Bump launch-editor from 2.12.0 to 2.14.1 in /docs

Bumps [launch-editor](https://github.com/vitejs/launch-editor) from 2.12.0 to 2.14.1.
- [Commits](https://github.com/vitejs/launch-editor/compare/v2.12.0...v2.14.1)

---
updated-dependencies:
- dependency-name: launch-editor
  dependency-version: 2.14.1
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>

.cspell/frigate-dictionary.txt

@@ -162,6 +162,7 @@ mpegts
 mqtt
 mse
 msenc
+muxing
 namedtuples
 nbytes
 nchw
@@ -197,6 +198,8 @@ OWASP
 paddleocr
 paho
 passwordless
+PCMA
+PCMU
 popleft
 posthog
 postprocess
@@ -222,7 +225,9 @@ radeontop
 rawvideo
 rcond
 RDONLY
+realmonitor
 rebranded
+recvonly
 referer
 reindex
 Reolink
@@ -239,8 +244,11 @@ rocminfo
 rootfs
 rtmp
 RTSP
+rtsps
+rtspx
 ruamel
 scroller
+sendonly
 setproctitle
 setpts
 shms
@@ -251,6 +259,7 @@ SNDMORE
 socs
 sqliteq
 sqlitevecq
+Srtp
 ssdlite
 statm
 stimeout

.github/workflows/pull_request.yml

@@ -125,5 +125,7 @@ jobs:
         run: devcontainer up --workspace-folder .
       - name: Run mypy in devcontainer
         run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m mypy --config-file frigate/mypy.ini frigate"
+      - name: Check API spec is up to date
+        run: devcontainer exec --workspace-folder . bash -lc "python3 generate_api_auth_spec.py --check"
       - name: Run unit tests in devcontainer
         run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m unittest"

AGENTS.md

@@ -235,6 +235,14 @@ ruff check frigate/
 
 # Type check
 python3 -u -m mypy --config-file frigate/mypy.ini frigate
+
+# Regenerate the OpenAPI spec after adding, changing, or removing an API
+# endpoint or its auth dependency — outputs docs/static/frigate-api.yaml,
+# annotated with each endpoint's auth requirement (admin / any / camera /
+# public). NEVER edit that file by hand. CI runs the --check variant and fails
+# if it is out of date. (from repo root)
+python3 generate_api_auth_spec.py
+python3 generate_api_auth_spec.py --check
 ```
 
 ### Frontend (from web/ directory)
@@ -316,6 +324,8 @@ async def get_events(request: Request, limit: int = 100):
     # Implementation
 ```
 
+After adding, changing, or removing an endpoint (or its auth dependency), regenerate the OpenAPI spec with `python3 generate_api_auth_spec.py` so `docs/static/frigate-api.yaml` stays in sync and the endpoint's auth requirement is documented. CI enforces this via the `--check` variant; never edit that file by hand.
+
 ### Configuration Access
 
 ```python

docker/main/Dockerfile

@@ -265,8 +265,8 @@ ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PA
 RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
     /deps/install_deps.sh
 
-ENV DEFAULT_FFMPEG_VERSION="7.0"
-ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:5.0"
+ENV DEFAULT_FFMPEG_VERSION="8.0"
+ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:7.0:5.0"
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \

docker/main/install_deps.sh

@@ -52,9 +52,13 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
     rm -rf ffmpeg.tar.xz
     mkdir -p /usr/lib/ffmpeg/7.0
-    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-03-19-13-03/ffmpeg-n7.1.3-43-g5a1f107b4c-linux64-gpl-7.1.tar.xz"
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
     tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
     rm -rf ffmpeg.tar.xz
+    mkdir -p /usr/lib/ffmpeg/8.0
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-06-02-14-20/ffmpeg-n8.1.1-9-g58d4114d36-linux64-gpl-8.1.tar.xz"
+    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/8.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
+    rm -rf ffmpeg.tar.xz
 fi
 
 # ffmpeg -> arm64
@@ -64,9 +68,13 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
     tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
     rm -f ffmpeg.tar.xz
     mkdir -p /usr/lib/ffmpeg/7.0
-    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-03-19-13-03/ffmpeg-n7.1.3-43-g5a1f107b4c-linuxarm64-gpl-7.1.tar.xz"
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
     tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
     rm -f ffmpeg.tar.xz
+    mkdir -p /usr/lib/ffmpeg/8.0
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-06-02-14-20/ffmpeg-n8.1.1-9-g58d4114d36-linuxarm64-gpl-8.1.tar.xz"
+    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/8.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
+    rm -f ffmpeg.tar.xz
 fi
 
 # arch specific packages

docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py

@@ -5,11 +5,7 @@ from typing import Any
 from ruamel.yaml import YAML
 
 sys.path.insert(0, "/opt/frigate")
-from frigate.const import (
-    DEFAULT_FFMPEG_VERSION,
-    INCLUDED_FFMPEG_VERSIONS,
-)
-from frigate.util.config import find_config_file
+from frigate.util.config import find_config_file, resolve_ffmpeg_path
 
 sys.path.remove("/opt/frigate")
 
@@ -29,9 +25,4 @@ except FileNotFoundError:
     config: dict[str, Any] = {}
 
 path = config.get("ffmpeg", {}).get("path", "default")
-if path == "default":
-    print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg")
-elif path in INCLUDED_FFMPEG_VERSIONS:
-    print(f"/usr/lib/ffmpeg/{path}/bin/ffmpeg")
-else:
-    print(f"{path}/bin/ffmpeg")
+print(resolve_ffmpeg_path(path, "ffmpeg"))

docker/main/rootfs/usr/local/go2rtc/create_config.py

@@ -11,12 +11,10 @@ sys.path.insert(0, "/opt/frigate")
 from frigate.config.env import substitute_frigate_vars
 from frigate.const import (
     BIRDSEYE_PIPE,
-    DEFAULT_FFMPEG_VERSION,
-    INCLUDED_FFMPEG_VERSIONS,
     LIBAVFORMAT_VERSION_MAJOR,
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
-from frigate.util.config import find_config_file
+from frigate.util.config import find_config_file, resolve_ffmpeg_path
 from frigate.util.services import is_restricted_go2rtc_source
 
 sys.path.remove("/opt/frigate")
@@ -81,12 +79,7 @@ if go2rtc_config.get("rtsp", {}).get("password") is not None:
 
 # ensure ffmpeg path is set correctly
 path = config.get("ffmpeg", {}).get("path", "default")
-if path == "default":
-    ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
-elif path in INCLUDED_FFMPEG_VERSIONS:
-    ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
-else:
-    ffmpeg_path = f"{path}/bin/ffmpeg"
+ffmpeg_path = resolve_ffmpeg_path(path, "ffmpeg")
 
 if go2rtc_config.get("ffmpeg") is None:
     go2rtc_config["ffmpeg"] = {"bin": ffmpeg_path}

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -252,6 +252,7 @@ http {
             include proxy.conf;
 
             proxy_cache api_cache;
+            proxy_cache_key "$scheme$proxy_host$request_uri|$role|$groups|$user";
             proxy_cache_lock on;
             proxy_cache_use_stale updating;
             proxy_cache_valid 200 5s;

docs/docs/configuration/advanced/reference.md

@@ -147,6 +147,13 @@ auth:
   # NOTE: changing this value will not automatically update password hashes, you
   #       will need to change each user password for it to apply
   hash_iterations: 600000
+  # Optional: Map roles to the list of cameras each role can access (default: none)
+  # NOTE: An empty list grants the role access to all cameras. Roles defined here can be
+  #       referenced by proxy header role mapping or assigned to native users.
+  roles:
+    my_custom_role:
+      - front_door
+      - back_yard
 
 # Optional: model modifications
 # NOTE: The default values are for the EdgeTPU detector.
@@ -166,6 +173,9 @@ model:
   # Required: Object detection model input tensor format
   # Valid values are nhwc or nchw (default: shown below)
   input_tensor: nhwc
+  # Optional: Data type of the model input tensor
+  # Valid values are float, float_denorm, or int (default: shown below)
+  input_dtype: int
   # Required: Object detection model type, currently only used with the OpenVINO detector
   # Valid values are ssd, yolox, yolonas (default: shown below)
   model_type: ssd
@@ -196,11 +206,12 @@ audio:
   #  - 500 - medium sensitivity
   #  - 1000 - low sensitivity
   min_volume: 500
+  # Optional: Number of threads to use for audio detection (default: shown below)
+  num_threads: 2
   # Optional: Types of audio to listen for (default: shown below)
   listen:
     - bark
     - fire_alarm
-    - scream
     - speech
     - yell
   # Optional: Filters to configure detection.
@@ -257,7 +268,7 @@ birdseye:
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
   # Optional: ffmpeg binary path (default: shown below)
-  # can also be set to `7.0` or `5.0` to specify one of the included versions
+  # can also be set to `8.0` or `5.0` to specify one of the included versions
   # or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
   path: "default"
   # Optional: global ffmpeg args (default: shown below)
@@ -469,6 +480,8 @@ review:
       - Animals in the garden
     # Optional: Preferred response language (default: English)
     preferred_language: English
+    # Optional: Save thumbnails sent to the GenAI provider for review/debugging purposes (default: shown below)
+    debug_save_thumbnails: False
 
 # Optional: Motion configuration
 # NOTE: Can be overridden at the camera level
@@ -500,6 +513,8 @@ motion:
   #  - 30 - medium sensitivity
   #  - 50 - low sensitivity
   contour_area: 10
+  # Optional: Alpha blending factor used in frame differencing for motion calculation (default: shown below)
+  delta_alpha: 0.2
   # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
   # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
   # Low values will cause things like moving shadows to be detected as motion for longer.
@@ -572,6 +587,8 @@ record:
     timelapse_args: "-vf setpts=0.04*PTS -r 30"
     # Optional: Global hardware acceleration settings for timelapse exports. (default: inherit)
     hwaccel_args: auto
+    # Optional: Maximum number of export jobs to process at the same time (default: shown below)
+    max_concurrent: 3
   # Optional: Recording Preview Settings
   preview:
     # Optional: Quality of recording preview (default: shown below).
@@ -714,28 +731,42 @@ lpr:
   enhancement: 0
   # Optional: Save plate images to /media/frigate/clips/lpr for debugging purposes (default: shown below)
   debug_save_plates: False
-  # Optional: List of regex replacement rules to normalize detected plates (default: shown below)
-  replace_rules: {}
+  # Optional: List of regex replacement rules to normalize detected plates before matching (default: none)
+  replace_rules:
+    # Required: regex pattern to match in the detected plate
+    - pattern: "O"
+      # Required: string to replace the matched pattern with
+      replacement: "0"
 
-# Optional: Configuration for AI / LLM provider
+# Optional: Configuration for AI / LLM providers
 # WARNING: Depending on the provider, this will send thumbnails over the internet
 # to Google or OpenAI's LLMs to generate descriptions. GenAI features can be configured at
 # the camera level to enhance privacy for indoor cameras.
+# NOTE: genai is a map of named providers. Each key is a name you choose for the provider,
+#       and each role (chat, descriptions, embeddings) may be assigned to exactly one provider.
 genai:
-  # Required: Provider must be one of ollama, gemini, or openai
-  provider: ollama
-  # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
-  base_url: http://localhost::11434
-  # Required if gemini or openai
-  api_key: "{FRIGATE_GENAI_API_KEY}"
-  # Required: The model to use with the provider.
-  model: gemini-1.5-flash
-  # Optional additional args to pass to the GenAI Provider (default: None)
-  provider_options:
-    keep_alive: -1
-  # Optional: Options to pass during inference calls (default: {})
-  runtime_options:
-    temperature: 0.7
+  # Required: name of the provider (chosen by you, used to reference it elsewhere)
+  my_provider:
+    # Required: Provider must be one of ollama, openai, azure_openai, gemini, or llamacpp
+    provider: ollama
+    # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
+    base_url: http://localhost::11434
+    # Required if gemini or openai
+    api_key: "{FRIGATE_GENAI_API_KEY}"
+    # Required: The model to use with the provider.
+    model: gemini-1.5-flash
+    # Optional: Roles this provider handles (default: shown below)
+    # Each role (chat, descriptions, embeddings) must be assigned to exactly one provider.
+    roles:
+      - chat
+      - descriptions
+      - embeddings
+    # Optional additional args to pass to the GenAI Provider (default: None)
+    provider_options:
+      keep_alive: -1
+    # Optional: Options to pass during inference calls (default: {})
+    runtime_options:
+      temperature: 0.7
 
 # Optional: Configuration for audio transcription
 # NOTE: only the enabled option can be overridden at the camera level
@@ -840,8 +871,8 @@ cameras:
   # Required: name of the camera
   back:
     # Optional: Enable/Disable the camera (default: shown below).
-    # If disabled: config is used but no live stream and no capture etc.
-    # Events/Recordings are still viewable.
+    # When False, ffmpeg is not started and the camera is hidden from the UI
+    # (except Camera Management). Re-enabling requires a Frigate restart.
     enabled: True
     # Optional: camera type used for some Frigate features (default: shown below)
     # Options are "generic" and "lpr"
@@ -908,6 +939,9 @@ cameras:
         inertia: 3
         # Optional: Number of seconds that an object must loiter to be considered in the zone (default: shown below)
         loitering_time: 0
+        # Optional: Minimum speed required for an object to be considered present in the zone (default: none)
+        # In real-world units if distances are set. Used for speed-based zone triggers.
+        speed_threshold: 2.5
         # Optional: List of objects that can trigger this zone (default: all tracked objects)
         objects:
           - person
@@ -945,6 +979,9 @@ cameras:
       order: 0
       # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
       dashboard: True
+      # Optional: Whether this camera is visible in review (the review page and its camera
+      # filter, motion review, and the history view) (default: shown below)
+      review: True
 
     # Optional: connect to ONVIF camera
     # to enable PTZ controls.
@@ -1083,22 +1120,6 @@ ui:
   # Optional: Set the time format used.
   # Options are browser, 12hour, or 24hour (default: shown below)
   time_format: browser
-  # Optional: Set the date style for a specified length.
-  # Options are: full, long, medium, short
-  # Examples:
-  #    short: 2/11/23
-  #    medium: Feb 11, 2023
-  #    full: Saturday, February 11, 2023
-  # (default: shown below).
-  date_style: short
-  # Optional: Set the time style for a specified length.
-  # Options are: full, long, medium, short
-  # Examples:
-  #    short: 8:14 PM
-  #    medium: 8:15:22 PM
-  #    full: 8:15:22 PM Mountain Standard Time
-  # (default: shown below).
-  time_style: medium
   # Optional: Set the unit system to either "imperial" or "metric" (default: metric)
   # Used in the UI and in MQTT topics
   unit_system: metric

docs/docs/configuration/advanced/system.md

@@ -1,7 +1,6 @@
 ---
-id: advanced
-title: Advanced Options
-sidebar_label: Advanced Options
+id: system
+title: System
 ---
 
 import ConfigTabs from "@site/src/components/ConfigTabs";
@@ -202,7 +201,7 @@ model:
 
 :::warning
 
-If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../configuration/review.md#restricting-alerts-to-specific-labels) for more info.
+If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../review.md#restricting-alerts-to-specific-labels) for more info.
 
 :::
 
@@ -234,26 +233,16 @@ Some labels have special handling and modifications can disable functionality.
 
 ## Network Configuration
 
-Changes to Frigate's internal network configuration can be made by bind mounting nginx.conf into the container. For example:
-
-```yaml
-services:
-  frigate:
-    container_name: frigate
-    ...
-    volumes:
-      ...
-      - /path/to/your/nginx.conf:/usr/local/nginx/conf/nginx.conf
-```
+Frigate exposes a few networking options. IPv6 and the listen ports are set in the `networking` configuration (or from the Settings UI); more advanced changes require [customizing the bundled Nginx configuration](#customizing-the-nginx-configuration).
 
 ### Enabling IPv6
 
-IPv6 is disabled by default. Enable it in the Frigate configuration.
+By default Frigate listens on IPv4 only. To also listen on IPv6 — on port `5000`, and on `8971` when TLS is configured — enable it in the `networking` configuration.
 
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Networking" /> and expand **IPv6 configuration**, then enable **Enable IPv6**.
+Navigate to <NavPath path="Settings > System > Networking" /> and enable **IPv6**.
 
 </TabItem>
 <TabItem value="yaml">
@@ -261,7 +250,7 @@ Navigate to <NavPath path="Settings > System > Networking" /> and expand **IPv6
 ```yaml
 networking:
   ipv6:
-    enabled: True
+    enabled: true
 ```
 
 </TabItem>
@@ -300,6 +289,20 @@ This setting is for advanced users. For the majority of use cases it's recommend
 
 :::
 
+### Customizing the Nginx configuration
+
+More advanced changes to Frigate's internal network configuration can be made by bind mounting your own `nginx.conf` into the container. For example:
+
+```yaml
+services:
+  frigate:
+    container_name: frigate
+    ...
+    volumes:
+      ...
+      - /path/to/your/nginx.conf:/usr/local/nginx/conf/nginx.conf
+```
+
 ## Base path
 
 By default, Frigate runs at the root path (`/`). However some setups require to run Frigate under a custom path prefix (e.g. `/frigate`), especially when Frigate is located behind a reverse proxy that requires path-based routing.

docs/docs/configuration/audio_detectors.md

@@ -54,7 +54,7 @@ The ffmpeg process for capturing audio will be a separate connection to the came
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add an input with the `audio` role pointing to a stream that includes audio.
+Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add an input with the `audio` role pointing to a stream that includes audio.
 
 </TabItem>
 <TabItem value="yaml">
@@ -88,7 +88,7 @@ Volume is considered motion for recordings, this means when the `record -> retai
 
 ### Configuring Audio Events
 
-The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `scream`, `speech`, and `yell` are enabled but these can be customized.
+The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `speech`, and `yell` are enabled but these can be customized.
 
 <ConfigTabs>
 <TabItem value="ui">
@@ -107,7 +107,6 @@ audio:
   listen:
     - bark
     - fire_alarm
-    - scream
     - speech
     - yell
 ```
@@ -115,6 +114,70 @@ audio:
 </TabItem>
 </ConfigTabs>
 
+### Common Audio Labels
+
+The labelmap includes hundreds of sound types. The labels below are the ones most users may find practical, grouped by what they're typically used for. Use the exact label string from the left column in your `listen` config, or search for the label in the Frigate UI directly.
+
+Some labels cover several related sounds: `yell` is triggered by shouting, yelling, children shouting, and screaming; `crying` covers baby cries, sobbing, and whimpering; and `speech` covers ordinary talking and conversation.
+
+**Safety and security**
+
+| Label            | Detects                            |
+| ---------------- | ---------------------------------- |
+| `yell`           | Shouting, yelling, screaming       |
+| `fire_alarm`     | Fire and smoke alarm sirens        |
+| `smoke_detector` | Smoke detector beeps               |
+| `alarm`          | General alarm sounds               |
+| `car_alarm`      | Car alarms                         |
+| `siren`          | Emergency vehicle and civil sirens |
+| `glass`          | Glass clinking                     |
+| `shatter`        | Breaking glass                     |
+| `breaking`       | Something breaking                 |
+| `gunshot`        | Gunshots                           |
+| `explosion`      | Explosions                         |
+
+**People and activity**
+
+| Label       | Detects                  |
+| ----------- | ------------------------ |
+| `speech`    | Talking and conversation |
+| `laughter`  | Laughing                 |
+| `crying`    | Baby crying and sobbing  |
+| `cough`     | Coughing                 |
+| `footsteps` | Footsteps and walking    |
+| `knock`     | Knocking on a door       |
+| `doorbell`  | Doorbell                 |
+| `ding-dong` | Doorbell chime           |
+
+**Pets and animals**
+
+| Label      | Detects          |
+| ---------- | ---------------- |
+| `bark`     | Dog barking      |
+| `dog`      | Other dog sounds |
+| `howl`     | Howling          |
+| `growling` | Growling         |
+| `meow`     | Cat meowing      |
+| `cat`      | Other cat sounds |
+| `hiss`     | Hissing          |
+
+**Vehicles and driveway**
+
+| Label             | Detects              |
+| ----------------- | -------------------- |
+| `car`             | Passing cars         |
+| `honk`            | Car horns            |
+| `truck`           | Trucks               |
+| `reversing_beeps` | Vehicle backup beeps |
+| `motorcycle`      | Motorcycles          |
+| `engine_starting` | Engines starting     |
+
+:::tip
+
+Frequently-heard labels like `speech` can generate a lot of events, and each event could save a snapshot and recording based on your configuration, so start with a focused set — the defaults (`bark`, `fire_alarm`, `speech`, `yell`) plus a few of the safety labels above cover most needs — and expand from there. See the [full audio labelmap](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) or the Frigate UI for every available type.
+
+:::
+
 ### Audio Transcription
 
 Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.

docs/docs/configuration/autotracking.md

@@ -167,7 +167,7 @@ A fast [detector](object_detectors.md) is recommended. CPU detectors will not pe
 
 A full-frame zone in `required_zones` is not recommended, especially if you've calibrated your camera and there are `movement_weights` defined in the configuration file. Frigate will continue to autotrack an object that has entered one of the `required_zones`, even if it moves outside of that zone.
 
-Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](index.md).
+Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](advanced/reference.md).
 
 ## Zooming
 

docs/docs/configuration/birdseye.md

@@ -6,10 +6,16 @@ import NavPath from "@site/src/components/NavPath";
 
 In addition to Frigate's Live camera dashboard, Birdseye allows a portable heads-up view of your cameras to see what is going on around your property / space without having to watch all cameras that may have nothing happening. Birdseye allows specific modes that intelligently show and disappear based on what you care about.
 
-Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the "+" icon on the Live page, and choose "Birdseye" as one of the cameras.
+Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the pencil icon in the sidebar on the Live page, and choose "Birdseye" as one of the cameras.
 
 Birdseye can also be used in Home Assistant dashboards, cast to media devices, etc.
 
+:::note
+
+Each camera tile in Birdseye is composed from the frames of the stream assigned the `detect` role, so a camera's image quality in Birdseye matches its detect stream resolution rather than a higher-resolution recording stream. If a camera looks low quality in Birdseye, increasing the detect width and height (or assigning the `detect` role to a higher-resolution stream) is what affects it. See [setting up camera inputs](./cameras.md#setting-up-camera-inputs) for how roles are assigned.
+
+:::
+
 ## Birdseye Behavior
 
 ### Birdseye Modes
@@ -35,10 +41,10 @@ To include a camera in Birdseye view only for specific circumstances, or exclude
 
 **Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.
 
-| Field | Description |
-|-------|-------------|
-| **Enable Birdseye** | Whether this camera appears in Birdseye view |
-| **Tracking mode** | When to show the camera: `continuous`, `motion`, or `objects` |
+| Field               | Description                                                   |
+| ------------------- | ------------------------------------------------------------- |
+| **Enable Birdseye** | Whether this camera appears in Birdseye view                  |
+| **Tracking mode**   | When to show the camera: `continuous`, `motion`, or `objects` |
 
 </TabItem>
 <TabItem value="yaml">
@@ -72,8 +78,8 @@ By default birdseye shows all cameras that have had the configured activity in t
 
 Navigate to <NavPath path="Settings > System > Birdseye" />.
 
-| Field | Description |
-|-------|-------------|
+| Field                    | Description                                                                 |
+| ------------------------ | --------------------------------------------------------------------------- |
 | **Inactivity threshold** | Seconds of inactivity before a camera is hidden from Birdseye (default: 30) |
 
 </TabItem>
@@ -100,9 +106,9 @@ The resolution and aspect ratio of birdseye can be configured. Resolution will i
 
 Navigate to <NavPath path="Settings > System > Birdseye" />.
 
-| Field | Description |
-|-------|-------------|
-| **Width** | Birdseye output width in pixels (default: 1280) |
+| Field      | Description                                     |
+| ---------- | ----------------------------------------------- |
+| **Width**  | Birdseye output width in pixels (default: 1280) |
 | **Height** | Birdseye output height in pixels (default: 720) |
 
 </TabItem>
@@ -161,8 +167,8 @@ It is possible to limit the number of cameras shown on birdseye at one time. Whe
 
 Navigate to <NavPath path="Settings > System > Birdseye" />.
 
-| Field | Description |
-|-------|-------------|
+| Field                    | Description                                                                         |
+| ------------------------ | ----------------------------------------------------------------------------------- |
 | **Layout > Max cameras** | Maximum number of cameras shown at once (e.g., `1` for only the most active camera) |
 
 </TabItem>
@@ -187,8 +193,8 @@ By default birdseye tries to fit 2 cameras in each row and then double in size u
 
 Navigate to <NavPath path="Settings > System > Birdseye" />.
 
-| Field | Description |
-|-------|-------------|
+| Field                       | Description                                              |
+| --------------------------- | -------------------------------------------------------- |
 | **Layout > Scaling factor** | Camera scaling factor between 1.0 and 5.0 (default: 2.0) |
 
 </TabItem>

docs/docs/configuration/cameras.md

@@ -24,12 +24,14 @@ Each role can only be assigned to one input per camera. The options for roles ar
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 | Field             | Description                                                         |
 | ----------------- | ------------------------------------------------------------------- |
 | **Camera inputs** | List of input stream definitions (paths and roles) for this camera. |
 
+For each input you can choose its source: select **Restream (go2rtc)** to pick an existing [go2rtc stream](restream.md) from a dropdown (Frigate uses the `rtsp://127.0.0.1:8554/<stream>` path and `preset-rtsp-restream` input args for that input automatically), or **Manual input path** to type the stream URL directly.
+
 Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
 
 | Field             | Description                                                                                            |
@@ -67,7 +69,7 @@ Additional cameras are simply added under the camera configuration section.
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Camera configuration > Management" /> and use the add camera button to configure each additional camera.
+Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and use the add camera button to configure each additional camera.
 
 </TabItem>
 <TabItem value="yaml">
@@ -143,6 +145,11 @@ If your ONVIF camera does not require authentication credentials, you may still
 
 :::
 
+If a camera connects but fails to authenticate, two optional fields can help:
+
+- `tls_insecure`: Skips TLS certificate verification and sends the ONVIF password as plaintext (`PasswordText`) instead of a hashed digest (`PasswordDigest`). Some cameras reject the digest token and only accept plaintext. This weakens connection security, so only enable it on a trusted local network.
+- `ignore_time_mismatch`: ONVIF authentication tokens include a timestamp, and a camera will reject the token if its clock differs too much from Frigate's. Enabling this makes Frigate compensate for the time offset so authentication can still succeed. Running NTP on both the camera and the Frigate host is the recommended fix; only use this in a "safe" environment, as it slightly weakens token validation.
+
 If your camera has multiple ONVIF profiles, you can specify which one to use for PTZ control with the `profile` option, matched by token or name. When not set, Frigate selects the first profile with a valid PTZ configuration. Check the Frigate debug logs (`frigate.ptz.onvif: debug`) to see available profile names and tokens for your camera.
 
 An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
@@ -174,7 +181,7 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
 | Hikvision DS-2DE3A404IWG-E/W |      ✅      |      ✅      |                                                                                                                                                                                                                                                                   |
 | Reolink                      |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Speco O8P32X                 |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
-| Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable.                                                                                                                                                              |
+| Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatible.                                                                                                                                                              |
 | Tapo                         |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                                                                                                                                   |
 | Uniview IPC672LR-AX4DUPK     |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                                                                                                                                    |
 | Uniview IPC6612SR-X33-VG     |      ✅      |      ✅      | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working.                                                                                                                                                             |

docs/docs/configuration/config.md

@@ -1,5 +1,5 @@
 ---
-id: index
+id: config
 title: Frigate Configuration
 ---
 
@@ -9,11 +9,54 @@ import NavPath from "@site/src/components/NavPath";
 
 Frigate can be configured through the **Settings UI** or by editing the YAML configuration file directly. The Settings UI is the recommended approach — it provides validation and a guided experience for all configuration options.
 
-It is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).
+## Using the Settings UI
+
+The Settings UI groups every configuration option into sections that are listed in the left-hand menu. Each section presents a guided form with validation, so you don't need to remember the structure of the YAML or look up option names by hand.
+
+### Global vs. camera-level configuration
+
+Settings are organized into two scopes:
+
+- **Global configuration** — values under <NavPath path="Settings > Global configuration" /> apply to every camera by default. This is where you set the baseline behavior for object detection, recording, snapshots, motion, and so on.
+- **Camera configuration** — values under <NavPath path="Settings > Camera configuration" /> apply to a single camera. Use the camera selector button at the top of these pages to choose which camera you are editing.
+
+When a camera-level section is left untouched, the camera simply inherits the global values. Changing a value on a camera page **overrides** the global value for that camera only — the global setting and every other camera are unaffected. This mirrors how the YAML works, where a value set under `cameras.<name>` takes precedence over the same value set at the top level.
+
+To undo an override and go back to inheriting from the parent scope, use the reset button at the bottom of the section:
+
+- On a camera section, the button is labeled **Reset to Global** and restores the camera to the global value.
+- On a global section, the button is labeled **Reset to Default** and restores Frigate's built-in default.
+
+Resetting asks for confirmation and cannot be undone once applied.
+
+### Saving changes and the Save All button
+
+Edits are not applied until you save them. As soon as you change a value, the UI tracks it as a pending change:
+
+- The edited section shows a **Modified** badge, and the changed fields are highlighted.
+- A **You have unsaved changes** notice appears above the section's **Save** and **Undo** buttons. **Save** commits just that section; **Undo** discards its pending edits.
+
+Because pending changes can span multiple sections — and multiple cameras — the header provides a **Save All** button that writes every pending change at once. Next to it, **Review pending changes** opens a summary that lists each pending edit with its scope (Global or a specific camera), the affected field, and the new value, so you can confirm exactly what will be written before committing. **Undo All** discards every pending change across all sections.
+
+### Restart-required indicators
+
+Most settings take effect immediately, but some require Frigate to restart before they apply. Fields that require a restart are marked with a small restart icon and a **Restart required** tooltip next to the field label.
+
+When you save a change that touches one of these fields, Frigate confirms the save and reminds you that a restart is needed (for example, _"Settings saved successfully. Restart Frigate to apply your changes."_). The notification includes a one-click **Restart Frigate** action so you can apply the change right away, or you can continue editing and restart later.
+
+### The colored dots in the camera configuration menu
+
+When you are working under <NavPath path="Settings > Camera configuration" />, small colored dots can appear next to a section's name in the menu. They give you an at-a-glance summary of that section's state for the selected camera:
+
+- **Blue dot** — this section **overrides the global configuration**. One or more values in the section have been set specifically for this camera and differ from the global defaults.
+- **Profile-colored dot** — when you are viewing a [camera profile](./profiles.md), a dot in that profile's assigned color indicates the section is **overridden by that profile**. Each profile is given its own distinct color so you can tell at a glance which sections it changes.
+- **Amber dot** — this section has **unsaved changes**. It appears alongside the **Modified** badge whenever you have pending edits in the section that haven't been saved yet.
+
+Hover over any dot to see a tooltip describing what it means. Open a section to see exactly which fields are overridden — the section header indicates how many fields differ from the global (or base) configuration.
 
 ## Configuration File Location
 
-For users who prefer to edit the YAML configuration file directly:
+For users who prefer to edit the YAML configuration file directly, it is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).
 
 - **Home Assistant App:** `/addon_configs/<addon_directory>/config.yml` — see [directory list](#accessing-app-config-dir)
 - **All other installations:** Map to `/config/config.yml` inside the container
@@ -57,7 +100,7 @@ VS Code supports JSON schemas for automatically validating configuration files.
 
