<div align="center">
<p align="center"><img src="assets/figs/logo/logo.png" alt="DeepTutor logo" height="56" style="vertical-align: middle;"> <img src="assets/figs/logo/banner.png" alt="DeepTutor" height="48" style="vertical-align: middle;"></p>
# DeepTutor: Lifelong Personalized Tutoring
<p align="center">
<a href="https://deeptutor.info" target="_blank"><img alt="Docs — deeptutor.info" src="https://img.shields.io/badge/Docs-deeptutor.info%20%E2%86%97-0A0A0A?style=for-the-badge&labelColor=F5F5F4" height="36"></a>
<a href="https://deeptutor.info/collaborate/" target="_blank"><img alt="Collaborate — work with us" src="https://img.shields.io/badge/Collaborate-work%20with%20us%20%E2%86%97-0A0A0A?style=for-the-badge&labelColor=F5F5F4" height="36"></a>
</p>
<p align="center">
<a href="https://www.star-history.com/hkuds/deeptutor"><picture><source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/badge?repo=HKUDS/DeepTutor&type=trending&theme=dark"><source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/badge?repo=HKUDS/DeepTutor&type=trending"><img alt="GitHub Trending Repository of the Day" src="https://api.star-history.com/badge?repo=HKUDS/DeepTutor&type=trending" height="55"></picture></a> <a href="https://trendshift.io/repositories/17099"><img src="https://trendshift.io/api/badge/trendshift/repositories/17099/daily" alt="HKUDS/DeepTutor | Trendshift" width="250" height="55"></a> <a href="https://www.star-history.com/hkuds/deeptutor"><picture><source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/badge?repo=HKUDS/DeepTutor&theme=dark"><source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/badge?repo=HKUDS/DeepTutor"><img alt="Star History Rank" src="https://api.star-history.com/badge?repo=HKUDS/DeepTutor" width="185" height="55"></picture></a>
</p>
<p align="center">
<a href="README.md"><img alt="English" height="40" src="https://img.shields.io/badge/English-BCDCF7"></a>
<a href="assets/README/README_CN.md"><img alt="简体中文" height="40" src="https://img.shields.io/badge/简体中文-CDCFD4"></a>
<a href="assets/README/README_TW.md"><img alt="繁體中文" height="40" src="https://img.shields.io/badge/繁體中文-CDCFD4"></a>
<a href="assets/README/README_JA.md"><img alt="日本語" height="40" src="https://img.shields.io/badge/日本語-CDCFD4"></a>
<a href="assets/README/README_ES.md"><img alt="Español" height="40" src="https://img.shields.io/badge/Español-CDCFD4"></a>
<a href="assets/README/README_FR.md"><img alt="Français" height="40" src="https://img.shields.io/badge/Français-CDCFD4"></a>
<a href="assets/README/README_AR.md"><img alt="Arabic" height="40" src="https://img.shields.io/badge/Arabic-CDCFD4"></a>
<a href="assets/README/README_RU.md"><img alt="Русский" height="40" src="https://img.shields.io/badge/Русский-CDCFD4"></a>
<a href="assets/README/README_HI.md"><img alt="Hindi" height="40" src="https://img.shields.io/badge/Hindi-CDCFD4"></a>
<a href="assets/README/README_PT.md"><img alt="Português" height="40" src="https://img.shields.io/badge/Português-CDCFD4"></a>
<a href="assets/README/README_TH.md"><img alt="Thai" height="40" src="https://img.shields.io/badge/Thai-CDCFD4"></a>
<a href="assets/README/README_PL.md"><img alt="Polski" height="40" src="https://img.shields.io/badge/Polski-CDCFD4"></a>
</p>
[](https://www.python.org/downloads/)
[](https://nextjs.org/)
[](LICENSE)
[](https://github.com/HKUDS/DeepTutor/releases)
[](https://arxiv.org/abs/2604.26962)
[](https://discord.gg/eRsjPgMU4t)
[](./Communication.md)
[](https://github.com/HKUDS/DeepTutor/issues/78)
[Features](#-key-features) · [Get Started](#-get-started) · [Explore](#-explore-deeptutor) · [CLI](#%EF%B8%8F-deeptutor-cli--agent-native-interface) · [Ecosystem](#-ecosystem--eduhub--the-skills-community) · [Community](#-community)
</div>
---
> 🤝 **We welcome any kinds of contributing!** Vote on roadmap items or propose new ones at [`Roadmap`](https://github.com/HKUDS/DeepTutor/issues/498), and see our [Contributing Guide](CONTRIBUTING.md) for branching strategy, coding standards, and how to get started.
### 📦 Releases
> **[2026.10.19]** [v1.6.15](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.15) — Reviewed learning-path changes, grouped assessments, bounded tutoring memory, reliable chat recovery, sturdier parsing, retrieval comparisons, and native Gemini speech.
<details>
<summary><b>Past releases (more than 1 week ago)</b></summary>
> **[2026.10.8]** [v1.6.14](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.14) — Original-source visual practice, recoverable indexing, unified video Reading, scientific research tools, managed plugins, a learning journal, and provider setup improvements.
> **[2026.10.4]** [v1.6.13](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.13) — Reading and listening improvements, question-bank practice, resilient document parsing, configurable speech, workspace-visible attachments, Polish, and new providers.
> **[2026.9.27]** [v1.6.12](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.12) — Workspace KB moves, Kiwix archives, source figures, Task Board, German UI, and chat recovery.
> **[2026.9.24]** [v1.6.11](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.11) — French and Ukrainian interfaces, reading folders with in-chat selection actions, figures sent to vision models, paged Office previews, and self-syncing knowledge bases.
> **[2026.9.22]** [v1.6.10](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.10) — Native LightRAG role models, a published index that records and enforces what built it, PDF attachments that follow your parsing engine, visible truncation, and unfiltered provider choices.
> **[2026.9.21]** [v1.6.9](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.9) — Folder-based learning workspaces, daily practice, redesigned Settings, clearer streaming conversations, persistent usage accounting, and recoverable archives with explicit permanent deletion.
> **[2026.9.14]** [v1.6.8](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.8) — A recycle bin for deleted chats, search across your whole conversation history, a tool that looks past your knowledge base, and a sweep of fixes for quiet failures.
> **[2026.9.11]** [v1.6.7](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.7) — A fix release: books that arrived as one empty chapter, quizzes that produced nothing, the model's scratchpad in the text, formulas printed raw, and cards you could not submit.
> **[2026.9.8]** [v1.6.6](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.6) — A fix release: answers that could not submit, a copy button that lied, connected knowledge bases for partners, Codex sign-in inside Docker, and a 100 KB lighter home route.
> **[2026.9.6]** [v1.6.5](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.5) — A content workspace you point at any folder, one `exec` tool for every language, Mastery Path modes that gate its tools, and Settings that grades readiness.
> **[2026.9.3]** [v1.6.4](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.4) — Faster isolated runtimes, controllable Book generation, source-complete Mastery paths and Chat hand-offs, durable Reading, unified activity UI, recoverable sessions, and explicit per-model API capabilities.
> **[2026.9.2]** [v1.6.3](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.3) — Breaking front/back-end refactor, strict canonical routes and recoverable streams, plus learner/guardian accounts, grounded Reading, WeKnora, broader parsing, Python 3.14, and DashScope media.
> **[2026.8.31]** [v1.6.2](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.2) — Immersive YouTube learning, a plugin-driven Visualize catalog, three new agent harnesses, safer reading citations, multi-format MinerU, live Partner channel status, and guided updates.
> **[2026.8.30]** [v1.6.1](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.1) — One vendor key linked to every service it serves, a task model for background work, Settings as a searchable navigator, a sidebar you arrange, and first-party **LightRAG**.
> **[2026.8.27]** [v1.6.0](https://github.com/HKUDS/DeepTutor/releases/tag/v1.6.0) — Faithful EPUB reading and annotations, Courses with Little Tutor and Ask Questions, bounded web-source sync, shared Books with private learning state, and Serply/native search.
> **[2026.8.25]** [v1.5.17](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.17) — Partners each member owns with private conversations and linkable chat accounts, GitHub repos as a knowledge source, **Antigravity CLI**, browser WeChat QR login, and `deeptutor doctor`.
> **[2026.8.22]** [v1.5.16](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.16) — **MarginNote 4** libraries you connect and its add-on fills, Book pages that turn again, and tool-call ids, embeddings and temperature limits that stop breaking behind a gateway.
> **[2026.8.20]** [v1.5.15](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.15) — **PageIndex OSS** you host yourself with reasoning retrieval, a question bank you can finally file into, third-party tool/capability plugins, and **Apache Tika** parsing.
> **[2026.8.19]** [v1.5.14](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.14) — **Immersive Reading**: a document open beside the thread, cited page by page; DeepTutor configures itself from chat; IMA libraries you browse and write to; a notebook console.
> **[2026.8.17]** [v1.5.13](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.13) — Books stream while they compile, track your progress, and export to Markdown; a cost estimate before you approve a spine; and home starter suggestions drawn from memory.
> **[2026.8.13]** [v1.5.12](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.12) — Web search rebuilt with six new providers (**Doubao**, **Bocha**, **Zhipu**, **Firecrawl**, **Qianfan**, **Aliyun IQS**), a **LiteParse** parsing engine, MCP servers that reconnect on credential change, and **CodeBuddy** + **OrcaRouter**.
> **[2026.8.10]** [v1.5.11](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.11) — Prose around a DSML tool call stops vanishing, a truncated reply continues instead of ending, live memory usage in Settings, and LightRAG indexing off the event loop.
> **[2026.8.7]** [v1.5.10](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.10) — Every account signs in to its own **Codex**, model output language becomes its own setting, empty tool calls are rejected instead of retried, and uploads stop blocking the loop.
> **[2026.8.4]** [v1.5.9](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.9) — Gemini **Embedding 2** on its native endpoint, a per-model **reasoning effort** control, a **Novita AI** gateway, retrieval roles for queries, and Compose deployments that keep all of `data/`.
> **[2026.8.2]** [v1.5.8](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.8) — Memory: a real heap ceiling for the dev server, source installs serve a production build, bounded LLM client and index caches, and a keep-alive fix for stray 500s.
> **[2026.7.31]** [v1.5.7](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.7) — A per-account **MCP Services** store, 101 **CLI Apps** the tutor can run, credentials moved out of the sandbox's reach, and a mobile layout.
> **[2026.7.29]** [v1.5.6](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.6) — Remote **Codex** sign-in completes behind an SSH tunnel, generated files get their own card in Activity, non-English languages stop collapsing to Chinese, and book creation no longer times out.
> **[2026.7.26]** [v1.5.5](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.5) — Sign in with your ChatGPT plan via **OpenAI Codex** OAuth, an **Eden AI** provider, knowledge bases that report what they hold, traceable `rag` citations, and GraphRAG indexing without a workaround.
> **[2026.7.24]** [v1.5.4](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.4) — Maintenance sweep: the post-answer "generating" stall is gone, IM partners render Markdown tables faithfully, LLM JSON parsing is sturdier, plus quiz, create-KB form, and Math Animator fixes.
> **[2026.7.24]** [v1.5.3](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.3) — Themeable code blocks, four more coding CLIs in My Agents (Gemini, Kimi, opencode, MiMo), an Atlas Cloud LLM provider, and a broad chat, memory, embedding, and parsing reliability sweep.
> **[2026.7.19]** [v1.5.2](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.2) — Configurable chat attachment limits, PageIndex retrieval that reasons across your documents via agentic tool calls, broader Anthropic/OpenAI model support, and steadier Book, Knowledge Base, and chat UI.
