DeepTutor is a command-line tutoring agent that quizzes you on study material, grades the explanation you write back, and keeps track of what you still have not understood. It is for people who study technical material on their own: developers reading documentation, a paper or a course, who are comfortable running a Python tool and attaching their own model provider to it. The project comes from HKUDS, is Apache-2.0 licensed, and has collected roughly 45,000 stars and 5,600 forks on GitHub since it was created at the end of December 2025.
What it does
The pitch is aimed at the weakness of multiple choice: you can pick the right option by elimination and learn nothing. DeepTutor asks you to explain the answer in your own words and then judges that explanation.
A few specifics the project describes:
- It labels an answer as incomplete, incorrect, or merely uncertain, so "nearly right" and "confidently wrong" are not the same result.
- It grounds that judgement in your own source material rather than in general knowledge alone.
- It remembers the mix-ups you never resolved and carries them into the next session, so tutoring does not restart from zero each time.
- It can group two to four questions together and score each one separately instead of giving a single blended grade.
That is the behaviour the project and its demo describe. No independent benchmark of grading accuracy appears in the material here, so treat the grading quality as the authors' claim until you have tried it on something you already know well.
How it works
The code is Python. The repository's own topics name retrieval-augmented generation, multi-agent systems and agent tooling, which lines up with what the feature list implies: your documents are indexed and used as the reference the grader checks you against, and the session history becomes state that persists between runs. The README as saved is mostly badges and pointers to the project's documentation site at deeptutor.info, so the detailed architecture lives there rather than on the repository page, and this write-up does not guess at the parts it does not show.
One thing is stated plainly: DeepTutor does not ship a model. You choose a model provider from DeepTutor's own provider catalog, and that provider does the actual grading and question writing.
Getting started
The software itself is free and the licence is permissive. The intelligence is not included: you supply a model provider, and if the provider you pick is a paid cloud API then every question, every explanation you write and every grade costs tokens on your account. The material does not list which providers are in the catalog, what they cost, or whether a locally hosted model is one of the options, so check the provider catalog in the documentation before you plan around a particular one.
The project does not list hardware requirements, an operating system or a memory figure anywhere in the material available here. Assume a normal Python environment and expect the real constraint to be the provider, not your machine.
Get it from github.com/HKUDS/DeepTutor, and read the install steps in the repository README or at deeptutor.info. The saved copy of the README used for this entry is truncated before the install section, so no specific command is quoted here rather than inventing one. There is no separate download, app store build or installer mentioned: this is a repository you clone and a command-line tool you run.
When to use it / when not
The main catch is the bring-your-own-provider model: you install the study app, but you also wire up an API key, accept a per-session cost that scales with how much you study, and send your study material and your own written answers to whichever provider you selected.
Use it instead of just asking Claude or ChatGPT to quiz you when you want the grading to be anchored to your documents and the record of your weak spots to survive past the end of the chat. Don't use it if you want a graphical app, a phone client, or a tutor that works with no account and no API key attached.
Alternatives
The honest comparison set is a plain chatbot session, which is free of setup but forgets you between conversations, and a flashcard system such as Anki, which remembers everything but only ever checks recall of what you wrote on the card yourself. DeepTutor sits between them: generated questions, written answers, and memory of the things you got wrong.
Take this one seriously if you study alone, already pay for a model API, and know that your real problem is the illusion of understanding rather than a lack of material to read. The combination of a grader that distinguishes "incomplete" from "incorrect" and a memory that drags your unresolved confusions into the next session is a genuinely different study loop from asking a chatbot to test you. If you want a finished consumer product, or you are unwilling to route your notes through a third-party model, this is not the tool to adopt yet.