 ## Environment Variable Substitution
 
-Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./reference.md). For example, the following values can be replaced at runtime by using environment variables:
+Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./advanced/reference.md). For example, the following values can be replaced at runtime by using environment variables:
 
 ```yaml
 mqtt:
@@ -92,7 +135,7 @@ genai:
 
 ## Common configuration examples
 
-Here are some common starter configuration examples. These can be configured through the Settings UI or via YAML. Refer to the [reference config](./reference.md) for detailed information about all config values.
+Here are some common starter configuration examples. These can be configured through the Settings UI or via YAML. Refer to the [reference config](./advanced/reference.md) for detailed information about all config values.
 
 ### Raspberry Pi Home Assistant App with USB Coral
 
@@ -113,7 +156,7 @@ Here are some common starter configuration examples. These can be configured thr
 3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
 4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-6. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
+6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
 7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
 
 </TabItem>
@@ -192,7 +235,7 @@ cameras:
 3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
 4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-6. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
+6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
 7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
 
 </TabItem>
@@ -270,7 +313,7 @@ cameras:
 4. On the same page, in the **Custom Model** tab, configure the OpenVINO model path and settings
 5. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 6. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-7. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
+7. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
 8. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
 
 </TabItem>

docs/docs/configuration/custom_classification/object_classification.md

@@ -149,9 +149,16 @@ For more detail, see [Frigate Tip: Best Practices for Training Face and Custom C
 - **The wizard is just the starting point**: You don't need to find and label every class upfront. Missing classes will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
 - **Preprocessing**: Ensure examples reflect object crops similar to Frigate's boxes; keep the subject centered.
-- **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
+- **Crop size**: Aim for crops of at least 100×100 pixels (a 10,000 pixel area). Crops smaller than ~80×80 get stretched 3-7× by the model's 224×224 input resize and tend to collapse into a generic "blob" region of feature space where identity becomes unreliable. If most of your detections are small because the camera is far from the subject, consider repositioning the camera for closer crops.
+- **Class balance**: Aim to keep your largest class within ~3× the count of your smallest. Beyond that, the model becomes biased toward the dominant class and tends to default borderline predictions to it (the "everything looks like Buddy" failure mode).
 - **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.
 
+:::tip `none` works differently from named classes
+
+Named classes work best with visually uniform examples — every Buddy photo should look like Buddy. The `none` class needs the opposite: visual diversity across sizes, framings, and qualities, because at inference it has to absorb everything that isn't one of your named classes. Don't apply the same "only keep large, well-framed images" rule to `none` that you would to a named class. Mix in small crops, partial views, and false positives deliberately - otherwise the model has no signal for "small/ambiguous thing = not one of my known classes" and will force those crops into a named class by default.
+
+:::
+
 ## Debugging Classification Models
 
 To troubleshoot issues with object classification models, enable debug logging to see detailed information about classification attempts, scores, and consensus calculations.

docs/docs/configuration/ffmpeg_presets.md

@@ -33,7 +33,7 @@ Select the appropriate hwaccel preset for your hardware.
 <TabItem value="ui">
 
 1. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to the appropriate preset for your hardware.
-2. To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and set **Hardware acceleration arguments** for that camera.
+2. To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and set **Hardware acceleration arguments** for that camera.
 
 </TabItem>
 <TabItem value="yaml">

docs/docs/configuration/genai/config.md

@@ -49,15 +49,14 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
 
 ### Model Types: Instruct vs Thinking
 
-Most vision-language models are available as **instruct** models, which are fine-tuned to follow instructions and respond concisely to prompts. However, some models (such as certain Qwen-VL or minigpt variants) offer both **instruct** and **thinking** versions.
+Vision-language models come in **instruct** variants (fine-tuned to follow instructions and respond concisely), **thinking** variants (fine-tuned for free-form, speculative reasoning), and **hybrid** variants that support both modes per request. Most modern vision-language models are hybrid.
 
-- **Instruct models** are always recommended for use with Frigate. These models generate direct, relevant, actionable descriptions that best fit Frigate's object and event summary use case.
-- **Reasoning / Thinking models** are fine-tuned for more free-form, open-ended, and speculative outputs, which are typically not concise and may not provide the practical summaries Frigate expects. For this reason, Frigate does **not** recommend or support using thinking models.
+Frigate manages reasoning per task automatically:
 
-Some models are labeled as **hybrid** (capable of both thinking and instruct tasks). In these cases, it is recommended to disable reasoning / thinking, which is generally model specific (see your models documentation).
+- **Description tasks** (object descriptions, review descriptions, review summaries) are synthesis-only and benefit from concise, direct output, so Frigate disables thinking for these calls when the model exposes a per-request toggle.
+- **Chat** lets you toggle thinking on or off from the composer when the configured model supports it.
 
-**Recommendation:**
-Always select the `-instruct` or documented instruct/tagged variant of any model you use in your Frigate configuration. If in doubt, refer to your model provider's documentation or model library for guidance on the correct model variant to use.
+You can use a pure instruct, hybrid, or thinking-capable model with Frigate — no extra configuration is required to disable thinking for descriptions.
 
 ### llama.cpp
 

docs/docs/configuration/go2rtc.md

@@ -0,0 +1,70 @@
+---
+id: go2rtc
+title: go2rtc
+---
+
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+Frigate uses the bundled go2rtc to power a number of key features:
+
+- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
+- Live stream support for cameras in Home Assistant Integration
+- RTSP relay for use with other consumers to reduce the number of connections to your camera streams
+
+:::tip[Most users no longer need to configure go2rtc by hand]
+
+The **camera setup wizard** is the recommended way to add cameras. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />, and the wizard probes your camera and writes its configuration for you — including the go2rtc restream and the live stream mapping — so go2rtc is set up automatically.
+
+This guide is mainly useful if you are **upgrading from an older version and have existing cameras that don't yet use go2rtc**, or if you want to fine-tune a stream by hand (for example, to transcode a codec your browser can't play). The [go2rtc troubleshooting guide](/troubleshooting/go2rtc) applies regardless of how your cameras were added.
+
+:::
+
+## Adding a go2rtc stream manually
+
+If you added your cameras with the wizard, go2rtc is already configured — you can skip straight to [troubleshooting](/troubleshooting/go2rtc). The steps below are for upgrading users with existing cameras that aren't using go2rtc yet, or for anyone who prefers to configure a stream by hand.
+
+Configure go2rtc to connect to your camera by adding the stream you want to use for live view. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#module-streams), not just rtsp.
+
+:::tip
+
+For the best experience, set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
+
+See [the live view docs](/configuration/live#setting-streams-for-live-ui) for more information.
+
+:::
+
+<ConfigTabs>
+<TabItem value="ui">
+
+Navigate to <NavPath path="Settings > System > go2rtc Streams" /> and click **Add stream**. Give the stream a name (use the camera's name so Frigate can auto-map it - for example, if your camera's name is `back`, use `back` as the go2rtc stream name), then paste the camera's stream URL into the **Source** field. Save the section.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+go2rtc:
+  streams:
+    back:
+      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+```
+
+</TabItem>
+</ConfigTabs>
+
+After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
+
+### Next steps
+
+1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
+2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
+3. If your camera supports two-way talk, you must configure your stream with `#backchannel=0` to prevent go2rtc from blocking other applications from accessing the camera's audio output. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
+
+## Troubleshooting
+
+If your stream won't play, has no audio, uses excessive CPU, or otherwise misbehaves, see the dedicated [go2rtc troubleshooting guide](/troubleshooting/go2rtc). It walks through how to isolate where the problem is and covers the most common issues — unsupported codecs, H.265/HEVC, audio, WebRTC and two-way talk, hardware-accelerated transcoding with FFmpeg 8, and camera-specific quirks.
+
+## Homekit Configuration
+
+To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to share export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.

docs/docs/configuration/hardware_acceleration_video.md

@@ -72,7 +72,7 @@ Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video
 
 :::note
 
-The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
+The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
 
 See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/000005505/processors.html) to figure out what generation your CPU is.
 
@@ -85,7 +85,7 @@ VAAPI supports automatic profile selection so it will work automatically with bo
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">
@@ -105,7 +105,7 @@ ffmpeg:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">
@@ -123,7 +123,7 @@ ffmpeg:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">
@@ -169,7 +169,7 @@ Frigate can utilize modern AMD integrated GPUs and AMD GPUs to accelerate video
 
 ### Configuring Radeon Driver
 
-You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
+You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
 
 ### Via VAAPI
 
@@ -178,7 +178,7 @@ VAAPI supports automatic profile selection so it will work automatically with bo
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">
@@ -193,7 +193,7 @@ ffmpeg:
 
 ## NVIDIA GPUs
 
-While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
+While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced/system#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
 
 A more complete list of cards and their compatible drivers is available in the [driver release readme](https://download.nvidia.com/XFree86/Linux-x86_64/525.85.05/README/supportedchips.html).
 
@@ -237,7 +237,7 @@ Using `preset-nvidia` ffmpeg will automatically select the necessary profile for
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">
@@ -300,7 +300,7 @@ If you are using the HA App, you may need to use the full access variant and tur
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">
@@ -420,7 +420,7 @@ For example, for H264 video, you'll select `preset-jetson-h264`.
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">
@@ -452,7 +452,7 @@ Set the FFmpeg hwaccel preset to enable hardware video processing.
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">
@@ -519,7 +519,7 @@ Set the FFmpeg hwaccel args to enable hardware video processing.
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
 
 </TabItem>
 <TabItem value="yaml">

docs/docs/configuration/license_plate_recognition.md

@@ -363,7 +363,7 @@ An example configuration for a dedicated LPR camera using a `license_plate`-dete
 
 Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available).
 
-Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.
+Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
 
 Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
 
@@ -475,7 +475,7 @@ Navigate to <NavPath path="Settings > Camera configuration > License plate recog
 | **Enable LPR**        | Set to on                                                                        |
 | **Enhancement level** | Set to `3` (optional — enhances the image before trying to recognize characters) |
 
-Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.
+Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
 
 Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
 

docs/docs/configuration/live.md

@@ -11,7 +11,7 @@ Frigate intelligently displays your camera streams on the Live view dashboard. B
 
 ### Live View technologies
 
-Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be configured as shown in the [step by step guide](/guides/configuring_go2rtc).
+Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be [configured](/configuration/go2rtc).
 
 The jsmpeg live view will use more browser and client GPU resources. Using go2rtc is highly recommended and will provide a superior experience.
 
@@ -88,8 +88,18 @@ Configure a "friendly name" for your stream followed by the go2rtc stream name.
 <ConfigTabs>
 <TabItem value="ui">
 
-1. Navigate to <NavPath path="Settings > Camera configuration > Live playback" />, then select your camera.
-   - Under **Live stream names**, add entries mapping a friendly name to each go2rtc stream name (e.g., `Main Stream` mapped to `test_cam`, `Sub Stream` mapped to `test_cam_sub`).
+1. Navigate to <NavPath path="Settings > Camera configuration > Live playback" /> and select your camera.
+2. Under **Live stream names**, click **Add stream** to add a new entry.
+3. In the **Stream name** field, enter a friendly name that will appear in the Live UI's stream dropdown (e.g., `Main Stream`).
+4. In the **go2rtc stream** field, open the dropdown and select the go2rtc stream this name should map to (e.g., `test_cam`). The dropdown lists every stream configured under `go2rtc.streams`. If the go2rtc stream hasn't been created yet, you can type the name and choose **Use "..."** to save a custom value.
+5. Repeat for each additional stream you want to expose (e.g., `Sub Stream` → `test_cam_sub`).
+6. Use the trash icon on a row to remove a stream, then **Save** the section.
+
+:::tip
+
+Configure your go2rtc streams first under <NavPath path="Settings > System > go2rtc streams" /> so the dropdown is populated with valid options.
+
+:::
 
 </TabItem>
 <TabItem value="yaml">
@@ -257,19 +267,47 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
-### Disabling cameras
+### Camera state
 
-Cameras can be temporarily disabled through the Frigate UI and through [MQTT](/integrations/mqtt#frigatecamera_nameenabledset) to conserve system resources. When disabled, Frigate's ffmpeg processes are terminated — recording stops, object detection is paused, and the Live dashboard displays a blank image with a disabled message. Review items, tracked objects, and historical footage for disabled cameras can still be accessed via the UI.
+Each camera has three possible states, surfaced as a status selector in **Settings → Global configuration → Camera management**:
 
-:::note
+- **On** — streams are processed normally. Object detection, recording, and Live view are active.
+- **Off** — Frigate's ffmpeg processes are paused. Recording stops, object detection is paused, and the Live dashboard displays a blank image with a "Camera is off" message. The camera is still visible in the Live dashboard and its past review items, tracked objects, and historical footage remain accessible via the UI. The Off state persists across Frigate restarts via a `.runtime_state.json` file alongside `config.yml` (see [Runtime toggle persistence](#runtime-toggle-persistence)).
+- **Disabled** — the change is saved to your configuration file (`enabled: False`). The camera stops immediately, Frigate stops ffmpeg processes, and all live and historical UI elements for the camera are no longer visible but remains retained on disk. The camera is still listed in **Settings → Global configuration → Camera management** so it can be re-enabled. **A restart of Frigate is required to bring a disabled camera back to On.**
 
-Disabling a camera via the Frigate UI or MQTT is temporary and does not persist through restarts of Frigate.
+#### Turning a camera on or off
 
-:::
+Turning a camera off is temporary and does not require a restart. The available controls are:
 
-For restreamed cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
+- The power button in the single-camera Live view header
+- The right-click context menu on a camera tile on the Live dashboard
+- The Camera management settings pane (status set to **Off**)
+- The mobile settings drawer on the single-camera Live view (admin users only)
+- The [MQTT topic](/integrations/mqtt#frigatecamera_nameenabledset) `frigate/<camera_name>/enabled/set` with payload `ON` or `OFF`
+- The Home Assistant integration via the [`camera.turn_on` / `camera.turn_off` actions](/integrations/home-assistant#camera-api)
 
-Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
+#### Disabling a camera
+
+Disabling a camera saves the change to your configuration file. Navigate to **Settings → Global configuration → Camera management** and set the camera's status to **Disabled**. Runtime processing stops immediately; the change persists across restarts.
+
+Re-enabling a disabled camera requires a restart of Frigate so that the ffmpeg processes and other camera-scoped resources can be initialized. The UI will prompt you to restart when you switch a disabled camera back to On.
+
+#### Restream behavior
+
+For both Off and Disabled cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
+
+#### Choosing Off versus Disabled
+
+If you want a camera's historical data (review items, tracked objects, footage) to stay accessible in the UI while you stop processing, set the camera to **Off**. If you want the camera fully removed from the Live dashboard, review filters, and other UI surfaces, set it to **Disabled**. The Disabled state still keeps the camera in Camera management so it can be re-enabled later; if you want to remove all traces of a camera including its configuration, delete it via Camera management instead.
+
+#### Runtime toggle persistence
+
+The Live view toggles for **camera on/off**, **detect**, **recordings**, **snapshots**, and **audio detection** — along with the equivalent MQTT `/set` topics — write the new state to `.runtime_state.json` next to your `config.yml`. The file is replayed on Frigate startup so your last-known toggle states survive a restart. Two interactions worth knowing:
+
+- **Settings UI saves win.** When you save a field through **Settings → Global configuration**, the matching entry is cleared from `.runtime_state.json` so the new value in your config file is the durable source.
+- **Switching profiles clears all runtime overrides.** Activating or deactivating a [profile](/configuration/profiles) is treated as a deliberate state change, so the file is wiped to avoid stale overrides replaying on top of the new profile.
+
+If you hand-edit `config.yml` while runtime overrides exist, the overrides will still replay on restart. Delete `.runtime_state.json` to reset to the YAML-defined defaults.
 
 ### Live player error messages
 
@@ -333,7 +371,7 @@ When your browser runs into problems playing back your camera streams, it will l
    - Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
    - Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
    - Test with a different stream via the UI dropdown (if `live -> streams` is configured).
-   - For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see (WebRTC Extra Configuration)(#webrtc-extra-configuration)).
+   - For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
    - If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
 
 3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
@@ -394,3 +432,5 @@ When your browser runs into problems playing back your camera streams, it will l
              roles:
                - detect
    ```
+
+   The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).

docs/docs/configuration/motion_detection.md

@@ -197,3 +197,7 @@ This option is handy when you want to prevent large transient changes from trigg
 When the skip threshold is exceeded, **no motion is reported** for that frame, meaning **nothing is recorded** for that frame. That means you can miss something important, like a PTZ camera auto-tracking an object or activity while the camera is moving. If you prefer to guarantee that every frame is saved, leave this unset and accept occasional recordings containing scene noise — they typically only take up a few megabytes and are quick to scan in the timeline UI.
 
 :::
+
+## Reviewing Detected Motion
+
+To review what the detector picked up — or to search past recordings for motion in a specific region — see [Reviewing Motion](/usage/review#reviewing-motion) on the Review page.

docs/docs/configuration/object_detectors.md

@@ -8,10 +8,13 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
 
-# Supported Hardware
+### Supported hardware
+
+Object detection is what allows Frigate to identify _what_ is in your camera's view — people, cars, animals, and more — rather than just reacting to pixel changes. When Frigate's motion detection finds activity in a frame, that region is sent to an **object detector**, which returns the objects it recognizes along with their location and a confidence score. These detections are what drive tracked objects, alerts, detections, and notifications.
+
+Object detection is computationally intensive, so Frigate is designed to run it on a dedicated AI accelerator or GPU rather than the CPU. A **detector** is the specific hardware-and-model backend Frigate uses to run inference. Choosing a detector that matches your hardware is one of the most important steps in getting good performance, and the right choice depends on what device Frigate is running on.
 
 :::info
-
 Frigate supports multiple different detectors that work on different types of hardware:
 
 **Most Hardware**
@@ -660,7 +663,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
 
 #### RF-DETR
 
-[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more informatoin on downloading the RF-DETR model for use in Frigate.
+[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more information on downloading the RF-DETR model for use in Frigate.
 
 :::warning
 

docs/docs/configuration/objects.md

@@ -158,4 +158,4 @@ Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use yo
 - EdgeTPU Model: `/edgetpu_model.tflite`
 - Labels: `/labelmap.txt`
 
-You also need to update the [model config](advanced.md#model) if they differ from the defaults.
+You also need to update the [model config](advanced/system.md#model) if they differ from the defaults.

docs/docs/configuration/profiles.md

@@ -33,10 +33,10 @@ The easiest way to define profiles is to use the Frigate UI. Profiles can also b
 <ConfigTabs>
 <TabItem value="ui">
 
-1. **Create a profile** — Navigate to <NavPath path="Settings > Camera configuration > Profiles" />. Click the **Add Profile** button, enter a name (and optionally a profile ID).
+1. **Create a profile** — Navigate to <NavPath path="Settings > Global configuration > Profiles" />. Click the **Add Profile** button, enter a name (and optionally a profile ID).
 2. **Configure overrides** — Navigate to a camera configuration section (e.g. Motion detection, Record, Notifications). In the top right, two buttons will appear - choose a camera and a profile from the profile selector to edit overrides for that camera and section. Only the fields you change will be stored as overrides — fields that require a restart are hidden since profiles are applied at runtime. You can click the **Remove Profile Override** button to clear overrides.
-3. **Activate a profile** — Use the **Profiles** option in Frigate's main menu to choose a profile. Alternatively, in Settings, navigate to <NavPath path="Settings > Camera configuration > Profiles" />, then choose a profile in the Active Profile dropdown to activate it. The active profile is also shown in the status bar at the bottom of the screen on desktop browsers.
-4. **Delete a profile** — Navigate to <NavPath path="Settings > Camera configuration > Profiles" />, then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.
+3. **Activate a profile** — Use the **Profiles** option in Frigate's main menu to choose a profile. Alternatively, in Settings, navigate to <NavPath path="Settings > Global configuration > Profiles" />, then choose a profile in the Active Profile dropdown to activate it. The active profile is also shown in the status bar at the bottom of the screen on desktop browsers.
+4. **Delete a profile** — Navigate to <NavPath path="Settings > Global configuration > Profiles" />, then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.
 
 </TabItem>
 <TabItem value="yaml">
@@ -126,7 +126,11 @@ Only the fields you explicitly set in a profile override are applied. All other
 
 ## Activating Profiles
 
-Profiles can be activated and deactivated from the Frigate UI. Open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
+Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), or the Home Assistant integration.
+
+In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
+
+Activating or deactivating a profile clears any [runtime toggle overrides](/configuration/live#runtime-toggle-persistence) so the profile's settings aren't silently undone by a stale toggle from before the switch.
 
 ## Example: Home / Away Setup
 
@@ -135,10 +139,10 @@ A common use case is having different detection and notification settings based
 <ConfigTabs>
 <TabItem value="ui">
 
-1. Navigate to <NavPath path="Settings > Camera configuration > Profiles" /> and create two profiles: **Home** and **Away**.
+1. Navigate to <NavPath path="Settings > Global configuration > Profiles" /> and create two profiles: **Home** and **Away**.
 2. From to the Camera configuration section in Settings, choose the **front_door** camera, and select the **Away** profile from the profile dropdown. Then, enable notifications from the Notifications pane, and set alert labels to `person` and `car` from the Review pane. Then, from the profile dropdown choose **Home** profile, then navigate to Notifications to disable notifications.
 3. For the **indoor_cam** camera, perform similar steps - configure the **Away** profile to enable the camera, detection, and recording. Configure the **Home** profile to disable the camera entirely for privacy.
-4. Activate the desired profile from <NavPath path="Settings > Camera configuration > Profiles" /> or from the **Profiles** option in Frigate's main menu.
+4. Activate the desired profile from <NavPath path="Settings > Global configuration > Profiles" /> or from the **Profiles** option in Frigate's main menu.
 
 </TabItem>
 <TabItem value="yaml">
@@ -207,3 +211,27 @@ In this example:
 - **Away profile**: The front door camera enables notifications and tracks specific alert labels. The indoor camera is fully enabled with detection and recording.
 - **Home profile**: The front door camera disables notifications. The indoor camera is completely disabled for privacy.
 - **No profile active**: All cameras use their base configuration values.
+
+## FAQ
+
+### Can I define a zone or mask in a profile but not have it in the base config?
+
+No. Profiles are pure overrides. Every zone and mask defined under a profile must reference an entry that already exists on the base camera config. Configurations that introduce profile-only zones or masks are rejected at startup.
+
+If you want a zone or mask to be active only under a specific profile, define it on the base config with `enabled: false`, then enable it in that profile's overrides.
+
+### How do I revert a profile zone or mask override back to the base configuration?
+
+Delete the override. In the Frigate UI, edit the profile and use the "Revert override" action (the trash can icon) on the zone or mask. The base entry is left untouched, and once the override is removed the profile inherits the base values for that zone or mask.
+
+### Can multiple profiles be active at the same time?
+
+No. Only one profile can be active at a time. Activating a new profile automatically deactivates the current one.
+
+### What happens to my profile overrides if I delete a zone or mask from the base?
+
+When you delete a base zone or mask in the Frigate UI, any profile overrides for that entry are deleted automatically as part of the same operation. If you remove a base entry by editing your config file directly and leave a profile override behind, the config will fail validation at startup until the orphaned override is removed as well.
+
+### Why are some settings missing when I configure a profile override?
+
+Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.

docs/docs/configuration/record.md

@@ -11,6 +11,12 @@ Recordings can be enabled and are stored at `/media/frigate/recordings`. The fol
 
 New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
 
+:::tip
+
+To keep a specific clip beyond your retention window, [export](/usage/exports) it rather than increasing retention for the whole camera. Exports are saved separately and are never removed by retention.
+
+:::
+
 H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other browsers require recordings to be encoded with H264.
 
 ## Common recording configurations

docs/docs/configuration/restream.md

@@ -61,7 +61,7 @@ Configure the go2rtc stream and point the camera inputs at the local restream.
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera. Then navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> for each camera and set the input paths to use the local restream URL (`rtsp://127.0.0.1:8554/<camera_name>`).
+Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera. Then navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> for each camera. For each input, choose **Restream (go2rtc)** and pick the matching stream from the dropdown — Frigate uses the local restream URL (`rtsp://127.0.0.1:8554/<camera_name>`) and the `preset-rtsp-restream` input args for that input automatically. (Choose **Manual input path** instead to type a URL directly.)
 
 </TabItem>
 <TabItem value="yaml">
@@ -111,7 +111,7 @@ Two connections are made to the camera. One for the sub stream, one for the rest
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera and its sub stream. Then navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> for each camera and configure separate inputs for the main and sub streams using the local restream URLs.
+Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera and its sub stream. Then navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> for each camera and add separate inputs for the main and sub streams. Set each input's source to **Restream (go2rtc)** and pick the matching stream from the dropdown — Frigate uses the local restream URL and the `preset-rtsp-restream` input args for that input automatically.
 
 </TabItem>
 <TabItem value="yaml">

docs/docs/configuration/review.md

@@ -23,7 +23,7 @@ In 0.14 and later, all of that is bundled into a single review item which starts
 
 ## Alerts and Detections
 
-Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate 0.14 categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.
+Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.
 
 :::note
 
@@ -130,3 +130,7 @@ By default a review item will be created if any `review -> alerts -> labels` and
 Because zones don't apply to audio, audio labels will always be marked as a detection by default.
 
 :::
+
+## Reviewing Motion
+
+The Review page can also surface periods of motion that didn't produce a tracked object, and lets you search past recordings for motion in a region you draw. See [Reviewing Motion](/usage/review#reviewing-motion) in the Usage docs for how to use **Motion Previews** and **Motion Search**, and [Tuning Motion Detection](motion_detection.md) for configuring the underlying motion detector.

docs/docs/configuration/semantic_search.md

@@ -222,12 +222,7 @@ See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_
 
 ## Usage and Best Practices
 
-1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
-2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
-3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the the ones without them are more relevant.
-4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
-5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
-6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.
+For tips on getting the best results from Semantic Search — choosing between thumbnail and description search, phrasing queries effectively, and combining search with the other Explore filters — see [Usage and best practices](/usage/explore#usage-and-best-practices) in the Usage docs.
 
 ## Triggers
 

docs/docs/configuration/snapshots.md

@@ -7,13 +7,17 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
 
-Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>-clean.webp`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)
+A snapshot is a single still image that captures a tracked object at its best moment — the clearest frame Frigate saw while following that object across the scene. Unlike a [recording](./record.md), which is continuous video, a snapshot is one representative image saved per tracked object once tracking ends.
 
-Snapshots are accessible in the UI in the Explore pane. This allows for quick submission to the Frigate+ service.
+When snapshots are enabled, Frigate saves one image to `/media/frigate/clips` for each tracked object, named `<camera>-<id>-clean.webp`. A clean image is always stored without any annotations (no timestamp, bounding boxes, or cropping) so you have an unmodified copy of the original frame. Annotations like bounding boxes and timestamps are applied on demand when a snapshot is requested [via the HTTP API](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx) — see [Rendering](#rendering) below.
 
-To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones)
+A few things to keep in mind:
 
-Snapshots sent via MQTT are configured separately under the camera MQTT settings, not here.
+- Snapshots are saved per tracked object, so a camera with no detected objects produces no snapshots even if recording is enabled.
+- Snapshots and recordings are configured and retained independently — enabling one does not enable the other.
+- Snapshots are accessible in the UI in the Explore pane, which allows for quick submission to the Frigate+ service.
+- To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones).
+- Snapshots sent via MQTT are configured separately under the camera MQTT settings, not here.
 
 ## Enabling Snapshots
 
@@ -107,7 +111,6 @@ Navigate to <NavPath path="Settings > Global configuration > Snapshots" />.
 | Field                                              | Description                                                                         |
 | -------------------------------------------------- | ----------------------------------------------------------------------------------- |
 | **Snapshot retention > Default retention**         | Number of days to retain snapshots (default: 10)                                    |
-| **Snapshot retention > Retention mode**            | Retention mode: `all`, `motion`, or `active_objects`                                |
 | **Snapshot retention > Object retention > Person** | Per-object overrides for retention days (e.g., keep `person` snapshots for 15 days) |
 
 </TabItem>
@@ -118,7 +121,6 @@ snapshots:
   enabled: True
   retain:
     default: 10
-    mode: motion
     objects:
       person: 15
 ```

docs/docs/frigate/camera_setup.md

@@ -5,7 +5,7 @@ title: Camera setup
 
 Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Firefox 134+/136+/137+ (Windows/Mac/Linux & Android), Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
 
-- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher (10fps is the recommended maximum for most users) for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
+- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The default frame rate of 5fps is correct for almost all cameras and rarely needs to be changed; see [Choosing a detect frame rate](#choosing-a-detect-frame-rate). Higher resolutions and frame rates will drive higher CPU usage on your server.
 
 - **Recording**: This stream should be the resolution you wish to store for reference. Typically, this will be the highest resolution your camera supports. I recommend setting this feed in your camera's firmware to 15 fps.
 
@@ -25,6 +25,44 @@ Larger resolutions **do** improve performance if the objects are very small in t
 
 ![Resolutions](/img/resolutions-min.jpg)
 
+### Choosing a detect frame rate
+
+`detect.fps` controls how many times per second Frigate runs object detection — it does **not** need to match your camera's frame rate. The default of **5** is correct for the vast majority of cameras.
+
+:::warning
+
+Most users who raise `detect.fps` above the default don't need to. Increasing it consumes more CPU/GPU (detection load scales directly with the frame rate) while providing **no benefit to tracking** once objects are already being followed smoothly. Leave it at **5** unless you have a specific scene that fails the test below, and confirm any change actually helps in the debug view.
+
+:::
+
+#### Why 5 is enough for almost everyone
+
+Frigate follows an object by matching its bounding box from one detection frame to the next, which requires the object to be detected often enough while it is on screen. At 5 fps this is satisfied in normal scenes: an object crossing a yard, porch, driveway, or walkway is in view for several seconds and produces ~15 or more detections, which is more than enough for a reliable track and a good snapshot. This includes fast subjects such as a running person or a bolting pet, which on a wide-angle view remain on screen for several seconds.
+
+A higher rate helps only when an object crosses the **entire frame in less than two seconds**, which is determined by camera framing rather than object speed - for example, a camera aimed down a street at fast cross-traffic. In those scenes 5 fps may produce too few detections to hold a track. Cameras covering normal approaches and open areas are unaffected.
+
+#### Checking whether a higher rate is needed
+
+Estimate how long an object is visible as it crosses the area of interest, aiming for roughly 8–10 detections during the pass:
+
+> **`detect.fps` ≈ 10 ÷ (seconds the object is in view)**
+
+Most objects — people walking or running, pets, and vehicles in a yard, driveway, or walkway — stay in view for two seconds or more, so the default of 5 fps is correct. Slowly try raising it to 10 (the recommended maximum) in increments only when objects routinely cross the entire frame in about a second, such as a camera aimed at a street or sidewalk with fast cross-traffic. Objects that transit in under a second cannot be tracked reliably at any practical rate, so reposition the camera instead.
+
+:::tip
+
+If the formula calls for more than 10, the fix is **camera placement, not frame rate**. Angle the camera so objects move toward it rather than across the view, or aim it where traffic slows. A higher `detect.fps` increases CPU load proportionally without producing more detections of a too-brief object.
+
+:::
+
+#### Verify in the debug view
+
+Confirm any change in the Debug view or Debug Replay. Watch a typical object cross the scene: if its bounding box follows it smoothly while visible, the rate is sufficient. A box that jumps erratically, drops out, or splits one object into multiple events indicates the rate should be increased one step.
+
+#### Dedicated LPR cameras
+
+A dedicated license plate recognition camera is the most common reason to use something higher than 5 fps: the camera is highly zoomed, the plate is small, and it moves at full vehicle speed, so it transits the frame quickly. However, the same ceiling applies: above 10 fps is unnecessary, and **placement matters most**: aim LPR cameras where vehicles slow down, such as gates, driveways, and parking entrances. A tight view of a fast through-road will not likely read plates reliably at any frame rate. See [License Plate Recognition](/configuration/license_plate_recognition) for details.
+
 ### Example Camera Configuration
 
 For the Dahua/Loryta 5442 camera, I use the following settings:

docs/docs/frigate/glossary.md

@@ -5,20 +5,40 @@ title: Glossary
 
 The glossary explains terms commonly used in Frigate's documentation.
 
+## Alert
+
+The higher-priority of the two [review item](#review-item) severities, the other being a [detection](#detection). By default a review item is an alert when it involves a `person` or `car`; the qualifying [labels](#label) and [zones](#zone) can be configured. [See the review docs for more info](/configuration/review)
+
+## Attribute
+
+A property detected on an [object](#object) that exists alongside its [label](#label). Unlike a [sub label](#sub-label), an object can carry several attributes at once. Some attributes come directly from the object detection [model](#model) — for example `face`, `license_plate`, or delivery carrier logos such as `amazon`, `ups`, and `fedex` — while others come from a [custom object classification model](/configuration/custom_classification/object_classification) configured with the `attribute` type. Attributes are visible in the Tracked Object Details pane in Explore, in `frigate/events` MQTT messages, and through the HTTP API.
+
 ## Bounding Box
 
-A box returned from the object detection model that outlines an object in the frame. These have multiple colors depending on object type in the debug live view.
+A box returned by the object detection [model](#model) that outlines a detected [object](#object) in the frame. In the Debug view, bounding boxes are colored by object [label](#label).
 
 ### Bounding Box Colors
 
 - At startup different colors will be assigned to each object label
 - A dark blue thin line indicates that object is not detected at this current point in time
 - A gray thin line indicates that object is detected as being stationary
-- A thick line indicates that object is the subject of autotracking (when enabled).
+- A thick line indicates that object is the subject of autotracking (when enabled)
+
+## Class
+
+The categories a classification [model](#model) is trained to distinguish between. Each class is a distinct visual category the model predicts, plus a `none` class for inputs that don't fit any category. For example, a custom object classification model for `person` objects might use the classes `delivery_person`, `resident`, and `none`. The predicted class is applied to the [object](#object) as either a [sub label](#sub-label) or an [attribute](#attribute), depending on the model's configuration. [See the object classification docs for more info](/configuration/custom_classification/object_classification)
+
+## Detection
+
+The lower-priority of the two [review item](#review-item) severities, the other being an [alert](#alert). By default, any review item that does not qualify as an alert is a detection; the qualifying [labels](#label) and [zones](#zone) can be configured. Despite the name, a detection is a category of review item — not the same as the object detection performed by the [model](#model). [See the review docs for more info](/configuration/review)
 
 ## False Positive
 
-An incorrect detection of an object type. For example a dog being detected as a person, a chair being detected as a dog, etc. A person being detected in an area you want to ignore is not a false positive.
+An incorrect result from the object detection [model](#model), where it assigns the wrong [label](#label) to something in the frame — for example a dog identified as a person, or a chair identified as a dog. A person correctly identified in an area you want to ignore is not a false positive.
+
+## Label
+
+The type assigned to a detected [object](#object) by the object detection [model](#model), drawn from the model's labelmap — for example `person`, `car`, or `dog`. Frigate tracks `person` by default; additional labels are tracked by adding them to the objects configuration. [See the available objects docs for the full list](/configuration/objects)
 
 ## Mask
 
@@ -26,44 +46,56 @@ There are two types of masks in Frigate. [See the mask docs for more info](/conf
 
 ### Motion Mask
 
-Motion masks prevent detection of [motion](#motion) in masked areas from triggering Frigate to run object detection, but do not prevent objects from being detected if object detection runs due to motion in nearby areas. For example: camera timestamps, skies, the tops of trees, etc.
+A motion mask stops [motion](#motion) in the masked area from triggering object detection. It does not stop an object from being detected when object detection runs because of motion in a nearby area. Use motion masks for parts of the frame that change constantly but never contain objects you care about — camera timestamps, the sky, the tops of trees, and so on.
 
 ### Object Mask
 
-Object filter masks drop any bounding boxes where the bottom center (overlap doesn't matter) is in the masked area. It forces them to be considered a [false positive](#false-positive) so that they are ignored.
+An object filter mask drops any [bounding box](#bounding-box) whose bottom center falls inside the masked area (overlap elsewhere doesn't matter). The object is forced to be treated as a [false positive](#false-positive) and ignored.
 
 ## Min Score
 
-The lowest score that an object can be detected with during tracking, any detection with a lower score will be assumed to be a false positive
+The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
+
+## Model
+
+A machine learning model that Frigate uses to detect or classify objects. The object detection model locates [objects](#object) in each frame and returns their [labels](#label) and [bounding boxes](#bounding-box). Additional enrichment models run on tracked objects to add detail: face recognition, license plate recognition, bird classification, custom object and state classification, and the embedding models used for semantic search. [See the object detectors docs for more info](/configuration/object_detectors)
 
 ## Motion
 
-When pixels in the current camera frame are different than previous frames. When many nearby pixels are different in the current frame they grouped together and indicated with a red motion box in the live debug view. [See the motion detection docs for more info](/configuration/motion_detection)
+A change in pixels between the current camera frame and previous frames. When many nearby pixels change together, they are grouped and shown as a red motion box in the debug live view. [See the motion detection docs for more info](/configuration/motion_detection)
+
+## Object
+
+Something Frigate can detect and follow in a camera frame, identified by its [label](#label) (for example a person or a car). The object types Frigate watches for are set in the `objects` configuration. Once an object is detected and followed across frames it becomes a [tracked object](#tracked-object-event-in-previous-versions), which may also carry a [sub label](#sub-label) and [attributes](#attribute). [See the available objects docs for more info](/configuration/objects)
 
 ## Region
 
-A portion of the camera frame that is sent to object detection, regions can be sent due to motion, active objects, or occasionally for stationary objects. These are represented by green boxes in the debug live view.
+A portion of the camera frame sent to the object detection [model](#model). Regions are selected because of [motion](#motion), active objects, or occasionally to recheck stationary objects, and are shown as green boxes in the debug live view.
 
 ## Review Item
 
-A review item is a time period where any number of events/tracked objects were active. [See the review docs for more info](/configuration/review)
+A period of time during which one or more [tracked objects](#tracked-object-event-in-previous-versions) were active, grouped together for review. Each review item is categorized as either an [alert](#alert) or a [detection](#detection). [See the review docs for more info](/configuration/review)
 
 ## Snapshot Score
 
-The score shown in a snapshot is the score of that object at that specific moment in time.
+The object's score at the specific moment the snapshot was captured.
+
+## Sub Label
+
+A more specific identity assigned to a [tracked object](#tracked-object-event-in-previous-versions) in addition to its [label](#label). A `person` may get the name of a recognized face, a `car` may get the name of a known license plate, and a `bird` may get its species. An object can have only one sub label at a time. Sub labels are produced by face recognition, license plate recognition, bird classification, custom object classification configured with the `sub label` type, and semantic search triggers.
 
 ## Threshold
 
-The threshold is the median score that an object must reach in order to be considered a true positive.
+The median score an object must reach to be considered a true positive.
 
 ## Top Score
 
-The top score for an object is the highest median score for an object.
+The highest median score an object reached over its lifetime.
 
 ## Tracked Object ("event" in previous versions)
 
-The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Tracked objects are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
+An [object](#object) followed from the moment it enters the frame until it leaves, including any time it stays still. A tracked object is saved once it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording.
 
 ## Zone
 
-Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
+A user-defined area of interest within the camera frame. Zones can be used for notifications and to limit where Frigate creates a [review item](#review-item). [See the zone docs for more info](/configuration/zones)

docs/docs/frigate/installation.md

@@ -600,7 +600,7 @@ There are several variants of the App available:
 
 If you are using hardware acceleration for ffmpeg, you **may** need to use the _Full Access_ variant of the App. This is because the Frigate App runs in a container with limited access to the host system. The _Full Access_ variant allows you to disable _Protection mode_ and give Frigate full access to the host system.
 
-You can also edit the Frigate configuration file through the [VS Code App](https://github.com/hassio-addons/addon-vscode) or similar. In that case, the configuration file will be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate App you are running. See the list of directories [here](../configuration/index.md#accessing-app-config-dir).
+You can also edit the Frigate configuration file through the [VS Code App](https://github.com/hassio-addons/addon-vscode) or similar. In that case, the configuration file will be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate App you are running. See the list of directories [here](../configuration/config.md#accessing-app-config-dir).
 
 ## Kubernetes
 
@@ -749,7 +749,7 @@ Failure to remap port 5000 on the host will result in the WebUI and all API endp
 
 :::
 
-Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native swift app). The difference in inference speeds is negligable, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
+Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native Swift app). The difference in inference speeds is negligible, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
 
 To allow Frigate to use the Apple Silicon Neural Engine / Processing Unit (NPU) the host must be running [Apple Silicon Detector](../configuration/object_detectors.md#apple-silicon-detector) on the host (outside Docker)
 
@@ -768,7 +768,7 @@ services:
       - /path/to/your/recordings:/recordings
     ports:
       - "8971:8971"
-      # If exposing on macOS map to a diffent host port like 5001 or any orher port with no conflicts
+      # If exposing on macOS map to a different host port like 5001 or any other port with no conflicts
       # - "5001:5000" # Internal unauthenticated access. Expose carefully.
       - "8554:8554" # RTSP feeds
     extra_hosts:

docs/docs/frigate/network_requirements.md

@@ -32,7 +32,7 @@ The following models are downloaded automatically the first time their associate
 | [License plate recognition](/configuration/license_plate_recognition)                         | PaddleOCR (detection, classification, recognition) + YOLOv9 plate detector | GitHub               |
 | [Bird classification](/configuration/bird_classification)                                     | MobileNetV2 bird model + label map                                         | GitHub               |
 | [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras)                              | Google storage       |
-| [Audio transcription](/configuration/advanced)                                                | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
+| [Audio transcription](/configuration/advanced/system)                                                | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
 
 ### Hardware-Specific Detector Models
 

docs/docs/guides/configuring_go2rtc.md

@@ -1,116 +0,0 @@
----
-id: configuring_go2rtc
-title: Configuring go2rtc
----
-
-Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect directly to your cameras. However, adding go2rtc to your configuration is required for the following features:
-
-- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
-- Live stream support for cameras in Home Assistant Integration
-- RTSP relay for use with other consumers to reduce the number of connections to your camera streams
-
-## Setup a go2rtc stream
-
-First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#module-streams), not just rtsp.
-
-:::tip
-
-For the best experience, you should set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
-
-See [the live view docs](../configuration/live.md#setting-streams-for-live-ui) for more information.
-
-:::
-
-```yaml
-go2rtc:
-  streams:
-    back:
-      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-```
-
-After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
-
-### What if my video doesn't play?
-
-- Check Logs:
-  - Access the go2rtc logs in the Frigate UI under Logs in the sidebar.
-  - If go2rtc is having difficulty connecting to your camera, you should see some error messages in the log.
-
-- Check go2rtc Web Interface: if you don't see any errors in the logs, try viewing the camera through go2rtc's web interface.
-  - Navigate to port 1984 in your browser to access go2rtc's web interface.
-    - If using Frigate through Home Assistant, enable the web interface at port 1984.
-    - If using Docker, forward port 1984 before accessing the web interface.
-  - Click `stream` for the specific camera to see if the camera's stream is being received.
-
-- Check Video Codec:
-  - If the camera stream works in go2rtc but not in your browser, the video codec might be unsupported.
-  - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#codecs-madness) in go2rtc documentation.
-  - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
-    ```yaml
-    go2rtc:
-      streams:
-        back:
-          - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          - "ffmpeg:back#video=h264#hardware"
-    ```
-
-- Switch to FFmpeg if needed:
-  - Some camera streams may need to use the ffmpeg module in go2rtc. This has the downside of slower startup times, but has compatibility with more stream types.
-
-    ```yaml
-    go2rtc:
-      streams:
-        back:
-          - ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-    ```
-
-  - If you can see the video but do not have audio, this is most likely because your camera's audio stream codec is not AAC.
-  - If possible, update your camera's audio settings to AAC in your camera's firmware.
-  - If your cameras do not support AAC audio, you will need to tell go2rtc to re-encode the audio to AAC on demand if you want audio. This will use additional CPU and add some latency. To add AAC audio on demand, you can update your go2rtc config as follows:
-
-    ```yaml
-    go2rtc:
-      streams:
-        back:
-          - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          - "ffmpeg:back#audio=aac"
-    ```
-
-    If you need to convert **both** the audio and video streams, you can use the following:
-
-    ```yaml
-    go2rtc:
-      streams:
-        back:
-          - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          - "ffmpeg:back#video=h264#audio=aac#hardware"
-    ```
-
-    When using the ffmpeg module, you would add AAC audio like this:
-
-    ```yaml
-    go2rtc:
-      streams:
-        back:
-          - "ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#video=copy#audio=copy#audio=aac#hardware"
-    ```
-
-:::warning
-
-To access the go2rtc stream externally when utilizing the Frigate App (for
-instance through VLC), you must first enable the RTSP Restream port.
-You can do this by visiting the Frigate App configuration page within Home
-Assistant and revealing the hidden options under the "Show disabled ports"
-section.
-
-:::
-
-### Next steps
-
-1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
-2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
-3. If your camera supports two-way talk, you must configure your stream with `#backchannel=0` to prevent go2rtc from blocking other applications from accessing the camera's audio output. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
-
-## Homekit Configuration
-
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to share export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.

docs/docs/guides/getting_started.md

@@ -144,7 +144,7 @@ At this point you should be able to start Frigate and a basic config will be cre
 
 ### Step 2: Add a camera
 
-Click the **Add Camera** button in <NavPath path="Settings > Camera configuration > Management" /> to use the camera setup wizard to get your first camera added into Frigate.
+Click the **Add Camera** button in <NavPath path="Settings > Global configuration > Camera management" /> to use the camera setup wizard to get your first camera added into Frigate.
 
 ### Step 3: Configure hardware acceleration (recommended)
 
@@ -301,7 +301,7 @@ cameras:
 
 More details on available detectors can be found [here](../configuration/object_detectors.md).
 
-Restart Frigate and you should start seeing detections for `person`. If you want to track other objects, they can be configured in <NavPath path="Settings > Global configuration > Objects" /> or via the [configuration file reference](../configuration/reference.md).
+Restart Frigate and you should start seeing detections for `person`. If you want to track other objects, they can be configured in <NavPath path="Settings > Global configuration > Objects" /> or via the [configuration file reference](../configuration/advanced/reference.md).
 
 ### Step 5: Setup motion masks
 
@@ -348,7 +348,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.
 <ConfigTabs>
 <TabItem value="ui">
 
-1. If you have separate streams for detect and record, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />, select your camera, and add a second input with the `record` role pointing to your high-resolution stream
+1. If you have separate streams for detect and record, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />, select your camera, and add a second input with the `record` role pointing to your high-resolution stream
 2. Navigate to <NavPath path="Settings > Global configuration > Recording" /> (or <NavPath path="Settings > Camera configuration > Recording" /> for a specific camera) and set **Enable recording** to on
 
 </TabItem>
@@ -388,21 +388,20 @@ If you only plan to use Frigate for recording, it is still recommended to define
 
 :::
 
-By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/reference.md).
+By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/advanced/reference.md).
 
 ### Step 7: Complete config
 
 At this point you have a complete config with basic functionality.
 
-- View [common configuration examples](../configuration/index.md#common-configuration-examples) for a list of common configuration examples.
-- View [full config reference](../configuration/reference.md) for a complete list of configuration options.
+- View [common configuration examples](../configuration/config.md#common-configuration-examples) for a list of common configuration examples.
+- View [full config reference](../configuration/advanced/reference.md) for a complete list of configuration options.
 
 ### Follow up
 
 Now that you have a working install, you can use the following documentation for additional features:
 
-1. [Configuring go2rtc](configuring_go2rtc.md) - Additional live view options and RTSP relay
-2. [Zones](../configuration/zones.md)
-3. [Review](../configuration/review.md)
-4. [Masks](../configuration/masks.md)
-5. [Home Assistant Integration](../integrations/home-assistant.md) - Integrate with Home Assistant
+1. [Zones](../configuration/zones.md)
+2. [Review](../configuration/review.md)
+3. [Masks](../configuration/masks.md)
+4. [Home Assistant Integration](../integrations/home-assistant.md) - Integrate with Home Assistant

docs/docs/guides/reverse_proxy.md

@@ -10,13 +10,14 @@ A reverse proxy is typically needed if you want to set up Frigate on a custom UR
 Before setting up a reverse proxy, check if any of the built-in functionality in Frigate suits your needs:
 |Topic|Docs|
 |-|-|
-|TLS|Please see the  `tls` [configuration option](../configuration/tls.md)|
+|TLS|Please see the `tls` [configuration option](../configuration/tls.md)|
 |Authentication|Please see the [authentication](../configuration/authentication.md) documentation|
-|IPv6|[Enabling IPv6](../configuration/advanced.md#enabling-ipv6)
+|IPv6|[Enabling IPv6](../configuration/advanced/system.md#enabling-ipv6)
 
-**Note about TLS**  
-When using a reverse proxy, the TLS session is usually terminated at the proxy, sending the internal request over plain HTTP. If this is the desired behavior, TLS must first be disabled in Frigate, or you will encounter an HTTP 400 error: "The plain HTTP request was sent to HTTPS port."  
+**Note about TLS**
+When using a reverse proxy, the TLS session is usually terminated at the proxy, sending the internal request over plain HTTP. If this is the desired behavior, TLS must first be disabled in Frigate, or you will encounter an HTTP 400 error: "The plain HTTP request was sent to HTTPS port."
 To disable TLS, set the following in your Frigate configuration:
+
 ```yml
 tls:
   enabled: false
@@ -24,18 +25,26 @@ tls:
 
 :::warning
 A reverse proxy can be used to secure access to an internal web server, but the user will be entirely reliant on the steps they have taken. You must ensure you are following security best practices.
-This page does not attempt to outline the specific steps needed to secure your internal website.  
+This page does not attempt to outline the specific steps needed to secure your internal website.
 Please use your own knowledge to assess and vet the reverse proxy software before you install anything on your system.
 :::
 
+## WebSocket support
+
+Frigate relies on WebSockets for real-time communication between the browser and the backend. Features such as camera controls (enabling/disabling a camera, audio, detect, recordings, and other toggles), live stream playback, and other live-updating parts of the UI will not function correctly if WebSocket connections are not proxied.
+
+Your reverse proxy must be configured to forward the `Upgrade` and `Connection` headers so that WebSocket connections can be established. Each proxy example below already includes the directives needed to do this, but if you are adapting your own configuration, ensure these headers are passed through.
+
+Note that some proxies disable WebSocket support by default — for example, Nginx Proxy Manager has a "Websockets Support" toggle that must be enabled.
+
 ## Proxies
 
 There are many solutions available to implement reverse proxies and the community is invited to help out documenting others through a contribution to this page.
 
