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* add onboarding wizard for new users * resolve hwaccel per camera and clarify recording retention The hwaccel step listed every preset Frigate ships, so an Intel box was offered Raspberry Pi and Rockchip decoding, and the codec specific presets (`preset-intel-qsv-h264` vs `-h265`) were offered as global values that break as soon as two cameras use different codecs. `/hardware/hwaccel` now returns the decoding families the probed hardware can actually use, each carrying a preset per codec, and the wizard resolves the family against the detect stream codec the camera wizard already probed: one global `ffmpeg.hwaccel_args` when every camera agrees, per-camera `cameras.<name>.ffmpeg.hwaccel_args` when they don't. The global stays on `auto` in that case so cameras added later still resolve at startup. A gen13+ Intel machine keeps its QuickSync recommendation with mixed h264 and h265 cameras instead of dropping to vaapi. The recording step's "Days to retain recordings" only wrote alert and detection retention, and the storage estimate under it assumed continuous recording. It now asks what to record in plain language, writes `record.continuous.days` to match, shows the estimate only for continuous, and drops the spinner arrows on the number input. * clean up * add light/dark mode icon switcher * use yml as default config file extension when not found * i18n tweaks * gate the setup wizard on cameras instead of a config key * render setup wizard steps by key * share the setup wizard e2e helpers and mock users * add an account step to the setup wizard * add setup wizard account step e2e coverage * cover the account step's restart behavior * button consistency * fix test * docs * fixes
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.