> **[2026.7.9]** [v1.5.1](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.1) — Remove a single failed document from a knowledge base — even one stuck in an **error** state — instead of deleting and rebuilding the whole base.
> **[2026.7.4]** [v1.5.0](https://github.com/HKUDS/DeepTutor/releases/tag/v1.5.0) — LlamaIndex ingestion now honors your **Document Parsing** engine with multimodal image extraction, Partner & Soul ids stay URL-safe for non-Latin names, and optional RAG extras install cleanly on Python 3.14+.
> **[2026.6.30]** [v1.4.15](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.15) — A native **Mattermost** channel for Partners, plus fixes so Guided Learning multiple-choice questions grade correctly and a configured zero chunk overlap is honored.
> **[2026.6.29]** [v1.4.14](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.14) — Click an assigned partner to chat in one step, Deep Research flags partial reports, LightRAG indexes without MinerU, FAISS handles non-ASCII paths, and PocketBase sessions are isolated per user.
> **[2026.6.27]** [v1.4.13](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.13) — Partners support non-Latin names and become assignable to users, logos render after login (#599), tiny knowledge bases retrieve reliably, and containers start cleanly under rootless Podman.
> **[2026.6.24]** [v1.4.12](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.12) — A new **LightRAG Server** retrieval engine, a lightweight **PyMuPDF4LLM** parsing engine, and a FAISS vector backend that makes large knowledge-base retrieval dramatically faster.
> **[2026.6.23]** [v1.4.11](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.11) — Native tool calling on every cloud OpenAI-compatible provider, a redesigned admin Users page, LaTeX in quiz options, an honest session-loading spinner, and configurable container host binding.
> **[2026.6.21]** [v1.4.10](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.10) — A self-service **Profile** page with avatars, a rootless-ready container guide with a single-port request-time proxy, and deny-by-default MCP tools for non-admin users.
> **[2026.6.19]** [v1.4.9](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.9) — Settings polish: Search shows only the fields your provider needs, connection profiles can be renamed and auto-named by provider, and graded Mastery Path questions flow into your Question Bank.
> **[2026.6.18]** [v1.4.8](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.8) — Connect your own **Partners** under **My Agents** and consult them live in chat — answering through their own persona, library and skills — each with its own private memory.
> **[2026.6.18]** [v1.4.7](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.7) — Connect your local **Claude Code / Codex** and consult it live mid-turn, **My Agents** graduates to a top-level `/agents`, and Partner conversations gain branch / resume / delete with a replayable trace.
> **[2026.6.17]** [v1.4.6](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.6) — Four-surface consolidation: a Space learning dashboard with importable **My Agents** and top-level Memory, a **Knowledge Center** with GraphRAG / PageIndex / LightRAG / linked-KB / Obsidian, opened-up Settings, and per-model capability gating.
> **[2026.6.14]** [v1.4.5](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.5) — Guided Learning rebuilt on the chat agent loop with a hard per-type mastery gate and a `/learning` dashboard, a new loop-plugin framework, plus Markdown export / save-to-notebook for Partner conversations.
> **[2026.6.13]** [v1.4.4](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.4) — Install community skills from [ClawHub](https://clawhub.ai/) with `deeptutor skill install` behind a security gate, plus real in-browser DOCX/XLSX previews for knowledge-base files.
> **[2026.6.12]** [v1.4.3](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.3) — TutorBot becomes **Partners** on a production-grade IM pipeline (15 channels, live streaming), Chat moves to a single agent loop, real per-user isolation, and a rebuilt Visualize.
> **[2026.5.28]** [v1.4.2](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.2) — Stability + polish: Gemini 2.5+ unblocked across Visualize and Chat, auth-routing fix (#485), smooth-streaming chat UX, a Recents sidebar, and Lemonade local-provider support.
> **[2026.5.27]** [v1.4.1](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.1) — Security + stability: TutorBot tool sandbox locked down, per-user resource isolation, multimodal image fallback, an HTTP/SSE API for TutorBots, and a v1.4.0 chat regression fix.
> **[2026.5.22]** [v1.4.0](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.0) — GA cut of v1.4: Auto Mode, three-layer Memory, agentic Deep Research / Solve / Question, LlamaIndex RAG refactor, Visualize/Animator merge, and restart-safe turn runtime.
> **[2026.5.21]** [v1.4.0-beta](https://github.com/HKUDS/DeepTutor/releases/tag/v1.4.0-beta) — Three-layer Memory workbench (L1/L2/L3), every chat capability rebuilt on a single agentic engine, LlamaIndex-only RAG, and a unified Settings + Capabilities surface.
> **[2026.5.10]** [v1.3.10](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.10) — Remote Docker CORS recovery, `DISABLE_SSL_VERIFY` across SDK providers, safer code-block citations, and optional Matrix E2EE add-on.
> **[2026.5.9]** [v1.3.9](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.9) — TutorBot Zulip and NVIDIA NIM support, safer thinking-model routing, `deeptutor start`, sidebar tooltips, and session-store parity.
> **[2026.5.8]** [v1.3.8](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.8) — Optional multi-user deployments with isolated user workspaces, admin grants, auth routes, and scoped runtime access.
> **[2026.5.4]** [v1.3.7](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.7) — Thinking-model/provider fixes, visible Knowledge index history, and safer Co-Writer clear/template editing.
> **[2026.5.3]** [v1.3.6](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.6) — Catalog-based model selection for chat and TutorBot, safer RAG re-indexing, OpenAI Responses token-limit fixes, and Skills editor validation.
> **[2026.5.2]** [v1.3.5](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.5) — Smoother local launch settings, safer RAG queries, cleaner local embedding auth, and Settings dark-mode polish.
> **[2026.5.1]** [v1.3.4](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.4) — Book page chat persistence and rebuild flows, chat-to-book references, stronger language/reasoning handling, RAG document extraction hardening.
> **[2026.4.30]** [v1.3.3](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.3) — NVIDIA NIM + Gemini embedding support, unified Space context for chat history/skills/memory, session snapshots, RAG re-index resilience.
> **[2026.4.29]** [v1.3.2](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.2) — Transparent embedding endpoint URLs, RAG re-index resilience for invalid persisted vectors, memory cleanup for thinking-model output, Deep Solve runtime fix.
> **[2026.4.28]** [v1.3.1](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.1) — Stability: safer RAG routing & embedding validation, Docker persistence, IME-safe input, Windows/GBK robustness.
> **[2026.4.27]** [v1.3.0](https://github.com/HKUDS/DeepTutor/releases/tag/v1.3.0) — Versioned KB indexes with re-index workflow, rebuilt Knowledge workspace, embedding auto-discovery with new adapters, Space hub.
> **[2026.4.25]** [v1.2.5](https://github.com/HKUDS/DeepTutor/releases/tag/v1.2.5) — Persistent chat attachments with file-preview drawer, attachment-aware capability pipelines, TutorBot Markdown export.
> **[2026.4.25]** [v1.2.4](https://github.com/HKUDS/DeepTutor/releases/tag/v1.2.4) — Text/code/SVG attachments, one-command Setup Tour, Markdown chat export, compact KB management UI.
> **[2026.4.24]** [v1.2.3](https://github.com/HKUDS/DeepTutor/releases/tag/v1.2.3) — Document attachments (PDF/DOCX/XLSX/PPTX), reasoning thinking-block display, Soul template editor, Co-Writer save-to-notebook.
> **[2026.4.22]** [v1.2.2](https://github.com/HKUDS/DeepTutor/releases/tag/v1.2.2) — User-authored Skills system, chat input performance overhaul, TutorBot auto-start, Book Library UI, visualization fullscreen.
> **[2026.4.21]** [v1.2.1](https://github.com/HKUDS/DeepTutor/releases/tag/v1.2.1) — Per-stage token limits, Regenerate response across all entry points, RAG & Gemma compatibility fixes.
> **[2026.4.20]** [v1.2.0](https://github.com/HKUDS/DeepTutor/releases/tag/v1.2.0) — Book Engine "living book" compiler, multi-document Co-Writer, interactive HTML visualizations, Question Bank @-mention.
> **[2026.4.18]** [v1.1.2](https://github.com/HKUDS/DeepTutor/releases/tag/v1.1.2) — Schema-driven Channels tab, RAG single-pipeline consolidation, externalized chat prompts.
> **[2026.4.17]** [v1.1.1](https://github.com/HKUDS/DeepTutor/releases/tag/v1.1.1) — Universal "Answer now", Co-Writer scroll sync, unified settings panel, streaming Stop button.
> **[2026.4.15]** [v1.1.0](https://github.com/HKUDS/DeepTutor/releases/tag/v1.1.0) — LaTeX block math overhaul, LLM diagnostic probe, Docker + local LLM guidance.
> **[2026.4.14]** [v1.1.0-beta](https://github.com/HKUDS/DeepTutor/releases/tag/v1.1.0-beta) — Bookmarkable sessions, Snow theme, WebSocket heartbeat & auto-reconnect, embedding registry overhaul.
> **[2026.4.13]** [v1.0.3](https://github.com/HKUDS/DeepTutor/releases/tag/v1.0.3) — Question Notebook with bookmarks & categories, Mermaid in Visualize, embedding mismatch detection, Qwen/vLLM compatibility, LM Studio & llama.cpp support, and Glass theme.
> **[2026.4.11]** [v1.0.2](https://github.com/HKUDS/DeepTutor/releases/tag/v1.0.2) — Search consolidation with SearXNG fallback, provider switch fix, and frontend resource leak fixes.
> **[2026.4.10]** [v1.0.1](https://github.com/HKUDS/DeepTutor/releases/tag/v1.0.1) — Visualize capability (Chart.js/SVG), quiz duplicate prevention, and o4-mini model support.
> **[2026.4.10]** [v1.0.0-beta.4](https://github.com/HKUDS/DeepTutor/releases/tag/v1.0.0-beta.4) — Embedding progress tracking with rate-limit retry, cross-platform dependency fixes, and MIME validation fix.
> **[2026.4.8]** [v1.0.0-beta.3](https://github.com/HKUDS/DeepTutor/releases/tag/v1.0.0-beta.3) — Native OpenAI/Anthropic SDK (drop litellm), Windows Math Animator support, robust JSON parsing, and full Chinese i18n.
> **[2026.4.7]** [v1.0.0-beta.2](https://github.com/HKUDS/DeepTutor/releases/tag/v1.0.0-beta.2) — Hot settings reload, MinerU nested output, WebSocket fix, and Python 3.11+ minimum.
> **[2026.4.4]** [v1.0.0-beta.1](https://github.com/HKUDS/DeepTutor/releases/tag/v1.0.0-beta.1) — Agent-native architecture rewrite (~200k lines): Tools + Capabilities plugin model, CLI & SDK, TutorBot, Co-Writer, Guided Learning, and persistent memory.
> **[2026.1.23]** [v0.6.0](https://github.com/HKUDS/DeepTutor/releases/tag/v0.6.0) — Session persistence, incremental document upload, flexible RAG pipeline import, and full Chinese localization.
> **[2026.1.18]** [v0.5.2](https://github.com/HKUDS/DeepTutor/releases/tag/v0.5.2) — Docling support for RAG-Anything, logging system optimization, and bug fixes.
> **[2026.1.15]** [v0.5.0](https://github.com/HKUDS/DeepTutor/releases/tag/v0.5.0) — Unified service configuration, RAG pipeline selection per knowledge base, question generation overhaul, and sidebar customization.
