add tests

* add ptz presets and default role widgets

* language tweaks

* fix width in triggers view

* tweak iOS PWA message in notifications settings

* deprecate ui.date_style and ui.time_style

these have been unused since date/time formatting has been pushed to i18n

* add config migrator to remove date_style and time_style

* remove date_style and time_style from reference config

* fix camera list scrolling in state classification wizard on mobile

.github/copilot-instructions.md

@@ -1,401 +0,0 @@
-# GitHub Copilot Instructions for Frigate NVR
-
-This document provides coding guidelines and best practices for contributing to Frigate NVR, a complete and local NVR designed for Home Assistant with AI object detection.
-
-## Project Overview
-
-Frigate NVR is a realtime object detection system for IP cameras that uses:
-
-- **Backend**: Python 3.13+ with FastAPI, OpenCV, TensorFlow/ONNX
-- **Frontend**: React with TypeScript, Vite, TailwindCSS
-- **Architecture**: Multiprocessing design with ZMQ and MQTT communication
-- **Focus**: Minimal resource usage with maximum performance
-
-## Code Review Guidelines
-
-When reviewing code, do NOT comment on:
-
-- Missing imports - Static analysis tooling catches these
-- Code formatting - Ruff (Python) and Prettier (TypeScript/React) handle formatting
-- Minor style inconsistencies already enforced by linters
-
-## Python Backend Standards
-
-### Python Requirements
-
-- **Compatibility**: Python 3.13+
-- **Language Features**: Use modern Python features:
-  - Pattern matching
-  - Type hints (comprehensive typing preferred)
-  - f-strings (preferred over `%` or `.format()`)
-  - Dataclasses
-  - Async/await patterns
-
-### Code Quality Standards
-
-- **Formatting**: Ruff (configured in `pyproject.toml`)
-- **Linting**: Ruff with rules defined in project config
-- **Type Checking**: Use type hints consistently
-- **Testing**: unittest framework - use `python3 -u -m unittest` to run tests
-- **Language**: American English for all code, comments, and documentation
-
-### Logging Standards
-
-- **Logger Pattern**: Use module-level logger
-
-  ```python
-  import logging
-
-  logger = logging.getLogger(__name__)
-  ```
-
-- **Format Guidelines**:
-  - No periods at end of log messages
-  - No sensitive data (keys, tokens, passwords)
-  - Use lazy logging: `logger.debug("Message with %s", variable)`
-- **Log Levels**:
-  - `debug`: Development and troubleshooting information
-  - `info`: Important runtime events (startup, shutdown, state changes)
-  - `warning`: Recoverable issues that should be addressed
-  - `error`: Errors that affect functionality but don't crash the app
-  - `exception`: Use in except blocks to include traceback
-
-### Error Handling
-
-- **Exception Types**: Choose most specific exception available
-- **Try/Catch Best Practices**:
-  - Only wrap code that can throw exceptions
-  - Keep try blocks minimal - process data after the try/except
-  - Avoid bare exceptions except in background tasks
-
-  Bad pattern:
-
-  ```python
-  try:
-      data = await device.get_data()  # Can throw
-      # ❌ Don't process data inside try block
-      processed = data.get("value", 0) * 100
-      result = processed
-  except DeviceError:
-      logger.error("Failed to get data")
-  ```
-
-  Good pattern:
-
-  ```python
-  try:
-      data = await device.get_data()  # Can throw
-  except DeviceError:
-      logger.error("Failed to get data")
-      return
-
-  # ✅ Process data outside try block
-  processed = data.get("value", 0) * 100
-  result = processed
-  ```
-
-### Async Programming
-
-- **External I/O**: All external I/O operations must be async
-- **Best Practices**:
-  - Avoid sleeping in loops - use `asyncio.sleep()` not `time.sleep()`
-  - Avoid awaiting in loops - use `asyncio.gather()` instead
-  - No blocking calls in async functions
-  - Use `asyncio.create_task()` for background operations
-- **Thread Safety**: Use proper synchronization for shared state
-
-### Documentation Standards
-
-- **Module Docstrings**: Concise descriptions at top of files
-  ```python
-  """Utilities for motion detection and analysis."""
-  ```
-- **Function Docstrings**: Required for public functions and methods
-
-  ```python
-  async def process_frame(frame: ndarray, config: Config) -> Detection:
-      """Process a video frame for object detection.
-
-      Args:
-          frame: The video frame as numpy array
-          config: Detection configuration
-
-      Returns:
-          Detection results with bounding boxes
-      """
-  ```
-
-- **Comment Style**:
-  - Explain the "why" not just the "what"
-  - Keep lines under 88 characters when possible
-  - Use clear, descriptive comments
-
-### File Organization
-
-- **API Endpoints**: `frigate/api/` - FastAPI route handlers
-- **Configuration**: `frigate/config/` - Configuration parsing and validation
-- **Detectors**: `frigate/detectors/` - Object detection backends
-- **Events**: `frigate/events/` - Event management and storage
-- **Utilities**: `frigate/util/` - Shared utility functions
-
-## Frontend (React/TypeScript) Standards
-
-### Internationalization (i18n)
-
-- **CRITICAL**: Never write user-facing strings directly in components
-- **Always use react-i18next**: Import and use the `t()` function
-
-  ```tsx
-  import { useTranslation } from "react-i18next";
-
-  function MyComponent() {
-    const { t } = useTranslation(["views/live"]);
-    return <div>{t("camera_not_found")}</div>;
-  }
-  ```
-
-- **Translation Files**: Add English strings to the appropriate json files in `web/public/locales/en`
-- **Namespaces**: Organize translations by feature/view (e.g., `views/live`, `common`, `views/system`)
-
-### Code Quality
-
-- **Linting**: ESLint (see `web/.eslintrc.cjs`)
-- **Formatting**: Prettier with Tailwind CSS plugin
-- **Type Safety**: TypeScript strict mode enabled
-- **Testing**: Vitest for unit tests
-
-### Component Patterns
-
-- **UI Components**: Use Radix UI primitives (in `web/src/components/ui/`)
-- **Styling**: TailwindCSS with `cn()` utility for class merging
-- **State Management**: React hooks (useState, useEffect, useCallback, useMemo)
-- **Data Fetching**: Custom hooks with proper loading and error states
-
-### ESLint Rules
-
-Key rules enforced:
-
-- `react-hooks/rules-of-hooks`: error
-- `react-hooks/exhaustive-deps`: error
-- `no-console`: error (use proper logging or remove)
-- `@typescript-eslint/no-explicit-any`: warn (always use proper types instead of `any`)
-- Unused variables must be prefixed with `_`
-- Comma dangles required for multiline objects/arrays
-
-### File Organization
-
-- **Pages**: `web/src/pages/` - Route components
-- **Views**: `web/src/views/` - Complex view components
-- **Components**: `web/src/components/` - Reusable components
-- **Hooks**: `web/src/hooks/` - Custom React hooks
-- **API**: `web/src/api/` - API client functions
-- **Types**: `web/src/types/` - TypeScript type definitions
-
-## Testing Requirements
-
-### Backend Testing
-
-- **Framework**: Python unittest
-- **Run Command**: `python3 -u -m unittest`
-- **Location**: `frigate/test/`
-- **Coverage**: Aim for comprehensive test coverage of core functionality
-- **Pattern**: Use `TestCase` classes with descriptive test method names
-  ```python
-  class TestMotionDetection(unittest.TestCase):
-      def test_detects_motion_above_threshold(self):
-          # Test implementation
-  ```
-
-### Test Best Practices
-
-- Always have a way to test your work and confirm your changes
-- Write tests for bug fixes to prevent regressions
-- Test edge cases and error conditions
-- Mock external dependencies (cameras, APIs, hardware)
-- Use fixtures for test data
-
-## Development Commands
-
-### Python Backend
-
-```bash
-# Run all tests
-python3 -u -m unittest
-
-# Run specific test file
-python3 -u -m unittest frigate.test.test_ffmpeg_presets
-
-# Check formatting (Ruff)
-ruff format --check frigate/
-
-# Apply formatting
-ruff format frigate/
-
-# Run linter
-ruff check frigate/
-```
-
-### Frontend (from web/ directory)
-
-```bash
-# Start dev server (AI agents should never run this directly unless asked)
-npm run dev
-
-# Build for production
-npm run build
-
-# Run linter
-npm run lint
-
-# Fix linting issues
-npm run lint:fix
-
-# Format code
-npm run prettier:write
-```
-
-### Docker Development
-
-AI agents should never run these commands directly unless instructed.
-
-```bash
-# Build local image
-make local
-
-# Build debug image
-make debug
-```
-
-## Common Patterns
-
-### API Endpoint Pattern
-
-```python
-from fastapi import APIRouter, Request
-from frigate.api.defs.tags import Tags
-
-router = APIRouter(tags=[Tags.Events])
-
-@router.get("/events")
-async def get_events(request: Request, limit: int = 100):
-    """Retrieve events from the database."""
-    # Implementation
-```
-
-### Configuration Access
-
-```python
-# Access Frigate configuration
-config: FrigateConfig = request.app.frigate_config
-camera_config = config.cameras["front_door"]
-```
-
-### Database Queries
-
-```python
-from frigate.models import Event
-
-# Use Peewee ORM for database access
-events = (
-    Event.select()
-    .where(Event.camera == camera_name)
-    .order_by(Event.start_time.desc())
-    .limit(limit)
-)
-```
-
-## Common Anti-Patterns to Avoid
-
-### ❌ Avoid These
-
-```python
-# Blocking operations in async functions
-data = requests.get(url)  # ❌ Use async HTTP client
-time.sleep(5)  # ❌ Use asyncio.sleep()
-
-# Hardcoded strings in React components
-<div>Camera not found</div>  # ❌ Use t("camera_not_found")
-
-# Missing error handling
-data = await api.get_data()  # ❌ No exception handling
-
-# Bare exceptions in regular code
-try:
-    value = await sensor.read()
-except Exception:  # ❌ Too broad
-    logger.error("Failed")
-
-# Returning exceptions in JSON responses
-except ValueError as e:
-    return JSONResponse(
-        content={"success": False, "message": str(e)},
-    )
-```
-
-### ✅ Use These Instead
-
-```python
-# Async operations
-import aiohttp
-async with aiohttp.ClientSession() as session:
-    async with session.get(url) as response:
-        data = await response.json()
-
-await asyncio.sleep(5)  # ✅ Non-blocking
-
-# Translatable strings in React
-const { t } = useTranslation();
-<div>{t("camera_not_found")}</div>  # ✅ Translatable
-
-# Proper error handling
-try:
-    data = await api.get_data()
-except ApiException as err:
-    logger.error("API error: %s", err)
-    raise
-
-# Specific exceptions
-try:
-    value = await sensor.read()
-except SensorException as err:  # ✅ Specific
-    logger.exception("Failed to read sensor")
-
-# Safe error responses
-except ValueError:
-    logger.exception("Invalid parameters for API request")
-    return JSONResponse(
-        content={
-            "success": False,
-            "message": "Invalid request parameters",
-        },
-    )
-```
-
-## Project-Specific Conventions
-
-### Configuration Files
-
-- Main config: `config/config.yml`
-
-### Directory Structure
-
-- Backend code: `frigate/`
-- Frontend code: `web/`
-- Docker files: `docker/`
-- Documentation: `docs/`
-- Database migrations: `migrations/`
-
-### Code Style Conformance
-
-Always conform new and refactored code to the existing coding style in the project:
-
-- Follow established patterns in similar files
-- Match indentation and formatting of surrounding code
-- Use consistent naming conventions (snake_case for Python, camelCase for TypeScript)
-- Maintain the same level of verbosity in comments and docstrings
-
-## Additional Resources
-
-- Documentation: https://docs.frigate.video
-- Main Repository: https://github.com/blakeblackshear/frigate
-- Home Assistant Integration: https://github.com/blakeblackshear/frigate-hass-integration

.github/copilot-instructions.md

@@ -0,0 +1 @@
+AGENTS.md

.gitignore

@@ -22,3 +22,8 @@ core
 !/web/**/*.ts
 .idea/*
 .ipynb_checkpoints
+
+# Auto-generated Docker Compose Generator config files
+docs/src/components/DockerComposeGenerator/config/devices.ts
+docs/src/components/DockerComposeGenerator/config/hardware.ts
+docs/src/components/DockerComposeGenerator/config/ports.ts

AGENTS.md

@@ -0,0 +1,439 @@
+# Agent Instructions for Frigate NVR
+
+This document provides coding guidelines and best practices for contributing to Frigate NVR, a complete and local NVR designed for Home Assistant with AI object detection.
+
+## Project Overview
+
+Frigate NVR is a realtime object detection system for IP cameras that uses:
+
+- **Backend**: Python 3.13+ with FastAPI, OpenCV, TensorFlow/ONNX
+- **Frontend**: React with TypeScript, Vite, TailwindCSS
+- **Architecture**: Multiprocessing design with ZMQ and MQTT communication
+- **Focus**: Minimal resource usage with maximum performance
+
+## Code Review Guidelines
+
+When reviewing code, do NOT comment on:
+
+- Missing imports - Static analysis tooling catches these
+- Code formatting - Ruff (Python) and Prettier (TypeScript/React) handle formatting
+- Minor style inconsistencies already enforced by linters
+
+## Python Backend Standards
+
+### Python Requirements
+
+- **Compatibility**: Python 3.13+
+- **Language Features**: Use modern Python features:
+  - Pattern matching
+  - Type hints (comprehensive typing preferred)
+  - f-strings (preferred over `%` or `.format()`)
+  - Dataclasses
+  - Async/await patterns
+
+### Code Quality Standards
+
+- **Formatting**: Ruff (configured in `pyproject.toml`)
+- **Linting**: Ruff with rules defined in project config
+- **Type Checking**: Use type hints consistently
+- **Testing**: unittest framework - use `python3 -u -m unittest` to run tests
+- **Language**: American English for all code, comments, and documentation
+
+### Logging Standards
+
+- **Logger Pattern**: Use module-level logger
+
+  ```python
+  import logging
+
+  logger = logging.getLogger(__name__)
+  ```
+
+- **Format Guidelines**:
+  - No periods at end of log messages
+  - No sensitive data (keys, tokens, passwords)
+  - Use lazy logging: `logger.debug("Message with %s", variable)`
+- **Log Levels**:
+  - `debug`: Development and troubleshooting information
+  - `info`: Important runtime events (startup, shutdown, state changes)
+  - `warning`: Recoverable issues that should be addressed
+  - `error`: Errors that affect functionality but don't crash the app
+  - `exception`: Use in except blocks to include traceback
+
+### Error Handling
+
+- **Exception Types**: Choose most specific exception available
+- **Try/Catch Best Practices**:
+  - Only wrap code that can throw exceptions
+  - Keep try blocks minimal - process data after the try/except
+  - Avoid bare exceptions except in background tasks
+
+  Bad pattern:
+
+  ```python
+  try:
+      data = await device.get_data()  # Can throw
+      # ❌ Don't process data inside try block
+      processed = data.get("value", 0) * 100
+      result = processed
+  except DeviceError:
+      logger.error("Failed to get data")
+  ```
+
+  Good pattern:
+
+  ```python
+  try:
+      data = await device.get_data()  # Can throw
+  except DeviceError:
+      logger.error("Failed to get data")
+      return
+
+  # ✅ Process data outside try block
+  processed = data.get("value", 0) * 100
+  result = processed
+  ```
+
+### Async Programming
+
+- **External I/O**: All external I/O operations must be async
+- **Best Practices**:
+  - Avoid sleeping in loops - use `asyncio.sleep()` not `time.sleep()`
+  - Avoid awaiting in loops - use `asyncio.gather()` instead
+  - No blocking calls in async functions
+  - Use `asyncio.create_task()` for background operations
+- **Thread Safety**: Use proper synchronization for shared state
+
+### Documentation Standards
+
+- **Module Docstrings**: Concise descriptions at top of files
+  ```python
+  """Utilities for motion detection and analysis."""
+  ```
+- **Function Docstrings**: Required for public functions and methods
+
+  ```python
+  async def process_frame(frame: ndarray, config: Config) -> Detection:
+      """Process a video frame for object detection.
+
+      Args:
+          frame: The video frame as numpy array
+          config: Detection configuration
+
+      Returns:
+          Detection results with bounding boxes
+      """
+  ```
+
+- **Comment Style**:
+  - Explain the "why" not just the "what"
+  - Keep lines under 88 characters when possible
+  - Use clear, descriptive comments
+
+### File Organization
+
+- **API Endpoints**: `frigate/api/` - FastAPI route handlers
+- **Configuration**: `frigate/config/` - Configuration parsing and validation
+- **Detectors**: `frigate/detectors/` - Object detection backends
+- **Events**: `frigate/events/` - Event management and storage
+- **Utilities**: `frigate/util/` - Shared utility functions
+
+## Frontend (React/TypeScript) Standards
+
+### Internationalization (i18n)
+
+- **CRITICAL**: Never write user-facing strings directly in components
+- **Always use react-i18next**: Import and use the `t()` function
+
+  ```tsx
+  import { useTranslation } from "react-i18next";
+
+  function MyComponent() {
+    const { t } = useTranslation(["views/live"]);
+    return <div>{t("camera_not_found")}</div>;
+  }
+  ```
+
+- **Translation Files**: Add English strings to the appropriate json files in `web/public/locales/en`
+- **Namespaces**: Organize translations by feature/view (e.g., `views/live`, `common`, `views/system`)
+
+### Code Quality
+
+- **Linting**: ESLint (see `web/.eslintrc.cjs`)
+- **Formatting**: Prettier with Tailwind CSS plugin
+- **Type Safety**: TypeScript strict mode enabled
+
+### Component Patterns
+
+- **UI Components**: Use Radix UI primitives (in `web/src/components/ui/`)
+- **Styling**: TailwindCSS with `cn()` utility for class merging
+- **State Management**: React hooks (useState, useEffect, useCallback, useMemo)
+- **Data Fetching**: Custom hooks with proper loading and error states
+
+### ESLint Rules
+
+Key rules enforced:
+
+- `react-hooks/rules-of-hooks`: error
+- `react-hooks/exhaustive-deps`: error
+- `no-console`: error (use proper logging or remove)
+- `@typescript-eslint/no-explicit-any`: warn (always use proper types instead of `any`)
+- Unused variables must be prefixed with `_`
+- Comma dangles required for multiline objects/arrays
+
+### File Organization
+
+- **Pages**: `web/src/pages/` - Route components
+- **Views**: `web/src/views/` - Complex view components
+- **Components**: `web/src/components/` - Reusable components
+- **Hooks**: `web/src/hooks/` - Custom React hooks
+- **API**: `web/src/api/` - API client functions
+- **Types**: `web/src/types/` - TypeScript type definitions
+
+## Testing Requirements
+
+### Backend Testing
+
+- **Framework**: Python unittest
+- **Run Command**: `python3 -u -m unittest`
+- **Location**: `frigate/test/`
+- **Coverage**: Aim for comprehensive test coverage of core functionality
+- **Pattern**: Use `TestCase` classes with descriptive test method names
+  ```python
+  class TestMotionDetection(unittest.TestCase):
+      def test_detects_motion_above_threshold(self):
+          # Test implementation
+  ```
+
+### Test Best Practices
+
+- Always have a way to test your work and confirm your changes
+- Write tests for bug fixes to prevent regressions
+- Test edge cases and error conditions
+- Mock external dependencies (cameras, APIs, hardware)
+- Use fixtures for test data
+
+## Development Commands
+
+### Python Backend
+
+```bash
+# Run all tests
+python3 -u -m unittest
+
+# Run specific test file
+python3 -u -m unittest frigate.test.test_ffmpeg_presets
+
+# Check formatting (Ruff)
+ruff format --check frigate/
+
+# Apply formatting
+ruff format frigate/
+
+# Run linter
+ruff check frigate/
+
+# Type check
+python3 -u -m mypy --config-file frigate/mypy.ini frigate
+```
+
+### Frontend (from web/ directory)
+
+```bash
+# Start dev server (AI agents should never run this directly unless asked)
+npm run dev
+
+# Build for production
+npm run build
+
+# Run linter
+npm run lint
+
+# Fix linting issues
+npm run lint:fix
+
+# Format code
+npm run prettier:write
+
+# E2E: first-time setup
+npm install
+npx playwright install chromium
+
+# E2E: build the app and run all tests
+npm run e2e:build && npm run e2e
+
+# E2E: interactive UI for debugging
+npm run e2e:ui
+
+# E2E: run a specific spec
+npx playwright test --config e2e/playwright.config.ts e2e/specs/live.spec.ts
+
+# E2E: filter by name, or run only desktop/mobile
+npx playwright test --config e2e/playwright.config.ts --grep="severity tab"
+npx playwright test --config e2e/playwright.config.ts --project=desktop
+
+# E2E: regenerate mock data after backend model changes (from repo root)
+PYTHONPATH=. python3 web/e2e/fixtures/mock-data/generate-mock-data.py
+
+# Regenerate config translations from Pydantic models — outputs to
+# web/public/locales/en/config/{global,cameras}.json. NEVER edit those
+# JSON files by hand; change the Pydantic field title/description and
+# re-run this script. (from repo root)
+python3 generate_config_translations.py
+
+# Extract i18n keys from source into the locale files after adding
+# new t() calls. Use the :ci variant to verify the locale files are
+# in sync with source (fails if extraction would change anything).
+npm run i18n:extract
+npm run i18n:extract:ci
+```
+
+### Docker Development
+
+AI agents should never run these commands directly unless instructed.
+
+```bash
+# Build local image
+make local
+
+# Build debug image
+make debug
+```
+
+## Common Patterns
+
+### API Endpoint Pattern
+
+```python
+from fastapi import APIRouter, Request
+from frigate.api.defs.tags import Tags
+
+router = APIRouter(tags=[Tags.Events])
+
+@router.get("/events")
+async def get_events(request: Request, limit: int = 100):
+    """Retrieve events from the database."""
+    # Implementation
+```
+
+### Configuration Access
+
+```python
+# Access Frigate configuration
+config: FrigateConfig = request.app.frigate_config
+camera_config = config.cameras["front_door"]
+```
+
+### Database Queries
+
+```python
+from frigate.models import Event
+
+# Use Peewee ORM for database access
+events = (
+    Event.select()
+    .where(Event.camera == camera_name)
+    .order_by(Event.start_time.desc())
+    .limit(limit)
+)
+```
+
+## Common Anti-Patterns to Avoid
+
+### ❌ Avoid These
+
+```python
+# Blocking operations in async functions
+data = requests.get(url)  # ❌ Use async HTTP client
+time.sleep(5)  # ❌ Use asyncio.sleep()
+
+# Hardcoded strings in React components
+<div>Camera not found</div>  # ❌ Use t("camera_not_found")
+
+# Missing error handling
+data = await api.get_data()  # ❌ No exception handling
+
+# Bare exceptions in regular code
+try:
+    value = await sensor.read()
+except Exception:  # ❌ Too broad
+    logger.error("Failed")
+
+# Returning exceptions in JSON responses
+except ValueError as e:
+    return JSONResponse(
+        content={"success": False, "message": str(e)},
+    )
+```
+
+### ✅ Use These Instead
+
+```python
+# Async operations
+import aiohttp
+async with aiohttp.ClientSession() as session:
+    async with session.get(url) as response:
+        data = await response.json()
+
+await asyncio.sleep(5)  # ✅ Non-blocking
+
+# Translatable strings in React
+const { t } = useTranslation();
+<div>{t("camera_not_found")}</div>  # ✅ Translatable
+
+# Proper error handling
+try:
+    data = await api.get_data()
+except ApiException as err:
+    logger.error("API error: %s", err)
+    raise
+
+# Specific exceptions
+try:
+    value = await sensor.read()
+except SensorException as err:  # ✅ Specific
+    logger.exception("Failed to read sensor")
+
+# Safe error responses
+except ValueError:
+    logger.exception("Invalid parameters for API request")
+    return JSONResponse(
+        content={
+            "success": False,
+            "message": "Invalid request parameters",
+        },
+    )
+```
+
+## WebSocket Broadcasts
+
+Outbound WebSocket broadcasts go through a per-recipient classifier in `frigate/comms/ws.py` that enforces camera-level access. **The classifier is fail-closed: any topic it doesn't recognize is dropped for every client.** New outbound topics must be classified there or they'll silently disappear.
+
+## Project-Specific Conventions
+
+### Configuration Files
+
+- Main config: `config/config.yml`
+
+### Directory Structure
+
+- Backend code: `frigate/`
+- Frontend code: `web/`
+- Docker files: `docker/`
+- Documentation: `docs/`
+- Database migrations: `migrations/`
+
+### Code Style Conformance
+
+Always conform new and refactored code to the existing coding style in the project:
+
+- Follow established patterns in similar files
+- Match indentation and formatting of surrounding code
+- Use consistent naming conventions (snake_case for Python, camelCase for TypeScript)
+- Maintain the same level of verbosity in comments and docstrings
+
+## Additional Resources
+
+- Documentation: https://docs.frigate.video
+- Main Repository: https://github.com/blakeblackshear/frigate
+- Home Assistant Integration: https://github.com/blakeblackshear/frigate-hass-integration

CLAUDE.md

@@ -0,0 +1 @@
+AGENTS.md

CONTRIBUTING.md

@@ -10,11 +10,14 @@ If you've found a bug and want to fix it, go for it. Link to the relevant issue
 
 ### New features
 
-Every new feature adds scope that the maintainers must test, maintain, and support long-term. Before writing code for a new feature:
+A pull request is more than just code — it's a request for the maintainers to review, integrate, and support the change long-term. We're selective about what we take on, and prioritize changes that align with the project's direction and can be responsibly maintained in the long term.
+
+**Large or highly-requested features** raise the bar even higher. Popularity signals demand, but it doesn't pre-approve any particular implementation. The bigger the change, the higher the long-term cost, and the more important it is that we're aligned on scope and approach before any code is written. A large PR that lands without prior discussion is unlikely to be merged as-is, no matter how well it's implemented.
+
+Before writing code for a new feature:
 
 1. **Check for existing discussion.** Search [feature requests](https://github.com/blakeblackshear/frigate/issues) and [discussions](https://github.com/blakeblackshear/frigate/discussions) to see if it's been proposed or discussed. Feature requests tagged with "planned" are on our radar — we plan to get to them, but we don't maintain a public roadmap or timeline. Check in with us first if you have interest in contributing to one.
 2. **Start a discussion or feature request first.** This helps ensure your idea aligns with Frigate's direction before you invest time building it. Community interest in a feature request helps us gauge demand, though a great idea is a great idea even without a crowd behind it.
-3. **Be open to "no".** We try to be thoughtful about what we take on, and sometimes that means saying no to good code if the feature isn't the right fit for the project. These calls are sometimes subjective, and we won't always get them right. We're happy to discuss and reconsider.
 
 ## AI usage policy
 
@@ -39,6 +42,8 @@ We're not trying to gatekeep how you write code. Use whatever tools make you pro
 
 Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term — often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
 
+One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated — where the author can't explain the design, debug issues independently, or engage substantively in design discussions — doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
+
 ## Pull request guidelines
 
 ### Before submitting

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

@@ -3,7 +3,6 @@
 import json
 import os
 import sys
-from pathlib import Path
 from typing import Any
 
 from ruamel.yaml import YAML
@@ -18,37 +17,12 @@ from frigate.const import (
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 from frigate.util.config import find_config_file
+from frigate.util.services import is_restricted_go2rtc_source
 
 sys.path.remove("/opt/frigate")
 
 yaml = YAML()
 
-# Check if arbitrary exec sources are allowed (defaults to False for security)
-allow_arbitrary_exec = None
-if "GO2RTC_ALLOW_ARBITRARY_EXEC" in os.environ:
-    allow_arbitrary_exec = os.environ.get("GO2RTC_ALLOW_ARBITRARY_EXEC")
-elif (
-    os.path.isdir("/run/secrets")
-    and os.access("/run/secrets", os.R_OK)
-    and "GO2RTC_ALLOW_ARBITRARY_EXEC" in os.listdir("/run/secrets")
-):
-    allow_arbitrary_exec = (
-        Path(os.path.join("/run/secrets", "GO2RTC_ALLOW_ARBITRARY_EXEC"))
-        .read_text()
-        .strip()
-    )
-# check for the add-on options file
-elif os.path.isfile("/data/options.json"):
-    with open("/data/options.json") as f:
-        raw_options = f.read()
-    options = json.loads(raw_options)
-    allow_arbitrary_exec = options.get("go2rtc_allow_arbitrary_exec")
-
-ALLOW_ARBITRARY_EXEC = allow_arbitrary_exec is not None and str(
-    allow_arbitrary_exec
-).lower() in ("true", "1", "yes")
-
-
 config_file = find_config_file()
 
 try:
@@ -128,18 +102,13 @@ if LIBAVFORMAT_VERSION_MAJOR < 59:
         go2rtc_config["ffmpeg"]["rtsp"] = rtsp_args
 
 
-def is_restricted_source(stream_source: str) -> bool:
-    """Check if a stream source is restricted (echo, expr, or exec)."""
-    return stream_source.strip().startswith(("echo:", "expr:", "exec:"))
-
-
 for name in list(go2rtc_config.get("streams", {})):
     stream = go2rtc_config["streams"][name]
 
     if isinstance(stream, str):
         try:
             formatted_stream = substitute_frigate_vars(stream)
-            if not ALLOW_ARBITRARY_EXEC and is_restricted_source(formatted_stream):
+            if is_restricted_go2rtc_source(formatted_stream):
                 print(
                     f"[ERROR] Stream '{name}' uses a restricted source (echo/expr/exec) which is disabled by default for security. "
                     f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
@@ -158,7 +127,7 @@ for name in list(go2rtc_config.get("streams", {})):
         for i, stream_item in enumerate(stream):
             try:
                 formatted_stream = substitute_frigate_vars(stream_item)
-                if not ALLOW_ARBITRARY_EXEC and is_restricted_source(formatted_stream):
+                if is_restricted_go2rtc_source(formatted_stream):
                     print(
                         f"[ERROR] Stream '{name}' item {i + 1} uses a restricted source (echo/expr/exec) which is disabled by default for security. "
                         f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."

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;

docker/rocm/Dockerfile

@@ -13,7 +13,7 @@ ARG ROCM
 
 RUN apt update -qq && \
     apt install -y wget gpg && \
-    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.2/ubuntu/jammy/amdgpu-install_7.2.70200-1_all.deb && \
+    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.2.3/ubuntu/jammy/amdgpu-install_7.2.3.70203-1_all.deb && \
     apt install -y ./rocm.deb && \
     apt update && \
     apt install -qq -y rocm
@@ -78,6 +78,10 @@ ENV MIGRAPHX_DISABLE_MIOPEN_FUSION=1
 ENV MIGRAPHX_DISABLE_SCHEDULE_PASS=1
 ENV MIGRAPHX_DISABLE_REDUCE_FUSION=1
 ENV MIGRAPHX_ENABLE_HIPRTC_WORKAROUNDS=1
+ENV MIOPEN_CUSTOM_CACHE_DIR=/config/model_cache/migraphx
+ENV MIOPEN_USER_DB_PATH=/config/model_cache/migraphx
+ENV AMD_COMGR_CACHE=1
+ENV AMD_COMGR_CACHE_DIR=/config/model_cache/migraphx
 
 COPY --from=rocm-dist / /
 

docker/rocm/requirements-wheels-rocm.txt

@@ -1 +1 @@
-onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.2.0/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl
+onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.2.3-1/onnxruntime_migraphx-1.24.4-cp311-cp311-linux_x86_64.whl

docker/rocm/rocm.hcl

@@ -1,5 +1,5 @@
 variable "ROCM" {
-  default = "7.2.0"
+  default = "7.2.3"
 }
 variable "HSA_OVERRIDE_GFX_VERSION" {
   default = ""

docs/docs/configuration/advanced.md

@@ -172,7 +172,7 @@ Custom models may also require different input tensor formats. The colorspace co
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detection model" /> to configure the model path, dimensions, and input format.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and open the **Custom Model** tab to configure the model path, dimensions, and input format.
 
 | Field                                         | Description                          |
 | --------------------------------------------- | ------------------------------------ |

docs/docs/configuration/cameras.md

@@ -67,7 +67,7 @@ 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 +143,11 @@ 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.

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/face_recognition.md

@@ -19,7 +19,7 @@ Face recognition requires a one-time internet connection to download detection a
 
 ### Face Detection
 
-When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
+When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/index.md#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
 
 When running a default COCO model or another model that does not include `face` as a detectable label, face detection will run via CV2 using a lightweight DNN model that runs on the CPU. In this case, you should _not_ define `face` in your list of objects to track.
 

