giuseppe99barchetta/SuggestArr

Description and use

**Brief Summary**
SuggestArr automatically generates movie, TV‑show, and anime recommendations for Jellyseer/Overseer by analysing the titles you’ve recently watched on Jellyfin, Plex or Emby. The tool runs on a simple Python‑based service, exposing an API/CLI that can be slotted into existing media‑library automation pipelines, keeping your collection fresh without any manual curation.

**Value**

• **Eliminates repetitive recommendation work** – instead of manually searching for new titles, SuggestArr continuously scans your watch history and pushes curated suggestions to Jellyseer/Overseer.
• **Integrates with existing media stacks** – it works with the three major self‑hosted media servers (Jellyfin, Plex, Emby), so you can reuse your current infrastructure.
• **Scalable, repeatable flows** – the API/CLI can be called from cron jobs, CI/CD pipelines, or orchestration tools (e.g., GitHub Actions, Airflow), enabling fully automated, scheduled recommendation updates.

**Practical Adoption Path**

1. **Deploy the Python service** – clone the repo, install dependencies (pip install -r requirements.txt), and run the containerised version (Dockerfile provided) or a virtual‑env instance.
2. **Configure credentials** – add API tokens for your Jellyfin/Plex/Emby server and for Jellyseer/Overseer in the supplied config.yaml.
3. **Connect to your workflow** – invoke the CLI (suggestarr recommend) or call the REST endpoint from a cron job, a Home Assistant automation, or a CI pipeline to run at your preferred cadence (e.g., nightly).
4. **Monitor & tune** – use the built‑in logging and optional Prometheus metrics to verify that recommendations are being generated and delivered correctly; adjust filters (genre, rating, watch‑time window) via the config file.

**Production Readiness**

• **Active development** – last commit on 2026‑05‑11, 1,133 GitHub stars, and a healthy fork count indicate strong community interest.
• **Mature ecosystem fit** – Python is a first‑class language for DevOps automation, and the project already ships a Docker image, API, CLI, and comprehensive documentation.
• **Low risk** – no known licensing or security red flags, though a final review of the dependency tree and maintainer activity is advisable.

Overall, SuggestArr is production‑ready for a pilot deployment in any self‑hosted media environment, offering a straightforward, automated way to keep your library populated with fresh content.

Use in a workflow

giuseppe99barchetta/SuggestArr helps remove repetitive manual operations from a workflow.

• remove manual work
• connect tools into repeatable flows
• schedule operational tasks

Limitations and review notes

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.