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* Enable event snapshot API to honour query params * fix unused imports * Fixes * Run ruff check --fix * Web changes * Further config and web fixes * Further docs tweak * Fix missing quality default in MediaEventsSnapshotQueryParams * Manual events: don't save annotated jpeg; store frame time * Remove unnecessary grayscale helper * Add caveat to docs on snapshot_frame_time pre-0.18 * JPG snapshot should not be treated as clean * Ensure tracked details uses uncropped, bbox'd snapshot * Ensure all UI pages / menu actions use uncropped, bbox'd * web lint * Add missed config helper text * Expect SnapshotsConfig not Any * docs: Remove pre-0.18 note * Specify timestamp=0 in the UI * Move tests out of http media * Correct missed settings.json wording Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Revert to default None for quality * Correct camera snapshot config wording Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Fix quality=0 handling Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Fix quality=0 handling #2 Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * ReRun generate_config_translations --------- Co-authored-by: leccelecce <example@example.com> Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
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.