic docstring.
(5) Hierarchical modules description era ranging from the underside to the highest.
(6) Utilizing Mkdocs and GitHub pages for automated documentation pushing and streaming: Remaining stage of the docstring pipeline, contemplating a recursive traversal throughout the venture’s modules and submodules. Hierarchy is predicated on the supply path; at every leaf-processing degree, a beforehand parsed construction is used to create an outline of which submodule is used, in accordance with the principle thought. As processing strikes to increased ranges of the hierarchy, generated submodules’ summaries are additionally used to offer extra context. The mannequin returns summaries in Markdown to make sure seamless integration with the mkdocs documentation era pipeline. The entire schema of the strategy is described within the picture under.
CI/CD and construction group
OSA provides an automatic CI/CD setup that works throughout totally different repository internet hosting platforms. It generates configurable workflows that make it simpler to run exams, test code high quality, and deploy initiatives. The device helps frequent utilities equivalent to Black for code formatting, unit_test for working exams, PEP8 and autopep8 for model checks, fix_pep8 for automated model fixes, pypi_publish for publishing packages, and slash_command_dispatch for dealing with instructions. Relying on the platform, these workflows are positioned within the applicable areas, for instance, .github/workflows/ for GitHub or a .gitlab-ci.yml file within the repository root for GitLab.
Customers can customise the generated workflows utilizing choices like –use-poetry to allow Poetry for dependency administration, –branches to outline which branches set off the workflows (by default, important and grasp), and code protection settings by way of --codecov-token and --include-codecov.
To make sure dependable testing, OSA additionally reorganizes the repository construction. It identifies check and instance information and strikes them into standardized exams and examples directories, permitting CI workflows to run exams persistently with out extra configuration.
Workflow information are created from templates that mix project-specific data with user-defined settings. This strategy retains workflows constant throughout initiatives whereas nonetheless permitting flexibility when wanted.
OSA additionally automates documentation deployment utilizing MkDocs. For GitHub repositories, it generates a YAML workflow within the .github/workflows listing and requires enabling learn/write permissions and choosing the gh-pages department for deployment within the repository settings. For GitLab, OSA creates or updates the .gitlab-ci.yml file to incorporate construct and deployment jobs utilizing Docker photographs, scripts, and artifact retention guidelines. Documentation is then mechanically printed when adjustments are merged into the principle department.
The right way to use OSA
To start utilizing OSA, select your repository with draft code that’s incomplete or underdocumented. Optionally, embrace a associated scientific paper or one other doc describing the library or algorithm carried out within the chosen repo. The paper is uploaded as a separate file and used to generate the README. It’s also possible to specify the LLM supplier (e.g., OpenAI) and the mannequin identify (equivalent to GPT-4o).
OSA generates suggestions for enhancing the repository, together with:
- A README file generated from code evaluation, utilizing commonplace templates and examples
- Docstrings for lessons and strategies which might be at present lacking, to allow automated documentation era with MkDocs
- Primary CI/CD scripts, together with linters and automatic exams
- A report with actionable suggestions for enhancing the repository
- Contribution pointers and information (Code of Conduct, pull request and challenge templates, and so on.)
You’ll be able to simply set up OSA by working:
pip set up osa_toolAfter organising the setting, it is best to select an LLM supplier (equivalent to OpenAI or an area mannequin). Subsequent, it is best to add GIT_TOKEN (GitHub token with commonplace repo permissions) and OPENAI_API_KEY (for those who use OpenAI-compatible API) as setting variables, or you’ll be able to retailer them within the .env file as effectively. Lastly, you’ll be able to launch OSA instantly from the command line. OSA is designed to work with an current open-source repository by offering its URL. The essential launch command consists of the repository tackle and non-obligatory parameters such because the operation mode, API endpoint, and mannequin identify:
osa_tool -r {repository} [--mode {mode}] [--api {api}] [--base-url {base_url}] [--model {model_name}]OSA helps three working modes:
- auto (default) – analyzes the repository and creates a custom-made enchancment plan utilizing the specialised LLM agent.
- primary – applies a predefined set of enhancements: generates a venture report, README, group pointers, an “About” part, and creates commonplace directories for exams and examples (if they’re lacking).
- superior – permits guide choice and configuration of actions earlier than execution.
