Strands Agents Manim
A minimal end-to-end tutorial to build an autonomous agent that generates mathematical animations using AWS Strands, Manim, and MCP.
Install
$ agentstack add mcp-danielpuentee-strands-agents-manim ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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How agent discovery & health will work →About
🧶 Strands Agents + MCP Tutorial — Manim Visualizations
A minimal end-to-end tutorial to build an autonomous agent that generates mathematical animations using AWS Strands, Manim, and MCP.
[](https://github.com/psf/black)
> Author: Daniel Puente Viejo
🎯 Objective
This repository shows how to combine AWS Strands Agents + Manim to enable model-driven visualization using the Model Context Protocol (MCP). We will build an autonomous agent that accepts natural language math queries and orchestrates a local rendering engine to produce high-quality videos using native AWS Bedrock models.
🧠 Core Idea
We move away from hard-coded workflows to a fluid, 4-step autonomous process:
- Query: You provide a prompt (e.g., "Create a grid and a unit square. Apply a shear transformation matrix [[1, 1], [0, 1]] to the grid to show how the space distorts.").
- Tool Use: The Strands Agent uses the MCP tool to write the necessary Python/Manim code.
- Execution: The local MCP server receives the code and runs the rendering engine.
- Generation: A video file is generated and saved to your output folder.
🧩 Requirements
This project uses uv for dependency management.
- Environment Variables:
Create a .env file with your AWS credentials: ``plaintext AWS_ACCESS_KEY_ID= AWS_SECRET_ACCESS_KEY= AWS_REGION= ``
🚀 How to Run
This project is set up with pyproject.toml and uv.lock. You will need two terminal windows to run the agent and the server simultaneously.
1. Start the MCP Server
In the first terminal, launch the server that handles the Manim engine:
uv run start_mcp_server.py2. Run the Agent
In a second terminal, launch the client application to start chatting with the agent:
uv run app.py📃 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: DanielPuentee
- Source: DanielPuentee/strands-agents-manim
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.