-* [Apache2](#apache2-reverse-proxy)
-* [Nginx](#nginx-reverse-proxy)
-* [Traefik](#traefik-reverse-proxy)
-* [Caddy](#caddy-reverse-proxy)
+- [Apache2](#apache2-reverse-proxy)
+- [Nginx](#nginx-reverse-proxy)
+- [Traefik](#traefik-reverse-proxy)
+- [Caddy](#caddy-reverse-proxy)
 
 ## Apache2 Reverse Proxy
 
@@ -159,7 +168,7 @@ The settings below enabled connection upgrade, sets up logging (optional) and pr
 
 ## Traefik Reverse Proxy
 
-This example shows how to add a `label` to the Frigate Docker compose file, enabling Traefik to automatically discover your Frigate instance.  
+This example shows how to add a `label` to the Frigate Docker compose file, enabling Traefik to automatically discover your Frigate instance.
 Before using the example below, you must first set up Traefik with the [Docker provider](https://doc.traefik.io/traefik/providers/docker/)
 
 ```yml
@@ -203,7 +212,7 @@ This example shows Frigate running under a subdomain with logging and a tls cert
 }
 
 frigate.YOUR_DOMAIN.TLD {
-        reverse_proxy http://localhost:8971 
+        reverse_proxy http://localhost:8971
         import tls
         import logging frigate.YOUR_DOMAIN.TLD
 }

docs/docs/integrations/home-assistant.md

@@ -195,7 +195,7 @@ For clips to be castable to media devices, audio is required and may need to be
 
 ## Camera API
 
-To disable a camera dynamically
+To turn a camera off (pauses Frigate's processing of the stream; does not persist across Frigate restarts; see [Camera state](/configuration/live#camera-state)):
 
 ```
 action: camera.turn_off
@@ -204,7 +204,7 @@ target:
   entity_id: camera.back_deck_cam  # your Frigate camera entity ID
 ```
 
-To enable a camera that has been disabled dynamically
+To turn a camera back on:
 