Extra CLI choices can be found here. You’ll be able to customise OSA by passing these choices as arguments to the CLI, or by choosing desired options within the interactive command-line mode.
As soon as launched, OSA performs an preliminary evaluation of the repository and shows key data: basic venture particulars, the present setting configuration, and tables with deliberate and inactive actions. The consumer is then prompted to both settle for the recommended plan, cancel the operation, or enter an interactive enhancing mode.
In interactive mode, the plan could be modified: actions toggled on or off, parameters (strings and lists) adjusted, and extra choices configured. The system guides the consumer by means of every motion’s description, doable values, and present settings. This course of continues till the consumer confirms the ultimate plan.
This CLI-based workflow ensures flexibility, from totally automated processing to specific guide management, making it appropriate for each speedy preliminary assessments and detailed venture refinements.
OSA additionally consists of an experimental conversational interplay mode that permits customers to specify desired repository enhancements utilizing free-form pure language by way of the CLI. If the request is ambiguous or insufficiently associated to repository processing, the system iteratively requests clarifications and permits the connected supplementary file to be up to date. As soon as a sound instruction is obtained, OSA analyzes the repository, selects the suitable inside modules, and executes the corresponding actions. This mode is at present underneath energetic improvement.
When OSA finishes, it creates a pull request (PR) within the repository. The PR consists of all proposed adjustments, such because the README, docstrings, documentation web page, CI/CD scripts, сontribution pointers, report, and extra. The consumer can simply evaluation the PR, make adjustments if wanted, and merge it into the venture’s important department.
Let’s take a look at an instance. GAN-MFS is a repository that gives a PyTorch implementation of Wasserstein GAN with Gradient Penalty (WGAN-GP). Right here is an instance of a command to launch OSA on this repo:
osa_tool -r github.com/Roman223/GAN_MFS --mode auto --api openai --base-url https://api.openai.com/v1 --model gpt-4.1-miniOSA made a number of contributions to the repository, together with a README file generated from the paper’s content material.
OSA additionally added a License file to the pull request, in addition to some primary CI/CD scripts.
OSA added docstrings to all lessons and strategies the place documentation was lacking. It additionally generated a structured, web-based documentation website utilizing these docstrings.
The generated report consists of an audit of the repository’s key elements: README, license, documentation, utilization examples, exams, and a venture abstract. It additionally analyzes key sections of the repository, equivalent to construction, README, and documentation. Primarily based on this evaluation, the system identifies key areas for enchancment and gives focused suggestion.
Lastly, OSA interacts with the goal repository by way of GitHub. The OSA bot creates a fork of the repository and opens a pull request that features all proposed adjustments. The developer solely must evaluation the options and alter something that appears incorrect. For my part, that is a lot simpler than writing the identical README from scratch. After evaluation, the repository maintainer efficiently merged the pull request. All adjustments proposed by OSA can be found here.
Though the variety of adjustments launched by the OSA is important, it’s tough to evaluate the general enchancment in repository high quality. To do that, we determined to look at the repository from a safety perspective. The scorecard device permits us to guage the repository utilizing the aggregated metric. Scorecard was created to assist open supply maintainers enhance their safety greatest practices and to assist open supply shoppers decide whether or not their dependencies are protected. The combination rating takes under consideration many repository parameters, together with the presence of binary artifacts, CI/CD exams, the variety of contributors, and a license. The aggregated rating of the unique repository was 2.2/10. After the processing by OSA, it rose to three.7/10. This occurred because of the addition of a license and CI/CD scripts. This rating should appear too low, however the repository being processed isn’t meant for integration into massive initiatives. It’s a small device for producing artificial information based mostly on a scientific article, so its safety necessities are decrease.
What’s Subsequent for OSA?
We plan to combine a RAG system into OSA, based mostly on greatest practices in open-source improvement. OSA will examine the goal repository with reference examples to establish lacking elements. For instance, if the repository already has a high-quality README, it received’t be regenerated. Initially, we used OSA for Python repositories, however we plan to help extra programming languages sooner or later.
If in case you have an open repository that requires enchancment, give OSA a attempt! We might additionally respect concepts for brand spanking new options which you could go away as points and PRs.
If you happen to want to use OSA in your works, it may be cited as:
Nikitin N. et al. An LLM-Powered Device for Enhancing Scientific Open-Supply Repositories // Championing Open-source DEvelopment in ML Workshop@ ICML25.