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
 
@@ -201,7 +200,7 @@ Cloud Generative AI providers require an active internet connection to send imag
 
 ### Ollama Cloud
 
-Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
+Ollama also supports [cloud models](https://ollama.com/cloud), where model inference is performed in the cloud. You can connect directly to Ollama Cloud by setting `base_url` to `https://ollama.com` and providing an API key. Alternatively, you can run Ollama locally and use a cloud model name so your local instance forwards requests to the cloud. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
 
 #### Configuration
 
@@ -210,7 +209,8 @@ Ollama also supports [cloud models](https://ollama.com/cloud), where your local
 
 1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
    - Set **Provider** to `ollama`
-   - Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`)
+   - Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`) or `https://ollama.com` for direct cloud inference
+   - Set **API key** if required by your endpoint (e.g., when using `https://ollama.com`)
    - Set **Model** to the cloud model name
 
 </TabItem>
@@ -223,6 +223,16 @@ genai:
   model: cloud-model-name
 ```
 
+or when using Ollama Cloud directly
+
+```yaml
+genai:
+  provider: ollama
+  base_url: https://ollama.com
+  model: cloud-model-name
+  api_key: your-api-key
+```
+
 </TabItem>
 </ConfigTabs>
 

docs/docs/configuration/hardware_acceleration_video.md

@@ -136,90 +136,32 @@ ffmpeg:
 </TabItem>
 </ConfigTabs>
 
-### Configuring Intel GPU Stats in Docker
+### Configuring Intel GPU Stats
 
-Additional configuration is needed for the Docker container to be able to access the `intel_gpu_top` command for GPU stats. There are two options:
+Frigate reads Intel GPU utilization directly from the kernel's per-client DRM usage counters exposed at `/proc/<pid>/fdinfo/<fd>`. This requires:
 
-1. Run the container as privileged.
-2. Add the `CAP_PERFMON` capability (note: you might need to set the `perf_event_paranoid` low enough to allow access to the performance event system.)
+- Linux kernel **5.19 or newer** for the `i915` driver, or any release of the `xe` driver.
+- Frigate running with permission to read other processes' fdinfo. Running as root inside the container (the default) satisfies this; non-root setups may need `CAP_SYS_PTRACE`.
 
-#### Run as privileged
+No `intel_gpu_top` binary, `CAP_PERFMON`, privileged mode, or `perf_event_paranoid` tuning is required.
 
-This method works, but it gives more permissions to the container than are actually needed.
+#### Stats for SR-IOV or specific devices
 
-##### Docker Compose - Privileged
-
-```yaml
-services:
-  frigate:
-    ...
-    image: ghcr.io/blakeblackshear/frigate:stable
-    # highlight-next-line
-    privileged: true
-```
-
-##### Docker Run CLI - Privileged
-
-```bash {4}
-docker run -d \
-  --name frigate \
-  ...
-  --privileged \
-  ghcr.io/blakeblackshear/frigate:stable
-```
-
-#### CAP_PERFMON
-
-Only recent versions of Docker support the `CAP_PERFMON` capability. You can test to see if yours supports it by running: `docker run --cap-add=CAP_PERFMON hello-world`
-
-##### Docker Compose - CAP_PERFMON
-
-```yaml {5,6}
-services:
-  frigate:
-    ...
-    image: ghcr.io/blakeblackshear/frigate:stable
-    cap_add:
-      - CAP_PERFMON
-```
-
-##### Docker Run CLI - CAP_PERFMON
-
-```bash {4}
-docker run -d \
-  --name frigate \
-  ...
-  --cap-add=CAP_PERFMON \
-  ghcr.io/blakeblackshear/frigate:stable
-```
-
-#### perf_event_paranoid
-
-_Note: This setting must be changed for the entire system._
-
-For more information on the various values across different distributions, see https://askubuntu.com/questions/1400874/what-does-perf-paranoia-level-four-do.
-
-Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
-
-#### Stats for SR-IOV or other devices
-
-When using virtualized GPUs via SR-IOV, you need to specify the device path to use to gather stats from `intel_gpu_top`. This example may work for some systems using SR-IOV:
+If the host has more than one Intel GPU (e.g. an iGPU plus a discrete GPU, or SR-IOV virtual functions), pin stats collection to a specific device by setting `intel_gpu_device` to either its PCI bus address or a DRM card/render-node path:
 
 ```yaml
 telemetry:
   stats:
-    intel_gpu_device: "sriov"
+    intel_gpu_device: "0000:00:02.0"
 ```
 
-For other virtualized GPUs, try specifying the direct path to the device instead:
-
 ```yaml
 telemetry:
   stats:
-    intel_gpu_device: "drm:/dev/dri/card0"
+    intel_gpu_device: "/dev/dri/card1"
 ```
 
-If you are passing in a device path, make sure you've passed the device through to the container.
+When passing a device path, make sure the device is also passed through to the container.
 
 ## AMD-based CPUs
 

docs/docs/configuration/index.md

@@ -110,10 +110,10 @@ Here are some common starter configuration examples. These can be configured thr
 
 1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the MQTT connection to your Home Assistant Mosquitto broker
 2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)`
-3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
+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>
@@ -189,10 +189,10 @@ cameras:
 
 1. Navigate to <NavPath path="Settings > System > MQTT" /> and set **Enable MQTT** to off
 2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
-3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
+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>
@@ -266,11 +266,11 @@ cameras:
 
 1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the connection to your MQTT broker
 2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
-3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `openvino` and **Device** `AUTO`
-4. Navigate to <NavPath path="Settings > System > Detection model" /> and configure the OpenVINO model path and settings
+3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `openvino` and **Device** `AUTO`
+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/live.md

@@ -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
 

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](review.md#reviewing-motion) on the Review page.

docs/docs/configuration/object_detectors.md

@@ -91,7 +91,7 @@ See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edg
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
 
 </TabItem>
 <TabItem value="yaml">
@@ -111,7 +111,7 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `usb:0` and `usb:1` as the device for each.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `usb:0` and `usb:1` as the device for each.
 
 </TabItem>
 <TabItem value="yaml">
@@ -136,7 +136,7 @@ _warning: may have [compatibility issues](https://github.com/blakeblackshear/fri
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then leave the device field empty.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then leave the device field empty.
 
 </TabItem>
 <TabItem value="yaml">
@@ -156,7 +156,7 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `pci`.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `pci`.
 
 </TabItem>
 <TabItem value="yaml">
@@ -176,7 +176,7 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `pci:0` and `pci:1` as the device for each.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `pci:0` and `pci:1` as the device for each.
 
 </TabItem>
 <TabItem value="yaml">
@@ -199,7 +199,7 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors with different device types (e.g., `usb` and `pci`).
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors with different device types (e.g., `usb` and `pci`).
 
 </TabItem>
 <TabItem value="yaml">
@@ -246,7 +246,7 @@ After placing the downloaded files for the tflite model and labels in your confi
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure the model settings:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`. Then on the same page, in the **Custom Model** tab, configure the model settings:
 
 | Field                                    | Value                                                             |
 | ---------------------------------------- | ----------------------------------------------------------------- |
@@ -309,7 +309,7 @@ Use this configuration for YOLO-based models. When no custom model path or URL i
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure the model settings:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then on the same page, in the **Custom Model** tab, configure the model settings:
 
 | Field                                    | Value                   |
 | ---------------------------------------- | ----------------------- |
@@ -365,7 +365,7 @@ For SSD-based models, provide either a model path or URL to your compiled SSD mo
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure the model settings:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then on the same page, in the **Custom Model** tab, configure the model settings:
 
 | Field                                   | Value  |
 | --------------------------------------- | ------ |
@@ -410,7 +410,7 @@ The Hailo detector supports all YOLO models compiled for Hailo hardware that inc
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure the model settings to match your custom model dimensions and format.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then on the same page, in the **Custom Model** tab, configure the model settings to match your custom model dimensions and format.
 
 </TabItem>
 <TabItem value="yaml">
@@ -465,7 +465,7 @@ When using many cameras one detector may not be enough to keep up. Multiple dete
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **OpenVINO** from the detector type dropdown and click **Add** to add multiple detectors, each targeting `GPU` or `NPU`.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **OpenVINO** from the detector type dropdown and click **Add** to add multiple detectors, each targeting `GPU` or `NPU`.
 
 </TabItem>
 <TabItem value="yaml">
@@ -494,7 +494,7 @@ detectors:
 | [YOLO-NAS](#yolo-nas)                 | ✅  | ✅  |                                                              |
 | [MobileNet v2](#ssdlite-mobilenet-v2) | ✅  | ✅  | Fast and lightweight model, less accurate than larger models |
 | [YOLOX](#yolox)                       | ✅  | ?   |                                                              |
-| [D-FINE](#d-fine)                     | ❌  | ❌  |                                                              |
+| [D-FINE / DEIMv2](#d-fine--deimv2)    | ❌  | ❌  |                                                              |
 
 #### SSDLite MobileNet v2
 
@@ -508,7 +508,7 @@ Use the model configuration shown below when using the OpenVINO detector with th
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                      |
 | ---------------------------------------- | ------------------------------------------ |
@@ -558,7 +558,7 @@ After placing the downloaded onnx model in your config folder, use the following
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                             |
 | ---------------------------------------- | ------------------------------------------------- |
@@ -620,7 +620,7 @@ After placing the downloaded onnx model in your config folder, use the following
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                                    |
 | ---------------------------------------- | -------------------------------------------------------- |
@@ -676,7 +676,7 @@ After placing the downloaded onnx model in your `config/model_cache` folder, use
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                   | Value                             |
 | --------------------------------------- | --------------------------------- |
@@ -710,13 +710,13 @@ model:
 
 </details>
 
-#### D-FINE
+#### D-FINE / DEIMv2
 
-[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
+[D-FINE](https://github.com/Peterande/D-FINE) and [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) are DETR based models that share the same ONNX input/output format. The ONNX exported models are supported, but not included by default. See the models section for downloading [D-FINE](#downloading-d-fine-model) or [DEIMv2](#downloading-deimv2-model) for use in Frigate.
 
 :::warning
 
-Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model
+Currently D-FINE / DEIMv2 models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model
 
 :::
 
@@ -728,7 +728,7 @@ After placing the downloaded onnx model in your config/model_cache folder, use t
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `CPU`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `CPU`. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                              |
 | ---------------------------------------- | ---------------------------------- |
@@ -766,6 +766,31 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
 
 </details>
 
+<details>
+  <summary>DEIMv2 Setup & Config</summary>
+
+After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
+
+```yaml
+detectors:
+  ov:
+    type: openvino
+    device: CPU
+
+model:
+  model_type: dfine
+  width: 640
+  height: 640
+  input_tensor: nchw
+  input_dtype: float
+  path: /config/model_cache/deimv2_hgnetv2_n.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
+Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
+
+</details>
+
 ## Apple Silicon detector
 
 The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
@@ -782,7 +807,7 @@ Using the detector config below will connect to the client:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://host.docker.internal:5555`.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://host.docker.internal:5555`.
 
 </TabItem>
 <TabItem value="yaml">
@@ -816,7 +841,7 @@ When Frigate is started with the following config it will connect to the detecto
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://host.docker.internal:5555`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://host.docker.internal:5555`. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                                    |
 | ---------------------------------------- | -------------------------------------------------------- |
@@ -947,7 +972,7 @@ The AMD GPU kernel is known problematic especially when converting models to mxr
 
 See [ONNX supported models](#supported-models) for supported models, there are some caveats:
 
-- D-FINE models are not supported
+- D-FINE / DEIMv2 models are not supported
 - YOLO-NAS models are known to not run well on integrated GPUs
 
 ## ONNX
@@ -977,7 +1002,7 @@ When using many cameras one detector may not be enough to keep up. Multiple dete
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **ONNX** from the detector type dropdown and click **Add** to add multiple detectors.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **ONNX** from the detector type dropdown and click **Add** to add multiple detectors.
 
 </TabItem>
 <TabItem value="yaml">
@@ -997,13 +1022,13 @@ detectors:
 
 ### ONNX Supported Models
 
-| Model                         | Nvidia GPU | AMD GPU | Notes                                               |
-| ----------------------------- | ---------- | ------- | --------------------------------------------------- |
-| [YOLOv9](#yolo-v3-v4-v7-v9-2) | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
-| [RF-DETR](#rf-detr)           | ✅         | ❌      | Supports CUDA Graphs for optimal Nvidia performance |
-| [YOLO-NAS](#yolo-nas-1)       | ⚠️         | ⚠️      | Not supported by CUDA Graphs                        |
-| [YOLOX](#yolox-1)             | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
-| [D-FINE](#d-fine)             | ⚠️         | ❌      | Not supported by CUDA Graphs                        |
+| Model                                | Nvidia GPU | AMD GPU | Notes                                               |
+| ------------------------------------ | ---------- | ------- | --------------------------------------------------- |
+| [YOLOv9](#yolo-v3-v4-v7-v9-2)        | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
+| [RF-DETR](#rf-detr)                  | ✅         | ⚠️      | Supports CUDA Graphs for optimal Nvidia performance |
+| [YOLO-NAS](#yolo-nas-1)              | ⚠️         | ⚠️      | Not supported by CUDA Graphs                        |
+| [YOLOX](#yolox-1)                    | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
+| [D-FINE / DEIMv2](#d-fine--deimv2-1) | ⚠️         | ❌      | Not supported by CUDA Graphs                        |
 
 There is no default model provided, the following formats are supported:
 
@@ -1025,7 +1050,7 @@ After placing the downloaded onnx model in your config folder, use the following
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **ONNX** from the detector type dropdown and click **Add**. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                             |
 | ---------------------------------------- | ------------------------------------------------- |
@@ -1084,7 +1109,7 @@ After placing the downloaded onnx model in your config folder, use the following
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **ONNX** from the detector type dropdown and click **Add**. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                                    |
 | ---------------------------------------- | -------------------------------------------------------- |
@@ -1133,7 +1158,7 @@ After placing the downloaded onnx model in your config folder, use the following
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **ONNX** from the detector type dropdown and click **Add**. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                                    |
 | ---------------------------------------- | -------------------------------------------------------- |
@@ -1182,7 +1207,7 @@ After placing the downloaded onnx model in your `config/model_cache` folder, use
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **ONNX** from the detector type dropdown and click **Add**. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                   | Value                             |
 | --------------------------------------- | --------------------------------- |
@@ -1215,9 +1240,9 @@ model:
 
 </details>
 
-#### D-FINE
+#### D-FINE / DEIMv2
 
-[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
+[D-FINE](https://github.com/Peterande/D-FINE) and [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) are DETR based models that share the same ONNX input/output format. The ONNX exported models are supported, but not included by default. See the models section for downloading [D-FINE](#downloading-d-fine-model) or [DEIMv2](#downloading-deimv2-model) for use in Frigate.
 
 <details>
   <summary>D-FINE Setup & Config</summary>
@@ -1227,7 +1252,7 @@ After placing the downloaded onnx model in your `config/model_cache` folder, use
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **ONNX** from the detector type dropdown and click **Add**. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                       |
 | ---------------------------------------- | ------------------------------------------- |
@@ -1262,6 +1287,28 @@ model:
 
 </details>
 
+<details>
+  <summary>DEIMv2 Setup & Config</summary>
+
+After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
+
+```yaml
+detectors:
+  onnx:
+    type: onnx
+
+model:
+  model_type: dfine
+  width: 640
+  height: 640
+  input_tensor: nchw
+  input_dtype: float
+  path: /config/model_cache/deimv2_hgnetv2_n.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
+</details>
+
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 ## CPU Detector (not recommended)
@@ -1281,7 +1328,7 @@ A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **CPU** from the detector type dropdown and click **Add**. Configure the number of threads and click **Add** again to add additional CPU detectors as needed (one per camera is recommended).
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **CPU** from the detector type dropdown and click **Add**. Configure the number of threads and click **Add** again to add additional CPU detectors as needed (one per camera is recommended).
 
 </TabItem>
 <TabItem value="yaml">
@@ -1317,7 +1364,7 @@ To integrate CodeProject.AI into Frigate, configure the detector as follows:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **DeepStack** from the detector type dropdown and click **Add**. Set the API URL to point to your CodeProject.AI server (e.g., `http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection`).
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **DeepStack** from the detector type dropdown and click **Add**. Set the API URL to point to your CodeProject.AI server (e.g., `http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection`).
 
 </TabItem>
 <TabItem value="yaml">
@@ -1356,7 +1403,7 @@ To configure the MemryX detector, use the following example configuration:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`.
 
 </TabItem>
 <TabItem value="yaml">
@@ -1376,7 +1423,7 @@ detectors:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **MemryX** from the detector type dropdown and click **Add** to add multiple detectors, specifying `PCIe:0`, `PCIe:1`, `PCIe:2`, etc. as the device for each.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **MemryX** from the detector type dropdown and click **Add** to add multiple detectors, specifying `PCIe:0`, `PCIe:1`, `PCIe:2`, etc. as the device for each.
 
 </TabItem>
 <TabItem value="yaml">
@@ -1405,7 +1452,7 @@ MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the
 
 #### YOLO-NAS
 
-The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded from the [Models Section](#downloading-yolo-nas-model) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
+The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded from the [Models Section](#downloading-yolo-nas-model) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
 
 **Note:** The default model for the MemryX detector is YOLO-NAS 320x320.
 
@@ -1420,7 +1467,7 @@ Below is the recommended configuration for using the **YOLO-NAS** (small) model
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                             |
 | ---------------------------------------- | ------------------------------------------------- |
@@ -1459,7 +1506,7 @@ model:
 
 #### YOLOv9
 
-The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
+The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
 
 ##### Configuration
 
@@ -1468,7 +1515,7 @@ Below is the recommended configuration for using the **YOLOv9** (small) model wi
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                             |
 | ---------------------------------------- | ------------------------------------------------- |
@@ -1515,7 +1562,7 @@ Below is the recommended configuration for using the **YOLOX** (small) model wit
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                   |
 | ---------------------------------------- | ----------------------- |
@@ -1562,7 +1609,7 @@ Below is the recommended configuration for using the **SSDLite MobileNet v2** mo
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                   |
 | ---------------------------------------- | ----------------------- |
@@ -1601,19 +1648,39 @@ model:
 
 #### Using a Custom Model
 
-To use your own model:
+To use your own custom model, first compile it into a [.dfp](https://developer.memryx.com/2p1/specs/files.html#dataflow-program) file, which is the format used by MemryX.
 
-1.  Package your compiled model into a `.zip` file.
+#### Compile the Model
 
-2.  The `.zip` must contain the compiled `.dfp` file.
+Custom models must be compiled using **MemryX SDK 2.1**.
 
-3.  Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
+Before compiling your model, install the MemryX Neural Compiler tools from the
+[Install Tools](https://developer.memryx.com/2p1/get_started/install_tools.html) page on the **host**.
 
-4.  Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
+> **Note:** It is recommended to compile the model on the host machine, or on another separate machine, rather than inside the Frigate Docker container. Installing the compiler inside Docker may conflict with container packages. It is recommended to create a Python virtual environment and install the compiler there.
 
-5.  Update the `labelmap_path` to match your custom model's labels.
+Once the SDK 2.1 environment is set up, follow the
+[MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) documentation to compile your model.
 
-For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/tutorials/tutorials.html).
+Example:
+
+```bash
+mx_nc -m yolonas.onnx -c 4 --autocrop -v --dfp_fname yolonas.dfp
+```
+
+For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/2p1/tutorials/tutorials.html).
+
+#### Package the Compiled Model
+
+1. Package your compiled model into a `.zip` file.
+
+2. The `.zip` file must contain the compiled `.dfp` file.
+
+3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
+
+4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
+
+5. Update `labelmap_path` to match your custom model's labels.
 
 ```yaml
 # The detector automatically selects the default model if nothing is provided in the config.
@@ -1701,7 +1768,7 @@ Use the config below to work with generated TRT models:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **TensorRT** from the detector type dropdown and click **Add**, then set the device to `0` (the default GPU index). Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **TensorRT** from the detector type dropdown and click **Add**, then set the device to `0` (the default GPU index). Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                                        |
 | ---------------------------------------- | ------------------------------------------------------------ |
@@ -1758,7 +1825,7 @@ Use the model configuration shown below when using the synaptics detector with t
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **Synaptics** from the detector type dropdown and click **Add**. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **Synaptics** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                        |
 | ---------------------------------------- | ---------------------------- |
@@ -1812,7 +1879,7 @@ When using many cameras one detector may not be enough to keep up. Multiple dete
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **RKNN** from the detector type dropdown and click **Add** to add multiple detectors, each with `num_cores` set to `0` for automatic selection.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **RKNN** from the detector type dropdown and click **Add** to add multiple detectors, each with `num_cores` set to `0` for automatic selection.
 
 </TabItem>
 <TabItem value="yaml">
@@ -1854,7 +1921,7 @@ This `config.yml` shows all relevant options to configure the detector and expla
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **RKNN** from the detector type dropdown and click **Add**. Set `num_cores` to `0` for automatic selection (increase for better performance on multicore NPUs, e.g., set to `3` on rk3588).
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **RKNN** from the detector type dropdown and click **Add**. Set `num_cores` to `0` for automatic selection (increase for better performance on multicore NPUs, e.g., set to `3` on rk3588).
 
 </TabItem>
 <TabItem value="yaml">
@@ -1891,7 +1958,7 @@ The inference time was determined on a rk3588 with 3 NPU cores.
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                                                   |
 | ---------------------------------------- | ----------------------------------------------------------------------- |
@@ -1937,7 +2004,7 @@ The pre-trained YOLO-NAS weights from DeciAI are subject to their license and ca
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                              |
 | ---------------------------------------- | -------------------------------------------------- |
@@ -1977,7 +2044,7 @@ model: # required
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                                          |
 | ---------------------------------------- | ---------------------------------------------- |
@@ -2071,7 +2138,7 @@ Once completed, configure the detector as follows:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **DeGirum** from the detector type dropdown and click **Add**. Set the location to your AI server (e.g., service name, container name, or `host:port`), the zoo to `degirum/public`, and provide your authentication token if needed.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **DeGirum** from the detector type dropdown and click **Add**. Set the location to your AI server (e.g., service name, container name, or `host:port`), the zoo to `degirum/public`, and provide your authentication token if needed.
 
 </TabItem>
 <TabItem value="yaml">
@@ -2114,7 +2181,7 @@ It is also possible to eliminate the need for an AI server and run the hardware
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **DeGirum** from the detector type dropdown and click **Add**. Set the location to `@local`, the zoo to `degirum/public`, and provide your authentication token.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **DeGirum** from the detector type dropdown and click **Add**. Set the location to `@local`, the zoo to `degirum/public`, and provide your authentication token.
 
 </TabItem>
 <TabItem value="yaml">
@@ -2151,7 +2218,7 @@ If you do not possess whatever hardware you want to run, there's also the option
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **DeGirum** from the detector type dropdown and click **Add**. Set the location to `@cloud`, the zoo to `degirum/public`, and provide your authentication token.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **DeGirum** from the detector type dropdown and click **Add**. Set the location to `@cloud`, the zoo to `degirum/public`, and provide your authentication token.
 
 </TabItem>
 <TabItem value="yaml">
@@ -2207,7 +2274,7 @@ Use the model configuration shown below when using the axengine detector with th
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and select **AXEngine NPU** from the detector type dropdown and click **Add**. Then navigate to <NavPath path="Settings > System > Detection model" /> and configure:
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **AXEngine NPU** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
 
 | Field                                    | Value                   |
 | ---------------------------------------- | ----------------------- |
@@ -2274,6 +2341,49 @@ COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL
 EOF
 ```
 
+### Downloading DEIMv2 Model
+
+[DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) can be exported as ONNX by running the command below. Pretrained weights are available on Hugging Face for two backbone families:
+
+- **HGNetv2** (smaller/faster): `atto`, `femto`, `pico`, `n`
+- **DINOv3** (larger/more accurate): `s`, `m`, `l`, `x`
+
+Set `BACKBONE` and `MODEL_SIZE` in the first line to match your desired variant. Hugging Face model names use uppercase (e.g. `HGNetv2_N`, `DINOv3_S`), while config files use lowercase (e.g. `hgnetv2_n`, `dinov3_s`).
+
+```sh
+docker build . --rm --build-arg BACKBONE=hgnetv2 --build-arg MODEL_SIZE=n --output . -f- <<'EOF'
+FROM python:3.11-slim AS build
+RUN apt-get update && apt-get install --no-install-recommends -y git libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
+COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
+WORKDIR /deimv2
+RUN git clone https://github.com/Intellindust-AI-Lab/DEIMv2.git .
+# Install CPU-only PyTorch first to avoid pulling CUDA variant
+RUN uv pip install --no-cache --system torch torchvision --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache --system -r requirements.txt
+RUN uv pip install --no-cache --system onnx safetensors huggingface_hub
+RUN mkdir -p output
+ARG BACKBONE
+ARG MODEL_SIZE
+# Download from Hugging Face and convert safetensors to pth
+RUN python3 -c "\
+from huggingface_hub import hf_hub_download; \
+from safetensors.torch import load_file; \
+import torch; \
+backbone = '${BACKBONE}'.replace('hgnetv2','HGNetv2').replace('dinov3','DINOv3'); \
+size = '${MODEL_SIZE}'.upper(); \
+st = load_file(hf_hub_download('Intellindust/DEIMv2_' + backbone + '_' + size + '_COCO', 'model.safetensors')); \
+torch.save({'model': st}, 'output/deimv2.pth')"
+RUN sed -i "s/data = torch.rand(2/data = torch.rand(1/" tools/deployment/export_onnx.py
+# HuggingFace safetensors omits frozen constants that the model constructor initializes
+RUN sed -i "s/cfg.model.load_state_dict(state)/cfg.model.load_state_dict(state, strict=False)/" tools/deployment/export_onnx.py
+RUN python3 tools/deployment/export_onnx.py -c configs/deimv2/deimv2_${BACKBONE}_${MODEL_SIZE}_coco.yml -r output/deimv2.pth
+FROM scratch
+ARG BACKBONE
+ARG MODEL_SIZE
+COPY --from=build /deimv2/output/deimv2.onnx /deimv2_${BACKBONE}_${MODEL_SIZE}.onnx
+EOF
+```
+
 ### Downloading RF-DETR Model
 
 RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.

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

@@ -195,7 +195,7 @@ Pre and post capture footage is included in the **recording timeline**, visible
 
 ## Will Frigate delete old recordings if my storage runs out?
 
-As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 hours of recordings will be deleted.
+If there is less than an hour left of storage, the oldest hour of recordings will be deleted and a message will be printed in the Frigate logs. This emergency cleanup deletes the oldest recordings first regardless of retention settings to reclaim space as quickly as possible.
 
 ## Configuring Recording Retention
 

docs/docs/configuration/reference.md

@@ -840,8 +840,8 @@ 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"
@@ -1083,22 +1083,6 @@ 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/restream.md

@@ -236,7 +236,7 @@ Enabling arbitrary exec sources allows execution of arbitrary commands through g
 
 ## Advanced Restream Configurations
 
-The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:
 
 :::warning
 
@@ -244,16 +244,11 @@ The `exec:`, `echo:`, and `expr:` sources are disabled by default for security.
 
 :::
 
-:::warning
-
-The `exec:`, `echo:`, and `expr:` sources are disabled by default for security. You must set `GO2RTC_ALLOW_ARBITRARY_EXEC=true` to use them. See [Security: Restricted Stream Sources](#security-restricted-stream-sources) for more information.
-
-:::
-
-NOTE: The output will need to be passed with two curly braces `{{output}}`
+NOTE: RTSP output will need to be passed with two curly braces `{{output}}`, whereas pipe output must be passed without curly braces.
 
 ```yaml
 go2rtc:
   streams:
     stream1: exec:ffmpeg -hide_banner -re -stream_loop -1 -i /media/BigBuckBunny.mp4 -c copy -rtsp_transport tcp -f rtsp {{output}}
+    stream2: exec:rpicam-vid -t 0 --libav-format h264 -o -
 ```

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 +130,61 @@ 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 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](motion_detection.md) for how the underlying motion detector is configured.
+
+### 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 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.
+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 percentage of the region of interest that must change for a frame to be considered significant. Raise it to ignore small movements (leaves, distant motion); lower it when the object you care about only covers a small slice of the ROI.                                                             |
+| **Frame Skip**            | Number of frames to skip between samples — at a camera recording 20 fps, a skip value of 20 takes motion samples roughly once per second. Higher values scan much faster and are usually the right choice; lower it only when you need to catch the exact appearance or disappearance of a fast-moving object. |
+| **Maximum Results**       | Maximum number of matching timestamps to return.                                                                                                                                                                                                                                                               |
+| **Parallel mode**         | Process multiple recording segments in parallel. Speeds up large time ranges at the cost of higher CPU usage.                                                                                                                                                                                                  |
+
+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 status panel shows live progress and metrics such as how many segments were scanned, how many were skipped because no motion was recorded for that segment (using the stored motion heatmap), how many frames were decoded, and the total wall-clock time. Segments with no recorded motion in the selected ROI are skipped automatically, which 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.
+
+#### 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.
+- Keep Frame Skip high. A higher value samples fewer frames and speeds up the search considerably, while still landing within a few seconds of when the motion or object appeared — close enough to seek to in the recording. Only lower it when you need to pinpoint the exact frame something appears or disappears.
+- Use Parallel mode to shorten wall-clock time on multi-core systems, at the cost of higher CPU usage while it runs.

docs/docs/frigate/hardware.md

@@ -223,10 +223,11 @@ Apple Silicon can not run within a container, so a ZMQ proxy is utilized to comm
 
 With the [ROCm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
 
-| Name      | YOLOv9 Inference Time       | YOLO-NAS Inference Time   |
-| --------- | --------------------------- | ------------------------- |
-| AMD 780M  | t-320: ~ 14 ms s-320: 20 ms | 320: ~ 25 ms 640: ~ 50 ms |
-| AMD 8700G |                             | 320: ~ 20 ms 640: ~ 40 ms |
+| Name           | YOLOv9 Inference Time       | YOLO-NAS Inference Time   | RF-DETR Inference Time |
+| -------------- | --------------------------- | ------------------------- | ---------------------- |
+| AMD 780M       | t-320: ~ 14 ms s-320: 20 ms | 320: ~ 25 ms 640: ~ 50 ms |                        |
+| AMD 8700G      |                             | 320: ~ 20 ms 640: ~ 40 ms |                        |
+| AMD 9060XT 16G | t-320: ~ 4 ms  s-320: 5 ms  | 320: ~ 6 ms               | Nano-320: ~ 90 ms      |
 
 ## Community Supported Detectors
 

docs/docs/frigate/installation.md

@@ -4,12 +4,15 @@ title: Installation
 ---
 
 import ShmCalculator from '@site/src/components/ShmCalculator'
+import DockerComposeGenerator from '@site/src/components/DockerComposeGenerator'
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 
 Frigate is a Docker container that can be run on any Docker host including as a [Home Assistant App](https://www.home-assistant.io/apps/). Note that the Home Assistant App is **not** the same thing as the integration. The [integration](/integrations/home-assistant) is required to integrate Frigate into Home Assistant, whether you are running Frigate as a standalone Docker container or as a Home Assistant App.
 
 :::tip
 
-If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started#configuring-frigate) to configure Frigate.
+If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started.md#configuring-frigate) to configure Frigate.
 
 :::
 
@@ -286,7 +289,7 @@ The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVM
 
 #### Installation
 
-To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/get_started/hardware_setup.html).
+To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/2p1/get_started/install_hardware.html).
 
 Then follow these steps for installing the correct driver/runtime configuration:
 
@@ -295,6 +298,12 @@ Then follow these steps for installing the correct driver/runtime configuration:
 3. Run the script with `./user_installation.sh`
 4. **Restart your computer** to complete driver installation.
 
+:::warning
+
+For manual setup, use **MemryX SDK 2.1** only. Other SDK versions are not supported for this setup. See the [SDK 2.1 documentation](https://developer.memryx.com/2p1/index.html)
+
+:::
+
 #### Setup
 
 To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
@@ -468,6 +477,16 @@ Finally, configure [hardware object detection](/configuration/object_detectors#a
 
 Running through Docker with Docker Compose is the recommended install method.
 
+<Tabs>
+  <TabItem value="domestic" label="Docker Compose Generator" default>
+
+Generate a Frigate Docker Compose configuration based on your hardware and requirements.
+
+<DockerComposeGenerator/>
+
+
+  </TabItem>
+  <TabItem value="original" label="Example Docker Compose File">
 ```yaml
 services:
   frigate:
@@ -501,6 +520,10 @@ services:
     environment:
       FRIGATE_RTSP_PASSWORD: "password"
 ```
+  </TabItem>
+</Tabs>
+
+**Docker CLI**
 
 If you can't use Docker Compose, you can run the container with something similar to this:
 

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)
 
@@ -192,7 +192,7 @@ cameras:
 
 ### Step 4: Configure detectors
 
-By default, Frigate will use a single CPU detector.
+By default, Frigate will use a single OpenVINO detector running on the CPU.
 
 In many cases, the integrated graphics on Intel CPUs provides sufficient performance for typical Frigate setups. If you have an Intel processor, you can follow the configuration below.
 
@@ -204,8 +204,8 @@ You need to refer to **Configure hardware acceleration** above to enable the con
 <ConfigTabs>
 <TabItem value="ui">
 
-1. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `OpenVINO` and **Device** `GPU`
-2. Navigate to <NavPath path="Settings > System > Detection model" /> and configure the model settings for OpenVINO:
+1. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `OpenVINO` and **Device** `GPU`
+2. On the same page, in the **Custom Model** tab, configure the model settings for OpenVINO:
 
 | Field                                    | Value                                      |
 | ---------------------------------------- | ------------------------------------------ |
@@ -273,7 +273,7 @@ services:
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`.
+Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`.
 
 </TabItem>
 <TabItem value="yaml">

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/integrations/plus.md

@@ -3,6 +3,8 @@ id: plus
 title: Frigate+
 ---
 
+import NavPath from "@site/src/components/NavPath";
+
 For more information about how to use Frigate+ to improve your model, see the [Frigate+ docs](/plus/).
 
 :::info
@@ -57,7 +59,7 @@ You can view all of your submitted images at [https://plus.frigate.video](https:
 
 Once you have [requested your first model](../plus/first_model.md) and gotten your own model ID, it can be used with a special model path. No other information needs to be configured for Frigate+ models because it fetches the remaining config from Frigate+ automatically.
 
-You can either choose the new model from the Frigate+ pane in the Settings page of the Frigate UI, or manually set the model at the root level in your config:
+You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:
 
 ```yaml
 detectors: ...

