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DeepTutor

by HKUDS36kPythonUpdated 2026-08-16

DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/.

DeepTutor is a lifelong personalized AI tutoring platform built on an agent-native architecture that unifies chat, problem-solving, quiz generation, research, visualization, and mastery practice in one extensible workspace. Developed by HKUDS, it connects learners' knowledge bases, books, notebooks, question banks, and multi-layer memory across every workflow so context follows the learner rather than being siloed in separate tools. The system supports MCP servers, CLI apps, partner sub-agents (Claude Code, Codex, Gemini, Kimi), multi-engine RAG (LlamaIndex, PageIndex, GraphRAG, LightRAG, Obsidian), and installable community skills from EduHub.

Key Features

Unified agent loop for chat, quiz, deep research, visualize, solve, and mastery path modes with shared learner context
Three-layer inspectable memory system (L1 traces, L2 summaries, L3 synthesis) with a Memory Graph tracing claims to evidence
Multi-engine knowledge bases supporting LlamaIndex, PageIndex, GraphRAG, LightRAG, and linked Obsidian vaults with versioned RAG indexes
Partner sub-agents for live consultation with Claude Code, Codex, Gemini, Kimi, opencode, or MiMo coding CLIs mid-turn
Persistent IM companions (15 channels) and TutorBot agents on the same brain with per-partner private memory
Book Engine living-book compiler, Co-Writer multi-document drafting, Question Bank, and interactive HTML visualizations
Extensible tools: built-in capabilities, MCP server integration, CLI apps (101+), image/video/voice generation, and community skills from EduHub/ClawHub
Multi-provider LLM and embedding support with catalog-based model selection, reasoning effort control per model, and native OpenAI/Anthropic SDK

Use Cases

  • 01Personalized lifelong tutoring with continuous learning context across quiz, research, problem-solving, and mastery practice
  • 02Deep research workflows combining multi-document RAG retrieval with agentic tool calls and partner sub-agent consultation
  • 03Interactive study sessions with quiz generation, question notebooks, guided learning paths, and graded mastery gates
  • 04Collaborative writing and visualization using Co-Writer, Book Engine, and Chart.js/SVG/Mermaid rendering
  • 05Multi-user educational deployments with isolated workspaces, admin grants, and scoped runtime access for institutions
  • 06Building custom tutoring workflows by combining MCP servers, community skills, and persistent memory graphs

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DeepTutor — FAQ

What is DeepTutor?+

DeepTutor is an open-source, agent-native learning workspace that provides personalized tutoring, problem-solving, quiz generation, research, and mastery practice in one extensible system. It connects knowledge bases, books, notebooks, and three-layer memory across every workflow so learners' context persists across all activities.

How do I install DeepTutor?+

Install from PyPI with `pip install -U deeptutor`, then run `deeptutor init` to configure ports and providers, followed by `deeptutor start`. Alternatively, install from source (Python 3.11–3.13 + Node.js 22) or run the Docker image `ghcr.io/hkuds/deeptutor:latest` with port 3782 published.

Which AI clients does DeepTutor work with?+

DeepTutor runs as a standalone Web application (Next.js 16 frontend + FastAPI backend) and integrates partner sub-agents like Claude Code, OpenAI Codex, Gemini, Kimi, opencode, and MiMo. It supports MCP server connections and can be deployed for multi-user access with authentication.

Do I need API keys to use DeepTutor?+

Yes, you need an LLM provider API key (OpenAI, Anthropic, Gemini, or compatible endpoints like Ollama, LM Studio, llama.cpp, vLLM). Optionally configure an embedding provider for knowledge-base RAG and API keys for specific tools like web search or image generation.

Is DeepTutor free and open source?+

Yes, DeepTutor is released under the Apache 2.0 license and available on GitHub. You only pay for the LLM and embedding API usage from your chosen providers; the platform itself is free to self-host.

What are the system requirements for DeepTutor?+

Python 3.11–3.13 and Node.js 20+ for PyPI or source installs. The Docker image is self-contained. You'll need an LLM provider endpoint (cloud or local) and sufficient disk space for knowledge-base indexes and memory graphs.

How do I install DeepTutor?+

Open the source repository on GitHub and follow its README. DeepTutor is a agent — MCP Agents Market links you directly to the official repo.

Is DeepTutor free?+

DeepTutor is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.

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