OpenMAIC Multi-Agent Interactive Classroom
One-click immersive multi-agent learning experiences for any topic or document.
Evidence shows: repository includes a security policy (SECURITY.md) with vulnerability reporting; OpenClaw skill claims confirmation at each step; SSRF hardening mentioned; many dependencies but no security audit evidence; external effects (web search, export) described but permission boundaries not detailed; rollback mechanism not mentioned; source attribution via license and third-party components. Deductions: least privilege not concretely implemented, user confirmation only claimed, data flow transparency insufficient, sensitive data handling details missing, dependency security lacks audit or vulnerability scan evidence, external effects permissions unclear, rollback missing, source attribution partial.
Evidence shows: README consistent with code structure, CI includes lint, typecheck, unit and e2e tests, indicating good self-consistency; many dependencies but no availability guarantee; failure messages tested in some cases (e.g., CORS failure returns error code), but overall failure handling documentation insufficient. Deductions: self-consistency supported by tests but not fully verified; dependency availability lacks lockfile or mirror backup; failure messages only partially tested, not covering all critical paths.
Evidence shows: README clearly defines target users (learners, teachers) and use cases (classroom generation, interactive learning); capability boundaries defined via feature list and architecture; trigger precision in OpenClaw skill has confirmation steps but not detailed trigger conditions; environment fit supports multiple deployments (local, Docker, Vercel) and many LLM providers. Deductions: trigger precision only partially described, not all trigger conditions; environment fit broad but lacks detailed configuration guide.
Evidence shows: README well-structured with quick start, features, use cases; install notes detailed (requirements, config, start); naming stability has version numbers but no naming convention; examples and FAQ present; known limitations not explicitly listed; license MIT with third-party components noted; changelog exists; maintenance responsibility via contribution guide and contact. Deductions: naming stability not explicitly stated; known limitations not separately listed; maintenance responsibility partial (contact but no clear maintenance team).
Evidence shows: output usability high (multiple export formats: PPTX, HTML, ZIP); marginal value clear (one-click interactive classroom, multi-agent interaction); cost-benefit not detailed (no performance benchmarks or cost estimates). Deductions: cost-benefit lacks data support.
Evidence shows: feature claims partially supported by tests (e.g., e2e); cross-source corroboration limited (only internal tests); fact-inference separation unclear. Deductions: claim traceability insufficient, no specific test mapping; cross-source corroboration lacks external validation; fact-inference separation not explicit.
- Many dependencies without security audit or vulnerability scan evidence; recommend dependency security review before deployment.
- OpenClaw skill claims confirmation but lacks implementation details; verify actual behavior.
- Rollback mechanism not mentioned; errors may be irreversible.
- Sensitive data handling details insufficient, especially API keys and user data storage.
What does this agent do, and when should you use it?
OpenMAIC is an open-source AI platform from Tsinghua University that turns any topic or document into a rich, interactive classroom experience. Powered by multi-agent orchestration, it generates slides, quizzes, interactive simulations, and project-based learning (PBL) activities, delivered by AI teachers and AI classmates who can speak, draw on a whiteboard, and engage in real-time discussions. Built with Next.js, React, and LangGraph, it supports major LLM providers like OpenAI, Anthropic, Gemini, and local Ollama, and integrates with OpenClaw to generate classrooms directly from messaging apps like Feishu and Slack. Licensed under MIT.
OpenMAIC ingests a user-provided topic or uploaded materials (documents, audio, video), and runs a two-stage generation pipeline (outline generation, then scene content generation) to build a complete course. It runs as a Next.js application, offering both the classic one-click generator and a new v1.0.0 Pro workbench with an agentic chat-based course builder. The platform supports multiple scene types, whiteboard drawing, text-to-speech (TTS), speech recognition (ASR), interactive HTML simulations, and project-based learning (PBL). It integrates with messaging apps via an OpenClaw skill, and can export courses as editable PowerPoint (.pptx) or self-contained interactive HTML, with optional PostgreSQL-backed persistence and Docker deployment.
- An instructor quickly converts a topic or slide deck into a full online course with slides, quizzes, and interactive exercises.
- A corporate trainer generates interactive training modules and distributes them directly in Feishu or Slack via OpenClaw.
- A self-learner describes a learning goal (e.g., "teach me Python from scratch in 30 min") and gets an immersive classroom with AI teachers and classmates.
- An edtech developer builds a customized learning system on top of OpenMAIC, leveraging its pluggable storage, model, and service providers.
- A school or institution needing offline or intranet courses uses the classroom ZIP export to play content in an air-gapped environment.
What are this agent's strengths and limitations?
- One-click generation of complete courses with multiple interactive scenes, drastically reducing course creation effort.
- Multi-agent classroom provides realistic student-teacher interaction with whiteboard, voice, and real-time discussion.
- Model-agnostic: supports major LLM providers, local models (Ollama), and multiple TTS/ASR services.
- Rich export formats (PPTX, HTML, ZIP) enable offline use and content reuse.
- MIT license allows commercial use and modification.
- Generation quality heavily depends on the configured LLM provider; different models yield varying results.
- Requires at least one LLM API key, incurring potential API costs.
- Advanced features like video export and advanced document parsing need additional service components (Docker containers, external APIs).
- Pro workbench and server persistence require extra PostgreSQL setup, which may be a hurdle for non-technical users.
- As a relatively new project, updates are rapid and some documentation may lag.
How do you install or deploy this agent?
- Clone the repo: git clone https://github.com/THU-MAIC/OpenMAIC.git 2. Install dependencies: cd OpenMAIC && pnpm install 3. Copy environment: cp .env.example .env.local 4. Configure at least one LLM provider key (e.g., OPENAI_API_KEY) 5. Run locally: pnpm dev, then open http://localhost:3000. Docker deployment is also supported: docker compose up --build. Optional: enable PostgreSQL persistence and the Pro workbench.
How do you use this agent?
Open http://localhost:3000, enter a topic or upload materials on the home page, and click generate to build a classroom. Watch AI teachers lecture, participate in quizzes, PBL activities, or discussions with AI classmates. Configure TTS/ASR and other providers in Settings. With OpenClaw integration, you can generate classrooms directly from a chat app.