> **[2026.1.9]** [v0.4.0](https://github.com/HKUDS/DeepTutor/releases/tag/v0.4.0) — Multi-provider LLM & embedding support, new home page, RAG module decoupling, and environment variable refactor.
> **[2026.1.5]** [v0.3.0](https://github.com/HKUDS/DeepTutor/releases/tag/v0.3.0) — Unified PromptManager architecture, GitHub Actions CI/CD, and pre-built Docker images on GHCR.
> **[2026.1.2]** [v0.2.0](https://github.com/HKUDS/DeepTutor/releases/tag/v0.2.0) — Docker deployment, Next.js 16 & React 19 upgrade, WebSocket security hardening, and critical vulnerability fixes.
</details>
> ✨ **v1.6.15 is live.** `pip install -U deeptutor` picks up the latest stable release.
### 📰 News
- **2026-09-20** 🎉 40k stars in 9 months! We'll keep expanding DeepTutor's learning ecosystem.
- **2026-05-22** 🌐 Official docs site live at [**deeptutor.info**](https://deeptutor.info/) — guides, references, and capability tours in one place.
- **2026-04-19** 🎉 20k stars in 111 days! Thank you for the support toward truly personalized, intelligent tutoring.
- **2026-04-10** 📄 Our paper is live on arXiv — read the [preprint](https://arxiv.org/abs/2604.26962) for the design and ideas behind DeepTutor.
- **2026-02-06** 🚀 10k stars in just 39 days! A huge thank you to our incredible community.
- **2026-01-01** 🎊 Happy New Year! Join our [Discord](https://discord.gg/eRsjPgMU4t), [WeChat](https://github.com/HKUDS/DeepTutor/issues/78), or [Discussions](https://github.com/HKUDS/DeepTutor/discussions) — let's shape DeepTutor together.
- **2025-12-29** 🎓 DeepTutor is officially released!
## ✨ Key Features
DeepTutor is an agent-native learning workspace that connects tutoring, problem solving, quiz generation, research, visualization, and mastery practice in one extensible system.
- **One runtime for every mode** — Chat, Ask Questions, Quiz, Research, Visualize, Solve, Course Study, Mastery Path, Immersive Reading, and Audio Overview share one capability runtime and session context while keeping purpose-built loops and pipelines.
- **Task Board** — Track study tasks in To do, In progress, and Done, with notes, drag-and-drop or keyboard-accessible move buttons, and an archive you can restore from. Cards stay in the current workspace and follow the existing appearance and language settings; no model configuration is required.
- **Connected learning context** — Knowledge bases, books, Co-Writer drafts, notebooks, question banks, personas, and Memory can be reused across the workflows that support them, subject to account grants and learning policies.
- **Immersive video learning** — add YouTube or Bilibili links to Reading for native playback, transcript search, timestamp-grounded tutoring, saved notes, and resumable progress; YouTube captions and browsing can use an administrator-configured Invidious instance.
- **Subagents and Partners** — from Chat, consult a live agent harness (Claude Code, Codex, Grok CLI, Antigravity, Kimi, opencode, MiMo, Hermes, OpenClaw, or DeepSeek) or a Partner, import past conversations, and run persistent IM companions on the same brain.
- **Multi-engine knowledge** — versioned RAG libraries across LlamaIndex, PageIndex, GraphRAG, LightRAG, a remote LightRAG Server, a self-hosted WeKnora knowledge base, a Tencent IMA or MarginNote 4 library, a connected Kiwix ZIM archive, or a linked Obsidian vault, with pluggable document parsing. See [native LightRAG role models](deeptutor/services/rag/pipelines/lightrag/README.md) for independent extraction, query and vision settings, default-only creation and confirmed rebuilds.
- **Extensible tools and skills** — built-in tools, MCP servers, CLI apps, image / video / voice generation models, and installable community skills from EduHub.
- **Inspectable memory** — L1 traces, L2 surface summaries, and L3 synthesis make personalization visible and editable; the Memory Graph links L2 facts to L1 evidence and L3 synthesis to contributing surfaces.
---
## 🚀 Get Started
### Let Your Agent Set Up DeepTutor
Copy this prompt into your terminal-capable AI agent (such as Codex or Claude Code):
```text
Help me install and configure DeepTutor locally from https://github.com/HKUDS/DeepTutor.
Clone the repository, or reuse my existing checkout, and read SKILL.md and
docs-for-user/AGENT_SETUP.md first. Follow that guide to prepare the environment,
configure my model provider, verify the setup, and start the Web app.
Ask me for any missing provider, model, or credential-source information;
do not print API keys. Preserve my existing files and settings, and finish
with the access URL and the results of your checks.
```
The [Agent Setup guide](docs-for-user/AGENT_SETUP.md) covers the full flow, including non-interactive CLI configuration. To install it yourself, choose one of the paths below.
DeepTutor ships four installation paths. They all share one runtime-home layout: private settings live in `data/user/settings/` under the directory you launch from (or under `DEEPTUTOR_HOME` / `deeptutor start --home` if you set one explicitly). For the full app, the recommended flow is **pick a runtime-home directory → install → `deeptutor init` → `deeptutor start`**.
### One-Command Docker Trial
If Docker is not installed yet, [install Docker](https://docs.docker.com/get-docker/) first. Then try the full app without setting up Python or Node.js:
```bash
docker run --rm --name deeptutor \
-p 127.0.0.1:3782:3782 \
-v deeptutor-data:/app/data \
ghcr.io/hkuds/deeptutor:latest
```
Open [http://127.0.0.1:3782](http://127.0.0.1:3782), configure model providers in **Settings → Providers**, and press `Ctrl+C` when you are done. The named `deeptutor-data` volume keeps settings and local data for the next run.
### Content Workspace
The **Content Workspace** is separate from DeepTutor's private runtime home. It
is the folder agents may read, with generated files under
`outputs/<capability>/<session>/<turn>/`. Custom workspaces isolate conversations,
learning materials, progress, and caches in a private `.deeptutor/data/` tree
that file tools cannot browse. Settings, credentials, and Memory stay shared
at the account level.
Without configuration, the content workspace is
`<runtime-home>/data/user/workspace`. Local PyPI, CLI, and source installs can
choose folders in **Settings → Workspaces**; set the default folder with:
```bash
deeptutor workspace show
deeptutor workspace set /absolute/path/to/my-folder
deeptutor workspace reset
```
Capabilities inspect their selected workspace through the built-in workspace
tools. The model only receives relative paths such as `outputs/...`; when it
uses `workspace_present`, the UI renders an authenticated, openable snapshot.
The same exact relative path also works in a normal Markdown link or image.
Changing the source file later does not change an already presented snapshot.
Learning Space manages the resource library. In **Settings → Workspaces**, assign
Skills, MCP services and knowledge bases to each workspace, or retain its
existing access rules. Existing workspaces keep their current access until a
selection is saved. Assignments reference the original resources without copying
credentials or knowledge indexes; workspace-specific skills can override shared
versions. See [workspace resource assignments](docs-for-user/workspaces.md).
Execution is read-only outside `outputs/`. Copying a generated file elsewhere
in the content workspace requires an explicit **Allow once** confirmation for
that exact source and destination. A system sandbox or the Docker runner
enforces this boundary when available; local restricted-subprocess fallback is
shown as **best effort** in Workspace settings.
<details>
<summary><b>Option 1 — Install From PyPI</b> · full local Web app + CLI, no clone required</summary>
Full local Web app + CLI, no clone required. Needs **Python 3.11–3.14** and a **Node.js 20+** runtime on PATH (the packaged Next.js standalone server is spawned by `deeptutor start`).
```bash
mkdir -p my-deeptutor && cd my-deeptutor
pip install -U deeptutor
deeptutor init # prompts for ports + LLM provider + optional embedding/search
deeptutor start # starts backend + frontend; keep the terminal open
```
`deeptutor init` prompts for backend port (default `8001`), frontend port (default `3782`), LLM provider / base URL / API key / model, an optional embedding provider for Knowledge Base / RAG, and an optional search provider for Web Search.
After `deeptutor start`, open the frontend URL printed in the terminal — by default [http://127.0.0.1:3782](http://127.0.0.1:3782). Press `Ctrl+C` in that terminal to stop both backend and frontend. Skipping `deeptutor init` is fine for a quick trial; the app boots with default ports and empty model settings, configure them later in **Settings → Providers** and **Language models**.
**Browser microphone transcription:** OpenAI-compatible STT adapters forward browser audio to the provider without local conversion. The native DashScope and Volcengine STT adapters convert browser WebM/Opus to 16 kHz WAV and require an `ffmpeg` executable on DeepTutor's `PATH`. A canonical 16 kHz mono PCM WAV bypasses this conversion. For Windows PyPI installations using either native adapter, install FFmpeg, add its `bin` directory to the service's `PATH`, then restart DeepTutor. Conversion failures appear below the chat input.
</details>
<details>
<summary><b>Option 2 — Install From Source</b> · develop against a checkout</summary>
For development against a checkout. Use **Python 3.11–3.14** and **Node.js 22 LTS** to match CI and Docker.
```bash
git clone https://github.com/HKUDS/DeepTutor.git
cd DeepTutor
# Create a venv (macOS/Linux). Windows PowerShell:
# py -3.11 -m venv .venv ; .\.venv\Scripts\Activate.ps1
python3 -m venv .venv && source .venv/bin/activate
python -m pip install --upgrade pip
# Install backend + frontend deps
python -m pip install -e .
( cd web && npm ci --legacy-peer-deps )
deeptutor init
deeptutor start --dev
```
`deeptutor start` builds the local `web/` frontend for production once and reuses it; `--dev` runs Next.js with HMR. Config layout, ports, and `Ctrl+C` match Option 1.
<details>
<summary><b>Conda environment</b> (instead of <code>venv</code>)</summary>
```bash
conda create -n deeptutor python=3.11
conda activate deeptutor
python -m pip install --upgrade pip
```
</details>
<details>
<summary><b>Optional install extras</b> — RAG engines / dev / partners / matrix / math-animator</summary>
```bash
pip install -e ".[rag-lightrag]" # Built-in LightRAG engine (exact supported SDK)
pip install -e ".[graphrag]" # Microsoft GraphRAG engine (Python 3.11–3.13)
pip install -e ".[dev]" # tests/lint tools
pip install -e ".[partners]" # Partner IM channel SDKs
pip install -e ".[video-learning]" # compatibility extra; captions ship in the full/CLI installs
pip install -e ".[matrix]" # Matrix channel without E2EE/libolm
pip install -e ".[matrix-e2e]" # Matrix E2EE; requires libolm
pip install -e ".[math-animator]" # Manim addon; requires LaTeX/ffmpeg/system libs
```
</details>
<details>
<summary><b>Frontend dependency tweaks & dev-server troubleshooting</b></summary>
**Changing frontend dependencies:** run `npm install --legacy-peer-deps` to refresh `web/package-lock.json`, then commit both `web/package.json` and `web/package-lock.json`.
**Stuck dev server:** if `deeptutor start --dev` reports an existing frontend that isn't responding, stop the PID it prints. If no Next.js process is actually running, the lock files are stale — remove them and retry:
```bash
rm -f web/.next/dev/lock web/.next/lock
deeptutor start --dev
```
</details>
</details>
<details>
<summary><b>Option 3 — Docker</b> · one self-contained container</summary>
One container for the full Web app. Images on GitHub Container Registry:
- `ghcr.io/hkuds/deeptutor:latest` — latest stable release
- `ghcr.io/hkuds/deeptutor:<version>` — exact release without the leading `v` (for example `:1.6.3`); pre-releases receive only their version tag
> See [CONTAINERIZATION.md](./docs-for-user/CONTAINERIZATION.md) for podman/rootless/read-only-rootfs deployments and the full per-installation guide.