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
@@ -37,6 +39,8 @@ The per-clip variation is typically quite low and is mostly an artifact of keyfr
 
 Debug Replay lets you re-run Frigate's detection pipeline against a section of recorded video without manually configuring a dummy camera. It automatically extracts the recording, creates a temporary camera with the same detection settings as the original, and loops the clip through the pipeline so you can observe detections in real time.
 
+Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real-time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
+
 ### When to use
 
 - Reproducing a detection or tracking issue from a specific time range
@@ -49,11 +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/package-lock.json

@@ -14,9 +14,11 @@
         "@docusaurus/theme-mermaid": "^3.7.0",
         "@inkeep/docusaurus": "^2.0.16",
         "@mdx-js/react": "^3.1.0",
+        "@types/js-yaml": "^4.0.9",
         "clsx": "^2.1.1",
         "docusaurus-plugin-openapi-docs": "^4.5.1",
         "docusaurus-theme-openapi-docs": "^4.5.1",
+        "js-yaml": "^4.1.1",
         "prism-react-renderer": "^2.4.1",
         "raw-loader": "^4.0.2",
         "react": "^18.3.1",
@@ -5747,6 +5749,11 @@
         "@types/istanbul-lib-report": "*"
       }
     },
+    "node_modules/@types/js-yaml": {
+      "version": "4.0.9",
+      "resolved": "https://mirrors.tencent.com/npm/@types/js-yaml/-/js-yaml-4.0.9.tgz",
+      "integrity": "sha512-k4MGaQl5TGo/iipqb2UDG2UwjXziSWkh0uysQelTlJpX1qGlpUZYm8PnO4DxG1qBomtJUdYJ6qR6xdIah10JLg=="
+    },
     "node_modules/@types/json-schema": {
       "version": "7.0.15",
       "resolved": "https://registry.npmjs.org/@types/json-schema/-/json-schema-7.0.15.tgz",
@@ -10964,9 +10971,9 @@
       "license": "MIT"
     },
     "node_modules/fast-uri": {
-      "version": "3.1.0",
-      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.0.tgz",
-      "integrity": "sha512-iPeeDKJSWf4IEOasVVrknXpaBV0IApz/gp7S2bb7Z4Lljbl2MGJRqInZiUrQwV16cpzw/D3S5j5Julj/gT52AA==",
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.2.tgz",
+      "integrity": "sha512-rVjf7ArG3LTk+FS6Yw81V1DLuZl1bRbNrev6Tmd/9RaroeeRRJhAt7jg/6YFxbvAQXUCavSoZhPPj6oOx+5KjQ==",
       "funding": [
         {
           "type": "github",
@@ -12883,7 +12890,7 @@
     },
     "node_modules/js-yaml": {
       "version": "4.1.1",
-      "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-4.1.1.tgz",
+      "resolved": "https://mirrors.tencent.com/npm/js-yaml/-/js-yaml-4.1.1.tgz",
       "integrity": "sha512-qQKT4zQxXl8lLwBtHMWwaTcGfFOZviOJet3Oy/xmGk2gZH677CJM9EvtfdSkgWcATZhj/55JZ0rmy3myCT5lsA==",
       "license": "MIT",
       "dependencies": {

docs/package.json

@@ -3,9 +3,10 @@
   "version": "0.0.0",
   "private": true,
   "scripts": {
+    "build:config": "node scripts/build-config.mjs",
     "docusaurus": "docusaurus",
-    "start": "npm run regen-docs && docusaurus start --host 0.0.0.0",
-    "build": "npm run regen-docs && docusaurus build",
+    "start": "npm run build:config && npm run regen-docs && docusaurus start --host 0.0.0.0",
+    "build": "npm run build:config && npm run regen-docs && docusaurus build",
     "swizzle": "docusaurus swizzle",
     "deploy": "docusaurus deploy",
     "clear": "docusaurus clear",
@@ -23,9 +24,11 @@
     "@docusaurus/theme-mermaid": "^3.7.0",
     "@inkeep/docusaurus": "^2.0.16",
     "@mdx-js/react": "^3.1.0",
+    "@types/js-yaml": "^4.0.9",
     "clsx": "^2.1.1",
     "docusaurus-plugin-openapi-docs": "^4.5.1",
     "docusaurus-theme-openapi-docs": "^4.5.1",
+    "js-yaml": "^4.1.1",
     "prism-react-renderer": "^2.4.1",
     "raw-loader": "^4.0.2",
     "react": "^18.3.1",

docs/scripts/build-config.mjs

@@ -0,0 +1,64 @@
+#!/usr/bin/env node
+
+/**
+ * Build script: reads config.yaml and generates TypeScript files
+ * for the Docker Compose Generator.
+ *
+ * Usage: node scripts/build-config.mjs
+ */
+
+import fs from "node:fs";
+import path from "node:path";
+import { fileURLToPath } from "node:url";
+import yaml from "js-yaml";
+
+const __dirname = path.dirname(fileURLToPath(import.meta.url));
+const CONFIG_DIR = path.resolve(__dirname, "../src/components/DockerComposeGenerator/config");
+const YAML_PATH = path.join(CONFIG_DIR, "config.yaml");
+
+// Read & parse YAML
+const raw = fs.readFileSync(YAML_PATH, "utf8");
+const config = yaml.load(raw);
+
+if (!config.devices || !config.hardware || !config.ports) {
+  console.error("config.yaml must contain 'devices', 'hardware', and 'ports' sections.");
+  process.exit(1);
+}
+
+/**
+ * Generate a .ts file from a section of the YAML config.
+ */
+function generateTsFile(sectionName, items, typeName, varName, mapVarName, yamlFilename) {
+  const jsonItems = JSON.stringify(items, null, 2);
+  // Indent JSON to fit inside the array literal
+  const indented = jsonItems
+    .split("\n")
+    .map((line, i) => (i === 0 ? line : "  " + line))
+    .join("\n");
+
+  const content = `/**
+ * AUTO-GENERATED FILE — do not edit directly.
+ * Source: ${yamlFilename}
+ * To update, edit the YAML file and run: npm run build:config
+ */
+
+import type { ${typeName} } from "./types";
+
+export const ${varName}: ${typeName}[] = ${indented};
+
+/** Lookup map for quick access by ID */
+export const ${mapVarName}: Map<string, ${typeName}> = new Map(${varName}.map((item) => [item.id, item]));
+`;
+
+  const outPath = path.join(CONFIG_DIR, `${sectionName}.ts`);
+  fs.writeFileSync(outPath, content, "utf8");
+  console.log(`  ✓ Generated ${sectionName}.ts (${items.length} items)`);
+}
+
+console.log("Building config from config.yaml...");
+
+generateTsFile("devices", config.devices, "DeviceConfig", "devices", "deviceMap", "config.yaml");
+generateTsFile("hardware", config.hardware, "HardwareOption", "hardwareOptions", "hardwareMap", "config.yaml");
+generateTsFile("ports", config.ports, "PortConfig", "ports", "portMap", "config.yaml");
+
+console.log("Done!");

docs/scripts/lib/nav_map.py

@@ -63,8 +63,8 @@ SYSTEM_NAV: dict[str, tuple[str, str]] = {
     "environment_vars": ("System", "Environment variables"),
     "telemetry": ("System", "Telemetry"),
     "birdseye": ("System", "Birdseye"),
-    "detectors": ("System", "Detector hardware"),
-    "model": ("System", "Detection model"),
+    "detectors": ("System", "Detectors and model"),
+    "model": ("System", "Detectors and model"),
 }
 
 # All known top-level config section keys

docs/src/components/DockerComposeGenerator/DockerComposeGenerator.tsx

@@ -0,0 +1,108 @@
+import React from "react";
+import Admonition from "@theme/Admonition";
+import DeviceSelector from "./components/DeviceSelector";
+import HardwareOptions from "./components/HardwareOptions";
+import PortConfigSection from "./components/PortConfig";
+import StoragePaths from "./components/StoragePaths";
+import NvidiaGpuConfig from "./components/NvidiaGpuConfig";
+import OtherOptions from "./components/OtherOptions";
+import GeneratedOutput from "./components/GeneratedOutput";
+import { useConfigGenerator } from "./hooks/useConfigGenerator";
+import styles from "./styles.module.css";
+
+/**
+ * Simple markdown-link-to-React renderer for help text.
+ * Only supports [text](url) syntax — no nested brackets.
+ */
+function renderHelpText(text: string): React.ReactNode {
+  const parts = text.split(/(\[[^\]]+\]\([^)]+\))/g);
+  return parts.map((part, i) => {
+    const match = part.match(/^\[([^\]]+)\]\(([^)]+)\)$/);
+    if (match) {
+      return (
+        <a key={i} href={match[2]}>
+          {match[1]}
+        </a>
+      );
+    }
+    return <React.Fragment key={i}>{part}</React.Fragment>;
+  });
+}
+
+export default function DockerComposeGenerator() {
+  const {
+    deviceId, device, hardwareEnabled,
+    portEnabled,
+    nvidiaGpuCount, nvidiaGpuDeviceId,
+    configPath, mediaPath, rtspPassword, timezone, shmSize,
+    shmSizeError, gpuDeviceIdError, configPathError, mediaPathError,
+    hasAnyHardware, generatedYaml,
+    selectDevice, toggleHardware, togglePort,
+    handleShmSizeChange, handleConfigPathChange, handleMediaPathChange,
+    handleNvidiaGpuCountChange, handleNvidiaGpuDeviceIdChange,
+    setRtspPassword, setTimezone, isHardwareDisabled,
+  } = useConfigGenerator();
+
+  return (
+    <div className={styles.generator}>
+      <div className={styles.card}>
+        <DeviceSelector selectedId={deviceId} onSelect={selectDevice} />
+
+        {device.helpText && (
+          <Admonition type={device.helpType || "info"}>
+            {renderHelpText(device.helpText)}
+          </Admonition>
+        )}
+
+        {device.needsNvidiaConfig && (
+          <NvidiaGpuConfig
+            gpuCount={nvidiaGpuCount}
+            gpuDeviceId={nvidiaGpuDeviceId}
+            gpuDeviceIdError={gpuDeviceIdError}
+            onGpuCountChange={handleNvidiaGpuCountChange}
+            onGpuDeviceIdChange={handleNvidiaGpuDeviceIdChange}
+          />
+        )}
+
+        <HardwareOptions
+          deviceId={deviceId}
+          hardwareEnabled={hardwareEnabled}
+          onToggle={toggleHardware}
+          isDisabled={isHardwareDisabled}
+        />
+
+        <StoragePaths
+          configPath={configPath}
+          mediaPath={mediaPath}
+          configPathError={configPathError}
+          mediaPathError={mediaPathError}
+          onConfigPathChange={handleConfigPathChange}
+          onMediaPathChange={handleMediaPathChange}
+        />
+
+        <PortConfigSection
+          portEnabled={portEnabled}
+          onTogglePort={togglePort}
+        />
+
+        <OtherOptions
+          rtspPassword={rtspPassword}
+          timezone={timezone}
+          shmSize={shmSize}
+          shmSizeError={shmSizeError}
+          onRtspPasswordChange={setRtspPassword}
+          onTimezoneChange={setTimezone}
+          onShmSizeChange={handleShmSizeChange}
+        />
+
+        <GeneratedOutput
+          yaml={generatedYaml}
+          configPath={configPath}
+          mediaPath={mediaPath}
+          hasAnyHardware={hasAnyHardware}
+          deviceId={deviceId}
+        />
+      </div>
+    </div>
+  );
+}

docs/src/components/DockerComposeGenerator/components/DeviceSelector.tsx

@@ -0,0 +1,147 @@
+import React from "react";
+import { useColorMode } from "@docusaurus/theme-common";
+import { devices } from "../config";
+import type { DeviceConfig } from "../config";
+import styles from "../styles.module.css";
+
+interface Props {
+  selectedId: string;
+  onSelect: (id: string) => void;
+}
+
+/**
+ * Determine the icon type from the icon string:
+ * - Starts with "<svg" → inline SVG
+ * - Starts with "/" or "http" → image URL/path
+ * - Otherwise → emoji text
+ */
+function getIconType(icon: string): "svg" | "image" | "emoji" {
+  const trimmed = icon.trim();
+  if (trimmed.startsWith("<svg")) return "svg";
+  if (trimmed.startsWith("/") || trimmed.startsWith("http://") || trimmed.startsWith("https://")) return "image";
+  return "emoji";
+}
+
+/**
+ * Check if the style object contains background-* properties,
+ * indicating the image should be rendered as a CSS background-image
+ * rather than an <img> tag.
+ */
+function hasBackgroundProps(style: React.CSSProperties | undefined): boolean {
+  if (!style) return false;
+  return Object.keys(style).some((key) => {
+    const k = key.toLowerCase().replace(/-/g, "");
+    return k === "backgroundsize" || k === "backgroundposition" || k === "backgroundrepeat" || k === "backgroundimage";
+  });
+}
+
+/**
+ * Convert a style object to CSS custom properties (e.g. { width: "24px" } → { "--svg-width": "24px" })
+ * so they can be consumed by CSS rules targeting child elements like <svg>.
+ */
+function toCssVars(style: React.CSSProperties | undefined, prefix: string): React.CSSProperties {
+  if (!style) return {};
+  const vars: Record<string, string> = {};
+  for (const [key, value] of Object.entries(style)) {
+    const cssKey = key.replace(/([A-Z])/g, "-$1").toLowerCase();
+    vars[`--${prefix}-${cssKey}`] = value;
+  }
+  return vars as React.CSSProperties;
+}
+
+function DeviceIcon({ device }: { device: DeviceConfig }) {
+  const { isDarkTheme } = useColorMode();
+  const iconStr = isDarkTheme && device.iconDark ? device.iconDark : device.icon;
+  const iconStyle = (isDarkTheme && device.iconDarkStyle
+    ? device.iconDarkStyle
+    : device.iconStyle) as React.CSSProperties | undefined;
+  const svgStyle = (isDarkTheme && device.svgDarkStyle
+    ? device.svgDarkStyle
+    : device.svgStyle) as React.CSSProperties | undefined;
+
+  const iconType = getIconType(iconStr);
+
+  if (iconType === "svg") {
+    return (
+      <div
+        className={styles.deviceIconSvg}
+        style={{ ...iconStyle, ...toCssVars(svgStyle, "svg") }}
+        dangerouslySetInnerHTML={{ __html: iconStr }}
+      />
+    );
+  }
+
+  if (iconType === "image") {
+    // When iconStyle contains background-* properties, render as background-image
+    // on the container div instead of an <img> tag, enabling background-size/position control.
+    if (hasBackgroundProps(iconStyle)) {
+      return (
+        <div
+          className={styles.deviceIconImage}
+          style={{
+            backgroundImage: `url(${iconStr})`,
+            backgroundRepeat: "no-repeat",
+            backgroundPosition: "center",
+            backgroundSize: "contain",
+            ...iconStyle,
+          }}
+        />
+      );
+    }
+    return (
+      <div className={styles.deviceIconImage}>
+        <img src={iconStr} alt={device.name} style={iconStyle} />
+      </div>
+    );
+  }
+
+  return (
+    <div className={styles.deviceIcon} style={iconStyle}>
+      {iconStr}
+    </div>
+  );
+}
+
+function DeviceCard({
+  device,
+  active,
+  onClick,
+}: {
+  device: DeviceConfig;
+  active: boolean;
+  onClick: () => void;
+}) {
+  return (
+    <div
+      className={`${styles.deviceCard} ${active ? styles.deviceCardActive : ""}`}
+      onClick={onClick}
+      role="button"
+      tabIndex={0}
+      onKeyDown={(e) => {
+        if (e.key === "Enter" || e.key === " ") onClick();
+      }}
+    >
+      <DeviceIcon device={device} />
+      <div className={styles.deviceName}>{device.name}</div>
+      <div className={styles.deviceDesc}>{device.description}</div>
+    </div>
+  );
+}
+
+export default function DeviceSelector({ selectedId, onSelect }: Props) {
+  return (
+    <div className={styles.formSection}>
+      <h4>Device Type</h4>
+      <div className={styles.deviceGrid}>
+        {devices.map((d) => (
+          <DeviceCard
+            key={d.id}
+            device={d}
+            active={selectedId === d.id}
+            onClick={() => onSelect(d.id)}
+          />
+        ))}
+      </div>
+    </div>
+  );
+}

docs/src/components/DockerComposeGenerator/components/GeneratedOutput.tsx

@@ -0,0 +1,60 @@
+import React, { useState, useCallback } from "react";
+import CodeBlock from "@theme/CodeBlock";
+import Admonition from "@theme/Admonition";
+import styles from "../styles.module.css";
+
+interface Props {
+  yaml: string;
+  configPath: string;
+  mediaPath: string;
+  hasAnyHardware: boolean;
+  deviceId: string;
+}
+
+export default function GeneratedOutput({
+  yaml,
+  configPath,
+  mediaPath,
+  hasAnyHardware,
+  deviceId,
+}: Props) {
+  const [copied, setCopied] = useState(false);
+
+  const handleCopy = useCallback(() => {
+    navigator.clipboard.writeText(yaml).then(() => {
+      setCopied(true);
+      setTimeout(() => setCopied(false), 2000);
+    });
+  }, [yaml]);
+
+  return (
+    <div className={styles.resultSection}>
+      <div className={styles.resultHeader}>
+        <h4>Generated Configuration</h4>
+        <button className="button button--primary button--sm" onClick={handleCopy}>
+          {copied ? "Copied!" : "Copy"}
+        </button>
+      </div>
+
+      {!configPath && (
+        <Admonition type="tip">
+          <p>You haven&apos;t specified a config file directory. You may want to modify the default path.</p>
+        </Admonition>
+      )}
+      {!mediaPath && (
+        <Admonition type="tip">
+          <p>You haven&apos;t specified a recording storage directory. You may want to modify the default path.</p>
+        </Admonition>
+      )}
+      {deviceId === "stable" && !hasAnyHardware && (
+        <Admonition type="warning">
+          <p>You haven&apos;t selected any hardware acceleration. Please check if you have supported hardware available.</p>
+        </Admonition>
+      )}
+
+      <CodeBlock language="yaml" title="docker-compose.yml">
+        {yaml}
+      </CodeBlock>
+    </div>
+  );
+}

docs/src/components/DockerComposeGenerator/components/HardwareOptions.tsx

@@ -0,0 +1,62 @@
+import React from "react";
+import { hardwareOptions } from "../config";
+import type { HardwareOption } from "../config";
+import styles from "../styles.module.css";
+
+interface Props {
+  deviceId: string;
+  hardwareEnabled: Record<string, boolean>;
+  onToggle: (hwId: string) => void;
+  isDisabled: (hwId: string) => boolean;
+}
+
+function renderDescription(text: string): React.ReactNode {
+  const parts = text.split(/(\[[^\]]+\]\([^)]+\))/g);
+  return parts.map((part, i) => {
+    const match = part.match(/^\[([^\]]+)\]\(([^)]+)\)$/);
+    if (match) {
+      return <a key={i} href={match[2]}>{match[1]}</a>;
+    }
+    return <React.Fragment key={i}>{part}</React.Fragment>;
+  });
+}
+
+function HardwareCheckbox({
+  hw, disabled, checked, onToggle,
+}: {
+  hw: HardwareOption; disabled: boolean; checked: boolean; onToggle: () => void;
+}) {
+  return (
+    <div className={styles.hardwareItem}>
+      <label className={`${styles.checkboxLabel} ${disabled ? styles.checkboxDisabled : ""}`}>
+        <input type="checkbox" checked={checked} onChange={onToggle} disabled={disabled} />
+        <span>{hw.label}</span>
+      </label>
+      {checked && hw.description && (
+        <div className={styles.hardwareDescription}>{renderDescription(hw.description)}</div>
+      )}
+    </div>
+  );
+}
+
+export default function HardwareOptions({ deviceId, hardwareEnabled, onToggle, isDisabled }: Props) {
+  return (
+    <div className={styles.formSection}>
+      <h4>Generic Hardware Devices</h4>
+      {deviceId !== "stable" && (
+        <p className={styles.helpText}>
+          Some options have been auto-configured based on your device type.
+        </p>
+      )}
+      <div className={styles.checkboxGrid}>
+        {hardwareOptions.map((hw) => {
+          const disabled = isDisabled(hw.id);
+          const checked = disabled ? false : !!hardwareEnabled[hw.id];
+          return (
+            <HardwareCheckbox key={hw.id} hw={hw} disabled={disabled} checked={checked} onToggle={() => onToggle(hw.id)} />
+          );
+        })}
+      </div>
+    </div>
+  );
+}

docs/src/components/DockerComposeGenerator/components/NvidiaGpuConfig.tsx

@@ -0,0 +1,64 @@
+import React from "react";
+import styles from "../styles.module.css";
+
+interface Props {
+  gpuCount: string;
+  gpuDeviceId: string;
+  gpuDeviceIdError: boolean;
+  onGpuCountChange: (value: string) => void;
+  onGpuDeviceIdChange: (value: string) => void;
+}
+
+export default function NvidiaGpuConfig({
+  gpuCount,
+  gpuDeviceId,
+  gpuDeviceIdError,
+  onGpuCountChange,
+  onGpuDeviceIdChange,
+}: Props) {
+  const showDeviceId = gpuCount !== "";
+
+  return (
+    <div className={styles.nvidiaConfig}>
+      <div className={styles.formGroup}>
+        <label htmlFor="dcg-gpu-count" className={styles.label}>
+          GPU count:
+        </label>
+        <input
+          id="dcg-gpu-count"
+          type="text"
+          inputMode="numeric"
+          pattern="[0-9]*"
+          className={styles.input}
+          value={gpuCount}
+          placeholder="all"
+          onChange={(e) => onGpuCountChange(e.target.value.replace(/\D/g, ""))}
+        />
+      </div>
+      {showDeviceId && (
+        <div className={styles.formGroup}>
+          <label htmlFor="dcg-gpu-device-id" className={styles.label}>
+            GPU device IDs (required, comma-separated):
+          </label>
+          <input
+            id="dcg-gpu-device-id"
+            type="text"
+            className={`${styles.input} ${gpuDeviceIdError ? styles.inputError : ""}`}
+            value={gpuDeviceId}
+            placeholder="0"
+            onChange={(e) => onGpuDeviceIdChange(e.target.value)}
+          />
+          {gpuDeviceIdError ? (
+            <p className={styles.helpText}>
+              ⚠️ GPU device IDs are required when GPU count is a number
+            </p>
+          ) : (
+            <p className={styles.helpText}>
+              Single GPU: 0 &nbsp;|&nbsp; Multiple GPUs: 0,1,2
+            </p>
+          )}
+        </div>
+      )}
+    </div>
+  );
+}

docs/src/components/DockerComposeGenerator/components/OtherOptions.tsx

@@ -0,0 +1,122 @@
+import React, { useMemo } from "react";
+import CodeInline from "@theme/CodeInline";
+import styles from "../styles.module.css";
+
+const AUTO_TIMEZONE_VALUE = "__auto__";
+
+function getTimezoneList(): string[] {
+  if (typeof Intl !== "undefined") {
+    const intl = Intl as typeof Intl & {
+      supportedValuesOf?: (key: string) => string[];
+    };
+    const supported = intl.supportedValuesOf?.("timeZone");
+    if (supported && supported.length > 0) {
+      return [...supported].sort();
+    }
+  }
+
+  const fallback = Intl.DateTimeFormat().resolvedOptions().timeZone;
+  return fallback ? [fallback] : ["UTC"];
+}
+
+interface Props {
+  rtspPassword: string;
+  timezone: string;
+  shmSize: string;
+  shmSizeError: boolean;
+  onRtspPasswordChange: (value: string) => void;
+  onTimezoneChange: (value: string) => void;
+  onShmSizeChange: (value: string) => void;
+}
+
+export default function OtherOptions({
+  rtspPassword,
+  timezone,
+  shmSize,
+  shmSizeError,
+  onRtspPasswordChange,
+  onTimezoneChange,
+  onShmSizeChange,
+}: Props) {
+  const timezones = useMemo(() => getTimezoneList(), []);
+  const systemTimezone =
+    Intl.DateTimeFormat().resolvedOptions().timeZone || "Etc/UTC";
+  const selectedValue = timezone || AUTO_TIMEZONE_VALUE;
+
+  return (
+    <div className={styles.formSection}>
+      <h4>Other Options</h4>
+      <div className={styles.formGrid}>
+        <div className={styles.formGroup}>
+          <label htmlFor="dcg-timezone" className={styles.label}>
+            Timezone:
+          </label>
+          <select
+            id="dcg-timezone"
+            className={`${styles.input} ${styles.select}`}
+            value={selectedValue}
+            onChange={(e) =>
+              onTimezoneChange(
+                e.target.value === AUTO_TIMEZONE_VALUE ? "" : e.target.value
+              )
+            }
+          >
+            <option value={AUTO_TIMEZONE_VALUE}>
+              Use browser timezone ({systemTimezone})
+            </option>
+            {timezones.map((tz) => (
+              <option key={tz} value={tz}>
+                {tz}
+              </option>
+            ))}
+          </select>
+        </div>
+        <div className={styles.formGroup}>
+          <label htmlFor="dcg-shm-size" className={styles.label}>
+            Shared memory (SHM):
+          </label>
+          <input
+            id="dcg-shm-size"
+            type="text"
+            className={`${styles.input} ${shmSizeError ? styles.inputError : ""}`}
+            value={shmSize}
+            placeholder="512mb"
+            onChange={(e) => onShmSizeChange(e.target.value)}
+          />
+          {shmSizeError ? (
+            <p className={styles.helpText}>
+              ⚠️ Invalid format. Use a number followed by a unit (e.g. 512mb, 1gb)
+            </p>
+          ) : (
+            <p className={styles.helpText}>
+              See{" "}
+              <a href="/frigate/installation#calculating-required-shm-size">
+                calculating required SHM size
+              </a>{" "}
+              for the correct value.
+            </p>
+          )}
+        </div>
+        <div className={styles.formGroup}>
+          <label htmlFor="dcg-rtsp-password" className={styles.label}>
+            RTSP password:
+          </label>
+          <input
+            id="dcg-rtsp-password"
+            type="text"
+            className={styles.input}
+            value={rtspPassword}
+            placeholder="password"
+            onChange={(e) => onRtspPasswordChange(e.target.value)}
+          />
+          <p className={styles.helpText}>
+            Optional. You can specify{" "}
+            <CodeInline>{"{FRIGATE_RTSP_PASSWORD}"}</CodeInline>{" "}
+            in the config file to reference camera stream passwords. This is NOT
+            the Frigate login password.
+          </p>
+        </div>
+      </div>
+    </div>
+  );
+}

docs/src/components/DockerComposeGenerator/components/PortConfig.tsx

@@ -0,0 +1,71 @@
+import React from "react";
+import Admonition from "@theme/Admonition";
+import { ports } from "../config";
+import styles from "../styles.module.css";
+
+interface Props {
+  portEnabled: Record<string, boolean>;
+  onTogglePort: (portId: string) => void;
+}
+
+function PortItem({
+  port,
+  enabled,
+  onToggle,
+}: {
+  port: typeof ports[number];
+  enabled: boolean;
+  onToggle: () => void;
+}) {
+  const showWarning = port.warningContent && (
+    port.warningWhen === "checked" ? enabled :
+    port.warningWhen === "unchecked" ? !enabled : enabled
+  );
+
+  return (
+    <div className={styles.hardwareItem}>
+      <label className={`${styles.checkboxLabel} ${port.locked ? styles.checkboxDisabled : ""}`}>
+        <input
+          type="checkbox"
+          checked={enabled}
+          onChange={onToggle}
+          disabled={port.locked}
+        />
+        <span>
+          {port.locked && "🔒 "}
+          Port {port.host}
+          {port.protocol !== "tcp" && `/${port.protocol}`}
+        </span>
+      </label>
+      {port.description && (
+        <div className={styles.hardwareDescription}>{port.description}</div>
+      )}
+      {showWarning && (
+        <Admonition type={port.warningType || "warning"}>
+          {port.warningContent}
+        </Admonition>
+      )}
+    </div>
+  );
+}
+
+export default function PortConfigSection({
+  portEnabled,
+  onTogglePort,
+}: Props) {
+  return (
+    <div className={styles.formSection}>
+      <h4>Port Configuration</h4>
+      <div className={styles.checkboxGrid}>
+        {ports.map((port) => (
+          <PortItem
+            key={port.id}
+            port={port}
+            enabled={!!portEnabled[port.id]}
+            onToggle={() => onTogglePort(port.id)}
+          />
+        ))}
+      </div>
+    </div>
+  );
+}

docs/src/components/DockerComposeGenerator/components/StoragePaths.tsx

@@ -0,0 +1,66 @@
+import React from "react";
+import styles from "../styles.module.css";
+
+interface Props {
+  configPath: string;
+  mediaPath: string;
+  configPathError: boolean;
+  mediaPathError: boolean;
+  onConfigPathChange: (value: string) => void;
+  onMediaPathChange: (value: string) => void;
+}
+
+export default function StoragePaths({
+  configPath,
+  mediaPath,
+  configPathError,
+  mediaPathError,
+  onConfigPathChange,
+  onMediaPathChange,
+}: Props) {
+  return (
+    <div className={styles.formSection}>
+      <h4>Storage Paths</h4>
+      <div className={styles.formGrid}>
+        <div className={styles.formGroup}>
+          <label htmlFor="dcg-config-path" className={styles.label}>
+            Config / DB / model cache directory (on your host):
+          </label>
+          <input
+            id="dcg-config-path"
+            type="text"
+            className={`${styles.input} ${configPathError ? styles.inputError : ""}`}
+            value={configPath}
+            placeholder="/path/to/your/config"
+            onChange={(e) => onConfigPathChange(e.target.value)}
+          />
+          {configPathError && (
+            <p className={styles.helpText}>
+              ⚠️ Path contains invalid characters. Only letters, numbers,
+              underscores, hyphens, slashes, and dots are allowed.
+            </p>
+          )}
+        </div>
+        <div className={styles.formGroup}>
+          <label htmlFor="dcg-media-path" className={styles.label}>
+            Recording storage directory (on your host):
+          </label>
+          <input
+            id="dcg-media-path"
+            type="text"
+            className={`${styles.input} ${mediaPathError ? styles.inputError : ""}`}
+            value={mediaPath}
+            placeholder="/path/to/your/storage"
+            onChange={(e) => onMediaPathChange(e.target.value)}
+          />
+          {mediaPathError && (
+            <p className={styles.helpText}>
+              ⚠️ Path contains invalid characters. Only letters, numbers,
+              underscores, hyphens, slashes, and dots are allowed.
+            </p>
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}

