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OpenMAIC

Turn any topic into an immersive multi-agent interactive classroom in one click
0TypeScriptGNU Affero General Public License v3.0
#multi-agent#ai-education#interactive-classroom#pbl#langgraph#llm
#whiteboard
#open-source

What is it?

OpenMAIC is an open-source AI education platform by Tsinghua University MAIC Lab. It converts any topic or document into a fully interactive classroom in one click, powered by a LangGraph-based multi-agent orchestration engine that coordinates AI teachers and AI classmates for real-time lectures, whiteboard drawing, discussions, debates, and Q&A. Scene types include slides with voice narration, interactive quizzes, HTML-based simulations, and project-based learning PBL tasks. Outputs can be exported as editable .pptx or standalone .html files. The platform supports OpenAI, Anthropic Claude, Google Gemini, DeepSeek, and any OpenAI-compatible API. Through OpenClaw integration, classrooms can be generated directly from Feishu, Slack, Telegram, and 20+ messaging apps. The underlying research is published in JCST'26.

Pain Points vs Innovation

✕Traditional Pain Points✓Innovative Solutions
Traditional MOOC platforms deliver fixed content, leaving learners in a passive viewing mode with minimal live interactionA two-stage pipeline creates the outline first and then generates rich scenes in parallel for better speed and density
Most AI tutoring tools remain single-turn Q&A systems without sustained classroom narrative or multi-role collaborationThe LangGraph 'director graph' orchestrates multiple agents and supports 28+ actions including speech, whiteboard, laser pointer, and spotlight
Building an in-house AI courseware stack requires integrating TTS, ASR, LLMs, rendering, and agent frameworks at high engineering costThe HTML simulation engine produces operable web experiments instead of static visual content
Mainstream document AI tools rarely generate full courses with simulations and PBL directly from one uploaded sourceOpenClaw integration exposes classroom generation inside 20+ IM tools and turns chat into a learning workflow
-Academic credibility is strong, backed by the Tsinghua team and a JCST'26 publication

Architecture Deep Dive

Two-Stage Lesson Pipeline
OpenMAIC splits generation into outline creation and scene creation. An LLM first defines chapters, objectives, and difficulty, then the system generates slides, quizzes, HTML simulations, and PBL modules in parallel. This preserves structural quality while reducing end-to-end latency. Progress is streamed through SSE so users can watch the classroom assemble in real time.
LangGraph Orchestration Engine
The runtime core is a LangGraph-based 'director graph' that manages roles such as teacher, classmate, and moderator. When the flow enters discussion or Q&A, the orchestration layer decides who speaks, which action fires, and how context is handed off. It supports a wide action surface including speech, whiteboard drawing, spotlighting, and laser-pointer behavior. State is synchronized to the frontend store so rendering stays aligned with agent behavior.
Playback and Live Interaction
A dedicated playback state machine manages idle, playing, paused, and live states. Pre-generated content runs as an ordered action sequence, while user interruption immediately switches the session into live mode for real-time response. This dual-state design prevents live inference latency from degrading playback smoothness. It is a practical architecture for educational products that need both consistency and interruption handling.
Provider and Media Abstraction
OpenMAIC abstracts LLMs, audio, and media through separate layers in lib/ai, lib/audio, and lib/media. It can connect to OpenAI, Anthropic, Gemini, DeepSeek, and OpenAI-compatible endpoints with runtime configurability. TTS, ASR, image generation, and video generation follow the same pluggable model. This keeps the core system adaptable as the model ecosystem and compliance requirements change.

Deployment Guide

1. Clone the repository and install dependencies with Node.js >= 20 and pnpm >= 10

bash
1git clone https://github.com/THU-MAIC/OpenMAIC.git2cd OpenMAIC3pnpm install

2. Copy the environment template and configure at least one LLM provider key

bash
1cp .env.example .env.local2# Edit .env.local3# OPENAI_API_KEY=sk-...4# ANTHROPIC_API_KEY=sk-ant-...5# GOOGLE_API_KEY=...