```bash
docker run --rm --name deeptutor \
-p 127.0.0.1:3782:3782 \
-v deeptutor-data:/app/data \
ghcr.io/hkuds/deeptutor:latest
```
To choose a host content folder at container startup, mount it at the stable
container path and lock DeepTutor to that path:
```bash
mkdir -p "$PWD/deeptutor-workspace/outputs"
docker run --rm --name deeptutor \
-p 127.0.0.1:3782:3782 \
-v deeptutor-data:/app/data \
-v "$PWD/deeptutor-workspace:/workspace" \
-e DEEPTUTOR_WORKSPACE_ROOT=/workspace \
-e DEEPTUTOR_WORKSPACE_ALLOWED_ROOTS=/workspace \
ghcr.io/hkuds/deeptutor:latest
```
For Compose, set `DEEPTUTOR_WORKSPACE_HOST=/absolute/host/folder` before
running `python scripts/docker_compose.py up -d`. When omitted it uses
`./data/user/workspace`. Docker paths are selected at startup and therefore
appear locked in the Web settings page.
> **Only `3782` needs to be published.** The browser talks exclusively to the frontend origin; the Next.js middleware (`web/proxy.ts`) forwards `/api/*` and `/ws/*` to the FastAPI backend **inside the container**. Publishing `8001` (`-p 127.0.0.1:8001:8001`) is optional — handy only for hitting the API directly with curl or scripts.
Open [http://127.0.0.1:3782](http://127.0.0.1:3782). The container creates `/app/data/user/settings/*.json` on first boot; configure model providers from the Web Settings page. Config, API keys, logs, the default Content Workspace, memory, and knowledge bases persist in the `deeptutor-data` volume. A separately mounted Content Workspace persists at its host path instead. Optional extras belong on the deployment, not in a shell: set `DEEPTUTOR_EXTRAS` (and `DEEPTUTOR_APT_PACKAGES` for system libraries) and every container started from it re-applies them, where a `docker exec … pip install` would be lost at the next `compose down`.
- **Different host ports:** change the left side of each `-p host:container` mapping (e.g. `-p 127.0.0.1:8088:3782`). If you change container-side ports in `/app/data/user/settings/system.json`, restart and update the right side of each mapping to match.
- **Detached:** add `-d`, then `docker logs -f deeptutor` to follow, `docker stop deeptutor` to stop, `docker rm deeptutor` before reusing the name. The `deeptutor-data` volume keeps private runtime data and the default Content Workspace across restarts; a separately mounted Content Workspace persists at its host path.
**Remote Docker / reverse proxy:** the browser only talks to the frontend
origin (`:3782`); the in-container Next.js middleware forwards `/api/*` and
`/ws/*` to the backend server-side. For the common single-container case you
don't configure an API base at all — just point your reverse proxy / TLS
terminator at `:3782`. You only need an API base for a **split deployment**
(backend in a separate container/host): set `next_public_api_base` in
`data/user/settings/system.json` to the in-network address the frontend server
uses to reach the backend (it's read server-side, never sent to the browser).
```json
{
"next_public_api_base": "http://backend:8001"
}
```
`next_public_api_base_external` (and its alias `public_api_base`) are accepted as
lower-precedence fallbacks. CORS uses frontend **origins**, not API URLs. With
auth disabled, DeepTutor permits normal HTTP/HTTPS browser origins by default.
With auth enabled, add exact frontend origins:
```json
{
"cors_origins": ["https://deeptutor.example.com"]
}
```
<details>
<summary><b>Connecting to Ollama / LM Studio / llama.cpp / vLLM / Lemonade on the host</b></summary>
Inside Docker, `localhost` is the container itself, not your host machine. To reach a model service running on the host, use the host gateway (recommended):
```bash
docker run --rm --name deeptutor \
-p 127.0.0.1:3782:3782 -p 127.0.0.1:8001:8001 \
--add-host=host.docker.internal:host-gateway \
-v deeptutor-data:/app/data \
ghcr.io/hkuds/deeptutor:latest
```
Then in **Settings → Providers**, point the provider Base URL at `host.docker.internal`:
- Ollama LLM: `http://host.docker.internal:11434/v1`
- Ollama embedding: `http://host.docker.internal:11434/api/embed`
- LM Studio: `http://host.docker.internal:1234/v1`
- llama.cpp: `http://host.docker.internal:8080/v1`
- Lemonade: `http://host.docker.internal:13305/api/v1`
Docker Desktop (macOS/Windows) usually resolves `host.docker.internal` without `--add-host`. On Linux, the flag is the portable way to create that hostname on modern Docker Engine.
**Linux alternative — host networking:** add `--network=host` and drop the `-p` flags. The container shares the host network directly, so open [http://127.0.0.1:3782](http://127.0.0.1:3782) (or the `frontend_port` in `system.json`), and host services can be reached with normal localhost URLs like `http://127.0.0.1:11434/v1`. Note that host networking exposes container ports directly on the host and may conflict with existing services — to keep them on loopback, set `BACKEND_HOST=127.0.0.1` and `FRONTEND_HOST=127.0.0.1` (see [CONTAINERIZATION.md](./docs-for-user/CONTAINERIZATION.md)).
</details>
</details>
<details>
<summary><b>Option 4 — CLI Only</b> · no Web UI, from a source checkout</summary>
When you don't need the Web UI. The CLI-only package is installed from a source checkout, not from PyPI.
```bash
git clone https://github.com/HKUDS/DeepTutor.git
cd DeepTutor
# Create a venv (macOS/Linux). Windows PowerShell:
# py -3.11 -m venv .venv-cli ; .\.venv-cli\Scripts\Activate.ps1
python3 -m venv .venv-cli && source .venv-cli/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ./packaging/deeptutor-cli
deeptutor init --cli
deeptutor chat
```
`deeptutor init --cli` shares the same `data/user/settings/` layout as the full app but skips the backend/frontend port prompts. It still offers the Embedding and Search selectors (choose **Skip** when you do not need them), writes the key runtime files (`system.json`, `auth.json`, `integrations.json`, `interface.json`, `model_catalog.json`, `main.yaml`, `agents.yaml`), and prompts for the active LLM provider and model.
<details>
<summary><b>Common commands</b></summary>
```bash
deeptutor chat # interactive REPL
deeptutor chat --capability deep_solve --tool rag --kb my-kb
deeptutor run chat "Explain Fourier transform"
deeptutor run deep_solve "Solve x^2 = 4" --tool rag --kb my-kb
deeptutor kb create my-kb --doc textbook.pdf
deeptutor memory show
deeptutor config show
```
</details>
The local `deeptutor-cli` install ships no Web assets or server dependencies. Keep the source checkout around — the editable install points to it. To add the Web app later, install the PyPI package (Option 1) and run `deeptutor init` + `deeptutor start` from the same workspace.
</details>
<details>
<summary><b>Code Execution Sandbox (office skills)</b> · running model-generated code for docx / pdf / pptx / xlsx</summary>
The built-in office skills — **docx / pdf / pptx / xlsx** — work by having the
model write a short Python script (`python-docx`, `reportlab`, `openpyxl`, …),
run it through the single `exec` tool, and present the saved workspace file.
Those tools mount whenever a sandbox backend is active. DeepTutor selects the
strongest configured backend in this order:
- **Runner sidecar:** `DEEPTUTOR_SANDBOX_RUNNER_URL` routes execution to the
hardened, least-privileged service from `Dockerfile.runner`.
- **Linux bubblewrap:** when available, `bwrap` isolates the process and files.
- **Restricted subprocess fallback:** local and single-container installs use
this only when allowed; under Docker the container remains another boundary.
The `sandbox_allow_subprocess` setting in `data/user/settings/system.json`
(default `true`) controls only the last fallback. Set it to `false` (or export
`DEEPTUTOR_SANDBOX_ALLOW_SUBPROCESS=0`) to refuse subprocess execution when no
runner or `bwrap` backend is available; it does not disable those stronger
backends.
</details>
<details>
<summary><b>Configuration reference</b> — config files under <code>data/user/settings/</code> (JSON/YAML)</summary>
Everything under `data/user/settings/` is plain JSON/YAML. The **Settings** page is the recommended editor; workspace registrations live separately in `data/user/.runtime/workspaces.sqlite3`.
| File | Purpose |
|:---|:---|
| `model_catalog.json` | Provider connections plus LLM, task, embedding, search, TTS, STT, image, and video profiles, credentials, and active selections |
| `system.json` | Backend/frontend ports, public API base, CORS, SSL verification, attachment directory and upload/extraction limits |
| `auth.json` | Optional auth toggle, username, password hash, token/cookie settings |
| `integrations.json` | Optional PocketBase and sidecar integration settings |
| `interface.json` | UI and model output language / theme / sidebar preferences |
| `document_parsing.json` | Parsing engine and image-description model selection, remote endpoints, and engine-specific options |
| `video_learning.json` | YouTube caption provider, Invidious origins and account access, and transcript settings |
| `main.yaml` | Runtime behavior defaults and path injection |
| `agents.yaml` | Capability/tool temperature and token settings |
Web Search references are filtered by default: only public `http`/`https` URLs
without embedded credentials or unusual ports are surfaced. Deployments can add
an education-focused domain policy in `data/user/settings/system.json`:
```json
{
"web_search_source_filtering": {
"enabled": true,
"blocked_domains": ["spam.example"],
"trusted_domains": ["edu.cn", "arxiv.org"]
}
}
```
When `trusted_domains` is non-empty, references are limited to those domains
and their subdomains; `blocked_domains` always takes precedence.
Project-root `.env` is **not** read as an application config file. For a minimal model setup, save a Base URL and API key in **Settings → Providers**, then add and select an LLM in **Language models**. Add an embedding profile only if you plan to use Knowledge Base / RAG features.
For API Route, select **API Route** in **Settings → Providers** and enter an API Route key. The preset uses `https://global.api-route.com/v1`; then add a model using its API Route model ID in **Language models**. See the [API Route quickstart](https://www.api-route.com/docs/quickstart) for key and model setup.
LLM and task-model profiles expose an **API format** setting when their provider
supports a choice. Keep `Auto` for normal routing and fallback, or choose
`OpenAI Chat Completions`, `OpenAI Responses`, or `Anthropic Messages`; forced
Responses remains fail-closed. The persisted field is `api_format` (`auto`,
`openai_chat`, `openai_responses`, or `anthropic`); `wire_api` is derived
compatibility state. Per-model `Auto` / `Supported` / `Not supported` overrides
cover tool calling, image input, JSON output, and reasoning controls.
</details>
<details>
<summary><b>Uninstall and cleanup</b></summary>
DeepTutor separates its installed code, private runtime home, and optional
Content Workspace. By default, the runtime home is the directory where you run
`deeptutor init` / `deeptutor start`; `--home PATH` or `DEEPTUTOR_HOME`
overrides it. Private application state is the `data` directory inside that
home, so the startup banner line beginning with `Workspace:` identifies that
runtime location. If **Settings → Workspace** points to another folder, back up
or remove that content folder separately; it is intentionally not erased by
uninstalling DeepTutor.
1. Stop the app. Press `Ctrl+C` in the terminal running `deeptutor start`, or
run `deeptutor stop [--home PATH]` for a launcher started with `--detach`;
stop any running Partner and detached Docker containers before deleting data.