docs/src/components/DockerComposeGenerator/config/index.ts

@@ -0,0 +1,12 @@
+export { devices, deviceMap } from "./devices";
+export { hardwareOptions, hardwareMap } from "./hardware";
+export { ports, portMap } from "./ports";
+
+export type {
+  DeviceConfig,
+  DeviceMapping,
+  VolumeMapping,
+  HardwareOption,
+  PortConfig,
+  NvidiaDeployConfig,
+} from "./types";

docs/src/components/DockerComposeGenerator/config/types.ts

@@ -0,0 +1,154 @@
+/**
+ * Type definitions for the Docker Compose Generator configuration.
+ * All device, hardware, and port options are declaratively defined
+ * so that adding a new device only requires editing config files.
+ */
+
+/** A single device mapping entry (e.g. /dev/dri:/dev/dri) */
+export interface DeviceMapping {
+  /** Host device path */
+  host: string;
+  /** Container device path (defaults to host if omitted) */
+  container?: string;
+  /** Inline comment for this device line */
+  comment?: string;
+}
+
+/** A single volume mapping entry */
+export interface VolumeMapping {
+  /** Host path */
+  host: string;
+  /** Container path */
+  container: string;
+  /** Whether the mount is read-only */
+  readOnly?: boolean;
+  /** Inline comment */
+  comment?: string;
+}
+
+/** NVIDIA deploy configuration for docker-compose */
+export interface NvidiaDeployConfig {
+  /** "all" or a specific number */
+  count: string;
+  /** Specific GPU device IDs (when count is a number) */
+  deviceIds?: string[];
+}
+
+/** Full device type definition */
+export interface DeviceConfig {
+  /** Unique identifier, e.g. "intel" */
+  id: string;
+  /** Display name, e.g. "Intel GPU" */
+  name: string;
+  /** Short description */
+  description: string;
+  /**
+   * Icon for the device card. Supports:
+   * - Emoji string (e.g. "🖥️")
+   * - Image URL or static path (e.g. "/img/intel.svg", "https://example.com/icon.png")
+   * - Inline SVG markup (e.g. "<svg>...</svg>")
+   */
+  icon: string;
+  /**
+   * Additional CSS properties applied to the icon element.
+   * - For image-type icons: if any `background-*` property (e.g. `background-size`,
+   *   `background-position`) is present, the image is rendered as a CSS `background-image`
+   *   on the container div, enabling full background positioning control.
+   *   Otherwise the image is rendered as an `<img>` tag and styles apply to it.
+   * - For emoji/SVG icons: styles apply to the container div.
+   */
+  iconStyle?: Record<string, string>;
+  /**
+   * Additional CSS properties applied directly to the inner `<svg>` element
+   * when the icon is an inline SVG. Use this to override the default
+   * `width: 100%; height: 100%` or set `fill`, `transform`, etc.
+   * Ignored for emoji and image-type icons.
+   */
+  svgStyle?: Record<string, string>;
+  /**
+   * Icon for dark mode. Same format as `icon`. When provided, this icon
+   * replaces `icon` when the user is in dark mode.
+   */
+  iconDark?: string;
+  /** Additional CSS properties for the dark mode icon container */
+  iconDarkStyle?: Record<string, string>;
+  /**
+   * SVG-specific styles for dark mode. Same as `svgStyle` but applied
+   * when dark mode is active. Merged over `svgStyle` in dark mode.
+   */
+  svgDarkStyle?: Record<string, string>;
+  /** Docker image tag, e.g. "stable" */
+  imageTag: string;
+  /**
+   * Image tag suffix appended to the base tag.
+   * e.g. "-standard-arm64" produces "stable-standard-arm64"
+   */
+  imageTagSuffix?: string;
+  /** Hardware option IDs to auto-enable when this device is selected */
+  autoHardware: string[];
+  /** Help text shown as an admonition when this device is selected */
+  helpText?: string;
+  /** Admonition type for help text */
+  helpType?: "info" | "warning" | "danger";
+  /** Device mappings always added for this device type */
+  devices?: DeviceMapping[];
+  /** Volume mappings always added for this device type */
+  volumes?: VolumeMapping[];
+  /** Extra environment variables for this device type */
+  env?: Record<string, string>;
+  /** NVIDIA deploy config (only for tensorrt) */
+  nvidiaDeploy?: NvidiaDeployConfig;
+  /** Runtime setting, e.g. "nvidia" for Jetson */
+  runtime?: string;
+  /** Extra hosts entries, e.g. "host.docker.internal:host-gateway" */
+  extraHosts?: string[];
+  /** Security options, e.g. ["apparmor=unconfined"] */
+  securityOpt?: string[];
+  /** Whether this device type needs the NVIDIA GPU config UI */
+  needsNvidiaConfig?: boolean;
+}
+
+/** Generic hardware acceleration option definition */
+export interface HardwareOption {
+  /** Unique identifier, e.g. "usbCoral" */
+  id: string;
+  /** Display label */
+  label: string;
+  /**
+   * Description shown below the checkbox when this option is enabled.
+   * Supports markdown link syntax: [text](url)
+   */
+  description?: string;
+  /** Device IDs that disable this option */
+  disabledWhen?: string[];
+  /** Device mappings added when this option is enabled */
+  devices?: DeviceMapping[];
+  /** Volume mappings added when this option is enabled */
+  volumes?: VolumeMapping[];
+  /** Extra environment variables */
+  env?: Record<string, string>;
+}
+
+/** Port definition */
+export interface PortConfig {
+  /** Unique identifier (also the default host port as string) */
+  id: string;
+  /** Host port number */
+  host: number;
+  /** Container port number */
+  container: number;
+  /** Protocol */
+  protocol?: "tcp" | "udp";
+  /** Description of the port's purpose */
+  description: string;
+  /** Whether enabled by default */
+  defaultEnabled: boolean;
+  /** Whether this port is locked (always enabled, cannot be toggled off) */
+  locked?: boolean;
+  /** Admonition type for the warning */
+  warningType?: "warning" | "danger";
+  /** Warning content (markdown) */
+  warningContent?: string;
+  /** When to show the warning: when the port is checked or unchecked */
+  warningWhen?: "checked" | "unchecked";
+}

docs/src/components/DockerComposeGenerator/generator/index.ts

@@ -0,0 +1,250 @@
+import type {
+  DeviceConfig,
+  DeviceMapping,
+  VolumeMapping,
+} from "../config/types";
+import { hardwareMap } from "../config";
+
+// ---------------------------------------------------------------------------
+// Input type
+// ---------------------------------------------------------------------------
+
+export interface GeneratorInput {
+  device: DeviceConfig;
+  selectedHardware: string[];
+  enabledPorts: string[];
+  configPath: string;
+  mediaPath: string;
+  rtspPassword?: string;
+  timezone: string;
+  shmSize: string;
+  nvidiaGpuCount?: string;
+  nvidiaGpuDeviceId?: string;
+}
+
+// ---------------------------------------------------------------------------
+// Helpers
+// ---------------------------------------------------------------------------
+
+function deviceLine(dm: DeviceMapping): string {
+  const host = dm.host;
+  const container = dm.container ?? dm.host;
+  const mapping = host === container ? host : `${host}:${container}`;
+  const comment = dm.comment ? ` # ${dm.comment}` : "";
+  return `      - ${mapping}${comment}`;
+}
+
+function volumeLine(vm: VolumeMapping): string {
+  const ro = vm.readOnly ? ":ro" : "";
+  const comment = vm.comment ? ` # ${vm.comment}` : "";
+  return `      - ${vm.host}:${vm.container}${ro}${comment}`;
+}
+
+// ---------------------------------------------------------------------------
+// YAML builder — each section returns an array of lines
+// ---------------------------------------------------------------------------
+
+function buildImage(device: DeviceConfig): string[] {
+  const tag = device.imageTagSuffix
+    ? `${device.imageTag}${device.imageTagSuffix}`
+    : device.imageTag;
+  return [`    image: ghcr.io/blakeblackshear/frigate:${tag}`];
+}
+
+function buildDevices(
+  device: DeviceConfig,
+  hwDevices: DeviceMapping[]
+): string[] {
+  const all: DeviceMapping[] = [
+    ...(device.devices ?? []),
+    ...hwDevices,
+  ];
+  if (all.length === 0) return [];
+  return [
+    "    devices:",
+    ...all.map(deviceLine),
+  ];
+}
+
+function buildVolumes(
+  device: DeviceConfig,
+  hwVolumes: VolumeMapping[],
+  configPath: string,
+  mediaPath: string
+): string[] {
+  const all: VolumeMapping[] = [
+    ...(device.volumes ?? []),
+    ...hwVolumes,
+  ];
+  return [
+    "    volumes:",
+    "      - /etc/localtime:/etc/localtime:ro # Sync host time",
+    `      - ${configPath}:/config # Config file directory`,
+    `      - ${mediaPath}:/media/frigate # Recording storage directory`,
+    "      - type: tmpfs # 1GB in-memory filesystem for recording segment storage",
+    "        target: /tmp/cache",
+    "        tmpfs:",
+    "          size: 1000000000",
+    ...all.map(volumeLine),
+  ];
+}
+
+function buildPorts(enabledPorts: string[]): string[] {
+  return [
+    "    ports:",
+    ...enabledPorts,
+  ];
+}
+
+function buildEnvironment(
+  device: DeviceConfig,
+  hwEnv: Record<string, string>,
+  rtspPassword: string | undefined,
+  timezone: string
+): string[] {
+  const allEnv: Record<string, string> = {
+    ...hwEnv,
+    ...(device.env ?? {}),
+  };
+
+  const lines: string[] = ["    environment:"];
+
+  if (rtspPassword) {
+    lines.push(
+      `      FRIGATE_RTSP_PASSWORD: "${rtspPassword}" # RTSP password — change to your own`
+    );
+  }
+
+  lines.push(`      TZ: "${timezone}" # Timezone`);
+
+  for (const [key, value] of Object.entries(allEnv)) {
+    lines.push(`      ${key}: "${value}"`);
+  }
+
+  return lines;
+}
+
+function buildDeploy(device: DeviceConfig, input: GeneratorInput): string[] {
+  if (device.id === "stable-tensorrt") {
+    const count = input.nvidiaGpuCount || "all";
+    const isAll = count === "all";
+    const deviceId = input.nvidiaGpuDeviceId?.trim();
+
+    if (isAll) {
+      return [
+        "    deploy:",
+        "      resources:",
+        "        reservations:",
+        "          devices:",
+        "            - driver: nvidia",
+        "              count: all # Use all GPUs",
+        "              capabilities: [gpu]",
+      ];
+    }
+
+    if (deviceId) {
+      const ids = deviceId
+        .split(",")
+        .map((s) => s.trim())
+        .filter(Boolean)
+        .map((s) => `'${s}'`)
+        .join(", ");
+      return [
+        "    deploy:",
+        "      resources:",
+        "        reservations:",
+        "          devices:",
+        "            - driver: nvidia",
+        `              device_ids: [${ids}] # GPU device IDs`,
+        `              count: ${count} # GPU count`,
+        "              capabilities: [gpu]",
+      ];
+    }
+
+    return [
+      "    deploy:",
+      "      resources:",
+      "        reservations:",
+      "          devices:",
+      "            - driver: nvidia",
+      `              count: ${count} # GPU count`,
+      "              capabilities: [gpu]",
+    ];
+  }
+
+  return [];
+}
+
+function buildRuntime(device: DeviceConfig): string[] {
+  if (device.runtime) {
+    return [`    runtime: ${device.runtime}`];
+  }
+  return [];
+}
+
+function buildExtraHosts(device: DeviceConfig): string[] {
+  if (!device.extraHosts?.length) return [];
+  return [
+    "    extra_hosts:",
+    ...device.extraHosts.map(
+      (h, i) =>
+        `      - "${h}"${i === 0 ? " # Required to talk to the NPU detector" : ""}`
+    ),
+  ];
+}
+
+function buildSecurityOpt(device: DeviceConfig): string[] {
+  if (!device.securityOpt?.length) return [];
+  return [
+    "    security_opt:",
+    ...device.securityOpt.map((s) => `      - ${s}`),
+  ];
+}
+
+// ---------------------------------------------------------------------------
+// Public API
+// ---------------------------------------------------------------------------
+
+/**
+ * Generate a docker-compose YAML string from the given input.
+ * The output is pure YAML with inline comments (no Shiki annotations).
+ */
+export function generateDockerCompose(input: GeneratorInput): string {
+  const { device } = input;
+
+  // Collect hardware-level devices, volumes, and env
+  const hwDevices: DeviceMapping[] = [];
+  const hwVolumes: VolumeMapping[] = [];
+  const hwEnv: Record<string, string> = {};
+
+  for (const hwId of input.selectedHardware) {
+    const hw = hardwareMap.get(hwId);
+    if (!hw) continue;
+    // Skip GPU device mapping for tensorrt images (it uses deploy instead)
+    if (hw.id === "gpu" && device.imageTag === "stable-tensorrt") continue;
+    hwDevices.push(...(hw.devices ?? []));
+    hwVolumes.push(...(hw.volumes ?? []));
+    Object.assign(hwEnv, hw.env ?? {});
+  }
+
+  const lines: string[] = [
+    "services:",
+    "  frigate:",
+    "    container_name: frigate",
+    "    privileged: true # This may not be necessary for all setups",
+    "    restart: unless-stopped",
+    "    stop_grace_period: 30s # Allow enough time to shut down the various services",
+    ...buildImage(device),
+    `    shm_size: "${input.shmSize || "512mb"}" # Update for your cameras based on SHM calculation`,
+    ...buildRuntime(device),
+    ...buildDeploy(device, input),
+    ...buildExtraHosts(device),
+    ...buildSecurityOpt(device),
+    ...buildDevices(device, hwDevices),
+    ...buildVolumes(device, hwVolumes, input.configPath, input.mediaPath),
+    ...buildPorts(input.enabledPorts),
+    ...buildEnvironment(device, hwEnv, input.rtspPassword, input.timezone),
+  ];
+
+  return lines.join("\n");
+}

docs/src/components/DockerComposeGenerator/hooks/useConfigGenerator.ts

@@ -0,0 +1,195 @@
+import { useState, useCallback, useMemo } from "react";
+import { deviceMap, hardwareMap, portMap } from "../config";
+import { generateDockerCompose } from "../generator";
+import type { GeneratorInput } from "../generator";
+
+/**
+ * Main hook that holds all form state and generates the Docker Compose output.
+ * Configuration is loaded synchronously from build-time generated .ts files.
+ */
+export function useConfigGenerator() {
+  const [deviceId, setDeviceId] = useState("stable");
+
+  const [hardwareEnabled, setHardwareEnabled] = useState<Record<string, boolean>>(() => {
+    const defaultDevice = deviceMap.get("stable");
+    const initial: Record<string, boolean> = {};
+    if (defaultDevice) {
+      for (const hwId of defaultDevice.autoHardware) {
+        initial[hwId] = true;
+      }
+    }
+    return initial;
+  });
+
+  const [portEnabled, setPortEnabled] = useState<Record<string, boolean>>(() => {
+    const initial: Record<string, boolean> = {};
+    for (const p of portMap.values()) {
+      initial[p.id] = p.defaultEnabled;
+    }
+    return initial;
+  });
+
+  const [nvidiaGpuCount, setNvidiaGpuCount] = useState("");
+  const [nvidiaGpuDeviceId, setNvidiaGpuDeviceId] = useState("");
+  const [configPath, setConfigPath] = useState("");
+  const [mediaPath, setMediaPath] = useState("");
+  const [rtspPassword, setRtspPassword] = useState("");
+  const [timezone, setTimezone] = useState("");
+  const [shmSize, setShmSize] = useState("512mb");
+  const [shmSizeError, setShmSizeError] = useState(false);
+  const [gpuDeviceIdError, setGpuDeviceIdError] = useState(false);
+  const [configPathError, setConfigPathError] = useState(false);
+  const [mediaPathError, setMediaPathError] = useState(false);
+
+  const device = useMemo(() => deviceMap.get(deviceId)!, [deviceId]);
+
+  const selectDevice = useCallback((id: string) => {
+    const newDevice = deviceMap.get(id);
+    if (!newDevice) return;
+    setDeviceId(id);
+    setHardwareEnabled(() => {
+      const next: Record<string, boolean> = {};
+      for (const hwId of newDevice.autoHardware) {
+        next[hwId] = true;
+      }
+      return next;
+    });
+    setNvidiaGpuCount("");
+    setNvidiaGpuDeviceId("");
+    setGpuDeviceIdError(false);
+  }, []);
+
+  const toggleHardware = useCallback((hwId: string) => {
+    setHardwareEnabled((prev) => ({ ...prev, [hwId]: !prev[hwId] }));
+  }, []);
+
+  const togglePort = useCallback((portId: string) => {
+    const port = portMap.get(portId);
+    if (port?.locked) return;
+    setPortEnabled((prev) => ({ ...prev, [portId]: !prev[portId] }));
+  }, []);
+
+  const isHardwareDisabled = useCallback(
+    (hwId: string): boolean => {
+      const hw = hardwareMap.get(hwId);
+      if (!hw) return false;
+      return hw.disabledWhen?.includes(deviceId) ?? false;
+    },
+    [deviceId]
+  );
+
+  const validateShmSize = useCallback((value: string): boolean => {
+    if (!value) return true;
+    return /^\d+(\.\d+)?[bkmgBKMG]{1,2}$/.test(value);
+  }, []);
+
+  const validatePath = useCallback((value: string): boolean => {
+    if (!value) return true;
+    return /^[a-zA-Z0-9_\-/./]+$/.test(value);
+  }, []);
+
+  const handleShmSizeChange = useCallback(
+    (value: string) => {
+      const filtered = value.replace(/[^0-9.bkmgBKMG]/g, "");
+      const valid = validateShmSize(filtered);
+      setShmSize(filtered);
+      setShmSizeError(!valid && filtered !== "");
+    },
+    [validateShmSize]
+  );
+
+  const handleConfigPathChange = useCallback(
+    (value: string) => {
+      const filtered = value.replace(/[^a-zA-Z0-9_\-/./]/g, "");
+      const valid = validatePath(filtered);
+      setConfigPath(filtered);
+      setConfigPathError(!valid && filtered !== "");
+    },
+    [validatePath]
+  );
+
+  const handleMediaPathChange = useCallback(
+    (value: string) => {
+      const filtered = value.replace(/[^a-zA-Z0-9_\-/./]/g, "");
+      const valid = validatePath(filtered);
+      setMediaPath(filtered);
+      setMediaPathError(!valid && filtered !== "");
+    },
+    [validatePath]
+  );
+
+  const handleNvidiaGpuCountChange = useCallback((value: string) => {
+    // Only allow digits
+    setNvidiaGpuCount(value);
+    if (value === "") {
+      setNvidiaGpuDeviceId("");
+      setGpuDeviceIdError(false);
+    } else {
+      setGpuDeviceIdError(false);
+    }
+  }, []);
+
+  const handleNvidiaGpuDeviceIdChange = useCallback((value: string) => {
+    setNvidiaGpuDeviceId(value.trim());
+    setGpuDeviceIdError(false);
+  }, []);
+
+  const enabledPortLines = useMemo(() => {
+    const lines: string[] = [];
+    for (const [id, enabled] of Object.entries(portEnabled)) {
+      if (!enabled) continue;
+      const p = portMap.get(id);
+      if (!p) continue;
+      const proto = p.protocol && p.protocol !== "tcp" ? `/${p.protocol}` : "";
+      const comment = p.description ? ` # ${p.description}` : "";
+      lines.push(`      - "${p.host}:${p.container}${proto}"${comment}`);
+    }
+    return lines;
+  }, [portEnabled]);
+
+  const selectedHardwareIds = useMemo(() => {
+    return Object.entries(hardwareEnabled)
+      .filter(([id, enabled]) => {
+        if (!enabled) return false;
+        const hw = hardwareMap.get(id);
+        if (!hw) return false;
+        if (hw.disabledWhen?.includes(deviceId)) return false;
+        return true;
+      })
+      .map(([id]) => id);
+  }, [hardwareEnabled, deviceId]);
+
+  const generatedYaml = useMemo(() => {
+    const input: GeneratorInput = {
+      device,
+      selectedHardware: selectedHardwareIds,
+      enabledPorts: enabledPortLines,
+      configPath: configPath || "/path/to/your/config",
+      mediaPath: mediaPath || "/path/to/your/storage",
+      rtspPassword,
+      timezone: timezone || Intl.DateTimeFormat().resolvedOptions().timeZone || "Etc/UTC",
+      shmSize: shmSize || "512mb",
+      nvidiaGpuCount,
+      nvidiaGpuDeviceId,
+    };
+    return generateDockerCompose(input);
+  }, [
+    device, selectedHardwareIds, enabledPortLines,
+    configPath, mediaPath, rtspPassword, timezone, shmSize,
+    nvidiaGpuCount, nvidiaGpuDeviceId,
+  ]);
+
+  const hasAnyHardware = selectedHardwareIds.length > 0 || !!device?.devices?.length;
+
+  return {
+    deviceId, device, hardwareEnabled, portEnabled,
+    nvidiaGpuCount, nvidiaGpuDeviceId,
+    configPath, mediaPath, rtspPassword, timezone, shmSize,
+    shmSizeError, gpuDeviceIdError, configPathError, mediaPathError,
+    hasAnyHardware, generatedYaml,
+    selectDevice, toggleHardware, togglePort,
+    handleShmSizeChange, handleConfigPathChange, handleMediaPathChange,
+    handleNvidiaGpuCountChange, handleNvidiaGpuDeviceIdChange,
+    setRtspPassword, setTimezone, isHardwareDisabled,
+  };
+}

docs/src/components/DockerComposeGenerator/index.ts

@@ -0,0 +1 @@
+export { default } from "./DockerComposeGenerator";

docs/src/components/DockerComposeGenerator/styles.module.css

@@ -0,0 +1,381 @@
+/* ===================================================================
+   Docker Compose Generator — styles
+   Uses Docusaurus / Infima CSS variables for theme compatibility.
+   =================================================================== */
+
+.generator {
+  margin: 2rem 0;
+}
+
+.card {
+  background: var(--ifm-background-surface-color);
+  border: 1px solid var(--ifm-color-emphasis-400);
+  border-radius: 12px;
+  padding: 2rem;
+  box-shadow: var(--ifm-global-shadow-lw);
+}
+
+[data-theme="light"] .card {
+  background: var(--ifm-color-emphasis-100);
+  border: 1px solid var(--ifm-color-emphasis-300);
+}
+
+/* --- Form sections --- */
+
+.formSection {
+  margin-bottom: 1.5rem;
+  padding-bottom: 1.5rem;
+  border-bottom: 1px solid var(--ifm-color-emphasis-400);
+}
+
+.formSection:last-child {
+  border-bottom: none;
+  margin-bottom: 0;
+  padding-bottom: 0;
+}
+
+.formSection h4 {
+  margin: 0 0 1rem 0;
+  color: var(--ifm-font-color-base);
+  font-size: 1.1rem;
+  font-weight: var(--ifm-font-weight-semibold);
+}
+
+/* --- Form controls --- */
+
+.formGroup {
+  margin-bottom: 1rem;
+}
+
+.formGroup:last-child {
+  margin-bottom: 0;
+}
+
+.label {
+  display: block;
+  margin-bottom: 0.25rem;
+  color: var(--ifm-font-color-base);
+  font-weight: var(--ifm-font-weight-semibold);
+  font-size: 0.9rem;
+}
+
+.input {
+  width: 100%;
+  padding: 0.5rem 0.75rem;
+  border: 1px solid var(--ifm-color-emphasis-400);
+  border-radius: 6px;
+  background: var(--ifm-background-color);
+  color: var(--ifm-font-color-base);
+  font-size: 0.95rem;
+  transition: border-color 0.2s, box-shadow 0.2s;
+}
+
+[data-theme="light"] .input {
+  background: #fff;
+  border: 1px solid #d0d7de;
+}
+
+.input:focus {
+  outline: none;
+  border-color: var(--ifm-color-primary);
+  box-shadow: 0 0 0 3px var(--ifm-color-primary-lightest);
+}
+
+[data-theme="dark"] .input {
+  border-color: var(--ifm-color-emphasis-300);
+}
+
+.inputError {
+  border-color: #e74c3c;
+  animation: shake 0.3s ease-in-out;
+}
+
+@keyframes shake {
+  0%,
+  100% {
+    transform: translateX(0);
+  }
+  25% {
+    transform: translateX(-5px);
+  }
+  75% {
+    transform: translateX(5px);
+  }
+}
+
+/* --- Select dropdown --- */
+
+.select {
+  cursor: pointer;
+  appearance: none;
+  -moz-appearance: none;
+  -webkit-appearance: none;
+  background: var(--ifm-background-color)
+    url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='12' viewBox='0 0 12 12'%3E%3Cpath fill='%23666' d='M6 8L1 3h10z'/%3E%3C/svg%3E")
+    no-repeat right 0.75rem center / 12px 12px;
+  padding-right: 2rem;
+}
+
+[data-theme="light"] .select {
+  background: #fff
+    url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='12' viewBox='0 0 12 12'%3E%3Cpath fill='%23555' d='M6 8L1 3h10z'/%3E%3C/svg%3E")
+    no-repeat right 0.75rem center / 12px 12px;
+}
+
+.helpText {
+  margin: 0.5rem 0 0 0;
+  font-size: 0.85rem;
+  color: var(--ifm-font-color-secondary);
+  line-height: 1.5;
+}
+
+.helpText a {
+  color: var(--ifm-color-primary);
+}
+
+/* --- Device grid --- */
+
+.deviceGrid {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(130px, 1fr));
+  gap: 0.75rem;
+  margin-top: 0.5rem;
+}
+
+.deviceCard {
+  padding: 0.75rem;
+  border: 2px solid var(--ifm-color-emphasis-400);
+  border-radius: 12px;
+  cursor: pointer;
+  transition: all 0.2s;
+  text-align: center;
+  background: var(--ifm-background-color);
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+}
+
+[data-theme="light"] .deviceCard {
+  border: 2px solid #d0d7de;
+  background: #fff;
+}
+
+.deviceCard:hover {
+  border-color: var(--ifm-color-primary);
+  background: var(--ifm-color-emphasis-100);
+  transform: translateY(-2px);
+}
+
+.deviceCardActive {
+  border-color: var(--ifm-color-primary);
+  background: var(--ifm-color-primary-lightest);
+  box-shadow: 0 0 0 1px var(--ifm-color-primary);
+}
+
+[data-theme="light"] .deviceCardActive {
+  background: color-mix(in srgb, var(--ifm-color-primary) 12%, #fff);
+}
+
+[data-theme="dark"] .deviceCardActive {
+  background: color-mix(in srgb, var(--ifm-color-primary) 25%, #1b1b1b);
+}
+
+[data-theme="dark"] .deviceCardActive .deviceName {
+  color: var(--ifm-color-primary-light);
+}
+
+[data-theme="dark"] .deviceCardActive .deviceDesc {
+  color: var(--ifm-color-primary-light);
+  opacity: 0.85;
+}
+
+.deviceIcon {
+  font-size: 2rem;
+  margin-bottom: 0.25rem;
+  height: 40px;
+  width: 50px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.deviceIconSvg {
+  margin-bottom: 0.25rem;
+  height: 40px;
+  width: 50px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  overflow: visible;
+  /* Allow iconStyle width/height to override */
+  flex-shrink: 0;
+}
+
+.deviceIconSvg svg {
+  width: var(--svg-width, 100%);
+  height: var(--svg-height, 100%);
+  fill: var(--svg-fill, currentColor);
+  transform: var(--svg-transform, none);
+}
+
+.deviceIconImage {
+  margin-bottom: 0.25rem;
+  height: 40px;
+  width: 50px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.deviceIconImage img {
+  max-width: 100%;
+  max-height: 100%;
+  object-fit: contain;
+}
+
+.deviceName {
+  font-weight: var(--ifm-font-weight-semibold);
+  color: var(--ifm-font-color-base);
+  margin-bottom: 0.15rem;
+  font-size: 0.9rem;
+}
+
+.deviceDesc {
+  font-size: 0.75rem;
+  color: var(--ifm-font-color-secondary);
+  line-height: 1.3;
+}
+
+/* --- Checkbox grid --- */
+
+.checkboxGrid {
+  display: grid;
+  grid-template-columns: repeat(2, 1fr);
+  gap: 0.5rem;
+}
+
+@media (max-width: 576px) {
+  .checkboxGrid {
+    grid-template-columns: 1fr;
+  }
+}
+
+.hardwareItem {
+  margin-bottom: 0;
+}
+
+.hardwareDescription {
+  margin: 0.15rem 0 0.4rem 1.6rem;
+  font-size: 0.8rem;
+  color: var(--ifm-font-color-secondary);
+  line-height: 1.5;
+}
+
+.hardwareDescription a {
+  color: var(--ifm-color-primary);
+  text-decoration: underline;
+  text-underline-offset: 2px;
+}
+
+.checkboxLabel {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  cursor: pointer;
+  padding: 0.4rem 0.5rem;
+  border-radius: 6px;
+  transition: background-color 0.2s;
+  font-size: 0.9rem;
+}
+
+.checkboxLabel:hover {
+  background: var(--ifm-color-emphasis-100);
+}
+
+.checkboxLabel input[type="checkbox"] {
+  width: 1.1rem;
+  height: 1.1rem;
+  cursor: pointer;
+  flex-shrink: 0;
+}
+
+.checkboxLabel span {
+  color: var(--ifm-font-color-base);
+}
+
+.checkboxDisabled {
+  cursor: not-allowed;
+}
+
+.checkboxDisabled:hover {
+  background: transparent;
+}
+
+.checkboxDisabled input[type="checkbox"] {
+  cursor: not-allowed;
+  opacity: 0.5;
+}
+
+/* --- Form grid (side-by-side) --- */
+
+.formGrid {
+  display: grid;
+  grid-template-columns: repeat(2, 1fr);
+  gap: 1rem;
+}
+
+@media (max-width: 576px) {
+  .formGrid {
+    grid-template-columns: 1fr;
+  }
+}
+
+.formGrid .formGroup {
+  margin-bottom: 0;
+}
+
+/* --- Port section --- */
+
+.portSection {
+  margin-bottom: 0.75rem;
+}
+
+.warningBadge {
+  margin-left: auto;
+  color: #e67e22;
+  font-size: 0.85rem;
+}
+
+/* --- NVIDIA config --- */
+
+.nvidiaConfig {
+  margin-top: 1rem;
+  margin-bottom: 1.5rem;
+  padding: 1rem;
+  background: var(--ifm-background-color);
+  border-radius: 8px;
+  border-left: 3px solid var(--ifm-color-primary);
+}
+
+[data-theme="light"] .nvidiaConfig {
+  background: #f6f8fa;
+  border-left: 3px solid var(--ifm-color-primary);
+}
+
+/* --- Result section --- */
+
+.resultSection {
+  margin-top: 2rem;
+}
+
+.resultHeader {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 1rem;
+}
+
+.resultHeader h4 {
+  margin: 0;
+  color: var(--ifm-font-color-base);
+}

docs/static/frigate-api.yaml

@@ -2058,6 +2058,47 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
+  /genai/models:
+    get:
+      tags:
+        - App
+      summary: List available GenAI models
+      description: Returns available models for each configured GenAI provider.
+      operationId: genai_models_genai_models_get
+      responses:
+        "200":
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+  /genai/probe:
+    post:
+      tags:
+        - App
+      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. Requires admin role.
+      operationId: genai_probe_genai_probe_post
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              $ref: "#/components/schemas/GenAIProbeBody"
+      responses:
+        "200":
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        "422":
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: "#/components/schemas/HTTPValidationError"
   /vainfo:
     get:
       tags:
@@ -5997,7 +6038,10 @@ paths:
       tags:
         - App
       summary: Start debug replay
-      description: Start a debug replay session from camera recordings.
+      description:
+        Start a debug replay session from camera recordings. Returns
+        immediately while clip generation runs as a background job; subscribe
+        to the 'debug_replay' job_state WS topic to track progress.
       operationId: start_debug_replay_debug_replay_start_post
       requestBody:
         required: true
@@ -6006,12 +6050,16 @@ paths:
             schema:
               $ref: "#/components/schemas/DebugReplayStartBody"
       responses:
-        "200":
+        "202":
           description: Successful Response
           content:
             application/json:
               schema:
                 $ref: "#/components/schemas/DebugReplayStartResponse"
+        "400":
+          description: Invalid camera, time range, or no recordings
+        "409":
+          description: A replay session is already active
         "422":
           description: Validation Error
           content:
@@ -6272,10 +6320,14 @@ components:
         replay_camera:
           type: string
           title: Replay Camera
+        job_id:
+          type: string
+          title: Job Id
       type: object
       required:
         - success
         - replay_camera
+        - job_id
       title: DebugReplayStartResponse
       description: Response for starting a debug replay session.
     DebugReplayStatusResponse:
@@ -7020,6 +7072,39 @@ components:
                 "john_doe": ["face1.webp", "face2.jpg"],
                 "jane_smith": ["face3.png"]
             }
+    GenAIProbeBody:
+      properties:
+        provider:
+          type: string
+          enum:
+            - openai
+            - azure_openai
+            - gemini
+            - ollama
+            - llamacpp
+          title: Provider
+          description: GenAI provider to probe
+        api_key:
+          anyOf:
+            - type: string
+            - type: "null"
+          title: API Key
+          description: API key for the provider (when applicable)
+        base_url:
+          anyOf:
+            - type: string
+            - type: "null"
+          title: Base URL
+          description: Base URL for self-hosted or compatible providers
+        provider_options:
+          type: object
+          title: Provider Options
+          description: Additional provider-specific options
+          default: {}
+      type: object
+      required:
+        - provider
+      title: GenAIProbeBody
     GenerateObjectExamplesBody:
       properties:
         model_name:

