<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> DeepTutor is an AI-powered personalized learning assistant enhancing educational and research workflows. It offers intelligent knowledge Q&A, interactive learning visualization, knowledge reinforcement, and advanced research/idea generation. Targeting students, researchers, and power users, it accelerates learning and discovery via personal knowledge base creation and insight synthesis.

How It Works

DeepTutor uses a multi-layered architecture: UI, Agent Modules (Solver, Research, IdeaGen), Tool Integration (RAG, web search, code execution), and Knowledge/Memory Foundation (Graph, Vector Store). Its core approach features multi-agent systems, including dual-loop reasoning for problem-solving and a dynamic topic queue for systematic research, enabling complex workflows from interactive learning to automated reporting.

Quick Start & Requirements

Installation: Clone repo, set up Python 3.10+ virtual environment (conda recommended), install dependencies (scripts/install_all.sh or pip install -r requirements.txt, npm install). Configure LLM API keys in .env. Prerequisites: Python 3.10+, Node.js/npm, LLM API keys. Optional demos available.

Highlighted Details

• Massive Document Q&A: Build knowledge bases from documents, with multi-agent problem-solving and RAG for step-by-step solutions.
• Interactive Learning Visualization: Transform concepts into visual aids and personalized, context-aware Q&A.
• Knowledge Reinforcement: Generate targeted quizzes, practice problems, and authentic exam simulations.
• Deep Research & Idea Generation: Conduct topic exploration, synthesize findings, and discover research directions.
• All-in-One Knowledge System: Features a personal knowledge base and notebook for tracking learning sessions.

Maintenance & Community

Active development is indicated by a recent December 2025 release. Community engagement via Feishu/WeChat groups (links in Communication.md) and GitHub Discussions.

Licensing & Compatibility

Licensed under AGPL-3.0. This strong copyleft license requires derivative works to be shared under the same license, potentially restricting integration into proprietary commercial products without open-sourcing modifications.

Limitations & Caveats

README omits explicit limitations or bugs. AGPL-3.0 license is a significant consideration for commercial adoption. Setup requires careful dependency management and API key configuration.