2. Remove runtime data only if you also want to erase all local state. This
includes settings and API keys, chat history, sessions, Memory, Notebooks,
Books, Reading state, Skills, Partners state, logs, Knowledge Bases, parse
caches, generated artifacts, and the packaged frontend runtime cache.
First copy the exact `Workspace:` path from the startup banner and verify
that its `data` child is the intended DeepTutor data directory. Back it up
if anything may be needed later, then move that exact directory to your
operating system's Trash/Recycle Bin. Do not run a recursive deletion
command against a relative path or an unresolved environment variable.
3. Remove the installed package. Use the command that matches the distribution:
```bash
python -m pip uninstall deeptutor
python -m pip uninstall deeptutor-cli
```
If the virtual environment was created only for DeepTutor, remove it through
your environment manager. For a source install, deactivate the environment,
leave the source directory, and run `git status --short` inside that exact
checkout. Only move the checkout to Trash/Recycle Bin after confirming it
contains no unrelated or uncommitted work.
4. For the Docker path, inspect the exact container and named volume before
removing them. Volume removal permanently erases the Docker-managed data:
```bash
docker ps -a --filter name=^/deeptutor$
docker volume inspect deeptutor-data
docker rm -f deeptutor
docker volume rm deeptutor-data
```
</details>
## 📖 Explore DeepTutor
Start with the main surfaces you will use day to day: Chat, Partners, My Agents, Co-Writer, Book, Knowledge Center, Learning Space, Memory, and Settings. The tour then covers Multi-User deployments for shared, isolated workspaces.
If an answer loses an earlier constraint, cites weak evidence, or disagrees with selected material, collect the diagnostics in [`REASONING_SAFETY_CHECKLIST.md`](./docs-for-user/REASONING_SAFETY_CHECKLIST.md) before opening an issue.
<div align="center">
<img src="assets/figs/web-1.6.5/OVERVIEW.png" alt="DeepTutor home — the Chat workspace with every surface in the sidebar" width="900">
</div>
> **Screenshot status:** The overview is current for v1.6.5. The surface screenshots below remain v1.4.6 references while a versioned refresh is in progress. Use them to understand workflows, not as exact navigation.
<details>
<summary><b>🏗️ System architecture</b></summary>
<div align="center">
<img src="assets/figs/system/system%20architecture.png" alt="DeepTutor system architecture" width="900">
</div>
</details>
<details>
<summary><b>💬 Chat — The Agent Loop You Actually Use</b></summary>
Chat is the default capability and where most work begins. A single thread can talk normally, call tools, ground itself in selected knowledge bases, read attachments, generate images, consult subagents, write notebook records, and continue with the same context across turns.
<div align="center">
<img src="assets/figs/web-1.4.6+/home/00-overview.png" alt="DeepTutor chat workspace" width="900">
</div>
The loop is deliberately simple: the model thinks in rounds, calls tools when useful, observes the results, and finishes with a tool-free message. `ask_user` is special — instead of guessing, the agent can pause the turn, ask a structured clarifying question, and resume once you answer.
<div align="center">
<img src="assets/figs/system/chat-agent-loop.png" alt="DeepTutor chat agent loop" width="900">
</div>
User-toggleable tools are `brainstorm`, `web_search`, `paper_search`, `zotero_search`, `reason`, and `geogebra_analysis` — plus `imagegen` and `videogen` once you configure the matching generation model. Contextual tools such as `rag`, `kb_files`, `knowledge_frontier`, `read_source`, `read_memory`, `write_memory`, `read_skill`, `load_tools`, `exec`, `web_fetch`, `ask_user`, `list_notebook`, `write_note`, `question_bank`, `github`, `consult_subagent`, `workspace_list`, `workspace_read`, `workspace_search`, `workspace_present`, and `workspace_export` mount automatically when the turn has the right context.
Context comes in two kinds: **sticky session context** (capability, workspace or course, tools, knowledge bases, persona, model, and Reading / Mastery state) persists across turns; **one-time references** (files, chat history, books, reading sections, notebooks, selected question-bank entries, imported agents) come from the `+` menu for a single turn. The voice button only transcribes the current message.
Home keeps **Chat**, **Ask Questions**, **Quiz**, and **Visualize** one click away; **Research** for cited reports and **Solve** for worked reasoning sit under *More Capabilities*. **Personalized Learning** groups Book, **Mastery Path**, **Immersive Reading**, and **Practice**; Reading brings documents, video, and audio together with verified citations, saved notes, natural read-aloud from source passages / study guidance / vocabulary / quiz / translation actions, and notebook capture, while Course Study keeps its course-bound context.
</details>
<details>
<summary><b>🤝 Partner — Persistent Companions on the Same Brain</b></summary>
<div align="center">
<img src="assets/figs/web-1.4.6+/partners/00-partners%20overview.png" alt="DeepTutor partners workspace" width="900">
</div>
Partners are persistent companions with their own soul, model policy, library, memory, and channels. They are not a separate bot engine: every inbound web or IM message becomes a normal `ChatOrchestrator` turn inside a partner-scoped workspace. A partner is "a chat that has a personality and a phone number."
<div align="center">
<img src="assets/figs/system/partners-architecture.png" alt="DeepTutor partners architecture" width="900">
</div>
Each partner has a `SOUL.md`, model selection, channels, tool policy, and assigned library. Its library either copies knowledge bases, skills, and notebooks into `data/partners/<id>/workspace/`, or stays linked to an existing workspace's files and resources; soul, conversations, and Partner memory remain separate. Authenticated non-admin users keep private partner sessions and relationship memory while the partner reads their personal memory read-only; admin, group, and unbound traffic use the shared partner scope.
<div align="center">
<img src="assets/figs/web-1.4.6+/partners/02-IM%20config%20for%20each%20partner.png" alt="Per-partner IM channel configuration" width="900">
</div>
The channel layer is schema-driven and can connect to IM platforms such as Feishu, Telegram, Slack, Discord, DingTalk, QQ/NapCat, WeCom, WhatsApp, Zulip, Mattermost, Matrix, Mochat, and Microsoft Teams depending on installed extras and configured credentials. A partner can also be connected as a subagent and consulted from a normal chat turn — see **My Agents** below.
For faster setup, the Partner channel page can create a Feishu/Lark app or WeCom AI bot, or sign a personal WeChat account in, from a QR scan drawn in the browser rather than the server log. Feishu/Lark detects the account domain and saves the scanning user as the initial allowed sender. WeCom keeps an existing allowlist and otherwise defaults to all users who can reach the bot, with a visible open-access warning; the manual channel forms remain available if a provider's scan protocol changes.
</details>
<details>
<summary><b>🧑🚀 My Agents — Consult & Import Other Agents</b></summary>
<div align="center">
<img src="assets/figs/web-1.4.6+/myagents/00-overview.png" alt="DeepTutor My Agents workspace" width="900">
</div>
My Agents turns other agents into context for DeepTutor, and does two distinct things. **Connect a live agent** — Claude Code, Codex, Grok CLI, Antigravity, Kimi, opencode, MiMo Code, Hermes Agent, OpenClaw, or DeepSeek Harness on your machine, a remote Hermes gateway, or one of your Partners — and consult it from inside a chat turn: DeepTutor actually *runs* the other agent and streams its work into the Activity panel via the `consult_subagent` tool. Select it and its round limit with the Agent chip, or filter the same connected-agent list with `@`; the choice stays attached to the session.
**Connect Grok CLI.** Install xAI's Grok CLI on the machine running the DeepTutor
backend, run `grok login` there, and verify that `grok --help` lists
`--output-format streaming-json`. Then open **My Agents → Connect**, select
**Grok CLI**, and choose a working directory. Detection checks the executable's
protocol support; it does not verify login or model access. The connector has
been exercised with Grok CLI 1.0.3; unrelated third-party commands also named
`grok` are not supported.
In **Settings → Partners & Agents → Grok CLI**, leave model and reasoning effort
empty to use the CLI defaults, or enter values supported by your account's
`grok models` output. System instructions are passed through `--rules`. The
default permission mode is `dontAsk`: Grok uses its existing rules and built-in
read-only handling, and denies operations that need approval. This is a CLI
permission policy, not a filesystem sandbox. Broader modes can be selected
explicitly in settings; advanced CLI flags remain available through
`backends.grok.extra_args` in the subagent settings API.
Grok uses its own authentication and session storage; DeepTutor does not copy
its credentials. Follow-up consults resume the connection's session in the same
working directory. Text and tool activity stream live; private thought payloads
are omitted. Cross-session Grok memory is disabled by default. This connector
supports text questions and CLI tools, not image forwarding or importing past
Grok conversations. In Docker, install and authenticate Grok inside the backend
container; a CLI installed only on the browser's computer is not reachable.
<div align="center">
<img src="assets/figs/web-1.4.6+/home/08-subagent%20demo%20with%20claude%20code.png" alt="Consulting a Claude Code subagent live" width="900">
</div>
**Import past conversations** — bring in your existing ChatGPT, Claude Code, and Codex history as searchable, resumable conversations. Select `conversations.json` from an official ChatGPT data export for a safe, idempotent snapshot import, choose Claude history by project / working directory, or choose Codex history by calendar date. Folder-based agents remain refreshable so their selected scope can pull in new conversations. Reference one from a Chat turn via `+` → My Agents, and DeepTutor reads it as a third-party transcript — it stays *their* conversation, not DeepTutor's own voice.
</details>
<details>
<summary><b>✍️ Co-Writer — Selection-Aware Markdown Drafting</b></summary>
<div align="center">
<img src="assets/figs/web-1.4.6+/co-writer/00-overview.png" alt="DeepTutor Co-Writer workspace" width="900">
</div>
Co-Writer is a split-view Markdown workspace for reports, tutorials, notes, and long-form learning artifacts. Documents autosave and render a live preview (KaTeX math, diagram fences), and can be saved back into notebooks when a draft becomes reusable context. Import a `.docx` to start a new draft, and export the current editor as Markdown or Word.
<div align="center">
<img src="assets/figs/web-1.4.6+/co-writer/01-edit%20panel.png" alt="Co-Writer editor with live preview" width="900">
</div>
Its defining idea is **surgical editing**: select a span and ask DeepTutor to rewrite, expand, or shorten it. Choose the edit model; it can use a knowledge base or web evidence and keeps a trace of its tool calls. If the draft has not changed while it works, the result replaces the selected text directly and remains reversible with **Undo**.
</details>
<details>
<summary><b>📖 Book — Living Books from Your Materials</b></summary>
<div align="center">
<img src="assets/figs/web-1.4.6+/book/00-book_overview.png" alt="DeepTutor book library" width="900">
</div>
Book turns selected sources into an interactive **living book** — not a static PDF, but a reading environment built from typed blocks. A book can start from knowledge bases, notebooks, question banks, or chat history; the creation flow proposes a chapter outline before content is generated, so you review the shape instead of accepting a blind one-shot output.
<p align="center">
<img src="assets/figs/web-1.4.6+/book/01-book-demo-quiz%20card.png" alt="Book quiz block" width="31%">
<img src="assets/figs/web-1.4.6+/book/02-book-demo-manim%20video.png" alt="Book Manim animation block" width="31%">
<img src="assets/figs/web-1.4.6+/book/03-book-demo%20interactive%20module.png" alt="Book interactive widget block" width="31%">
</p>
Each chapter compiles into editable typed blocks — text, callouts, quizzes, flash cards, timelines, code, figures, interactive HTML, animations, concept graphs, deep dives, and user notes — with its own Page Chat. Insert, move, regenerate, rewrite, or switch a block type; selected passages enter a reviewable learning-capture inbox. Progress, bookmarks, quiz attempts, captures, and Page Chat stay private per reader even when an admin book is shared read-only or for collaborative editing; shared deletion stays admin-only. Any book exports to Markdown, long compiles pause and resume, and `deeptutor book health` / `refresh-fingerprints` flag source drift.
