explaingit

hkuds/deeptutor

📈 Trending★ 24,085PythonAudience · developerComplexity · 4/5ActiveLicenseSetup · hard

TLDR

AI tutoring assistant that lets you have conversations with documents, ask questions, get explanations, and learn interactively instead of just reading passively.

Mindmap

mindmap
  root((DeepTutor))
    What it does
      Chat with documents
      Ask questions interactively
      Get tailored explanations
    How it works
      Multi-agent AI system
      RAG for document search
      Knowledge base from PDFs
    Features
      TutorBot guided learning
      Co-writing note mode
      Living books compiler
      CLI for power users
    Use cases
      Study research papers
      Learn from textbooks
      Deep document understanding
    Tech stack
      Python backend
      Next.js frontend
      RAG architecture
    Audience
      Students and researchers
      Lifelong learners

Things people build with this

USE CASE 1

Upload a research paper and ask the AI to explain complex sections in simpler terms, then quiz yourself on key concepts.

USE CASE 2

Study a textbook by having interactive conversations with the material instead of passively reading chapters.

USE CASE 3

Build a searchable knowledge base from multiple PDFs and use the TutorBot to guide structured learning sessions.

USE CASE 4

Take notes while learning by using the co-writing mode to capture insights as you discuss the material with the AI.

Tech stack

PythonNext.jsRAGMulti-agent AI

Getting it running

Difficulty · hard Time to first run · 1h+

Requires API keys for LLM services, database setup, and coordinating Python backend with Next.js frontend.

Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

DeepTutor is an AI-powered personalized tutoring assistant designed to help people learn from documents and study materials through interactive conversation. The core problem it solves is passive reading: instead of just reading a PDF or research paper and hoping it sticks, you can have a back-and-forth conversation with an AI that has read the document, ask questions, get explanations tailored to your level, and work through problems interactively. The system uses a multi-agent architecture, meaning several specialized AI components work together behind the scenes. One handles question answering from documents using RAG (retrieval-augmented generation, a technique that lets the AI search relevant passages before answering), another manages a "TutorBot" that can autonomously guide learning sessions, and another supports a co-writing mode for taking notes. You can upload documents like PDFs and have the AI build a searchable knowledge base from them, then chat with that knowledge base. There is also a CLI for power users and a Book Engine that compiles study materials into interactive "living books." You would use this as a student, researcher, or lifelong learner who wants to deeply understand documents rather than skim them, or who benefits from having complex material explained in different ways on demand. The backend is Python (version 3.11 or higher) and the frontend uses Next.js.

Copy-paste prompts

Prompt 1
I have a PDF of a research paper on machine learning. How do I upload it to DeepTutor and start asking questions about its content?
Prompt 2
Show me how to use the TutorBot feature to create a guided learning session from my study materials.
Prompt 3
How do I set up the CLI version of DeepTutor to process multiple documents at once?
Prompt 4
Can you walk me through creating a 'living book' from my course notes using the Book Engine?
Prompt 5
What's the best way to use the co-writing mode to take notes while learning from a document?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.