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Open-Source AI Presentation Generator and API (Gamma, Beautiful AI, Decktopus Alternative)

✨ Why Presenton

No SaaS lock-in · No forced subscriptions · Full control over models and data

What makes Presenton different?

• Fully self-hosted; Web (Docker) & Desktop (Mac, Windows & Linux)
• Works with OpenAI, Gemini, Anthropic, Ollama, or custom models
• API deployable
• Fully open-source (Apache 2.0)
• Use your existing PPTX files as templates for AI presentation generation

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🎛 Features

💻 Presenton Desktop

Create AI-powered presentations using your own model provider (BYOK) or run everything locally on your own machine for full control and data privacy.

Available Platforms

Platform	Architecture	Package	Download
macOS	Apple Silicon / Intel	.dmg	Download ↗
Windows	x64	.exe	Download ↗
Linux	x64	.deb	Download ↗

Presenton gives you complete control over your AI presentation workflow. Choose your models, customize your experience, and keep your data private.

• Custom Templates & Themes — Create unlimited presentation designs with HTML and Tailwind CSS
• AI Template Generation — Create presentation templates from existing Powerpoint documents.
• Flexible Generation — Build presentations from prompts or uploaded documents
• Export Ready — Save as PowerPoint (PPTX) and PDF with professional formatting
• Built-In MCP Server — Generate presentations over Model Context Protocol
• Bring Your Own Key — Use your own API keys for OpenAI, Google Gemini, Anthropic Claude, or any compatible provider. Only pay for what you use, no hidden fees or subscriptions.
• Ollama Integration — Run open-source models locally with full privacy
• OpenAI API Compatible — Connect to any OpenAI-compatible endpoint with your own models
• Multi-Provider Support — Mix and match text and image generation providers
• Versatile Image Generation — Choose from DALL-E 3, Gemini Flash, Pexels, or Pixabay
• Rich Media Support — Icons, charts, and custom graphics for professional presentations
• Runs Locally — All processing happens on your device, no cloud dependencies
• API Deployment — Host as your own API service for your team
• Fully Open-Source — Apache 2.0 licensed, inspect, modify, and contribute
• Docker Ready — One-command deployment with GPU support for local models
• Electron Desktop App — Run Presenton as a native desktop application on Windows, macOS, and Linux (no browser required)
• Sign in with ChatGPT — Use your free or paid ChatGPT account to sign in and start creating presentations instantly — no separate API key required

☁️ Presenton Cloud

Run Presenton directly in your browser — no installation, no setup required. Start creating presentations instantly from anywhere.

⚡ Running Presenton

You can run Presenton in two ways: Docker for a one-command setup without installing a local dev stack, or the Electron desktop app for a native app experience (ideal for development or offline use).

Option 1: Electron (Desktop App)

Run Presenton as a native desktop application. LLM and image provider (API keys, etc.) can be configured in the app. The same environment variables used for Docker apply when running the bundled backend.

Prerequisites: Node.js (LTS), npm, Python 3.11, and uv (for the Electron FastAPI backend in electron/servers/fastapi).

• Setup (First Time)

  cd electron
  npm run setup:env

  This installs Node dependencies, runs uv sync in the FastAPI server, and installs Next.js dependencies.

• Run in Development

  npm run dev

  This compiles TypeScript and starts Electron. The backend and UI run locally inside the desktop window.

• Build Distributable (Optional) To create installers for Windows, macOS, or Linux:

  npm run build:all
  npm run dist

  Output files are written to electron/dist (or as configured in your electron-builder settings).

Option 2: Docker

• Start Presenton Linux/MacOS (Bash/Zsh Shell):

  docker run -it --name presenton -p 5000:80 -v "./app_data:/app_data" ghcr.io/presenton/presenton:latest

  Windows (PowerShell):

  docker run -it --name presenton -p 5000:80 -v "${PWD}\app_data:/app_data" ghcr.io/presenton/presenton:latest

• Open Presenton

  Open http://localhost:5000 in the 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.

⚙️ Deployment Configurations

These settings apply to both Docker and the Electron app's backend. 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.