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())]
@@ -96,11 +111,46 @@ def version():
 
 
 @router.get("/stats", dependencies=[Depends(allow_any_authenticated())])
-def stats(request: Request):
-    return JSONResponse(content=request.app.stats_emitter.get_latest_stats())
+def stats(
+    request: Request,
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+):
+    stats_data = request.app.stats_emitter.get_latest_stats()
+
+    # Admins see the full snapshot
+    if request.headers.get("remote-role") == "admin":
+        return JSONResponse(content=stats_data)
+
+    allowed_set = set(allowed_cameras)
+
+    # Shallow-copy so we don't mutate the cached stats history entry.
+    filtered = {**stats_data}
+
+    cameras = stats_data.get("cameras")
+    if cameras is not None:
+        filtered["cameras"] = {
+            name: data for name, data in cameras.items() if name in allowed_set
+        }
+
+    bandwidth = stats_data.get("bandwidth_usages")
+    if bandwidth is not None:
+        filtered["bandwidth_usages"] = {
+            name: data for name, data in bandwidth.items() if name in allowed_set
+        }
+
+    # cmdline can leak camera URLs/paths; strip but keep cpu/mem so
+    # client-side problem heuristics still work.
+    cpu_usages = stats_data.get("cpu_usages")
+    if cpu_usages is not None:
+        filtered["cpu_usages"] = {
+            pid: {k: v for k, v in usage.items() if k != "cmdline"}
+            for pid, usage in cpu_usages.items()
+        }
+
+    return JSONResponse(content=filtered)
 
 
-@router.get("/stats/history", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/stats/history", dependencies=[Depends(require_role(["admin"]))])
 def stats_history(request: Request, keys: str = None):
     if keys:
         keys = keys.split(",")
@@ -135,6 +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
@@ -146,25 +285,28 @@ def config(request: Request):
         for name, detector in config_obj.detectors.items()
     }
 
-    # remove the mqtt password
-    config["mqtt"].pop("password", None)
+    # remove environment_vars for non-admin users
+    if request.headers.get("remote-role") != "admin":
+        config.pop("environment_vars", None)
 
-    # remove the proxy secret
-    config["proxy"].pop("auth_secret", None)
+    # redact mqtt credentials
+    redact_credential(config["mqtt"], "password")
 
-    # remove genai api keys
-    for genai_name, genai_cfg in config.get("genai", {}).items():
+    # redact proxy secret
+    redact_credential(config["proxy"], "auth_secret")
+
+    # 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", []):
@@ -494,6 +636,40 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
         )
 
 
+def _restore_masked_camera_paths(config_data: dict, config: FrigateConfig) -> None:
+    """Substitute incoming `*:*` masked credentials with the in-memory ones.
+
+    The /config response masks ffmpeg input credentials, so the settings UI
+    sends the masked path back when sibling fields (e.g. hwaccel_args) are
+    edited.  Without this we'd write `rtsp://*:*@host` into YAML and lose
+    the real credentials.  Mutates `config_data` in place.
+    """
+    cameras = config_data.get("cameras")
+    if not isinstance(cameras, dict):
+        return
+
+    for camera_name, camera_data in cameras.items():
+        if not isinstance(camera_data, dict):
+            continue
+        inputs = camera_data.get("ffmpeg", {}).get("inputs")
+        if not isinstance(inputs, list):
+            continue
+        existing = config.cameras.get(camera_name)
+        if existing is None:
+            continue
+        existing_paths = [inp.path for inp in existing.ffmpeg.inputs]
+        for index, input_obj in enumerate(inputs):
+            if not isinstance(input_obj, dict):
+                continue
+            path = input_obj.get("path")
+            if not isinstance(path, str):
+                continue
+            if ("://*:*@" in path or "user=*&password=*" in path) and index < len(
+                existing_paths
+            ):
+                input_obj["path"] = existing_paths[index]
+
+
 def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONResponse:
     """Apply config changes in-memory only, without writing to YAML.
 
@@ -504,8 +680,13 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
     try:
         updates = {}
         if body.config_data:
+            _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(
@@ -569,6 +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,
@@ -610,9 +825,19 @@ def config_set(request: Request, body: AppConfigSetBody):
                 if query_string:
                     updates = process_config_query_string(query_string)
                 elif body.config_data:
+                    _restore_masked_camera_paths(
+                        body.config_data, request.app.frigate_config
+                    )
                     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(
@@ -683,6 +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
@@ -696,6 +926,8 @@ def config_set(request: Request, body: AppConfigSetBody):
 
                 if request.app.dispatcher is not None:
                     request.app.dispatcher.config = config
+                    for comm in request.app.dispatcher.comms:
+                        comm.config = config
 
                 if body.update_topic:
                     if body.update_topic.startswith("config/cameras/"):
@@ -792,7 +1024,7 @@ def nvinfo():
 @router.get(
     "/logs/{service}",
     tags=[Tags.logs],
-    dependencies=[Depends(allow_any_authenticated())],
+    dependencies=[Depends(require_role(["admin"]))],
 )
 async def logs(
     service: str = Path(enum=["frigate", "nginx", "go2rtc"]),
@@ -997,12 +1229,27 @@ def get_media_sync_status(job_id: str):
 
 
 @router.get("/labels", dependencies=[Depends(allow_any_authenticated())])
-def get_labels(camera: str = ""):
+def get_labels(
+    camera: str = "",
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+):
     try:
         if camera:
+            if camera not in allowed_cameras:
+                return JSONResponse(
+                    content={
+                        "success": False,
+                        "message": f"Access denied to camera '{camera}'",
+                    },
+                    status_code=403,
+                )
             events = Event.select(Event.label).where(Event.camera == camera).distinct()
         else:
-            events = Event.select(Event.label).distinct()
+            events = (
+                Event.select(Event.label)
+                .where(Event.camera << allowed_cameras)
+                .distinct()
+            )
     except Exception as e:
         logger.error(e)
         return JSONResponse(
@@ -1015,9 +1262,16 @@ def get_labels(camera: str = ""):
 
 
 @router.get("/sub_labels", dependencies=[Depends(allow_any_authenticated())])
-def get_sub_labels(split_joined: Optional[int] = None):
+def get_sub_labels(
+    split_joined: Optional[int] = None,
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+):
     try:
-        events = Event.select(Event.sub_label).distinct()
+        events = (
+            Event.select(Event.sub_label)
+            .where(Event.camera << allowed_cameras)
+            .distinct()
+        )
     except Exception:
         return JSONResponse(
             content=({"success": False, "message": "Failed to get sub_labels"}),

frigate/api/auth.py

@@ -26,6 +26,7 @@ from frigate.api.defs.request.app_body import (
     AppPutRoleBody,
 )
 from frigate.api.defs.tags import Tags
+from frigate.api.media_auth import check_camera_access, deny_response_for_media_uri
 from frigate.config import AuthConfig, NetworkingConfig, ProxyConfig
 from frigate.const import CONFIG_DIR, JWT_SECRET_ENV_VAR, PASSWORD_HASH_ALGORITHM
 from frigate.models import User
@@ -633,6 +634,9 @@ def auth(request: Request):
         logger.debug("X-Proxy-Secret header does not match configured secret value")
         return fail_response
 
+    original_url = request.headers.get("x-original-url")
+    frigate_config = request.app.frigate_config
+
     # if auth is disabled, just apply the proxy header map and return success
     if not auth_config.enabled:
         # pass the user header value from the upstream proxy if a mapping is specified
@@ -649,6 +653,11 @@ def auth(request: Request):
         role = resolve_role(request.headers, proxy_config, config_roles_set)
 
         success_response.headers["remote-role"] = role
+
+        deny_status = deny_response_for_media_uri(original_url, role, frigate_config)
+        if deny_status is not None:
+            return Response("", status_code=deny_status)
+
         return success_response
 
     # now apply authentication
@@ -743,6 +752,11 @@ def auth(request: Request):
 
         success_response.headers["remote-user"] = user
         success_response.headers["remote-role"] = role
+
+        deny_status = deny_response_for_media_uri(original_url, role, frigate_config)
+        if deny_status is not None:
+            return Response("", status_code=deny_status)
+
         return success_response
     except Exception as e:
         logger.error(f"Error parsing jwt: {e}")
@@ -812,6 +826,11 @@ limiter = Limiter(key_func=get_remote_addr)
 )
 @limiter.limit(limit_value=rateLimiter.get_limit)
 def login(request: Request, body: AppPostLoginBody):
+    if not request.app.frigate_config.auth.enabled:
+        return JSONResponse(
+            content={"message": "Authentication is disabled"}, status_code=404
+        )
+
     JWT_COOKIE_NAME = request.app.frigate_config.auth.cookie_name
     JWT_COOKIE_SECURE = request.app.frigate_config.auth.cookie_secure
     JWT_SESSION_LENGTH = request.app.frigate_config.auth.session_length
@@ -1064,19 +1083,19 @@ async def require_camera_access(
         raise HTTPException(status_code=current_user.status_code, detail=detail)
 
     role = current_user["role"]
-    all_camera_names = set(request.app.frigate_config.cameras.keys())
-    roles_dict = request.app.frigate_config.auth.roles
-    allowed_cameras = User.get_allowed_cameras(role, roles_dict, all_camera_names)
+    frigate_config = request.app.frigate_config
 
-    # Admin or full access bypasses
-    if role == "admin" or not roles_dict.get(role):
+    if check_camera_access(role, camera_name, frigate_config):
         return
 
-    if camera_name not in allowed_cameras:
-        raise HTTPException(
-            status_code=403,
-            detail=f"Access denied to camera '{camera_name}'. Allowed: {allowed_cameras}",
-        )
+    all_camera_names = set(frigate_config.cameras.keys())
+    allowed_cameras = User.get_allowed_cameras(
+        role, frigate_config.auth.roles, all_camera_names
+    )
+    raise HTTPException(
+        status_code=403,
+        detail=f"Access denied to camera '{camera_name}'. Allowed: {allowed_cameras}",
+    )
 
 
 def _get_stream_owner_cameras(request: Request, stream_name: str) -> set[str]:

frigate/api/camera.py

@@ -19,7 +19,9 @@ from zeep.exceptions import Fault, TransportError
 from zeep.transports import AsyncTransport
 
 from frigate.api.auth import (
+    _get_stream_owner_cameras,
     allow_any_authenticated,
+    get_current_user,
     require_go2rtc_stream_access,
     require_role,
 )
@@ -31,11 +33,12 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateTopic,
 )
 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.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
+from frigate.util.services import ffprobe_stream, is_restricted_go2rtc_source
 
 logger = logging.getLogger(__name__)
 
@@ -66,7 +69,7 @@ def _is_valid_host(host: str) -> bool:
 
 
 @router.get("/go2rtc/streams", dependencies=[Depends(allow_any_authenticated())])
-def go2rtc_streams():
+async def go2rtc_streams(request: Request):
     r = requests.get("http://127.0.0.1:1984/api/streams")
     if not r.ok:
         logger.error("Failed to fetch streams from go2rtc")
@@ -75,6 +78,24 @@ def go2rtc_streams():
             status_code=500,
         )
     stream_data = r.json()
+
+    # Roles with an explicit camera list see only streams owned by an allowed
+    # camera. Admin and full-access roles (no list / empty list) see all streams.
+    current_user = await get_current_user(request)
+    if not isinstance(current_user, JSONResponse):
+        role = current_user["role"]
+        roles_dict = request.app.frigate_config.auth.roles
+        if role != "admin" and roles_dict.get(role):
+            all_camera_names = set(request.app.frigate_config.cameras.keys())
+            allowed_cameras = set(
+                User.get_allowed_cameras(role, roles_dict, all_camera_names)
+            )
+            stream_data = {
+                name: data
+                for name, data in stream_data.items()
+                if _get_stream_owner_cameras(request, name) & allowed_cameras
+            }
+
     for data in stream_data.values():
         for producer in data.get("producers") or []:
             producer["url"] = clean_camera_user_pass(producer.get("url", ""))
@@ -126,9 +147,24 @@ def go2rtc_add_stream(request: Request, stream_name: str, src: str = ""):
         params = {"name": stream_name}
         if src:
             try:
-                params["src"] = substitute_frigate_vars(src)
+                resolved_src = substitute_frigate_vars(src)
             except KeyError:
-                params["src"] = src
+                resolved_src = src
+
+            if is_restricted_go2rtc_source(resolved_src):
+                logger.warning(
+                    "Rejected go2rtc stream '%s' with restricted source type (echo/expr/exec)",
+                    stream_name,
+                )
+                return JSONResponse(
+                    content={
+                        "success": False,
+                        "message": "Restricted stream source type",
+                    },
+                    status_code=400,
+                )
+
+            params["src"] = resolved_src
 
         r = requests.put(
             "http://127.0.0.1:1984/api/streams",
@@ -493,6 +529,68 @@ 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"]))],
@@ -569,34 +667,10 @@ 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",
@@ -608,10 +682,9 @@ 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 (
@@ -649,10 +722,9 @@ 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
@@ -684,10 +756,9 @@ 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:
@@ -840,10 +911,9 @@ 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 []:
@@ -966,7 +1036,6 @@ async def onvif_probe(
                         probe = ffprobe_stream(
                             request.app.frigate_config.ffmpeg, test_uri, detailed=False
                         )
-                        print(probe)
                         ok = probe is not None and getattr(probe, "returncode", 1) == 0
                         tested_candidates.append(
                             {

frigate/api/chat.py

@@ -10,7 +10,7 @@ from functools import reduce
 from typing import Any, Dict, List, Optional
 
 import cv2
-from fastapi import APIRouter, Body, Depends, Request
+from fastapi import APIRouter, Body, Depends, HTTPException, Request
 from fastapi.responses import JSONResponse, StreamingResponse
 from pydantic import BaseModel
 
@@ -35,8 +35,13 @@ from frigate.api.defs.response.chat_response import (
     ToolCall,
 )
 from frigate.api.defs.tags import Tags
-from frigate.api.event import events
+from frigate.api.event import _build_attribute_filter_clause, events
 from frigate.config import FrigateConfig
+from frigate.genai.prompts import (
+    build_chat_system_prompt,
+    get_attribute_classifications,
+    get_tool_definitions,
+)
 from frigate.genai.utils import build_assistant_message_for_conversation
 from frigate.jobs.vlm_watch import (
     get_vlm_watch_job,
@@ -67,338 +72,21 @@ class VLMMonitorRequest(BaseModel):
     zones: List[str] = []
 
 
-def get_tool_definitions() -> List[Dict[str, Any]]:
-    """
-    Get OpenAI-compatible tool definitions for Frigate.
-
-    Returns a list of tool definitions that can be used with OpenAI-compatible
-    function calling APIs.
-    """
-    return [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_objects",
-                "description": (
-                    "Search the historical record of detected objects in Frigate. "
-                    "Use this ONLY for questions about the PAST — e.g. 'did anyone come by today?', "
-                    "'when was the last car?', 'show me detections from yesterday'. "
-                    "Do NOT use this for monitoring or alerting requests about future events — "
-                    "use start_camera_watch instead for those. "
-                    "An 'object' in Frigate represents a tracked detection (e.g., a person, package, car). "
-                    "When the user asks about a specific name (person, delivery company, animal, etc.), "
-                    "filter by sub_label only and do not set label."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "camera": {
-                            "type": "string",
-                            "description": "Camera name to filter by (optional).",
-                        },
-                        "label": {
-                            "type": "string",
-                            "description": "Object label to filter by (e.g., 'person', 'package', 'car').",
-                        },
-                        "sub_label": {
-                            "type": "string",
-                            "description": "Name of a person, delivery company, animal, etc. When filtering by a specific name, use only sub_label; do not set label.",
-                        },
-                        "after": {
-                            "type": "string",
-                            "description": "Start time in ISO 8601 format (e.g., '2024-01-01T00:00:00Z').",
-                        },
-                        "before": {
-                            "type": "string",
-                            "description": "End time in ISO 8601 format (e.g., '2024-01-01T23:59:59Z').",
-                        },
-                        "zones": {
-                            "type": "array",
-                            "items": {"type": "string"},
-                            "description": "List of zone names to filter by.",
-                        },
-                        "limit": {
-                            "type": "integer",
-                            "description": "Maximum number of objects to return (default: 25).",
-                            "default": 25,
-                        },
-                    },
-                },
-                "required": [],
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "find_similar_objects",
-                "description": (
-                    "Find tracked objects that are visually and semantically similar "
-                    "to a specific past event. Use this when the user references a "
-                    "particular object they have seen and wants to find other "
-                    "sightings of the same or similar one ('that green car', 'the "
-                    "person in the red jacket', 'the package that was delivered'). "
-                    "Prefer this over search_objects whenever the user's intent is "
-                    "'find more like this specific one.' Use search_objects first "
-                    "only if you need to locate the anchor event. Requires semantic "
-                    "search to be enabled."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "event_id": {
-                            "type": "string",
-                            "description": "The id of the anchor event to find similar objects to.",
-                        },
-                        "after": {
-                            "type": "string",
-                            "description": "Start time in ISO 8601 format (e.g., '2024-01-01T00:00:00Z').",
-                        },
-                        "before": {
-                            "type": "string",
-                            "description": "End time in ISO 8601 format (e.g., '2024-01-01T23:59:59Z').",
-                        },
-                        "cameras": {
-                            "type": "array",
-                            "items": {"type": "string"},
-                            "description": "Optional list of cameras to restrict to. Defaults to all.",
-                        },
-                        "labels": {
-                            "type": "array",
-                            "items": {"type": "string"},
-                            "description": "Optional list of labels to restrict to. Defaults to the anchor event's label.",
-                        },
-                        "sub_labels": {
-                            "type": "array",
-                            "items": {"type": "string"},
-                            "description": "Optional list of sub_labels (names) to restrict to.",
-                        },
-                        "zones": {
-                            "type": "array",
-                            "items": {"type": "string"},
-                            "description": "Optional list of zones. An event matches if any of its zones overlap.",
-                        },
-                        "similarity_mode": {
-                            "type": "string",
-                            "enum": ["visual", "semantic", "fused"],
-                            "description": "Which similarity signal(s) to use. 'fused' (default) combines visual and semantic.",
-                            "default": "fused",
-                        },
-                        "min_score": {
-                            "type": "number",
-                            "description": "Drop matches with a similarity score below this threshold (0.0-1.0).",
-                        },
-                        "limit": {
-                            "type": "integer",
-                            "description": "Maximum number of matches to return (default: 10).",
-                            "default": 10,
-                        },
-                    },
-                    "required": ["event_id"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "set_camera_state",
-                "description": (
-                    "Change a camera's feature state (e.g., turn detection on/off, enable/disable recordings). "
-                    "Use camera='*' to apply to all cameras at once. "
-                    "Only call this tool when the user explicitly asks to change a camera setting. "
-                    "Requires admin privileges."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "camera": {
-                            "type": "string",
-                            "description": "Camera name to target, or '*' to target all cameras.",
-                        },
-                        "feature": {
-                            "type": "string",
-                            "enum": [
-                                "detect",
-                                "record",
-                                "snapshots",
-                                "audio",
-                                "motion",
-                                "enabled",
-                                "birdseye",
-                                "birdseye_mode",
-                                "improve_contrast",
-                                "ptz_autotracker",
-                                "motion_contour_area",
-                                "motion_threshold",
-                                "notifications",
-                                "audio_transcription",
-                                "review_alerts",
-                                "review_detections",
-                                "object_descriptions",
-                                "review_descriptions",
-                                "profile",
-                            ],
-                            "description": (
-                                "The feature to change. Most features accept ON or OFF. "
-                                "birdseye_mode accepts CONTINUOUS, MOTION, or OBJECTS. "
-                                "motion_contour_area and motion_threshold accept a number. "
-                                "profile accepts a profile name or 'none' to deactivate (requires camera='*')."
-                            ),
-                        },
-                        "value": {
-                            "type": "string",
-                            "description": "The value to set. ON or OFF for toggles, a number for thresholds, a profile name or 'none' for profile.",
-                        },
-                    },
-                    "required": ["camera", "feature", "value"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "get_live_context",
-                "description": (
-                    "Get the current live image and detection information for a 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."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "camera": {
-                            "type": "string",
-                            "description": "Camera name to get live context for.",
-                        },
-                    },
-                    "required": ["camera"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "start_camera_watch",
-                "description": (
-                    "Start a continuous VLM watch job that monitors a camera and sends a notification "
-                    "when a specified condition is met. Use this when the user wants to be alerted about "
-                    "a future event, e.g. 'tell me when guests arrive' or 'notify me when the package is picked up'. "
-                    "Only one watch job can run at a time. Returns a job ID."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "camera": {
-                            "type": "string",
-                            "description": "Camera ID to monitor.",
-                        },
-                        "condition": {
-                            "type": "string",
-                            "description": (
-                                "Natural-language description of the condition to watch for, "
-                                "e.g. 'a person arrives at the front door'."
-                            ),
-                        },
-                        "max_duration_minutes": {
-                            "type": "integer",
-                            "description": "Maximum time to watch before giving up (minutes, default 60).",
-                            "default": 60,
-                        },
-                        "labels": {
-                            "type": "array",
-                            "items": {"type": "string"},
-                            "description": "Object labels that should trigger a VLM check (e.g. ['person', 'car']). If omitted, any detection on the camera triggers a check.",
-                        },
-                        "zones": {
-                            "type": "array",
-                            "items": {"type": "string"},
-                            "description": "Zone names to filter by. If specified, only detections in these zones trigger a VLM check.",
-                        },
-                    },
-                    "required": ["camera", "condition"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "stop_camera_watch",
-                "description": (
-                    "Cancel the currently running VLM watch job. Use this when the user wants to "
-                    "stop a previously started watch, e.g. 'stop watching the front door'."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {},
-                    "required": [],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "get_profile_status",
-                "description": (
-                    "Get the current profile status including the active profile and "
-                    "timestamps of when each profile was last activated. Use this to "
-                    "determine time periods for recap requests — e.g. when the user asks "
-                    "'what happened while I was away?', call this first to find the relevant "
-                    "time window based on profile activation history."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {},
-                    "required": [],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "get_recap",
-                "description": (
-                    "Get a recap of all activity (alerts and detections) for a given time period. "
-                    "Use this after calling get_profile_status to retrieve what happened during "
-                    "a specific window — e.g. 'what happened while I was away?'. Returns a "
-                    "chronological list of activity with camera, objects, zones, and GenAI-generated "
-                    "descriptions when available. Summarize the results for the user."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "after": {
-                            "type": "string",
-                            "description": "Start of the time period in ISO 8601 format (e.g. '2025-03-15T08:00:00').",
-                        },
-                        "before": {
-                            "type": "string",
-                            "description": "End of the time period in ISO 8601 format (e.g. '2025-03-15T17:00:00').",
-                        },
-                        "cameras": {
-                            "type": "string",
-                            "description": "Comma-separated camera IDs to include, or 'all' for all cameras. Default is 'all'.",
-                        },
-                        "severity": {
-                            "type": "string",
-                            "enum": ["alert", "detection"],
-                            "description": "Filter by severity level. Omit to include both alerts and detections.",
-                        },
-                    },
-                    "required": ["after", "before"],
-                },
-            },
-        },
-    ]
-
-
 @router.get(
     "/chat/tools",
     dependencies=[Depends(allow_any_authenticated())],
     summary="Get available tools",
     description="Returns OpenAI-compatible tool definitions for function calling.",
 )
-def get_tools() -> JSONResponse:
+def get_tools(request: Request) -> JSONResponse:
     """Get list of available tools for LLM function calling."""
-    tools = get_tool_definitions()
+    config = request.app.frigate_config
+    semantic_search_enabled = bool(getattr(config.semantic_search, "enabled", False))
+    attribute_classifications = get_attribute_classifications(config)
+    tools = get_tool_definitions(
+        semantic_search_enabled=semantic_search_enabled,
+        attribute_classifications=attribute_classifications,
+    )
     return JSONResponse(content={"tools": tools})
 
 
@@ -431,16 +119,29 @@ def _resolve_zones(
 
 
 async def _execute_search_objects(
+    request: Request,
     arguments: Dict[str, Any],
     allowed_cameras: List[str],
-    config: FrigateConfig,
 ) -> JSONResponse:
     """
     Execute the search_objects tool.
 
-    This searches for detected objects (events) in Frigate using the same
-    logic as the events API endpoint.
+    Routes to the semantic path when the LLM supplied a `semantic_query`
+    and semantic search is enabled; otherwise delegates to the standard
+    events API logic.
     """
+    config = request.app.frigate_config
+    semantic_query = arguments.get("semantic_query")
+    if isinstance(semantic_query, str):
+        semantic_query = semantic_query.strip() or None
+    else:
+        semantic_query = None
+
+    if semantic_query and getattr(config.semantic_search, "enabled", False):
+        return await _execute_search_objects_semantic(
+            request, arguments, allowed_cameras, semantic_query
+        )
+
     # Parse after/before as server local time; convert to Unix timestamp
     after = arguments.get("after")
     before = arguments.get("before")
@@ -476,11 +177,14 @@ async def _execute_search_objects(
     elif zones is None:
         zones = "all"
 
+    attribute = arguments.get("attribute")
+
     # Build query parameters compatible with EventsQueryParams
     query_params = EventsQueryParams(
         cameras=arguments.get("camera", "all"),
         labels=arguments.get("label", "all"),
         sub_labels=arguments.get("sub_label", "all"),  # case-insensitive on the backend
+        attributes=attribute if attribute else "all",
         zones=zones,
         zone=zones,
         after=after,
@@ -507,6 +211,124 @@ async def _execute_search_objects(
         )
 
 
+async def _execute_search_objects_semantic(
+    request: Request,
+    arguments: Dict[str, Any],
+    allowed_cameras: List[str],
+    semantic_query: str,
+) -> JSONResponse:
+    """Search objects via fused thumbnail + description embeddings.
+
+    Runs both visual and description vec searches against `semantic_query`,
+    intersects the candidates with the structured filters (camera, label,
+    sub_label, zones, time window) the LLM supplied, and ranks the survivors
+    by fused similarity. Mirrors the candidate-then-filter pattern used by
+    find_similar_objects since sqlite-vec's IN filter is unreliable.
+    """
+    from peewee import fn
+
+    config = request.app.frigate_config
+    context = request.app.embeddings
+    if context is None:
+        logger.warning(
+            "semantic_query supplied but embeddings context is unavailable; "
+            "returning empty results."
+        )
+        return JSONResponse(content=[])
+
+    after = parse_iso_to_timestamp(arguments.get("after"))
+    before = parse_iso_to_timestamp(arguments.get("before"))
+
+    camera_arg = arguments.get("camera")
+    if camera_arg and camera_arg != "all":
+        if camera_arg not in allowed_cameras:
+            return JSONResponse(content=[])
+        cameras = [camera_arg]
+    else:
+        cameras = list(allowed_cameras) if allowed_cameras else []
+
+    if not cameras:
+        return JSONResponse(content=[])
+
+    label = arguments.get("label")
+    sub_label = arguments.get("sub_label")
+    attribute = arguments.get("attribute")
+
+    zones = arguments.get("zones")
+    if isinstance(zones, list) and zones:
+        zones = _resolve_zones(zones, config, cameras)
+    else:
+        zones = None
+
+    limit = int(arguments.get("limit", 25))
+    limit = max(1, min(limit, 100))
+
+    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}
+    except Exception:
+        logger.exception(
+            "search_thumbnail failed for semantic_query: %s", semantic_query
+        )
+
+    try:
+        rows = context.search_description(semantic_query)
+        description_distances = {row[0]: row[1] for row in rows}
+    except Exception:
+        logger.exception(
+            "search_description failed for semantic_query: %s", semantic_query
+        )
+
+    vec_ids = set(visual_distances) | set(description_distances)
+    if not vec_ids:
+        return JSONResponse(content=[])
+
+    clauses = [Event.id.in_(list(vec_ids)), Event.camera.in_(cameras)]
+    if after is not None:
+        clauses.append(Event.start_time >= after)
+    if before is not None:
+        clauses.append(Event.start_time <= before)
+    if label:
+        clauses.append(Event.label == label)
+    if sub_label:
+        # case-insensitive match to mirror events() behavior
+        clauses.append(fn.LOWER(Event.sub_label.cast("text")) == sub_label.lower())
+    if attribute:
+        attribute_clause = _build_attribute_filter_clause(attribute)
+        if attribute_clause is not None:
+            clauses.append(attribute_clause)
+    if zones:
+        zone_clauses = [Event.zones.cast("text") % f'*"{zone}"*' for zone in zones]
+        clauses.append(reduce(operator.or_, zone_clauses))
+
+    eligible = {e.id: e for e in Event.select().where(reduce(operator.and_, clauses))}
+
+    scored: List[tuple[str, float]] = []
+    for eid in eligible:
+        v_score = (
+            distance_to_score(visual_distances[eid], context.thumb_stats)
+            if eid in visual_distances
+            else None
+        )
+        d_score = (
+            distance_to_score(description_distances[eid], context.desc_stats)
+            if eid in description_distances
+            else None
+        )
+        fused = fuse_scores(v_score, d_score)
+        if fused is None:
+            continue
+        scored.append((eid, fused))
+
+    scored.sort(key=lambda pair: pair[1], reverse=True)
+    scored = scored[:limit]
+
+    results = [hydrate_event(eligible[eid], score=score) for eid, score in scored]
+    return JSONResponse(content=results)
+
+
 async def _execute_find_similar_objects(
     request: Request,
     arguments: Dict[str, Any],
@@ -695,9 +517,7 @@ async def execute_tool(
     logger.debug(f"Executing tool: {tool_name} with arguments: {arguments}")
 
     if tool_name == "search_objects":
-        return await _execute_search_objects(
-            arguments, allowed_cameras, request.app.frigate_config
-        )
+        return await _execute_search_objects(request, arguments, allowed_cameras)
 
     if tool_name == "find_similar_objects":
         result = await _execute_find_similar_objects(
@@ -727,9 +547,21 @@ async def _execute_get_live_context(
     camera: str,
     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:
@@ -877,9 +709,7 @@ async def _execute_tool_internal(
     This is used by the chat completion endpoint to execute tools.
     """
     if tool_name == "search_objects":
-        response = await _execute_search_objects(
-            arguments, allowed_cameras, request.app.frigate_config
-        )
+        response = await _execute_search_objects(request, arguments, allowed_cameras)
         try:
             if hasattr(response, "body"):
                 body_str = response.body.decode("utf-8")
@@ -903,7 +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)
@@ -1292,52 +1129,21 @@ async def chat_completion(
             status_code=400,
         )
 
-    tools = get_tool_definitions()
+    config = request.app.frigate_config
+    semantic_search_enabled = bool(getattr(config.semantic_search, "enabled", False))
+    attribute_classifications = get_attribute_classifications(config)
+    tools = get_tool_definitions(
+        semantic_search_enabled=semantic_search_enabled,
+        attribute_classifications=attribute_classifications,
+    )
     conversation = []
 