 ```
 action: camera.turn_on
@@ -213,6 +213,12 @@ target:
   entity_id: camera.back_deck_cam  # your Frigate camera entity ID
 ```
 
+:::note
+
+These actions toggle Frigate's runtime On/Off state. To permanently disable a camera, set its status to **Disabled** in **Settings → Camera Management** in the Frigate UI.
+
+:::
+
 ## Notification API
 
 Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.

docs/docs/integrations/mqtt.md

@@ -306,7 +306,7 @@ Publishes the current health status of each role that is enabled (`audio`, `dete
 
 - `online`: Stream is running and being processed
 - `offline`: Stream is offline and is being restarted
-- `disabled`: Camera is currently disabled
+- `disabled`: Camera is currently turned off (either at runtime via the `enabled/set` topic, or persistently via the configuration file). See [Camera state](/configuration/live#camera-state) for the distinction.
 
 ### `frigate/<camera_name>/<object_name>`
 
@@ -368,15 +368,15 @@ The published value is the detected state class name (e.g., `open`, `closed`, `o
 
 ### `frigate/<camera_name>/enabled/set`
 
-Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn Frigate's processing of a camera on or off at runtime. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)). To permanently change the configured value, use **Settings → Global configuration → Camera management** in the Frigate UI. See [Camera state](/configuration/live#camera-state) for the difference between turning a camera off and disabling it.
 
 ### `frigate/<camera_name>/enabled/state`
 
-Topic with current state of processing for a camera. Published values are `ON` and `OFF`.
+Topic with current runtime state of processing for a camera. Published values are `ON` and `OFF`.
 
 ### `frigate/<camera_name>/detect/set`
 
-Topic to turn object detection for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn object detection for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
 
 ### `frigate/<camera_name>/detect/state`
 
@@ -384,7 +384,7 @@ Topic with current state of object detection for a camera. Published values are
 
 ### `frigate/<camera_name>/audio/set`
 
-Topic to turn audio detection for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn audio detection for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
 
 ### `frigate/<camera_name>/audio/state`
 
@@ -392,7 +392,7 @@ Topic with current state of audio detection for a camera. Published values are `
 
 ### `frigate/<camera_name>/recordings/set`
 
-Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
 
 ### `frigate/<camera_name>/recordings/state`
 
@@ -400,7 +400,7 @@ Topic with current state of recordings for a camera. Published values are `ON` a
 
 ### `frigate/<camera_name>/snapshots/set`
 
-Topic to turn snapshots for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn snapshots for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
 
 ### `frigate/<camera_name>/snapshots/state`
 

docs/docs/troubleshooting/cpu.md

@@ -24,7 +24,7 @@ Video decoding is one of the most CPU-intensive tasks in Frigate. While an AI ac
 
 ### Configuration
 
-Frigate provides preset configurations for common hardware acceleration scenarios. Set up `hwaccel_args` based on your hardware in your [configuration](../configuration/reference) as described in the [getting started guide](../guides/getting_started).
+Frigate provides preset configurations for common hardware acceleration scenarios. Set up `hwaccel_args` based on your hardware in your [configuration](../configuration/advanced/reference) as described in the [getting started guide](../guides/getting_started).
 
 ### Troubleshooting Hardware Acceleration
 

docs/docs/troubleshooting/dummy-camera.md

@@ -3,6 +3,8 @@ id: dummy-camera
 title: Analyzing Object Detection
 ---
 
+import NavPath from "@site/src/components/NavPath";
+
 Frigate provides several tools for investigating object detection and tracking behavior: reviewing recorded detections through the UI, using the built-in Debug Replay feature, and manually setting up a dummy camera for advanced scenarios.
 
 ## Reviewing Detections in the UI
@@ -51,11 +53,25 @@ Only one replay session can be active at a time. If a session is already running
 
 :::
 
+### Starting Debug Replay
+
+Debug Replay can be started from several places in the UI. The starting point determines the time range that gets replayed.
+
+- **History — Actions menu.** Navigate to <NavPath path="History > {camera}" />, open the **Actions** menu in the toolbar, and choose **Debug Replay**. From here you can pick a preset (**Last 1 Minute**, **Last 5 Minutes**), select a range directly on the timeline with **From Timeline**, or enter exact start and end times with **Custom**. This is the most flexible option and the best choice when you want to add padding around a detection. On mobile, the same options appear in the Actions drawer.
+- **History — Detail Stream event menu.** While viewing a review item in the Detail Stream, open the menu on a tracked object's event card and choose **Debug Replay**. The replay range is set automatically to that object's start and end times.
+- **Explore — search result menu.** From an Explore card, open the kebab menu and choose **Debug Replay**. The range is taken from the tracked object's lifecycle.
+- **Explore — Tracking Details Actions menu.** Open a tracked object's **Tracking Details** dialog, then choose **Debug Replay** from the Actions menu. Same automatic range as the search result menu.
+- **Exports — export card menu.** From <NavPath path="Exports" />, open the menu on an export and choose **Debug Replay** to loop the exported clip through the detection pipeline for the camera it was exported from.
+
+The Detail Stream, Explore, and Exports entry points use the underlying recording or export's bounds with a small amount of padding. This can be convenient for quick checks, but if a detection is short or you want extra "settle" time for motion and the detector, start the replay from the History Actions menu instead and widen the range manually.
+
 ### Variables to consider
 
 - The replay will not always produce identical results to the original run. Different frames may be selected on replay, which can change detections and tracking.
 - Motion detection depends on the exact frames used; small frame shifts can change motion regions and therefore what gets passed to the detector.
 - Object detection is not fully deterministic: models and post-processing can yield slightly different results across runs.
+- In cases where a detection is short and a replay may only be a small number of frames, it is recommended to manually add some padding before and after the detection so that the motion and object detectors have time to settle into the scene. Rather than starting Debug Replay from Explore, navigate to History for your camera, choose Debug Replay from the Actions menu, and click the "From Timeline" or "Custom" option.
+- The replay camera inherits the source camera's zones. Any automations that trigger on those zone names will fire for the replay camera as well. This can be helpful when debugging zone behavior, but may be unexpected. You can add a condition on the source camera's name in your automation if you want to exclude replay triggers.
 
 Treat the replay as a close approximation rather than an exact reproduction. Run multiple loops and examine the debug overlays and logs to understand the behavior.
 

docs/docs/troubleshooting/faqs.md

@@ -55,7 +55,7 @@ If you see repeated "On connect called" messages in your logs, check for another
 
 ### Error: Database Is Locked
 
-SQLite does not work well on a network share, if the `/media` folder is mapped to a network share then [this guide](../configuration/advanced.md#database) should be used to move the database to a location on the internal drive.
+SQLite does not work well on a network share, if the `/media` folder is mapped to a network share then [this guide](../configuration/advanced/system.md#database) should be used to move the database to a location on the internal drive.
 
 ### Unable to publish to MQTT: client is not connected
 

docs/docs/troubleshooting/go2rtc.md

@@ -0,0 +1,235 @@
+---
+id: go2rtc
+title: Troubleshooting go2rtc
+---
+
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+This page covers common problems with the bundled [go2rtc](/configuration/go2rtc) and how to resolve them, whether your cameras were added with the setup wizard or configured by hand.
+
+When a stream won't play or behaves oddly, the most important first step is to figure out **where** in the pipeline it breaks. Frigate's live view is a chain — _camera → go2rtc → your browser_ — and each stage fails for different reasons. Work through the checks below in order, then jump to the matching problem category.
+
+## Start by isolating the problem
+
+### 1. Read the go2rtc logs
+
+Access the go2rtc logs in the Frigate UI under <NavPath path="System Logs" /> in the sidebar (select the **go2rtc** tab). If go2rtc cannot connect to your camera you will usually see a clear error here — `401 Unauthorized` (bad or incorrectly encoded credentials), `Connection refused` / `timeout` (wrong IP, port, or the camera is at its connection limit), or `404 Not Found` (wrong RTSP path, or the referenced stream name does not exist).
+
+### 2. Test the stream in the go2rtc web interface
+
+If the logs look clean, open go2rtc's own web interface on port `1984`. This is the single most useful diagnostic, because it takes Frigate's UI out of the equation entirely.
+
+- If using Frigate through Home Assistant, enable the web interface at port `1984` (it is disabled by default — see [Home Assistant ports](#home-assistant-and-port-access)).
+- If using Docker, forward port `1984` before accessing the web interface.
+
+Open the stream page for your camera (`http://<frigate_host>:1984/stream.html?src=back`) and try each player link:
+
+- **If nothing plays here**, the problem is between the camera and go2rtc (codec, credentials, or transport), _not_ your browser. Fix it at the source before touching anything in Frigate.
+- **If a player works here but Frigate's live view does not**, the problem is browser/codec related — compare the **MSE** and **WebRTC** links. Frigate prefers MSE and only attempts WebRTC when MSE fails (or for two-way talk). If `mode=mse` plays but `mode=webrtc` does not, you have a [WebRTC codec problem](#webrtc-and-two-way-talk); if neither plays, your browser cannot decode the codec (commonly H.265 — see [H.265 / HEVC cameras](#h265--hevc-cameras)).
+
+### 3. Inspect the negotiated codecs
+
+You can view detailed stream info — including the exact video and audio codecs go2rtc negotiated with the camera — at `http://frigate_ip:5000/api/go2rtc/streams` (or `http://frigate_ip:5000/api/go2rtc/streams/back` for a single camera). This is the authoritative answer to "what is my camera actually sending?" and is far more reliable than guessing from the camera's web UI. It also shows whether the audio track is `sendonly`/`recvonly`, which matters for [two-way talk](#webrtc-and-two-way-talk).
+
+### 4. Fix the codec with the FFmpeg module
+
+If the camera plays in go2rtc but not in your browser, the video or audio codec is unsupported. Browsers can reliably play **H.264** video and **AAC** audio; many cannot play H.265/HEVC, and some camera audio (G.711/PCM, MJPEG containers, etc.) is not playable at all. The fix is to have go2rtc re-encode the stream on demand using its FFmpeg module.
+
+In the Frigate UI this is the **Use compatibility mode (ffmpeg)** toggle on a stream source; in YAML it is the `ffmpeg:` prefix on the source URL.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+1. Navigate to <NavPath path="Settings > System > go2rtc Streams" /> and expand your camera's stream.
+2. On the source you want to convert, click the **Use compatibility mode (ffmpeg)** button (the sliders icon next to the URL). This routes the source through go2rtc's FFmpeg module and reveals the transcoding options.
+3. Set **Video** to **Transcode to H.264** if your browser can't play the camera's video codec (e.g. H.265). Leave it on **Copy** to pass the video through untouched — this is much cheaper and should be your default whenever only the audio needs converting.
+4. Set **Audio** to **Transcode to AAC** (for MSE) or **Transcode to Opus** (for WebRTC) if the camera's audio codec is unsupported. Leave it on **Copy** to keep the original, or **Exclude** to drop audio entirely.
+5. When transcoding **video**, set **Hardware acceleration** to **Automatic (recommended)** so the encode runs on your GPU instead of the CPU. See [hardware-accelerated transcoding](#hardware-accelerated-transcoding-with-ffmpeg-8) for an important FFmpeg 8 caveat.
+6. **Save** the section, then reload the live view.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+go2rtc:
+  streams:
+    back:
+      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+      # transcode video to H.264 on the GPU; only needed if the browser can't play the source codec
+      - "ffmpeg:back#video=h264#hardware"
+```
+
+To convert audio only (leaving video untouched), or to convert both:
+
+```yaml
+go2rtc:
+  streams:
+    back:
+      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+      - "ffmpeg:back#audio=aac" # audio only — preferred when the video already plays
+      # or, to convert both video and audio:
+      # - "ffmpeg:back#video=h264#audio=aac#hardware"
+```
+
+</TabItem>
+</ConfigTabs>
+
+:::warning
+
+The `#`-modifiers (`#video=`, `#audio=`, `#hardware`, `#backchannel=0`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing — go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
+
+:::
+
+Transcoding video is resource intensive. Always prefer `#video=copy` (the **Copy** option) and only convert the track that is actually unsupported. If you must transcode video and have no hardware encoder available, the built-in jsmpeg view may be the better option.
+
+## Live view is black, buffering, or stuck in "low-bandwidth mode"
+
+When the live view shows a black screen, spins forever, or repeatedly drops to the lower-quality jsmpeg player ("low-bandwidth mode"), the stream almost always contains something the browser cannot decode over MSE — usually H.265 video or a non-AAC audio track. Confirm this in the go2rtc web UI (port `1984`): if MSE won't play there, Frigate can't play it either, since it uses the same pipeline.
+
+The fix is to produce an **H.264 + AAC** stream, either by changing your camera's firmware codecs or by transcoding in go2rtc (see [Fix the codec with the FFmpeg module](#4-fix-the-codec-with-the-ffmpeg-module)). A few other things worth checking:
+
+- **Set the camera's I-frame (keyframe) interval to match its frame rate** (or "1x" on Reolink), and avoid "smart"/"+" codecs like _H.264+_ or _H.265+_. A long keyframe interval delays the first decodable frame past Frigate's startup timeout, which forces the fallback to jsmpeg. See [camera settings recommendations](/configuration/live#camera-settings-recommendations).
+- **A spinner that never clears, even though video plays in VLC**, is often an unplayable _audio_ track stalling playback. Drop or transcode the audio (see below).
+- **Remote/VPN viewing that buffers** while the LAN is fine is usually latency/jitter exceeding MSE's startup buffer — set up [WebRTC](/configuration/live#webrtc-extra-configuration), which drops late frames instead of buffering.
+
+The general live-view behavior (smart streaming, the MSE → WebRTC → jsmpeg fallback chain, and how to read browser console errors) is documented in detail in the [Live view FAQ](/configuration/live#live-view-faq).
+
+## H.265 / HEVC cameras
+
+H.265/HEVC playback in the browser is unreliable and version-dependent. WebRTC does not support H.265 on some browsers, and MSE/HEVC support varies by browser, OS, and whether a hardware decoder is present. An H.265 stream that plays fine in VLC, the go2rtc web UI, and Frigate's recordings can still be blank in a live view.
+
+For dependable live viewing, use **H.264** for the stream the live view consumes:
+
+- Point the live view at the camera's H.264 **substream** and keep the H.265 main stream for recording only, or
+- Transcode H.265 → H.264 in go2rtc with the FFmpeg module and `#hardware` (software HEVC transcoding is very CPU heavy).
+
+Treat browser HEVC playback as best-effort. See also [H.265 cameras via Safari](/configuration/camera_specific#h265-cameras-via-safari).
+
+## No audio in Live view
+
+Live view audio has strict codec requirements that differ by player: **MSE requires AAC, PCMA, or PCMU**, and **WebRTC requires Opus, PCMA, or PCMU**. Many cameras default to a codec outside these sets (or to PCM/G.711), so the player loads video only and no audio control appears.
+
+The most robust approach is to provide both an AAC track (for MSE) and an Opus track (for WebRTC) on the same stream by transcoding audio with the FFmpeg module while copying the video:
+
+<ConfigTabs>
+<TabItem value="ui">
+
+1. Navigate to <NavPath path="Settings > System > go2rtc Streams" /> and expand the camera's stream.
+2. Add a second **Source** that references the stream by name (e.g. the URL `ffmpeg:back`), enable **Use compatibility mode (ffmpeg)**, and set **Audio** to **Transcode to Opus** for WebRTC support.
+3. Keep the original source as **Source 1** so MSE can use the camera's AAC (or transcode the first source's audio to AAC if the camera doesn't provide it).
+4. **Save** the section.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+go2rtc:
+  streams:
+    back:
+      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2 # video + AAC for MSE
+      - "ffmpeg:back#audio=opus" # adds an Opus track for WebRTC
+```
+
+If the camera's native audio isn't AAC either, transcode both:
+
+```yaml
+go2rtc:
+  streams:
+    back:
+      - "ffmpeg:rtsp://user:password@10.0.10.10:554/live0#video=copy#audio=aac" # video copy + AAC for MSE
+      - "ffmpeg:back#audio=opus" # Opus for WebRTC
+```
+
+</TabItem>
+</ConfigTabs>
+
+Setting the camera firmware to AAC (and H.264) avoids transcoding entirely and is always preferable when the camera supports it. For more detail and examples, see [Audio Support](/configuration/live#audio-support).
+
+## WebRTC and two-way talk
+
+WebRTC is only attempted when MSE fails or when using a camera's two-way talk feature; the "All Cameras" dashboard never uses it. When it doesn't work, the cause is almost always one of:
+
+- **Codec mismatch** — WebRTC cannot carry H.265 or AAC. The stream backing the WebRTC view must provide Opus (or PCMA/PCMU) audio and H.264 video. Add an `ffmpeg:back#audio=opus` source as shown above.
+- **Port `8555` not reachable, or no candidates set** — WebRTC needs port `8555` (both TCP and UDP) open and a reachable candidate advertised. On Docker installs running on a custom/overlay network, go2rtc may advertise unreachable container IPs as ICE candidates; setting `webrtc.filters.candidates: []` and supplying only your host's LAN IP resolves this. See [WebRTC extra configuration](/configuration/live#webrtc-extra-configuration).
+- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly — go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
+
+## High CPU usage
+
+If go2rtc is using a lot of CPU, it is almost always transcoding in software. An FFmpeg source with a codec modifier like `#video=h264` or `#audio=aac` but **no** `#hardware` re-encodes on the CPU. (Frigate's `ffmpeg.hwaccel_args` only applies to Frigate's own detect/record processes — it does _not_ accelerate go2rtc's transcodes.)
+
+To keep CPU usage down:
+
+- Only transcode the track that is genuinely unsupported, and use `#video=copy` to pass video through untouched whenever possible.
+- When you must transcode video, always add `#hardware` (the **Automatic** hardware option in the UI) so the encode runs on the GPU. Note the [FFmpeg 8 device requirement](#hardware-accelerated-transcoding-with-ffmpeg-8) below.
+- Don't restream a high-resolution main stream just to feed the live view — even with `#video=copy`, muxing a 4K/8MP+ stream is inherently expensive. Use the camera's lower-resolution substream for live and detect, and let Frigate pull the main stream directly for recording.
+
+## Connection, authentication, and complex passwords
+
+If go2rtc logs `401 Unauthorized` for a URL that works in VLC, the password almost certainly contains reserved URL characters. **Frigate URL-encodes passwords for its own `cameras.ffmpeg.inputs`, but it does not touch what you write under `go2rtc.streams`** — go2rtc parses that URL itself. You must URL-encode special characters yourself in the `go2rtc.streams` section (`@` → `%40`, `#` → `%23`, `?` → `%3F`, `%` → `%25`, etc.).
+
+Note the asymmetry: under `cameras.ffmpeg.inputs` you should use the **raw** password (Frigate encodes it for you) — pre-encoding it there causes a double-encode and fails. See [Handling Complex Passwords](/configuration/restream#handling-complex-passwords).
+
+Repeated `401`/`Connection refused` errors can also mean the camera hit its **concurrent connection limit** or triggered a login lockout. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) means the camera only ever sees one connection from go2rtc.
+
+## Stream names must match everywhere
+
+A surprising number of "the better live options aren't available" or `404 Not Found` problems come down to a name mismatch. The same string must be used consistently:
+
+- the **go2rtc stream key** (`go2rtc.streams.<name>`),
+- any `ffmpeg:<name>#…` source that references it,
+- the camera's restream input path (`rtsp://127.0.0.1:8554/<name>`), and
+- the camera name itself (so Frigate auto-maps it for MSE/WebRTC) — or an explicit `live -> streams` mapping pointing at the go2rtc stream **name** (never a path).
+
+If you rename or remove a go2rtc stream while experimenting and the live stream selector then shows a blank entry, clear your browser's site data for the Frigate URL — the selected stream is cached per-device in local storage.
+
+## Camera-specific behavior
+
+Several camera brands have well-known quirks with go2rtc. Rather than repeat them here, see the [camera-specific configuration](/configuration/camera_specific) page, which covers them in detail. The highlights:
+
+- **Reolink** — RTSP is unreliable on many models; the **http-flv** stream through the FFmpeg module is recommended, and you must enable HTTP/RTMP in the camera and **reboot** it. 6MP+ models stream H.265 over http-flv-enhanced, which requires FFmpeg 8.0. See [Reolink Cameras](/configuration/camera_specific#reolink-cameras).
+- **TP-Link Tapo** — use go2rtc's native `tapo://` source for stability and two-way audio; a stale RTSP credential can often be revived by clicking play once in the go2rtc web UI.
+- **Ubiquiti/UniFi Protect** — use the `rtspx://` scheme (not `rtsps://…?enableSrtp`).
+- **Amcrest/Dahua** — use the `/cam/realmonitor?channel=1&subtype=N` scheme, where `subtype=0` is the main stream. See [Amcrest & Dahua](/configuration/camera_specific#amcrest--dahua).
+
+## Non-RTSP sources and the FFmpeg module
+
+go2rtc's native zero-copy handling only supports well-formed RTSP H.264/H.265. Anything else — MJPEG, HTTP/HTTP-FLV, RTMP, or unusual codecs — must be handed to the FFmpeg module by prefixing the source with `ffmpeg:`. This is also necessary for some camera streams to be parsed at all, at the cost of slightly slower startup. MJPEG and other non-H.264 sources additionally need `#video=h264` (with `#hardware`) before they can be used for the `record`, `detect`, or restream roles. See [MJPEG Cameras](/configuration/camera_specific#mjpeg-cameras) for a complete example.
+
+## Hardware-accelerated transcoding with FFmpeg 8
+
+Frigate 0.18 ships **FFmpeg 8.0** as the default, and FFmpeg 8 is stricter about hardware-accelerated filtering than earlier versions. Whenever go2rtc transcodes video with hardware acceleration (any source using `#hardware`, `#hardware=vaapi`, or the **Automatic** hardware option in the UI), it builds a filter chain that uploads frames to the GPU with the `hwupload` filter. FFmpeg 8 now refuses to do this unless it is told **which device** to use — earlier versions selected one automatically. The result is that an otherwise-working transcode fails to start, the live view never loads, and go2rtc logs:
+
+```
+[hwupload] A hardware device reference is required to upload frames to.
+[AVFilterGraph] Error initializing filters
+Error opening output files: Invalid argument
+```
+
+The fix is to tell go2rtc's bundled FFmpeg which hardware device to use via the `go2rtc -> ffmpeg -> global` option. For **VAAPI**-based acceleration — which covers most Intel and AMD GPUs, and is what go2rtc selects automatically on that hardware — point it at your render device:
+
+```yaml
+go2rtc:
+  ffmpeg:
+    global: "-vaapi_device /dev/dri/renderD128"
+  streams:
+    back:
+      - "ffmpeg:rtsp://user:password@10.0.10.10:554/live0#video=h264#hardware"
+```
+
+`/dev/dri/renderD128` is the usual render node; on a system with more than one GPU you may need `renderD129` (or higher), and the device must be passed into the container (e.g. `devices: - /dev/dri:/dev/dri` in Docker Compose).
+
+If you use a **different hardware acceleration backend**, you will likely need to specify its device in the same way, using the option that matches that backend instead of `-vaapi_device`. See the [go2rtc FFmpeg source documentation](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-ffmpeg) and the upstream report ([go2rtc issue #1984](https://github.com/AlexxIT/go2rtc/issues/1984)) for background and other examples.
+
+:::tip
+
+If you don't transcode in go2rtc with hardware acceleration, this does not affect you. If you want to avoid the change entirely, you can pin Frigate (and the go2rtc it bundles) back to FFmpeg 7.0 by setting `ffmpeg -> path: "7.0"` in your config.
+
+:::
+
+## Home Assistant and port access
+
+When running Frigate as a Home Assistant add-on, the go2rtc API (port `1984`), the RTSP restream (port `8554`), and WebRTC (port `8555`) are **disabled and hidden by default**. To use them — for example to reach the go2rtc web interface for troubleshooting, or to open a go2rtc stream externally in an app like VLC — go to <NavPath path="Settings > Add-ons > Frigate > Configuration > Network" />, click **Show disabled ports**, enable the port you need, and save. Use the host's IP address rather than an mDNS name like `homeassistant.local`.
+
+If live view works in the Frigate UI but not in Home Assistant, the most common cause is the go2rtc stream name not matching the camera name — name the primary go2rtc stream exactly like the camera, or add a `live -> streams` mapping, so the integration can resolve the restream.

docs/docs/troubleshooting/recordings.md

@@ -121,6 +121,12 @@ If segments are only ~1 second instead of ~10 seconds, the camera is sending cor
 - **Changing codec, bitrate, or resolution mid-stream** — Any encoding changes during an active stream can cause unpredictable segment splitting.
 - **Camera firmware bugs** — Check for firmware updates from your camera manufacturer.
 
+:::tip
+
+You don't have to run `ffprobe` by hand to catch this. Open a camera's **Camera Probe Info** dialog (the info icon on the System → Metrics → Cameras page) and check the **Keyframe analysis** section. It probes the record stream and flags sparse or variable keyframes, which is what smart/"+" codecs (H.264+/H.265+) and long keyframe intervals produce.
+
+:::
+
 ### Step 4: Check for a stuck detector
 
 If the detect stream is not processing frames, segments will accumulate. Common causes:

docs/docs/usage/explore.md

@@ -0,0 +1,95 @@
+---
+id: explore
+title: Explore
+---
+
+import NavPath from "@site/src/components/NavPath";
+
+**Explore** is where you browse and search every **tracked object** Frigate has saved. By default it groups recent objects by label; when [Semantic Search](/configuration/semantic_search) is enabled, you can also search by natural-language description or visual similarity. Selecting any object opens a detail pane with its snapshot, lifecycle, and metadata.
+
+This page describes how to _use_ the Explore view. For how the underlying features are _configured_, see [Semantic Search](/configuration/semantic_search) and [Generative AI descriptions](/configuration/genai/genai_objects).
+
+## Browsing tracked objects
+
+The default view shows your most recent tracked objects grouped into rows by label — _Person_, _Car_, _Dog_, and so on — each row labeled with the object type and a count. The arrow at the end of a row opens the full, filterable grid for that label.
+
+Clicking a thumbnail opens its [detail dialog](#tracked-object-details); right-clicking or long-pressing a thumbnail opens an [actions menu](#actions-and-bulk-selection). You can switch to a denser grid layout and adjust the number of columns from the view's settings.
+
+## Searching
+
+When [Semantic Search](/configuration/semantic_search) is enabled, a search bar appears that combines two things in one input:
+
+- **Natural-language search** — type a free-text query and press Enter to run a semantic search over your tracked objects.
+- **Filter tokens** — type a `key:` to get suggestions, then a value, to add a structured filter. Each filter becomes a removable chip, and you can chain several together.
+
+You can save a search with the star icon and reload it later, and clear everything with the clear-search icon. A help popover explains the token syntax, for example:
+
+```
+cameras:front_door label:person before:01012024 time_range:3:00PM-4:00PM
+```
+
+### Filter reference
+
+The most common filter tokens are:
+
+| Filter                       | Description                                                                        |
+| ---------------------------- | ---------------------------------------------------------------------------------- |
+| **Cameras**                  | Limit to one or more cameras.                                                      |
+| **Labels**                   | Object labels (person, car, etc.).                                                 |
+| **Sub Labels**               | Recognized sub labels (e.g. a recognized face or name).                            |
+| **Attributes**               | Classification attributes applied to the object.                                   |
+| **Recognized License Plate** | Match a recognized plate.                                                          |
+| **Zones**                    | Objects that entered specific zones.                                               |
+| **Before / After**           | Restrict to a date range.                                                          |
+| **Time Range**               | Restrict to a time of day (`HH:MM-HH:MM`).                                         |
+| **Min / Max Score**          | Restrict by the object's confidence score.                                         |
+| **Min / Max Speed**          | Restrict by estimated speed (when speed estimation is configured).                 |
+| **Has Snapshot / Has Clip**  | Only objects that saved a snapshot or recording.                                   |
+| **Submitted to Frigate+**    | Only objects already submitted (when Frigate+ is enabled).                         |
+| **Search Type**              | Whether semantic search matches the object's **Thumbnail** or its **Description**. |
+
+### Sorting
+
+When a filter or search is active, a **Sort** control lets you order results by **date**, **object score**, or **estimated speed** (ascending or descending). When a semantic query or similarity search is active, results can also be ordered by **relevance**.
+
+### Thumbnail and description search
+
+- The **Search Type** setting controls whether a text query is matched against each object's **thumbnail** or its **description**. Each result indicates which one it matched and the confidence.
+
+Natural-language search, thumbnail search, and description search all require [Semantic Search](/configuration/semantic_search) to be enabled.
+
+## Tracked Object Details
+
+Selecting an object opens the **Tracked Object Details** dialog. Use the arrows (or the left/right keys) to step to the previous or next object. The dialog has two tabs:
+
+- **Snapshot** or **Thumbnail** — the saved snapshot (or thumbnail).
+- **Tracking Details** — the object's lifecycle, available when the object has a recording. It lists each significant moment (detected, entered a zone, became active or stationary, left, and so on); clicking a moment plays that part of the recording with the bounding box overlaid. A settings popover lets you show all zones and adjust the annotation offset.
+
+The details pane shows the object's **label**, **scores**, **camera**, **timestamp**, estimated **speed**, any **recognized license plate** and **classification attributes**, and its **description**. Admins can edit the sub label, license plate, and attributes inline.
+
+The **description** can be edited by hand, and — when [Generative AI descriptions](/configuration/genai/genai_objects) are enabled and the object's lifecycle has ended — regenerated from the snapshot or from thumbnails. For `speech` objects, a **Transcribe** action is available when audio transcription is enabled. When [Frigate+](/integrations/plus) is enabled, admins can submit a snapshot to improve their model directly from this pane.
+
+## Actions and bulk selection
+
+Right-clicking or long-pressing an object (in the grid or its thumbnail) opens an actions menu with options to **download** the video, snapshot, or a clean snapshot; **view tracking details**; **find similar**; **add a trigger**; **view in History**; and **delete the tracked object**.
+
+:::note
+
+Deleting a tracked object removes its snapshot, embeddings, and tracking-details entries, but the recorded footage of that object in [History](/usage/history) is **not** deleted.
+
+:::
+
+To act on many objects at once, Ctrl/Cmd-click or right-click to start a selection (selected tiles gain a blue ring), then use the toolbar to select all, clear the selection, or delete (admins).
+
+## Semantic Search - Usage and best practices {#usage-and-best-practices}
+
+1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
+2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
+3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the the ones without them are more relevant.
+4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
+5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
+6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.
+
+## Triggers
+
+From an object's actions menu, **Add trigger** sets up a per-camera trigger that uses Semantic Search to automate an action (a notification, sub label, or attribute) whenever a similar object appears. Triggers require Semantic Search and are managed under <NavPath path="Settings > Enrichments > Triggers" />. See [Triggers](/configuration/semantic_search#triggers) for full configuration and best practices.

docs/docs/usage/exports.md

@@ -0,0 +1,43 @@
+---
+id: exports
+title: Exports
+---
+
+**Exports** are how you keep a specific piece of footage permanently.
+
+Frigate's recordings are governed by your [retention settings](/configuration/record): once footage ages past its retention window — or, depending on your configuration, once it is only kept where motion, alerts, or detections occurred — it is deleted to free up disk space. An **export** saves a copy of a chosen time range to a separate location that is **never removed by retention**, so it stays available until you delete it yourself.
+
+This is the answer to the common question _"how do I stop Frigate from deleting an important clip?"_ Instead of increasing retention for an entire camera (which uses far more storage to protect a single moment), export just the footage you want to keep.
+
+:::tip
+
+Exports are stored under `/media/frigate/exports`, separate from your recordings, and are not counted against or removed by recording retention. They remain on disk until you delete them, so be aware that they accumulate over time.
+
+:::
+
+## Creating an export
+
+There are a few ways to create an export:
+
+- **From Review** — select (right click or long-press) an individual review item directly, and choose Export from the header menu. You can also select multiple review items and export them all at once, optionally grouping them into a [case](#cases).
+- **From History** — open the **Actions** menu and choose **Export**. You can export a preset duration (the last 1, 4, 8, 12, or 24 hours), enter a custom start and end time, or select a range directly on the timeline. A **multi-camera** option lets you export the same time range across several cameras at once.
+
+In every case you can give the export a name. Frigate then saves the footage from your recordings as a single video file. Larger ranges take time to process; the export is marked _in progress_ until it finishes, and you can keep using Frigate while it runs.
+
+## Managing exports
+
+All of your exports live on the **Exports** page, reachable from the main navigation, where you can search for one by name. Each export offers the following actions:
+
+- **Play** it in the browser,
+- **Download** it to save the footage outside of Frigate,
+- **Share** it — copies a direct link to the export (or uses your device's share sheet),
+- **Rename** it, and
+- **Delete** it — deleting is the only way an export is removed.
+
+You can also select multiple exports at once to **delete** them in bulk, or to **add them to** (or **remove them from**) a [case](#cases).
+
+## Cases
+
+A **case** groups related exports together — for example, all the clips from a single incident across multiple cameras. On the **Exports** page you can create a case with a name and description, add existing exports to it (or create a new case while exporting), and **download the entire case as a single archive** to hand off as one package.
+
+Exports that don't belong to a case appear under **Uncategorized Exports**. Deleting a case lets you either keep its exports (they move back to uncategorized) or delete them along with the case.

docs/docs/usage/history.md

@@ -0,0 +1,75 @@
+---
+id: history
+title: History
+---
+
+import NavPath from "@site/src/components/NavPath";
+
+**History** is Frigate's full-resolution recording viewer. Unlike Live, Review, and Explore, there is no menu item for it — you reach it from within another view, then scrub the timeline, switch cameras, inspect a tracked object's lifecycle, and export or share any moment.
+
+This page describes how to _use_ the History view. For how recordings are _configured_ (retention, pre/post capture), see [Recording](/configuration/record).
+
+## Opening History
+
+You can open History from several places:
+
+- **From [Review](/usage/review):** clicking a review item opens its recording, scrubbed to just before the activity on that camera.
+- **From [Live](/usage/live):** the **History** button in a camera's single-camera view opens that camera about 30 seconds in the past.
+- **From a share link:** opening a shared timestamp link (see [Share Timestamp](#the-actions-menu) below) jumps straight to that camera and moment.
+
+Use the **Back** button to return where you came from, or the **Live** button to jump to the current camera's live view.
+
+:::tip
+
+If you see **"No recordings found for this time"**, the most common causes are: recording was not enabled for that camera at the time of the event; the retention window has since expired and those segments were removed; or storage ran low and Frigate deleted them early to free space. See [Recording](/configuration/record) to verify your retention settings.
+
+:::
+
+## Timeline, Events, and Detail
+
+A toggle (a drawer on mobile) switches the side panel between three modes:
+
+- **Timeline** — a scrubbable vertical timeline of the selected camera, annotated with a motion line, review-item markers, and gaps where no recording exists.
+- **Events** — a scrollable list of the camera's review items for the time range; clicking one seeks the player to it.
+- **Detail** — the [tracking details inspector](#the-detail-view) for the objects in view.
+
+While you are selecting a range to export, the panel temporarily switches to Timeline.
+
+## Scrubbing and previews
+
+Drag the timeline handlebar to move through time; the main player and any secondary camera previews scrub together so everything stays in sync. Press the zoom buttons on the timeline to change its zoom level (from coarse to fine segments). Sections of the timeline with no recordings are shown as gaps.
+
+On desktop, when more than one camera is available, a **row of secondary previews** shows the other cameras at the same moment. Clicking one of them makes it the main camera at the current timestamp, so you can follow activity across cameras without losing your place. On mobile, use the camera drawer to switch cameras.
+
+## Filtering and the calendar
+
+You can filter History by **cameras** and **date**. The calendar behaves the same as it does in [Review](/usage/review#filtering-and-the-calendar): an **underline** under a day means recordings exist for that day, and a **colored dot** (red for unreviewed alerts, orange for unreviewed detections) marks days with unreviewed activity.
+
+## The Detail view
+
+The **Detail** mode turns the side panel into a tracking details inspector. It lists one card per review item, each showing the item's severity, start time, the object labels involved, a count of tracked objects, and the duration. The active card is highlighted as the video plays, and clicking a card seeks to it.
+
+Expanding a card reveals the **lifecycle** of each tracked object — a row for each significant moment (detected, entered a zone, became active, became stationary, left, and so on), with a progress line that follows the current playback position. Hovering a row shows that moment's score, ratio, and area, and clicking a row seeks the video to that exact timestamp.
+
+The **Detail View Settings** at the bottom let you toggle whether the active item's objects expand automatically, and adjust the **annotation offset** — a fine timing correction that aligns the bounding-box overlays with the recorded video when your camera's snapshot and recording timestamps drift. Admins can save the offset to the camera's configuration.
+
+## The Actions menu
+
+On desktop, the **Actions** menu (the film icon) collects the things you can do with the footage you are viewing:
+
+- **Export** — save a clip of a chosen time range so it is never removed by retention. The dialog pre-selects the last hour; adjust the range or drag the timeline handles, then export. See [Exports](/usage/exports) for managing and downloading exports.
+- **Share Timestamp** — generate a link to the current moment (or a custom timestamp) to share with another Frigate user. This is an internal link, not a public share URL.
+- **Motion Search** — scan this camera's recordings for changes in a region you draw. This is the same tool documented under [Reviewing Motion](/usage/review#motion-search).
+- **Debug Replay** (admins) — replay a recorded range back through Frigate's detection pipeline to see how it would be processed.
+
+You can also capture an instant snapshot of the current frame, and submit a frame to [Frigate+](/integrations/plus) directly from the player (admins only).
+
+## AI review summaries
+
+When [Generative AI review](/configuration/genai/genai_review) is configured, Frigate can generate a title, description, and threat classification for review items and surface them as you scrub through History. A review item that has an AI summary exposes its details in a few places:
+
+- **Over the video** — when the item is on screen, a popup appears over the player.
+- **In the Events side panel** — items with a summary show the title below the thumbnail.
+- **In the Detail side panel** — the item's card shows the title alongside its tracking details.
+
+Clicking any of these opens the **AI Analysis** dialog with the generated detail and any flagged concerns for that item.

docs/docs/usage/live.md

@@ -0,0 +1,118 @@
+---
+id: live
+title: Live View
+---
+
+import NavPath from "@site/src/components/NavPath";
+
+**Live view** is Frigate's real-time dashboard and the page you land on by default. It shows all of your cameras at a glance, streams your most recent alerts across the top, and lets you open any camera in a full-resolution single-camera view with audio, two-way talk, PTZ, and on-demand recording controls.
+
+This page describes how to _use_ the Live view. For how to _configure_ live streaming — go2rtc, stream selection, smart streaming, WebRTC, and audio — see the [Live View configuration](/configuration/live) docs.
+
+## The dashboard at a glance
+
+The default **All Cameras** dashboard shows every camera, with a filmstrip of recent **alerts** scrolling across the top. Clicking an alert opens it in [Review](/usage/review); each card also has a check button to mark it reviewed without leaving the dashboard. Only **alerts** appear in the filmstrip — to suppress a label or zone from showing there, configure it as a detection instead (see [Alerts and Detections](/configuration/review#alerts-and-detections)).
+
+By default Frigate uses **smart streaming**: a camera's image updates roughly once per minute while nothing is happening, and switches to a full live stream the moment activity is detected. This conserves bandwidth and resources. You can change this per camera or per group (see [Streaming settings](#streaming-settings-and-the-right-click-menu) below), and the behavior is explained in detail under [Live view technologies](/configuration/live#live-view-technologies).
+
+On mobile, a toggle in the header switches between a **grid** layout and a single-column **list** layout. On desktop a **fullscreen** button is available in the lower-right corner.
+
+## Switching dashboards and camera groups
+
+The icon rail (top-left on desktop, a horizontal strip on mobile) switches between dashboards:
+
+- The **home** icon is the **All Cameras** dashboard, which shows every camera enabled for the dashboard.
+- Each **camera group** you create appears as its own icon. Selecting a group shows only that group's cameras.
+
+Camera groups are useful for organizing cameras by location (for example, _Front of House_ or _Backyard_) and for giving each group its own dashboard layout and streaming preferences.
+
+You can also view [Birdseye](/configuration/birdseye) on the dashboard, or open it directly at `http://<frigate_host>:5000/#birdseye`. Clicking a camera inside the Birdseye view jumps to that camera's live feed.
+
+## Creating and editing camera groups
+
+Admins can manage groups from the pencil icon next to the group rail, which opens the **Camera Groups** dialog. From there you can add a group, or edit and delete existing ones. When creating a group you choose:
+
+- a **Name** (spaces are converted to underscores),
+- the **cameras** to include — each camera has a toggle and a gear that opens its [streaming settings](#streaming-settings-and-the-right-click-menu), and
+- an **icon** used for the group's button in the rail.
+
+Deleting a group also clears any custom layout you saved for it.
+
+## Rearranging a camera group layout
+
+On desktop and tablet, each camera group has its own freely-arrangeable grid. Enter **Edit Layout** mode from the layout button in the lower-right corner: camera tiles gain a drag handle and corner resize handles. Drag a tile to reposition it and drag a corner to resize it (the aspect ratio is preserved). Exit edit mode to save. The layout is stored in your browser per device, so each device can have its own arrangement.
+
+The default **All Cameras** dashboard is not manually arrangeable — it automatically sizes tiles based on each camera's aspect ratio (wide cameras span two columns, tall cameras span two rows).
+
+## Reading the tile indicators
+
+Each camera tile surfaces its current state with a few overlays:
+
+- A **pulsing red dot** in the corner means **motion is currently detected** on that camera.
+- A **red outline** around the tile means an **active tracked object** is on that camera.
+- A small **label chip** lists the object types currently detected (for example, _Person_, _Car_).
+- A **camera-name label** appears when you have enabled always-on camera names, or when a camera is offline or disabled.
+- A **Stream Offline** or **Camera is off** placeholder appears when no frames are being received or the camera has been turned off.
+
+You can optionally overlay live streaming statistics (stream type, bandwidth, latency, and frame counts) on a tile to diagnose playback issues.
+
+## Streaming settings and the right-click menu
+
+Right-clicking (or long-pressing) a camera tile opens a context menu with quick controls: an **audio volume** control for streams that support audio, **Mute / Unmute all cameras**, **show or hide streaming statistics**, the **debug view**, **notification** options, and — for admins — turning the camera on or off. If the audio control doesn't appear, see [Audio Support](/configuration/live#audio-support) — audio requires go2rtc configured with a compatible codec.
+
+A **Low-bandwidth mode** notice may also appear in the context menu with a **Reset** option appears when Frigate has fallen back to the lower-quality jsmpeg stream — see the [Live view FAQ](/configuration/live#live-view-faq) for why this happens.
+
+For non-default groups, the context menu also exposes **Streaming Settings** for that camera, which let you choose:
+
+- the **stream** to display (the dropdown lists the streams you configured under [`live -> streams`](/configuration/live#setting-streams-for-live-ui), and indicates whether audio is available),
+- the **streaming method** — **No Streaming**, **Smart Streaming** (recommended), or **Continuous Streaming** (higher bandwidth), and
+- **compatibility mode**, for devices that have trouble rendering the default player.
+
+These settings are saved per group and per device in your browser, not in your config file.
+
+## The single-camera view
+
+Clicking a camera tile opens its full-resolution single-camera view. The top bar provides:
+
+- **Back** (also the `Esc` key) to return to the dashboard,
+- **History** to jump to the [recordings](/usage/history) for this camera, starting about 30 seconds in the past,
+- **Fullscreen** and **Picture-in-Picture** (if supported by your browser),
+- **Two-way talk** (the microphone button — requires a supported camera and WebRTC; keyboard shortcut `t`), and
+- **Camera audio muting** (the speaker button; keyboard shortcut `m`).
+
+You can pinch or scroll to zoom into the feed. A **settings** gear provides a **stream** selector (with audio and two-way-talk availability indicators), **Play in background**, **Show stats**, and a **Debug view** that overlays Frigate's detection regions and bounding boxes.
+
+:::tip
+
+Two-way talk and camera audio have specific codec and port requirements. See [Audio Support](/configuration/live#audio-support) and [WebRTC](/configuration/live#webrtc-extra-configuration) for setup details.
+
+:::
+
+## Camera controls
+
+Admins get a row of toggles in the single-camera view (a settings drawer on mobile) to turn camera features on and off in real time:
+
+- **Camera** on/off,
+- **Object detection**,
+- **Recording** (only available when recording is enabled in the camera's config),
+- **Snapshots**,
+- **Audio detection**,
+- **Live audio transcription** (when audio detection is enabled), and
+- **Autotracking** (for [autotracking-capable PTZ cameras](/configuration/autotracking)).
+
+These toggles change runtime behavior immediately. Whether a change persists across a restart depends on the feature — see the relevant configuration page.
+
+## On-demand recording and snapshots
+
+The single-camera view can capture footage on demand:
+
+- **Start on-demand recording** begins a manual recording based on the camera's recording retention settings (the button pulses while active). If recording is disabled for the camera, only a snapshot is saved. Use **End on-demand recording** to stop.
+- **Download instant snapshot** saves a still image of the current frame.
+
+See [Recording](/configuration/record) and [Snapshots](/configuration/snapshots) for how retention is configured, and [Exports](/usage/exports) for keeping a clip permanently.
+
+## PTZ controls
+
+For ONVIF cameras that support it, a control panel provides pan/tilt arrows, **zoom**, **focus**, and saved **presets**. You can also enable a **click-to-move / drag-to-zoom** overlay: click a point in the frame to center the camera there, or drag a box to pan and zoom to that area (dragging top-left to bottom-right zooms in, the reverse zooms out).
+
+For continuous, automatic tracking of a moving object, see [Autotracking](/configuration/autotracking).

docs/docs/usage/review.md

