Hello Algo (《Hello 算法》) is an open-source book that teaches data structures and algorithms through animated diagrams and code you can run on the spot. It started as a Chinese-language project and grew into a multilingual one, and the GitHub repository carries both the prose and a parallel implementation of every example in more than a dozen programming languages. Treat it as a textbook with a working code lab attached, not as a library you install.
That framing matters because GitHub labels the repo as “Java,” which is misleading. The Java tag only reflects which language directory happens to weigh the most. The actual content is polyglot: the same algorithm is written out in Python, Java, C++, C, C#, JavaScript, TypeScript, Go, Swift, Rust, Ruby, Kotlin, Dart, and Zig, so you can read the book in your own language and study the code in whichever one you already know.
The gap it fills
Most people learning algorithms land in one of two uncomfortable places. Classic references such as CLRS or Deng Junhui’s textbook are rigorous but steep, and beginners often stall on the math before they build intuition. Interview-grinding sites push you straight into problem sets, which trains pattern matching without explaining why a structure behaves the way it does. Hello Algo aims at the on-ramp between those two. Its core bet is that animations plus runnable code build intuition faster than static pseudocode, and the bet paid off: the project went from its first commit in late 2022 to roughly 127,000 stars as of 2026-06, with the steepest climb across 2023.
Two endorsements in the README are worth taking at face value because of who gave them. Deng Junhui, the Tsinghua professor behind a widely used Chinese DSA textbook, recommends it for beginners, and Mu Li (Amazon) says he would have learned the material far faster with it. Those are signals that the book is pedagogically sound, not just popular.
What you actually get
The book walks through the foundational curriculum: complexity analysis, arrays and linked lists, stacks and queues, hash tables, trees and binary search trees, heaps, graphs, then the algorithm families of searching, sorting, divide and conquer, backtracking, dynamic programming, and greedy methods. Each topic pairs an animated illustration with a minimal implementation.
The differentiator is the codes/ directory. Every example exists as a runnable file per language, so you are not copying pseudocode and guessing at the gaps. You can read the chapter in your browser, then open the same algorithm in Python to confirm your understanding and in Rust to see how ownership changes the shape of the code. For anyone switching languages or refreshing fundamentals, that parallel layout is the feature that keeps people coming back.
You can read it three ways: online at the official site, as a downloadable PDF or EPUB from the GitHub releases, or as a local docs site. The latest tagged edition is 1.3.0 (January 2026), and releases have settled into a roughly annual cadence, which tells you the book is mature rather than half-written.
How to read and run it
Read online at hello-algo.com, or grab the PDF/EPUB from the releases page.
To run the code locally, clone the repo and open the directory for your language:
git clone https://github.com/krahets/hello-algo.git
cd hello-algo/codes/python
# run any example with your normal toolchain, e.g.
python chapter_array_and_linkedlist/array.pyTo serve the book site locally, the repo ships a Docker setup:
docker-compose up -d
# then open http://localhost:8000There is nothing to “install” as a dependency. The code files use each language’s standard runtime, and the book itself is static content.
Who it is for, and who should look elsewhere
It fits absolute beginners, self-taught programmers, and students who want intuition before formalism, plus anyone using a new language who wants idiomatic reference implementations of the classics. Visual learners get the most out of it.
It is a poor fit if you are cramming for interviews under deadline, where a problem-driven plan like Coding Interview University is a better match, or if you need advanced material. Hello Algo deliberately stops at the foundations. It does not cover advanced graph algorithms, string algorithms in depth, computational geometry, or formal proofs of correctness. For that depth you still want a rigorous textbook.
How it compares
| Project | Stars (2026-06) | What it is | Best for |
|---|---|---|---|
| krahets/hello-algo | ~127k | Animated DSA book + runnable code in 12+ languages | Building intuition from zero |
| jwasham/coding-interview-university | ~353k | Self-study checklist for big-tech interviews | Interview prep with a plan |
| TheAlgorithms/Python | ~222k | Reference implementations of algorithms | Looking up a clean implementation |
The three are complements more than rivals. Hello Algo teaches the concept, TheAlgorithms gives you a catalog of implementations, and Coding Interview University tells you what to study for a job.
What the star curve shows
The growth is the shape of a resource that found durable demand rather than a viral spike that fades. After launching in late 2022 it climbed sharply through 2023, then kept rising steadily toward 127,000. Star-history curves like this, a fast ramp followed by a long steady slope, usually mean a project crossed from “trending” into “recommended by word of mouth,” which is the healthier pattern for a learning resource.
Things the README plays down
The license is the big one. Everything in the repo, text and code, is under CC BY-NC-SA 4.0. The “NC” is non-commercial: you can read, share, and adapt it for free, but you cannot drop the material into a paid course, a commercial product, or monetized content without permission. The README buries this at the bottom, and it surprises people who assume an MIT-style free-for-anything license.
Second, the non-Chinese editions lag. English, Japanese, and Russian translations are community efforts, and the open issues make clear that translation and code transpilation are ongoing “help wanted” work. The Chinese original is the most complete; other languages may trail it on newer chapters.
Third, mind the scope. This is a foundations book by design, so do not expect coverage of the harder, research-adjacent topics.
Related reading
- jwasham/coding-interview-university for the interview-prep angle
- TheAlgorithms/Python when you want a working implementation to copy and study
FAQ
Is Hello Algo free? Yes. The book and all code are free to read and use under CC BY-NC-SA 4.0. You can read it online, download a PDF or EPUB, or clone the repo. The only catch is the non-commercial clause, covered below.
What programming languages does Hello Algo support? The examples are implemented in over a dozen languages, including Python, Java, C++, C, C#, JavaScript, TypeScript, Go, Swift, Rust, Ruby, Kotlin, Dart, and Zig. You read the book once and pick whichever language you study the code in.
Is Hello Algo good for coding interviews? It is good for the foundations that interviews assume, but it is not an interview plan. It teaches you how the structures and algorithms work. For a deadline-driven study schedule and practice routine, pair it with Coding Interview University.
Can I use Hello Algo commercially? Not without permission. The CC BY-NC-SA 4.0 license forbids commercial use, so you cannot use the text or code in a paid course or product without contacting the author. Personal study, classroom use, and non-commercial sharing are fine.
Hello Algo vs LeetCode, which should I use? They serve different stages. Hello Algo builds the conceptual model with animations and readable code; LeetCode drills you on problems once you have that model. Most people read Hello Algo first, then practice on a problem site.