RadaRec answers the question that follows every good film: what next? It reads your Radarr library, lets you tick the titles you liked, and asks TMDB for each one’s recommendations, filtering the results by rating, vote count and language before you ever see them. What comes back is a poster wall of candidates, each with a button to push it straight into Radarr at your chosen root folder, quality profile and metadata profile.
The point is to keep discovery inside the tool that already holds your collection. Instead of browsing a website for a title, copying its ID and adding it by hand in Radarr, you select what you enjoyed from a list and walk away with a shortlist you can add in one click. It is a small, single-purpose container — one Python process, one config file, one Radarr instance — which is the right size for the job.
radarr_address, tmdb_api_key, quality_profile_id, and the rest).Recommendation engines are built to sell you something; RadaRec is built to fill a shelf you already own. Because the seed comes from your own library, the suggestions tend to land inside the taste you have already collected — prestige thrillers beget prestige thrillers, and a Denis Villeneuve run turns into a sensible shortlist instead of a streaming service’s front page.
The three filters do most of the quiet work. Rating and vote floors remove the obscure titles that look fine until you read the vote count, and the language filter stops a search for British crime drama from returning films you cannot watch. What survives that is usually worth adding, and adding it is one click rather than a trip to Radarr’s search page.
It is used from a browser on the same network as the host, and its configuration lives in a single config directory that you mount as a volume.
Free and open source — GPL-3.0. It needs two things you supply: a reachable Radarr instance with its API key, and a free TMDB API key for the recommendation data.