Math-To-Manim

See It In Action

Brownian Motion: From Pollen to Portfolio

A journey from Robert Brown's microscope to Einstein's heat equation, arriving at the Black-Scholes model for financial options pricing.

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Recursive Rhombicosidodecahedron

A fractal Archimedean solid where every vertex spawns another complete rhombicosidodecahedron.

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The Hopf Fibration

Stereographic projection of S3 fibers creating nested tori - pure topology rendered in 3D.

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The Whiskering Exchange

Visualizing the commutative property of 2-cell composition in higher category theory.

Three AI Pipelines, One Goal

Math-To-Manim offers three distinct AI pipelines. Choose based on your API access and preferences:

Pipeline Comparison

Feature	Gemini 3 (Google ADK)	Claude Sonnet 4.5	Kimi K2
Framework	Google Agent Development Kit	Anthropic Agent SDK	OpenAI-compatible API
Architecture	Six-Agent Swarm	Six-Agent Pipeline	Three-Stage Enrichment
Strengths	Complex topology, physics reasoning	Reliable code generation, recursion	Chain-of-thought, structured tools
Best For	Advanced 3D math, Kerr metrics	General purpose, production use	LaTeX-heavy explanations
Setup Complexity	Moderate	Simple	Simple

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Pipeline 1: Google Gemini 3 (ADK)

Location: Gemini3/

The Gemini pipeline uses the Google Agent Development Kit with a six-agent swarm architecture. Each agent is a specialist with a specific role in the animation generation process.

How It Works

Quick Start

# Set API key
echo "GOOGLE_API_KEY=your_key_here" >> .env

# Run the pipeline
python Gemini3/run_pipeline.py "Explain the Hopf Fibration"

Key Files

• Gemini3/run_pipeline.py - Entry point
• Gemini3/src/agents.py - Agent definitions with system prompts
• Gemini3/src/pipeline.py - Orchestration logic
• Gemini3/docs/GOOGLE_ADK_AGENTS.md - Full documentation

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Pipeline 2: Claude Sonnet 4.5 (Anthropic SDK)

Location: src/

The Claude pipeline uses the Anthropic Agent SDK with automatic context management and built-in tools.

How It Works

Key Files

• src/app_claude.py - Gradio UI entry point
• src/agents/prerequisite_explorer_claude.py - Claude SDK agent
• docs/ARCHITECTURE.md - System design details

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Pipeline 3: Kimi K2 Thinking Model

Location: KimiK2Thinking/

The Kimi pipeline uses Moonshot AI's K2 thinking model with an OpenAI-compatible API and tool-calling interface.

How It Works

Quick Start

# Set API key
echo "MOONSHOT_API_KEY=your_key_here" >> .env

# Run prerequisite exploration
python KimiK2Thinking/examples/test_kimi_integration.py

# Run full enrichment pipeline
python KimiK2Thinking/examples/run_enrichment_pipeline.py path/to/tree.json

Key Files

• KimiK2Thinking/kimi_client.py - API client
• KimiK2Thinking/agents/enrichment_chain.py - Three-stage pipeline
• KimiK2Thinking/README.md - Complete documentation

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Installation

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

# Install dependencies
pip install -r requirements.txt

# Set up your preferred API key
echo "ANTHROPIC_API_KEY=your_key" >> .env    # For Claude
echo "GOOGLE_API_KEY=your_key" >> .env       # For Gemini
echo "MOONSHOT_API_KEY=your_key" >> .env     # For Kimi

# Install FFmpeg (required for video rendering)
# Windows: choco install ffmpeg
# Linux: sudo apt-get install ffmpeg
# macOS: brew install ffmpeg

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Run Example Animations

We have 55+ working examples organized by topic:

# Physics - Black Hole Symphony
manim -pql examples/physics/black_hole_symphony.py BlackHoleSymphony

# Mathematics - Hopf Fibration
manim -pql examples/misc/epic_hopf.py HopfFibrationEpic

# Finance - Option Pricing
manim -pql examples/finance/optionskew.py OptionSkewScene

# Computer Science - Neural Networks
manim -pql examples/computer_science/machine_learning/AlexNet.py AlexNetScene

Flags: -p preview, -q quality (l low, m medium, h high, k 4K)

Browse all examples: docs/EXAMPLES.md

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Repository Structure

Math-To-Manim/
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+-- src/                    # Claude Sonnet 4.5 pipeline
|   +-- agents/             # Agent implementations
|   +-- app_claude.py       # Gradio UI
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+-- Gemini3/                # Google Gemini 3 pipeline
|   +-- src/                # Agent definitions
|   +-- docs/               # Gemini-specific docs
|   +-- run_pipeline.py     # Entry point
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+-- KimiK2Thinking/         # Kimi K2 pipeline
|   +-- agents/             # Enrichment chain
|   +-- examples/           # Usage examples
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+-- examples/               # 55+ working animations
|   +-- physics/            # Quantum, gravity, particles
|   +-- mathematics/        # Geometry, topology, analysis
|   +-- computer_science/   # ML, algorithms
|   +-- cosmology/          # Cosmic evolution
|   +-- finance/            # Option pricing
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+-- docs/                   # Documentation
+-- tests/                  # Test suite
+-- tools/                  # Utility scripts

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Why LaTeX-Rich Prompting Works

The Problem with Vague Prompts

"Create an animation showing quantum field theory"

Result: Generic, incorrect, or broken code.

The Solution: Verbose LaTeX Prompts

"Begin with Minkowski spacetime showing the metric:

$$ds^2 = -c^2 dt^2 + dx^2 + dy^2 + dz^2$$

Each component highlighted in different hues. Introduce the QED Lagrangian:

$$\mathcal{L}_{\text{QED}} = \bar{\psi}(i \gamma^\mu D_\mu - m)\psi - \tfrac{1}{4}F_{\mu\nu}F^{\mu\nu}$$

with Dirac spinor $\psi$ in orange, covariant derivative $D_\mu$ in green..."

Result: Perfect animations with correct LaTeX, camera movements, and timing.

Our agents generate these verbose prompts automatically by walking the knowledge tree.

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Common Pitfalls (And How We Solve Them)

Problem	Traditional Approach	Our Solution
LaTeX Errors	Hope for the best	Verbose prompts show exact formulas
Vague Cinematography	"Show quantum field"	Specify colors, angles, timing
Missing Prerequisites	Jump to advanced topics	Recursive dependency discovery
Inconsistent Notation	Mixed symbols	Mathematical enricher maintains consistency

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Technical Requirements

• Python: 3.10+
• **