The problem
How it works
Researchers built TheoremExplainAgent, which plans a video about a STEM theorem, writes Manim Community Edition code for each scene, renders it and narrates it with Kokoro text-to-speech. They also released TheoremExplainBench, 240 theorems scored with five automated metrics. The MIT-licensed code reaches models through LiteLLM, so OpenAI, Gemini, Azure and others can drive it, and it has an optional RAG mode over the Manim documentation.
Results and limitations
The paper reports that agentic planning was needed to produce detailed long videos, and that the o3-mini agent reached a 93.8% success rate with an overall score of 0.77. The authors also found that most videos had minor problems with how visual elements were laid out.
Before you try it
This is research code tested on macOS and Linux; it needs LaTeX, Manim's system dependencies, the Kokoro model files and model API keys, and long videos use many model calls. It relies on Manim Community Edition, not 3Blue1Brown's ManimGL. Check the mathematics and the visuals before publishing, since automated scores do not prove an explanation is correct. Results depend on your setup and were not independently reproduced by Top 200 Repos.
Tools and repositories
No ranked repository is involved. This use case still stands on its own source.
- Tools
- TheoremExplainAgent · Manim Community Edition with LaTeX · Kokoro text-to-speech · An LLM through LiteLLM
- Prerequisites
- macOS or Linux, Python 3.12, LaTeX, the Kokoro model files and an API key for a LiteLLM-supported model.
Try it yourself: Storyboard a theorem video before rendering
Fill the fields to adapt the prompt. Empty fields stay as visible [placeholders].
Plan a [Target length] explainer video on [Theorem] for [Audience]. Produce a scene-by-scene storyboard: for each scene give the learning goal, the narration, the objects on screen and how they animate, and the Manim Community Edition code that renders it. Keep text and objects inside the frame and avoid overlapping labels. State the theorem precisely, include one worked example, and flag any step you are not sure is mathematically correct so I can check it before rendering.Render one scene first and check layout before generating the whole video.
Sources
- TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding (opens in a new tab)
Paper · arxiv.org · Published 26 Feb 2025 · Source checked 11 Nov 2026, 22:04 UTC
What this source supports
- Agent generates theorem explanation videos longer than five minutes using Manim
- TheoremExplainBench covers 240 theorems with five automated metrics
- The o3-mini agent reached a 93.8% success rate and an overall score of 0.77
- Most videos had minor visual layout problems
- TIGER-AI-Lab/TheoremExplainAgent README (opens in a new tab)
Project documentation · github.com · Undated · Source checked 11 Nov 2026, 22:05 UTC
What this source supports
- Uses Manim Community Edition and Kokoro text-to-speech
- Models are configured through LiteLLM
- Optional RAG over the Manim documentation
- MIT licence; tested on macOS and Linux