@@ -0,0 +1,140 @@
+---
+id: review
+title: Review
+---
+
+import NavPath from "@site/src/components/NavPath";
+
+**Review** is where you triage what happened on your cameras. It groups activity into **review items** — segments of time on a single camera that bundle together the objects and audio that were active at once — and sorts them into **Alerts**, **Detections**, and **Motion**. From here you can scrub through activity, mark items as reviewed, filter, export, and jump to the full recording in [History](/usage/history).
+
+This page describes how to _use_ the Review view. For how alerts and detections are _configured_ (labels, zones, required zones, retention), see the [Review configuration](/configuration/review) docs.
+
+:::info
+
+Review items are only created for a camera when **object tracking and recording are enabled** for that camera. See [Recording](/configuration/record).
+
+:::
+
+## Alerts, Detections, and Motion
+
+Not every segment of video captured by Frigate is of the same level of interest. The people who enter your property may be a higher priority than those just walking by on the sidewalk. For this reason, Frigate sorts **review items** by importance into **alerts** and **detections**, with a separate **Motion** category for significant motion.
+
+The toggle at the top of the page switches between these three severities. One is always selected.
+
+| Tab            | Indicator color | What it shows                                                                                                    |
+| -------------- | --------------- | ---------------------------------------------------------------------------------------------------------------- |
+| **Alerts**     | dark red        | The activity you most want to see. By default, all `person` and `car` tracked objects are alerts.                |
+| **Detections** | orange          | Everything else Frigate tracked that wasn't promoted to an alert.                                                |
+| **Motion**     | yellow          | Periods of significant motion, with the ability to filter to periods which did **not** produce a tracked object. |
+
+This same color coding is used for the ring around a selected item and the dots on the calendar. How an object is categorized as an alert vs. a detection — and how required zones refine that — is covered in [Alerts and Detections](/configuration/review#alerts-and-detections).
+
+The **Alerts** and **Detections** tabs show a count next to their label. With **Show Reviewed** turned off (the default), this is the number of items still left to review; with it on, the count reflects every item in the selected time range.
+
+## Marking items as reviewed
+
+Review items are shown as a grid of thumbnail cards next to a vertical activity timeline. Hovering a card (desktop) or swiping to the right (mobile) plays a short preview inline.
+
+- **Clicking** a card opens its recording in [History](/usage/history) and marks the item as reviewed.
+- The object chip on each card is **gray** when the item is unreviewed and turns **green** once it has been reviewed.
+- The **Mark these items as reviewed** button marks everything currently shown as reviewed at once.
+
+Reviewed state is tracked per user, so marking an item reviewed does not hide it for other users. Marking an item reviewed does not delete anything — the footage and the review item itself remain until they expire via retention.
+
+## Selecting and acting on multiple items
+
+To act on several items at once, start a selection by **Ctrl/Cmd-clicking** a card (desktop) or **long-pressing** one (mobile). Selected cards gain a colored ring matching their severity. Keyboard shortcuts speed this up: `Ctrl+A` selects all, `R` marks the selection reviewed, and `Esc` clears it.
+
+With items selected, an action bar appears with options to:
+
+- **Export** the selected items (a single item exports directly; multiple items open the batch [export](/usage/exports) dialog),
+- **Mark as reviewed** or **Mark as unreviewed**, and
+- **Delete** them (admins only).
+
+## Filtering and the calendar
+
+Use the filter controls in the header to narrow what's shown. The available filters depend on the tab: Alerts and Detections can be filtered by **cameras**, **date**, **labels**, **zones**, and whether items are already reviewed; the Motion tab can be filtered by **cameras**, **date**, and **motion only**.
+
+The **calendar** filter lets you jump to a specific day (it shows **Last 24 Hours** until you pick one). On each day:
+
+- An **underline** under the day number means **recordings exist** for that day. Days without recordings are dimmed.
+- A **colored dot** under the day number means there is **unreviewed activity** that day — a **red dot** for unreviewed alerts, or an **orange dot** for unreviewed detections when there are no unreviewed alerts. Motion is not represented by a dot.
+
+Future dates are disabled, and the week start and time zone follow your configuration.
+
+## Reviewing Motion
+
+The Review page also can show periods of motion that didn't produce a tracked object, and provides a way to search past recordings for motion in a specific region. These tools complement the alerts and detections workflow above — see [Tuning Motion Detection](/configuration/motion_detection) for how the underlying motion detector is configured.
+
+The **Motion** tab itself shows a multi-camera grid scrubbed to a shared point in time, with a draggable timeline and a playback-speed selector. A camera tile gains a colored ring when a review item or significant motion overlaps the current time, and clicking a tile opens that camera's recording at that moment. Each camera's options menu (the kebab in the corner of its tile) is where you open **Motion Previews** and **Motion Search**, described below.
+
+### Motion Previews
+
+The Motion Previews pane shows preview clips for periods of significant motion that did not produce a tracked object. It is useful for spotting things that motion detection picked up but object detection did not, which can help validate tuning or catch missed objects.
+
+On the <NavPath path="Review > Motion" /> page, click the kebab menu on a camera and choose **Motion Previews**. Each card represents a continuous range of motion-only activity and plays back the recorded preview for that range. A heatmap overlay dims areas of the frame with no motion so the moving regions stand out.
+
+The pane provides a few controls:
+
+- **Speed** — speeds up or slows down all of the preview clips at once.
+- **Dim** — controls how strongly non-motion areas are darkened by the heatmap overlay. Higher values increase motion area visibility.
+- **Filter** — opens a 16×16 grid overlaid on a snapshot of the camera. Select one or more cells to only show clips with motion in those regions. This is helpful for filtering out motion in areas like a busy street while keeping motion in your driveway.
+
+Clicking a preview clip seeks the recording player to that timestamp so you can review the full footage.
+
+### Motion Search
+
+Motion Search lets you scan recorded footage for changes inside a region of interest you draw on the camera. Unlike Motion Previews, which surfaces what Frigate's motion detector flagged in real time, Motion Search re-analyzes the saved recordings, so it can find changes that were missed (for example, an object that appeared while motion detection was paused by `lightning_threshold`, or in a region that is normally motion-masked).
+
+To start a search, open the Actions menu in [History](/usage/history) or click the kebab menu on a camera in the <NavPath path="Review > Motion" /> page and choose **Motion Search**. In the dialog:
+
+1. Pick the camera and time range to scan. In the date pickers, days that have recordings available are underlined.
+2. Draw a polygon on the camera frame to define the region of interest.
+3. Adjust the search parameters if needed:
+
+| Field                     | Description                                                                                                                                                                                                                                                                                                                   |
+| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Sensitivity Threshold** | Per-pixel luminance change required to count as motion inside the ROI. Behaves like Frigate's motion detection `threshold` setting.                                                                                                                                                                                           |
+| **Minimum Change Area**   | Minimum size of a single moving region, as a percentage of the ROI, for a frame to count as significant. Raise it to ignore small movements (leaves, distant motion); lower it when your subject covers only a small slice of the ROI. Every result shows the percentage it scored, so you can use those values to tune this. |
+| **Maximum Results**       | Maximum number of matching timestamps to return. The search stops once it reaches this many results, so a lower value finishes sooner while a higher value scans further into the range.                                                                                                                                      |
+| **Parallel mode**         | Decode multiple recording ranges at the same time. Speeds up large time ranges at the cost of higher decoding and CPU usage.                                                                                                                                                                                                  |
+
+Motion Search samples each recording's keyframes automatically, so there is no frame-rate or sampling setting to tune.
+
+Once running, Frigate scans the recording segments that overlap the time range and reports timestamps where changes were detected inside the polygon, along with the percentage of the ROI that changed. Clicking a result seeks the player to that moment so you can review what happened.
+
+The results panel shows the time range being scanned, a live progress bar with the timestamp currently being analyzed, and the running result count. A collapsible **Search Metrics** section reports how many segments were scanned and processed, how many were skipped because no motion was recorded in the ROI (using the stored motion heatmap), how many frames were decoded, and the total search time. Skipping segments with no recorded motion in the selected ROI is what makes searching long time ranges practical.
+
+#### Common use cases
+
+Frigate's main use case is to record and surface tracked objects, so Motion Search is most useful for the cases where object detection produced nothing — there is no object to find in Explore, but you suspect something happened.
+
+- **Locating an unattributed change.** You know something appeared, disappeared, or moved in a window of footage — a package now gone, a gate left open — but no detection points to it. A search returns the candidate timestamps instead of scrubbing the timeline by hand.
+- **An object that was never detected.** Something Frigate doesn't have a model label for, an object too small or distant to be detected, or movement in a region where detection isn't running. The activity left no tracked object but did change the pixels, so a search can still find it.
+- **Activity while detection was effectively paused.** Changes that occurred while object detection was disabled, motion was suppressed by `skip_motion_threshold`, or inside an area covered by a motion mask, won't appear as review items or tracked objects but can be recovered by searching the recordings directly.
+
+#### Examples
+
+These show how to choose the ROI and **Minimum Change Area** for two common goals. Minimum Change Area is the size of a single moving region as a percentage of the ROI you draw, so the right value depends on how much of the ROI your subject — and its movement between samples — covers.
+
+Because samples are a second or more apart, a moving subject usually appears in two places at once in the comparison, so even ordinary motion often scores tens of percent and a low threshold lets in almost everything. The most reliable approach is to **run a search, look at the percentage each result scored, and set Minimum Change Area just below the values for the events you care about.** The default is 20%; the suggestions below are starting points.
+
+- **When did this item first appear (or disappear)?** A package was dropped off, a car parked, or a trash can was moved, and you want the exact moment. Draw a **tight ROI** around the spot the item occupies and **raise Minimum Change Area** (start around 40–60%). Because the item fills most of a tight ROI, its arrival or removal is a large change, while smaller nearby motion (shadows, a passing pedestrian) stays below the threshold. The **earliest result** is when it appeared; if you only care about that moment, a low Maximum Results finishes faster. If you get no hits, the ROI is probably looser than the item — lower the threshold or tighten the ROI.
+- **What's been getting into the garden?** Something has been trampling a flower bed overnight and no object was ever tracked. Draw a **looser ROI** covering the whole bed and use a **lower Minimum Change Area than the case above** — start near the 20% default and lower it (toward 5–10%) only if a small or distant subject is missed, since it covers just a slice of a large region. Expect more results to scan through — step through the timestamps and jump to each to see what triggered it. If wind-blown plants add noise, raise Minimum Change Area or the Sensitivity Threshold.
+
+#### Expected performance
+
+Motion Search analyzes the saved recordings on demand rather than reading a pre-built index, so a search over a long range takes longer than browsing Motion Previews. Cost scales mainly with how much footage has to be examined: segments with no recorded motion in your ROI are skipped using the stored motion heatmap (shown as "segments skipped" in the status panel), so a quiet range finishes quickly while a busy one takes longer.
+
+To increase the speed of searches:
+
+- Draw a tight ROI. Because **Minimum Change Area** is measured as a percentage of the region you draw, a tight ROI around where you expect the change makes the object fill a larger share of the area, so it clears the threshold more easily. A loose ROI makes the same object a small fraction of the region, so it can fall below the threshold and be missed — forcing you to lower Minimum Change Area, which lets in more noise.
+- Narrow the time range to the window you care about, so there is less footage to examine.
+- Lower **Maximum Results** when you only need the first few hits. Because the search stops once it reaches that many results, a smaller value lets a busy range finish early instead of scanning the whole window.
+- Use Parallel mode to shorten wall-clock time on multi-core systems, at the cost of higher decoding and CPU usage while it runs.
+
+## AI review summaries
+
+When [Generative AI review](/configuration/genai/genai_review) is configured, Frigate can generate a title, description, and threat classification for review items and surface them automatically in Review and History. Clicking the summary chip opens an **AI Analysis** dialog with the generated detail and any flagged concerns.
+
+In Review, an additional icon appears on unreviewed items that the AI classified as **suspicious** (Level 1) or **critical** (Level 2), so the activity that most warrants attention stands out before you open it. The icon goes away once the item has been reviewed.

docs/package-lock.json

@@ -13090,13 +13090,13 @@
       }
     },
     "node_modules/launch-editor": {
-      "version": "2.12.0",
-      "resolved": "https://registry.npmjs.org/launch-editor/-/launch-editor-2.12.0.tgz",
-      "integrity": "sha512-giOHXoOtifjdHqUamwKq6c49GzBdLjvxrd2D+Q4V6uOHopJv7p9VJxikDsQ/CBXZbEITgUqSVHXLTG3VhPP1Dg==",
+      "version": "2.14.1",
+      "resolved": "https://registry.npmjs.org/launch-editor/-/launch-editor-2.14.1.tgz",
+      "integrity": "sha512-QWBrQsMpH7gPr965dsKD/3cKWiNoTjpATQf++Xq63N6sKRGMwlVXz41O1IZTMfZQgBctD/K5Zt06+/I6pP6+HA==",
       "license": "MIT",
       "dependencies": {
         "picocolors": "^1.1.1",
-        "shell-quote": "^1.8.3"
+        "shell-quote": "^1.8.4"
       }
     },
     "node_modules/layout-base": {
@@ -20902,9 +20902,9 @@
       }
     },
     "node_modules/shell-quote": {
-      "version": "1.8.3",
-      "resolved": "https://registry.npmjs.org/shell-quote/-/shell-quote-1.8.3.tgz",
-      "integrity": "sha512-ObmnIF4hXNg1BqhnHmgbDETF8dLPCggZWBjkQfhZpbszZnYur5DUljTcCHii5LC3J5E0yeO/1LIMyH+UvHQgyw==",
+      "version": "1.8.4",
+      "resolved": "https://registry.npmjs.org/shell-quote/-/shell-quote-1.8.4.tgz",
+      "integrity": "sha512-VsC6n6vz1ihYYyZZwX7YZSF5l5x36ca17OC+a69h94YqB7X6XLwf+5MOgynYir2SLFUbl8gIYvBo8K8RoNQ6bQ==",
       "license": "MIT",
       "engines": {
         "node": ">= 0.4"

docs/sidebars.ts

@@ -17,91 +17,126 @@ const sidebars: SidebarsConfig = {
     ],
     Guides: [
       "guides/getting_started",
-      "guides/configuring_go2rtc",
       "guides/ha_notifications",
       "guides/ha_network_storage",
       "guides/reverse_proxy",
     ],
-    Configuration: {
-      "Configuration Files": [
-        "configuration/index",
-        "configuration/reference",
-        {
-          type: "link",
-          label: "Go2RTC Configuration Reference",
-          href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.13#configuration",
-        } as PropSidebarItemLink,
-      ],
-      Detectors: [
-        "configuration/object_detectors",
-        "configuration/audio_detectors",
-      ],
-      Enrichments: [
-        "configuration/semantic_search",
-        "configuration/face_recognition",
-        "configuration/license_plate_recognition",
-        "configuration/bird_classification",
-        {
-          type: "category",
-          label: "Custom Classification",
-          link: {
-            type: "generated-index",
-            title: "Custom Classification",
-            description: "Configuration for custom classification models",
+    Usage: [
+      "usage/live",
+      "usage/review",
+      "usage/history",
+      "usage/explore",
+      "usage/exports",
+    ],
+    Configuration: [
+      "configuration/config",
+      {
+        type: "category",
+        label: "Detectors",
+        items: [
+          "configuration/object_detectors",
+          "configuration/audio_detectors",
+        ],
+      },
+      {
+        type: "category",
+        label: "Enrichments",
+        items: [
+          "configuration/semantic_search",
+          "configuration/face_recognition",
+          "configuration/license_plate_recognition",
+          "configuration/bird_classification",
+          {
+            type: "category",
+            label: "Custom Classification",
+            link: {
+              type: "generated-index",
+              title: "Custom Classification",
+              description: "Configuration for custom classification models",
+            },
+            items: [
+              "configuration/custom_classification/state_classification",
+              "configuration/custom_classification/object_classification",
+            ],
           },
-          items: [
-            "configuration/custom_classification/state_classification",
-            "configuration/custom_classification/object_classification",
-          ],
-        },
-        {
-          type: "category",
-          label: "Generative AI",
-          link: {
-            type: "generated-index",
-            title: "Generative AI",
-            description: "Generative AI Features",
+          {
+            type: "category",
+            label: "Generative AI",
+            link: {
+              type: "generated-index",
+              title: "Generative AI",
+              description: "Generative AI Features",
+            },
+            items: [
+              "configuration/genai/genai_config",
+              "configuration/genai/genai_review",
+              "configuration/genai/genai_objects",
+            ],
           },
-          items: [
-            "configuration/genai/genai_config",
-            "configuration/genai/genai_review",
-            "configuration/genai/genai_objects",
-          ],
-        },
-      ],
-      Cameras: [
-        "configuration/cameras",
-        "configuration/review",
-        "configuration/record",
-        "configuration/snapshots",
-        "configuration/motion_detection",
-        "configuration/birdseye",
-        "configuration/live",
-        "configuration/restream",
-        "configuration/autotracking",
-        "configuration/camera_specific",
-      ],
-      Objects: [
-        "configuration/object_filters",
-        "configuration/masks",
-        "configuration/zones",
-        "configuration/objects",
-        "configuration/stationary_objects",
-      ],
-      "Hardware Acceleration": [
-        "configuration/hardware_acceleration_video",
-        "configuration/hardware_acceleration_enrichments",
-      ],
-      "Extra Configuration": [
-        "configuration/authentication",
-        "configuration/notifications",
-        "configuration/profiles",
-        "configuration/ffmpeg_presets",
-        "configuration/pwa",
-        "configuration/tls",
-        "configuration/advanced",
-      ],
-    },
+        ],
+      },
+      {
+        type: "category",
+        label: "Cameras",
+        items: [
+          "configuration/cameras",
+          "configuration/review",
+          "configuration/record",
+          "configuration/snapshots",
+          "configuration/motion_detection",
+          "configuration/birdseye",
+          "configuration/live",
+          "configuration/restream",
+          "configuration/autotracking",
+          "configuration/camera_specific",
+        ],
+      },
+      {
+        type: "category",
+        label: "Objects",
+        items: [
+          "configuration/object_filters",
+          "configuration/masks",
+          "configuration/zones",
+          "configuration/objects",
+          "configuration/stationary_objects",
+        ],
+      },
+      {
+        type: "category",
+        label: "Hardware Acceleration",
+        items: [
+          "configuration/hardware_acceleration_video",
+          "configuration/hardware_acceleration_enrichments",
+        ],
+      },
+      {
+        type: "category",
+        label: "Extra Configuration",
+        items: [
+          "configuration/authentication",
+          "configuration/notifications",
+          "configuration/profiles",
+          "configuration/go2rtc",
+          "configuration/ffmpeg_presets",
+          "configuration/pwa",
+          "configuration/tls",
+        ],
+      },
+      {
+        type: "category",
+        label: "Advanced Configuration",
+        items: [
+          "configuration/advanced/system",
+          "configuration/advanced/reference",
+          {
+            type: "link",
+            label: "Go2RTC Configuration Reference",
+            href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.13#configuration",
+          } as PropSidebarItemLink,
+        ],
+      },
+    ],
     Integrations: [
       "integrations/plus",
       "integrations/home-assistant",
@@ -130,6 +165,7 @@ const sidebars: SidebarsConfig = {
     ],
     Troubleshooting: [
       "troubleshooting/faqs",
+      "troubleshooting/go2rtc",
       "troubleshooting/recordings",
       "troubleshooting/dummy-camera",
       {

frigate/api/app.py

@@ -34,15 +34,18 @@ from frigate.api.auth import (
 from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
 from frigate.api.defs.request.app_body import (
     AppConfigSetBody,
+    GenAIProbeBody,
     MediaSyncBody,
 )
 from frigate.api.defs.tags import Tags
-from frigate.config import FrigateConfig
+from frigate.config import FrigateConfig, GenAIConfig, GenAIProviderEnum
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateTopic,
 )
+from frigate.const import REDACTED_CREDENTIAL_SENTINEL
 from frigate.ffmpeg_presets import FFMPEG_HWACCEL_VAAPI, _gpu_selector
+from frigate.genai import PROVIDERS, load_providers
 from frigate.jobs.media_sync import (
     get_current_media_sync_job,
     get_media_sync_job_by_id,
@@ -59,7 +62,11 @@ from frigate.util.builtin import (
     process_config_query_string,
     update_yaml_file_bulk,
 )
-from frigate.util.config import apply_section_update, find_config_file
+from frigate.util.config import (
+    apply_section_update,
+    find_config_file,
+    redact_credential,
+)
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
     get_nvidia_driver_info,
@@ -75,6 +82,14 @@ logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.app])
 
+# Short timeout for the /genai/probe path. The probe is interactive — fail
+# fast on hung providers rather than holding an API worker thread.
+_PROBE_TIMEOUT_SECONDS = 10
+# Outer cap that returns control to the caller even if the underlying sync
+# HTTP call ignores its timeout. The sync work continues in the background
+# thread; only the response is bounded.
+_PROBE_OUTER_TIMEOUT_SECONDS = 15
+
 
 @router.get(
     "/", response_class=PlainTextResponse, dependencies=[Depends(allow_public())]
@@ -170,6 +185,95 @@ def genai_models(request: Request):
     return JSONResponse(content=request.app.genai_manager.list_models())
 
 
+@router.post(
+    "/genai/probe",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Probe a GenAI provider without saving config",
+    description=(
+        "Builds a transient client from the request body and returns its "
+        "available models. Used to validate provider credentials in the UI "
+        "before saving the configuration."
+    ),
+)
+async def genai_probe(body: GenAIProbeBody):
+    load_providers()
+
+    provider_cls = PROVIDERS.get(body.provider)
+    if not provider_cls:
+        return JSONResponse(
+            status_code=400,
+            content={"success": False, "message": "Unknown provider"},
+        )
+
+    # The OpenAI-compatible SDKs accept "timeout" as a constructor kwarg via
+    # provider_options; other plugins use GenAIClient.timeout passed below.
+    # Don't inject timeout for Gemini — its HttpOptions interprets the value
+    # in milliseconds and would clash with the plugin's own default.
+    probe_provider_options: dict[str, Any] = dict(body.provider_options or {})
+    if body.provider in (GenAIProviderEnum.openai, GenAIProviderEnum.azure_openai):
+        probe_provider_options.setdefault("timeout", _PROBE_TIMEOUT_SECONDS)
+
+    try:
+        transient_cfg = GenAIConfig(
+            provider=body.provider,
+            api_key=body.api_key,
+            base_url=body.base_url,
+            provider_options=probe_provider_options,
+            # model is required by the schema but irrelevant for listing.
+            model="probe",
+            roles=[],
+        )
+    except ValidationError:
+        logger.exception("GenAI probe: invalid configuration")
+        return JSONResponse(
+            status_code=400,
+            content={"success": False, "message": "Invalid provider configuration"},
+        )
+
+    try:
+        client = provider_cls(
+            transient_cfg,
+            timeout=_PROBE_TIMEOUT_SECONDS,
+            validate_model=False,
+        )
+    except Exception:
+        logger.exception("GenAI probe: failed to construct client")
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "Failed to connect to provider",
+            },
+        )
+
+    try:
+        models = await asyncio.wait_for(
+            asyncio.to_thread(client.list_models),
+            timeout=_PROBE_OUTER_TIMEOUT_SECONDS,
+        )
+    except asyncio.TimeoutError:
+        return JSONResponse(
+            content={"success": False, "message": "Probe timed out"},
+        )
+    except Exception:
+        logger.exception("GenAI probe: list_models failed")
+        return JSONResponse(
+            content={"success": False, "message": "Provider returned no models"},
+        )
+
+    if not models:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": (
+                    "No models returned. Check the API key, base URL, and "
+                    "that the provider is reachable."
+                ),
+            },
+        )
+
+    return JSONResponse(content={"success": True, "models": models})
+
+
 @router.get("/config", dependencies=[Depends(allow_any_authenticated())])
 def config(request: Request):
     config_obj: FrigateConfig = request.app.frigate_config
@@ -185,26 +289,24 @@ def config(request: Request):
     if request.headers.get("remote-role") != "admin":
         config.pop("environment_vars", None)
 
-    # remove mqtt credentials
-    config["mqtt"].pop("password", None)
-    config["mqtt"].pop("user", None)
+    # redact mqtt credentials
+    redact_credential(config["mqtt"], "password")
 
-    # remove the proxy secret
-    config["proxy"].pop("auth_secret", None)
+    # redact proxy secret
+    redact_credential(config["proxy"], "auth_secret")
 
-    # remove genai api keys
-    for genai_name, genai_cfg in config.get("genai", {}).items():
+    # redact genai api keys
+    for _genai_name, genai_cfg in config.get("genai", {}).items():
         if isinstance(genai_cfg, dict):
-            genai_cfg.pop("api_key", None)
+            redact_credential(genai_cfg, "api_key")
 
     for camera_name, camera in request.app.frigate_config.cameras.items():
         camera_dict = config["cameras"][camera_name]
 
-        # remove onvif credentials
+        # redact onvif credentials
         onvif_dict = camera_dict.get("onvif", {})
         if onvif_dict:
-            onvif_dict.pop("user", None)
-            onvif_dict.pop("password", None)
+            redact_credential(onvif_dict, "password")
 
         # clean paths
         for input in camera_dict.get("ffmpeg", {}).get("inputs", []):
@@ -581,6 +683,10 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
             _restore_masked_camera_paths(body.config_data, request.app.frigate_config)
             updates = flatten_config_data(body.config_data)
             updates = {k: ("" if v is None else v) for k, v in updates.items()}
+            # Drop any field whose value is still the redaction sentinel
+            updates = {
+                k: v for k, v in updates.items() if v != REDACTED_CREDENTIAL_SENTINEL
+            }
 
         if not updates:
             return JSONResponse(
@@ -644,6 +750,40 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
                     settings,
                 )
 
+                # detect resize also republishes motion + objects so other
+                # processes pick up the rebuilt masks, and fires refresh so
+                # the camera maintainer recycles the camera process to pick
+                # up the new ffmpeg cmd / SHM sizing
+                if field == "detect":
+                    cam_cfg = config.cameras.get(camera)
+                    if cam_cfg is not None:
+                        if cam_cfg.motion is not None:
+                            request.app.config_publisher.publish_update(
+                                CameraConfigUpdateTopic(
+                                    CameraConfigUpdateEnum.motion, camera
+                                ),
+                                cam_cfg.motion,
+                            )
+                        request.app.config_publisher.publish_update(
+                            CameraConfigUpdateTopic(
+                                CameraConfigUpdateEnum.objects, camera
+                            ),
+                            cam_cfg.objects,
+                        )
+                        if cam_cfg.zones:
+                            request.app.config_publisher.publish_update(
+                                CameraConfigUpdateTopic(
+                                    CameraConfigUpdateEnum.zones, camera
+                                ),
+                                cam_cfg.zones,
+                            )
+                        request.app.config_publisher.publish_update(
+                            CameraConfigUpdateTopic(
+                                CameraConfigUpdateEnum.refresh, camera
+                            ),
+                            cam_cfg,
+                        )
+
         return JSONResponse(
             content={"success": True, "message": "Config applied in-memory"},
             status_code=200,
@@ -691,6 +831,13 @@ def config_set(request: Request, body: AppConfigSetBody):
                     updates = flatten_config_data(body.config_data)
                     # Convert None values to empty strings for deletion (e.g., when deleting masks)
                     updates = {k: ("" if v is None else v) for k, v in updates.items()}
+                    # Drop sentinel-valued fields so untouched credential
+                    # placeholders don't clobber the saved YAML value.
+                    updates = {
+                        k: v
+                        for k, v in updates.items()
+                        if v != REDACTED_CREDENTIAL_SENTINEL
+                    }
 
                 if not updates:
                     return JSONResponse(
@@ -761,6 +908,11 @@ def config_set(request: Request, body: AppConfigSetBody):
                     status_code=500,
                 )
 
+            # drop runtime overrides for any fields the user just rewrote in
+            # yaml so a stale override doesn't silently win after restart
+            if request.app.dispatcher is not None:
+                request.app.dispatcher.clear_runtime_state_for_yaml_keys(updates.keys())
+
             if body.requires_restart == 0 or body.update_topic:
                 old_config: FrigateConfig = request.app.frigate_config
                 request.app.frigate_config = config

frigate/api/camera.py

@@ -34,11 +34,15 @@ from frigate.config.camera.updater import (
 )
 from frigate.config.env import substitute_frigate_vars
 from frigate.models import User
-from frigate.util.builtin import clean_camera_user_pass
+from frigate.util.builtin import clean_camera_user_pass, get_record_segment_time
 from frigate.util.camera_cleanup import cleanup_camera_db, cleanup_camera_files
 from frigate.util.config import find_config_file
 from frigate.util.image import run_ffmpeg_snapshot
-from frigate.util.services import ffprobe_stream, is_restricted_go2rtc_source
+from frigate.util.services import (
+    analyze_record_keyframes,
+    ffprobe_stream,
+    is_restricted_go2rtc_source,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -362,6 +366,48 @@ def ffprobe(request: Request, paths: str = "", detailed: bool = False):
     return JSONResponse(content=output)
 
 
+@router.get("/keyframe_analysis", dependencies=[Depends(require_role(["admin"]))])
+async def keyframe_analysis(request: Request, camera: str = ""):
+    """Probe a camera's record stream and classify its keyframe spacing.
+
+    Detects smart/+ codecs and long/variable GOPs that degrade recording.
+    """
+    config: FrigateConfig = request.app.frigate_config
+
+    if camera not in config.cameras:
+        return JSONResponse(
+            content={"success": False, "message": f"{camera} is not a valid camera."},
+            status_code=404,
+        )
+
+    camera_config = config.cameras[camera]
+
+    if not camera_config.enabled:
+        return JSONResponse(
+            content={"success": False, "message": f"{camera} is not enabled."},
+            status_code=404,
+        )
+
+    # keyframe spacing only matters when this camera is recording
+    if not camera_config.record.enabled:
+        return JSONResponse(content={"severity": "record_disabled"})
+
+    # recording guarantees an input carries the record role; its index matches
+    # the "Stream N" numbering the ffprobe endpoint surfaces (same input order)
+    record_index, record_input = next(
+        (idx, i)
+        for idx, i in enumerate(camera_config.ffmpeg.inputs)
+        if "record" in i.roles
+    )
+
+    segment_time = get_record_segment_time(camera_config)
+    result = await analyze_record_keyframes(
+        config.ffmpeg, record_input.path, segment_time
+    )
+    result["stream_index"] = record_index
+    return JSONResponse(content=result)
+
+
 @router.get("/ffprobe/snapshot", dependencies=[Depends(require_role(["admin"]))])
 def ffprobe_snapshot(request: Request, url: str = "", timeout: int = 10):
     """Get a snapshot from a stream URL using ffmpeg."""
@@ -529,6 +575,68 @@ def _extract_fps(r_frame_rate: str) -> float | None:
         return None
 
 
+def _build_digest_transport(username: str, password: str) -> AsyncTransport:
+    """Build a zeep transport backed by an httpx client using HTTP digest auth."""
+    auth = httpx.DigestAuth(username, password)
+    client = httpx.AsyncClient(auth=auth, timeout=10.0)
+    return AsyncTransport(client=client)
+
+
+async def _connect_onvif_camera(
+    host: str,
+    port: int,
+    username: str,
+    password: str,
+    wsdl_base: str | None,
+    auth_type: str,
+) -> ONVIFCamera:
+    """Connect to an ONVIF device, trying both WS-Security password encodings.
+
+    Cameras disagree on whether the WS-Security UsernameToken should carry a
+    hashed PasswordDigest or a plaintext PasswordText. The wizard can't know
+    which a given camera expects, so we try PasswordDigest first (the common
+    case) and fall back to PasswordText when the device rejects the token. This
+    is independent of auth_type, which controls HTTP transport-level auth.
+    """
+    first_error: Fault | None = None
+
+    # encrypt=True -> PasswordDigest, encrypt=False -> PasswordText
+    for encrypt in (True, False):
+        onvif_camera = ONVIFCamera(
+            host,
+            port,
+            username or "",
+            password or "",
+            wsdl_dir=wsdl_base,
+            encrypt=encrypt,
+        )
+
+        try:
+            await onvif_camera.update_xaddrs()
+        except Fault as e:
+            # A SOAP fault here is how a camera signals the wrong password
+            # encoding, so retry with the other encoding before giving up.
+            logger.debug(
+                "ONVIF connect with %s rejected, trying alternate encoding",
+                "PasswordDigest" if encrypt else "PasswordText",
+            )
+            if first_error is None:
+                first_error = e
+            continue
+
+        if auth_type == "digest" and username and password:
+            transport = _build_digest_transport(username, password)
+            for service in ("devicemgmt", "media", "ptz"):
+                if hasattr(onvif_camera, service):
+                    getattr(onvif_camera, service).zeep_client.transport = transport
+            logger.debug("Configured digest authentication")
+
+        return onvif_camera
+
+    # Both encodings failed authentication; surface the original fault.
+    raise first_error
+
+
 @router.get(
     "/onvif/probe",
     dependencies=[Depends(require_role(["admin"]))],
@@ -605,34 +713,10 @@ async def onvif_probe(
         except Exception:
             wsdl_base = None
 
-        onvif_camera = ONVIFCamera(
-            host, port, username or "", password or "", wsdl_dir=wsdl_base
+        onvif_camera = await _connect_onvif_camera(
+            host, port, username, password, wsdl_base, auth_type
         )
 
-        # Configure digest authentication if requested
-        if auth_type == "digest" and username and password:
-            # Create httpx client with digest auth
-            auth = httpx.DigestAuth(username, password)
-            client = httpx.AsyncClient(auth=auth, timeout=10.0)
-
-            # Replace the transport in the zeep client
-            transport = AsyncTransport(client=client)
-
-            # Update the xaddr before setting transport
-            await onvif_camera.update_xaddrs()
-
-            # Replace transport in all services
-            if hasattr(onvif_camera, "devicemgmt"):
-                onvif_camera.devicemgmt.zeep_client.transport = transport
-            if hasattr(onvif_camera, "media"):
-                onvif_camera.media.zeep_client.transport = transport
-            if hasattr(onvif_camera, "ptz"):
-                onvif_camera.ptz.zeep_client.transport = transport
-
-            logger.debug("Configured digest authentication")
-        else:
-            await onvif_camera.update_xaddrs()
-
         # Get device information
         device_info = {
             "manufacturer": "Unknown",
@@ -644,10 +728,9 @@ async def onvif_probe(
 
             # Update transport for device service if digest auth
             if auth_type == "digest" and username and password:
-                auth = httpx.DigestAuth(username, password)
-                client = httpx.AsyncClient(auth=auth, timeout=10.0)
-                transport = AsyncTransport(client=client)
-                device_service.zeep_client.transport = transport
+                device_service.zeep_client.transport = _build_digest_transport(
+                    username, password
+                )
 
             device_info_resp = await device_service.GetDeviceInformation()
             manufacturer = getattr(device_info_resp, "Manufacturer", None) or (
@@ -685,10 +768,9 @@ async def onvif_probe(
 
             # Update transport for media service if digest auth
             if auth_type == "digest" and username and password:
-                auth = httpx.DigestAuth(username, password)
-                client = httpx.AsyncClient(auth=auth, timeout=10.0)
-                transport = AsyncTransport(client=client)
-                media_service.zeep_client.transport = transport
+                media_service.zeep_client.transport = _build_digest_transport(
+                    username, password
+                )
 
             profiles = await media_service.GetProfiles()
             profiles_count = len(profiles) if profiles else 0
@@ -720,10 +802,9 @@ async def onvif_probe(
 
             # Update transport for PTZ service if digest auth
             if auth_type == "digest" and username and password:
-                auth = httpx.DigestAuth(username, password)
-                client = httpx.AsyncClient(auth=auth, timeout=10.0)
-                transport = AsyncTransport(client=client)
-                ptz_service.zeep_client.transport = transport
+                ptz_service.zeep_client.transport = _build_digest_transport(
+                    username, password
+                )
 
             # Check if PTZ service is available
             try:
@@ -876,10 +957,9 @@ async def onvif_probe(
 
             # Update transport for media service if digest auth
             if auth_type == "digest" and username and password:
-                auth = httpx.DigestAuth(username, password)
-                client = httpx.AsyncClient(auth=auth, timeout=10.0)
-                transport = AsyncTransport(client=client)
-                media_service.zeep_client.transport = transport
+                media_service.zeep_client.transport = _build_digest_transport(
+                    username, password
+                )
 
             if profiles_count and media_service:
                 for p in profiles or []:

frigate/api/chat.py

@@ -7,7 +7,7 @@ import operator
 import time
 from datetime import datetime
 from functools import reduce
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 import cv2
 from fastapi import APIRouter, Body, Depends, HTTPException, Request
@@ -59,7 +59,7 @@ class ToolExecuteRequest(BaseModel):
     """Request model for tool execution."""
 
     tool_name: str
-    arguments: Dict[str, Any]
+    arguments: dict[str, Any]
 
 
 class VLMMonitorRequest(BaseModel):
@@ -68,8 +68,8 @@ class VLMMonitorRequest(BaseModel):
     camera: str
     condition: str
     max_duration_minutes: int = 60
-    labels: List[str] = []
-    zones: List[str] = []
+    labels: list[str] = []
+    zones: list[str] = []
 
 
 @router.get(
@@ -91,10 +91,10 @@ def get_tools(request: Request) -> JSONResponse:
 
 
 def _resolve_zones(
-    zones: List[str],
+    zones: list[str],
     config: FrigateConfig,
-    target_cameras: List[str],
-) -> List[str]:
+    target_cameras: list[str],
+) -> list[str]:
     """Map zone names to their canonical config keys, case-insensitively.
 
     LLMs frequently echo a user's casing ("Front Yard") instead of the
@@ -107,7 +107,7 @@ def _resolve_zones(
     if not zones:
         return zones
 
-    lookup: Dict[str, str] = {}
+    lookup: dict[str, str] = {}
     for camera_id in target_cameras:
         camera_config = config.cameras.get(camera_id)
         if camera_config is None:
@@ -120,8 +120,8 @@ def _resolve_zones(
 
 async def _execute_search_objects(
     request: Request,
-    arguments: Dict[str, Any],
-    allowed_cameras: List[str],
+    arguments: dict[str, Any],
+    allowed_cameras: list[str],
 ) -> JSONResponse:
     """
     Execute the search_objects tool.
@@ -213,8 +213,8 @@ async def _execute_search_objects(
 
 async def _execute_search_objects_semantic(
     request: Request,
-    arguments: Dict[str, Any],
-    allowed_cameras: List[str],
+    arguments: dict[str, Any],
+    allowed_cameras: list[str],
     semantic_query: str,
 ) -> JSONResponse:
     """Search objects via fused thumbnail + description embeddings.
@@ -263,8 +263,8 @@ async def _execute_search_objects_semantic(
     limit = int(arguments.get("limit", 25))
     limit = max(1, min(limit, 100))
 
-    visual_distances: Dict[str, float] = {}
-    description_distances: Dict[str, float] = {}
+    visual_distances: dict[str, float] = {}
+    description_distances: dict[str, float] = {}
     try:
         rows = context.search_thumbnail(semantic_query)
         visual_distances = {row[0]: row[1] for row in rows}
@@ -305,7 +305,7 @@ async def _execute_search_objects_semantic(
 
     eligible = {e.id: e for e in Event.select().where(reduce(operator.and_, clauses))}
 
-    scored: List[tuple[str, float]] = []
+    scored: list[tuple[str, float]] = []
     for eid in eligible:
         v_score = (
             distance_to_score(visual_distances[eid], context.thumb_stats)
@@ -331,9 +331,9 @@ async def _execute_search_objects_semantic(
 
 async def _execute_find_similar_objects(
     request: Request,
-    arguments: Dict[str, Any],
-    allowed_cameras: List[str],
-) -> Dict[str, Any]:
+    arguments: dict[str, Any],
+    allowed_cameras: list[str],
+) -> dict[str, Any]:
     """Execute the find_similar_objects tool.
 
     Returns a plain dict (not JSONResponse) so the chat loop can embed it
@@ -403,8 +403,8 @@ async def _execute_find_similar_objects(
     # version (see frigate/embeddings/__init__.py). Mirror the pattern used by
     # frigate/api/event.py events_search: fetch top-k globally, then intersect
     # with the structured filters via Peewee.
-    visual_distances: Dict[str, float] = {}
-    description_distances: Dict[str, float] = {}
+    visual_distances: dict[str, float] = {}
+    description_distances: dict[str, float] = {}
 
     try:
         if similarity_mode in ("visual", "fused"):
@@ -462,7 +462,7 @@ async def _execute_find_similar_objects(
     eligible = {e.id: e for e in Event.select().where(reduce(operator.and_, clauses))}
 
     # 6. Fuse and rank.
-    scored: List[tuple[str, float]] = []
+    scored: list[tuple[str, float]] = []
     for eid in eligible:
         v_score = (
             distance_to_score(visual_distances[eid], context.thumb_stats)
@@ -503,7 +503,7 @@ async def _execute_find_similar_objects(
 async def execute_tool(
     request: Request,
     body: ToolExecuteRequest = Body(...),
-    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
 ) -> JSONResponse:
     """
     Execute a tool function call.
@@ -545,11 +545,23 @@ async def execute_tool(
 async def _execute_get_live_context(
     request: Request,
     camera: str,
-    allowed_cameras: List[str],
-) -> Dict[str, Any]:
+    allowed_cameras: list[str],
+) -> dict[str, Any]:
+    # Reject wildcards explicitly so models retry with a real camera name
+    # instead of silently fanning out across every camera.
+    if camera in ("*", "all"):
+        return {
+            "error": (
+                "get_live_context requires a single camera name; wildcards "
+                "are not supported. Call this tool once per camera."
+            ),
+            "available_cameras": allowed_cameras,
+        }
+
     if camera not in allowed_cameras:
         return {
             "error": f"Camera '{camera}' not found or access denied",
+            "available_cameras": allowed_cameras,
         }
 
     if camera not in request.app.frigate_config.cameras:
@@ -581,7 +593,7 @@ async def _execute_get_live_context(
                     "stationary": obj_dict.get("stationary", False),
                 }
 
-        result: Dict[str, Any] = {
+        result: dict[str, Any] = {
             "camera": camera,
             "timestamp": frame_time,
             "detections": list(tracked_objects_dict.values()),
@@ -608,7 +620,7 @@ async def _execute_get_live_context(
 async def _get_live_frame_image_url(
     request: Request,
     camera: str,
-    allowed_cameras: List[str],
+    allowed_cameras: list[str],
 ) -> Optional[str]:
     """
     Fetch the current live frame for a camera as a base64 data URL.
@@ -647,8 +659,8 @@ async def _get_live_frame_image_url(
 
 async def _execute_set_camera_state(
     request: Request,
-    arguments: Dict[str, Any],
-) -> Dict[str, Any]:
+    arguments: dict[str, Any],
+) -> dict[str, Any]:
     role = request.headers.get("remote-role", "")
     if "admin" not in [r.strip() for r in role.split(",")]:
         return {"error": "Admin privileges required to change camera settings."}
@@ -687,10 +699,10 @@ async def _execute_set_camera_state(
 
 async def _execute_tool_internal(
     tool_name: str,
-    arguments: Dict[str, Any],
+    arguments: dict[str, Any],
     request: Request,
-    allowed_cameras: List[str],
-) -> Dict[str, Any]:
+    allowed_cameras: list[str],
+) -> dict[str, Any]:
     """
     Internal helper to execute a tool and return the result as a dict.
 
@@ -721,7 +733,14 @@ async def _execute_tool_internal(
                 "Arguments: %s",
                 json.dumps(arguments),
             )
-            return {"error": "Camera parameter is required"}
+            return {
+                "error": (
+                    "get_live_context requires a single camera name; "
+                    "wildcards and empty values are not supported. "
+                    "Call this tool once per camera."
+                ),
+                "available_cameras": allowed_cameras,
+            }
         return await _execute_get_live_context(request, camera, allowed_cameras)
     elif tool_name == "start_camera_watch":
         return await _execute_start_camera_watch(request, arguments)
@@ -744,8 +763,8 @@ async def _execute_tool_internal(
 
 async def _execute_start_camera_watch(
     request: Request,
-    arguments: Dict[str, Any],
-) -> Dict[str, Any]:
+    arguments: dict[str, Any],
+) -> dict[str, Any]:
     camera = arguments.get("camera", "").strip()
     condition = arguments.get("condition", "").strip()
     max_duration_minutes = int(arguments.get("max_duration_minutes", 60))
@@ -795,14 +814,14 @@ async def _execute_start_camera_watch(
     }
 
 
-def _execute_stop_camera_watch() -> Dict[str, Any]:
+def _execute_stop_camera_watch() -> dict[str, Any]:
     cancelled = stop_vlm_watch_job()
     if cancelled:
         return {"success": True, "message": "Watch job cancelled."}
     return {"success": False, "message": "No active watch job to cancel."}
 
 
-def _execute_get_profile_status(request: Request) -> Dict[str, Any]:
+def _execute_get_profile_status(request: Request) -> dict[str, Any]:
     """Return profile status including active profile and activation timestamps."""
     profile_manager = getattr(request.app, "profile_manager", None)
     if profile_manager is None:
@@ -827,9 +846,9 @@ def _execute_get_profile_status(request: Request) -> Dict[str, Any]:
 
 
 def _execute_get_recap(
-    arguments: Dict[str, Any],
-    allowed_cameras: List[str],
-) -> Dict[str, Any]:
+    arguments: dict[str, Any],
+    allowed_cameras: list[str],
+) -> dict[str, Any]:
     """Fetch review segments with GenAI metadata for a time period."""
     from functools import reduce
 
@@ -890,7 +909,7 @@ def _execute_get_recap(
             .iterator()
         )
 
-        events: List[Dict[str, Any]] = []
+        events: list[dict[str, Any]] = []
 
         for row in rows:
             data = row.get("data") or {}
@@ -901,7 +920,7 @@ def _execute_get_recap(
                     data = {}
 
             camera = row["camera"]
