
Presenton is an open-source application for generating presentations with AI — all running locally on your device. Stay in control of your data and privacy while using models like OpenAI and Gemini, or use your own hosted models through Ollama.
✨ Now, generate presentations with your existing PPTX file! Just upload your presentation file to create template design and then use that template to generate on brand and on design presentation on any topic.

[!NOTE] Enterprise Inquiries: For enterprise use, custom deployments, or partnership opportunities, contact us at suraj@presenton.ai.
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[!TIP] For detailed setup guides, API documentation, and advanced configuration options, visit our Official Documentation
Presenton gives you complete control over your AI presentation workflow. Choose your models, customize your experience, and keep your data private.
We're launching Presenton Cloud which will make it very easy to create presentations through UI, API and MCP. Join our waitlist for early beta.
docker run -it --name presenton -p 5000:80 -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
docker run -it --name presenton -p 5000:80 -v "${PWD}\app_data:/app_data" ghcr.io/presenton/presenton:latest
Open http://localhost:5000 on browser of your choice to use Presenton.
Note: You can replace 5000 with any other port number of your choice to run Presenton on a different port number.
You may want to directly provide your API KEYS as environment variables and keep them hidden. You can set these environment variables to achieve it.
You can also set the following environment variables to customize the image generation provider and API keys:
You can disable anonymous telemetry using the following environment variable: - DISABLE_ANONYMOUS_TELEMETRY=[true/false]: Set this to true to disable anonymous telemetry.
Note: You can freely choose both the LLM (text generation) and the image provider. Supported image providers: pexels, pixabay, gemini_flash (Google), and dall-e-3 (OpenAI).
docker run -it --name presenton -p 5000:80 -e LLM="openai" -e OPENAI_API_KEY="******" -e IMAGE_PROVIDER="dall-e-3" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
docker run -it --name presenton -p 5000:80 -e LLM="google" -e GOOGLE_API_KEY="******" -e IMAGE_PROVIDER="gemini_flash" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
docker run -it --name presenton -p 5000:80 -e LLM="ollama" -e OLLAMA_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="*******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
docker run -it --name presenton -p 5000:80 -e LLM="anthropic" -e ANTHROPIC_API_KEY="******" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
docker run -it -p 5000:80 -e CAN_CHANGE_KEYS="false" -e LLM="custom" -e CUSTOM_LLM_URL="http://*****" -e CUSTOM_LLM_API_KEY="*****" -e CUSTOM_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="********" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
To use GPU acceleration with Ollama models, you need to install and configure the NVIDIA Container Toolkit. This allows Docker containers to access your NVIDIA GPU.
Once the NVIDIA Container Toolkit is installed and configured, you can run Presenton with GPU support by adding the --gpus=all flag:
docker run -it --name presenton --gpus=all -p 5000:80 -e LLM="ollama" -e OLLAMA_MODEL="llama3.2:3b" -e IMAGE_PROVIDER="pexels" -e PEXELS_API_KEY="*******" -e CAN_CHANGE_KEYS="false" -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest
Note: GPU acceleration significantly improves the performance of Ollama models, especially for larger models. Make sure you have sufficient GPU memory for your chosen model.
Endpoint: /api/v1/ppt/presentation/generate
Method: POST
Content-Type: multipart/form-data
Note: Make sure to set
Content-Typeasmultipart/form-dataand notapplication/json.
| Parameter | Type | Required | Description |
|---|---|---|---|
| prompt | string | Yes | The main topic or prompt for generating the presentation |
| n_slides | integer | No | Number of slides to generate (default: 8, min: 5, max: 15) |
| language | string | No | Language for the presentation (default: "English") |
| template | string | No | Presentation template (default: "general"). Available options: "classic", "general", "modern", "professional" + Custom templates |
| documents | File[] | No | Optional list of document files to include in the presentation. Supported file types: PDF, TXT, PPTX, DOCX |
| export_as | string | No | Export format ("pptx" or "pdf", default: "pptx") |
{
"presentation_id": "string",
"path": "string",
"edit_path": "string"
}
curl -X POST http://localhost:5000/api/v1/ppt/presentation/generate \
-F "prompt=Introduction to Machine Learning" \
-F "n_slides=5" \
-F "language=English" \
-F "template=general" \
-F "export_as=pptx"
{
"presentation_id": "d3000f96-096c-4768-b67b-e99aed029b57",
"path": "/static/user_data/d3000f96-096c-4768-b67b-e99aed029b57/Introduction_to_Machine_Learning.pptx",
"edit_path": "/presentation?id=d3000f96-096c-4768-b67b-e99aed029b57"
}
Note: Make sure to prepend your server's root URL to the path and edit_path fields in the response to construct valid links.
For detailed info checkout API documentation.







Apache 2.0
$ claude mcp add presenton \
-- python -m otcore.mcp_server <graph>