• CAN_CHANGE_KEYS=[true/false]: Set this to false if you want to keep API Keys hidden and make them unmodifiable.
• LLM=[openai/google/anthropic/ollama/custom]: Select LLM of your choice.
• OPENAI_API_KEY=[Your OpenAI API Key]: Provide this if LLM is set to openai
• OPENAI_MODEL=[OpenAI Model ID]: Provide this if LLM is set to openai (default: "gpt-4.1")
• GOOGLE_API_KEY=[Your Google API Key]: Provide this if LLM is set to google
• GOOGLE_MODEL=[Google Model ID]: Provide this if LLM is set to google (default: "models/gemini-2.0-flash")
• ANTHROPIC_API_KEY=[Your Anthropic API Key]: Provide this if LLM is set to anthropic
• ANTHROPIC_MODEL=[Anthropic Model ID]: Provide this if LLM is set to anthropic (default: "claude-3-5-sonnet-20241022")
• OLLAMA_URL=[Custom Ollama URL]: Provide this if you want to custom Ollama URL and LLM is set to ollama
• OLLAMA_MODEL=[Ollama Model ID]: Provide this if LLM is set to ollama
• CUSTOM_LLM_URL=[Custom OpenAI Compatible URL]: Provide this if LLM is set to custom
• CUSTOM_LLM_API_KEY=[Custom OpenAI Compatible API KEY]: Provide this if LLM is set to custom
• CUSTOM_MODEL=[Custom Model ID]: Provide this if LLM is set to custom
• TOOL_CALLS=[Enable/Disable Tool Calls on Custom LLM]: If true, LLM will use Tool Call instead of Json Schema for Structured Output.
• DISABLE_THINKING=[Enable/Disable Thinking on Custom LLM]: If true, Thinking will be disabled.
• WEB_GROUNDING=[Enable/Disable Web Search for OpenAI, Google And Anthropic]: If true, LLM will be able to search web for better results.
• MEM0_ENABLED=[true/false]: Enables mem0 OSS presentation memory. Default is true.
• MEM0_DIR=[Path]: Directory for mem0 OSS local storage (Qdrant path + history DB). Default is /app_data/mem0.
• MEM0_EMBEDDER_PROVIDER=[Provider]: Embedder provider for mem0 OSS. Default is fastembed.
• MEM0_EMBEDDER_MODEL=[Model]: Mid-range local embedding model for memory search. Default is BAAI/bge-small-en-v1.5.
• MEM0_EMBEDDING_DIMS=[Number]: Embedding dimensions used by mem0 embedder and qdrant collection. Default is 384.
• LITEPARSE_DPI=[Number]: LiteParse OCR render DPI (higher can increase memory use). Default is 120.
• LITEPARSE_NUM_WORKERS=[Number]: LiteParse OCR worker count. Default is 1 for stable Docker parsing.

Mem0 in Docker uses OSS/self-hosted mode (not Mem0 Platform API). Memory is isolated per presentation ID. Prompt context, extracted document text, generated outline context, and subsequent edit interactions are stored and retrieved only for that same presentation (including when revisiting later).

You can also set the following environment variables to customize the image generation provider and API keys:

• DISABLE_IMAGE_GENERATION: If true, Image Generation will be disabled for slides.
• IMAGE_PROVIDER=[dall-e-3/gpt-image-1.5/gemini_flash/nanobanana_pro/pexels/pixabay/comfyui]: Select the image provider of your choice.
  • Required if DISABLE_IMAGE_GENERATION is not set to true.
• OPENAI_API_KEY=[Your OpenAI API Key]: Required if using dall-e-3 or gpt-image-1.5 as the image provider.
• DALL_E_3_QUALITY=[standard/hd]: Optional quality setting for dall-e-3 (default: standard).
• GPT_IMAGE_1_5_QUALITY=[low/medium/high]: Optional quality setting for gpt-image-1.5 (default: medium).
• GOOGLE_API_KEY=[Your Google API Key]: Required if using gemini_flash or nanobanana_pro as the image provider.
• PEXELS_API_KEY=[Your Pexels API Key]: Required if using pexels as the image provider.
• PIXABAY_API_KEY=[Your Pixabay API Key]: Required if using pixabay as the image provider.
• COMFYUI_URL=[Your ComfyUI server URL] and COMFYUI_WORKFLOW=[Workflow JSON]: Required if using comfyui to route prompts to a self-hosted ComfyUI workflow.

You can disable anonymous telemetry using the following environment variable:

• DISABLE_ANONYMOUS_TRACKING=[true/false]: Set this to true to disable anonymous telemetry.

