你还记得以前折腾数学物理教学动画时那种抓狂的感觉吗？

Manim要手写、LaTeX要配、镜头运动要一帧帧调，光环境搭建就能劝退一半人。

现在有个开源工具直接把这条路打通了——Math-To-Manim，一句话描述，自动生成完整交互动画。

不是那种糊弄人的“生成”，它会从概念拆解开始，自动补齐LaTeX公式、镜头设计，连学习笔记文档都一起给你出了，还附带55+示例覆盖数理CS方向。

https://github.com/HarleyCoops/Math-To-Manim…

HarleyCoops/Math-To-Manim

Source: https://github.com/HarleyCoops/Math-To-Manim

Math to Manim

Ask a question -> get a freakin’ movie

Mythos pipeline · Motion showcase · Architecture · Prime RL · Roadmap · Agent guide

Math-To-Manim is now a Claude Mythos-native pipeline: six reasoning agents turn a question into a cinematic Manim film — and every artifact that produced it: intent briefs, knowledge maps, curricula, math dossiers, shot lists, scene specs, generated code, validation reports, and render evidence.

The Mythos pipeline

This repo is now built around Claude Mythos. The six-agent reasoning chain has been rebuilt on Claude-native tooling: the agents are Claude Code subagents, a custom harness drives them headlessly through the Claude CLI, and a Mythos-class model writes every frame with the camera as narrator — plain-language headlines before symbols, flights into the exact term being explained, pull-backs to restore context, true-3D set pieces.

The chain: intent → cartographer → curriculum → math-director → cinematographer → scene-composer, then codegen → static checks → render → self-repair.

uv sync --extra render

# the whole chain, one line
python -m mythos.harness "explain quantum field theory" --render -q m

# or render the flagship directly
manim -qh examples/mythos/qft_cinematic.py QFTCinematicJourney

Stills from the Mythos cut of the QED journey: the camera inside the Lagrangian (left); the e⁻e⁻γ vertex as α resolves to 1/137 (right).

The original Codex/OpenAI chain remains available as a legacy provider — nothing was removed, Mythos is simply the way the films get made now.

What this is

Math-To-Manim started on the morning of Donald Trump’s inauguration. I do not think it was an accident that the Chinese decided to release the R1 model on that day.

I was awake, saw the model hit Hugging Face, and quickly built a .ipynb to load the model and run it.

I created this repo at 2025-01-20T11:04:50Z / 04:04:50 MST.

Within a couple of minutes I realized what this meant. If the Chinese, via GRPO, had reasoning on a chip, recursive reasoning was not far behind. In my tweet I wrote “Wrap it up, its over” and I still believe it.

09a2f22  2025-01-20T04:24:50-07:00  updated
A        DeepSeek_R1_zero.ipynb
A        Readme.md

Three hours later, the first Manim file landed: pythagorean.py at 2025-01-20T07:18:12-07:00.

“I asked #R1 to visually explain to me the Pythagorean theorem. This was done in one shot with no errors in less than 30 seconds. Wrap it up, its over: #DeepSeek #R1”

— Christian H. Cooper, January 20, 2025

What I saw with R1 is that the model was already good with Manim code out of the box. What actually runs under the hood with Math-To-Manim is a series of six planning agents that recursively reason over the prompt you gave it before code generation, validation, rendering, and review. This all runs on Codex 5.5.

However, since Prime Intellect rolled out hosted evals, and since I understand Recursive Learning Models better now, I am using the reasoning traces for RL training.

But this will always just work. If you are a teacher or a parent, you can always ask for an explanation and just get an MP4 back. You never have to see or worry about the reasoning training.

For the curious, follow along here: Prime Intellect M2M hub: harleycooper/math-to-manim.

-christian

“Hey man, I just want to see a demo, I don’t need a calculus lecture”

Fair. The whole point is that the pipeline should turn a one-sentence idea into something moving on screen before you have to read the architecture docs.

WSL quickstart:

cd /mnt/c/Users/$USER

git clone https://github.com/HarleyCoops/Math-To-Manim.git
cd Math-To-Manim

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -e ".[dev,render]"
./scripts/bootstrap-render.sh  # Debian/Ubuntu/WSL system deps for real MP4 output

m2m2 generate \
  "Show why the quantum harmonic oscillator only allows discrete energies: start with a springy potential well, zoom into the wavefunctions, then reveal the ladder of allowed energy levels." \
  --codegen-provider codex-cli \
  --codex-full-auto \
  --style cinematic \
  --quality l \
  --runs-dir runs

Generated bundles and videos stay in repo-local runs/<run_id>/ by default; the --runs-dir runs flag above is intentionally explicit so agent-driven runs do not disappear into /tmp.