-    current_datetime = datetime.now()
-    current_date_str = current_datetime.strftime("%Y-%m-%d")
-    current_time_str = current_datetime.strftime("%I:%M:%S %p")
-
-    cameras_info = []
-    config = request.app.frigate_config
-    for camera_id in allowed_cameras:
-        if camera_id not in config.cameras:
-            continue
-        camera_config = config.cameras[camera_id]
-        friendly_name = (
-            camera_config.friendly_name
-            if camera_config.friendly_name
-            else camera_id.replace("_", " ").title()
-        )
-        zone_names = list(camera_config.zones.keys())
-        if zone_names:
-            cameras_info.append(
-                f"  - {friendly_name} (ID: {camera_id}, zones: {', '.join(zone_names)})"
-            )
-        else:
-            cameras_info.append(f"  - {friendly_name} (ID: {camera_id})")
-
-    cameras_section = ""
-    if cameras_info:
-        cameras_section = (
-            "\n\nAvailable cameras:\n"
-            + "\n".join(cameras_info)
-            + "\n\nWhen users refer to cameras by their friendly name (e.g., 'Back Deck Camera'), use the corresponding camera ID (e.g., 'back_deck_cam') in tool calls."
-        )
-
-    system_prompt = f"""You are a helpful assistant for Frigate, a security camera NVR system. You help users answer questions about their cameras, detected objects, and events.
-
-Current server local date and time: {current_date_str} at {current_time_str}
-
-Do not start your response with phrases like "I will check...", "Let me see...", or "Let me look...". Answer directly.
-
-Always present times to the user in the server's local timezone. When tool results include start_time_local and end_time_local, use those exact strings when listing or describing detection times—do not convert or invent timestamps. Do not use UTC or ISO format with Z for the user-facing answer unless the tool result only provides Unix timestamps without local time fields.
-When users ask about "today", "yesterday", "this week", etc., use the current date above as reference.
-When searching for objects or events, use ISO 8601 format for dates (e.g., {current_date_str}T00:00:00Z for the start of today).
-Always be accurate with time calculations based on the current date provided.
-
-When a user refers to a specific object they have seen or describe with identifying details ("that green car", "the person in the red jacket", "a package left today"), prefer the find_similar_objects tool over search_objects. Use search_objects first only to locate the anchor event, then pass its id to find_similar_objects. For generic queries like "show me all cars today", keep using search_objects. If a user message begins with [attached_event:<id>], treat that event id as the anchor for any similarity or "tell me more" request in the same message and call find_similar_objects with that id.{cameras_section}"""
+    system_prompt = build_chat_system_prompt(
+        config=config,
+        allowed_cameras=allowed_cameras,
+        semantic_search_enabled=semantic_search_enabled,
+        attribute_classifications=attribute_classifications,
+    )
 
     conversation.append(
         {
@@ -1386,6 +1192,7 @@ When a user refers to a specific object they have seen or describe with identify
                     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")
@@ -1398,6 +1205,18 @@ When a user refers to a specific object they have seen or describe with identify
                             )
                             + b"\n"
                         )
+                    elif kind == "reasoning_delta":
+                        yield (
+                            json.dumps({"type": "reasoning", "delta": value}).encode(
+                                "utf-8"
+                            )
+                            + b"\n"
+                        )
+                    elif kind == "stats":
+                        yield (
+                            json.dumps({"type": "stats", **value}).encode("utf-8")
+                            + b"\n"
+                        )
                     elif kind == "message":
                         msg = value
                         if msg.get("finish_reason") == "error":
@@ -1468,6 +1287,7 @@ When a user refers to a specific object they have seen or describe with identify
                 messages=conversation,
                 tools=tools if tools else None,
                 tool_choice="auto",
+                enable_thinking=body.enable_thinking,
             )
 
             if response.get("finish_reason") == "error":
@@ -1493,6 +1313,7 @@ When a user refers to a specific object they have seen or describe with identify
                 final_content = response.get("content") or ""
 
                 if body.stream:
+                    final_reasoning = response.get("reasoning")
 
                     async def stream_body() -> Any:
                         if tool_calls:
@@ -1507,6 +1328,15 @@ When a user refers to a specific object they have seen or describe with identify
                                 ).encode("utf-8")
                                 + b"\n"
                             )
+                        # Emit the full reasoning trace up front when the
+                        # underlying client did not stream it
+                        if final_reasoning:
+                            yield (
+                                json.dumps(
+                                    {"type": "reasoning", "delta": final_reasoning}
+                                ).encode("utf-8")
+                                + b"\n"
+                            )
                         # Stream content in word-sized chunks for smooth UX
                         for part in chunk_content(final_content):
                             yield (
@@ -1527,6 +1357,7 @@ When a user refers to a specific object they have seen or describe with identify
                         message=ChatMessageResponse(
                             role="assistant",
                             content=final_content,
+                            reasoning=response.get("reasoning"),
                             tool_calls=None,
                         ),
                         finish_reason=response.get("finish_reason", "stop"),
@@ -1628,6 +1459,7 @@ async def start_vlm_monitor(
             dispatcher=request.app.dispatcher,
             labels=body.labels,
             zones=body.zones,
+            username=request.headers.get("remote-user", ""),
         )
     except RuntimeError as e:
         logger.error("Failed to start VLM watch job: %s", e, exc_info=True)
@@ -1648,10 +1480,22 @@ async def start_vlm_monitor(
     summary="Get current VLM watch job",
     description="Returns the current (or most recently completed) VLM watch job.",
 )
-async def get_vlm_monitor() -> JSONResponse:
+async def get_vlm_monitor(request: Request) -> JSONResponse:
     job = get_vlm_watch_job()
     if job is None:
         return JSONResponse(content={"active": False}, status_code=200)
+
+    role = request.headers.get("remote-role", "viewer")
+    username = request.headers.get("remote-user", "")
+
+    # Admin and the job's creator always see the job. Other users only see it
+    # if they have access to the camera being watched; otherwise hide it.
+    if role != "admin" and username != job.username:
+        try:
+            await require_camera_access(job.camera, request=request)
+        except HTTPException:
+            return JSONResponse(content={"active": False}, status_code=200)
+
     return JSONResponse(content={"active": True, **job.to_dict()}, status_code=200)
 
 
@@ -1661,7 +1505,27 @@ async def get_vlm_monitor() -> JSONResponse:
     summary="Cancel the current VLM watch job",
     description="Cancels the running watch job if one exists.",
 )
-async def cancel_vlm_monitor() -> JSONResponse:
+async def cancel_vlm_monitor(request: Request) -> JSONResponse:
+    job = get_vlm_watch_job()
+    if job is None:
+        return JSONResponse(
+            content={"success": False, "message": "No active watch job to cancel."},
+            status_code=404,
+        )
+
+    role = request.headers.get("remote-role", "viewer")
+    username = request.headers.get("remote-user", "")
+
+    # Admin can cancel any job; other users can only cancel jobs they started.
+    if role != "admin" and username != job.username:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "Not authorized to cancel this watch job.",
+            },
+            status_code=403,
+        )
+
     cancelled = stop_vlm_watch_job()
     if not cancelled:
         return JSONResponse(

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

@@ -6,10 +6,18 @@ from datetime import datetime
 
 from fastapi import APIRouter, Depends, Request
 from fastapi.responses import JSONResponse
+from peewee import DoesNotExist
 from pydantic import BaseModel, Field
 
 from frigate.api.auth import require_role
 from frigate.api.defs.tags import Tags
+from frigate.jobs.debug_replay import (
+    ExportDebugReplaySource,
+    RecordingDebugReplaySource,
+    start_debug_replay_job,
+)
+from frigate.models import Export
+from frigate.util.services import get_video_properties
 
 logger = logging.getLogger(__name__)
 
@@ -24,15 +32,28 @@ class DebugReplayStartBody(BaseModel):
     end_time: float = Field(title="End timestamp")
 
 
+class DebugReplayStartFromExportBody(BaseModel):
+    """Request body for starting a debug replay session from an export."""
+
+    export_id: str = Field(title="Export id")
+
+
 class DebugReplayStartResponse(BaseModel):
     """Response for starting a debug replay session."""
 
     success: bool
     replay_camera: str
+    job_id: str
 
 
 class DebugReplayStatusResponse(BaseModel):
-    """Response for debug replay status."""
+    """Response for debug replay status.
+
+    Returns only session-presence fields. Startup progress and error
+    details flow through the job_state WebSocket topic via the
+    debug_replay job (see frigate.jobs.debug_replay); the
+    Replay page subscribes there with useJobStatus("debug_replay").
+    """
 
     active: bool
     replay_camera: str | None = None
@@ -51,15 +72,40 @@ class DebugReplayStopResponse(BaseModel):
 @router.post(
     "/debug_replay/start",
     response_model=DebugReplayStartResponse,
+    status_code=202,
+    responses={
+        400: {"description": "Invalid camera, time range, or no recordings"},
+        409: {"description": "A replay session is already active"},
+    },
     dependencies=[Depends(require_role(["admin"]))],
     summary="Start debug replay",
-    description="Start a debug replay session from camera recordings.",
+    description="Start a debug replay session from camera recordings. Returns "
+    "immediately while clip generation runs as a background job; subscribe "
+    "to the 'debug_replay' job_state WS topic to track progress.",
 )
 async def start_debug_replay(request: Request, body: DebugReplayStartBody):
-    """Start a debug replay session."""
+    """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])
 
-    if replay_manager.active:
+    source = RecordingDebugReplaySource(
+        source_camera=body.camera,
+        start_ts=body.start_time,
+        end_ts=body.end_time,
+        internal_port=internal_port,
+    )
+
+    try:
+        job_id = await asyncio.to_thread(
+            start_debug_replay_job,
+            source=source,
+            frigate_config=request.app.frigate_config,
+            config_publisher=request.app.config_publisher,
+            replay_manager=replay_manager,
+        )
+    except RuntimeError:
         return JSONResponse(
             content={
                 "success": False,
@@ -67,38 +113,102 @@ async def start_debug_replay(request: Request, body: DebugReplayStartBody):
             },
             status_code=409,
         )
-
-    try:
-        replay_camera = await asyncio.to_thread(
-            replay_manager.start,
-            source_camera=body.camera,
-            start_ts=body.start_time,
-            end_ts=body.end_time,
-            frigate_config=request.app.frigate_config,
-            config_publisher=request.app.config_publisher,
-        )
     except ValueError:
-        logger.exception("Invalid parameters for debug replay start request")
+        logger.exception("Rejected debug replay start request")
         return JSONResponse(
             content={
                 "success": False,
-                "message": "Invalid debug replay request parameters",
+                "message": "Invalid debug replay parameters",
             },
             status_code=400,
         )
-    except RuntimeError:
-        logger.exception("Error while starting debug replay session")
+
+    return JSONResponse(
+        content={
+            "success": True,
+            "replay_camera": replay_manager.replay_camera_name,
+            "job_id": job_id,
+        },
+        status_code=202,
+    )
+
+
+@router.post(
+    "/debug_replay/start_from_export",
+    response_model=DebugReplayStartResponse,
+    status_code=202,
+    responses={
+        400: {"description": "Invalid export, time range, or no recordings"},
+        404: {"description": "Export not found"},
+        409: {"description": "A replay session is already active"},
+    },
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Start debug replay from an export",
+    description="Start a debug replay session covering an existing export's "
+    "time range. The end time is derived from the export's video duration.",
+)
+async def start_debug_replay_from_export(
+    request: Request, body: DebugReplayStartFromExportBody
+):
+    """Start a debug replay session from an existing export."""
+    try:
+        export: Export = Export.get(Export.id == body.export_id)
+    except DoesNotExist:
+        return JSONResponse(
+            content={"success": False, "message": "Export not found"},
+            status_code=404,
+        )
+
+    properties = await get_video_properties(
+        request.app.frigate_config.ffmpeg, export.video_path, get_duration=True
+    )
+    duration = properties.get("duration", -1)
+
+    if duration is None or duration <= 0:
         return JSONResponse(
             content={
                 "success": False,
-                "message": "An internal error occurred while starting debug replay",
+                "message": "Could not determine export duration",
             },
-            status_code=500,
+            status_code=400,
         )
 
-    return DebugReplayStartResponse(
-        success=True,
-        replay_camera=replay_camera,
+    replay_manager = request.app.replay_manager
+    source = ExportDebugReplaySource(export=export, duration=float(duration))
+
+    try:
+        job_id = await asyncio.to_thread(
+            start_debug_replay_job,
+            source=source,
+            frigate_config=request.app.frigate_config,
+            config_publisher=request.app.config_publisher,
+            replay_manager=replay_manager,
+        )
+    except RuntimeError:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "A replay session is already active",
+            },
+            status_code=409,
+        )
+    except ValueError:
+        logger.exception("Rejected debug replay start request")
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "Invalid debug replay parameters",
+            },
+            status_code=400,
+        )
+
+    return JSONResponse(
+        content={
+            "success": True,
+            "replay_camera": replay_manager.replay_camera_name,
+            "job_id": job_id,
+        },
+        status_code=202,
     )
 
 
@@ -118,12 +228,16 @@ def get_debug_replay_status(request: Request):
 
     if replay_manager.active and replay_camera:
         frame_processor = request.app.detected_frames_processor
-        frame = frame_processor.get_current_frame(replay_camera)
+        frame = (
+            frame_processor.get_current_frame(replay_camera)
+            if frame_processor is not None
+            else None
+        )
 
         if frame is not None:
             frame_time = frame_processor.get_current_frame_time(replay_camera)
             camera_config = request.app.frigate_config.cameras.get(replay_camera)
-            retry_interval = 10
+            retry_interval = 10.0
 
             if camera_config is not None:
                 retry_interval = float(camera_config.ffmpeg.retry_interval or 10)

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

@@ -36,3 +36,10 @@ 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

@@ -20,6 +20,10 @@ class ChatMessageResponse(BaseModel):
     content: Optional[str] = Field(
         default=None, description="Message content (None if tool calls present)"
     )
+    reasoning: Optional[str] = Field(
+        default=None,
+        description="Separated reasoning/thinking trace if the model emitted one",
+    )
     tool_calls: Optional[list[ToolCallInvocation]] = Field(
         default=None, description="Tool calls if LLM wants to call tools"
     )

frigate/api/export.py

@@ -398,7 +398,7 @@ class _StreamingZipBuffer:
 def _unique_archive_name(export: Export, used: set[str]) -> str:
     base = sanitize_filename(export.name) if export.name else None
     if not base:
-        base = f"{export.camera}_{int(datetime.datetime.timestamp(export.date))}"
+        base = f"{export.camera}_{int(export.date)}"
 
     candidate = f"{base}.mp4"
     counter = 1

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/media.py

@@ -174,12 +174,10 @@ async def latest_frame(
     }
     quality_params = get_image_quality_params(extension.value, params.quality)
 
-    if camera_name in request.app.frigate_config.cameras:
+    camera_config = request.app.frigate_config.cameras.get(camera_name)
+    if camera_config is not None:
         frame = frame_processor.get_current_frame(camera_name, draw_options)
-        retry_interval = float(
-            request.app.frigate_config.cameras.get(camera_name).ffmpeg.retry_interval
-            or 10
-        )
+        retry_interval = float(camera_config.ffmpeg.retry_interval or 10)
 
         is_offline = False
         if frame is None or datetime.now().timestamp() > (

frigate/api/media_auth.py

@@ -0,0 +1,291 @@
+"""URI-aware authorization for nginx-served static media.
+
+The `/auth` endpoint (used as nginx `auth_request` target) calls into this
+module to classify the requested URI from the `X-Original-URL` header and, for
+camera-scoped resources, decide whether the current role may access them.
+
+Without this, `auth_request` only verifies the JWT — every authenticated user
+could read clips, recordings, and exports for *any* camera, bypassing the
+per-camera authorization the regular API enforces via `require_camera_access`.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from enum import Enum
+from typing import Optional
+from urllib.parse import unquote, urlparse
+
+from peewee import DoesNotExist
+
+from frigate.config import FrigateConfig
+from frigate.const import EXPORT_DIR
+from frigate.models import Export, User
+
+logger = logging.getLogger(__name__)
+
+
+class MediaAuthResolution(str, Enum):
+    """Classification of an `X-Original-URL` path for media-auth purposes."""
+
+    CAMERA = "camera"
+    ADMIN_ONLY = "admin_only"
+    LISTING_MULTI_CAMERA = "listing_multi_camera"
+    LISTING_NEUTRAL = "listing_neutral"
+    # Under a recognized media root (/clips, /recordings, /exports) but
+    # unclassifiable (unknown subtree, no matching DB row, DB error).
+    # Restricted users are denied; admins/full-access roles are allowed
+    # (nginx will likely return 404 if the file genuinely doesn't exist).
+    UNRESOLVED_MEDIA = "unresolved_media"
+    # Not a media URI at all (e.g. /api/events, /login).
+    UNKNOWN = "unknown"
+
+
+def extract_path(original_url: Optional[str]) -> Optional[str]:
+    """Return the decoded path component of nginx's `X-Original-URL` header.
+
+    nginx forwards the *raw* request URI (with `..` segments intact) via
+    `$request_uri`. nginx normalizes the path before serving the file, so a
+    request like `/recordings/.../allowed_cam/../forbidden_cam/file.mp4`
+    would (1) parse as the allowed camera in our auth check, (2) be served
+    as the forbidden camera by nginx. To close the bypass we reject any URI
+    whose path contains `.` or `..` segments outright.
+    """
+    if not original_url:
+        return None
+
+    parsed = urlparse(original_url)
+    raw_path = parsed.path or original_url
+    decoded = unquote(raw_path)
+    if not decoded:
+        return None
+
+    if not decoded.startswith("/"):
+        decoded = "/" + decoded
+
+    segments = decoded.split("/")
+    if ".." in segments or "." in segments:
+        return None
+
+    return decoded
+
+
+def resolve_media_uri(
+    uri: str, frigate_config: Optional[FrigateConfig] = None
+) -> tuple[MediaAuthResolution, Optional[str]]:
+    """Classify a URI and return the owning camera if applicable.
+
+    `frigate_config` is used to disambiguate clip/review filenames whose
+    camera name contains hyphens by matching against the longest configured
+    camera-name prefix.
+    """
+    if not uri:
+        return MediaAuthResolution.UNKNOWN, None
+
+    parts = [p for p in uri.split("/") if p]
+    if not parts:
+        return MediaAuthResolution.UNKNOWN, None
+
+    root = parts[0]
+    if root == "recordings":
+        return _resolve_recording(parts)
+    if root == "clips":
+        return _resolve_clip(parts, frigate_config)
+    if root == "exports":
+        return _resolve_export(parts)
+
+    return MediaAuthResolution.UNKNOWN, None
+
+
+def _resolve_recording(
+    parts: list[str],
+) -> tuple[MediaAuthResolution, Optional[str]]:
+    # /recordings                          → neutral
+    # /recordings/{date}                   → neutral
+    # /recordings/{date}/{hour}            → multi-camera listing
+    # /recordings/{date}/{hour}/{cam}/...  → camera
+    if len(parts) <= 2:
+        return MediaAuthResolution.LISTING_NEUTRAL, None
+    if len(parts) == 3:
+        return MediaAuthResolution.LISTING_MULTI_CAMERA, None
+    return MediaAuthResolution.CAMERA, parts[3]
+
+
+def _resolve_clip(
+    parts: list[str], frigate_config: Optional[FrigateConfig]
+) -> tuple[MediaAuthResolution, Optional[str]]:
+    # /clips                                          → multi-camera listing
+    # /clips/thumbs/{cam}/...                         → camera
+    # /clips/previews/{cam}/...                       → camera
+    # /clips/review/thumb-{cam}-{review_id}.webp      → camera (parsed)
+    # /clips/faces/...                                → admin-only
+    # /clips/genai-requests/...                       → admin-only
+    # /clips/preview_restart_cache/...                → admin-only
+    # /clips/{model}/train|dataset/...                → admin-only
+    # /clips/{cam}-{event_id}[-clean].{ext}           → camera (parsed)
+    # other /clips/{subdir}/...                       → unresolved (deny restricted)
+    if len(parts) == 1:
+        return MediaAuthResolution.LISTING_MULTI_CAMERA, None
+
+    second = parts[1]
+
+    if second in ("thumbs", "previews"):
+        if len(parts) == 2:
+            return MediaAuthResolution.LISTING_MULTI_CAMERA, None
+        return MediaAuthResolution.CAMERA, parts[2]
+
+    if second == "review":
+        if len(parts) == 2:
+            return MediaAuthResolution.LISTING_MULTI_CAMERA, None
+        camera = _camera_from_thumb_filename(parts[2], frigate_config)
+        if camera:
+            return MediaAuthResolution.CAMERA, camera
+        return MediaAuthResolution.UNRESOLVED_MEDIA, None
+
+    if second in ("faces", "genai-requests", "preview_restart_cache"):
+        return MediaAuthResolution.ADMIN_ONLY, None
+
+    if len(parts) >= 3 and parts[2] in ("train", "dataset"):
+        return MediaAuthResolution.ADMIN_ONLY, None
+
+    if len(parts) == 2:
+        camera = _camera_from_clip_filename(second, frigate_config)
+        if camera:
+            return MediaAuthResolution.CAMERA, camera
+        return MediaAuthResolution.UNRESOLVED_MEDIA, None
+
+    return MediaAuthResolution.UNRESOLVED_MEDIA, None
+
+
+def _longest_prefix_camera(
+    stem: str, frigate_config: Optional[FrigateConfig]
+) -> Optional[str]:
+    if frigate_config is None:
+        return None
+    for cam in sorted(frigate_config.cameras.keys(), key=len, reverse=True):
+        if stem.startswith(cam + "-"):
+            return cam
+    return None
+
+
+def _camera_from_clip_filename(
+    filename: str, frigate_config: Optional[FrigateConfig]
+) -> Optional[str]:
+    """Match a flat clip filename `{camera}-{event_id}[-clean].{ext}` against
+    configured camera names. Longest-prefix wins so camera names containing
+    hyphens (e.g. `front-door`) resolve correctly.
+    """
+    dot = filename.rfind(".")
+    stem = filename[:dot] if dot > 0 else filename
+    return _longest_prefix_camera(stem, frigate_config)
+
+
+def _camera_from_thumb_filename(
+    filename: str, frigate_config: Optional[FrigateConfig]
+) -> Optional[str]:
+    """Match a review thumbnail filename `thumb-{camera}-{review_id}.webp`."""
+    if not filename.startswith("thumb-"):
+        return None
+    dot = filename.rfind(".")
+    stem = filename[len("thumb-") : dot] if dot > 0 else filename[len("thumb-") :]
+    return _longest_prefix_camera(stem, frigate_config)
+
+
+def _resolve_export(
+    parts: list[str],
+) -> tuple[MediaAuthResolution, Optional[str]]:
+    # /exports                  → multi-camera listing
+    # /exports/{filename}.mp4   → camera (DB lookup by exact path)
+    if len(parts) == 1:
+        return MediaAuthResolution.LISTING_MULTI_CAMERA, None
+    if len(parts) != 2:
+        return MediaAuthResolution.UNRESOLVED_MEDIA, None
+
+    filename = parts[1]
+    full_path = os.path.join(EXPORT_DIR, filename)
+    try:
+        export = Export.get(Export.video_path == full_path)
+        return MediaAuthResolution.CAMERA, export.camera
+    except DoesNotExist:
+        return MediaAuthResolution.UNRESOLVED_MEDIA, None
+    except Exception as e:
+        logger.warning("Export DB lookup failed for %s: %s", filename, e)
+        return MediaAuthResolution.UNRESOLVED_MEDIA, None
+
+
+def check_camera_access(role: str, camera: str, frigate_config: FrigateConfig) -> bool:
+    """Return True iff `role` may access `camera`.
+
+    Mirrors the gating logic in `require_camera_access`: admin and any role
+    without a non-empty allow-list bypass the check.
+    """
+    if role == "admin":
+        return True
+
+    roles_dict = frigate_config.auth.roles
+    if not roles_dict.get(role):
+        return True
+
+    all_camera_names = set(frigate_config.cameras.keys())
+    allowed = User.get_allowed_cameras(role, roles_dict, all_camera_names)
+    return camera in allowed
+
+
+def is_role_restricted(role: str, frigate_config: FrigateConfig) -> bool:
+    """True if `role` has a non-empty allow-list (i.e. not full-access)."""
+    if role == "admin":
+        return False
+    return bool(frigate_config.auth.roles.get(role))
+
+
+def deny_response_for_media_uri(
+    original_url: Optional[str], role: Optional[str], frigate_config: FrigateConfig
+) -> Optional[int]:
+    """Decide whether the current role should be blocked from `original_url`.
+
+    Returns an HTTP status code (403) when access should be denied, or `None`
+    when the request is allowed.
+    """
+    if not original_url:
+        return None
+
+    path = extract_path(original_url)
+
+    # `extract_path` returns None for URIs containing `.` or `..` segments.
+    # For media-root URIs that's a traversal attempt — deny outright. For
+    # non-media URIs, pass through (nginx / the backend handle them).
+    if path is None:
+        raw = urlparse(original_url).path or original_url
+        decoded = unquote(raw)
+        first = decoded.lstrip("/").split("/", 1)[0] if decoded else ""
+        if first in ("clips", "recordings", "exports"):
+            return 403
+        return None
+
+    resolution, camera = resolve_media_uri(path, frigate_config)
+    if resolution == MediaAuthResolution.UNKNOWN:
+        return None
+
+    if not role or role == "admin":
+        return None
+
+    if not is_role_restricted(role, frigate_config):
+        return None
+
+    if resolution == MediaAuthResolution.LISTING_NEUTRAL:
+        return None
+
+    if resolution in (
+        MediaAuthResolution.LISTING_MULTI_CAMERA,
+        MediaAuthResolution.ADMIN_ONLY,
+        MediaAuthResolution.UNRESOLVED_MEDIA,
+    ):
+        return 403
+
+    if resolution == MediaAuthResolution.CAMERA:
+        if camera and check_camera_access(role, camera, frigate_config):
+            return None
+        return 403
+
+    return 403

frigate/api/motion_search.py

@@ -42,9 +42,9 @@ class MotionSearchRequest(BaseModel):
         description="Minimum change area as a percentage of the ROI",
     )
     frame_skip: int = Field(
-        default=5,
+        default=30,
         ge=1,
-        le=30,
+        le=120,
         description="Process every Nth frame (1=all frames, 5=every 5th frame)",
     )
     parallel: bool = Field(

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

@@ -658,6 +658,11 @@ 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

@@ -144,7 +144,7 @@ class FrigateApp:
         for d in dirs:
             if not os.path.exists(d) and not os.path.islink(d):
                 logger.info(f"Creating directory: {d}")
-                os.makedirs(d)
+                os.makedirs(d, exist_ok=True)
             else:
                 logger.debug(f"Skipping directory: {d}")
 
@@ -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():
@@ -428,18 +440,11 @@ class FrigateApp:
         self.camera_maintainer.start()
 
     def start_audio_processor(self) -> None:
-        audio_cameras = [
-            c
-            for c in self.config.cameras.values()
-            if c.enabled and c.audio.enabled_in_config
-        ]
-
-        if audio_cameras:
-            self.audio_process = AudioProcessor(
-                self.config, audio_cameras, self.camera_metrics, self.stop_event
-            )
-            self.audio_process.start()
-            self.processes["audio_detector"] = self.audio_process.pid or 0
+        self.audio_process = AudioProcessor(
+            self.config, self.camera_metrics, self.stop_event
+        )
+        self.audio_process.start()
+        self.processes["audio_detector"] = self.audio_process.pid or 0
 
     def start_timeline_processor(self) -> None:
         self.timeline_processor = TimelineProcessor(
@@ -619,6 +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/comms/webpush.py

@@ -429,7 +429,10 @@ class WebPushClient(Communicator):
             else:
                 title = base_title
 
-            message = payload["after"]["data"]["metadata"]["shortSummary"]
+            if payload["after"]["data"]["metadata"].get("shortSummary"):
+                message = payload["after"]["data"]["metadata"]["shortSummary"]
+            else:
+                message = f"Detected on {camera_name}"
         else:
             zone_names = payload["after"]["data"]["zones"]
             formatted_zone_names = []

frigate/comms/ws.py

@@ -17,9 +17,408 @@ from ws4py.websocket import WebSocket as WebSocket_
 
 from frigate.comms.base_communicator import Communicator
 from frigate.config import FrigateConfig
+from frigate.const import (
+    CLEAR_ONGOING_REVIEW_SEGMENTS,
+    EXPIRE_AUDIO_ACTIVITY,
+    INSERT_MANY_RECORDINGS,
+    INSERT_PREVIEW,
+    NOTIFICATION_TEST,
+    REQUEST_REGION_GRID,
+    UPDATE_AUDIO_ACTIVITY,
+    UPDATE_AUDIO_TRANSCRIPTION_STATE,
+    UPDATE_BIRDSEYE_LAYOUT,
+    UPDATE_CAMERA_ACTIVITY,
+    UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
+    UPDATE_EVENT_DESCRIPTION,
+    UPDATE_MODEL_STATE,
+    UPDATE_REVIEW_DESCRIPTION,
+    UPSERT_REVIEW_SEGMENT,
+)
+from frigate.models import User
+from frigate.output.ws_auth import ws_has_camera_access
 
 logger = logging.getLogger(__name__)
 