Web login (Docker / self-hosted)

The web image can require a single admin username and password before the app and API are usable. Credentials are stored under your app_data volume (hashed in userConfig.json). Optional environment variables (also wired in docker-compose.yml for production, production-gpu, development, and development-gpu):

• AUTH_USERNAME / AUTH_PASSWORD — Preseed the admin login on first boot (password at least 6 characters). If credentials already exist, these are ignored unless AUTH_OVERRIDE_FROM_ENV is set.
• AUTH_OVERRIDE_FROM_ENV=[true/false] — If true, overwrite stored credentials from AUTH_USERNAME / AUTH_PASSWORD on every container start and rotate the session signing secret (all existing sessions end). Remove after a one-off rotation.
• RESET_AUTH=[true/false] — If true, clear stored login data on startup (recovery). Use for one boot only, then unset so credentials are not wiped again.

Examples

Default (first visit opens the setup UI on /):

docker compose up -d production
# open http://localhost:5000

Preseed credentials via .env then start:

# .env
AUTH_USERNAME=admin
AUTH_PASSWORD=your-secure-password

docker compose up -d production

Rotate password from the environment (then remove AUTH_OVERRIDE_FROM_ENV from .env and redeploy):

AUTH_USERNAME=admin
AUTH_PASSWORD=new-password
AUTH_OVERRIDE_FROM_ENV=true
docker compose up -d production

Locked out — reset and set up again:

RESET_AUTH=true docker compose up -d production
# after one successful start, remove RESET_AUTH from .env and run compose again

Manual reset: stop the container, edit ./app_data/userConfig.json, delete AUTH_USERNAME, AUTH_PASSWORD_HASH, and AUTH_SECRET_KEY, save, and start again.

Sign out from the app: Settings → Other → Sign out.

Note: You can freely choose both the LLM (text generation) and the image provider. Supported image providers: dall-e-3, gpt-image-1.5 (OpenAI), gemini_flash, nanobanana_pro (Google), pexels, pixabay, and comfyui (self-hosted).

Docker Run Examples by Provider

• Using 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

• Using Google

  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

• Using Ollama

  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

• Using Anthropic

  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

• Using OpenAI Compatible API

  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

• Running Presenton with GPU Support 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

✨ Generate Presentation via API

Generate Presentation

Endpoint: /api/v1/ppt/presentation/generate
Method: POST
Content-Type: application/json

Request Body

Parameter	Type	Required	Description
content	string	Yes	Main content used to generate the presentation.
slides_markdown	string[] | null	No	Provide custom slide markdown instead of auto-generation.
instructions	string | null	No	Additional generation instructions.
tone	string	No	Text tone (default: "default"). Options: default, casual, professional, funny, educational, sales_pitch
verbosity	string	No	Content density (default: "standard"). Options: concise, standard, text-heavy
web_search	boolean	No	Enable web search grounding (default: false).
n_slides	integer	No	Number of slides to generate (default: 8).
language	string	No	Presentation language (default: "English").
template	string	No	Template name (default: "general").
include_table_of_contents	boolean	No	Include table of contents slide (default: false).
include_title_slide	boolean	No	Include title slide (default: true).
files	string[] | null	No	Files to use in generation. Upload first via /api/v1/ppt/files/upload.
export_as	string	No	Export format (default: "pptx"). Options: pptx, pdf

Response

{
  "presentation_id": "string",
  "path": "string",
  "edit_path": "string"
}

Example Request

curl -X POST http://localhost:5000/api/v1/ppt/presentation/generate \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Introduction to Machine Learning",
    "n_slides": 5,
    "language": "English",
    "template": "general",
    "export_as": "pptx"
  }'

Example Response

{
  "presentation_id": "d3000f96-096c-4768-b67b-e99aed029b57",
  "path": "/app_data/d3000f96-096c-4768-b67b-e99aed029b57/Introduction_to_Machine_Learning.pptx",
  "edit_path": "/presentation?id=d3000f96-096c-4768-b67b-e99aed029b57"
}

Note: Prepend your server’s root URL to path and edit_path to construct valid links.

Documentation & Tutorials

• Full API Documentation
• Generate Presentations via API in 5 Minutes
• Create Presentations from CSV using AI
• Create Data Reports Using AI

🚀 Roadmap

• Support for custom HTML templates by developers
• Support for accessing custom templates over API
• Implement MCP server
• Ability for users to change system prompt
• Support external SQL database

🚀 Roadmap

Track the public roadmap on GitHub Projects: https://github.com/orgs/presenton/projects/2