</details>
<details>
<summary><b>📚 Knowledge Center — Multi-Engine RAG Libraries</b></summary>
<div align="center">
<img src="assets/figs/web-1.4.6+/knowledge/00-overview.png" alt="DeepTutor Knowledge Center" width="900">
</div>
Knowledge bases are the document collections behind RAG — they ground Chat turns, Co-Writer edits, Book generation, and Partner conversations. What's distinctive is a **choice of retrieval engines**: **LlamaIndex** (the default, hybrid vector + BM25 with optional cross-encoder reranking and exact-flat or HNSW FAISS indexes), **PageIndex** (reasoning retrieval with page-level citations, hosted or self-hosted OSS), **GraphRAG** and **LightRAG** (knowledge-graph retrieval), **LightRAG Server** (retrieval offloaded to an external LightRAG instance you connect over HTTP), **WeKnora** (retrieval from a knowledge base in your self-hosted deployment, without a local index or document copy), **Tencent IMA** (a library you curate in IMA — searched, browsed, and written back to over its OpenAPI), **MarginNote 4** (your MN4 study data — documents, excerpts, mind-map cards and the links between them — pushed in by the app's Add-on and navigated with dedicated tools), **Kiwix** (a searchable ZIM archive served over HTTP without local indexing), or a linked **Obsidian** vault the tutor reads and writes in place. Each KB is bound to one engine.
<div align="center">
<img src="assets/figs/web-1.4.6+/knowledge/01-create%20knowledge%20base.png" alt="Create a knowledge base" width="900">
</div>
Migrating an existing Obsidian, Hermes, or Markdown library? See [Knowledge migration guide](./docs-for-user/KNOWLEDGE_MIGRATION.md) for connected-vault and indexed-copy paths.
Creating a KB, you can **create new** (upload and index documents), **link existing** (reuse an index in place), or **connect Kiwix** (search one served ZIM archive on demand). Choose its storage workspace at creation; an existing KB can move between workspaces after a preview, keeping assignments and saved references. A KB can also track **GitHub repositories** (repo, branch, glob) or **documentation-site URLs** (bounded crawl depth and page count, re-synced every 24 h by default); sync hash-diffs added, changed, and removed content so followed documentation stays current without re-uploading, and **linked folders** pick up new or changed local files on sync. Re-indexing writes a new flat `version-N` directory and keeps prior ones, so a working index is never destroyed mid-rebuild. A single document can be removed even from an **error**-state base — dropping a file that failed to parse without a full delete-and-rebuild. Document parsing — Text-only, MinerU, Docling, Tika, markitdown, PyMuPDF4LLM, or LiteParse — and an optional image-description model are chosen in **Settings → Knowledge & documents**, with local model downloads off by default. Docling can also run in **remote** mode against a Docling Serve server (no local install or models needed), configured on that page (`mode=remote`, a server base URL, and an optional API key) or the `DOCLING_MODE` / `DOCLING_API_BASE_URL` / `DOCLING_API_TOKEN` environment variables. Tika is remote-only and points at the Apache Tika server configured on that page. The CLI mirrors the lifecycle with `list/info/create/connect-kiwix/add/search/eval/eval-diff/set-default/delete`, source add/remove commands, `list-sources`, and `sync`.
The built-in LightRAG engine is installed with `pip install 'deeptutor[rag-lightrag]'`. That extra contains the supported LightRAG SDK but does not install MinerU. Choose MinerU independently in Document Parsing and either configure its cloud mode or install its current local CLI when structured parsing is wanted. MinerU accepts PDF, common raster images, DOCX, PPTX, and XLSX; the legacy `magic-pdf` command remains PDF-only. Text-only and the other parsing engines do not require MinerU.
Native LightRAG queries and incremental indexing require the embedding configuration recorded by the published index, including the model, dimension, and endpoint identity. If it changes, restore the original configuration or rebuild with the current embedding; indexes without a recorded embedding identity require a rebuild. The knowledge-base detail and index-version views show recovery guidance, while files remain available for viewing and download.
**Scoring a knowledge base.** With several engines available, "which one retrieves best here?" is an empirical question — so `deeptutor kb eval` answers it. Write a QA set (one JSON object per line: a `query` plus the `gold` passages an ideal retrieval should return), then score the KB with `deeptutor kb eval <name> --dataset qa.jsonl --top-k 5 [--mode hybrid] [--save baseline.json]`. Each case is retrieved through the same path a chat turn uses, its ranked citations are matched against the gold passages using exact text or lexical overlap (thresholds are tunable with `--min-ratio`; semantic equivalence is not assessed), and the run reports **Recall@k / Precision@k / nDCG@k / MRR / MAP / Hit@k**. Metrics are pure functions over the ranking; no model judges the output. Compare saved reports with `deeptutor kb eval-diff baseline.json candidate.json` for per-metric and per-case changes. Use `--rewrite off` or `--rewrite standalone` on `kb eval` to compare original and rewritten queries; reports retain rewrite provenance. Cases whose search failed (a missing index, bad credentials) carry no score and are reported separately rather than dragging the means down. PageIndex uses reasoning as retrieval and returns no ranked chunks, so it is excluded.
```jsonl
{"query": "Why do transformers scale attention?", "gold": ["Scaling keeps the dot products from growing with the dimension."]}
```
</details>
<details>
<summary><b>🌐 Learning Space — Skills, Personas, and Reusable Context</b></summary>
<div align="center">
<img src="assets/figs/web-1.4.6+/learning-space/00-overview.png" alt="DeepTutor Learning Space hub" width="900">
</div>
Learning Space is the library, organization, and personalization layer. **Conversations & Materials** holds Chat History, notebooks with movable records and Markdown export, and a question bank with answers and explanations. Start **Practice** from a question-bank selection or Personalized Learning for review sessions, mistake tracking, and scheduled repetition. **Personalization** holds personas, skills (`SKILL.md` playbooks), one-click **MCP Services**, and **CLI Apps** from the [CLI-Anything](https://github.com/HKUDS/CLI-Anything) catalog, each with an on-demand usage guide. The separate **My Courses** workspace groups subject conversations and tutor threads; each asset is offered only in the workflows that support it.
<div align="center">
<img src="assets/figs/web-1.4.6+/learning-space/07-%20download%20skills%20from%20eduhub.png" alt="Import skills from EduHub" width="900">
</div>
You don't have to write every skill yourself — **Import from EduHub** browses the community catalog and downloads a skill straight into your library through a security gate (see [Ecosystem](#-ecosystem--eduhub--the-skills-community)).
</details>
<details>
<summary><b>🧠 Memory — Inspectable Personalization</b></summary>
<div align="center">
<img src="assets/figs/web-1.4.6+/memory/00-overview.png" alt="DeepTutor memory overview" width="900">
</div>
Memory is a file-backed, three-layer system you can read, curate, and audit — deliberately *not* a hidden vector store. **L1** is the workspace mirror plus an append-only event trace (`trace/<surface>/<date>.jsonl`); **L2** is per-surface curated facts (`L2/<surface>.md`) with references to L1 entities; **L3** is cross-surface synthesis (`L3/<profile|recent|scope|preferences>.md`) that records its contributing L2 surfaces.
<div align="center">
<img src="assets/figs/web-1.4.6+/memory/01-3%20layer%20memory%20graph.png" alt="DeepTutor memory graph" width="900">
</div>
The Memory Graph shows the whole pyramid — L3 synthesis at the centre, L2 in the middle ring, L1 traces on the outside — with exact L2 → L1 evidence edges and L3 → contributing-surface links. Memory is tracked across `chat`, `notebook`, `quiz`, `kb`, `book`, partner, and `cowriter` surfaces; the consolidator's Update / Audit / Dedup budgets are tuned in **Settings → Memory**.
</details>
<details>
<summary><b>⚙️ Settings — One Control Plane</b></summary>
<div align="center">
<img src="assets/figs/web-1.4.6+/settings/00-setting%20overview.png" alt="DeepTutor settings hub" width="900">
</div>
Settings is the operational control plane, opening on **General** for interface and model output language. Its searchable navigator links to independent pages: **Personal** covers Workspaces, Data migration, Appearance, and Usage statistics; **Learning & conversation** covers starting points, attachments, Video Learning, learner and guardian controls, Learning progress, and Memory; **Models & services** covers Providers, Language models, Task models, Embedding, Search, Voice, and Multimodal generation; **Features & integrations** covers tools, capability parameters, Partners & agents, and Knowledge & documents. **System** holds Network, Runtime status, and About; **Archived chats** lets you search, restore, or permanently delete archived conversations. Runtime status contains backend health, resident memory, and the **Readiness** matrix grading capability blockers, warnings, and suggestions. Workspaces keeps topic files and learning state separate, with verified migration and export under Data migration. A **provider** holds a vendor's address and credential for reuse by its service models; the model pages choose saved providers and configure model names and capabilities. **Task models** pin a small, fast model for background work — naming conversations and writing starting points — and resolve to the active default when empty. Voice groups speech synthesis and transcription; Multimodal generation groups image and video models. Partners & agents configures local harnesses and a remote Hermes gateway.
**Xiaomi MiMo speech.** Add a Xiaomi MiMo provider with `https://api.xiaomimimo.com/v1` and its API key, then add `mimo-v2.5-tts` under Settings → Voice. Choose a preset such as `mimo_default`, `冰糖`, or `苏打`, use `wav` or `pcm16` output, and audition it before applying. Voice instructions control style and speaking speed. This adapter supports preset speech only; voice design and voice cloning require separate models and are not supported. If an older MiMo speech model was configured through the generic OpenAI-compatible adapter, recreate its speech entry using the Xiaomi MiMo provider so it uses the chat-completions protocol. See the [official MiMo speech guide](https://mimo.mi.com/docs/zh-CN/quick-start/usage-guide/audio/speech-synthesis-v2.5).
**MiniMax speech** — in Settings → Voice, choose MiniMax for text-to-speech and select `speech-2.8-hd`. Configure a MiniMax API key and explicitly select a system voice or enter a custom voice ID. The API base defaults to `https://api.minimax.io/v1`; for the China region use `https://api.minimaxi.com/v1`. Read-aloud and voice previews use the native speech endpoint, with MP3, WAV, FLAC or PCM output, sample rate, speed and language controls. See the [MiniMax speech API](https://platform.minimax.io/docs/api-reference/speech-t2a-http) for voice IDs and account availability.
**Video Learning** under Settings → Learning & conversation configures YouTube captions and Invidious access for Reading. To use Invidious, set its administrator-managed backend API origin (for example `http://127.0.0.1:3000`) and browser-accessible public origin, test the connection, select Invidious, and save. **Browse Invidious** in Reading's add-material dialog searches videos and accesses connected subscriptions and playlists. YouTube playback uses the official privacy-enhanced player; missing captions remain visible and limit the tutor's evidence without blocking native playback. Install `.[video-learning]` for the default YouTube caption loader. Existing Watching links migrate into Reading with conversation history, compatible transcripts, notes, and progress preserved; new timestamp notes are Reading annotations. Bilibili uses its own playback and caption path.