If you want Hermes to run the harness like an operator instead of driving the CLI by hand:

hermes --skills manim-video,systematic-debugging,codebase-inspection \
  -z "Run the M2M2 pipeline on the quantum harmonic oscillator demo prompt with --runs-dir runs, inspect the repo-local run bundle, try a low-quality render, and report the generated movie path or the exact blocker. Do not put user-visible outputs in /tmp."

That gives you the practical loop: ask for the movie, inspect the run bundle, then tell the agent what to fix.

Hermes Agent

Hermes is the contributor/operator agent around this repository. It is not imported by Math-To-Manim and is not a runtime dependency; it uses the repo the way a developer would: read files, search code, patch docs and code, run terminal checks, inspect generated artifacts, review frames or GIFs, track todos, delegate larger work, and preserve stable context through skills.

That makes Hermes useful for maintaining the reverse-reasoning pipeline without becoming part of it. A Hermes session can inspect AGENTS.md, pyproject.toml, schemas, tests, and runs/<run_id>/ bundles; run pytest, CLI smoke commands, Manim, FFmpeg, and git checks; then verify that docs, code, and showcase media still match the artifact contracts.

Repo-local Hermes skills live under hermes/skills/. The old Claude ./skill path is historical; current contributor guidance is in AGENTS.md, with launch notes in docs/HERMES_LEARNS_MANIM.md.

Reverse reasoning pipeline

A normal text-to-code demo jumps from request to Python. Math-To-Manim takes the long way on purpose: it reasons backward from the final concept to the prerequisites, then walks forward through a teachable visual sequence.

The code path is explicit in math_to_manim/pipeline/runner.py. AnimationPipeline.generate() runs a fixed stage chain: IntentAgent, PrerequisiteGraphAgent, CurriculumAgent, MathAgent, StoryboardAgent, SceneSpecAgent, ManimCodeAgent, StaticReviewAgent, RenderAgent, VideoReviewAgent, and PublisherAgent.

That gives every run a memory: JSON contracts, generated code, render results, review notes, and a manifest. The output is not just a video; it is an inspectable path from question to understanding to animation.

For current editable-video status and the planned prompt/spec/code edit loop, see the roadmap.

Prime Intellect RL repair loop

Math-To-Manim is also becoming a Prime Intellect reinforcement-learning environment. The first RL target is not “make the whole video in one shot.” It is the edit move that matters after a base model produces a plausible but flawed scene: text overlaps formulas, equations are too small, the camera angle hides the point, or the zoom never lands on the symbol the learner needs to read.

A concrete target is the quantum-physics homepage-style failure mode: a beautiful Manim pass that still has text/formula collisions. The experiment is to give the model the typed scene plan, the generated Python, validation/render evidence, and a human request such as “fix the overlap,” “change the POV angle,” or “zoom into the formulas before the narration moves on.” The policy should return a sparse code edit that preserves the scene while making the movie more readable.

Run bundle as environment	Reward function as critic	Policy update as repair engine

The current hub environment is harleycooper/math-to-manim. A repair task carries the original prompt, typed scene_spec, generated Manim Python, static-validation report, and render/recovery evidence when available. The model must return one strict GeneratedCode JSON block. The Verifiers reward checks whether the proposed code parses, defines the expected Manim scene, avoids unsafe imports and calls, preserves expected math terms, and reduces obvious text/layout crowding hazards.

generated_scene.py + scene_spec + validation/render evidence
  -> Prime Intellect Verifiers environment
  -> model proposes corrected GeneratedCode JSON
  -> static reward checks parseability, scene shape, safety, terms, layout
  -> hosted RL updates the repair policy
  -> corrected, renderable Manim Python flows back into M2M2 recovery

That keeps the fast RL loop text-and-AST based while the slower Manim renderer remains the audit gate. The intended result is a model that learns the house style of this repo: cinematic but readable scenes, sparse formulas, staged captions, safe Manim code, and edits that can respond to text or voice change requests without throwing away the whole movie.