-            event: Dict[str, Any] = {
+            event: dict[str, Any] = {
                 "camera": camera.replace("_", " ").title(),
                 "severity": row.get("severity", "detection"),
             }
@@ -965,10 +984,10 @@ def _execute_get_recap(
 
 
 async def _execute_pending_tools(
-    pending_tool_calls: List[Dict[str, Any]],
+    pending_tool_calls: list[dict[str, Any]],
     request: Request,
-    allowed_cameras: List[str],
-) -> tuple[List[ToolCall], List[Dict[str, Any]], List[Dict[str, Any]]]:
+    allowed_cameras: list[str],
+) -> tuple[list[ToolCall], list[dict[str, Any]], list[dict[str, Any]]]:
     """
     Execute a list of tool calls.
 
@@ -977,9 +996,9 @@ async def _execute_pending_tools(
          tool result dicts for conversation,
          extra messages to inject after tool results — e.g. user messages with images)
     """
-    tool_calls_out: List[ToolCall] = []
-    tool_results: List[Dict[str, Any]] = []
-    extra_messages: List[Dict[str, Any]] = []
+    tool_calls_out: list[ToolCall] = []
+    tool_results: list[dict[str, Any]] = []
+    extra_messages: list[dict[str, Any]] = []
     for tool_call in pending_tool_calls:
         tool_name = tool_call["name"]
         tool_args = tool_call.get("arguments") or {}
@@ -1087,7 +1106,7 @@ async def _execute_pending_tools(
 async def chat_completion(
     request: Request,
     body: ChatCompletionRequest = Body(...),
-    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
 ):
     """
     Chat completion endpoint with tool calling support.
@@ -1119,19 +1138,23 @@ async def chat_completion(
     )
     conversation = []
 
-    system_prompt = build_chat_system_prompt(
-        config=config,
-        allowed_cameras=allowed_cameras,
-        semantic_search_enabled=semantic_search_enabled,
-        attribute_classifications=attribute_classifications,
-    )
-
-    conversation.append(
-        {
-            "role": "system",
-            "content": system_prompt,
-        }
-    )
+    # Build the system message only when the client hasn't already pinned one.
+    # The first turn has no system message; we generate it (with the current
+    # timestamp) and return the whole chain so the client persists it. Later
+    # turns send it back verbatim, freezing the timestamp so the prompt prefix
+    # stays byte-identical and the model server's prompt cache keeps hitting.
+    if not body.messages or body.messages[0].role != "system":
+        conversation.append(
+            {
+                "role": "system",
+                "content": build_chat_system_prompt(
+                    config=config,
+                    allowed_cameras=allowed_cameras,
+                    semantic_search_enabled=semantic_search_enabled,
+                    attribute_classifications=attribute_classifications,
+                ),
+            }
+        )
 
     for msg in body.messages:
         msg_dict = {
@@ -1142,11 +1165,13 @@ async def chat_completion(
             msg_dict["tool_call_id"] = msg.tool_call_id
         if msg.name:
             msg_dict["name"] = msg.name
+        if msg.tool_calls is not None:
+            msg_dict["tool_calls"] = msg.tool_calls
 
         conversation.append(msg_dict)
 
     tool_iterations = 0
-    tool_calls: List[ToolCall] = []
+    tool_calls: list[ToolCall] = []
     max_iterations = body.max_tool_iterations
 
     logger.debug(
@@ -1156,11 +1181,20 @@ async def chat_completion(
 
     # True LLM streaming when client supports it and stream requested
     if body.stream and hasattr(genai_client, "chat_with_tools_stream"):
-        stream_tool_calls: List[ToolCall] = []
         stream_iterations = 0
 
         async def stream_body_llm():
-            nonlocal conversation, stream_tool_calls, stream_iterations
+            nonlocal conversation, stream_iterations
+
+            def _emit_chain(extra: Optional[list[dict[str, Any]]] = None):
+                # Return the full conversation (including the system message) so
+                # the client persists and replays it verbatim next turn.
+                chain = conversation + (extra or [])
+                return (
+                    json.dumps({"type": "messages", "messages": chain}).encode("utf-8")
+                    + b"\n"
+                )
+
             while stream_iterations < max_iterations:
                 if await request.is_disconnected():
                     logger.debug("Client disconnected, stopping chat stream")
@@ -1173,6 +1207,7 @@ async def chat_completion(
                     messages=conversation,
                     tools=tools if tools else None,
                     tool_choice="auto",
+                    enable_thinking=body.enable_thinking,
                 ):
                     if await request.is_disconnected():
                         logger.debug("Client disconnected, stopping chat stream")
@@ -1224,31 +1259,33 @@ async def chat_completion(
                                 )
                                 return
                             (
-                                executed_calls,
+                                _executed_calls,
                                 tool_results,
                                 extra_msgs,
                             ) = await _execute_pending_tools(
                                 pending, request, allowed_cameras
                             )
-                            stream_tool_calls.extend(executed_calls)
                             conversation.extend(tool_results)
                             conversation.extend(extra_msgs)
-                            yield (
-                                json.dumps(
-                                    {
-                                        "type": "tool_calls",
-                                        "tool_calls": [
-                                            tc.model_dump() for tc in stream_tool_calls
-                                        ],
-                                    }
-                                ).encode("utf-8")
-                                + b"\n"
-                            )
+                            # Emit the running chain so the client can render tool
+                            # calls live and replay them verbatim next turn.
+                            yield _emit_chain()
                             break
                         else:
+                            # Streaming never appends the final assistant message
+                            # to the conversation, so add it to the chain.
+                            yield _emit_chain(
+                                extra=[
+                                    {
+                                        "role": "assistant",
+                                        "content": msg.get("content"),
+                                    }
+                                ]
+                            )
                             yield (json.dumps({"type": "done"}).encode("utf-8") + b"\n")
                             return
             else:
+                yield _emit_chain()
                 yield json.dumps({"type": "done"}).encode("utf-8") + b"\n"
 
         return StreamingResponse(
@@ -1267,6 +1304,7 @@ async def chat_completion(
                 messages=conversation,
                 tools=tools if tools else None,
                 tool_choice="auto",
+                enable_thinking=body.enable_thinking,
             )
 
             if response.get("finish_reason") == "error":
@@ -1294,19 +1332,15 @@ async def chat_completion(
                 if body.stream:
                     final_reasoning = response.get("reasoning")
 
+                    chain = list(conversation)
+
                     async def stream_body() -> Any:
-                        if tool_calls:
-                            yield (
-                                json.dumps(
-                                    {
-                                        "type": "tool_calls",
-                                        "tool_calls": [
-                                            tc.model_dump() for tc in tool_calls
-                                        ],
-                                    }
-                                ).encode("utf-8")
-                                + b"\n"
+                        yield (
+                            json.dumps({"type": "messages", "messages": chain}).encode(
+                                "utf-8"
                             )
+                            + b"\n"
+                        )
                         # Emit the full reasoning trace up front when the
                         # underlying client did not stream it
                         if final_reasoning:
@@ -1342,6 +1376,7 @@ async def chat_completion(
                         finish_reason=response.get("finish_reason", "stop"),
                         tool_iterations=tool_iterations,
                         tool_calls=tool_calls,
+                        messages=list(conversation),
                     ).model_dump(),
                 )
 
@@ -1374,6 +1409,7 @@ async def chat_completion(
                 finish_reason="length",
                 tool_iterations=tool_iterations,
                 tool_calls=tool_calls,
+                messages=list(conversation),
             ).model_dump(),
         )
 

frigate/api/classification.py

@@ -280,7 +280,7 @@ async def create_face(request: Request, name: str):
     success response with details about the registration, or an error if face recognition
     is not enabled or the image cannot be processed.""",
 )
-async def register_face(request: Request, name: str, file: UploadFile):
+def register_face(request: Request, name: str, file: UploadFile):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
             status_code=400,
@@ -288,7 +288,7 @@ async def register_face(request: Request, name: str, file: UploadFile):
         )
 
     context: EmbeddingsContext = request.app.embeddings
-    result = None if context is None else context.register_face(name, await file.read())
+    result = None if context is None else context.register_face(name, file.file.read())
 
     if not isinstance(result, dict):
         return JSONResponse(
@@ -313,7 +313,7 @@ async def register_face(request: Request, name: str, file: UploadFile):
     registered faces in the system. Returns the recognized face name and confidence score,
     or an error if face recognition is not enabled or the image cannot be processed.""",
 )
-async def recognize_face(request: Request, file: UploadFile):
+def recognize_face(request: Request, file: UploadFile):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
             status_code=400,
@@ -321,7 +321,7 @@ async def recognize_face(request: Request, file: UploadFile):
         )
 
     context: EmbeddingsContext = request.app.embeddings
-    result = context.recognize_face(await file.read())
+    result = context.recognize_face(file.file.read())
 
     if not isinstance(result, dict):
         return JSONResponse(

frigate/api/debug_replay.py

@@ -86,10 +86,15 @@ class DebugReplayStopResponse(BaseModel):
 async def start_debug_replay(request: Request, body: DebugReplayStartBody):
     """Start a debug replay session asynchronously."""
     replay_manager = request.app.replay_manager
+    internal_port = request.app.frigate_config.networking.listen.internal
+    if type(internal_port) is str:
+        internal_port = int(internal_port.split(":")[-1])
+
     source = RecordingDebugReplaySource(
         source_camera=body.camera,
         start_ts=body.start_time,
         end_ts=body.end_time,
+        internal_port=internal_port,
     )
 
     try:

frigate/api/defs/request/app_body.py

@@ -2,6 +2,8 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Field
 
+from frigate.config import GenAIProviderEnum
+
 
 class AppConfigSetBody(BaseModel):
     requires_restart: int = 1
@@ -10,6 +12,13 @@ class AppConfigSetBody(BaseModel):
     skip_save: bool = False
 
 
+class GenAIProbeBody(BaseModel):
+    provider: GenAIProviderEnum
+    api_key: Optional[str] = None
+    base_url: Optional[str] = None
+    provider_options: Dict[str, Any] = Field(default_factory=dict)
+
+
 class AppPutPasswordBody(BaseModel):
     password: str
     old_password: Optional[str] = None

frigate/api/defs/request/chat_body.py

@@ -1,6 +1,6 @@
 """Chat API request models."""
 
-from typing import Optional
+from typing import Any, Optional
 
 from pydantic import BaseModel, Field
 
@@ -11,13 +11,29 @@ class ChatMessage(BaseModel):
     role: str = Field(
         description="Message role: 'user', 'assistant', 'system', or 'tool'"
     )
-    content: str = Field(description="Message content")
+    content: Optional[Any] = Field(
+        default=None,
+        description=(
+            "Message content. Usually a string, but may be a multimodal content "
+            "list (e.g. text + image_url) or null for assistant turns that only "
+            "request tool calls."
+        ),
+    )
     tool_call_id: Optional[str] = Field(
         default=None, description="For tool messages, the ID of the tool call"
     )
     name: Optional[str] = Field(
         default=None, description="For tool messages, the tool name"
     )
+    tool_calls: Optional[list[dict[str, Any]]] = Field(
+        default=None,
+        description=(
+            "For assistant messages replayed from prior turns, the OpenAI-format "
+            "tool calls the model previously requested. Replaying these verbatim "
+            "keeps the conversation prefix byte-for-byte identical so the model "
+            "server's prompt cache hits on follow-up turns."
+        ),
+    )
 
 
 class ChatCompletionRequest(BaseModel):
@@ -36,3 +52,10 @@ class ChatCompletionRequest(BaseModel):
         default=False,
         description="If true, stream the final assistant response in the body as newline-delimited JSON.",
     )
+    enable_thinking: Optional[bool] = Field(
+        default=None,
+        description=(
+            "Per-request thinking toggle. None means use the provider default. "
+            "Ignored by providers that do not expose a per-request thinking switch."
+        ),
+    )

frigate/api/defs/response/chat_response.py

@@ -56,3 +56,12 @@ class ChatCompletionResponse(BaseModel):
         default_factory=list,
         description="List of tool calls that were executed during this completion",
     )
+    messages: list[dict[str, Any]] = Field(
+        default_factory=list,
+        description=(
+            "The full conversation chain, including the system message. Persist "
+            "and replay this verbatim on the next request so the prompt prefix "
+            "stays byte-identical and the model server's prompt cache keeps "
+            "hitting."
+        ),
+    )

frigate/api/fastapi_app.py

@@ -1,3 +1,4 @@
+import asyncio
 import logging
 import re
 from typing import Optional
@@ -36,7 +37,7 @@ from frigate.comms.event_metadata_updater import (
 from frigate.config import FrigateConfig
 from frigate.config.camera.updater import CameraConfigUpdatePublisher
 from frigate.config.profile_manager import ProfileManager
-from frigate.debug_replay import DebugReplayManager
+from frigate.debug_replay import DebugReplayManager, debug_replay_auto_stop_watchdog
 from frigate.embeddings import EmbeddingsContext
 from frigate.genai import GenAIClientManager
 from frigate.ptz.onvif import OnvifController
@@ -116,6 +117,11 @@ def create_fastapi_app(
     @app.on_event("startup")
     async def startup():
         logger.info("FastAPI started")
+        asyncio.create_task(
+            debug_replay_auto_stop_watchdog(
+                replay_manager, frigate_config, config_publisher
+            )
+        )
 
     # Rate limiter (used for login endpoint)
     if frigate_config.auth.failed_login_rate_limit is None:

frigate/api/motion_search.py

@@ -41,12 +41,6 @@ class MotionSearchRequest(BaseModel):
         le=100.0,
         description="Minimum change area as a percentage of the ROI",
     )
-    frame_skip: int = Field(
-        default=5,
-        ge=1,
-        le=30,
-        description="Process every Nth frame (1=all frames, 5=every 5th frame)",
-    )
     parallel: bool = Field(
         default=False,
         description="Enable parallel scanning across segments",
@@ -97,6 +91,8 @@ class MotionSearchStatusResponse(BaseModel):
     total_frames_processed: Optional[int] = None
     error_message: Optional[str] = None
     metrics: Optional[MotionSearchMetricsResponse] = None
+    scanning_timestamp: Optional[float] = None
+    progress: Optional[float] = None
 
 
 @router.post(
@@ -151,7 +147,6 @@ async def start_motion_search(
         polygon_points=body.polygon_points,
         threshold=body.threshold,
         min_area=body.min_area,
-        frame_skip=body.frame_skip,
         parallel=body.parallel,
         max_results=body.max_results,
     )
@@ -231,6 +226,9 @@ async def get_motion_search_status_endpoint(
     if job.metrics:
         response_content["metrics"] = job.metrics.to_dict()
 
+    response_content["scanning_timestamp"] = job.scanning_timestamp
+    response_content["progress"] = job.progress
+
     return JSONResponse(content=response_content)
 
 

frigate/api/record.py

@@ -299,22 +299,36 @@ async def no_recordings(
         .iterator()
     )
 
-    # Convert recordings to list of (start, end) tuples
+    # Convert recordings to list of (start, end) tuples, ordered by start_time
     recordings = [(r["start_time"], r["end_time"]) for r in data]
 
+    # Merge overlapping/adjacent recordings into covered intervals. The query
+    # orders by start_time, so a single pass merges them
+    covered: list[tuple[float, float]] = []
+    for rec_start, rec_end in recordings:
+        if covered and rec_start <= covered[-1][1]:
+            covered[-1] = (covered[-1][0], max(covered[-1][1], rec_end))
+        else:
+            covered.append((rec_start, rec_end))
+
     # Iterate through time segments and check if each has any recording
     no_recording_segments = []
     current = after
     current_gap_start = None
+    idx = 0
+    covered_count = len(covered)
 
     while current < before:
         segment_end = min(current + scale, before)
 
-        # Check if this segment overlaps with any recording
-        has_recording = any(
-            rec_start < segment_end and rec_end > current
-            for rec_start, rec_end in recordings
-        )
+        # Advance past covered intervals that end before this segment begins;
+        # they cannot overlap this or any later segment.
+        while idx < covered_count and covered[idx][1] <= current:
+            idx += 1
+
+        # A covered interval overlaps the segment when it starts before the
+        # segment ends (its end is already known to be > current).
+        has_recording = idx < covered_count and covered[idx][0] < segment_end
 
         if not has_recording:
             # This segment has no recordings

frigate/api/review.py

@@ -605,9 +605,10 @@ def motion_activity(
         if not filtered:
             return JSONResponse(content=[])
         camera_list = list(filtered)
-        clauses.append((Recordings.camera << camera_list))
     else:
-        clauses.append((Recordings.camera << allowed_cameras))
+        camera_list = list(allowed_cameras)
+
+    clauses.append((Recordings.camera << camera_list))
 
     data: list[Recordings] = (
         Recordings.select(
@@ -635,14 +636,12 @@ def motion_activity(
     df.set_index(["start_time"], inplace=True)
 
     # normalize data
-    motion = (
-        df["motion"]
-        .resample(f"{scale}s")
-        .apply(lambda x: max(x, key=abs, default=0.0))
-        .fillna(0.0)
-        .to_frame()
-    )
-    cameras = df["camera"].resample(f"{scale}s").agg(lambda x: ",".join(set(x)))
+    motion = df["motion"].resample(f"{scale}s").max().fillna(0.0).to_frame()
+
+    if len(camera_list) == 1:
+        cameras = df["camera"].resample(f"{scale}s").first().fillna("")
+    else:
+        cameras = df["camera"].resample(f"{scale}s").agg(lambda x: ",".join(set(x)))
     df = motion.join(cameras)
 
     length = df.shape[0]
@@ -658,6 +657,11 @@ def motion_activity(
         else:
             df.iloc[i : i + chunk, 0] = 0.0
 
+    # Drop resample gap-fill buckets. The resample above emits a row for every
+    # {scale}s bucket spanning the range, and buckets with no recording get a
+    # motion of 0 (from fillna) and an empty camera (from joining an empty set).
+    df = df[df["camera"] != ""]
+
     # change types for output
     df.index = df.index.astype(int) // (10**9)
     normalized = df.reset_index().to_dict("records")

frigate/app.py

@@ -343,12 +343,24 @@ class FrigateApp:
         )
         self.dispatcher.profile_manager = self.profile_manager
 
+    def restore_active_profile(self) -> None:
+        """Re-activate the persisted profile after subscribers are connected.
+
+        ZMQ PUB/SUB drops messages with no subscribers, so activation must
+        run after every config_updater subscriber is up.
+        """
+        if self.profile_manager is None:
+            return
+
         persisted = ProfileManager.load_persisted_profile()
         if persisted and any(
             persisted in cam.profiles for cam in self.config.cameras.values()
         ):
             logger.info("Restoring persisted profile '%s'", persisted)
-            self.profile_manager.activate_profile(persisted)
+            # runtime overrides are layered on top via restore_runtime_state()
+            self.profile_manager.activate_profile(
+                persisted, clear_runtime_overrides=False
+            )
 
     def start_detectors(self) -> None:
         for name in self.config.cameras.keys():
@@ -612,6 +624,10 @@ class FrigateApp:
         self.start_record_cleanup()
         self.start_watchdog()
 
+        # restore persisted runtime overrides on top of config
+        self.restore_active_profile()
+        self.dispatcher.restore_runtime_state()
+
         self.init_auth()
 
         try:

frigate/camera/maintainer.py

@@ -14,6 +14,7 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
 )
+from frigate.const import REPLAY_CAMERA_PREFIX
 from frigate.models import Regions
 from frigate.util.builtin import empty_and_close_queue
 from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
@@ -50,6 +51,7 @@ class CameraMaintainer(threading.Thread):
             [
                 CameraConfigUpdateEnum.add,
                 CameraConfigUpdateEnum.remove,
+                CameraConfigUpdateEnum.refresh,
             ],
         )
         self.shm_count = self.__calculate_shm_frame_count()
@@ -202,6 +204,25 @@ class CameraMaintainer(threading.Thread):
                 capture_process.terminate()
                 capture_process.join()
 
+    def __unlink_camera_frame_slots(self, camera: str) -> None:
+        """Drop the camera's per-frame YUV SHM segments from this
+        process's frame_manager and unlink them at the OS level.
+
+        Safe to call after the camera's capture/processor subprocesses
+        have been joined — they no longer hold mappings, so unlink frees
+        the segments immediately. Other long-lived processes that opened
+        these slots will continue using their existing mappings until
+        they call frame_manager.get with a shape that no longer fits
+        (the get path drops and reopens stale refs).
+        """
+        prefix = f"{camera}_frame"
+        names = [n for n in list(self.frame_manager.shm_store) if n.startswith(prefix)]
+        for name in names:
+            try:
+                self.frame_manager.delete(name)
+            except Exception as exc:
+                logger.debug("Could not unlink SHM %s: %s", name, exc)
+
     def __stop_camera_process(self, camera: str) -> None:
         camera_process = self.camera_processes.get(camera)
         if camera_process is not None:
@@ -253,12 +274,45 @@ class CameraMaintainer(threading.Thread):
                     for camera in updated_cameras:
                         self.__stop_camera_capture_process(camera)
                         self.__stop_camera_process(camera)
+                        self.__unlink_camera_frame_slots(camera)
                         self.capture_processes.pop(camera, None)
                         self.camera_processes.pop(camera, None)
                         self.camera_stop_events.pop(camera, None)
                         self.region_grids.pop(camera, None)
                         self.camera_metrics.pop(camera, None)
                         self.ptz_metrics.pop(camera, None)
+                elif update_type == CameraConfigUpdateEnum.refresh.name:
+                    # Recycle replay cameras so detect width/height/fps
+                    # propagate through ffmpeg args, SHM sizing, and the
+                    # region grid. Regular cameras detect change still
+                    # requires a full restart.
+                    for camera in updated_cameras:
+                        if not camera.startswith(REPLAY_CAMERA_PREFIX):
+                            continue
+
+                        new_config = self.update_subscriber.camera_configs.get(camera)
+                        if new_config is None:
+                            # remove arrived in the same batch
+                            continue
+
+                        if (
+                            camera not in self.camera_processes
+                            and camera not in self.capture_processes
+                        ):
+                            continue
+
+                        # rebuild ffmpeg cmds on the shared config so the
+                        # new subprocesses spawn with current args
+                        new_config.recreate_ffmpeg_cmds()
+
+                        self.__stop_camera_capture_process(camera)
+                        self.__stop_camera_process(camera)
+                        self.__unlink_camera_frame_slots(camera)
+                        self.capture_processes.pop(camera, None)
+                        self.camera_processes.pop(camera, None)
+
+                        self.__start_camera_processor(camera, new_config, runtime=True)
+                        self.__start_camera_capture(camera, new_config, runtime=True)
 
         # ensure the capture processes are done
         for camera in self.capture_processes.keys():

frigate/camera/state.py

@@ -45,6 +45,7 @@ class CameraState:
         self.frame_cache: dict[float, dict[str, Any]] = {}
         self.zone_objects: defaultdict[str, list[Any]] = defaultdict(list)
         self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
+        self._last_frame_shape: tuple[int, int] = self.camera_config.frame_shape_yuv
         self.current_frame_lock = threading.Lock()
         self.current_frame_time = 0.0
         self.motion_boxes: list[tuple[int, int, int, int]] = []
@@ -303,6 +304,42 @@ class CameraState:
     def on(self, event_type: str, callback: Callable[..., Any]) -> None:
         self.callbacks[event_type].append(callback)
 
+    def _discard_stale_resolution_state(
+        self, current_detections: dict[str, dict[str, Any]]
+    ) -> bool:
+        """Drop tracked state when the camera's detect resolution has
+        changed, and signal the caller to skip this batch if it contains
+        out-of-bounds boxes from the pre-recycle detect process.
+
+        Returns True when the batch should be skipped entirely.
+        """
+        # detect resolution changed — drop tracked state so old-grid
+        # boxes don't leak through end-callbacks
+        current_shape = self.camera_config.frame_shape_yuv
+        if current_shape != self._last_frame_shape:
+            logger.debug(
+                f"{self.name}: detect resolution changed {self._last_frame_shape} -> {current_shape}, dropping tracked state"
+            )
+            with self.current_frame_lock:
+                self.tracked_objects.clear()
+                self.motion_boxes = []
+                self.regions = []
+            self._last_frame_shape = current_shape
+
+        # drop in-flight batches from the pre-recycle detect process
+        # whose boxes exceed the current detect resolution
+        detect = self.camera_config.detect
+        if detect.width is not None and detect.height is not None:
+            for obj in current_detections.values():
+                box = obj.get("box")
+                if box and (box[2] > detect.width or box[3] > detect.height):
+                    logger.debug(
+                        f"{self.name}: dropping stale-resolution detection batch (box {box} exceeds {detect.width}x{detect.height})"
+                    )
+                    return True
+
+        return False
+
     def update(
         self,
         frame_name: str,
@@ -311,6 +348,9 @@ class CameraState:
         motion_boxes: list[tuple[int, int, int, int]],
         regions: list[tuple[int, int, int, int]],
     ) -> None:
+        if self._discard_stale_resolution_state(current_detections):
+            return
+
         current_frame = self.frame_manager.get(
             frame_name, self.camera_config.frame_shape_yuv
         )
@@ -332,14 +372,18 @@ class CameraState:
                 current_detections[id],
             )
 
-            # add initial frame to frame cache
-            logger.debug(
-                f"{self.name}: New object, adding {frame_time} to frame cache for {id}"
-            )
-            self.frame_cache[frame_time] = {
-                "frame": np.copy(current_frame),  # type: ignore[arg-type]
-                "object_id": id,
-            }
+            # Skip caching when the frame buffer isn't readable — e.g.
+            # frame_manager.get returned None because the SHM segment was
+            # unlinked or hasn't been recreated yet during a camera
+            # add/remove cycle.
+            if current_frame is not None:
+                logger.debug(
+                    f"{self.name}: New object, adding {frame_time} to frame cache for {id}"
+                )
+                self.frame_cache[frame_time] = {
+                    "frame": np.copy(current_frame),
+                    "object_id": id,
+                }
 
             # save initial thumbnail data and best object
             thumbnail_data = {

frigate/comms/dispatcher.py

@@ -3,11 +3,13 @@
 import datetime
 import json
 import logging
+from collections.abc import Iterable
 from typing import Any, Callable, Optional, cast
 
 from frigate.camera import PTZMetrics
 from frigate.camera.activity_manager import AudioActivityManager, CameraActivityManager
 from frigate.comms.base_communicator import Communicator
+from frigate.comms.runtime_state import RuntimeStatePersistence
 from frigate.comms.webpush import WebPushClient
 from frigate.config import BirdseyeModeEnum, FrigateConfig
 from frigate.config.camera.updater import (
@@ -67,6 +69,7 @@ class Dispatcher:
         self.embeddings_reindex: dict[str, Any] = {}
         self.birdseye_layout: dict[str, Any] = {}
         self.audio_transcription_state: str = "idle"
+        self._runtime_state = RuntimeStatePersistence()
         self._camera_settings_handlers: dict[str, Callable] = {
             "audio": self._on_audio_command,
             "audio_transcription": self._on_audio_transcription_command,
@@ -397,6 +400,60 @@ class Dispatcher:
         for comm in self.comms:
             comm.stop()
 
+    def restore_runtime_state(self) -> None:
+        """Replay persisted runtime overrides through the camera settings handlers.
+
+        Called once after Frigate startup completes so processing threads can
+        receive the resulting ``config_updater`` broadcasts. Unknown cameras
+        and topics are skipped; handler exceptions are logged and replay
+        continues for remaining entries.
+        """
+        state = self._runtime_state.load()
+        for camera_name, features in state.items():
+            if camera_name not in self.config.cameras:
+                continue
+            for topic, value in features.items():
+                handler = self._camera_settings_handlers.get(topic)
+                if handler is None:
+                    continue
+                payload = "ON" if value else "OFF"
+                try:
+                    handler(camera_name, payload)
+                except Exception:
+                    logger.exception(
+                        "Failed to restore runtime state %s.%s=%s",
+                        camera_name,
+                        topic,
+                        payload,
+                    )
+                    continue
+                logger.info(
+                    "Restored runtime state: %s.%s=%s",
+                    camera_name,
+                    topic,
+                    payload,
+                )
+
+    def clear_runtime_state_for_yaml_keys(self, dotted_keys: Iterable[str]) -> None:
+        """Clear stored runtime overrides for YAML keys that were just rewritten.
+
+        Called by ``/api/config/set`` after a successful YAML save so an
+        explicit settings-UI save isn't silently overridden by an older
+        runtime toggle on the next restart.
+        """
+        self._runtime_state.clear_for_yaml_keys(dotted_keys)
+
+    def clear_runtime_state(self) -> None:
+        """Wipe every stored runtime override.
+
+        Called when a profile is activated or deactivated. A profile switch
+        changes the layer below the runtime overrides, so the stored
+        "steady state" is no longer valid and must be reset; otherwise a
+        subsequent restart would replay stale overrides on top of the new
+        profile-derived in-memory state.
+        """
+        self._runtime_state.clear_all()
+
     def _on_detect_command(self, camera_name: str, payload: str) -> None:
         """Callback for detect topic."""
         detect_settings = self.config.cameras[camera_name].detect
@@ -428,6 +485,7 @@ class Dispatcher:
             CameraConfigUpdateTopic(CameraConfigUpdateEnum.detect, camera_name),
             detect_settings,
         )
+        self._runtime_state.set(camera_name, "detect", detect_settings.enabled)
         self.publish(f"{camera_name}/detect/state", payload, retain=True)
 
     def _on_enabled_command(self, camera_name: str, payload: str) -> None:
@@ -452,6 +510,7 @@ class Dispatcher:
             CameraConfigUpdateTopic(CameraConfigUpdateEnum.enabled, camera_name),
             camera_settings.enabled,
         )
+        self._runtime_state.set(camera_name, "enabled", camera_settings.enabled)
         self.publish(f"{camera_name}/enabled/state", payload, retain=True)
 
     def _on_motion_command(self, camera_name: str, payload: str) -> None:
@@ -614,6 +673,7 @@ class Dispatcher:
             CameraConfigUpdateTopic(CameraConfigUpdateEnum.audio, camera_name),
             audio_settings,
         )
+        self._runtime_state.set(camera_name, "audio", audio_settings.enabled)
         self.publish(f"{camera_name}/audio/state", payload, retain=True)
 
     def _on_audio_transcription_command(self, camera_name: str, payload: str) -> None:
@@ -670,6 +730,7 @@ class Dispatcher:
             CameraConfigUpdateTopic(CameraConfigUpdateEnum.record, camera_name),
             record_settings,
         )
+        self._runtime_state.set(camera_name, "recordings", record_settings.enabled)
         self.publish(f"{camera_name}/recordings/state", payload, retain=True)
 
     def _on_snapshots_command(self, camera_name: str, payload: str) -> None:
@@ -689,6 +750,7 @@ class Dispatcher:
             CameraConfigUpdateTopic(CameraConfigUpdateEnum.snapshots, camera_name),
             snapshots_settings,
         )
+        self._runtime_state.set(camera_name, "snapshots", snapshots_settings.enabled)
         self.publish(f"{camera_name}/snapshots/state", payload, retain=True)
 
     def _on_ptz_command(self, camera_name: str, payload: str | bytes) -> None:

frigate/comms/runtime_state.py

@@ -0,0 +1,163 @@
+"""Persistence layer for dispatcher runtime state overrides."""
+
+import json
+import logging
+import os
+from collections.abc import Iterable
+from typing import Any
+
+from filelock import FileLock, Timeout
+
+from frigate.util.config import find_config_file
+
+logger = logging.getLogger(__name__)
+
+
+class RuntimeStatePersistence:
+    """Persist last-known runtime states for dispatcher toggles.
+
+    Stores boolean overrides applied to camera-level toggles by the dispatcher.
+    Overrides are replayed at startup on top of the YAML-derived in-memory
+    config, so changes made via MQTT or the live-view UI survive a restart.
+    """
+
+    # Maps dispatcher topic name -> YAML key suffix under cameras.<cam>
+    TRACKED_TOPICS: dict[str, str] = {
+        "enabled": "enabled",
+        "detect": "detect.enabled",
+        "snapshots": "snapshots.enabled",
+        "recordings": "record.enabled",
+        "audio": "audio.enabled",
+    }
+
+    _SUFFIX_TO_TOPIC: dict[str, str] = {v: k for k, v in TRACKED_TOPICS.items()}
+
+    def __init__(self) -> None:
+        self._path = os.path.join(
+            os.path.dirname(find_config_file()), ".runtime_state.json"
+        )
+        self._lock_path = f"{self._path}.lock"
+        self._lock_timeout = 5
+
+    def load(self) -> dict[str, dict[str, bool]]:
+        """Return {camera: {topic: bool}} or {} if missing/corrupt."""
+        try:
+            with FileLock(self._lock_path, timeout=self._lock_timeout):
+                data = self._read_locked()
+        except Timeout:
+            logger.error("Timed out acquiring runtime state lock for load")
+            return {}
+        cameras = data.get("cameras", {})
+        if not isinstance(cameras, dict):
+            return {}
+        # Filter out malformed camera entries so callers can trust the shape.
+        return {
+            name: features
+            for name, features in cameras.items()
+            if isinstance(features, dict)
+        }
+
+    def set(self, camera: str, topic: str, value: bool) -> None:
+        """Persist a single (camera, topic, value). No-op if topic untracked."""
+        if topic not in self.TRACKED_TOPICS:
+            return
+        try:
+            with FileLock(self._lock_path, timeout=self._lock_timeout):
+                data = self._read_locked()
+                cameras = data.setdefault("cameras", {})
+                if not isinstance(cameras, dict):
+                    cameras = {}
+                    data["cameras"] = cameras
+                cam = cameras.setdefault(camera, {})
+                if not isinstance(cam, dict):
+                    cam = {}
+                    cameras[camera] = cam
+                cam[topic] = bool(value)
+                self._write_locked(data)
+        except Timeout:
+            logger.error("Timed out persisting runtime state for %s/%s", camera, topic)
+        except OSError:
+            logger.exception("Failed to persist runtime state for %s/%s", camera, topic)
+
+    def clear_all(self) -> None:
+        """Wipe every stored runtime override.
+
+        Called when the "layer below" changes in a way that invalidates all
+        runtime overrides for the current session (currently: profile
+        activation or deactivation).
+        """
+        try:
+            with FileLock(self._lock_path, timeout=self._lock_timeout):
+                if not os.path.exists(self._path):
+                    return
+                self._write_locked({"cameras": {}})
+        except Timeout:
+            logger.error("Timed out clearing runtime state")
+        except OSError:
+            logger.exception("Failed to clear runtime state")
+
+    def clear_for_yaml_keys(self, dotted_keys: Iterable[str]) -> None:
+        """Remove stored entries whose YAML key was just rewritten.
+
+        Each dotted key must be of the form ``cameras.<camera>.<suffix>``.
+        Keys that don't match a tracked topic are ignored.
+        """
+        to_remove: list[tuple[str, str]] = []
+        for key in dotted_keys:
+            parts = key.split(".")
+            if len(parts) < 3 or parts[0] != "cameras":
+                continue
+            camera = parts[1]
+            suffix = ".".join(parts[2:])
+            topic = self._SUFFIX_TO_TOPIC.get(suffix)
+            if topic is not None:
+                to_remove.append((camera, topic))
+
+        if not to_remove:
+            return
+
+        try:
+            with FileLock(self._lock_path, timeout=self._lock_timeout):
+                data = self._read_locked()
+                cameras = data.get("cameras")
+                if not isinstance(cameras, dict):
+                    return
+                changed = False
+                for camera, topic in to_remove:
+                    cam = cameras.get(camera)
+                    if isinstance(cam, dict) and topic in cam:
+                        del cam[topic]
+                        changed = True
+                        if not cam:
+                            del cameras[camera]
+                if changed:
+                    self._write_locked(data)
+        except Timeout:
+            logger.error("Timed out clearing runtime state for YAML keys")
+        except OSError:
+            logger.exception("Failed to clear runtime state for YAML keys")
+
+    def _read_locked(self) -> dict[str, Any]:
+        """Read the JSON file while the FileLock is held.
+
+        Returns ``{}`` on a missing or corrupt file so the caller can write a
+        fresh structure on the next mutation.
+        """
+        if not os.path.exists(self._path):
+            return {}
+        try:
+            with open(self._path, "r") as f:
+                data = json.load(f)
+        except (OSError, json.JSONDecodeError):
+            logger.exception(
+                "Failed to read runtime state file %s; starting fresh", self._path
+            )
+            return {}
+        return data if isinstance(data, dict) else {}
+
+    def _write_locked(self, data: dict[str, Any]) -> None:
+        """Atomically write the JSON file while the FileLock is held."""
+        tmp_path = f"{self._path}.tmp"
+        with open(tmp_path, "w") as f:
+            json.dump(data, f, indent=2, sort_keys=True)
+        os.replace(tmp_path, self._path)

frigate/config/camera/audio.py

@@ -9,7 +9,7 @@ from ..base import FrigateBaseModel
 __all__ = ["AudioConfig", "AudioFilterConfig"]
 
 
-DEFAULT_LISTEN_AUDIO = ["bark", "fire_alarm", "scream", "speech", "yell"]
+DEFAULT_LISTEN_AUDIO = ["bark", "fire_alarm", "speech", "yell"]
 
 
 class AudioFilterConfig(FrigateBaseModel):
@@ -41,7 +41,7 @@ class AudioConfig(FrigateBaseModel):
     listen: list[str] = Field(
         default=DEFAULT_LISTEN_AUDIO,
         title="Listen types",
-        description="List of audio event types to detect (for example: bark, fire_alarm, scream, speech, yell).",
+        description="List of audio event types to detect (for example: bark, fire_alarm, speech, yell).",
     )
     filters: Optional[dict[str, AudioFilterConfig]] = Field(
         None,

frigate/config/camera/camera.py

@@ -100,8 +100,8 @@ class CameraConfig(FrigateBaseModel):
         description="Settings for face detection and recognition for this camera.",
     )
     ffmpeg: CameraFfmpegConfig = Field(
-        title="FFmpeg",
-        description="FFmpeg settings including binary path, args, hwaccel options, and per-role output args.",
+        title="Streams (FFmpeg)",
+        description="Camera stream inputs and FFmpeg options, including binary path, args, hwaccel, and per-role output args.",
     )
     live: CameraLiveConfig = Field(
         default_factory=CameraLiveConfig,
@@ -146,7 +146,7 @@ class CameraConfig(FrigateBaseModel):
     timestamp_style: TimestampStyleConfig = Field(
         default_factory=TimestampStyleConfig,
         title="Timestamp style",
-        description="Styling options for in-feed timestamps applied to recordings and snapshots.",
+        description="Styling options for timestamps applied to snapshots and Debug view.",
     )
 
     # Options without global fallback

frigate/config/camera/ffmpeg.py

@@ -3,7 +3,7 @@ from typing import Union
 
 from pydantic import Field, field_validator
 
-from frigate.const import DEFAULT_FFMPEG_VERSION, INCLUDED_FFMPEG_VERSIONS
+from frigate.util.config import resolve_ffmpeg_path
 
 from ..base import FrigateBaseModel
 from ..env import EnvString
@@ -49,7 +49,7 @@ class FfmpegConfig(FrigateBaseModel):
     path: str = Field(
         default="default",
         title="FFmpeg path",
-        description='Path to the FFmpeg binary to use or a version alias ("5.0" or "7.0").',
+        description='Path to the FFmpeg binary to use or a version alias ("7.0" or "8.0").',
     )
     global_args: Union[str, list[str]] = Field(
         default=FFMPEG_GLOBAL_ARGS_DEFAULT,
@@ -90,21 +90,11 @@ class FfmpegConfig(FrigateBaseModel):
 
     @property
     def ffmpeg_path(self) -> str:
-        if self.path == "default":
-            return f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
-        elif self.path in INCLUDED_FFMPEG_VERSIONS:
-            return f"/usr/lib/ffmpeg/{self.path}/bin/ffmpeg"
-        else:
-            return f"{self.path}/bin/ffmpeg"
+        return resolve_ffmpeg_path(self.path, "ffmpeg")
 
     @property
     def ffprobe_path(self) -> str:
-        if self.path == "default":
-            return f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffprobe"
-        elif self.path in INCLUDED_FFMPEG_VERSIONS:
-            return f"/usr/lib/ffmpeg/{self.path}/bin/ffprobe"
-        else:
-            return f"{self.path}/bin/ffprobe"
+        return resolve_ffmpeg_path(self.path, "ffprobe")
 
 
 class CameraRoleEnum(str, Enum):

frigate/config/camera/genai.py

@@ -37,7 +37,7 @@ class GenAIConfig(FrigateBaseModel):
         description="Base URL for self-hosted or compatible providers (for example an Ollama instance).",
     )
     model: str = Field(
-        default="gpt-4o",
+        default="",
         title="Model",
         description="The model to use from the provider for generating descriptions or summaries.",
     )

frigate/config/camera/snapshots.py

@@ -3,7 +3,6 @@ from typing import Optional
 from pydantic import Field
 
 from ..base import FrigateBaseModel
-from .record import RetainModeEnum
 
 __all__ = ["SnapshotsConfig", "RetainConfig"]
 
@@ -14,11 +13,6 @@ class RetainConfig(FrigateBaseModel):
         title="Default retention",
         description="Default number of days to retain snapshots.",
     )
-    mode: RetainModeEnum = Field(
-        default=RetainModeEnum.motion,
-        title="Retention mode",
-        description="Mode for retention: all (save all segments), motion (save segments with motion), or active_objects (save segments with active objects).",
-    )
     objects: dict[str, float] = Field(
         default_factory=dict,
         title="Object retention",

frigate/config/camera/ui.py

@@ -16,3 +16,8 @@ class CameraUiConfig(FrigateBaseModel):
         title="Show in UI",
         description="Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again.",
     )
+    review: bool = Field(
+        default=True,
+        title="Show in review",
+        description="Toggle whether this camera is visible in review (the review page and its camera filter, motion review, and the history view).",
+    )

frigate/config/camera/updater.py

@@ -26,6 +26,7 @@ class CameraConfigUpdateEnum(str, Enum):
     object_genai = "object_genai"
     onvif = "onvif"
     record = "record"
+    refresh = "refresh"  # signals the camera maintainer to recycle the camera process
     remove = "remove"  # for removing a camera
     review = "review"
     review_genai = "review_genai"
@@ -72,7 +73,12 @@ class CameraConfigUpdateSubscriber:
 
         base_topic = "config/cameras"
 
-        if len(self.camera_configs) == 1:
+        # global subscribers must hear every camera; only narrow per-camera workers
+        is_global_subscriber = (
+            CameraConfigUpdateEnum.add in self.topics
+            or CameraConfigUpdateEnum.remove in self.topics
+        )
+        if not is_global_subscriber and len(self.camera_configs) == 1:
             base_topic += f"/{list(self.camera_configs.keys())[0]}"
 
         self.subscriber = ConfigSubscriber(
@@ -84,8 +90,8 @@ class CameraConfigUpdateSubscriber:
         self, camera: str, update_type: CameraConfigUpdateEnum, updated_config: Any
     ) -> None:
         if update_type == CameraConfigUpdateEnum.add:
-            self.config.cameras[camera] = updated_config
-            self.camera_configs[camera] = updated_config
+            shared = self.config.cameras.setdefault(camera, updated_config)
+            self.camera_configs[camera] = shared
             return
         elif update_type == CameraConfigUpdateEnum.remove:
             self.config.cameras.pop(camera, None)

frigate/config/config.py

@@ -326,6 +326,47 @@ def verify_required_zones_exist(camera_config: CameraConfig) -> None:
             )
 
 
+def verify_profile_overrides_match_base(camera_config: CameraConfig) -> None:
+    """Verify that profile zone and mask IDs reference entries defined on the base camera."""
+    for profile_name, profile in camera_config.profiles.items():
+        if profile.zones:
+            for zone_name in profile.zones:
+                if zone_name not in camera_config.zones:
+                    raise ValueError(
+                        f"Camera '{camera_config.name}' profile '{profile_name}' defines "
+                        f"zone '{zone_name}' that does not exist on the base config"
+                    )
+
+        if profile.motion and profile.motion.mask:
+            for mask_name in profile.motion.mask:
+                if mask_name not in camera_config.motion.mask:
+                    raise ValueError(
+                        f"Camera '{camera_config.name}' profile '{profile_name}' defines "
+                        f"motion mask '{mask_name}' that does not exist on the base config"
+                    )
+
+        if profile.objects:
+            for mask_name in profile.objects.mask or {}:
+                if mask_name not in (camera_config.objects.mask or {}):
+                    raise ValueError(
+                        f"Camera '{camera_config.name}' profile '{profile_name}' defines "
+                        f"object mask '{mask_name}' that does not exist on the base config"
+                    )
+            for label, filter_config in (profile.objects.filters or {}).items():
+                base_filter = (camera_config.objects.filters or {}).get(label)
+                profile_filter_masks = (
+                    filter_config.mask if filter_config else None
+                ) or {}
+                base_filter_masks = (base_filter.mask if base_filter else None) or {}
+                for mask_name in profile_filter_masks:
+                    if mask_name not in base_filter_masks:
+                        raise ValueError(
+                            f"Camera '{camera_config.name}' profile '{profile_name}' defines "
+                            f"object mask '{mask_name}' for '{label}' that does not exist "
+                            f"on the base config"
+                        )
+
+
 def verify_autotrack_zones(camera_config: CameraConfig) -> ValueError | None:
     """Verify that required_zones are specified when autotracking is enabled."""
     if (
@@ -639,6 +680,13 @@ class FrigateConfig(FrigateBaseModel):
         if self.ffmpeg.hwaccel_args == "auto":
             self.ffmpeg.hwaccel_args = auto_detect_hwaccel()
 
+        # Resolve global export hwaccel_args so it matches the per-camera
+        # resolution below. Without this, every camera reads as overriding
+        # record.export.hwaccel_args because the global stays "auto" while
+        # the camera value gets resolved to the actual args list.
+        if self.record.export.hwaccel_args == "auto":
+            self.record.export.hwaccel_args = self.ffmpeg.hwaccel_args
+
         # Populate global audio filters from listen. Existing user-defined
         # entries for labels not in listen are preserved but unused at runtime.
         if self.audio.filters is None:
@@ -952,6 +1000,7 @@ class FrigateConfig(FrigateBaseModel):
             verify_recording_segments_setup_with_reasonable_time(camera_config)
             verify_zone_objects_are_tracked(camera_config)
             verify_required_zones_exist(camera_config)
+            verify_profile_overrides_match_base(camera_config)
             verify_autotrack_zones(camera_config)
             verify_motion_and_detect(camera_config)
             verify_objects_track(camera_config, labelmap_objects)

frigate/config/profile_manager.py

@@ -5,7 +5,7 @@ import json
 import logging
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Optional
+from typing import Any, Callable, Optional
 
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