<div align="center">
<img src="assets/figs/web-1.4.6+/settings/01-appearance%20settings.png" alt="DeepTutor appearance settings and themes" width="900">
</div>
Most sections use a draft-and-apply flow, so you can test a provider before committing it. You can also just ask in Chat: the assistant reads the current configuration, applies a change, and says whether it needs a restart or a re-index — probing a new model before it commits, so it cannot switch itself onto something unreachable. API keys never pass through the model, which opens the matching form for you instead. Four themes ship in the box — Default, Cream, Dark, and Glass. Project-root `.env` files are intentionally ignored; runtime configuration lives under `data/user/settings/*.json` unless `DEEPTUTOR_HOME` or `deeptutor start --home` points the app elsewhere.
**OpenAI Codex OAuth (experimental).** Adding **OpenAI Codex** under Settings → Providers opens a browser sign-in that runs against your own ChatGPT plan, so no `OPENAI_API_KEY` is needed. Tokens live only in `data/system/user-secrets/<owner>/private/openai-codex/` — in the multi-container Compose deployment, outside every tree the exec sandbox can reach — and DeepTutor never reads or modifies your `~/.codex` CLI login. The model list comes from that account's live catalog; signing in publishes the profile but only becomes the active model when no LLM is configured yet. Because a token authorizes one person's plan, the profile is not shareable through user grants — each account signs in for itself, ordinary users included: their sign-in card sits under Providers, and the resulting models, catalog, and sign-out stay private to that account.
On sign-in and **Refresh models**, DeepTutor reads the latest stable `@openai/codex` version from the official npm registry and uses it as the catalog request's `client_version`. This is metadata-only: it does not install, download, or upgrade the Codex CLI, and the npm request carries no OAuth credentials. Discovery has a three-second deadline and a 64 KiB response limit. If it fails, DeepTutor uses the last successful version for that account, or its built-in fallback when none is cached. Only a successfully parsed live catalog saves a new version; validators are never reused across versions or credential generations. A version rejection or incompatible catalog structure permits one retry with the previous version; authentication, rate-limit, and TLS failures are not retried as version problems. A failed manual refresh reports an error rather than presenting an old cached catalog as refreshed.
Successful version history survives same-account token renewal and catalog authentication errors, without making invalidated model data or ETags usable again. Signing out clears that account's local cache, including the version history. Ordinary status reads and inference do not query npm. New catalog entries do not replace a model you already selected or restart learning sessions; appearing in the catalog does not establish full inference-protocol compatibility. There is no background version updater or user version setting.
Default local Docker and Podman deployments use separate loopback networks and need a temporary bridge during sign-in. Follow the [temporary local Codex OAuth bridge guide](./docs-for-user/CONTAINERIZATION.md#temporary-local-codex-oauth-bridge) for the exact Docker, Compose, Podman, and teardown commands.
For a remote deployment, the browser's `localhost` and the server's `localhost` are different machines, so an ordinary reverse proxy alone cannot carry the browser's localhost callback to the server. Use an SSH tunnel as the callback bridge. The tunnel reaches the already-published Web port; Next.js rewrites only the exact callback path to the public callback broker, and the broker validates `state` before routing to the original OAuth operation. The callback listener remains on the backend loopback, ports `1455` and `1457` are not published, and this path supports the default Docker bridge network.
```bash
ssh -N -L 1455:127.0.0.1:3782 <ssh-user>@<server-host>
```
If DeepTutor reports fallback callback port `1457`, use:
```bash
ssh -N -L 1457:127.0.0.1:3782 <ssh-user>@<server-host>
```
Run only the one command that matches the actual callback port; never run both. `3782` is only the example Web port: it is the configured frontend/container port reported as `callback_forward_port`. That value does not guarantee that the same port is listening on the SSH host's `127.0.0.1`. If Docker or Podman publishes a different host port, or a reverse proxy listens on a different port, replace only the right-hand target port (`3782` above) with the Web port actually listening on the SSH host's `127.0.0.1`; keep the left-hand callback port as `1455` or `1457`. `<server-host>` is the SSH host whose loopback owns that listening port. If the browser URL names a reverse proxy or load balancer, replace it with the correct SSH frontend host.
The CLI prints the tunnel command and then immediately tries to open the browser. On a remote deployment, keep the authorization page open without completing it, establish the printed tunnel in another terminal, and only then continue authorization.
Remote-topology detection has a localhost boundary. If Web itself is reached through an SSH or IDE localhost forward, the browser cannot tell that the server is remote. For the current Web operation, leave its authorization page unfinished, read `redirect_uri` in that operation's authorize URL to identify callback port `1455` or `1457`, and create the second tunnel from that local port to the actual Web port. Alternatively, cancel that Web operation and start a new one with the CLI; the CLI output belongs to the new operation and must not be used for the existing Web operation. Quota errors and catalog failures are reported as-is and never fall back to a paid provider. This compatibility path is experimental: the upstream interface may change.
</details>
<details>
<summary><b>👥 Multi-User — Shared Deployments</b> · optional auth, isolated per-user workspaces</summary>
Authentication is **off by default** — DeepTutor runs single-user. Turn it on and one `data/` tree hosts an admin workspace, isolated per-user workspaces, and partner workspaces side by side:
```text
data/
├── user/ # Admin workspace + global settings
├── users/<uid>/ # Per-user scope: chat history, memory, notebooks, KBs
├── partners/<id>/workspace/ # Partner (synthetic-user) scope
├── cli-apps/ # Installed CLI apps, mounted read-only into the sandbox
└── system/ # auth · grants · audit · user-secrets/<owner> (OAuth tokens)
```
The **first registered user becomes admin** and owns model catalogs, provider credentials, shared knowledge bases, skills, canonical shared books, and per-user grants. Admin-created local users choose Standard, Learner, or Custom. Learner locks learning capabilities and material policy, adds an adaptive profile, and supports revocable device credentials with expiry and daily limits; authorized guardians can view reports, approve materials, and reset credentials. Other users get isolated workspaces plus scoped models, KBs, skills, partners, and shared-book access without receiving raw API keys. If `auth.json` already carries a `username` + `password_hash`, that account *is* the admin: `/register` stays closed and accounts created from `/admin/users` are always `role=user` until you promote them.
**Enable it:** turn auth on in `data/user/settings/auth.json`, restart `deeptutor start`, register the first admin at `/register`, then add users from `/admin/users` and assign models, KBs, skills, partners, tool/MCP/CLI-app policy, and code-execution access through grants; configure shared books under each user's **Book access** panel.
For split private/public origins, set `auth.private_login_hosts` (or `AUTH_PRIVATE_LOGIN_HOSTS`) to the private frontend hosts that may show password sign-in and registration. The frontend forwards the incoming HTTP `Host` to the backend as its frontend-host assertion. Preserve the browser's `Host` through your reverse proxy, and make the public ingress reject requests naming a private host; do not expose a raw Next.js port that accepts arbitrary `Host` values. HTTP `Host` can be forged by a direct client, so private-host authorization depends on that ingress boundary. The backend accepts frontend-host assertions only from loopback by default. If the Web frontend connects from another container or host, set `AUTH_TRUSTED_FRONTEND_PROXY_IPS` to that frontend proxy's exact IP address on the backend process (comma-separated for more than one). Keep the backend API private to the frontend proxy and do not include general client networks in this list. Loopback is always allowed; when the list is non-empty, an authenticated user on a private origin can open **Profile → Public device sign-in** and create a short-lived, host-bound pairing link for an HTTPS public origin. The public `/handoff` page exchanges the one-time code for an equally short JWE ticket in a POST body, consumes the ticket once, and receives the normal HttpOnly session cookie.
> PocketBase stays a single-user integration — keep `integrations.pocketbase_url` blank for multi-user deployments unless you've wired up an external user store.
</details>
## ⌨️ DeepTutor CLI — Agent-Native Interface
One `deeptutor` binary, two ways in: an interactive **REPL** for people who live in the terminal, and structured **JSON** for other agents that drive DeepTutor as a tool. Same capabilities, tools, and knowledge bases either way.
<details>
<summary><b>Drive it yourself</b></summary>
`deeptutor chat` opens an interactive REPL and selects a mode with `--capability`; `deeptutor run <capability> "<message>"` takes that capability as its first positional argument and exits after one turn. Both accept `--tool`, `--kb`, `--config`, and `--workspace` to select a registered workspace.
```bash
deeptutor chat # interactive REPL
deeptutor chat --capability deep_solve --kb my-kb --tool rag
deeptutor run chat "Explain the Fourier transform" --tool rag --kb textbook
deeptutor run chat "Find recent work beyond this material" --kb textbook --tool knowledge_frontier
deeptutor run deep_research "Survey 2026 papers on RAG" \
--config mode=report --config depth=standard
```
Core workspace management is here too — knowledge bases (`kb`), sessions (`session`), partners (`partner`), skills (`skill`), notebooks, memory, and config; course and session organization remain in the Web app. Full list below.
</details>
<details>
<summary><b>Let an agent drive it</b></summary>
DeepTutor is built to be *operated by another agent*. Add `--format json` to any `run` and each turn streams **NDJSON — one event per line** (`content`, `tool_call`, `tool_result`, `done`, …), every line tagged with its `session_id`. Runs are headless-safe: an `ask_user` pause with no TTY auto-resolves with an empty reply instead of hanging.
```bash
# One shot, machine-readable
deeptutor run deep_solve "Find d/dx[sin(x^2)]" --tool reason --format json
# Chain turns in one stateful session — capture the id, reuse it
SID=$(deeptutor run deep_research "Survey 2026 papers on RAG" \
--config mode=report --config depth=standard --format json \
| jq -r 'select(.type=="done").session_id')
deeptutor run deep_question "Quiz me on that survey" --session "$SID" --format json
```
The repo ships a root [`SKILL.md`](SKILL.md) — a compact handover doc for the CLI. Ask your agent to read it explicitly; its [Agent Setup guide](docs-for-user/AGENT_SETUP.md) covers installation and configuration. You can also wrap `deeptutor run` as a tool in a LangChain / AutoGen loop. Full recipes: [Agent Handoff](https://deeptutor.info/docs/cli/agent-handoff/).
