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Josh Hawkins
cddd17f36e
Cameras can now add lower quality live streams that go2rtc transcodes to H.264 on demand. `live.transcode` takes a source stream and a list of heights and bitrates, and each quality becomes a `{camera}_transcode_{height}p` stream. The config validator adds them to `live.streams` without moving any the user already placed, and drops them when transcoding is disabled or a height changes. `create_config.py` writes them into go2rtc's generated config at startup, and saving the config or deleting a camera syncs them through go2rtc's API, so no restart is needed. They use `#hardware`, so go2rtc picks a hardware encoder and falls back to the CPU when there isn't one.
The Live playback settings stream list can be reordered by drag, since its order is the auto ladder. Auto order sorts it by bitrate, measuring native streams through a new admin-only `/go2rtc/streams/{name}/bitrate` endpoint and using the configured bitrate for transcoded ones. A pure reorder wasn't saved before because RJSF, the settings form, and `update_yaml` all ignore map key order. Sections can now mark a map with `orderedMaps`, which sends the whole map with `replace_paths` so `config_set` rewrites it in order.
Transcoded streams aren't in `go2rtc.streams`, which only lists yaml streams, so the frontend treated them as not restreamed and fell back to jsmpeg. Every restream check now goes through `isRestreamedStream`.
Auto treated any stall with no bytes in the last 2 seconds as a dead camera and handed it to the error fallback, which went straight to jsmpeg. Heavy congestion can stop delivery completely, so congested viewers skipped every lower stream. Auto now declines only when stats show the camera offline, and a stall on a live camera steps down. The stream picker also has a Try highest quality button that sends auto back to the top stream.
Frigate NVR™ - Realtime Object Detection for IP Cameras
[English] | 简体中文
A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.
- Tight integration with Home Assistant via a custom component
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection
- Object detection with TensorFlow runs in separate processes for maximum FPS
- Communicates over MQTT for easy integration into other systems
- Records video with retention settings based on detected objects
- 24/7 recording
- Re-streaming via RTSP to reduce the number of connections to your camera
- WebRTC & MSE support for low-latency live view
Documentation
View the documentation at https://docs.frigate.video
Donations
If you would like to make a donation to support development, please use Github Sponsors.
License
This project is licensed under the MIT License.
- Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
- Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.
Please see our Trademark Policy for details on acceptable use of our brand assets.
Screenshots
Live dashboard
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Translations
We use Weblate to support language translations. Contributions are always welcome.
Copyright © 2026 Frigate, Inc.