grokking_algorithms: the book's code repository, and what it is actually for

What this repository is, and the problem it solves

Grokking Algorithms is a book by Aditya Bhargava, published by Manning. This repository is its companion code. The README opens with one sentence: "This is the code in my book Grokking Algorithms." That framing matters more than it looks. The repository is not a package, not a framework, and not a collection of drop-in utilities. It exists so a reader can see the algorithms from the book written out and run them.

The audience follows from that. If you are reading the book and want to type along, or you finished a chapter and want to see how the same idea looks in another language, this is the place. If you are looking for a sorting library to import into an application, you are in the wrong repository, and the contribution guidelines confirm the intent: the maintainer writes that the main purpose of the repo is "to have easy to read examples that help people understand concepts."

The top-level layout maps directly onto the book's chapters. Folders run from 01_introduction_to_algorithms through 12_knn, with 07_trees and 09_dijkstras_algorithm present and 08 absent from the listing. Each folder is named for the topic rather than for a module, which tells you the organising principle is pedagogy, not API design.

How the code is organised across languages and chapters

The repository's primary language is JavaScript, but the chapter folders are not JavaScript-only. The README lists contributing an example in a new language as one of the most useful kinds of pull request, which only makes sense if the existing chapters already carry several languages side by side. The practical consequence is that a folder like the quicksort chapter contains more than one implementation of the same algorithm, and you have to check which language subfolder you are in before you copy anything.

This is a real difference from a normal library repository. There is no single entry point, no manifest at the root tying the chapters together, and no build step described in the README. Each chapter stands alone. Data flow is whatever the example needs: an array in, a sorted array or a path or a prediction out. Nothing is shared between chapters.

The images folder is a separate deliverable. The README states the repository "contains every image in Grokking Algorithms in high resolution" and that they are available for non-commercial use, with the credit line "copyright Manning Publications, drawn by adit.io". For someone building slides for a study group or a course, that folder may be the more useful half of the repository.

Getting the code and running a first example

The README gives no install instructions, no package manager command, and no dependency list. There is no homepage and no release. The only route the README describes is the book itself, linked to the Manning page, and the errata page at adit.io/errata.html. So the first step is cloning the repository rather than installing anything.

git clone https://github.com/egonSchiele/grokking_algorithms.git
cd grokking_algorithms

After that, list the chapter folders to find the topic you are reading about. The names are numbered, so the book's ordering is visible in the directory listing.

ls

You should see the numbered chapter directories, along with LICENSE, README.md and images. From there, enter a chapter and look at which language subfolders it contains before deciding what to run. Because the README documents no interpreter version, no virtual environment and no runtime setup, the command you use depends on the language of the file you picked and is not specified by the project. If you are working in Python, the README points to Python Tutor at pythontutor.com as a site that walks through Python code line by line, which is a reasonable way to step through a chapter example without setting anything up locally.

Where this repository stops being the right tool

The clearest limitation is stated by the maintainer rather than discovered by a user. In the contributing section: "I'm less likely to merge code that involves stylistic changes, and I'm very unlikely to merge PRs that add complex optimizations, as the main purpose of this repo is to have easy to read examples that help people understand concepts." Read that as a boundary on what the code will ever become. If you need a tuned implementation, the repository is designed not to provide one, and a pull request adding one will likely sit unmerged.

The second limitation is responsiveness. The same section says: "It takes me a long time to respond to PRs. It's something I'm trying to get better at, but I apologize in advance for how long it will take." The README also directs mistakes in the book and questions to email rather than the issue tracker, describing email as "the best way to get a response." So the issue tracker is not the primary support channel, and a reader expecting prompt triage should adjust expectations. The last push to the repository was on 2026-04-12.

The third is correctness tracking. The README sends readers to an external errata page rather than maintaining a list of known code errors in the repository. If you are relying on a snippet, the errata page is where corrections are pointed to, and the repository itself does not surface them.

How it compares with a reference text like CLRS

The natural comparison is with a comprehensive algorithms reference such as CLRS, which readers search for alongside this book. The difference is in what each one optimises for. A reference text covers proofs, asymptotic analysis and variants in depth, and expects the reader to bring mathematical maturity. Grokking Algorithms is written for people who want the idea first, and this repository mirrors that choice: the code is short, the folder names are plain, and the maintainer explicitly refuses optimizations that would obscure the example.

That trade-off cuts both ways. You will not find a proof of correctness in a chapter folder, and you will not find the edge cases a reference text would enumerate. What you get instead is a runnable version of the same idea, in more than one language, that you can step through. If your goal is interview preparation, the comparison people search for against Cracking the Coding Interview is also apt: that book is organised around problem patterns and practice, while this one is organised around teaching a technique, and the repository follows the book's structure.

Licence and the images carve-out

The repository's licence is reported as NOASSERTION, which means the licence file could not be automatically classified. That is a signal to read LICENSE yourself rather than assume a standard open source licence applies. Nothing in the README describes the code's terms; the only licensing language in the README concerns the images, which it says are "available for non-commercial use" and require the credit "copyright Manning Publications, drawn by adit.io" when used. Teaching materials and presentations are named as welcome uses.

The gap is worth stating plainly: the README grants a use right for the images and says nothing about the code's terms, while the repository carries a LICENSE file whose contents the metadata could not classify. If you plan to redistribute the code, or to use the figures in anything commercial, that is the file to open first. This is not legal advice, and the README's non-commercial phrasing for images should be read as the author's stated intent rather than as a full licence grant.

Editorial conclusion

Adopt this repository if you are working through Grokking Algorithms and want to run or adapt the examples, or if you teach from the book and need the figures, which the README permits for non-commercial use with the credit line "copyright Manning Publications, drawn by adit.io". Do not adopt it as a dependency or as a source of production-ready implementations: the README states the maintainer is unlikely to merge complex optimizations because the point is readable examples. Before using any snippet, open the chapter folder, check which language subfolder you are reading, and compare it against the errata page at adit.io/errata.html, since the README points there for corrections rather than tracking them in the repo.

Frequently asked questions

Official sources

1. egonSchiele/grokking_algorithms on GitHub
2. Issues
3. README