</details>
<details>
<summary><b>Command reference</b></summary>
| Command | Description |
|:---|:---|
| `deeptutor init [--non-interactive] [--home PATH]` | Run the setup wizard, or create missing defaults without prompts |
| `deeptutor doctor [--online]` | Check whether the runtime is ready to start a session; `--online` also probes the configured model provider, `--format json` prints the report |
| `deeptutor start [--home PATH] [--dev] [--detach] [--no-browser]` | Launch backend + frontend together; optionally detach or suppress browser opening |
| `deeptutor stop [--home PATH]` | Stop a launcher started with `--detach` |
| `deeptutor serve [--port PORT]` | Start only the FastAPI backend |
| `deeptutor workspace show/set/reset` | Inspect, select, or restore the per-user Content Workspace |
| `deeptutor run <capability> <message>` | Run a single capability turn (`chat`, `ask_questions`, `deep_solve`, `deep_question`, `deep_research`, `visualize`, `math_animator`, `mastery_path`, `immersive_reading`, `course_study`, `audio_overview`); add `--format json` for NDJSON output |
| `deeptutor chat` | Interactive REPL with capability, tool, KB, notebook, and history controls |
| `deeptutor partner list/create/start/stop` | Manage IM-connected partners |
| `deeptutor kb list/info/create/connect-kiwix/add/search/eval/eval-diff/set-default/delete/list-sources/sync` | Manage knowledge bases, connect Kiwix archives, score and compare retrieval runs, and synchronize registered GitHub/web sources (with source add/remove commands) |
| `deeptutor skill search/install/list/remove/login/logout/publish/update` | Manage skills, install from hubs, and publish your own (`eduhub:<slug>` by default, see Ecosystem) |
| `deeptutor memory show/clear` | Inspect L2/L3 memory docs or clear L1/all memory |
| `deeptutor session list/show/open/rename/delete` | Manage shared sessions |
| `deeptutor notebook list/create/show/add-md/replace-md/remove-record` | Manage notebooks from Markdown files |
| `deeptutor book list/health/refresh-fingerprints` | Inspect books and refresh source fingerprints |
| `deeptutor plugin list/info/state/search/show/install/approve/enable/disable/rollback/uninstall` | Inspect tools and capabilities; manage reviewed plugin packages and their permission approvals |
| `deeptutor config show [--home PATH]` | Show resolved runtime configuration with credentials redacted |
| `deeptutor config providers` | List supported setup providers and defaults as JSON |
| `deeptutor config apply FILE [--check] [--home PATH]` | Apply setup JSON without prompts, or validate it without writing settings |
| `deeptutor provider login <provider>` | Provider auth (`openai-codex` OAuth login; `github-copilot` GitHub device login; `codebuddy` validates CodeBuddy SDK auth and starts login when needed) |
GitHub Copilot credentials belong to the signed-in DeepTutor owner and live only at
`<runtime-home>/data/system/user-secrets/<owner-id>/private/github-copilot/credentials.v1.json`,
outside sandbox workspaces. CLI/admin partners use the administrator's credentials;
other users sign in separately, and Copilot profiles cannot be shared through model grants.
External nanobot/Copilot token files are never imported: after upgrading, run
`deeptutor provider login github-copilot` again.
Login validates inference with a currently discovered model; `init` validates the selected
model and aborts on failure without saving the draft configuration. Successful GitHub
authentication alone does not establish Copilot model access (the saved login is retained
if validation fails). Runtime requests follow the API endpoint returned by token exchange,
including refreshes, and respect each model's Responses/Chat Completions endpoint metadata.
Copilot-specific Responses compatibility is applied at dispatch without rewriting stored history.
</details>
<details>
<summary><b>CLI-only distribution</b></summary>
The CLI-only package lives in `packaging/deeptutor-cli`. In this checkout, install it from source:
```bash
python -m pip install -e ./packaging/deeptutor-cli
```
It isn't published to PyPI yet, so the main [Get Started](#-get-started) section keeps the source-install path.
</details>
## 🧩 Ecosystem — EduHub & the Skills Community
DeepTutor skills use the open **Agent-Skills** format — a folder with a `SKILL.md` playbook (YAML frontmatter + Markdown) and optional reference files. Nothing about it is DeepTutor-specific, so any registry that speaks the format becomes a source for your library. DeepTutor ships with **[EduHub](https://eduhub.deeptutor.info/)** — our own education-focused skill registry — wired in as the default hub.
<details>
<summary><b>EduHub — DeepTutor's skill ecosystem</b></summary>
[**EduHub**](https://eduhub.deeptutor.info/) is the community hub DeepTutor launched for sharing teaching-oriented agent skills — Socratic tutors, flashcard builders, essay feedback, exam blueprints, concept explainers, and more. It is built into DeepTutor, so there's nothing to configure: a bare slug or an `eduhub:` prefix resolves to it.
**Find and install** — in the browser, open **Learning Space → Skills → Import from EduHub** to browse the catalog and download a skill straight into your library. From the terminal:
```bash
deeptutor skill search "socratic tutor" # search EduHub (the default hub)
deeptutor skill install socratic-tutor # fetch → verify → register
deeptutor skill install eduhub:socratic-tutor@1.2.0 # pin a hub and a version
deeptutor skill list # local skills with their hub provenance
```
**Publish your own** — package a `SKILL.md` and share it back to the community:
```bash
deeptutor skill login # browser sign-in to EduHub
deeptutor skill publish ./my-skill # interactive: pick a track + tags, then upload
deeptutor skill update # roll back or release a new version
```
EduHub is also a standalone, ClawHub-compatible registry, so agents that aren't DeepTutor (Claude Code, Codex, …) can use it directly through the `eduhub` CLI — `npx eduhub install socratic-tutor`.
</details>
<details>
<summary><b>The import safety gate</b></summary>
Whatever the source, every import passes the **same safety gate** before anything touches your workspace:
- the registry's **security verdict** is checked first — flagged packages are refused unless you pass `--allow-unverified`;
- archives are extracted defensively with path-traversal, entry-count, size, compression-ratio, suffix, and symlink checks; executable bits are stripped, while extensionless files remain allowed;
- frontmatter is normalized to DeepTutor's schema and `always:` is **stripped**, so a downloaded skill can never force itself into every system prompt;
- provenance — hub, version, verdict, and install time — is written to `.hub-lock.json` for audits and updates.
In multi-user deployments, browser imports land in the authenticated caller's skill layer, while CLI and admin-console installs target the owner/admin workspace; admin skills stay hidden and read-only for ordinary users until granted.
</details>
<details>
<summary><b>Also compatible with ClawHub</b></summary>
Because DeepTutor speaks the open Agent-Skills format, **[ClawHub](https://clawhub.ai/)** works as a first-class source too — it's built in alongside EduHub. Pick it with the hub prefix:
```bash
deeptutor skill search "git release notes" --hub clawhub
deeptutor skill install clawhub:git-release-notes@1.0.1
deeptutor skill install clawhub:udiedrichsen/stock-analysis
```
When several publishers share the same slug, search shows each publisher and a
fully scoped install ref (`clawhub:<ownerHandle>/<slug>`).
Add more registries in `data/user/settings/skill_hubs.json`: a `type: "clawhub"` entry points at any compatible HTTP API (EduHub and ClawHub both speak it), `type: "command"` wraps whatever fetch CLI a registry ships, and `"default"` chooses the hub used for bare slugs. All of them feed the same import gate.
</details>
## 🤝 Open Source Partners
<p align="center">
<a href="https://github.com/VectifyAI/PageIndex" target="_blank">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="assets/figs/partners/pageindex-mark-dark.svg">
<source media="(prefers-color-scheme: light)" srcset="assets/figs/partners/pageindex-mark.svg">
<img src="assets/figs/partners/pageindex-mark.svg" alt="PageIndex" height="38">
</picture>
</a>
</p>
<p align="center">
Using code: <b><code>DEEPTUTOR20</code></b> — get $20 off for your first <a href="https://developer.pageindex.ai/">PageIndex subscription</a>!
</p>
## 🌐 Community
### 🔗 Maintainers
<table>
<tr>
<td align="center"><a href="https://github.com/pancacake"><img src="https://avatars.githubusercontent.com/u/150592536?v=4&s=80" width="80" height="80" alt="Bingxi Zhao"><br><strong>Bingxi Zhao</strong></a></td>
<td align="center"><a href="https://github.com/TyrionH-is-coding"><img src="https://avatars.githubusercontent.com/u/275607548?v=4&s=80" width="80" height="80" alt="Xingyu Hou"><br><strong>Xingyu Hou</strong></a></td>
<td align="center"><a href="https://github.com/zzhtx258"><img src="https://avatars.githubusercontent.com/u/175302980?v=4&s=80" width="80" height="80" alt="Jiahao Zhang"><br><strong>Jiahao Zhang</strong></a></td>
</tr>
</table>
### 📮 Contact
DeepTutor is an open-source project led by [Bingxi Zhao](https://github.com/pancacake) within the [HKUDS](https://github.com/HKUDS) Group, and it iterates in a **fully open-source form**, built together with the community. So far, we **DO NOT** have paid online products of any form. Feel free to reach out at **bingxizhao39@gmail.com** for discussions, ideas, or collaboration.
### 🙏 Appreciation
Heartfelt thanks to [**Chao Huang**](https://sites.google.com/view/chaoh), director of the Data Intelligence Lab @ HKU, and to our HKUDS labmates for their warm support — especially [**Jiahao Zhang**](https://github.com/zzhtx258), [**Zirui Guo**](https://github.com/LarFii), and [**Xubin Ren**](https://github.com/Re-bin). We're also deeply grateful to the **open-source community**: your stars, issues, pull requests, and discussions shape DeepTutor every single day.
DeepTutor also stands on the shoulders of outstanding open-source projects that gave us both tools and inspiration:
| Project | Role / Inspiration |
|:---|:---|
| [**LlamaIndex**](https://github.com/run-llama/llama_index) | RAG pipeline and document-indexing backbone |
| [**nanobot**](https://github.com/HKUDS/nanobot) | Ultra-lightweight agent engine that powered the original TutorBot *(HKUDS)* |
| [**LightRAG**](https://github.com/HKUDS/LightRAG) | Simple & fast RAG *(HKUDS)* |
| [**AutoAgent**](https://github.com/HKUDS/AutoAgent) | Zero-code agent framework *(HKUDS)* |
| [**AI-Researcher**](https://github.com/HKUDS/AI-Researcher) | Automated research pipeline *(HKUDS)* |
| [**OpenClaw**](https://github.com/openclaw/openclaw) | Open agent gateway and skill ecosystem behind ClawHub |
| [**Codex**](https://github.com/openai/codex) | Agent-native coding CLI that inspired our CLI workflow |
| [**Claude Code**](https://github.com/anthropics/claude-code) | Agentic coding CLI that inspired the DeepTutor agent loop |
| [**ManimCat**](https://github.com/Wing900/ManimCat) | AI-driven math animation generation for Math Animator |
### 🗺️ Roadmap & Contribute
We want DeepTutor to keep iterating and improving — and ultimately to become a gift we give back to the open-source community. Our [**roadmap**](https://github.com/HKUDS/DeepTutor/issues/498) is updated continuously; vote on items there or propose new ones. If you'd like to contribute, see the [**Contributing Guide**](CONTRIBUTING.md) for branching strategy, coding standards, and how to get started.
<div align="center">
We hope DeepTutor becomes a gift for the community. 🎁
<a href="https://github.com/HKUDS/DeepTutor/graphs/contributors">
<img src="https://contrib.rocks/image?repo=HKUDS/DeepTutor&max=999" alt="Contributors" />
</a>
</div>
<p align="center">
<a href="https://www.star-history.com/hkuds/deeptutor">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/badge?repo=HKUDS/DeepTutor&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/badge?repo=HKUDS/DeepTutor" />
<img alt="Star History Rank" src="https://api.star-history.com/badge?repo=HKUDS/DeepTutor" />
</picture>
</a>
</p>
<div align="center">
Licensed under the [Apache License 2.0](LICENSE).
<p>
<img src="https://visitor-badge.laobi.icu/badge?page_id=HKUDS.DeepTutor&style=for-the-badge&color=00d4ff" alt="Views">
</p>
</div>
### Mastery turn grounding
Mastery turns read the active path's status before the first model request, using
the same status tool and mastery gates as an explicit tool call. This snapshot is
fresh for each turn, includes the active session mode, and is never cached across
turns. A failed read falls back to `mastery_status`; after changing the path, mode,
outline, or progress, the tutor can refresh through that tool. Card grading and
its existing handoff remain unchanged.
### Repeated image attachments
The agent loop sends identical inline user images once per request and replaces later
copies with references to the retained image. Stable request-only labels identify
the retained inline images across provider translations and appended turns. Unique
image blocks, remote URLs, differing
image options, assistant messages, and tool results remain complete. Saved history
keeps every original attachment; references are rebuilt after history is trimmed.
This reduces repeated image payloads, but provider-reported usage remains the
source of truth for billing and cache hits.