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Open-Source DevOps · giuseppe99barchetta

SuggestArr

SuggestArr is a self-hosted automation tool that watches your Jellyfin, Plex, or Emby media library and automatically requests similar movies, TV shows, and anime via Seer/Overseer. It uses TMDb for content discovery and optionally integrates OpenAI-compatible LLMs for AI-powered personalized recommendations.

Source: GitHub — github.com/giuseppe99barchetta/SuggestArr
1.2k
GitHub stars
24
Forks
Python
Primary language
MIT
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositorygiuseppe99barchetta/SuggestArr
Ownergiuseppe99barchetta
Primary languagePython
LicenseMIT — OSI-approved
Stars1.2k
Forks24
Open issues28
Latest releasev2.9.1 (2026-06-19)
Last updated2026-07-06
Sourcehttps://github.com/giuseppe99barchetta/SuggestArr

What SuggestArr is

Python-based daemon that periodically polls media server APIs (Jellyfin, Plex, Emby) for watch history, queries TMDb or LLM providers for recommendations, and submits requests to Seer via API. Supports SQLite or external databases (PostgreSQL, MySQL), cron scheduling, Trakt watch-history enrichment, and optional AI reasoning via OpenAI-compatible endpoints.

Quickstart

Get the SuggestArr source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/giuseppe99barchetta/SuggestArr.gitcd SuggestArr# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Automated Library Growth for Self-Hosted Media Centers

Organizations running Jellyfin, Plex, or Emby can let SuggestArr watch user activity and automatically feed Seer with curated requests, reducing manual discovery work while respecting user approvals in Seer.

Personalized Recommendations with User Watch History

Each user can link their Trakt account to seed recommendations from their own history. SuggestArr generates content suggestions per user and avoids re-requesting already-watched titles, improving library freshness.

AI-Driven Content Curation for Media Teams

Teams operating their own media infrastructure can enable LLM-powered recommendations (via OpenAI, Ollama, or OpenRouter) to generate contextual suggestions with reasoning, then review and approve them before download.

Implementation considerations

  • Requires active TMDb API key and configured Seer instance with network accessibility from the deployment host.
  • Database choice (SQLite vs. PostgreSQL/MySQL) affects scalability; external databases recommended for multi-user or high-volume automation scenarios.
  • AI features are optional and require valid LLM credentials (OpenAI API key, Ollama endpoint, OpenRouter token, or LiteLLM proxy URL).
  • Cron scheduling and job-pause logic mean automation runs on a defined interval; real-time event-driven recommendations are not supported.
  • Cleanup automation must be tested in dry-run mode before enabling real deletions to avoid unintended loss of user-favorited content.

When to avoid it — and what to weigh

  • Closed or Proprietary Media Platforms — SuggestArr only supports Jellyfin, Plex, Emby, and Seer. If your media stack uses different platforms (e.g., Kaleidescape, custom systems), integration is not feasible.
  • Offline or Air-Gapped Environments Without LLM Access — The AI features require connectivity to OpenAI, Ollama, or another LLM provider. Purely offline deployments will fall back to TMDb-only recommendations, reducing feature value.
  • Highly Restrictive Library Governance — SuggestArr automates requests without human pre-approval; if your organization requires centralized control over all acquisition before submission, the automatic flow may conflict with compliance requirements.
  • Production Systems Requiring Commercial SLA Support — SuggestArr is MIT-licensed open source with no commercial support contract or SLA. Organizations requiring guaranteed uptime and vendor accountability should seek commercial alternatives.

License & commercial use

MIT License. Permissive open-source license allowing commercial use, modification, and redistribution with attribution and no warranty. No copyleft obligations.

MIT License explicitly permits commercial deployment and use. No license restrictions on commercial operation. However, no commercial support, SLA, or warranty provided by the project; users assume all operational risk.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityClear
Deployment complexityModerate
DEV.co fitGood
Assessment confidenceHigh
Security considerations

SuggestArr stores API keys, tokens, and user credentials in configuration files or database. No explicit encryption mentioned for secrets at rest. Supports local Seer users only (no OAuth delegation). Network traffic to external services (TMDb, OpenAI, Trakt, Ollama) should be assumed as-is; consider network isolation if sensitive. Input validation and CSRF/XSS protections for web UI not explicitly documented—requires code review. Docker image from Docker Hub; no signed releases or supply-chain security details provided.

Alternatives to consider

Overseerr / Seer (direct)

Seer has built-in recommendation features and automation rules. Use if you do not need media-server watch-history seeding or prefer a single integrated platform over a companion automation tool.

Radarr / Sonarr Rules Engine

Radarr and Sonarr support custom filters and automation for acquisition. Use if you want rule-based curation without AI or watch-history integration; simpler operational footprint but less personalization.

Lidarr for Music / Custom Scripts

For music libraries or highly bespoke workflows, home-grown Python/bash automation may offer more control. Trade complexity for flexibility; no pre-built UI or multi-media-server support.

Software development agency

Build on SuggestArr with DEV.co software developers

Deploy SuggestArr via Docker Compose and let AI-powered recommendations keep your Jellyfin, Plex, or Emby library fresh. Start with our quick-setup guide.

Talk to DEV.co

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SuggestArr FAQ

Does SuggestArr work with Seer only, or can it submit requests to Radarr/Sonarr?
SuggestArr is designed for Seer/Overseerr only. Direct Radarr/Sonarr integration is not supported in the current implementation.
Can I run SuggestArr without an LLM, using only TMDb?
Yes. AI features are optional. The default recommendation engine queries TMDb for similar titles. LLM integration (OpenAI, Ollama, etc.) is a beta feature that enhances suggestions with reasoning but is not required.
What happens if Seer is offline or unreachable?
SuggestArr will log errors and skip submission for that run. Jobs can be configured to pause if Seer has pending requests, preventing cascading failures. Explicit fallback or retry logic is not clearly documented.
Is there a way to manually trigger a recommendation run outside the cron schedule?
Yes. The web interface offers single-job execution and force-run-all options, allowing ad-hoc recommendations without waiting for the next scheduled interval.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If SuggestArr is part of your open-source devops roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Automate Your Media Library?

Deploy SuggestArr via Docker Compose and let AI-powered recommendations keep your Jellyfin, Plex, or Emby library fresh. Start with our quick-setup guide.