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Open-source tools & agent skills to build the things you’re paying for.

Frigate

Project36.4K

A local network video recorder with real-time object detection for IP cameras.

Added 2026-10-07
Replaces
GitHub

What you can build

  • Build a processor for MQTT event messages and an operator-maintained schedule that groups detections by camera and time, then produces a prioritized incident digest.
  • Build a monitor that compares incoming MQTT event counts with an operator-maintained camera schedule, flags cameras with unexpectedly quiet periods, and writes an audit report.
  • Build a feed planner that takes camera names and consumer requests, assigns shared RTSP restreams to reduce direct camera connections, and reports unassigned requests.
  • Build a companion that accepts operator-selected Frigate recordings and notes, orders their paths and annotations by time, and produces a shareable incident summary; clip selection stays manual.
  • Build a calculator using operator-entered storage capacity, observed usage, and retention settings to estimate storage needs and flag policies that exceed the available space.

Try this prompt

Build a small external incident-digest tool for Frigate. Consume MQTT event messages and a user-maintained schedule, group related detections by camera and time, and produce a prioritized report for review. Keep the schedule editable, explain grouping and priority rules, and preserve the original event messages so the report can be audited. Use only documented Frigate interfaces; do not assume undocumented topic names or payload fields. Include setup guidance, sample inputs, and tests for duplicate and out-of-schedule events.

Get started

  1. Review installation requirements

    Use the official documentation at https://docs.frigate.video for current installation and configuration instructions; the supplied README does not specify deployment commands.

  2. Plan cameras and detection

    Choose compatible IP cameras and review the documented supported object detectors. A GPU or AI accelerator is highly recommended for detection.

  3. Provision local resources

    Plan a host, camera network, and storage for recorded video, then configure recording and retention settings for your needs.

License & notes

MITThe repository is MIT-licensed; retain its copyright and license notice when distributing copies. Frigate is designed to run locally, so plan for your own compute, camera network, and video storage. A GPU or AI accelerator is highly recommended. The supplied README identifies no paid API or service price; it does not establish hosting or hardware costs.

  • Local detection needs suitable compute; a GPU or AI accelerator is highly recommended.
  • Video storage, host setup, and camera compatibility are the operator’s responsibility.
  • The supplied README does not document MQTT topic or payload details; consult the official documentation before building integrations.
  • MIT permits commercial use, but redistributed copies must retain the copyright and license notice.

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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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Added 2026-10-07
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