The Algorithms

What it is

The Algorithms is a community of programmers who maintain one repository per language, each implementing classic algorithms and data structures: sorting and searching, graphs, dynamic programming, strings, maths, cryptography, machine learning basics and more. The repositories are MIT licensed, and the project also runs a website, the-algorithms.com, where you can browse implementations across languages.

Its repositories are among the most starred educational projects on GitHub. As of September 2026 the Python repository had about 225k stars, Java about 66k, C++ about 35k, JavaScript about 34k and Go about 18k, and the organisation had around 64k followers.

Using it to learn

• When a concept has not clicked. Reading a clean implementation of Dijkstra, a segment tree or the KMP string algorithm, alongside the explanation in your textbook or course, often makes the idea concrete.
• Compare languages. Seeing the same algorithm in Python and in C++ is a good way to learn a second language's idioms.
• Read the tests. Many implementations, especially in the Python repository, include doctests showing inputs and expected outputs, which help you understand edge cases.

Keep in mind that implementations are written for clarity, not performance, and quality varies between contributors and languages. For competitive programming or production use, write or pick an optimised version.

Using it for a first open source contribution

The repositories are welcoming to newcomers, but they have standards. The Python repository's contributing guide, for example, asks that:

• New implementations are welcome, but identical copies of an existing one are not. Search before you start.
• Code must pass automated checks on GitHub Actions.
• Functions have type hints and doctests covering valid and invalid inputs.
• Algorithms perform real computation rather than wrapping an existing library call.
• Issues are not assigned; you simply open a pull request with a fix.

A good path is to improve tests or documentation first, then add an algorithm that is missing from your language's repository. Read the contributing guide for the specific repository; each has its own rules.

Weak spots

• Because of their popularity, the repositories receive many low-effort pull requests, so reviews can be slow and strict.
• Coverage and code style vary a lot between languages.
• It is a code collection, not a course: explanations are minimal, so pair it with a proper algorithms resource.

Common questions

Can I copy these into my assignments? Understanding is the point. Copying code into graded work is usually plagiarism, and the repositories themselves reject plagiarised contributions.

Is it good preparation for coding interviews? As a reference, yes. For practice, you still need to solve problems yourself on a problem site.

Best for: students learning algorithms who want working code to study, and developers looking for a structured, well-reviewed first open source contribution.