@@ -34,6 +34,45 @@ PROFILE_SECTION_UPDATES: dict[str, CameraConfigUpdateEnum] = {
     "zones": CameraConfigUpdateEnum.zones,
 }
 
+# Retained MQTT switch topics per profile section, with a payload getter.
+# Republished on profile change so MQTT/HA don't show a stale toggle.
+SECTION_STATE_TOPICS: dict[str, list[tuple[str, Callable[[Any], Any]]]] = {
+    "audio": [("audio", lambda c: "ON" if c.audio.enabled else "OFF")],
+    "birdseye": [
+        ("birdseye", lambda c: "ON" if c.birdseye.enabled else "OFF"),
+        (
+            "birdseye_mode",
+            lambda c: c.birdseye.mode.value.upper() if c.birdseye.enabled else "OFF",
+        ),
+    ],
+    "detect": [("detect", lambda c: "ON" if c.detect.enabled else "OFF")],
+    "motion": [
+        ("motion", lambda c: "ON" if c.motion.enabled else "OFF"),
+        ("improve_contrast", lambda c: "ON" if c.motion.improve_contrast else "OFF"),
+        ("motion_threshold", lambda c: c.motion.threshold),
+        ("motion_contour_area", lambda c: c.motion.contour_area),
+    ],
+    "notifications": [
+        ("notifications", lambda c: "ON" if c.notifications.enabled else "OFF"),
+    ],
+    "objects": [
+        ("object_descriptions", lambda c: "ON" if c.objects.genai.enabled else "OFF"),
+    ],
+    "record": [("recordings", lambda c: "ON" if c.record.enabled else "OFF")],
+    "review": [
+        ("review_alerts", lambda c: "ON" if c.review.alerts.enabled else "OFF"),
+        (
+            "review_detections",
+            lambda c: "ON" if c.review.detections.enabled else "OFF",
+        ),
+        (
+            "review_descriptions",
+            lambda c: "ON" if c.review.genai.enabled else "OFF",
+        ),
+    ],
+    "snapshots": [("snapshots", lambda c: "ON" if c.snapshots.enabled else "OFF")],
+}
+
 PERSISTENCE_FILE = Path(CONFIG_DIR) / ".profiles"
 
 
@@ -124,11 +163,24 @@ class ProfileManager:
                 self.config.active_profile = None
                 self._persist_active_profile(None)
 
-    def activate_profile(self, profile_name: Optional[str]) -> Optional[str]:
+            # drop all runtime overrides so they don't replay stale values on restart
+            if self.dispatcher is not None:
+                self.dispatcher.clear_runtime_state()
+
+    def activate_profile(
+        self,
+        profile_name: Optional[str],
+        clear_runtime_overrides: bool = True,
+    ) -> Optional[str]:
         """Activate a profile by name, or deactivate if None.
 
         Args:
             profile_name: Profile name to activate, or None to deactivate.
+            clear_runtime_overrides: When True (the default, for user-initiated
+                activations) drop the dispatcher's runtime override file because
+                the layer below changed. Startup callers that are replaying a
+                persisted profile pass False so the runtime state stays
+                available for the subsequent replay step.
 
         Returns:
             None on success, or an error message string on failure.
@@ -156,6 +208,11 @@ class ProfileManager:
 
         self.config.active_profile = profile_name
         self._persist_active_profile(profile_name)
+
+        # a profile switch invalidates the steady-state runtime overrides
+        if clear_runtime_overrides and self.dispatcher is not None:
+            self.dispatcher.clear_runtime_state()
+
         logger.info(
             "Profile %s",
             f"'{profile_name}' activated" if profile_name else "deactivated",
@@ -292,6 +349,15 @@ class ProfileManager:
                         settings,
                     )
 
+                # republish MQTT switch states
+                if self.dispatcher is not None:
+                    for suffix, get_payload in SECTION_STATE_TOPICS.get(section, ()):
+                        self.dispatcher.publish(
+                            f"{cam_name}/{suffix}/state",
+                            get_payload(cam_config),
+                            retain=True,
+                        )
+
     def _persist_active_profile(self, profile_name: Optional[str]) -> None:
         """Persist the active profile state to disk as JSON."""
         try:

frigate/config/proxy.py

@@ -45,7 +45,7 @@ class ProxyConfig(FrigateBaseModel):
     default_role: Optional[str] = Field(
         default="viewer",
         title="Default role",
-        description="Default role assigned to proxy-authenticated users when no role mapping applies (admin or viewer).",
+        description="Default role assigned to proxy-authenticated users when no role mapping applies.",
     )
     separator: Optional[str] = Field(
         default=",",

frigate/config/ui.py

@@ -5,7 +5,7 @@ from pydantic import Field
 
 from .base import FrigateBaseModel
 
-__all__ = ["TimeFormatEnum", "DateTimeStyleEnum", "UnitSystemEnum", "UIConfig"]
+__all__ = ["TimeFormatEnum", "UnitSystemEnum", "UIConfig"]
 
 
 class TimeFormatEnum(str, Enum):
@@ -14,13 +14,6 @@ class TimeFormatEnum(str, Enum):
     hours24 = "24hour"
 
 
-class DateTimeStyleEnum(str, Enum):
-    full = "full"
-    long = "long"
-    medium = "medium"
-    short = "short"
-
-
 class UnitSystemEnum(str, Enum):
     imperial = "imperial"
     metric = "metric"
@@ -37,16 +30,6 @@ class UIConfig(FrigateBaseModel):
         title="Time format",
         description="Time format to use in the UI (browser, 12hour, or 24hour).",
     )
-    date_style: DateTimeStyleEnum = Field(
-        default=DateTimeStyleEnum.short,
-        title="Date style",
-        description="Date style to use in the UI (full, long, medium, short).",
-    )
-    time_style: DateTimeStyleEnum = Field(
-        default=DateTimeStyleEnum.medium,
-        title="Time style",
-        description="Time style to use in the UI (full, long, medium, short).",
-    )
     unit_system: UnitSystemEnum = Field(
         default=UnitSystemEnum.metric,
         title="Unit system",

frigate/const.py

@@ -21,6 +21,8 @@ PLUS_API_HOST = "https://api.frigate.video"
 
 SHM_FRAMES_VAR = "SHM_MAX_FRAMES"
 
+REDACTED_CREDENTIAL_SENTINEL = "__FRIGATE_SAVED_CREDENTIAL__"
+
 # Attribute & Object constants
 
 DEFAULT_ATTRIBUTE_LABEL_MAP = {

frigate/debug_replay.py

@@ -5,6 +5,7 @@ frigate.jobs.debug_replay. This module owns only session presence
 (active), session metadata, and post-session cleanup.
 """
 
+import asyncio
 import logging
 import os
 import shutil
@@ -40,6 +41,9 @@ from frigate.util.config import find_config_file
 
 logger = logging.getLogger(__name__)
 
+MAX_SESSION_DURATION_SECONDS = 12 * 60 * 60
+AUTO_STOP_CHECK_INTERVAL_SECONDS = 60
+
 
 class DebugReplayManager:
     """Owns the lifecycle pointers for a single debug replay session.
@@ -58,6 +62,7 @@ class DebugReplayManager:
         self.clip_path: str | None = None
         self.start_ts: float | None = None
         self.end_ts: float | None = None
+        self.session_started_at: float | None = None
         self._job_state_publisher = JobStatePublisher()
 
     @property
@@ -83,6 +88,7 @@ class DebugReplayManager:
             self.start_ts = start_ts
             self.end_ts = end_ts
             self.clip_path = None
+            self.session_started_at = time.time()
 
     def mark_session_ready(self, clip_path: str) -> None:
         """Record the on-disk clip path after the camera has been published."""
@@ -104,6 +110,7 @@ class DebugReplayManager:
         self.clip_path = None
         self.start_ts = None
         self.end_ts = None
+        self.session_started_at = None
 
     def publish_camera(
         self,
@@ -169,6 +176,7 @@ class DebugReplayManager:
                     CameraConfigUpdateTopic(CameraConfigUpdateEnum.remove, replay_name),
                     frigate_config.cameras[replay_name],
                 )
+                frigate_config.cameras.pop(replay_name, None)
 
             if replay_name is not None:
                 self._cleanup_db(replay_name)
@@ -237,6 +245,10 @@ class DebugReplayManager:
             zone_dump.setdefault("coordinates", zone_config.coordinates)
             zones_dict[zone_name] = zone_dump
 
+        # Extract LPR and face recognition configs
+        lpr_dict = source_config.lpr.model_dump()
+        face_recognition_dict = source_config.face_recognition.model_dump()
+
         # Extract motion config (exclude runtime fields)
         motion_dict = {}
         if source_config.motion is not None:
@@ -245,11 +257,23 @@ class DebugReplayManager:
                     "frame_shape",
                     "raw_mask",
                     "mask",
-                    "improved_contrast_enabled",
+                    "enabled_in_config",
                     "rasterized_mask",
                 }
             )
 
+            if source_config.motion.mask:
+                motion_dict["mask"] = {
+                    mask_id: (
+                        mask_cfg.model_dump(
+                            exclude={"raw_coordinates", "enabled_in_config"}
+                        )
+                        if mask_cfg is not None
+                        else None
+                    )
+                    for mask_id, mask_cfg in source_config.motion.mask.items()
+                }
+
         return {
             "enabled": True,
             "ffmpeg": {
@@ -274,8 +298,8 @@ class DebugReplayManager:
             },
             "birdseye": {"enabled": False},
             "audio": {"enabled": False},
-            "lpr": {"enabled": False},
-            "face_recognition": {"enabled": False},
+            "lpr": lpr_dict,
+            "face_recognition": face_recognition_dict,
         }
 
     def _cleanup_db(self, camera_name: str) -> None:
@@ -334,3 +358,41 @@ def cleanup_replay_cameras() -> None:
             shutil.rmtree(REPLAY_DIR)
         except Exception as e:
             logger.error("Failed to remove replay cache directory: %s", e)
+
+
+async def debug_replay_auto_stop_watchdog(
+    manager: DebugReplayManager,
+    frigate_config: FrigateConfig,
+    config_publisher: CameraConfigUpdatePublisher,
+) -> None:
+    """Auto-stop debug replay sessions that exceed MAX_SESSION_DURATION_SECONDS.
+
+    Backstop against a session left running for days. The cap is intentionally
+    generous so realistic tuning and overnight soak workflows aren't disrupted.
+    """
+    while True:
+        try:
+            await asyncio.sleep(AUTO_STOP_CHECK_INTERVAL_SECONDS)
+
+            started_at = manager.session_started_at
+            if not manager.active or started_at is None:
+                continue
+
+            if time.time() - started_at < MAX_SESSION_DURATION_SECONDS:
+                continue
+
+            replay_name = manager.replay_camera_name
+            await asyncio.to_thread(
+                manager.stop,
+                frigate_config=frigate_config,
+                config_publisher=config_publisher,
+            )
+            logger.info(
+                "Debug replay auto-stopped after exceeding max session duration of %d hours: %s",
+                MAX_SESSION_DURATION_SECONDS // 3600,
+                replay_name,
+            )
+        except asyncio.CancelledError:
+            raise
+        except Exception:
+            logger.exception("Error in debug replay auto-stop watchdog")

frigate/detectors/detection_runners.py

@@ -15,6 +15,9 @@ from frigate.util.rknn_converter import auto_convert_model, is_rknn_compatible
 
 logger = logging.getLogger(__name__)
 
+# Process-wide lock serializing all OpenVINO compile/inference calls
+_OPENVINO_LOCK = threading.Lock()
+
 
 def is_arm64_platform() -> bool:
     """Check if we're running on an ARM platform."""
@@ -326,19 +329,17 @@ class OpenVINOModelRunner(BaseModelRunner):
             except Exception as e:
                 logger.debug(f"NPU_TURBO not supported by driver: {e}")
 
-        # Compile model
-        self.compiled_model = self.ov_core.compile_model(
-            model=model_path, device_name=device
-        )
+        # Compile model under the shared lock
+        with _OPENVINO_LOCK:
+            self.compiled_model = self.ov_core.compile_model(
+                model=model_path, device_name=device
+            )
+
+            # Create reusable inference request
+            self.infer_request = self.compiled_model.create_infer_request()
 
-        # Create reusable inference request
-        self.infer_request = self.compiled_model.create_infer_request()
         self.input_tensor: ov.Tensor | None = None
 
-        # Thread lock to prevent concurrent inference (needed for JinaV2 which shares
-        # one runner between text and vision embeddings called from different threads)
-        self._inference_lock = threading.Lock()
-
         if not self.complex_model:
             try:
                 input_shape = self.compiled_model.inputs[0].get_shape()
@@ -382,9 +383,11 @@ class OpenVINOModelRunner(BaseModelRunner):
         Returns:
             List of output tensors
         """
-        # Lock prevents concurrent access to infer_request
-        # Needed for JinaV2: genai thread (text) + embeddings thread (vision)
-        with self._inference_lock:
+        # Shared lock serializes inference across every OpenVINO runner in this
+        # process — both the shared-runner JinaV2 case (genai text thread +
+        # embeddings vision thread) and distinct runners running on separate
+        # threads (e.g. the ArcFace face-model build vs the LPR detector).
+        with _OPENVINO_LOCK:
             from frigate.embeddings.types import EnrichmentModelTypeEnum
 
             if self.model_type in [EnrichmentModelTypeEnum.arcface.value]:

frigate/embeddings/maintainer.py

@@ -98,10 +98,17 @@ class EmbeddingMaintainer(threading.Thread):
             [
                 CameraConfigUpdateEnum.add,
                 CameraConfigUpdateEnum.remove,
+                CameraConfigUpdateEnum.detect,
+                CameraConfigUpdateEnum.face_recognition,
+                CameraConfigUpdateEnum.ffmpeg,
+                CameraConfigUpdateEnum.lpr,
+                CameraConfigUpdateEnum.motion,
+                CameraConfigUpdateEnum.objects,
                 CameraConfigUpdateEnum.object_genai,
                 CameraConfigUpdateEnum.review,
                 CameraConfigUpdateEnum.review_genai,
                 CameraConfigUpdateEnum.semantic_search,
+                CameraConfigUpdateEnum.zones,
             ],
         )
         self.enrichment_config_subscriber = ConfigSubscriber("config/")

frigate/events/audio.py

@@ -94,9 +94,21 @@ class AudioProcessor(FrigateProcess):
         self.camera_metrics = camera_metrics
         self.config = config
 
+    def __stop_audio_thread(self, camera: str) -> None:
+        thread = self.audio_threads.pop(camera, None)
+        if thread is None:
+            return
+
+        thread.stop()
+        thread.join(10)
+        if thread.is_alive():
+            self.logger.warning(f"Audio maintainer thread for {camera} is still alive")
+        else:
+            self.logger.info(f"Audio maintainer stopped for {camera}")
+
     def run(self) -> None:
         self.pre_run_setup(self.config.logger)
-        audio_threads: dict[str, AudioEventMaintainer] = {}
+        self.audio_threads: dict[str, AudioEventMaintainer] = {}
 
         threading.current_thread().name = "process:audio_manager"
 
@@ -120,12 +132,13 @@ class AudioProcessor(FrigateProcess):
                 CameraConfigUpdateEnum.add,
                 CameraConfigUpdateEnum.audio,
                 CameraConfigUpdateEnum.ffmpeg,
+                CameraConfigUpdateEnum.remove,
             ],
         )
 
         def spawn_if_needed(camera: CameraConfig) -> None:
             name = camera.name
-            if name is None or name in audio_threads:
+            if name is None or name in self.audio_threads:
                 return
             if not camera.enabled or not camera.audio.enabled:
                 return
@@ -139,7 +152,7 @@ class AudioProcessor(FrigateProcess):
                 self.transcription_model_runner,
                 self.stop_event,  # type: ignore[arg-type]
             )
-            audio_threads[name] = thread
+            self.audio_threads[name] = thread
             thread.start()
             self.logger.info(f"Audio maintainer started for {name}")
 
@@ -148,21 +161,31 @@ class AudioProcessor(FrigateProcess):
 
         self.logger.info(f"Audio processor started (pid: {self.pid})")
 
-        # poll for newly added cameras or cameras flipped to audio.enabled at runtime
+        # poll for newly added/removed cameras or cameras flipped to
+        # audio.enabled at runtime
         while not self.stop_event.wait(timeout=1.0):
-            config_subscriber.check_for_updates()
+            updated_topics = config_subscriber.check_for_updates()
+
+            # stop maintainers for removed cameras so their ffmpeg process is
+            # torn down and they stop touching camera_metrics (which the camera
+            # maintainer has already popped for the removed camera)
+            for removed_camera in updated_topics.get(
+                CameraConfigUpdateEnum.remove.name, []
+            ):
+                self.__stop_audio_thread(removed_camera)
+
             for camera in self.config.cameras.values():
                 spawn_if_needed(camera)
 
         config_subscriber.stop()
 
-        for thread in audio_threads.values():
+        for thread in self.audio_threads.values():
             thread.join(1)
             if thread.is_alive():
                 self.logger.info(f"Waiting for thread {thread.name:s} to exit")
                 thread.join(10)
 
-        for thread in audio_threads.values():
+        for thread in self.audio_threads.values():
             if thread.is_alive():
                 self.logger.warning(f"Thread {thread.name} is still alive")
 
@@ -184,6 +207,9 @@ class AudioEventMaintainer(threading.Thread):
         self.camera_config = camera
         self.camera_metrics = camera_metrics
         self.stop_event = stop_event
+        # per-camera stop signal so a single maintainer can be torn down at
+        # runtime (e.g. on camera removal) without stopping the whole process
+        self.camera_stop_event = threading.Event()
         self.detector = AudioTfl(stop_event, self.camera_config.audio.num_threads)
         self.shape = (int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE)),)
         self.chunk_size = int(round(AUDIO_DURATION * AUDIO_SAMPLE_RATE * 2))
@@ -233,7 +259,11 @@ class AudioEventMaintainer(threading.Thread):
         self.was_audio_enabled = camera.audio.enabled
 
     def detect_audio(self, audio: np.ndarray) -> None:
-        if not self.camera_config.audio.enabled or self.stop_event.is_set():
+        if (
+            not self.camera_config.audio.enabled
+            or self.stop_event.is_set()
+            or self.camera_stop_event.is_set()
+        ):
             return
 
         audio_as_float: np.ndarray = audio.astype(np.float32)
@@ -352,11 +382,15 @@ class AudioEventMaintainer(threading.Thread):
             self.logger.error(f"Error reading audio data from ffmpeg process: {e}")
             log_and_restart()
 
+    def stop(self) -> None:
+        """Signal this maintainer to exit its run loop and clean up."""
+        self.camera_stop_event.set()
+
     def run(self) -> None:
         if self.camera_config.enabled:
             self.start_or_restart_ffmpeg()
 
-        while not self.stop_event.is_set():
+        while not self.stop_event.is_set() and not self.camera_stop_event.is_set():
             # check if there is an updated config
             self.config_subscriber.check_for_updates()
 

frigate/ffmpeg_presets.py

@@ -465,16 +465,6 @@ PRESETS_RECORD_OUTPUT = {
         "-c:a",
         "aac",
     ],
-    # NOTE: This preset originally used "-c:a copy" to pass through audio
-    # without re-encoding. FFmpeg 7.x introduced a threaded pipeline where
-    # demuxing, encoding, and muxing run in parallel via a Scheduler. This
-    # broke audio streamcopy from RTSP sources: packets are demuxed correctly
-    # but silently dropped before reaching the muxer (0 bytes written). The
-    # issue is specific to RTSP + streamcopy; file inputs and transcoding both
-    # work. Transcoding AAC audio is very lightweight (~30KiB per 10s segment)
-    # and adds negligible CPU overhead, so this is an acceptable workaround.
-    # The benefits of FFmpeg 7.x — particularly the removal of gamma correction
-    # hacks required by earlier versions — outweigh this trade-off.
     "preset-record-generic-audio-copy": [
         "-f",
         "segment",
@@ -486,10 +476,8 @@ PRESETS_RECORD_OUTPUT = {
         "1",
         "-strftime",
         "1",
-        "-c:v",
+        "-c",
         "copy",
-        "-c:a",
-        "aac",
     ],
     "preset-record-mjpeg": [
         "-f",

frigate/genai/__init__.py

@@ -5,7 +5,8 @@ import json
 import logging
 import os
 import re
-from typing import Any, Callable, Optional
+import time
+from typing import Any, AsyncGenerator, Callable, Optional
 
 import numpy as np
 from pydantic import ValidationError
@@ -50,10 +51,48 @@ def register_genai_provider(key: GenAIProviderEnum) -> Callable:
 class GenAIClient:
     """Generative AI client for Frigate."""
 
-    def __init__(self, genai_config: GenAIConfig, timeout: int = 120) -> None:
+    # Minimum seconds between re-initialization attempts when the provider was
+    # offline at startup
+    REINIT_INTERVAL = 60.0
+
+    def __init__(
+        self,
+        genai_config: GenAIConfig,
+        timeout: int = 120,
+        validate_model: bool = True,
+    ) -> None:
         self.genai_config: GenAIConfig = genai_config
         self.timeout = timeout
+        self.validate_model = validate_model
         self.provider = self._init_provider()
+        self._last_init_attempt = time.monotonic()
+
+    def ensure_provider(self) -> bool:
+        """Ensure a provider is available, retrying initialization if needed.
+
+        Providers can fail to initialize at startup when their backing service
+        isn't online yet (common when both are started together). This retries
+        ``_init_provider`` lazily — throttled to ``REINIT_INTERVAL`` — so the
+        client recovers on its own once the service is reachable, without a
+        config reload.
+
+        Returns True if a provider is available.
+        """
+        if self.provider is not None:
+            return True
+
+        now = time.monotonic()
+        if now - self._last_init_attempt < self.REINIT_INTERVAL:
+            return False
+
+        self._last_init_attempt = now
+        self.provider = self._init_provider()
+        if self.provider is not None:
+            logger.info(
+                "GenAI provider %s is now available",
+                self.genai_config.provider,
+            )
+        return self.provider is not None
 
     def generate_review_description(
         self,
@@ -216,8 +255,15 @@ class GenAIClient:
         prompt: str,
         images: list[bytes],
         response_format: Optional[dict] = None,
+        enable_thinking: bool = False,
     ) -> Optional[str]:
-        """Submit a request to the provider."""
+        """Submit a request to the provider.
+
+        ``enable_thinking`` is honored only by providers that report
+        ``supports_toggleable_thinking``. Description-style callers leave it
+        at the default (off) since synthesis tasks don't benefit from
+        reasoning traces.
+        """
         return None
 
     @property
@@ -229,6 +275,11 @@ class GenAIClient:
         """
         return True
 
+    @property
+    def supports_toggleable_thinking(self) -> bool:
+        """Whether the configured model exposes a per-request thinking toggle."""
+        return False
+
     def list_models(self) -> list[str]:
         """Return the list of model names available from this provider.
 
@@ -272,6 +323,7 @@ class GenAIClient:
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> dict[str, Any]:
         """
         Send chat messages to LLM with optional tool definitions.
@@ -295,7 +347,9 @@ class GenAIClient:
                 - 'none': Model must not call tools
                 - 'required': Model must call at least one tool
                 - Or a dict specifying a specific tool to call
-            **kwargs: Additional provider-specific parameters.
+            enable_thinking: Per-request thinking toggle. None means use the
+                provider default. Ignored by providers without a per-request
+                toggle (see `supports_toggleable_thinking`).
 
         Returns:
             Dictionary with:
@@ -338,6 +392,41 @@ class GenAIClient:
             "finish_reason": "error",
         }
 
+    async def chat_with_tools_stream(
+        self,
+        messages: list[dict[str, Any]],
+        tools: Optional[list[dict[str, Any]]] = None,
+        tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
+    ) -> AsyncGenerator[tuple[str, Any], None]:
+        """Streaming counterpart to `chat_with_tools`.
+
+        Yields ``(kind, value)`` tuples where ``kind`` is one of:
+            - 'content_delta': value is a string fragment of the answer
+            - 'reasoning_delta': value is a string fragment of the reasoning
+              trace (emitted before content for thinking models)
+            - 'stats': value is a usage stats dict
+            - 'message': value is the final dict shape described in
+              `chat_with_tools`
+
+        Argument semantics — including ``enable_thinking`` — match
+        `chat_with_tools`. Providers that don't support streaming should
+        override this and yield an error 'message' event.
+        """
+        logger.warning(
+            f"{self.__class__.__name__} does not support chat_with_tools_stream. "
+            "This method should be overridden by the provider implementation."
+        )
+        yield (
+            "message",
+            {
+                "content": None,
+                "reasoning": None,
+                "tool_calls": None,
+                "finish_reason": "error",
+            },
+        )
+
 
 def load_providers() -> None:
     plugins_dir = os.path.join(os.path.dirname(__file__), "plugins")

frigate/genai/manager.py

@@ -6,7 +6,7 @@ no chat feature is active) are never initialized.
 """
 
 import logging
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 from frigate.config import FrigateConfig
 from frigate.config.camera.genai import GenAIConfig, GenAIRoleEnum
@@ -62,7 +62,9 @@ class GenAIClientManager:
     def _get_client(self, name: str) -> "Optional[GenAIClient]":
         """Return the client for *name*, creating it on first access."""
         if name in self._clients:
-            return self._clients[name]
+            client = self._clients[name]
+            client.ensure_provider()
+            return client
 
         from frigate.genai import PROVIDERS
 
@@ -78,7 +80,7 @@ class GenAIClientManager:
             return None
 
         try:
-            client: "GenAIClient" = provider_cls(genai_cfg)
+            client = provider_cls(genai_cfg)
         except Exception as e:
             logger.exception(
                 "Failed to create GenAI client for provider %s: %s",
@@ -108,11 +110,16 @@ class GenAIClientManager:
         name = self._role_map.get(GenAIRoleEnum.embeddings)
         return self._get_client(name) if name else None
 
-    def list_models(self) -> dict[str, list[str]]:
-        """Return available models keyed by config entry name."""
-        result: dict[str, list[str]] = {}
-        for name in self._configs:
+    def list_models(self) -> dict[str, dict[str, Any]]:
+        """Return per-entry model lists and capabilities, keyed by config entry name."""
+        result: dict[str, dict[str, Any]] = {}
+        for name, genai_cfg in self._configs.items():
             client = self._get_client(name)
-            if client:
-                result[name] = client.list_models()
+            if not client:
+                continue
+            result[name] = {
+                "models": client.list_models(),
+                "roles": [r.value for r in genai_cfg.roles],
+                "supports_toggleable_thinking": client.supports_toggleable_thinking,
+            }
         return result

frigate/genai/plugins/gemini.py

@@ -1,5 +1,7 @@
 """Gemini Provider for Frigate AI."""
 
+import base64
+import binascii
 import json
 import logging
 from typing import Any, AsyncGenerator, Optional
@@ -14,6 +16,27 @@ from frigate.genai import GenAIClient, register_genai_provider
 logger = logging.getLogger(__name__)
 
 
+def _decode_thought_signature(value: Any) -> Optional[bytes]:
+    """Decode a base64-encoded thought_signature carried across conversation turns."""
+    if not value:
+        return None
+    if isinstance(value, bytes):
+        return value
+    if isinstance(value, str):
+        try:
+            return base64.b64decode(value)
+        except (binascii.Error, ValueError):
+            return None
+    return None
+
+
+def _encode_thought_signature(signature: Optional[bytes]) -> Optional[str]:
+    """Encode bytes thought_signature as base64 so it survives JSON-friendly transport."""
+    if not signature:
+        return None
+    return base64.b64encode(signature).decode("ascii")
+
+
 def _stats_from_gemini_usage(usage: Any) -> Optional[dict[str, Any]]:
     """Build a stats dict from a Gemini usage_metadata object."""
     prompt_tokens = getattr(usage, "prompt_token_count", None)
@@ -62,6 +85,7 @@ class GeminiClient(GenAIClient):
         prompt: str,
         images: list[bytes],
         response_format: Optional[dict] = None,
+        enable_thinking: bool = False,
     ) -> Optional[str]:
         """Submit a request to Gemini."""
         contents = [prompt] + [
@@ -119,11 +143,14 @@ class GeminiClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> dict[str, Any]:
         """
         Send chat messages to Gemini with optional tool definitions.
 
-        Implements function calling/tool usage for Gemini models.
+        Implements function calling/tool usage for Gemini models. Thinking is
+        configured at the model level for Gemini, so ``enable_thinking`` is
+        accepted for interface parity and ignored.
         """
         try:
             # Convert messages to Gemini format
@@ -165,11 +192,17 @@ class GeminiClient(GenAIClient):
                         if not isinstance(tc_args, dict):
                             tc_args = {}
                         if tc_name:
-                            parts.append(
-                                types.Part.from_function_call(
-                                    name=tc_name, args=tc_args
-                                )
+                            fc_part = types.Part.from_function_call(
+                                name=tc_name, args=tc_args
                             )
+                            # Thinking-capable Gemini models require the original
+                            # thought_signature to be echoed back on functionCall
+                            # parts after a tool response, or the next request
+                            # fails with INVALID_ARGUMENT.
+                            sig = _decode_thought_signature(tc.get("thought_signature"))
+                            if sig:
+                                fc_part.thought_signature = sig
+                            parts.append(fc_part)
                     if not parts:
                         parts.append(types.Part.from_text(text=" "))
                     gemini_messages.append(types.Content(role="model", parts=parts))
@@ -306,6 +339,9 @@ class GeminiClient(GenAIClient):
                                 "id": part.function_call.name or "",
                                 "name": part.function_call.name or "",
                                 "arguments": arguments,
+                                "thought_signature": _encode_thought_signature(
+                                    getattr(part, "thought_signature", None)
+                                ),
                             }
                         )
 
@@ -365,11 +401,14 @@ class GeminiClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> AsyncGenerator[tuple[str, Any], None]:
         """
         Stream chat with tools; yields content deltas then final message.
 
         Implements streaming function calling/tool usage for Gemini models.
+        ``enable_thinking`` is accepted for interface parity; Gemini configures
+        thinking at the model level, so it is ignored here.
         """
         try:
             # Convert messages to Gemini format
@@ -411,11 +450,17 @@ class GeminiClient(GenAIClient):
                         if not isinstance(tc_args, dict):
                             tc_args = {}
                         if tc_name:
-                            parts.append(
-                                types.Part.from_function_call(
-                                    name=tc_name, args=tc_args
-                                )
+                            fc_part = types.Part.from_function_call(
+                                name=tc_name, args=tc_args
                             )
+                            # Thinking-capable Gemini models require the original
+                            # thought_signature to be echoed back on functionCall
+                            # parts after a tool response, or the next request
+                            # fails with INVALID_ARGUMENT.
+                            sig = _decode_thought_signature(tc.get("thought_signature"))
+                            if sig:
+                                fc_part.thought_signature = sig
+                            parts.append(fc_part)
                     if not parts:
                         parts.append(types.Part.from_text(text=" "))
                     gemini_messages.append(types.Content(role="model", parts=parts))
@@ -581,6 +626,7 @@ class GeminiClient(GenAIClient):
                                     "id": tool_call_id,
                                     "name": tool_call_name,
                                     "arguments": "",
+                                    "thought_signature": None,
                                 }
 
                             # Accumulate arguments
@@ -591,6 +637,13 @@ class GeminiClient(GenAIClient):
                                     else str(arguments)
                                 )
 
+                            # Capture latest thought_signature for this call
+                            chunk_sig = getattr(part, "thought_signature", None)
+                            if chunk_sig:
+                                tool_calls_by_index[found_index][
+                                    "thought_signature"
+                                ] = chunk_sig
+
             # Build final message
             full_content = "".join(content_parts).strip() or None
             full_reasoning = "".join(reasoning_parts).strip() or None
@@ -611,6 +664,9 @@ class GeminiClient(GenAIClient):
                             "id": tc["id"],
                             "name": tc["name"],
                             "arguments": parsed_args,
+                            "thought_signature": _encode_thought_signature(
+                                tc.get("thought_signature")
+                            ),
                         }
                     )
                 finish_reason = "tool_calls"

frigate/genai/plugins/llama_cpp.py

@@ -122,6 +122,7 @@ class LlamaCppClient(GenAIClient):
     _supports_vision: bool
     _supports_audio: bool
     _supports_tools: bool
+    _supports_reasoning: bool
     _image_token_cache: dict[tuple[int, int], int]
     _text_baseline_tokens: int | None
     _media_marker: str
@@ -135,6 +136,7 @@ class LlamaCppClient(GenAIClient):
         self._supports_vision = False
         self._supports_audio = False
         self._supports_tools = False
+        self._supports_reasoning = False
         self._image_token_cache = {}
         self._text_baseline_tokens = None
         self._media_marker = "<__media__>"
@@ -150,6 +152,10 @@ class LlamaCppClient(GenAIClient):
         else:
             base_url = base_url.replace("/v1", "")  # Strip /v1 if included in base_url
 
+        if not self.validate_model:
+            # Probe path
+            return base_url
+
         configured_model = self.genai_config.model
         info = self._get_model_info(base_url, configured_model)
 
@@ -160,15 +166,17 @@ class LlamaCppClient(GenAIClient):
         self._supports_vision = info["supports_vision"]
         self._supports_audio = info["supports_audio"]
         self._supports_tools = info["supports_tools"]
+        self._supports_reasoning = info["supports_reasoning"]
         self._media_marker = info["media_marker"]
 
         logger.info(
-            "llama.cpp model '%s' initialized — context: %s, vision: %s, audio: %s, tools: %s",
+            "llama.cpp model '%s' initialized — context: %s, vision: %s, audio: %s, tools: %s, reasoning: %s",
             configured_model,
             self._context_size or "unknown",
             self._supports_vision,
             self._supports_audio,
             self._supports_tools,
+            self._supports_reasoning,
         )
 
         return base_url
@@ -196,6 +204,7 @@ class LlamaCppClient(GenAIClient):
             "supports_vision": False,
             "supports_audio": False,
             "supports_tools": False,
+            "supports_reasoning": False,
             "media_marker": "<__media__>",
         }
 
@@ -275,10 +284,17 @@ class LlamaCppClient(GenAIClient):
                 info["supports_vision"] = bool(modalities.get("vision", False))
                 info["supports_audio"] = bool(modalities.get("audio", False))
 
+            chat_caps = props.get("chat_template_caps") or {}
+
             if not info["supports_tools"]:
-                chat_caps = props.get("chat_template_caps", {})
                 info["supports_tools"] = bool(chat_caps.get("supports_tools", False))
 
+            # llama.cpp does not advertise per-template reasoning support, so
+            # detect it by looking for the `enable_thinking` toggle variable
+            # in the Jinja chat template itself.
+            chat_template = props.get("chat_template") or ""
+            info["supports_reasoning"] = "enable_thinking" in chat_template
+
             media_marker = props.get("media_marker")
             if isinstance(media_marker, str) and media_marker:
                 info["media_marker"] = media_marker
@@ -296,6 +312,7 @@ class LlamaCppClient(GenAIClient):
         prompt: str,
         images: list[bytes],
         response_format: Optional[dict] = None,
+        enable_thinking: bool = False,
     ) -> Optional[str]:
         """Submit a request to llama.cpp server."""
         if self.provider is None:
@@ -323,7 +340,7 @@ class LlamaCppClient(GenAIClient):
                 )
 
             # Build request payload with llama.cpp native options
-            payload = {
+            payload: dict[str, Any] = {
                 "model": self.genai_config.model,
                 "messages": [
                     {
@@ -337,6 +354,9 @@ class LlamaCppClient(GenAIClient):
             if response_format:
                 payload["response_format"] = response_format
 
+            if self.supports_toggleable_thinking:
+                payload["chat_template_kwargs"] = {"enable_thinking": enable_thinking}
+
             response = requests.post(
                 f"{self.provider}/v1/chat/completions",
                 json=payload,
@@ -373,6 +393,10 @@ class LlamaCppClient(GenAIClient):
         """Whether the loaded model supports tool/function calling."""
         return self._supports_tools
 
+    @property
+    def supports_toggleable_thinking(self) -> bool:
+        return self._supports_reasoning
+
     def list_models(self) -> list[str]:
         """Return available model IDs from the llama.cpp server."""
         base_url = self.provider or (
@@ -500,6 +524,7 @@ class LlamaCppClient(GenAIClient):
         tools: Optional[list[dict[str, Any]]],
         tool_choice: Optional[str],
         stream: bool = False,
+        enable_thinking: Optional[bool] = None,
     ) -> dict[str, Any]:
         """Build request payload for chat completions (sync or stream)."""
         openai_tool_choice = None
@@ -515,14 +540,21 @@ class LlamaCppClient(GenAIClient):
             "messages": messages,
             "model": self.genai_config.model,
         }
+
         if stream:
             payload["stream"] = True
             payload["stream_options"] = {"include_usage": True}
             payload["timings_per_token"] = True
+
         if tools:
             payload["tools"] = tools
+
             if openai_tool_choice is not None:
                 payload["tool_choice"] = openai_tool_choice
+
+        if enable_thinking is not None and self._supports_reasoning:
+            payload["chat_template_kwargs"] = {"enable_thinking": enable_thinking}
+
         provider_opts = {
             k: v for k, v in self.provider_options.items() if k != "context_size"
         }
@@ -728,6 +760,7 @@ class LlamaCppClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> dict[str, Any]:
         """
         Send chat messages to llama.cpp server with optional tool definitions.
@@ -745,7 +778,13 @@ class LlamaCppClient(GenAIClient):
                 "finish_reason": "error",
             }
         try:
-            payload = self._build_payload(messages, tools, tool_choice, stream=False)
+            payload = self._build_payload(
+                messages,
+                tools,
+                tool_choice,
+                stream=False,
+                enable_thinking=enable_thinking,
+            )
             response = requests.post(
                 f"{self.provider}/v1/chat/completions",
                 json=payload,
@@ -793,6 +832,7 @@ class LlamaCppClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> AsyncGenerator[tuple[str, Any], None]:
         """Stream chat with tools via OpenAI-compatible streaming API."""
         if self.provider is None:
@@ -809,7 +849,13 @@ class LlamaCppClient(GenAIClient):
             )
             return
         try:
-            payload = self._build_payload(messages, tools, tool_choice, stream=True)
+            payload = self._build_payload(
+                messages,
+                tools,
+                tool_choice,
+                stream=True,
+                enable_thinking=enable_thinking,
+            )
             content_parts: list[str] = []
             reasoning_parts: list[str] = []
             tool_calls_by_index: dict[int, dict[str, Any]] = {}

frigate/genai/plugins/ollama.py

@@ -98,6 +98,22 @@ class OllamaClient(GenAIClient):
 
     provider: ApiClient | None
     provider_options: dict[str, Any]
+    _supports_thinking_cache: Optional[bool] = None
+
+    @property
+    def supports_toggleable_thinking(self) -> bool:
+        if self._supports_thinking_cache is not None:
+            return self._supports_thinking_cache
+        if self.provider is None:
+            return False
+        try:
+            response = self.provider.show(self.genai_config.model)
+            capabilities = response.get("capabilities") or []
+            self._supports_thinking_cache = "thinking" in capabilities
+        except Exception as e:
+            logger.debug("Failed to query Ollama model capabilities: %s", e)
+            self._supports_thinking_cache = False
+        return self._supports_thinking_cache
 
     def _auth_headers(self) -> dict | None:
         if self.genai_config.api_key:
@@ -118,6 +134,9 @@ class OllamaClient(GenAIClient):
                 timeout=self.timeout,
                 headers=self._auth_headers(),
             )
+            if not self.validate_model:
+                # Probe path
+                return client
             # ensure the model is available locally
             response = client.show(self.genai_config.model)
             if response.get("error"):
@@ -175,6 +194,7 @@ class OllamaClient(GenAIClient):
         prompt: str,
         images: list[bytes],
         response_format: Optional[dict] = None,
+        enable_thinking: bool = False,
     ) -> Optional[str]:
         """Submit a request to Ollama"""
         if self.provider is None:
@@ -191,6 +211,8 @@ class OllamaClient(GenAIClient):
                 schema = response_format.get("json_schema", {}).get("schema")
                 if schema:
                     ollama_options["format"] = self._clean_schema_for_ollama(schema)
+            if self.supports_toggleable_thinking:
+                ollama_options["think"] = enable_thinking
             logger.debug(
                 "Ollama generate request: model=%s, prompt_len=%s, image_count=%s, "
                 "has_format=%s, options=%s",
@@ -271,6 +293,7 @@ class OllamaClient(GenAIClient):
         tools: Optional[list[dict[str, Any]]],
         tool_choice: Optional[str],
         stream: bool = False,
+        enable_thinking: Optional[bool] = None,
     ) -> dict[str, Any]:
         """Build request_messages and params for chat (sync or stream)."""
         request_messages = []
@@ -315,6 +338,8 @@ class OllamaClient(GenAIClient):
             request_params["stream"] = True
         if tools:
             request_params["tools"] = tools
+        if enable_thinking is not None and self.supports_toggleable_thinking:
+            request_params["think"] = enable_thinking
         return request_params
 
     def _message_from_response(self, response: dict[str, Any]) -> dict[str, Any]:
@@ -362,6 +387,7 @@ class OllamaClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> dict[str, Any]:
         if self.provider is None:
             logger.warning(
@@ -374,7 +400,11 @@ class OllamaClient(GenAIClient):
             }
         try:
             request_params = self._build_request_params(
-                messages, tools, tool_choice, stream=False
+                messages,
+                tools,
+                tool_choice,
+                stream=False,
+                enable_thinking=enable_thinking,
             )
             response = self.provider.chat(**request_params)
             return self._message_from_response(response)
@@ -398,6 +428,7 @@ class OllamaClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> AsyncGenerator[tuple[str, Any], None]:
         """Stream chat with tools; yields content deltas then final message.
 
@@ -427,7 +458,11 @@ class OllamaClient(GenAIClient):
                     "Ollama: tools provided, using non-streaming call for tool support"
                 )
                 request_params = self._build_request_params(
-                    messages, tools, tool_choice, stream=False
+                    messages,
+                    tools,
+                    tool_choice,
+                    stream=False,
+                    enable_thinking=enable_thinking,
                 )
                 async_client = OllamaAsyncClient(
                     host=self.genai_config.base_url,
@@ -449,7 +484,11 @@ class OllamaClient(GenAIClient):
                 return
 
             request_params = self._build_request_params(
-                messages, tools, tool_choice, stream=True
+                messages,
+                tools,
+                tool_choice,
+                stream=True,
+                enable_thinking=enable_thinking,
             )
             async_client = OllamaAsyncClient(
                 host=self.genai_config.base_url,

frigate/genai/plugins/openai.py

@@ -61,6 +61,7 @@ class OpenAIClient(GenAIClient):
         prompt: str,
         images: list[bytes],
         response_format: Optional[dict] = None,
+        enable_thinking: bool = False,
     ) -> Optional[str]:
         """Submit a request to OpenAI."""
         encoded_images = [base64.b64encode(image).decode("utf-8") for image in images]
@@ -187,11 +188,14 @@ class OpenAIClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> dict[str, Any]:
         """
         Send chat messages to OpenAI with optional tool definitions.
 
-        Implements function calling/tool usage for OpenAI models.
+        Implements function calling/tool usage for OpenAI models. The OpenAI
+        chat completions API does not expose a per-request thinking toggle,
+        so ``enable_thinking`` is accepted for interface parity and ignored.
         """
         try:
             openai_tool_choice = None
@@ -305,11 +309,15 @@ class OpenAIClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
+        enable_thinking: Optional[bool] = None,
     ) -> AsyncGenerator[tuple[str, Any], None]:
         """
         Stream chat with tools; yields content deltas then final message.
 
         Implements streaming function calling/tool usage for OpenAI models.
+        The OpenAI chat completions API does not expose a per-request thinking
+        toggle, so ``enable_thinking`` is accepted for interface parity and
+        ignored.
         """
         try:
             openai_tool_choice = None

frigate/genai/prompts.py

@@ -63,8 +63,8 @@ Describe the scene based on observable actions and movements, evaluate the activ
 ## Analysis Guidelines
 
 When forming your description:
-- **CRITICAL: Only describe objects explicitly listed in "Objects in Scene" below.** Do not infer or mention additional people, vehicles, or objects not present in this list, even if visual patterns suggest them. If only a car is listed, do not describe a person interacting with it unless "person" is also in the objects list.
-- **Only describe actions actually visible in the frames.** Do not assume or infer actions that you don't observe happening. If someone walks toward furniture but you never see them sit, do not say they sat. Stick to what you can see across the sequence.
+- **Treat "Objects in Scene" as the list of tracked subjects to describe.** Do not introduce additional people or vehicles that are not present in this list. You may freely reference other items, surfaces, and environmental details visible in the frames when describing what the listed subjects are doing.
+- **Describe the most likely activity from visible cues across the sequence** — the subject's path, what they are carrying, and what they interact with. Avoid asserting completed outcomes you do not observe; describe in-progress actions rather than results.
 - Describe what you observe: actions, movements, interactions with objects and the environment. Include any observable environmental changes (e.g., lighting changes triggered by activity).
 - Note visible details such as clothing, items being carried or placed, tools or equipment present, and how they interact with the property or objects.
 - Consider the full sequence chronologically: what happens from start to finish, how duration and actions relate to the location and objects involved.
@@ -518,16 +518,21 @@ def get_tool_definitions(
             "function": {
                 "name": "get_live_context",
                 "description": (
-                    "Get the current live image and detection information for a camera: objects being tracked, "
+                    "Get the current live image and detection information for a single camera: objects being tracked, "
                     "zones, timestamps. Use this to understand what is visible in the live view. "
-                    "Call this when answering questions about what is happening right now on a specific camera."
+                    "Call this when answering questions about what is happening right now on a specific camera. "
+                    "Operates on one camera at a time; call the tool again for each additional camera. "
+                    "Wildcards and empty values are not accepted."
                 ),
                 "parameters": {
                     "type": "object",
                     "properties": {
                         "camera": {
                             "type": "string",
-                            "description": "Camera name to get live context for.",
+                            "description": (
+                                "Exact name of a single camera to get live context for. "
+                                "Wildcards (e.g. '*', 'all') and empty strings are not accepted."
+                            ),
                         },
                     },
                     "required": ["camera"],

frigate/genai/utils.py

@@ -69,6 +69,14 @@ def build_assistant_message_for_conversation(
                     "name": tc["name"],
                     "arguments": json.dumps(tc.get("arguments") or {}),
                 },
+                # Gemini-only: opaque signature that must be echoed back on
+                # the same functionCall part in the next turn. Other providers
+                # do not set or read this.
+                **(
+                    {"thought_signature": tc["thought_signature"]}
