AI - Manim Video Generator

Automatic video generation using an agentic LLM flow in combination with the manim python library.

🌟 Inspiration

Following the release of Deepseek-R1, users found it was particularly adept at generating manim code for mathematical animations. A good example of this ability can be seen in this X post and in the repository related to that post: Math-To-Manim

Goal

The Goal of this project is to experiment with the automatic creation of manim based videos. Based on any input data / information you want to visualize.

As of now the project consist of an (agentic) flow with one Code Reviewer and one Code Writer that interact in a feedback loop.

Manim code generated in a one shot fashion often contains errors or has visual inconsistency. The approach in this project tries to reduce the error rate and improve the quality of the videos through better prompting, feedback loops and the use VLMs (Image Capable LLMs).

The use of LiteLLM in the project ensures that future models can also be easily benchmarked and compared on this task.

Benchmarking future model releases on this task will also be interesting, since it requires good spatial understanding which most LLMs struggle with.

❗ It's important to note that creating a coherent video that compiles and has no issues is still pushing the current SOTA LLMs to their limits

Current Flow (subject to change)

🚀 Installation

1. Clone the repository:

git clone https://github.com/makefinks/manim-generator.git
cd manim-generator

2. Install the requirements

With uv (Recommended)

uv sync

Note: uv offers faster installation times and better dependency resolution

Or using the requirements.txt directly

pip install -r requirements.txt

You also need to install ffmpeg and for some cases a LaTeX distribution

Windows (using Chocolatey):

choco install ffmpeg
choco install miktex

macOS (using Homebrew):

brew install ffmpeg
brew install --cask mactex

Linux (Debian/Ubuntu):

sudo apt-get update
sudo apt-get install texlive texlive-latex-extra texlive-fonts-extra texlive-science

3. Setup Environment Variables

Either by creating a .env file or by defining the API key of your preferred provider system wide.

Recommendation: use openrouter to access all llms

⚡ Usage

1. Execute the script

python manim_generator.py

2. CLI Arguments

The script supports the following command-line arguments:

Video Data Input

Argument	Description	Default
--video_data	Description of the video to generate (text string)	-
--video_data_file	Path to file containing video description	"video_data.txt"

Model Configuration

Argument	Description	Default
--manim_model	Model to use for generating Manim code	"openrouter/anthropic/claude-sonnet-4"
--review_model	Model to use for reviewing code	"openrouter/anthropic/claude-sonnet-4"
--streaming	Enable streaming responses from the model	False
--temperature	Temperature for the LLM Model	0.4
--force_vision	Adds images to the review process, regardless if LiteLLM reports vision is not supported	-
--provider	Specific provider to use for OpenRouter requests (e.g., 'anthropic', 'openai')	-

Process Configuration

Argument	Description	Default
--review_cycles	Number of review cycles to perform	5
--manim_logs	Show Manim execution logs	False
--success_threshold	Percentage of scenes that must render successfully to trigger enhanced visual review mode	100

Reasoning Tokens Configuration

Argument	Description	Default
--reasoning_effort	Reasoning effort level for OpenAI-style models (choices: "low", "medium", "high")	"high"
--reasoning_max_tokens	Maximum tokens for reasoning (Anthropic-style)	-
--reasoning_exclude	Exclude reasoning tokens from response (model still uses reasoning internally)	-

Note: You cannot use both --reasoning_effort and --reasoning_max_tokens at the same time.

Example with specific models and video data:

python manim_generator.py --video_data "Explain the concept of neural networks with visual examples" --manim_model "openrouter/anthropic/claude-sonnet-4" --review_model "openrouter/anthropic/claude-sonnet-4" --review_cycles 3

3. Tweak the config

Manually configure what llm the coder and reviewer should use inside manim_generator.py.

Images will only be used if the reviewer model supports image inputs.

Check compatibility:

• https://openrouter.ai/models?modality=text+image-%3Etext
• https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json (can be tweaked manually if necessary)

🤝 Contributing

Contributions are welcome!

Areas for Contribution

• Improving the prompt engineering
• Enhancing the review feedback loop -> extending it with more agentic patterns
• Improving code quality
• Adding / Proposing new features or optimizations

Known Issues:

• Streaming: current streaming implementation is broken, contains lots of flickering and leaves artifacts in scroll history.
• Prompting / Environment Setup: There may be a mismatch of what version the llm chooses to use and what is currently installed on the system.