Current hosted-training status: the environment action passes on Prime, the hub package is published as harleycooper/[email protected], a 1-step smoke completed, and a 25-step W&B-enabled pilot has been launched on Qwen/Qwen3.5-35B-A3B.

See the full integration notes in docs/PRIME_INTELLECT_RL.md.

Clone and run

1. Clone

Windows PowerShell:

git clone https://github.com/HarleyCoops/Math-To-Manim.git
cd Math-To-Manim
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -U pip
python -m pip install -e ".[dev]"
python -m pytest

macOS / Linux / WSL:

git clone https://github.com/HarleyCoops/Math-To-Manim.git
cd Math-To-Manim
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -e ".[dev]"
python -m pytest

2. Run a no-API smoke test

This proves the CLI, artifact contracts, and validators are wired before you spend model or render time:

math-to-manim generate "Explain why derivatives are slopes" --deterministic --no-render

Equivalent module form:

python -m math_to_manim.cli generate "Explain why derivatives are slopes" --deterministic --no-render

3. Generate with model calls

Set an OpenAI key and choose a model if desired:

export OPENAI_API_KEY="sk-..."
export OPENAI_MODEL="gpt-4.1"
math-to-manim generate "Explain Fourier epicycles as rotating vectors" --no-render

PowerShell:

$env:OPENAI_API_KEY = "sk-..."
$env:OPENAI_MODEL = "gpt-4.1"
math-to-manim generate "Explain Fourier epicycles as rotating vectors" --no-render

4. Install render extras when you want MP4 output

Python render dependency:

python -m pip install -e ".[dev,render]"

System render dependencies are also needed for real Manim output, especially FFmpeg and LaTeX for MathTex. On Debian/Ubuntu/WSL:

./scripts/bootstrap-render.sh

The package list lives in requirements-system.txt.

Codex CLI codegen path

Math-To-Manim can keep the typed planning pipeline while sending the Manim codegen and repair loop through a locally authenticated Codex CLI session.

Check Codex first:

codex --version
codex exec "Say ready from inside this repo"

Then route codegen through Codex:

math-to-manim generate "Explain derivatives as slopes with a cinematic tangent-line reveal" \
  --codegen-provider codex-cli \
  --codex-full-auto \
  --style cinematic \
  --quality l

Earlier planning stages remain on the typed adapters; only the generated-code and repair stages move first. That makes the migration incremental instead of all-or-nothing.

What lands on disk

A generation writes a self-contained run bundle:

runs/<run_id>/
  request.json
  intent.json
  knowledge_graph.json
  curriculum.json
  math_packet.json
  storyboard.json
  scene_spec.json
  generated_code.json
  generated_scene.py
  validation_report.json
  render_result.json
  review_report.json
  trace.jsonl  # stage-boundary events when tracing is enabled
  recovery_manifest.json  # after recover-render
  draft_review/
    draft_review.md
    contact_sheet.png
    frames/
  animation_package.json
  manifest.json

After editing generated_scene.py inside a run bundle, rerun the recovery path:

math-to-manim recover-render runs/<run_id> --quality l

That command refreshes validation, render, review, draft-review assets, and recovery_manifest.json without regenerating upstream planning artifacts.

Package layout:

math_to_manim/
  agents/      # stage adapters
  schemas/     # versioned artifact contracts
  tools/       # graph, validation, rendering, video, artifact helpers
  pipeline/    # orchestration, tracing, repair loop
  rendering/   # Manim and FFmpeg wrappers
  review/      # static and visual review scoring

Motion showcase

Sixteen curated GIFs are tracked under docs/showcase/assets/ as the art direction target for Math-To-Manim’s visual explanations.

Geometry as spectacle	Topology as choreography	Chaos as intuition

See the full gallery with descriptions: docs/showcase/README.md.

Make a README-sized GIF from a render

MP4="media/videos/your_scene/480p15/YourScene.mp4"

ffmpeg -y -ss 95 -t 24 -i "$MP4" \
  -vf "fps=12,scale=720:-1:flags=lanczos,split[s0][s1];[s0]palettegen=max_colors=96[p];[s1][p]paletteuse=dither=bayer:bayer_scale=5" \
  docs/showcase/assets/your-clip.gif

Adjust -ss and -t to capture the teaching beat you want.

License

MIT.