+                    if tc.get("thought_signature")
+                    else {}
+                ),
             }
             for tc in tool_calls_raw
         ]

frigate/jobs/debug_replay.py

@@ -1,4 +1,4 @@
-"""Debug replay startup job: ffmpeg concat + camera config publish.
+"""Debug replay startup job: ffmpeg remux + camera config publish.
 
 The runner orchestrates the async portion of starting a debug replay
 session. The DebugReplayManager (in frigate.debug_replay) owns session
@@ -153,15 +153,22 @@ class DebugReplaySource(ABC):
 class RecordingDebugReplaySource(DebugReplaySource):
     """Replay source backed by the Recordings table.
 
-    Builds a concat playlist of recording files covering the time range
-    and feeds it to ffmpeg's concat demuxer.
+    Feeds ffmpeg the internal VOD endpoint so segments with mismatched
+    SPS/PPS (e.g. across day/night transitions) stitch cleanly via HLS
+    discontinuities.
     """
 
-    def __init__(self, source_camera: str, start_ts: float, end_ts: float) -> None:
+    def __init__(
+        self,
+        source_camera: str,
+        start_ts: float,
+        end_ts: float,
+        internal_port: int,
+    ) -> None:
         self._camera = source_camera
         self._start_ts = start_ts
         self._end_ts = end_ts
-        self._concat_file: Optional[str] = None
+        self._internal_port = internal_port
 
     @property
     def source_camera(self) -> str:
@@ -185,18 +192,16 @@ class RecordingDebugReplaySource(DebugReplaySource):
             )
 
     def ffmpeg_input_args(self, working_dir: str) -> list[str]:
-        replay_name = f"{REPLAY_CAMERA_PREFIX}{self._camera}"
-        concat_file = os.path.join(working_dir, f"{replay_name}_concat.txt")
-        recordings = query_recordings(self._camera, self._start_ts, self._end_ts)
-        with open(concat_file, "w") as f:
-            for recording in recordings:
-                f.write(f"file '{recording.path}'\n")
-        self._concat_file = concat_file
-        return ["-f", "concat", "-safe", "0", "-i", concat_file]
-
-    def cleanup(self, working_dir: str) -> None:
-        if self._concat_file:
-            _remove_silent(self._concat_file)
+        playlist_url = (
+            f"http://127.0.0.1:{self._internal_port}/vod/{self._camera}"
+            f"/start/{self._start_ts}/end/{self._end_ts}/index.m3u8"
+        )
+        return [
+            "-protocol_whitelist",
+            "pipe,file,http,tcp",
+            "-i",
+            playlist_url,
+        ]
 
 
 class ExportDebugReplaySource(DebugReplaySource):

frigate/jobs/motion_search.py

@@ -3,6 +3,8 @@
 import logging
 import os
 import threading
+import time
+from collections.abc import Callable, Generator, Iterable
 from concurrent.futures import Future, ThreadPoolExecutor, as_completed
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
@@ -19,6 +21,18 @@ from frigate.jobs.manager import (
     get_job_by_id,
     set_current_job,
 )
+from frigate.jobs.motion_search_batch import (
+    build_segment_time_map,
+    coalesce_runs,
+    stream_time_to_absolute,
+)
+from frigate.jobs.motion_search_decode import (
+    iter_vod_frames,
+    keyframe_sampling_eligible,
+    probe_video_dimensions,
+    probe_vod_keyframe_pts,
+    resolve_motion_decode_args,
+)
 from frigate.models import Recordings
 from frigate.types import JobStatusTypesEnum
 
@@ -26,6 +40,18 @@ logger = logging.getLogger(__name__)
 
 # Constants
 HEATMAP_GRID_SIZE = 16
+# Max wall-clock span of one VOD run request (seconds). Bounds per-request size
+# and gives streaming/cancel/early-exit granularity.
+MAX_RUN_SECONDS = 600.0
+# Treat segments within this many seconds end-to-start as time-contiguous.
+RUN_GAP_EPSILON = 1.0
+# Longest-side pixels for the ROI downscale before motion detection.
+SCALE_TARGET = 400
+# Minimum wall seconds between intra-run progress broadcasts.
+PROGRESS_BROADCAST_INTERVAL = 1.0
+# Output frame rate for the fixed-cadence fallback used on long-GOP cameras
+# (where keyframe sampling is too sparse). Keyframe cameras ignore this.
+FALLBACK_SAMPLE_FPS = 2.0
 
 
 @dataclass
@@ -69,13 +95,16 @@ class MotionSearchJob(Job):
     polygon_points: list[list[float]] = field(default_factory=list)
     threshold: int = 30
     min_area: float = 5.0
-    frame_skip: int = 5
     parallel: bool = False
     max_results: int = 25
 
     # Track progress
     total_frames_processed: int = 0
 
+    # Live progress (ride the existing to_dict() websocket broadcast)
+    scanning_timestamp: Optional[float] = None
+    progress: float = 0.0
+
     # Metrics for observability
     metrics: Optional[MotionSearchMetrics] = None
 
@@ -100,6 +129,113 @@ def create_polygon_mask(
     return mask
 
 
+def compute_roi_crop_and_scale(
+    polygon_points: list[list[float]],
+    frame_width: int,
+    frame_height: int,
+    scale_target: int,
+) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
+    """Compute the ROI crop box and never-upscale scaled dimensions.
+
+    Returns ((crop_w, crop_h, crop_x, crop_y), (scaled_w, scaled_h)) in pixels.
+    The crop is the polygon's bounding box in frame pixels; the scaled size fits
+    the crop's longest side to ``scale_target`` without ever enlarging it.
+    """
+    xs = [p[0] for p in polygon_points]
+    ys = [p[1] for p in polygon_points]
+    # nv12 (4:2:0) hwdownload requires even crop offsets and even crop/scale
+    # dimensions; otherwise ffmpeg rounds the chroma planes and the raw byte
+    # stream stops matching the expected frame size. Force even values, and the
+    # mask is built from these same values so the two stay aligned.
+    crop_x = int(min(xs) * frame_width)
+    crop_y = int(min(ys) * frame_height)
+    crop_x -= crop_x % 2
+    crop_y -= crop_y % 2
+    crop_w = max(2, int(max(xs) * frame_width) - crop_x)
+    crop_h = max(2, int(max(ys) * frame_height) - crop_y)
+    crop_w -= crop_w % 2
+    crop_h -= crop_h % 2
+
+    longest = max(crop_w, crop_h)
+    factor = min(1.0, scale_target / longest)
+    scaled_w = max(2, round(crop_w * factor))
+    scaled_h = max(2, round(crop_h * factor))
+    scaled_w -= scaled_w % 2
+    scaled_h -= scaled_h % 2
+    return (crop_w, crop_h, crop_x, crop_y), (scaled_w, scaled_h)
+
+
+def build_scaled_roi_mask(
+    polygon_points: list[list[float]],
+    frame_width: int,
+    frame_height: int,
+    crop: tuple[int, int, int, int],
+    scaled: tuple[int, int],
+) -> np.ndarray:
+    """Rasterize the polygon mask at the scaled ROI size.
+
+    Builds the full-resolution mask, crops it to the ROI box, and nearest-
+    neighbor resizes it to the scaled dimensions so it lines up exactly with the
+    frames ffmpeg crops and scales.
+    """
+    crop_w, crop_h, crop_x, crop_y = crop
+    scaled_w, scaled_h = scaled
+    full_mask = create_polygon_mask(polygon_points, frame_width, frame_height)
+    cropped = full_mask[crop_y : crop_y + crop_h, crop_x : crop_x + crop_w]
+    return cv2.resize(cropped, (scaled_w, scaled_h), interpolation=cv2.INTER_NEAREST)
+
+
+def detect_motion_scaled(
+    frames: Iterable[tuple[int, np.ndarray]],
+    mask: np.ndarray,
+    threshold: int,
+    min_area: float,
+    timestamp_fn: Callable[[int], float],
+) -> list[MotionSearchResult]:
+    """Detect motion across pre-cropped, pre-scaled gray frames.
+
+    ``frames`` yields (absolute_frame_index, gray_roi_frame); ``mask`` is the
+    scaled ROI mask. ``min_area`` is a percentage of the masked ROI. Mirrors the
+    full-res detection math (absdiff -> blur -> threshold -> dilate -> contours)
+    on the already-reduced frames.
+    """
+    results: list[MotionSearchResult] = []
+    mask_area = np.count_nonzero(mask)
+    if mask_area == 0:
+        return results
+    min_area_pixels = int((min_area / 100.0) * mask_area)
+
+    prev: np.ndarray | None = None
+    for frame_idx, gray in frames:
+        masked = cv2.bitwise_and(gray, gray, mask=mask)
+        if prev is not None:
+            diff = cv2.absdiff(prev, masked)
+            diff_blurred = cv2.GaussianBlur(diff, (3, 3), 0)
+            _, thresh = cv2.threshold(diff_blurred, threshold, 255, cv2.THRESH_BINARY)
+            thresh_dilated = cv2.dilate(thresh, None, iterations=1)  # type: ignore[call-overload]
+            thresh_masked = cv2.bitwise_and(thresh_dilated, thresh_dilated, mask=mask)
+            change_pixels = cv2.countNonZero(thresh_masked)
+            if change_pixels > min_area_pixels:
+                contours, _ = cv2.findContours(
+                    thresh_masked, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+                )
+                total_change_area = sum(
+                    cv2.contourArea(c)
+                    for c in contours
+                    if cv2.contourArea(c) >= min_area_pixels
+                )
+                if total_change_area > 0:
+                    change_percentage = (total_change_area / mask_area) * 100
+                    results.append(
+                        MotionSearchResult(
+                            timestamp=timestamp_fn(frame_idx),
+                            change_percentage=round(change_percentage, 2),
+                        )
+                    )
+        prev = masked
+    return results
+
+
 def compute_roi_bbox_normalized(
     polygon_points: list[list[float]],
 ) -> tuple[float, float, float, float]:
@@ -184,6 +320,22 @@ def segment_passes_heatmap_gate(
     return heatmap_overlaps_roi(heatmap, roi_bbox)
 
 
+def resolve_internal_port(config: FrigateConfig) -> int:
+    """Return the unauthenticated internal nginx port for VOD requests."""
+    listen = config.networking.listen.internal
+    if isinstance(listen, str):
+        return int(listen.split(":")[-1])
+    return int(listen)
+
+
+def build_vod_url(internal_port: int, camera: str, start: float, end: float) -> str:
+    """Build the internal VOD HLS URL for a camera time range."""
+    return (
+        f"http://127.0.0.1:{internal_port}/vod/{camera}"
+        f"/start/{start}/end/{end}/index.m3u8"
+    )
+
+
 class MotionSearchRunner(threading.Thread):
     """Thread-based runner for motion search jobs with parallel verification."""
 
@@ -206,6 +358,23 @@ class MotionSearchRunner(threading.Thread):
         cpu_count = os.cpu_count() or 1
         self.max_workers = min(4, cpu_count)
 
+        # Resolved once per job in _execute_search
+        self.ffmpeg_path: str = "ffmpeg"
+        self.ffprobe_path: str = "ffprobe"
+        self.decode_args: list[str] = []
+        # Keyframe sampling decision, decided once per job from the first run's
+        # GOP. The fallback cadence is a fixed rate (see FALLBACK_SAMPLE_FPS).
+        self.use_keyframe: bool = True
+        self.fps_rate: float = FALLBACK_SAMPLE_FPS
+        # ROI crop/scale + scaled mask, computed once from the VOD-stream
+        # dimensions (which can differ from the detect resolution).
+        self.crop: tuple[int, int, int, int] = (0, 0, 0, 0)
+        self.scaled: tuple[int, int] = (0, 0)
+        self.scaled_mask: np.ndarray = np.zeros((0, 0), dtype=np.uint8)
+        self.channels: int = 1
+        self.internal_port: int = 5000
+        self._last_progress_broadcast: float = 0.0
+
     def run(self) -> None:
         """Execute the motion search job."""
         try:
@@ -281,6 +450,9 @@ class MotionSearchRunner(threading.Thread):
         if frame_width is None or frame_height is None:
             raise ValueError(f"Camera {camera_name} detect dimensions not configured")
 
+        self.ffmpeg_path = camera_config.ffmpeg.ffmpeg_path
+        self.ffprobe_path = camera_config.ffmpeg.ffprobe_path
+
         # Create polygon mask
         polygon_mask = create_polygon_mask(
             self.job.polygon_points, frame_width, frame_height
@@ -384,205 +556,274 @@ class MotionSearchRunner(threading.Thread):
             self.metrics.heatmap_roi_skip_segments,
         )
 
-        if self.job.parallel:
-            return self._search_motion_parallel(filtered_recordings, polygon_mask)
+        # Resolve decode backend (allowlisted hwaccel or software), coalesce the
+        # gate-passing segments into time-contiguous runs, and probe the first
+        # run's VOD stream once for dimensions + keyframe layout. VOD output is
+        # what we decode, so crop/scale/mask are computed against it.
+        self.internal_port = resolve_internal_port(self.config)
+        self.decode_args = resolve_motion_decode_args(camera_config)
+        ffprobe_path = self.ffprobe_path
 
-        return self._search_motion_sequential(filtered_recordings, polygon_mask)
+        runs = coalesce_runs(filtered_recordings, MAX_RUN_SECONDS, RUN_GAP_EPSILON)
+        if not runs:
+            return []
 
-    def _search_motion_parallel(
-        self,
-        recordings: list[Recordings],
-        polygon_mask: np.ndarray,
-    ) -> list[MotionSearchResult]:
-        """Search for motion in parallel across segments, streaming results."""
-        all_results: list[MotionSearchResult] = []
-        total_frames = 0
-        next_recording_idx_to_merge = 0
+        first_run = runs[0]
+        first_url = build_vod_url(
+            self.internal_port,
+            camera_name,
+            float(first_run[0].start_time),
+            float(first_run[-1].end_time),
+        )
+        dims = probe_video_dimensions(ffprobe_path, first_url)
+        if dims is None:
+            raise ValueError(f"Could not probe VOD dimensions for camera {camera_name}")
+        rec_width, rec_height, _rec_fps = dims
+
+        self.crop, self.scaled = compute_roi_crop_and_scale(
+            self.job.polygon_points, rec_width, rec_height, SCALE_TARGET
+        )
+        self.scaled_mask = build_scaled_roi_mask(
+            self.job.polygon_points, rec_width, rec_height, self.crop, self.scaled
+        )
+        self.channels = 1  # always gray output
+
+        # Decide keyframe vs fixed-cadence sampling once from the first run's GOP
+        # (keyframe structure is a per-camera constant).
+        first_pts = probe_vod_keyframe_pts(ffprobe_path, first_url)
+        self.use_keyframe = keyframe_sampling_eligible(first_pts)
 
         logger.debug(
-            "Motion search job %s: starting motion search with %d workers "
-            "across %d segments",
+            "Motion search job %s: %d runs, sampling=%s, hwaccel=%s, vod=%dx%d",
             self.job.id,
-            self.max_workers,
-            len(recordings),
+            len(runs),
+            "keyframe" if self.use_keyframe else "cadence",
+            bool(self.decode_args),
+            rec_width,
+            rec_height,
         )
 
-        # Initialize partial results on the job so they stream to the frontend
+        return self._search_runs(runs)
+
+    def _emit_progress(self, abs_ts: float) -> None:
+        """Throttled intra-run progress broadcast (scanning cursor)."""
+        now = time.monotonic()
+        if now - self._last_progress_broadcast < PROGRESS_BROADCAST_INTERVAL:
+            return
+        self._last_progress_broadcast = now
+        self.job.scanning_timestamp = abs_ts
+        self._broadcast_status()
+
+    def _detect_with_progress(
+        self,
+        indexed_frames: list[tuple[int, np.ndarray]],
+        timestamp_fn: Callable[[int], float],
+    ) -> list[MotionSearchResult]:
+        """Run detection while firing throttled progress as frames are scanned."""
+
+        def _gen() -> Generator[tuple[int, np.ndarray], None, None]:
+            for i, frame in indexed_frames:
+                if not self._should_stop():
+                    self._emit_progress(timestamp_fn(i))
+                yield i, frame
+
+        return detect_motion_scaled(
+            _gen(),
+            self.scaled_mask,
+            self.job.threshold,
+            self.job.min_area,
+            timestamp_fn,
+        )
+
+    def _process_run(
+        self, run: list[Recordings]
+    ) -> tuple[list[MotionSearchResult], int]:
+        """Decode one run's VOD stream and detect motion.
+
+        Keyframe mode compares every decoded keyframe (free recall, since they
+        are all decoded anyway) paired with its probed PTS; if the decoded and
+        probed counts disagree (the decoder ignored ``-skip_frame nokey`` or the
+        stream is corrupt) this run re-runs in the fixed-cadence fallback.
+        Returns ``(results, frame_count)``.
+        """
+        run_start: float = run[0].start_time  # type: ignore[assignment]
+        run_end: float = run[-1].end_time  # type: ignore[assignment]
+        vod_url = build_vod_url(self.internal_port, self.job.camera, run_start, run_end)
+        time_map = build_segment_time_map(run)
+
+        if self.use_keyframe:
+            kf_pts = probe_vod_keyframe_pts(self.ffprobe_path, vod_url)
+            frames = list(
+                iter_vod_frames(
+                    self.ffmpeg_path,
+                    vod_url,
+                    self.scaled[0],
+                    self.scaled[1],
+                    self.channels,
+                    self.decode_args,
+                    self.crop,
+                    self.scaled,
+                    True,
+                    self._should_stop,
+                    skip_nonkey=True,
+                    fps_rate=None,
+                )
+            )
+            if kf_pts and len(frames) == len(kf_pts):
+                abs_times = [stream_time_to_absolute(time_map, p) for p in kf_pts]
+                indexed = list(enumerate(frames))
+
+                def _ts_kf(i: int) -> float:
+                    return abs_times[i]
+
+                results = self._detect_with_progress(indexed, _ts_kf)
+                return results, len(frames)
+
+            logger.debug(
+                "Keyframe count mismatch (%d decoded vs %d probed), using cadence",
+                len(frames),
+                len(kf_pts),
+            )
+
+        return self._process_run_cadence(vod_url, time_map)
+
+    def _process_run_cadence(
+        self, vod_url: str, time_map: list[tuple[float, float, float]]
+    ) -> tuple[list[MotionSearchResult], int]:
+        """Fixed-cadence fallback: fps-filtered VOD decode, evenly spaced times."""
+        frames = list(
+            iter_vod_frames(
+                self.ffmpeg_path,
+                vod_url,
+                self.scaled[0],
+                self.scaled[1],
+                self.channels,
+                self.decode_args,
+                self.crop,
+                self.scaled,
+                True,
+                self._should_stop,
+                skip_nonkey=False,
+                fps_rate=self.fps_rate,
+            )
+        )
+        indexed = list(enumerate(frames))
+
+        def _ts_fps(i: int) -> float:
+            return stream_time_to_absolute(time_map, i / self.fps_rate)
+
+        results = self._detect_with_progress(indexed, _ts_fps)
+        return results, len(frames)
+
+    def _merge_run(
+        self,
+        run: list[Recordings],
+        run_results: list[MotionSearchResult],
+        frames: int,
+        state: dict[str, Any],
+    ) -> bool:
+        """Fold one run's output into the running results; stream + dedup.
+
+        Returns True once ``max_results`` deduped hits have accumulated.
+        """
+        state["completed_runs"] += 1
+        state["all_results"].extend(run_results)
+        state["total_frames"] += frames
+        self.job.total_frames_processed = state["total_frames"]
+        self.metrics.frames_decoded = state["total_frames"]
+        self.metrics.segments_processed += len(run)
+        self.job.progress = state["completed_runs"] / state["total_runs"]
+
+        state["all_results"].sort(key=lambda r: r.timestamp)
+        deduped = self._deduplicate_results(state["all_results"])[
+            : self.job.max_results
+        ]
+        self.job.results = {
+            "results": [r.to_dict() for r in deduped],
+            "total_frames_processed": state["total_frames"],
+        }
+        self._broadcast_status()
+        return len(deduped) >= self.job.max_results
+
+    def _search_runs(self, runs: list[list[Recordings]]) -> list[MotionSearchResult]:
+        """Decode runs (parallel pool when enabled), merge in order, stream."""
+        state: dict[str, Any] = {
+            "all_results": [],
+            "total_frames": 0,
+            "completed_runs": 0,
+            "total_runs": len(runs),
+        }
         self.job.results = {"results": [], "total_frames_processed": 0}
 
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-            futures: dict[Future, int] = {}
-            completed_segments: dict[int, tuple[list[MotionSearchResult], int]] = {}
+        logger.debug(
+            "Motion search job %s: searching %d runs (parallel=%s, workers=%d)",
+            self.job.id,
+            len(runs),
+            self.job.parallel,
+            self.max_workers,
+        )
 
-            for idx, recording in enumerate(recordings):
-                if self._should_stop():
-                    break
+        if self.job.parallel and len(runs) > 1:
+            with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
+                futures: dict[Future, int] = {}
+                for idx, run in enumerate(runs):
+                    if self._should_stop():
+                        break
+                    futures[executor.submit(self._process_run, run)] = idx
 
-                rec_start: float = recording.start_time  # type: ignore[assignment]
-                rec_end: float = recording.end_time  # type: ignore[assignment]
-                future = executor.submit(
-                    self._process_recording_for_motion,
-                    str(recording.path),
-                    rec_start,
-                    rec_end,
-                    self.job.start_time_range,
-                    self.job.end_time_range,
-                    polygon_mask,
-                    self.job.threshold,
-                    self.job.min_area,
-                    self.job.frame_skip,
-                )
-                futures[future] = idx
+                completed: dict[int, tuple[list[MotionSearchResult], int]] = {}
+                next_idx = 0
+                for future in as_completed(futures):
+                    if self._should_stop():
+                        break
+                    run_idx = futures[future]
+                    try:
+                        completed[run_idx] = future.result()
+                    except Exception as e:
+                        self.metrics.segments_with_errors += 1
+                        logger.warning("Error processing run %d: %s", run_idx, e)
+                        completed[run_idx] = ([], 0)
 
-            for future in as_completed(futures):
-                if self._should_stop():
-                    # Cancel remaining futures
-                    for f in futures:
-                        f.cancel()
-                    break
-
-                recording_idx = futures[future]
-                recording = recordings[recording_idx]
-
-                try:
-                    results, frames = future.result()
-                    self.metrics.segments_processed += 1
-                    completed_segments[recording_idx] = (results, frames)
-
-                    while next_recording_idx_to_merge in completed_segments:
-                        segment_results, segment_frames = completed_segments.pop(
-                            next_recording_idx_to_merge
-                        )
-
-                        all_results.extend(segment_results)
-                        total_frames += segment_frames
-                        self.job.total_frames_processed = total_frames
-                        self.metrics.frames_decoded = total_frames
-
-                        if segment_results:
-                            deduped = self._deduplicate_results(all_results)
-                            self.job.results = {
-                                "results": [
-                                    r.to_dict() for r in deduped[: self.job.max_results]
-                                ],
-                                "total_frames_processed": total_frames,
-                            }
-
-                        self._broadcast_status()
-
-                        if segment_results and len(deduped) >= self.job.max_results:
+                    while next_idx in completed:
+                        run_results, frames = completed.pop(next_idx)
+                        if self._merge_run(runs[next_idx], run_results, frames, state):
                             self.internal_stop_event.set()
-                            for pending_future in futures:
-                                pending_future.cancel()
+                            for pending in futures:
+                                pending.cancel()
                             break
-
-                        next_recording_idx_to_merge += 1
+                        next_idx += 1
 
                     if self.internal_stop_event.is_set():
                         break
-
+        else:
+            for run in runs:
+                if self._should_stop():
+                    break
+                try:
+                    run_results, frames = self._process_run(run)
                 except Exception as e:
-                    self.metrics.segments_processed += 1
                     self.metrics.segments_with_errors += 1
+                    self.metrics.segments_processed += len(run)
                     self._broadcast_status()
-                    logger.warning(
-                        "Error processing segment %s: %s",
-                        recording.path,
-                        e,
-                    )
-
-        self.job.total_frames_processed = total_frames
-        self.metrics.frames_decoded = total_frames
-
-        logger.debug(
-            "Motion search job %s: motion search complete, "
-            "found %d raw results, decoded %d frames, %d segment errors",
-            self.job.id,
-            len(all_results),
-            total_frames,
-            self.metrics.segments_with_errors,
-        )
-
-        # Sort and deduplicate results
-        all_results.sort(key=lambda x: x.timestamp)
-        return self._deduplicate_results(all_results)[: self.job.max_results]
-
-    def _search_motion_sequential(
-        self,
-        recordings: list[Recordings],
-        polygon_mask: np.ndarray,
-    ) -> list[MotionSearchResult]:
-        """Search for motion sequentially across segments, streaming results."""
-        all_results: list[MotionSearchResult] = []
-        total_frames = 0
-
-        logger.debug(
-            "Motion search job %s: starting sequential motion search across %d segments",
-            self.job.id,
-            len(recordings),
-        )
-
-        self.job.results = {"results": [], "total_frames_processed": 0}
-
-        for recording in recordings:
-            if self.cancel_event.is_set():
-                break
-
-            try:
-                rec_start: float = recording.start_time  # type: ignore[assignment]
-                rec_end: float = recording.end_time  # type: ignore[assignment]
-                results, frames = self._process_recording_for_motion(
-                    str(recording.path),
-                    rec_start,
-                    rec_end,
-                    self.job.start_time_range,
-                    self.job.end_time_range,
-                    polygon_mask,
-                    self.job.threshold,
-                    self.job.min_area,
-                    self.job.frame_skip,
-                )
-                all_results.extend(results)
-                total_frames += frames
-
-                self.job.total_frames_processed = total_frames
-                self.metrics.frames_decoded = total_frames
-                self.metrics.segments_processed += 1
-
-                if results:
-                    all_results.sort(key=lambda x: x.timestamp)
-                    deduped = self._deduplicate_results(all_results)[
-                        : self.job.max_results
-                    ]
-                    self.job.results = {
-                        "results": [r.to_dict() for r in deduped],
-                        "total_frames_processed": total_frames,
-                    }
-
-                self._broadcast_status()
-
-                if results and len(deduped) >= self.job.max_results:
+                    logger.warning("Error processing run: %s", e)
+                    continue
+                if self._merge_run(run, run_results, frames, state):
                     break
 
-            except Exception as e:
-                self.metrics.segments_processed += 1
-                self.metrics.segments_with_errors += 1
-                self._broadcast_status()
-                logger.warning("Error processing segment %s: %s", recording.path, e)
-
-        self.job.total_frames_processed = total_frames
-        self.metrics.frames_decoded = total_frames
+        all_results: list[MotionSearchResult] = state["all_results"]
+        self.job.total_frames_processed = state["total_frames"]
+        self.metrics.frames_decoded = state["total_frames"]
+        self.job.progress = 1.0
 
         logger.debug(
-            "Motion search job %s: sequential motion search complete, "
-            "found %d raw results, decoded %d frames, %d segment errors",
+            "Motion search job %s: complete, %d raw results, %d frames, %d errors",
             self.job.id,
             len(all_results),
-            total_frames,
+            state["total_frames"],
             self.metrics.segments_with_errors,
         )
 
-        all_results.sort(key=lambda x: x.timestamp)
+        all_results.sort(key=lambda r: r.timestamp)
         return self._deduplicate_results(all_results)[: self.job.max_results]
 
     def _deduplicate_results(
@@ -602,160 +843,6 @@ class MotionSearchRunner(threading.Thread):
 
         return deduplicated
 
-    def _process_recording_for_motion(
-        self,
-        recording_path: str,
-        recording_start: float,
-        recording_end: float,
-        search_start: float,
-        search_end: float,
-        polygon_mask: np.ndarray,
-        threshold: int,
-        min_area: float,
-        frame_skip: int,
-    ) -> tuple[list[MotionSearchResult], int]:
-        """Process a single recording file for motion detection.
-
-        This method is designed to be called from a thread pool.
-
-        Args:
-            min_area: Minimum change area as a percentage of the ROI (0-100).
-        """
-        results: list[MotionSearchResult] = []
-        frames_processed = 0
-
-        if not os.path.exists(recording_path):
-            logger.warning("Recording file not found: %s", recording_path)
-            return results, frames_processed
-
-        cap = cv2.VideoCapture(recording_path)
-        if not cap.isOpened():
-            logger.error("Could not open recording: %s", recording_path)
-            return results, frames_processed
-
-        try:
-            fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
-            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-            recording_duration = recording_end - recording_start
-
-            # Calculate frame range
-            start_offset = max(0, search_start - recording_start)
-            end_offset = min(recording_duration, search_end - recording_start)
-            start_frame = int(start_offset * fps)
-            end_frame = int(end_offset * fps)
-            start_frame = max(0, min(start_frame, total_frames - 1))
-            end_frame = max(0, min(end_frame, total_frames))
-
-            if start_frame >= end_frame:
-                return results, frames_processed
-
-            cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
-
-            # Get ROI bounding box
-            roi_bbox = cv2.boundingRect(polygon_mask)
-            roi_x, roi_y, roi_w, roi_h = roi_bbox
-
-            prev_frame_gray = None
-            frame_step = max(frame_skip, 1)
-            frame_idx = start_frame
-
-            while frame_idx < end_frame:
-                if self._should_stop():
-                    break
-
-                ret, frame = cap.read()
-                if not ret:
-                    frame_idx += 1
-                    continue
-
-                if (frame_idx - start_frame) % frame_step != 0:
-                    frame_idx += 1
-                    continue
-
-                frames_processed += 1
-
-                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-                # Handle frame dimension changes
-                if gray.shape != polygon_mask.shape:
-                    resized_mask = cv2.resize(
-                        polygon_mask,
-                        (gray.shape[1], gray.shape[0]),
-                        interpolation=cv2.INTER_NEAREST,
-                    )
-                    current_bbox = cv2.boundingRect(resized_mask)
-                else:
-                    resized_mask = polygon_mask
-                    current_bbox = roi_bbox
-
-                roi_x, roi_y, roi_w, roi_h = current_bbox
-                cropped_gray = gray[roi_y : roi_y + roi_h, roi_x : roi_x + roi_w]
-                cropped_mask = resized_mask[
-                    roi_y : roi_y + roi_h, roi_x : roi_x + roi_w
-                ]
-
-                cropped_mask_area = np.count_nonzero(cropped_mask)
-                if cropped_mask_area == 0:
-                    frame_idx += 1
-                    continue
-
-                # Convert percentage to pixel count for this ROI
-                min_area_pixels = int((min_area / 100.0) * cropped_mask_area)
-
-                masked_gray = cv2.bitwise_and(
-                    cropped_gray, cropped_gray, mask=cropped_mask
-                )
-
-                if prev_frame_gray is not None:
-                    diff = cv2.absdiff(prev_frame_gray, masked_gray)  # type: ignore[unreachable]
-                    diff_blurred = cv2.GaussianBlur(diff, (3, 3), 0)
-                    _, thresh = cv2.threshold(
-                        diff_blurred, threshold, 255, cv2.THRESH_BINARY
-                    )
-                    thresh_dilated = cv2.dilate(thresh, None, iterations=1)
-                    thresh_masked = cv2.bitwise_and(
-                        thresh_dilated, thresh_dilated, mask=cropped_mask
-                    )
-
-                    change_pixels = cv2.countNonZero(thresh_masked)
-                    if change_pixels > min_area_pixels:
-                        contours, _ = cv2.findContours(
-                            thresh_masked, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
-                        )
-                        total_change_area = sum(
-                            cv2.contourArea(c)
-                            for c in contours
-                            if cv2.contourArea(c) >= min_area_pixels
-                        )
-                        if total_change_area > 0:
-                            frame_time_offset = (frame_idx - start_frame) / fps
-                            timestamp = (
-                                recording_start + start_offset + frame_time_offset
-                            )
-                            change_percentage = (
-                                total_change_area / cropped_mask_area
-                            ) * 100
-                            results.append(
-                                MotionSearchResult(
-                                    timestamp=timestamp,
-                                    change_percentage=round(change_percentage, 2),
-                                )
-                            )
-
-                prev_frame_gray = masked_gray
-                frame_idx += 1
-
-        finally:
-            cap.release()
-
-        logger.debug(
-            "Motion search segment complete: %s, %d frames processed, %d results found",
-            recording_path,
-            frames_processed,
-            len(results),
-        )
-        return results, frames_processed
-
 
 # Module-level state for managing per-camera jobs
 _motion_search_jobs: dict[str, tuple[MotionSearchJob, threading.Event]] = {}
@@ -779,7 +866,6 @@ def start_motion_search_job(
     polygon_points: list[list[float]],
     threshold: int = 30,
     min_area: float = 5.0,
-    frame_skip: int = 5,
     parallel: bool = False,
     max_results: int = 25,
 ) -> str:
@@ -794,7 +880,6 @@ def start_motion_search_job(
         polygon_points=polygon_points,
         threshold=threshold,
         min_area=min_area,
-        frame_skip=frame_skip,
         parallel=parallel,
         max_results=max_results,
     )
@@ -812,14 +897,13 @@ def start_motion_search_job(
     logger.debug(
         "Started motion search job %s for camera %s: "
         "time_range=%.1f-%.1f, threshold=%d, min_area=%.1f%%, "
-        "frame_skip=%d, parallel=%s, max_results=%d, polygon_points=%d vertices",
+        "parallel=%s, max_results=%d, polygon_points=%d vertices",
         job.id,
         camera_name,
         start_time,
         end_time,
         threshold,
         min_area,
-        frame_skip,
         parallel,
         max_results,
         len(polygon_points),

frigate/jobs/motion_search_batch.py

@@ -0,0 +1,75 @@
+"""Pure helpers for VOD-batched motion search.
+
+Coalescing gate-passing segments into time-contiguous runs, mapping a frame's
+VOD stream time back to an absolute timestamp, and thinning sample times to a
+target interval. No I/O or ffmpeg here so the tricky math stays unit-testable.
+"""
+
+from bisect import bisect_right
+from typing import Any
+
+
+def coalesce_runs(
+    segments: list[Any], max_seconds: float, epsilon: float
+) -> list[list[Any]]:
+    """Group gate-passing segments into time-contiguous runs.
+
+    A run extends while each segment's ``start_time`` is within ``epsilon`` of
+    the previous segment's ``end_time`` (no recording gap) and the run's total
+    span stays at or below ``max_seconds``. A gap or the cap starts a new run.
+    Each segment must expose ``start_time`` / ``end_time``.
+    """
+    runs: list[list[Any]] = []
+    current: list[Any] = []
+    for seg in segments:
+        if not current:
+            current = [seg]
+            continue
+        prev_end = float(current[-1].end_time)
+        run_start = float(current[0].start_time)
+        contiguous = abs(float(seg.start_time) - prev_end) <= epsilon
+        within_cap = (float(seg.end_time) - run_start) <= max_seconds
+        if contiguous and within_cap:
+            current.append(seg)
+        else:
+            runs.append(current)
+            current = [seg]
+    if current:
+        runs.append(current)
+    return runs
+
+
+def build_segment_time_map(
+    run: list[Any],
+) -> list[tuple[float, float, float]]:
+    """Build a (stream_offset, abs_start, duration) row per segment in a run.
+
+    ``stream_offset`` is the segment's start in continuous VOD stream time (the
+    cumulative sum of preceding segment durations); ``abs_start`` is its absolute
+    ``start_time``. Built from each segment's own duration; for a gap-free run
+    this makes stream time equal ``run_start + offset``.
+    """
+    rows: list[tuple[float, float, float]] = []
+    offset = 0.0
+    for seg in run:
+        duration = float(seg.end_time) - float(seg.start_time)
+        rows.append((offset, float(seg.start_time), duration))
+        offset += duration
+    return rows
+
+
+def stream_time_to_absolute(
+    time_map: list[tuple[float, float, float]], stream_time: float
+) -> float:
+    """Map a VOD stream time to an absolute timestamp via the run's table.
+
+    Binary-searches the segment whose stream range contains ``stream_time`` and
+    returns ``abs_start + (stream_time - stream_offset)``. Times past the last
+    segment map into the last segment (clamped at the run edge).
+    """
+    offsets = [row[0] for row in time_map]
+    idx = bisect_right(offsets, stream_time) - 1
+    if idx < 0:
+        idx = 0
+    stream_offset, abs_start, _duration = time_map[idx]
+    return abs_start + (stream_time - stream_offset)

frigate/jobs/motion_search_decode.py

@@ -0,0 +1,382 @@
+"""Hardware-accelerated ffmpeg decode for motion search.
+
+Decodes a recording run's VOD/HLS stream with an ffmpeg subprocess, optionally
+selecting only keyframes, and streams raw frames over a pipe for the motion
+math. Output is the requested ``pix_fmt`` (gray or ``bgr24``) with optional
+crop/scale applied in the filter graph so downstream pixels are unchanged.
+"""
+
+import json
+import logging
+import subprocess as sp
+import tempfile
+from collections.abc import Callable, Generator
+from typing import IO
+
+import numpy as np
+
+from frigate.config import CameraConfig
+from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_decode
+from frigate.util.services import auto_detect_hwaccel
+
+logger = logging.getLogger(__name__)
+
+# Output-format surfaces that download cleanly to nv12 via the fixed
+# ``hwdownload,format=nv12`` step the decode path appends. Other surfaces
+# (drm_prime from rkmpp, vulkan, amf) need a different download step, so motion
+# search decodes them in software to keep results byte-identical rather than risk
+# a wrong-but-valid-sized frame the zero-frame fallback gate would not catch.
+_NV12_OUTPUT_FORMATS = frozenset({"vaapi", "cuda", "qsv"})
+
+
+def _hwaccel_output_format(decode_args: list[str]) -> str | None:
+    """Return the ``-hwaccel_output_format`` value in ffmpeg args, or None."""
+    try:
+        idx = decode_args.index("-hwaccel_output_format")
+    except ValueError:
+        return None
+    return decode_args[idx + 1] if idx + 1 < len(decode_args) else None
+
+
+def resolve_motion_decode_args(camera_config: CameraConfig) -> list[str]:
+    """Resolve the ffmpeg hwaccel decode args for a camera's recordings.
+
+    ``auto`` is resolved via ``auto_detect_hwaccel`` and the preset is expanded
+    by ``parse_preset_hardware_acceleration_decode`` (the same table the live
+    pipeline uses). Acceleration is kept only when the decoded surface downloads
+    cleanly to nv12 -- decided by reading ``-hwaccel_output_format`` back from the
+    resolved args rather than a separate preset allowlist that could drift from
+    ``PRESETS_HW_ACCEL_DECODE``. Anything else (custom args, a software-only
+    preset, or an nv12-incompatible surface) returns an empty list, meaning
+    software decode, so results stay byte-identical.
+    """
+    raw = camera_config.ffmpeg.hwaccel_args
+    preset = auto_detect_hwaccel() if raw == "auto" else raw
+
+    # Custom args (a list) decode in software so results stay byte-identical.
+    if not isinstance(preset, str):
+        return []
+
+    decode_args = parse_preset_hardware_acceleration_decode(
+        preset,
+        camera_config.detect.fps,
+        camera_config.detect.width or 0,
+        camera_config.detect.height or 0,
+        camera_config.ffmpeg.gpu,
+    )
+    if not decode_args:
+        return []
+
+    if _hwaccel_output_format(decode_args) not in _NV12_OUTPUT_FORMATS:
+        return []
+
+    return decode_args
+
+
+def _read_exact(stream: IO[bytes], size: int) -> bytes | None:
+    """Read exactly ``size`` bytes from a pipe, or None at clean EOF.
+
+    Pipe reads can return fewer bytes than requested, so loop until the frame
+    is complete. A short read at the start of a frame means end-of-stream.
+    """
+    buf = bytearray()
+    while len(buf) < size:
+        chunk = stream.read(size - len(buf))
+        if not chunk:
+            return None
+        buf.extend(chunk)
+    return bytes(buf)
+
+
+def _terminate(proc: sp.Popen[bytes]) -> None:
+    """Stop an ffmpeg decode process promptly."""
+    # Close the read end first so a blocked ffmpeg write unblocks (ffmpeg then
+    # sees a broken pipe), then signal it. The resulting ffmpeg write error is
+    # harmless and goes to the captured stderr.
+    if proc.stdout is not None:
+        try:
+            proc.stdout.close()
+        except OSError:
+            pass
+    if proc.poll() is None:
+        proc.terminate()
+        try:
+            proc.wait(timeout=5)
+        except sp.TimeoutExpired:
+            proc.kill()
+            proc.wait()
+
+
+KEYFRAME_MAX_GAP_SECONDS = 2.0
+
+
+def keyframe_sampling_eligible(
+    keyframe_pts: list[float], max_gap: float = KEYFRAME_MAX_GAP_SECONDS
+) -> bool:
+    """True if keyframes are dense and regular enough for keyframe-only sampling.
+
+    Requires at least two keyframes and no gap longer than ``max_gap`` seconds, so
+    a multi-second motion event necessarily spans a sampled keyframe.
+    """
+    if len(keyframe_pts) < 2:
+        return False
+    gaps = [b - a for a, b in zip(keyframe_pts, keyframe_pts[1:])]
+    return max(gaps) <= max_gap
+
+
+VOD_PROTOCOL_ARGS = ["-protocol_whitelist", "pipe,file,http,tcp"]
+
+
+def build_vod_decode_command(
+    ffmpeg_path: str,
+    vod_url: str,
+    decode_args: list[str],
+    crop: tuple[int, int, int, int] | None,
+    scale: tuple[int, int] | None,
+    gray: bool,
+    *,
+    skip_nonkey: bool,
+    fps_rate: float | None,
+) -> list[str]:
+    """Build the ffmpeg argv to decode a VOD HLS URL.
+
+    ``skip_nonkey`` adds ``-skip_frame nokey`` (keyframe-only). ``fps_rate`` adds
+    an ``fps`` filter for the fixed-cadence fallback. They are mutually
+    exclusive: keyframe mode passes ``skip_nonkey=True``/``fps_rate=None``; the
+    fallback passes ``skip_nonkey=False`` with a rate.
+    """
+    filters: list[str] = []
+    # With hwaccel the decoded frames are GPU surfaces; pull them back to system
+    # memory before the CPU fps/crop/scale filters and the rawvideo encoder.
+    if decode_args:
+        filters.append("hwdownload")
+        filters.append("format=nv12")
+    if fps_rate is not None:
+        filters.append(f"fps={fps_rate}")
+    if crop is not None:
+        cw, ch, cx, cy = crop
+        filters.append(f"crop={cw}:{ch}:{cx}:{cy}")
+    if scale is not None:
+        sw, sh = scale
+        filters.append(f"scale={sw}:{sh}")
+
+    pix_fmt = "gray" if gray else "bgr24"
+    cmd = [ffmpeg_path, "-hide_banner", "-loglevel", "error"]
+    if skip_nonkey:
+        cmd += ["-skip_frame", "nokey"]
+    cmd += [*decode_args, *VOD_PROTOCOL_ARGS, "-i", vod_url, "-an"]
+    if filters:
+        cmd += ["-vf", ",".join(filters)]
+    cmd += ["-vsync", "0", "-f", "rawvideo", "-pix_fmt", pix_fmt, "pipe:"]
+    return cmd
+
+
+def _run_vod_decode(
+    ffmpeg_path: str,
+    vod_url: str,
+    out_width: int,
+    out_height: int,
+    channels: int,
+    decode_args: list[str],
+    crop: tuple[int, int, int, int] | None,
+    scale: tuple[int, int] | None,
+    gray: bool,
+    should_stop: Callable[[], bool],
+    *,
+    skip_nonkey: bool,
+    fps_rate: float | None,
+    software_retry: bool,
+) -> Generator[np.ndarray, None, None]:
+    """Run one VOD decode, yielding raw frames; retry in software if empty."""
+    cmd = build_vod_decode_command(
+        ffmpeg_path,
+        vod_url,
+        decode_args,
+        crop,
+        scale,
+        gray,
+        skip_nonkey=skip_nonkey,
+        fps_rate=fps_rate,
+    )
+    frame_size = out_width * out_height * channels
+    stderr_file = tempfile.SpooledTemporaryFile(max_size=65536)
+    proc = sp.Popen(cmd, stdout=sp.PIPE, stderr=stderr_file)
+    assert proc.stdout is not None
+
+    count = 0
+    try:
+        while True:
+            if should_stop():
+                break
+            buf = _read_exact(proc.stdout, frame_size)
+            if buf is None:
+                break
+            if channels == 1:
+                frame = np.frombuffer(buf, dtype=np.uint8).reshape(
+                    (out_height, out_width)
+                )
+            else:
+                frame = np.frombuffer(buf, dtype=np.uint8).reshape(
+                    (out_height, out_width, channels)
+                )
+            count += 1
+            yield frame
+    finally:
+        _terminate(proc)
+        stderr_file.close()
+
+    if count == 0 and software_retry and not should_stop():
+        logger.warning("Hardware VOD decode produced no frames, retrying in software")
+        yield from _run_vod_decode(
+            ffmpeg_path,
+            vod_url,
+            out_width,
+            out_height,
+            channels,
+            [],
+            crop,
+            scale,
+            gray,
+            should_stop,
+            skip_nonkey=skip_nonkey,
+            fps_rate=fps_rate,
+            software_retry=False,
+        )
+
+
+def iter_vod_frames(
+    ffmpeg_path: str,
+    vod_url: str,
+    out_width: int,
+    out_height: int,
+    channels: int,
+    decode_args: list[str],
+    crop: tuple[int, int, int, int] | None,
+    scale: tuple[int, int] | None,
+    gray: bool,
+    should_stop: Callable[[], bool],
+    *,
+    skip_nonkey: bool,
+    fps_rate: float | None,
+) -> Generator[np.ndarray, None, None]:
+    """Decode a VOD HLS URL and yield raw frames in order.
+
+    Pair keyframe-mode output with probed keyframe PTS; pair fallback output with
+    a fixed cadence. Falls back once to software decode if a hwaccel decode yields
+    no frames.
+    """
+    yield from _run_vod_decode(
+        ffmpeg_path,
+        vod_url,
+        out_width,
+        out_height,
+        channels,
+        decode_args,
+        crop,
+        scale,
+        gray,
+        should_stop,
+        skip_nonkey=skip_nonkey,
+        fps_rate=fps_rate,
+        software_retry=bool(decode_args),
+    )
+
+
+def probe_vod_keyframe_pts(ffprobe_path: str, vod_url: str) -> list[float]:
+    """Return keyframe presentation timestamps (VOD stream time) in order.
+
+    Reads packet flags via ffprobe over the VOD URL (no decode). Returns [] on
+    any failure so the caller can fall back.
+    """
+    cmd = [
+        ffprobe_path,
+        "-v",
+        "error",
+        *VOD_PROTOCOL_ARGS,
+        "-i",
+        vod_url,
+        "-select_streams",
+        "v:0",
+        "-show_packets",
+        "-show_entries",
+        "packet=pts_time,flags",
+        "-of",
+        "json",
+    ]
+    try:
+        completed = sp.run(cmd, capture_output=True, text=True, timeout=120)
+    except (OSError, sp.SubprocessError):
+        logger.warning("ffprobe failed for VOD keyframe probe")
+        return []
+
+    if completed.returncode != 0 or not completed.stdout:
+        return []
+
+    try:
+        packets = json.loads(completed.stdout).get("packets", [])
+    except json.JSONDecodeError:
+        return []
+
+    pts: list[float] = []
+    for pkt in packets:
+        flags = pkt.get("flags", "")
+        pts_time = pkt.get("pts_time")
+        if flags.startswith("K") and pts_time is not None:
+            try:
+                pts.append(float(pts_time))
+            except ValueError:
+                continue
+    return sorted(pts)
+
+
+def probe_video_dimensions(
+    ffprobe_path: str, recording_path: str
+) -> tuple[int, int, float] | None:
+    """Return (width, height, fps) for a recording's video stream, or None.
+
+    Reads stream metadata via ffprobe (no decode). The record stream resolution
+    can differ from the camera's detect resolution, so this is probed once per
+    job against a real segment.
+    """
+    cmd = [
+        ffprobe_path,
+        "-v",
+        "error",
+        "-select_streams",
+        "v:0",
+        "-show_entries",
+        "stream=width,height,avg_frame_rate",
+        "-of",
+        "json",
+        recording_path,
+    ]
+    try:
+        completed = sp.run(cmd, capture_output=True, text=True, timeout=30)
+    except (OSError, sp.SubprocessError):
+        return None
+
+    if completed.returncode != 0 or not completed.stdout:
+        return None
+
+    try:
+        streams = json.loads(completed.stdout).get("streams", [])
+    except json.JSONDecodeError:
+        return None
+
+    if not streams:
+        return None
+
+    stream = streams[0]
+    width = int(stream.get("width", 0) or 0)
+    height = int(stream.get("height", 0) or 0)
+    rate = stream.get("avg_frame_rate", "0/0") or "0/0"
+    try:
+        num, _, den = rate.partition("/")
+        fps = float(num) / float(den) if float(den) != 0 else 0.0
+    except (ValueError, ZeroDivisionError):
+        fps = 0.0
+
+    if width <= 0 or height <= 0:
+        return None
+
+    return width, height, fps

frigate/object_detection/base.py

@@ -167,8 +167,9 @@ class DetectorRunner(FrigateProcess):
 
             # detect and send the output
             self.start_time.value = datetime.datetime.now().timestamp()
+            mono_start = time.monotonic()
             detections = object_detector.detect_raw(input_frame)
-            duration = datetime.datetime.now().timestamp() - self.start_time.value
+            duration = time.monotonic() - mono_start
             frame_manager.close(connection_id)
 
             if connection_id not in self.outputs: