What is the difference between DeepTutor and standard RAG tools?

Unlike standard RAG, DeepTutor uses a hybrid personalization engine and LightRAG knowledge graphs to track a learner's progress and conceptual mastery, ensuring pedagogical context is preserved.

Does DeepTutor support image and formula recognition?

Yes, DeepTutor is built to interpret complex charts, formulas, and multi-modal content within academic papers and textbooks.

Can I host DeepTutor on my own server?

Yes, DeepTutor is an open-source framework with a full-stack CLI (deeptutor start) available on GitHub for local or private deployment.

What is 'Imitation Mode' in the question generator?

Imitation Mode allows you to upload a reference exam or paper, and the AI will generate new practice questions that match the specific style and difficulty level of that document.

How does the Deep Research module work?

It follows a three-stage architecture (Planning, Research, Report) using a dynamic topic queue and parallel execution to synthesize information from web and academic sources.

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Learning

Free

DeepTutor

An AI tutoring system from HKUDS featuring multi-agent reasoning, RAG-driven problem solving, and personalized research assistants for academic and professional learning.

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Rating

Reviews

Upvotes

4 months ago

Listed

Learning

Education

Research

Academic Writing

RAG

Knowledge Graph

Tutoring

Question Generation

Study Tool

Tool information

Provider

HKUDS (University of Hong Kong)

Platforms

Web

GitHub (Self-hosted)

Languages

English

Chinese

Multi-language support

API Available

Yes

Added to directory

4 months ago

Last updated

about 2 months ago

Rating

Not available

Pricing

Free

About DeepTutor

What the tool does and who it's for

DeepTutor is an advanced AI-powered personalized tutoring framework developed by the Data Intelligence Laboratory at the University of Hong Kong (HKUDS). It features a multi-agent collaborative architecture that integrates Retrieval-Augmented Generation (RAG) with LightRAG-powered knowledge graphs to provide a deep understanding of complex educational materials. The platform offers a comprehensive suite of tools including a Smart Problem Solver that provides step-by-step solutions with citations, a Question Generator for creating targeted quizzes or mimicking exam styles, and a Deep Research module for systematic literature reviews. Designed for students and researchers, DeepTutor supports multiple modalities including PDF, LaTeX, images, and code execution, transforming static documents into interactive learning journeys.

Key capabilities

Smart Problem Solver: Delivers step-by-step solutions using dual-loop reasoning and web/paper search.

Interactive Knowledge Bases: Build repositories from PDF, TXT, and MD with automated vector indexing.

Practice Question Generator: Creates custom quizzes or mimics the style and difficulty of reference exams.

Deep Research Module: Conducts multi-stage systematic literature reviews and generates structured reports.

LightRAG Integration: Uses knowledge graphs to map semantic connections and prerequisite concepts.

Multi-Agent Collaboration: Features TutorBot for proactive companionship and autonomous review sessions.

Interactive Visualizations: Renders HTML components and simulators for complex technical concepts.

Code Execution: Built-in Python execution to solve mathematical or technical problems in real-time.

Pricing

Free — plans below

Open Source / Free

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