+# Internal IPC topics — NEVER allowed from WebSocket, regardless of role
+_WS_BLOCKED_TOPICS = frozenset(
+    {
+        INSERT_MANY_RECORDINGS,
+        INSERT_PREVIEW,
+        REQUEST_REGION_GRID,
+        UPSERT_REVIEW_SEGMENT,
+        CLEAR_ONGOING_REVIEW_SEGMENTS,
+        UPDATE_CAMERA_ACTIVITY,
+        UPDATE_AUDIO_ACTIVITY,
+        EXPIRE_AUDIO_ACTIVITY,
+        UPDATE_EVENT_DESCRIPTION,
+        UPDATE_REVIEW_DESCRIPTION,
+        UPDATE_MODEL_STATE,
+        UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
+        UPDATE_BIRDSEYE_LAYOUT,
+        UPDATE_AUDIO_TRANSCRIPTION_STATE,
+        NOTIFICATION_TEST,
+    }
+)
+
+# Read-only topics any authenticated user (including viewer) can send
+_WS_VIEWER_TOPICS = frozenset(
+    {
+        "onConnect",
+        "modelState",
+        "audioTranscriptionState",
+        "birdseyeLayout",
+        "embeddingsReindexProgress",
+        "jobState",
+    }
+)
+
+
+def _check_ws_authorization(
+    topic: str,
+    role_header: str | None,
+    separator: str,
+) -> bool:
+    """Check if a WebSocket message is authorized.
+
+    Args:
+        topic: The message topic.
+        role_header: The HTTP_REMOTE_ROLE header value, or None.
+        separator: The role separator character from proxy config.
+
+    Returns:
+        True if authorized, False if blocked.
+    """
+    # Block IPC-only topics unconditionally
+    if topic in _WS_BLOCKED_TOPICS:
+        return False
+
+    # No role header: default to viewer (fail-closed)
+    if role_header is None:
+        return topic in _WS_VIEWER_TOPICS
+
+    # Check if any role is admin
+    roles = [r.strip() for r in role_header.split(separator)]
+    if "admin" in roles:
+        return True
+
+    # Non-admin: only viewer topics allowed
+    return topic in _WS_VIEWER_TOPICS
+
+
+# ---- Outbound filtering ---------------------------------------------------
+#
+# Every WebSocket broadcast is classified into one of a small set of scopes,
+# then materialized per recipient. Connections with restricted roles only see
+# data for cameras they are authorized to access; admin and full-access roles
+# behave as today.
+
+# Topics that are safe to broadcast to every authenticated client.
+_WS_GLOBAL_OUTBOUND_TOPICS = frozenset(
+    {
+        "model_state",
+        "embeddings_reindex_progress",
+        "audio_transcription_state",
+        "profile/state",
+        "notifications/state",
+        "notification_test",
+    }
+)
+
+# Topics that restricted roles must never receive. Birdseye composites span
+# all cameras, so the existing JSMPEG policy already restricts birdseye access
+# to unrestricted roles; the layout broadcast follows the same rule.
+_WS_UNRESTRICTED_ONLY_TOPICS = frozenset(
+    {
+        "birdseye_layout",
+    }
+)
+
+# Topics whose payload (parsed as JSON) names a single owning camera at the
+# given key path. Used to scope events, reviews, triggers, etc.
+_WS_PAYLOAD_CAMERA_TOPICS: dict[str, tuple[str, ...]] = {
+    "events": ("after", "camera"),
+    "reviews": ("after", "camera"),
+    "tracked_object_update": ("camera",),
+    "triggers": ("camera",),
+    "camera_monitoring": ("camera",),
+}
+
+# Topics whose payload is a dict keyed by camera name; filter keys per
+# recipient.
+_WS_RESHAPE_BY_CAMERA_KEY_TOPICS = frozenset(
+    {
+        "camera_activity",
+        "audio_detections",
+    }
+)
+
+# Topics whose payload is a dict keyed by job_type, where each entry may
+# contain a "camera" or "source_camera" field, or a nested ``results.jobs``
+# list of per-camera sub-jobs (export broadcasts).
+_WS_RESHAPE_JOB_STATE_TOPICS = frozenset(
+    {
+        "job_state",
+    }
+)
+
+# Topics whose payload mixes global aggregates with a ``cameras`` sub-dict
+# keyed by camera name. Aggregates and detector data stay; per-camera entries
+# are filtered.
+_WS_RESHAPE_STATS_TOPICS = frozenset(
+    {
+        "stats",
+    }
+)
+
+
+def _collect_zone_names(config: FrigateConfig) -> set[str]:
+    """Return the set of all zone names defined across cameras."""
+    names: set[str] = set()
+    for camera in config.cameras.values():
+        zones = getattr(camera, "zones", None) or {}
+        names.update(zones.keys())
+    return names
+
+
+def _parse_json_payload(payload: Any) -> Any:
+    """Return payload parsed as JSON if it is a string, else as-is."""
+    if isinstance(payload, str):
+        try:
+            return json.loads(payload)
+        except (ValueError, TypeError):
+            return None
+    return payload
+
+
+def _scope_job_entry_to_allowed(entry: Any, allowed: set[str]) -> dict[str, Any] | None:
+    """Filter a single job_state entry to the recipient's allowed cameras.
+
+    Returns the (possibly reshaped) entry, or None to drop it. Four shapes
+    are handled:
+
+    * Top-level ``camera`` or ``source_camera`` (motion_search, vlm_watch,
+      export sub-job dicts): drop the entry if not allowed.
+    * Nested ``results.jobs`` list of per-camera sub-jobs (the aggregated
+      export broadcast): filter the list; drop the entry if nothing remains.
+    * Nested ``results.camera`` or ``results.source_camera`` (debug_replay,
+      which puts replay-specific fields inside ``results``): drop the entry
+      if not allowed.
+    * No camera anywhere (e.g. ``media_sync``): treat as global and keep.
+    """
+    if not isinstance(entry, dict):
+        return None
+
+    cam = entry.get("camera") or entry.get("source_camera")
+
+    if cam is None:
+        results = entry.get("results")
+        if isinstance(results, dict):
+            sub_jobs = results.get("jobs")
+            if isinstance(sub_jobs, list):
+                filtered_jobs = [
+                    j
+                    for j in sub_jobs
+                    if isinstance(j, dict)
+                    and (j.get("camera") or j.get("source_camera")) in allowed
+                ]
+                if not filtered_jobs:
+                    return None
+                reshaped = dict(entry)
+                reshaped["results"] = dict(results)
+                reshaped["results"]["jobs"] = filtered_jobs
+                return reshaped
+
+            cam = results.get("camera") or results.get("source_camera")
+
+    if cam is not None:
+        return entry if cam in allowed else None
+
+    return entry
+
+
+def _extract_payload_camera(payload: Any, path: tuple[str, ...]) -> str | None:
+    """Walk the dotted path through a (possibly JSON-encoded) payload."""
+    cur = _parse_json_payload(payload)
+    for key in path:
+        if not isinstance(cur, dict):
+            return None
+        cur = cur.get(key)
+    return cur if isinstance(cur, str) else None
+
+
+def _classify_outbound(
+    topic: str, all_cameras: set[str], all_zones: set[str]
+) -> tuple[str, Any]:
+    """Classify an outbound topic into (kind, extra).
+
+    kind values:
+      - "global"             : send to every authenticated client
+      - "drop"               : send to nobody (fail-closed for unknowns)
+      - "unrestricted_only"  : send only to admin/full-access roles
+      - "camera"             : extra is the owning camera name
+      - "payload_camera"     : extra is the JSON key path to the camera name
+      - "reshape_by_camera_key"
+      - "reshape_job_state"
+      - "reshape_stats"
+    """
+    if topic in _WS_GLOBAL_OUTBOUND_TOPICS:
+        return ("global", None)
+    if topic in _WS_UNRESTRICTED_ONLY_TOPICS:
+        return ("unrestricted_only", None)
+    if topic in _WS_RESHAPE_BY_CAMERA_KEY_TOPICS:
+        return ("reshape_by_camera_key", None)
+    if topic in _WS_RESHAPE_JOB_STATE_TOPICS:
+        return ("reshape_job_state", None)
+    if topic in _WS_RESHAPE_STATS_TOPICS:
+        return ("reshape_stats", None)
+    if topic in _WS_PAYLOAD_CAMERA_TOPICS:
+        return ("payload_camera", _WS_PAYLOAD_CAMERA_TOPICS[topic])
+
+    # Topic-prefix based: first segment names the owning camera or zone.
+    first = topic.split("/", 1)[0]
+    if first in all_cameras:
+        return ("camera", first)
+    if first in all_zones:
+        # Zone aggregates span cameras; restricted users see nothing here.
+        return ("unrestricted_only", None)
+
+    return ("drop", None)
+
+
+def _ws_role_header(ws: Any) -> str | None:
+    """Return the HTTP_REMOTE_ROLE header value, if any."""
+    environ = getattr(ws, "environ", None)
+    if not environ:
+        return None
+    value = environ.get("HTTP_REMOTE_ROLE")
+    return value if isinstance(value, str) else None
+
+
+def _ws_valid_roles(ws: Any, config: FrigateConfig) -> list[str]:
+    """Return the list of recognized roles for this connection."""
+    header = _ws_role_header(ws)
+    if not header:
+        return []
+    roles = [r.strip() for r in header.split(config.proxy.separator) if r.strip()]
+    return [r for r in roles if r in config.auth.roles]
+
+
+def _ws_is_unrestricted(ws: Any, config: FrigateConfig) -> bool:
+    """True when the connection has unrestricted camera access.
+
+    Mirrors the policy in ``frigate.output.ws_auth``: admin or any role with
+    an empty allow-list grants full access.
+    """
+    roles = _ws_valid_roles(ws, config)
+    if not roles:
+        return False
+    roles_dict = config.auth.roles
+    return any(r == "admin" or not roles_dict.get(r) for r in roles)
+
+
+def _ws_allowed_cameras(ws: Any, config: FrigateConfig) -> set[str]:
+    """Return the union of cameras this connection may access across its roles."""
+    roles = _ws_valid_roles(ws, config)
+    if not roles:
+        return set()
+    all_cameras = set(config.cameras.keys())
+    allowed: set[str] = set()
+    for role in roles:
+        if role == "admin" or not config.auth.roles.get(role):
+            return all_cameras
+        allowed.update(User.get_allowed_cameras(role, config.auth.roles, all_cameras))
+    return allowed
+
+
+def _wrap_envelope(topic: str, inner_payload: Any) -> str:
+    """Re-serialize a (topic, payload) message after payload reshaping.
+
+    Frigate's wire format keeps payloads as JSON-encoded strings inside the
+    outer envelope, mirroring what producers send today.
+    """
+    return json.dumps({"topic": topic, "payload": json.dumps(inner_payload)})
+
+
+def _materialize_for_ws(
+    ws: Any,
+    topic: str,
+    full_message: str,
+    scope: tuple[str, Any],
+    parsed_payload: Any,
+    config: FrigateConfig,
+) -> str | None:
+    """Return the JSON string to deliver to ``ws``, or None to skip it."""
+    kind, extra = scope
+    has_role = _ws_role_header(ws) is not None
+
+    if kind == "drop":
+        return None
+
+    if kind == "global":
+        # Globals still require an authenticated connection. Missing role
+        # falls back to viewer semantics (matching the inbound rule).
+        return full_message
+
+    # Beyond globals, an authenticated role header is required (fail-closed).
+    if not has_role:
+        return None
+
+    if kind == "unrestricted_only":
+        return full_message if _ws_is_unrestricted(ws, config) else None
+
+    if kind == "camera":
+        return full_message if ws_has_camera_access(ws, extra, config) else None
+
+    if kind == "payload_camera":
+        camera = _extract_payload_camera(parsed_payload, extra)
+        if camera is None:
+            return None
+        return full_message if ws_has_camera_access(ws, camera, config) else None
+
+    if kind == "reshape_by_camera_key":
+        if _ws_is_unrestricted(ws, config):
+            return full_message
+        if not isinstance(parsed_payload, dict):
+            return None
+        allowed = _ws_allowed_cameras(ws, config)
+        filtered = {cam: data for cam, data in parsed_payload.items() if cam in allowed}
+        if not filtered:
+            return None
+        return _wrap_envelope(topic, filtered)
+
+    if kind == "reshape_job_state":
+        if _ws_is_unrestricted(ws, config):
+            return full_message
+        if not isinstance(parsed_payload, dict):
+            return None
+        allowed = _ws_allowed_cameras(ws, config)
+        filtered_jobs: dict[str, Any] = {}
+        for job_type, job_payload in parsed_payload.items():
+            scoped = _scope_job_entry_to_allowed(job_payload, allowed)
+            if scoped is not None:
+                filtered_jobs[job_type] = scoped
+        if not filtered_jobs:
+            return None
+        return _wrap_envelope(topic, filtered_jobs)
+
+    if kind == "reshape_stats":
+        if _ws_is_unrestricted(ws, config):
+            return full_message
+        if not isinstance(parsed_payload, dict):
+            return None
+        allowed = _ws_allowed_cameras(ws, config)
+        cameras_block = parsed_payload.get("cameras")
+        if isinstance(cameras_block, dict):
+            filtered_cameras = {
+                name: data for name, data in cameras_block.items() if name in allowed
+            }
+            reshaped = dict(parsed_payload)
+            reshaped["cameras"] = filtered_cameras
+            return _wrap_envelope(topic, reshaped)
+        return full_message
+
+    return None
+
 
 class WebSocket(WebSocket_):  # type: ignore[misc]
     def unhandled_error(self, error: Any) -> None:
@@ -49,6 +448,7 @@ class WebSocketClient(Communicator):
 
         class _WebSocketHandler(WebSocket):
             receiver = self._dispatcher
+            role_separator = self.config.proxy.separator or ","
 
             def received_message(self, message: WebSocket.received_message) -> None:  # type: ignore[name-defined]
                 try:
@@ -63,11 +463,25 @@ class WebSocketClient(Communicator):
                     )
                     return
 
-                logger.debug(
-                    f"Publishing mqtt message from websockets at {json_message['topic']}."
+                topic = json_message["topic"]
+
+                # Authorization check (skip when environ is None — direct internal connection)
+                role_header = (
+                    self.environ.get("HTTP_REMOTE_ROLE") if self.environ else None
                 )
+                if self.environ is not None and not _check_ws_authorization(
+                    topic, role_header, self.role_separator
+                ):
+                    logger.warning(
+                        "Blocked unauthorized WebSocket message: topic=%s, role=%s",
+                        topic,
+                        role_header,
+                    )
+                    return
+
+                logger.debug(f"Publishing mqtt message from websockets at {topic}.")
                 self.receiver(
-                    json_message["topic"],
+                    topic,
                     json_message["payload"],
                 )
 
@@ -87,6 +501,10 @@ class WebSocketClient(Communicator):
         self.websocket_thread.start()
 
     def publish(self, topic: str, payload: Any, _: bool = False) -> None:
+        if self.websocket_server is None:
+            logger.debug("Skipping message, websocket not connected yet")
+            return
+
         try:
             ws_message = json.dumps(
                 {
@@ -99,14 +517,42 @@ class WebSocketClient(Communicator):
             logger.debug(f"payload for {topic} wasn't text. Skipping...")
             return
 
-        if self.websocket_server is None:
-            logger.debug("Skipping message, websocket not connected yet")
+        all_cameras = set(self.config.cameras.keys())
+        all_zones = _collect_zone_names(self.config)
+        scope = _classify_outbound(topic, all_cameras, all_zones)
+
+        if scope[0] == "drop":
             return
 
-        try:
-            self.websocket_server.manager.broadcast(ws_message)
-        except ConnectionResetError:
-            pass
+        # Pre-parse payload once for topics that need to read its contents.
+        parsed_payload: Any = None
+        if scope[0] in (
+            "payload_camera",
+            "reshape_by_camera_key",
+            "reshape_job_state",
+            "reshape_stats",
+        ):
+            parsed_payload = _parse_json_payload(payload)
+            if parsed_payload is None:
+                # malformed payload — fail closed
+                return
+
+        manager = self.websocket_server.manager
+        with manager.lock:
+            websockets = list(manager.websockets.values())
+
+        for ws in websockets:
+            if getattr(ws, "terminated", False):
+                continue
+            message = _materialize_for_ws(
+                ws, topic, ws_message, scope, parsed_payload, self.config
+            )
+            if message is None:
+                continue
+            try:
+                ws.send(message)
+            except (ConnectionResetError, BrokenPipeError, ValueError):
+                pass
 
     def stop(self) -> None:
         if self.websocket_server is not None:

frigate/config/camera/camera.py

@@ -76,7 +76,7 @@ class CameraConfig(FrigateBaseModel):
     # Options with global fallback
     audio: AudioConfig = Field(
         default_factory=AudioConfig,
-        title="Audio events",
+        title="Audio detection",
         description="Settings for audio-based event detection for this camera.",
     )
     audio_transcription: CameraAudioTranscriptionConfig = Field(
@@ -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/genai.py

@@ -37,12 +37,11 @@ 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.",
     )
-    provider: GenAIProviderEnum | None = Field(
-        default=None,
+    provider: GenAIProviderEnum = Field(
         title="Provider",
         description="The GenAI provider to use (for example: ollama, gemini, openai).",
     )

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"
@@ -84,8 +85,8 @@ 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)
@@ -121,7 +122,10 @@ class CameraConfigUpdateSubscriber:
         elif update_type == CameraConfigUpdateEnum.objects:
             config.objects = updated_config
         elif update_type == CameraConfigUpdateEnum.record:
+            old_enabled_in_config = config.record.enabled_in_config
             config.record = updated_config
+            if old_enabled_in_config != updated_config.enabled_in_config:
+                config.recreate_ffmpeg_cmds()
         elif update_type == CameraConfigUpdateEnum.review:
             config.review = updated_config
         elif update_type == CameraConfigUpdateEnum.review_genai:

frigate/config/classification.py

@@ -26,6 +26,11 @@ class EnrichmentsDeviceEnum(str, Enum):
     CPU = "CPU"
 
 
+class ModelSizeEnum(str, Enum):
+    small = "small"
+    large = "large"
+
+
 class TriggerType(str, Enum):
     THUMBNAIL = "thumbnail"
     DESCRIPTION = "description"
@@ -53,13 +58,13 @@ class AudioTranscriptionConfig(FrigateBaseModel):
         title="Transcription language",
         description="Language code used for transcription/translation (for example 'en' for English). See https://whisper-api.com/docs/languages/ for supported language codes.",
     )
-    device: Optional[EnrichmentsDeviceEnum] = Field(
+    device: EnrichmentsDeviceEnum = Field(
         default=EnrichmentsDeviceEnum.CPU,
         title="Transcription device",
         description="Device key (CPU/GPU) to run the transcription model on. Only NVIDIA CUDA GPUs are currently supported for transcription.",
     )
-    model_size: str = Field(
-        default="small",
+    model_size: ModelSizeEnum = Field(
+        default=ModelSizeEnum.small,
         title="Model size",
         description="Model size to use for offline audio event transcription.",
     )
@@ -189,8 +194,8 @@ class SemanticSearchConfig(FrigateBaseModel):
                 return v
         return v
 
-    model_size: str = Field(
-        default="small",
+    model_size: ModelSizeEnum = Field(
+        default=ModelSizeEnum.small,
         title="Model size",
         description="Select model size; 'small' runs on CPU and 'large' typically requires GPU.",
     )
@@ -253,8 +258,8 @@ class FaceRecognitionConfig(FrigateBaseModel):
         title="Enable face recognition",
         description="Enable or disable face recognition for all cameras; can be overridden per-camera.",
     )
-    model_size: str = Field(
-        default="small",
+    model_size: ModelSizeEnum = Field(
+        default=ModelSizeEnum.small,
         title="Model size",
         description="Model size to use for face embeddings (small/large); larger may require GPU.",
     )
@@ -335,8 +340,8 @@ class LicensePlateRecognitionConfig(FrigateBaseModel):
         title="Enable LPR",
         description="Enable or disable license plate recognition for all cameras; can be overridden per-camera.",
     )
-    model_size: str = Field(
-        default="small",
+    model_size: ModelSizeEnum = Field(
+        default=ModelSizeEnum.small,
         title="Model size",
         description="Model size used for text detection/recognition. Most users should use 'small'.",
     )

frigate/config/config.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import io
 import json
 import logging
 import os
@@ -25,7 +26,6 @@ from frigate.plus import PlusApi
 from frigate.util.builtin import (
     deep_merge,
     get_ffmpeg_arg_list,
-    load_labels,
 )
 from frigate.util.config import (
     CURRENT_CONFIG_VERSION,
@@ -80,17 +80,41 @@ logger = logging.getLogger(__name__)
 
 yaml = YAML()
 
+# Pydantic field default applied when an existing config omits `detectors:`.
+# Kept as cpu tflite for backwards compatibility with 0.17 configs.
+DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
+
+# Used by the openvino branch below and rendered into the new-config YAML
+# template so first-time setups default to openvino on CPU.
+DEFAULT_MODEL = {
+    "width": 300,
+    "height": 300,
+    "input_tensor": "nhwc",
+    "input_pixel_format": "bgr",
+    "path": "/openvino-model/ssdlite_mobilenet_v2.xml",
+    "labelmap_path": "/openvino-model/coco_91cl_bkgr.txt",
+}
+NEW_CONFIG_DETECTORS = {"ov": {"type": "openvino", "device": "CPU"}}
+DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}
+
+
+def _render_default_yaml(data: dict) -> str:
+    buf = io.StringIO()
+    _yaml_writer = YAML()
+    _yaml_writer.indent(mapping=2, sequence=4, offset=2)
+    _yaml_writer.dump(data, buf)
+    return buf.getvalue()
+
+
 DEFAULT_CONFIG = f"""
 mqtt:
   enabled: False
 
+{_render_default_yaml({"detectors": NEW_CONFIG_DETECTORS, "model": DEFAULT_MODEL})}
 cameras: {{}}  # No cameras defined, UI wizard should be used
 version: {CURRENT_CONFIG_VERSION}
 """
 
-DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
-DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}
-
 # stream info handler
 stream_info_retriever = StreamInfoRetriever()
 
@@ -302,6 +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 (
@@ -453,7 +518,7 @@ class FrigateConfig(FrigateBaseModel):
     cameras: Dict[str, CameraConfig] = Field(title="Cameras", description="Cameras")
     audio: AudioConfig = Field(
         default_factory=AudioConfig,
-        title="Audio events",
+        title="Audio detection",
         description="Settings for audio-based event detection for all cameras; can be overridden per-camera.",
     )
     birdseye: BirdseyeConfig = Field(
@@ -605,26 +670,29 @@ class FrigateConfig(FrigateBaseModel):
 
         # set default min_score for object attributes
         for attribute in self.model.all_attributes:
-            if not self.objects.filters.get(attribute):
+            existing = self.objects.filters.get(attribute)
+            if existing is None:
                 self.objects.filters[attribute] = FilterConfig(min_score=0.7)
-            elif self.objects.filters[attribute].min_score == 0.5:
-                self.objects.filters[attribute].min_score = 0.7
+            elif "min_score" not in existing.model_fields_set:
+                existing.min_score = 0.7
 
         # auto detect hwaccel args
         if self.ffmpeg.hwaccel_args == "auto":
             self.ffmpeg.hwaccel_args = auto_detect_hwaccel()
 
-        # Populate global audio filters for all audio labels
-        all_audio_labels = {
-            label
-            for label in load_labels("/audio-labelmap.txt", prefill=521).values()
-            if label
-        }
+        # 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:
             self.audio.filters = {}
 
-        for key in sorted(all_audio_labels - self.audio.filters.keys()):
+        for key in sorted(set(self.audio.listen) - self.audio.filters.keys()):
             self.audio.filters[key] = AudioFilterConfig()
 
         self.audio.filters = dict(sorted(self.audio.filters.items()))
@@ -679,6 +747,9 @@ class FrigateConfig(FrigateBaseModel):
                     model_config["path"] = "/cpu_model.tflite"
                 elif detector_config.type == "edgetpu":
                     model_config["path"] = "/edgetpu_model.tflite"
+                elif detector_config.type == "openvino":
+                    for default_key, default_value in DEFAULT_MODEL.items():
+                        model_config.setdefault(default_key, default_value)
 
             model = ModelConfig.model_validate(model_config)
             model.check_and_load_plus_model(self.plus_api, detector_config.type)
@@ -813,7 +884,9 @@ class FrigateConfig(FrigateBaseModel):
             if camera_config.audio.filters is None:
                 camera_config.audio.filters = {}
 
-            for key in sorted(all_audio_labels - camera_config.audio.filters.keys()):
+            for key in sorted(
+                set(camera_config.audio.listen) - camera_config.audio.filters.keys()
+            ):
                 camera_config.audio.filters[key] = AudioFilterConfig()
 
             camera_config.audio.filters = dict(
@@ -835,7 +908,9 @@ class FrigateConfig(FrigateBaseModel):
                     if mask_config:
                         coords = mask_config.coordinates
                         relative_coords = get_relative_coordinates(
-                            coords, camera_config.frame_shape
+                            coords,
+                            camera_config.frame_shape,
+                            camera_name=camera_config.name,
                         )
                         # Create a new ObjectMaskConfig with raw_coordinates set
                         processed_global_masks[mask_id] = ObjectMaskConfig(
@@ -925,6 +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/telemetry.py

@@ -25,8 +25,8 @@ class StatsConfig(FrigateBaseModel):
     )
     intel_gpu_device: Optional[str] = Field(
         default=None,
-        title="SR-IOV device",
-        description="Device identifier used when treating Intel GPUs as SR-IOV to fix GPU stats.",
+        title="Intel GPU device",
+        description="PCI bus address or DRM device path (e.g. /dev/dri/card1) used to pin Intel GPU stats to a specific device when multiple are present.",
     )
 
 

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/data_processing/common/license_plate/mixin.py

@@ -1073,10 +1073,6 @@ class LicensePlateProcessingMixin:
                     top_score = score
                     top_box = bbox
 
-                if score > top_score:
-                    top_score = score
-                    top_box = bbox
-
         # Return the top scoring bounding box if found
         if top_box is not None:
             # expand box by 5% to help with OCR
@@ -1092,9 +1088,6 @@ class LicensePlateProcessingMixin:
                 ]
             ).clip(0, [input.shape[1], input.shape[0]] * 2)
 
-            logger.debug(
-                f"{camera}: Found license plate. Bounding box: {expanded_box.astype(int)}"
-            )
             return tuple(int(x) for x in expanded_box)  # type: ignore[return-value]
         else:
             return None  # No detection above the threshold
@@ -1360,8 +1353,8 @@ class LicensePlateProcessingMixin:
                 )
 
                 # check that license plate is valid
-                # double the value because we've doubled the size of the car
-                if license_plate_area < self.config.cameras[camera].lpr.min_area * 2:
+                # quadruple the value because we've doubled both dimensions of the car
+                if license_plate_area < self.config.cameras[camera].lpr.min_area * 4:
                     logger.debug(f"{camera}: License plate is less than min_area")
                     return
 
@@ -1465,6 +1458,7 @@ class LicensePlateProcessingMixin:
                     license_plate_frame,
                 )
 
+        logger.debug(f"{camera}: Found license plate. Bounding box: {list(plate_box)}")
         logger.debug(f"{camera}: Running plate recognition for id: {id}.")
 
         # run detection, returns results sorted by confidence, best first

frigate/data_processing/post/object_descriptions.py

@@ -269,7 +269,9 @@ class ObjectDescriptionProcessor(PostProcessorApi):
 
         if event.has_snapshot and camera_config.objects.genai.use_snapshot:
             snapshot_image = self._read_and_crop_snapshot(event)
+
             if not snapshot_image:
+                self.cleanup_event(event_id)
                 return
 
         num_thumbnails = len(self.tracked_events.get(event_id, []))

frigate/data_processing/post/types.py

@@ -2,7 +2,7 @@ from typing import Annotated
 
 from pydantic import BaseModel, ConfigDict, Field, StringConstraints
 
-ObservationItem = Annotated[str, StringConstraints(min_length=20, max_length=160)]
+ObservationItem = Annotated[str, StringConstraints(min_length=20, max_length=200)]
 
 
 class ReviewMetadata(BaseModel):
@@ -11,33 +11,22 @@ class ReviewMetadata(BaseModel):
     observations: list[ObservationItem] = Field(
         ...,
         min_length=3,
-        max_length=15,
-        description=(
-            "Enumerate the significant observations across all frames, in "
-            "chronological order, BEFORE composing the scene narrative. "
-            "Include the very start of the activity — for example, a vehicle "
-            "entering the frame or pulling into the driveway — even if it "
-            "lasts only a few frames and the rest of the clip is dominated "
-            "by a longer activity. Include each arrival, departure, motion "
-            "event, object handled, and notable change in position or state. "
-            "Each item is a single concrete fact written as a complete "
-            "sentence. Do not summarize, interpret, or assign meaning here — "
-            "that belongs in the scene field."
-        ),
-    )
-    title: str = Field(
-        max_length=80,
-        description="Under 10 words. Name the apparent purpose or outcome of the activity together with the location involved. Do not narrate or list the sequence of actions step by step.",
+        max_length=8,
+        description="Enumerate the significant observations across all frames, in chronological order.",
     )
     scene: str = Field(
         min_length=150,
         max_length=600,
         description="A chronological narrative of what happens from start to finish, drawing directly from the items in observations.",
     )
+    title: str = Field(
+        max_length=80,
+        description="Title for the activity.",
+    )
     shortSummary: str = Field(
         min_length=70,
-        max_length=120,
-        description="A brief 2-sentence summary of the scene, suitable for notifications.",
+        max_length=140,
+        description="A brief summary for the activity.",
     )
     confidence: float = Field(
         ge=0.0,

frigate/data_processing/real_time/face.py

@@ -229,9 +229,10 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 logger.debug(f"No person box available for {id}")
                 return
 
-            rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
+            # YuNet (cv2.FaceDetectorYN) is trained on BGR
+            bgr = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
             left, top, right, bottom = person_box
-            person = rgb[top:bottom, left:right]
+            person = bgr[top:bottom, left:right]
             face_box = self.__detect_face(person, self.face_config.detection_threshold)
 
             if not face_box:
@@ -250,11 +251,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 )
                 return
 
-            try:
-                face_frame = cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)
-            except Exception as e:
-                logger.debug(f"Failed to convert face frame color for {id}: {e}")
-                return
         else:
             # don't run for object without attributes
             if not obj_data.get("current_attributes"):

frigate/debug_replay.py

@@ -1,10 +1,16 @@
-"""Debug replay camera management for replaying recordings with detection overlays."""
+"""Debug replay camera management for replaying recordings with detection overlays.
 
+The startup work (ffmpeg concat + camera config publish) lives in
+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
-import subprocess as sp
 import threading
+import time
 
 from ruamel.yaml import YAML
 
@@ -21,15 +27,33 @@ from frigate.const import (
     REPLAY_DIR,
     THUMB_DIR,
 )
-from frigate.models import Recordings
+from frigate.jobs.debug_replay import (
+    JOB_TYPE as DEBUG_REPLAY_JOB_TYPE,
+)
+from frigate.jobs.debug_replay import (
+    cancel_debug_replay_job,
+    wait_for_runner,
+)
+from frigate.jobs.export import JobStatePublisher
+from frigate.types import JobStatusTypesEnum
 from frigate.util.camera_cleanup import cleanup_camera_db, cleanup_camera_files
 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:
-    """Manages a single debug replay session."""
+    """Owns the lifecycle pointers for a single debug replay session.
+
+    A session exists from the moment mark_starting is called (synchronously,
+    inside the API handler) until clear_session runs (on success cleanup,
+    failure, or stop). The active property is the source of truth that the
+    status bar consumes — broader than the startup job, which only covers the
+    preparing_clip / starting_camera window.
+    """
 
     def __init__(self) -> None:
         self._lock = threading.Lock()
@@ -38,147 +62,73 @@ 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
     def active(self) -> bool:
-        """Whether a replay session is currently active."""
+        """True from mark_starting until clear_session."""
         return self.replay_camera_name is not None
 
-    def start(
+    def mark_starting(
         self,
         source_camera: str,
+        replay_camera_name: str,
         start_ts: float,
         end_ts: float,
-        frigate_config: FrigateConfig,
-        config_publisher: CameraConfigUpdatePublisher,
-    ) -> str:
-        """Start a debug replay session.
+    ) -> None:
+        """Synchronously claim the session before the job runner starts.
 
-        Args:
-            source_camera: Name of the source camera to replay
-            start_ts: Start timestamp
-            end_ts: End timestamp
-            frigate_config: Current Frigate configuration
-            config_publisher: Publisher for camera config updates
-
-        Returns:
-            The replay camera name
-
-        Raises:
-            ValueError: If a session is already active or parameters are invalid
-            RuntimeError: If clip generation fails
+        Called inside the API handler so the status bar sees active=True
+        immediately, before the worker thread does any ffmpeg work.
         """
         with self._lock:
-            return self._start_locked(
-                source_camera, start_ts, end_ts, frigate_config, config_publisher
-            )
+            self.replay_camera_name = replay_camera_name
+            self.source_camera = source_camera
+            self.start_ts = start_ts
+            self.end_ts = end_ts
+            self.clip_path = None
+            self.session_started_at = time.time()
 
-    def _start_locked(
+    def mark_session_ready(self, clip_path: str) -> None:
+        """Record the on-disk clip path after the camera has been published."""
+        with self._lock:
+            self.clip_path = clip_path
+
+    def clear_session(self) -> None:
+        """Reset session pointers without publishing camera removal.
+
+        Used by the job runner on failure paths. stop() does the camera
+        teardown plus this clear in one step.
+        """
+        with self._lock:
+            self._clear_locked()
+
+    def _clear_locked(self) -> None:
+        self.replay_camera_name = None
+        self.source_camera = None
+        self.clip_path = None
+        self.start_ts = None
+        self.end_ts = None
+        self.session_started_at = None
+
+    def publish_camera(
         self,
         source_camera: str,
-        start_ts: float,
-        end_ts: float,
+        replay_name: str,
+        clip_path: str,
         frigate_config: FrigateConfig,
         config_publisher: CameraConfigUpdatePublisher,
-    ) -> str:
-        if self.active:
-            raise ValueError("A replay session is already active")
+    ) -> None:
+        """Build the in-memory replay camera config and publish the add event.
 