3. Start the development environment and open the local URL

bash
1pnpm dev

4. Optionally deploy with Docker for containerized setup

bash
1cp .env.example .env.local2# After editing3docker compose up --build

5. Optionally deploy on Vercel by importing the repository and setting environment variables

bash
1# Visit https://vercel.com/new2# Import your fork and set API keys

Use Cases

Core SceneTarget AudienceSolutionOutcome
Personal Learning AcceleratorSelf-learners who want to master new skills fastEnter a topic or upload books and papers to auto-generate lessons with narration, quizzes, and simulationsFaster structured learning with noticeably higher knowledge absorption
Enterprise Training Content FactoryCorporate L&D teams producing training materials at high frequencyUpload internal SOPs or knowledge docs to generate interactive courseware and export .pptxDramatically shorter production cycles and lower labor cost
Smart Teaching Assistant for SchoolsHigher-ed and K12 teachers needing differentiated resourcesGenerate interactive classrooms with discussion and PBL directly from Feishu or SlackBetter class engagement and broader teaching coverage with lower setup cost

Limitations & Gotchas

Limitations & Gotchas
  • Generation quality is highly dependent on the underlying LLM, and weaker models may produce factual drift or unstable lesson structure
  • HTML simulations are model-generated, so complex scenarios may suffer from logic bugs or limited interaction accuracy
  • A full classroom triggers many model and voice calls, making API cost material under high concurrency
  • Real-time TTS can reduce immersion when network jitter or provider latency becomes visible
  • AGPL-3.0 creates strong constraints for closed-source SaaS, so commercialization requires licensing review

Frequently Asked Questions

What is the core difference between OpenMAIC and a traditional MOOC platform?▾
A traditional MOOC is built around fixed recorded content with weak interactivity. OpenMAIC generates the classroom from the current topic and uses multiple agents to teach, discuss, and answer in real time, which fits long-tail and personalized learning much better.
Why does the project use LangGraph instead of AutoGen or CrewAI?▾
Classroom products need explicit state control and interruption handling, which task-only agent frameworks often manage less predictably. LangGraph is better suited to modeling playback, discussion, and live switching as controlled graph states.
How much time and cost does a full classroom usually require?▾
It depends on the number of scenes, the model tier, and whether TTS is enabled. A medium-sized classroom can finish in minutes, but premium models and complex simulations can raise cost materially.
Can HTML interactive simulations be trusted to run reliably?▾
Simple simulations tend to work well, but complex interactions can still produce script defects or behavioral drift. For production teaching, manual preview and stronger models for simulation generation are recommended.
What is the real value of OpenClaw integration?▾
It moves classroom generation into everyday chat workflows such as Feishu, Slack, and Telegram. Users can launch jobs, track progress, and receive links without opening a terminal or admin panel.
What is the biggest engineering gap for enterprise private deployment?▾
The current architecture is more single-user and self-host oriented, with no native auth, RBAC, or tenant isolation. Enterprise rollout usually requires additional identity, middleware, and persistent database work.
How does AGPL-3.0 affect commercialization?▾
If you modify the code and provide it as a network service, you will typically need to release those modifications under the same license. Closed-source SaaS plans should therefore go through a commercial licensing path early.
How does it compare with AI PPT tools like Gamma.app?▾
Gamma is stronger in presentation polish and business-facing visual templates. OpenMAIC is stronger in instructional flow, agent interaction, whiteboard actions, and learning-oriented classroom logic.
View on GitHub

Project Metrics

Stars0
LanguageTypeScript
LicenseGNU Affero General Public License v3.0
Deploy DifficultyEasy

Table of Contents

  1. 01What is it?
  2. 02Pain Points vs Innovation
  3. 03Architecture Deep Dive
  4. 04Deployment Guide
  5. 05Use Cases
  6. 06Limitations & Gotchas
  7. 07Frequently Asked Questions

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