8000 GitHub - fingerthief/recommendarr: An LLM driven recommendation system based on Radarr and Sonarr library or watch history information
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An LLM driven recommendation system based on Radarr and Sonarr library or watch history information

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Recommendarr

mockup

Recommendarr is a web application that generates personalized TV show and movie recommendations based on your Sonarr, Radarr, Plex, and Jellyfin libraries using AI.

For detailed documentation, please visit the Recommendarr Wiki.

⚠️ IMPORTANT: When accessing this application from outside your network, you must open the application port on your router/firewall (default: 3000). Alternatively, see the Reverse Proxy Setup wiki page for secure setup guidance.

⚠️ PORT CONFIGURATION: The application now uses a single port (default: 3000) for both the frontend and API, configurable via the PORT environment variable. See Environment Variables.

🌟 Features

  • AI-Powered Recommendations: Get personalized TV show and movie suggestions based on your existing library.
  • Sonarr & Radarr Integration: Connects directly to your media servers to analyze your TV and movie collections.
  • Plex, Jellyfin, Tautulli & Trakt Integration: Analyzes your watch history for better recommendations.
  • Flexible AI Support: Works with OpenAI, local models (Ollama/LM Studio), or any OpenAI-compatible API. See Compatible AI Services.
  • Customization Options: Adjust recommendation count, model parameters, and more.
  • Dark/Light Mode: Toggle between themes based on your preference.
  • Poster Images: Displays media posters with fallback generation.

For a full list, see Features.

📋 Prerequisites

Before installing, ensure you have the necessary services and access. See the Prerequisites page on the wiki for details.

🚀 Quick Start (Docker Hub - Easiest)

The simplest way to get started with Recommendarr:

# Pull and run with default port 3000
docker run -d \
  --name recommendarr \
  -p 3000:3000 \
  -v recommendarr-data:/app/server/data \
  tannermiddleton/recommendarr:latest

Then visit http://localhost:3000 in your browser.

Default Login:

  • Username: admin
  • Password: 1234 (Change immediately after first login!)

For other installation methods (Docker Compose, Build from Source, Manual), please see the Installation page on the wiki.

🔧 Configuration & Usage

After installation, you'll need to connect your media services and set up an AI provider.

🌐 Advanced Setup

🔧 Troubleshooting

Encountering issues? Check the Troubleshooting page on the wiki for common problems and solutions.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

0