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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
48aaafba3c
/api/config/set and camera deletion re-parse yaml into a fresh FrigateConfig and swap it in, then re-layered the persisted runtime toggle overrides so a camera the user turned off wouldn't come back on. That re-layer ran apply_runtime_state, which replays each override through the command handlers, so every save re-published a ZMQ config update, a retained MQTT state message, and a runtime-state disk write for every camera with a stored toggle. All of it was redundant: the worker processes were never swapped and still hold the live toggle values, so only the in-process config object the API and dispatcher read was out of date. The extra traffic churned the retained MQTT topics, amplified disk writes, and co-drained enabled updates with other topics on the config socket. Add Dispatcher.reapply_runtime_state_to_config, which corrects only the swapped-in config object, mirroring the field mutations and gates of the _on_*_command handlers with no ZMQ, MQTT, or disk writes. swap_runtime_config now calls it instead of apply_runtime_state; apply_runtime_state is unchanged and still used at startup, where the workers genuinely must be told. |
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| .cursor/rules | ||
| .devcontainer | ||
| .github | ||
| .vscode | ||
| config | ||
| docker | ||
| docs | ||
| frigate | ||
| migrations | ||
| notebooks | ||
| testing-scripts | ||
| web | ||
| .dockerignore | ||
| .gitignore | ||
| .pylintrc | ||
| AGENTS.md | ||
| audio-labelmap.txt | ||
| CLAUDE.md | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| cspell.json | ||
| docker-compose.yml | ||
| generate_api_auth_spec.py | ||
| generate_config_translations.py | ||
| labelmap.txt | ||
| LICENSE | ||
| Makefile | ||
| netlify.toml | ||
| package-lock.json | ||
| pyproject.toml | ||
| README_CN.md | ||
| README.md | ||
| TRADEMARK.md | ||
Frigate NVR™ - Realtime Object Detection for IP Cameras
EnglishA 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.