-        if source_camera not in frigate_config.cameras:
-            raise ValueError(f"Camera '{source_camera}' not found")
-
-        if end_ts <= start_ts:
-            raise ValueError("End time must be after start time")
-
-        # Query recordings for the source camera in the time range
-        recordings = (
-            Recordings.select(
-                Recordings.path,
-                Recordings.start_time,
-                Recordings.end_time,
-            )
-            .where(
-                Recordings.start_time.between(start_ts, end_ts)
-                | Recordings.end_time.between(start_ts, end_ts)
-                | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
-            )
-            .where(Recordings.camera == source_camera)
-            .order_by(Recordings.start_time.asc())
-        )
-
-        if not recordings.count():
-            raise ValueError(
-                f"No recordings found for camera '{source_camera}' in the specified time range"
-            )
-
-        # Create replay directory
-        os.makedirs(REPLAY_DIR, exist_ok=True)
-
-        # Generate replay camera name
-        replay_name = f"{REPLAY_CAMERA_PREFIX}{source_camera}"
-
-        # Build concat file for ffmpeg
-        concat_file = os.path.join(REPLAY_DIR, f"{replay_name}_concat.txt")
-        clip_path = os.path.join(REPLAY_DIR, f"{replay_name}.mp4")
-
-        with open(concat_file, "w") as f:
-            for recording in recordings:
-                f.write(f"file '{recording.path}'\n")
-
-        # Concatenate recordings into a single clip with -c copy (fast)
-        ffmpeg_cmd = [
-            frigate_config.ffmpeg.ffmpeg_path,
-            "-hide_banner",
-            "-y",
-            "-f",
-            "concat",
-            "-safe",
-            "0",
-            "-i",
-            concat_file,
-            "-c",
-            "copy",
-            "-movflags",
-            "+faststart",
-            clip_path,
-        ]
-
-        logger.info(
-            "Generating replay clip for %s (%.1f - %.1f)",
-            source_camera,
-            start_ts,
-            end_ts,
-        )
-
-        try:
-            result = sp.run(
-                ffmpeg_cmd,
-                capture_output=True,
-                text=True,
-                timeout=120,
-            )
-            if result.returncode != 0:
-                logger.error("FFmpeg error: %s", result.stderr)
-                raise RuntimeError(
-                    f"Failed to generate replay clip: {result.stderr[-500:]}"
-                )
-        except sp.TimeoutExpired:
-            raise RuntimeError("Clip generation timed out")
-        finally:
-            # Clean up concat file
-            if os.path.exists(concat_file):
-                os.remove(concat_file)
-
-        if not os.path.exists(clip_path):
-            raise RuntimeError("Clip file was not created")
-
-        # Build camera config dict for the replay camera
+        Called by the job runner during the starting_camera phase.
+        """
         source_config = frigate_config.cameras[source_camera]
         camera_dict = self._build_camera_config_dict(
             source_config, replay_name, clip_path
         )
 
-        # Build an in-memory config with the replay camera added
         config_file = find_config_file()
         yaml_parser = YAML()
         with open(config_file, "r") as f:
@@ -191,75 +141,65 @@ class DebugReplayManager:
         try:
             new_config = FrigateConfig.parse_object(config_data)
         except Exception as e:
-            raise RuntimeError(f"Failed to validate replay camera config: {e}")
-
-        # Update the running config
+            raise RuntimeError(f"Failed to validate replay camera config: {e}") from e
         frigate_config.cameras[replay_name] = new_config.cameras[replay_name]
 
-        # Publish the add event
         config_publisher.publish_update(
             CameraConfigUpdateTopic(CameraConfigUpdateEnum.add, replay_name),
             new_config.cameras[replay_name],
         )
 
-        # Store session state
-        self.replay_camera_name = replay_name
-        self.source_camera = source_camera
-        self.clip_path = clip_path
-        self.start_ts = start_ts
-        self.end_ts = end_ts
-
-        logger.info("Debug replay started: %s -> %s", source_camera, replay_name)
-        return replay_name
-
     def stop(
         self,
         frigate_config: FrigateConfig,
         config_publisher: CameraConfigUpdatePublisher,
     ) -> None:
-        """Stop the active replay session and clean up all artifacts.
+        """Cancel any in-flight startup job and tear down the active session.
 
-        Args:
-            frigate_config: Current Frigate configuration
-            config_publisher: Publisher for camera config updates
+        Safe to call when no session is active (no-op with a warning).
         """
+        cancel_debug_replay_job()
+        wait_for_runner(timeout=2.0)
+
         with self._lock:
-            self._stop_locked(frigate_config, config_publisher)
+            if not self.active:
+                logger.warning("No active replay session to stop")
+                return
 
-    def _stop_locked(
-        self,
-        frigate_config: FrigateConfig,
-        config_publisher: CameraConfigUpdatePublisher,
-    ) -> None:
-        if not self.active:
-            logger.warning("No active replay session to stop")
-            return
+            replay_name = self.replay_camera_name
+            source_camera = self.source_camera
 
-        replay_name = self.replay_camera_name
+            # Only publish remove if the camera was actually added to the live
+            # config (i.e. the runner reached the starting_camera phase).
+            if replay_name is not None and replay_name in frigate_config.cameras:
+                config_publisher.publish_update(
+                    CameraConfigUpdateTopic(CameraConfigUpdateEnum.remove, replay_name),
+                    frigate_config.cameras[replay_name],
+                )
+                frigate_config.cameras.pop(replay_name, None)
 
-        # Publish remove event so subscribers stop and remove from their config
-        if replay_name in frigate_config.cameras:
-            config_publisher.publish_update(
-                CameraConfigUpdateTopic(CameraConfigUpdateEnum.remove, replay_name),
-                frigate_config.cameras[replay_name],
+            if replay_name is not None:
+                self._cleanup_db(replay_name)
+                self._cleanup_files(replay_name)
+
+            self._job_state_publisher.publish(
+                {
+                    "id": "stopped",
+                    "job_type": DEBUG_REPLAY_JOB_TYPE,
+                    "status": JobStatusTypesEnum.cancelled,
+                    "start_time": None,
+                    "end_time": time.time(),
+                    "error_message": None,
+                    "results": {
+                        "source_camera": source_camera,
+                        "replay_camera_name": replay_name,
+                    },
+                }
             )
-            # Do NOT pop here — let subscribers handle removal from the shared
-            # config dict when they process the ZMQ message to avoid race conditions
 
-        # Defensive DB cleanup
-        self._cleanup_db(replay_name)
+            self._clear_locked()
 
-        # Remove filesystem artifacts
-        self._cleanup_files(replay_name)
-
-        # Reset state
-        self.replay_camera_name = None
-        self.source_camera = None
-        self.clip_path = None
-        self.start_ts = None
-        self.end_ts = None
-
-        logger.info("Debug replay stopped and cleaned up: %s", replay_name)
+            logger.info("Debug replay stopped and cleaned up: %s", replay_name)
 
     def _build_camera_config_dict(
         self,
@@ -267,16 +207,7 @@ class DebugReplayManager:
         replay_name: str,
         clip_path: str,
     ) -> dict:
-        """Build a camera config dictionary for the replay camera.
-
-        Args:
-            source_config: Source camera's CameraConfig
-            replay_name: Name for the replay camera
-            clip_path: Path to the replay clip file
-
-        Returns:
-            Camera config as a dictionary
-        """
+        """Build a camera config dictionary for the replay camera."""
         # Extract detect config (exclude computed fields)
         detect_dict = source_config.detect.model_dump(
             exclude={"min_initialized", "max_disappeared", "enabled_in_config"}
@@ -311,10 +242,13 @@ class DebugReplayManager:
             zone_dump = zone_config.model_dump(
                 exclude={"contour", "color"}, exclude_defaults=True
             )
-            # Always include required fields
             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:
@@ -323,11 +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": {
@@ -352,8 +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:
@@ -412,3 +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

@@ -79,7 +79,11 @@ def is_openvino_gpu_npu_available() -> bool:
     available_devices = get_openvino_available_devices()
     # Check for GPU, NPU, or other acceleration devices (excluding CPU)
     acceleration_devices = ["GPU", "MYRIAD", "NPU", "GNA", "HDDL"]
-    return any(device in available_devices for device in acceleration_devices)
+    return any(
+        avail_dev == accel_dev or avail_dev.startswith(accel_dev + ".")
+        for avail_dev in available_devices
+        for accel_dev in acceleration_devices
+    )
 
 
 class BaseModelRunner(ABC):
@@ -132,7 +136,6 @@ class ONNXModelRunner(BaseModelRunner):
         return model_type in [
             EnrichmentModelTypeEnum.paddleocr.value,
             EnrichmentModelTypeEnum.jina_v2.value,
-            EnrichmentModelTypeEnum.arcface.value,
             ModelTypeEnum.rfdetr.value,
             ModelTypeEnum.dfine.value,
         ]
@@ -279,6 +282,13 @@ class OpenVINOModelRunner(BaseModelRunner):
             EnrichmentModelTypeEnum.arcface.value,
         ]
 
+    @staticmethod
+    def is_detection_model(model_type: str) -> bool:
+        # Import here to avoid circular imports
+        from frigate.detectors.detector_config import ModelTypeEnum
+
+        return model_type in [m.value for m in ModelTypeEnum]
+
     def __init__(self, model_path: str, device: str, model_type: str, **kwargs):
         self.model_path = model_path
         self.device = device
@@ -307,9 +317,15 @@ class OpenVINOModelRunner(BaseModelRunner):
         # Apply performance optimization
         self.ov_core.set_property(device, {"PERF_COUNT": "NO"})
 
-        if device in ["GPU", "AUTO"]:
+        if device in ["GPU", "AUTO", "NPU"]:
             self.ov_core.set_property(device, {"PERFORMANCE_HINT": "LATENCY"})
 
+        if device == "NPU" and OpenVINOModelRunner.is_detection_model(model_type):
+            try:
+                self.ov_core.set_property(device, {"NPU_TURBO": "YES"})
+            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

frigate/detectors/plugins/openvino.py

@@ -52,6 +52,12 @@ class OvDetector(DetectionApi):
         self.h = detector_config.model.height
         self.w = detector_config.model.width
 
+        logger.info(
+            "Loading OpenVINO model %s on device %s",
+            detector_config.model.path,
+            detector_config.device,
+        )
+
         self.runner = OpenVINOModelRunner(
             model_path=detector_config.model.path,
             device=detector_config.device,

frigate/embeddings/maintainer.py

@@ -60,7 +60,11 @@ from frigate.data_processing.real_time.license_plate import (
 )
 from frigate.data_processing.types import DataProcessorMetrics, PostProcessDataEnum
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
-from frigate.events.types import EventTypeEnum, RegenerateDescriptionEnum
+from frigate.events.types import (
+    EventStateEnum,
+    EventTypeEnum,
+    RegenerateDescriptionEnum,
+)
 from frigate.genai import GenAIClientManager
 from frigate.models import Event, Recordings, ReviewSegment, Trigger
 from frigate.types import TrackedObjectUpdateTypesEnum
@@ -94,10 +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/")
@@ -228,7 +239,7 @@ class EmbeddingMaintainer(threading.Thread):
                 )
             )
 
-        if self.config.audio_transcription.enabled and any(
+        if any(
             c.enabled_in_config and c.audio_transcription.enabled
             for c in self.config.cameras.values()
         ):
@@ -435,7 +446,7 @@ class EmbeddingMaintainer(threading.Thread):
         if update is None:
             return
 
-        source_type, _, camera, frame_name, data = update
+        source_type, event_type, camera, frame_name, data = update
 
         logger.debug(
             f"Received update - source_type: {source_type}, camera: {camera}, data label: {data.get('label') if data else 'None'}"
@@ -485,6 +496,12 @@ class EmbeddingMaintainer(threading.Thread):
 
         for processor in self.post_processors:
             if isinstance(processor, ObjectDescriptionProcessor):
+                # skip end events — _process_finalized handles them via event_end_subscriber.
+                # processing them here can re-create tracked_events entries after cleanup
+                # when the event_subscriber queue is backlogged behind event_end_subscriber.
+                if event_type == EventStateEnum.end:
+                    continue
+
                 processor.process_data(
                     {
                         "camera": camera,

frigate/events/audio.py

@@ -84,7 +84,6 @@ class AudioProcessor(FrigateProcess):
     def __init__(
         self,
         config: FrigateConfig,
-        cameras: list[CameraConfig],
         camera_metrics: DictProxy,
         stop_event: MpEvent,
     ):
@@ -93,16 +92,30 @@ class AudioProcessor(FrigateProcess):
         )
 
         self.camera_metrics = camera_metrics
-        self.cameras = cameras
         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: list[AudioEventMaintainer] = []
+        self.audio_threads: dict[str, AudioEventMaintainer] = {}
 
         threading.current_thread().name = "process:audio_manager"
 
-        if self.config.audio_transcription.enabled:
+        if any(
+            c.enabled_in_config and c.audio_transcription.enabled
+            for c in self.config.cameras.values()
+        ):
             self.transcription_model_runner: AudioTranscriptionModelRunner | None = (
                 AudioTranscriptionModelRunner(
                     self.config.audio_transcription.device or "AUTO",
@@ -112,32 +125,67 @@ class AudioProcessor(FrigateProcess):
         else:
             self.transcription_model_runner = None
 
-        if len(self.cameras) == 0:
-            return
+        config_subscriber = CameraConfigUpdateSubscriber(
+            self.config,
+            self.config.cameras,
+            [
+                CameraConfigUpdateEnum.add,
+                CameraConfigUpdateEnum.audio,
+                CameraConfigUpdateEnum.ffmpeg,
+                CameraConfigUpdateEnum.remove,
+            ],
+        )
 
-        for camera in self.cameras:
-            audio_thread = AudioEventMaintainer(
+        def spawn_if_needed(camera: CameraConfig) -> None:
+            name = camera.name
+            if name is None or name in self.audio_threads:
+                return
+            if not camera.enabled or not camera.audio.enabled:
+                return
+            # ffmpeg update may not have arrived yet; wait for next poll
+            if not any("audio" in i.roles for i in camera.ffmpeg.inputs):
+                return
+            thread = AudioEventMaintainer(
                 camera,
                 self.config,
                 self.camera_metrics,
                 self.transcription_model_runner,
                 self.stop_event,  # type: ignore[arg-type]
             )
-            audio_threads.append(audio_thread)
-            audio_thread.start()
+            self.audio_threads[name] = thread
+            thread.start()
+            self.logger.info(f"Audio maintainer started for {name}")
+
+        for camera in self.config.cameras.values():
+            spawn_if_needed(camera)
 
         self.logger.info(f"Audio processor started (pid: {self.pid})")
 
-        while not self.stop_event.wait():
-            pass
+        # poll for newly added/removed cameras or cameras flipped to
+        # audio.enabled at runtime
+        while not self.stop_event.wait(timeout=1.0):
+            updated_topics = config_subscriber.check_for_updates()
 
-        for thread in audio_threads:
+            # 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 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:
+        for thread in self.audio_threads.values():
             if thread.is_alive():
                 self.logger.warning(f"Thread {thread.name} is still alive")
 
@@ -159,6 +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))
@@ -184,7 +235,7 @@ class AudioEventMaintainer(threading.Thread):
         self.detection_publisher = DetectionPublisher(DetectionTypeEnum.audio.value)
 
         if (
-            self.config.audio_transcription.enabled
+            self.camera_config.audio_transcription.enabled
             and self.audio_transcription_model_runner is not None
         ):
             # init the transcription processor for this camera
@@ -208,7 +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)
@@ -327,11 +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/genai/__init__.py

@@ -1,21 +1,25 @@
 """Generative AI module for Frigate."""
 
-import datetime
 import importlib
 import json
 import logging
 import os
 import re
-from typing import Any, Callable, Optional
+from typing import Any, AsyncGenerator, Callable, Optional
 
 import numpy as np
-from playhouse.shortcuts import model_to_dict
 from pydantic import ValidationError
 
 from frigate.config import CameraConfig, GenAIConfig, GenAIProviderEnum
 from frigate.const import CLIPS_DIR
 from frigate.data_processing.post.types import ReviewMetadata
 from frigate.genai.manager import GenAIClientManager
+from frigate.genai.prompts import (
+    build_object_description_prompt,
+    build_review_description_prompt,
+    build_review_description_response_format,
+    build_review_summary_prompt,
+)
 from frigate.models import Event
 
 logger = logging.getLogger(__name__)
@@ -46,9 +50,15 @@ def register_genai_provider(key: GenAIProviderEnum) -> Callable:
 class GenAIClient:
     """Generative AI client for Frigate."""
 
-    def __init__(self, genai_config: GenAIConfig, timeout: int = 120) -> None:
+    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()
 
     def generate_review_description(
@@ -61,74 +71,14 @@ class GenAIClient:
         activity_context_prompt: str,
     ) -> ReviewMetadata | None:
         """Generate a description for the review item activity."""
+        context_prompt = build_review_description_prompt(
+            review_data,
+            thumbnails,
+            concerns,
+            preferred_language,
+            activity_context_prompt,
+        )
 
-        def get_concern_prompt() -> str:
-            if concerns:
-                concern_list = "\n    - ".join(concerns)
-                return f"""- `other_concerns` (list of strings): Include a list of any of the following concerns that are occurring:
-    - {concern_list}"""
-            else:
-                return ""
-
-        def get_language_prompt() -> str:
-            if preferred_language:
-                return f"Provide your answer in {preferred_language}"
-            else:
-                return ""
-
-        def get_objects_list() -> str:
-            if review_data["unified_objects"]:
-                return "\n- " + "\n- ".join(review_data["unified_objects"])
-            else:
-                return "\n- (No objects detected)"
-
-        context_prompt = f"""
-Your task is to analyze a sequence of images taken in chronological order from a security camera.
-
-## Normal Activity Patterns for This Property
-
-{activity_context_prompt}
-
-## Task Instructions
-
-Describe the scene based on observable actions and movements, evaluate the activity against the Activity Indicators above, and assign a potential_threat_level (0, 1, or 2) by applying the threat level indicators consistently.
-
-## 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.
-- 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.
-- **Use the actual timestamp provided in "Activity started at"** below for time of day context—do not infer time from image brightness or darkness. Unusual hours (late night/early morning) should increase suspicion when the observable behavior itself appears questionable. However, recognize that some legitimate activities can occur at any hour.
-- **Consider duration as a primary factor**: Apply the duration thresholds defined in the activity patterns above. Brief sequences during normal hours with apparent purpose typically indicate normal activity unless explicit suspicious actions are visible.
-- **Weigh all evidence holistically**: Match the activity against the normal and suspicious patterns defined above, then evaluate based on the complete context (zone, objects, time, actions, duration). Apply the threat level indicators consistently. Use your judgment for edge cases.
-
-## Response Field Guidelines
-
-Respond with a JSON object matching the provided schema. Field-specific guidance:
-- `scene`: Describe how the sequence begins, then the progression of events — all significant movements and actions in order. For example, if a vehicle arrives and then a person exits, describe both sequentially. For named subjects (those with a `←` separator in "Objects in Scene"), always use their name — do not replace them with generic terms. For unnamed objects (e.g., "person", "car"), refer to them naturally with articles (e.g., "a person", "the car"). Your description should align with and support the threat level you assign.
-- `title`: Characterize **what took place and where** — interpret the overall purpose or outcome, do not simply compress the scene description into fewer words. Include the relevant location (zone, area, or entry point). For named subjects, always use their name. For unnamed objects, refer to them naturally with articles. No editorial qualifiers like "routine" or "suspicious."
-- `potential_threat_level`: Must be consistent with your scene description and the activity patterns above.
-{get_concern_prompt()}
-
-## Sequence Details
-
-- Camera: {review_data["camera"]}
-- Total frames: {len(thumbnails)} (Frame 1 = earliest, Frame {len(thumbnails)} = latest)
-- Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds
-- Zones involved: {", ".join(review_data["zones"]) if review_data["zones"] else "None"}
-
-## Objects in Scene
-
-Each line represents a detection state, not necessarily unique individuals. The `←` symbol separates a recognized subject's name from their object type — use only the name (before the `←`) in your response, not the type after it. The same subject may appear across multiple lines if detected multiple times.
-
-**Note: Unidentified objects (without names) are NOT indicators of suspicious activity—they simply mean the system hasn't identified that object.**
-{get_objects_list()}
-
-{get_language_prompt()}
-"""
         logger.debug(
             f"Sending {len(thumbnails)} images to create review description on {review_data['camera']}"
         )
@@ -142,25 +92,7 @@ Each line represents a detection state, not necessarily unique individuals. The
             ) as f:
                 f.write(context_prompt)
 
-        # Build JSON schema for structured output from ReviewMetadata model
-        schema = ReviewMetadata.model_json_schema()
-        schema.get("properties", {}).pop("time", None)
-
-        if "time" in schema.get("required", []):
-            schema["required"].remove("time")
-        if not concerns:
-            schema.get("properties", {}).pop("other_concerns", None)
-            if "other_concerns" in schema.get("required", []):
-                schema["required"].remove("other_concerns")
-
-        response_format = {
-            "type": "json_schema",
-            "json_schema": {
-                "name": "review_metadata",
-                "strict": True,
-                "schema": schema,
-            },
-        }
+        response_format = build_review_description_response_format(concerns)
 
         response = self._send(context_prompt, thumbnails, response_format)
 
@@ -239,61 +171,9 @@ Each line represents a detection state, not necessarily unique individuals. The
         debug_save: bool,
     ) -> str | None:
         """Generate a summary of review item descriptions over a period of time."""
-        time_range = f"{datetime.datetime.fromtimestamp(start_ts).strftime('%B %d, %Y at %I:%M %p')} to {datetime.datetime.fromtimestamp(end_ts).strftime('%B %d, %Y at %I:%M %p')}"
-        timeline_summary_prompt = f"""
-You are a security officer writing a concise security report.
-
-Time range: {time_range}
-
-Input format: Each event is a JSON object with:
-- "title", "scene", "confidence", "potential_threat_level" (0-2), "other_concerns", "camera", "time", "start_time", "end_time"
-- "context": array of related events from other cameras that occurred during overlapping time periods
-
-**Note: Use the "scene" field for event descriptions in the report. Ignore any "shortSummary" field if present.**
-
-Report Structure - Use this EXACT format:
-
-# Security Summary - {time_range}
-
-## Overview
-[Write 1-2 sentences summarizing the overall activity pattern during this period.]
-
----
-
-## Timeline
-
-[Group events by time periods (e.g., "Morning (6:00 AM - 12:00 PM)", "Afternoon (12:00 PM - 5:00 PM)", "Evening (5:00 PM - 9:00 PM)", "Night (9:00 PM - 6:00 AM)"). Use appropriate time blocks based on when events occurred.]
-
-### [Time Block Name]
-
-**HH:MM AM/PM** | [Camera Name] | [Threat Level Indicator]
-- [Event title]: [Clear description incorporating contextual information from the "context" array]
-- Context: [If context array has items, mention them here, e.g., "Delivery truck present on Front Driveway Cam (HH:MM AM/PM)"]
-- Assessment: [Brief assessment incorporating context - if context explains the event, note it here]
-
-[Repeat for each event in chronological order within the time block]
-
----
-
-## Summary
-[One sentence summarizing the period. If all events are normal/explained: "Routine activity observed." If review needed: "Some activity requires review but no security concerns." If security concerns: "Security concerns requiring immediate attention."]
-
-Guidelines:
-- List ALL events in chronological order, grouped by time blocks
-- Threat level indicators: ✓ Normal, ⚠️ Needs review, 🔴 Security concern
-- Integrate contextual information naturally - use the "context" array to enrich each event's description
-- If context explains the event (e.g., delivery truck explains person at door), describe it accordingly (e.g., "delivery person" not "unidentified person")
-- Be concise but informative - focus on what happened and what it means
-- If contextual information makes an event clearly normal, reflect that in your assessment
-- Only create time blocks that have events - don't create empty sections
-"""
-
-        timeline_summary_prompt += "\n\nEvents:\n"
-        for event in events:
-            timeline_summary_prompt += f"\n{event}\n"
-
-        if preferred_language:
-            timeline_summary_prompt += f"\nProvide your answer in {preferred_language}"
+        timeline_summary_prompt = build_review_summary_prompt(
+            start_ts, end_ts, events, preferred_language
+        )
 
         if debug_save:
             with open(
@@ -325,10 +205,7 @@ Guidelines:
     ) -> Optional[str]:
         """Generate a description for the frame."""
         try:
-            prompt = camera_config.objects.genai.object_prompts.get(
-                str(event.label),
-                camera_config.objects.genai.prompt,
-            ).format(**model_to_dict(event))
+            prompt = build_object_description_prompt(camera_config, event)
         except KeyError as e:
             logger.error(f"Invalid key in GenAI prompt: {e}")
             return None
@@ -345,8 +222,15 @@ Guidelines:
         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
@@ -358,6 +242,11 @@ Guidelines:
         """
         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.
 
@@ -401,6 +290,7 @@ Guidelines:
         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.
@@ -424,11 +314,17 @@ Guidelines:
                 - '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:
             - 'content': Optional[str] - The text response from the LLM, None if tool calls
+            - 'reasoning': Optional[str] - The separated reasoning/thinking trace
+              if the model emitted one (e.g. via OpenAI-compatible
+              `reasoning_content`). None when the model does not surface a
+              trace or the provider does not parse it.
             - 'tool_calls': Optional[List[Dict]] - List of tool calls if LLM wants to call tools.
               Each tool call dict has:
                 - 'id': str - Unique identifier for this tool call
@@ -440,6 +336,14 @@ Guidelines:
                 - 'length': Hit token limit
                 - 'error': An error occurred
 
+        Streaming counterpart `chat_with_tools_stream` 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 above
+
         Raises:
             NotImplementedError: If the provider doesn't implement this method.
         """
@@ -450,14 +354,50 @@ Guidelines:
         )
         return {
             "content": None,
+            "reasoning": None,
             "tool_calls": None,
             "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:
-    package_dir = os.path.dirname(__file__)
-    for filename in os.listdir(package_dir):
+    plugins_dir = os.path.join(os.path.dirname(__file__), "plugins")
+    for filename in os.listdir(plugins_dir):
         if filename.endswith(".py") and filename != "__init__.py":
-            module_name = f"frigate.genai.{filename[:-3]}"
+            module_name = f"frigate.genai.plugins.{filename[:-3]}"
             importlib.import_module(module_name)

frigate/genai/azure-openai.py

@@ -1,305 +0,0 @@
-"""Azure OpenAI Provider for Frigate AI."""
-
-import base64
-import json
-import logging
-from typing import Any, AsyncGenerator, Optional
-from urllib.parse import parse_qs, urlparse
-
-from openai import AzureOpenAI
-
-from frigate.config import GenAIProviderEnum
-from frigate.genai import GenAIClient, register_genai_provider
-
-logger = logging.getLogger(__name__)
-
-
-@register_genai_provider(GenAIProviderEnum.azure_openai)
-class OpenAIClient(GenAIClient):
-    """Generative AI client for Frigate using Azure OpenAI."""
-
-    provider: AzureOpenAI
-
-    def _init_provider(self) -> AzureOpenAI | None:
-        """Initialize the client."""
-        try:
-            parsed_url = urlparse(self.genai_config.base_url or "")
-            query_params = parse_qs(parsed_url.query)
-            api_version = query_params.get("api-version", [None])[0]
-            azure_endpoint = f"{parsed_url.scheme}://{parsed_url.netloc}/"
-
-            if not api_version:
-                logger.warning("Azure OpenAI url is missing API version.")
-                return None
-
-        except Exception as e:
-            logger.warning("Error parsing Azure OpenAI url: %s", str(e))
-            return None
-
-        return AzureOpenAI(
-            api_key=self.genai_config.api_key,
-            api_version=api_version,
-            azure_endpoint=azure_endpoint,
-        )
-
-    def _send(
-        self,
-        prompt: str,
-        images: list[bytes],
-        response_format: Optional[dict] = None,
-    ) -> Optional[str]:
-        """Submit a request to Azure OpenAI."""
-        encoded_images = [base64.b64encode(image).decode("utf-8") for image in images]
-        try:
-            request_params = {
-                "model": self.genai_config.model,
-                "messages": [
-                    {
-                        "role": "user",
-                        "content": [{"type": "text", "text": prompt}]
-                        + [
-                            {
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/jpeg;base64,{image}",
-                                    "detail": "low",
-                                },
-                            }
-                            for image in encoded_images
-                        ],
-                    },
-                ],
-                "timeout": self.timeout,
-                **self.genai_config.runtime_options,
-            }
-            if response_format:
-                request_params["response_format"] = response_format
-            result = self.provider.chat.completions.create(**request_params)
-        except Exception as e:
-            logger.warning("Azure OpenAI returned an error: %s", str(e))
-            return None
-        if len(result.choices) > 0:
-            return str(result.choices[0].message.content.strip())
-        return None
-
-    def list_models(self) -> list[str]:
-        """Return available model IDs from Azure OpenAI."""
-        try:
-            return sorted(m.id for m in self.provider.models.list().data)
-        except Exception as e:
-            logger.warning("Failed to list Azure OpenAI models: %s", e)
-            return []
-
-    def get_context_size(self) -> int:
-        """Get the context window size for Azure OpenAI."""
-        return 128000
-
-    def chat_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: Optional[list[dict[str, Any]]] = None,
-        tool_choice: Optional[str] = "auto",
-    ) -> dict[str, Any]:
-        try:
-            openai_tool_choice = None
-            if tool_choice:
-                if tool_choice == "none":
-                    openai_tool_choice = "none"
-                elif tool_choice == "auto":
-                    openai_tool_choice = "auto"
-                elif tool_choice == "required":
-                    openai_tool_choice = "required"
-
-            request_params = {
-                "model": self.genai_config.model,
-                "messages": messages,
-                "timeout": self.timeout,
-            }
-
-            if tools:
-                request_params["tools"] = tools
-                if openai_tool_choice is not None:
-                    request_params["tool_choice"] = openai_tool_choice
-
-            result = self.provider.chat.completions.create(**request_params)  # type: ignore[call-overload]
-
-            if (
-                result is None
-                or not hasattr(result, "choices")
-                or len(result.choices) == 0
-            ):
-                return {
-                    "content": None,
-                    "tool_calls": None,
-                    "finish_reason": "error",
-                }
-
-            choice = result.choices[0]
-            message = choice.message
-
-            content = message.content.strip() if message.content else None
-
-            tool_calls = None
-            if message.tool_calls:
-                tool_calls = []
-                for tool_call in message.tool_calls:
-                    try:
-                        arguments = json.loads(tool_call.function.arguments)
-                    except (json.JSONDecodeError, AttributeError) as e:
-                        logger.warning(
-                            f"Failed to parse tool call arguments: {e}, "
-                            f"tool: {tool_call.function.name if hasattr(tool_call.function, 'name') else 'unknown'}"
-                        )
-                        arguments = {}
-
-                    tool_calls.append(
-                        {
-                            "id": tool_call.id if hasattr(tool_call, "id") else "",
-                            "name": tool_call.function.name
-                            if hasattr(tool_call.function, "name")
-                            else "",
-                            "arguments": arguments,
-                        }
-                    )
-
-            finish_reason = "error"
-            if hasattr(choice, "finish_reason") and choice.finish_reason:
-                finish_reason = choice.finish_reason
-            elif tool_calls:
-                finish_reason = "tool_calls"
-            elif content:
-                finish_reason = "stop"
-
-            return {
-                "content": content,
-                "tool_calls": tool_calls,
-                "finish_reason": finish_reason,
-            }
-
-        except Exception as e:
-            logger.warning("Azure OpenAI returned an error: %s", str(e))
-            return {
-                "content": None,
-                "tool_calls": None,
-                "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",
-    ) -> AsyncGenerator[tuple[str, Any], None]:
-        """
-        Stream chat with tools; yields content deltas then final message.
-
-        Implements streaming function calling/tool usage for Azure OpenAI models.
-        """
-        try:
-            openai_tool_choice = None
-            if tool_choice:
-                if tool_choice == "none":
-                    openai_tool_choice = "none"
-                elif tool_choice == "auto":
-                    openai_tool_choice = "auto"
-                elif tool_choice == "required":
-                    openai_tool_choice = "required"
-
-            request_params = {
-                "model": self.genai_config.model,
-                "messages": messages,
-                "timeout": self.timeout,
-                "stream": True,
-            }
-
-            if tools:
-                request_params["tools"] = tools
-                if openai_tool_choice is not None:
-                    request_params["tool_choice"] = openai_tool_choice
-
-            # Use streaming API
-            content_parts: list[str] = []
-            tool_calls_by_index: dict[int, dict[str, Any]] = {}
-            finish_reason = "stop"
-
-            stream = self.provider.chat.completions.create(**request_params)  # type: ignore[call-overload]
-
-            for chunk in stream:
-                if not chunk or not chunk.choices:
-                    continue
-
-                choice = chunk.choices[0]
-                delta = choice.delta
-
-                # Check for finish reason
-                if choice.finish_reason:
-                    finish_reason = choice.finish_reason
-
-                # Extract content deltas
-                if delta.content:
-                    content_parts.append(delta.content)
-                    yield ("content_delta", delta.content)
-
-                # Extract tool calls
-                if delta.tool_calls:
-                    for tc in delta.tool_calls:
-                        idx = tc.index
-                        fn = tc.function
-
-                        if idx not in tool_calls_by_index:
-                            tool_calls_by_index[idx] = {
-                                "id": tc.id or "",
-                                "name": fn.name if fn and fn.name else "",
-                                "arguments": "",
-                            }
-
-                        t = tool_calls_by_index[idx]
-                        if tc.id:
-                            t["id"] = tc.id
-                        if fn and fn.name:
-                            t["name"] = fn.name
-                        if fn and fn.arguments:
-                            t["arguments"] += fn.arguments
-
-            # Build final message
-            full_content = "".join(content_parts).strip() or None
-
-            # Convert tool calls to list format
-            tool_calls_list = None
-            if tool_calls_by_index:
-                tool_calls_list = []
-                for tc in tool_calls_by_index.values():
-                    try:
-                        # Parse accumulated arguments as JSON
-                        parsed_args = json.loads(tc["arguments"])
-                    except (json.JSONDecodeError, Exception):
-                        parsed_args = tc["arguments"]
-
-                    tool_calls_list.append(
-                        {
-                            "id": tc["id"],
-                            "name": tc["name"],
-                            "arguments": parsed_args,
-                        }
-                    )
-                finish_reason = "tool_calls"
-
-            yield (
-                "message",
-                {
-                    "content": full_content,
-                    "tool_calls": tool_calls_list,
-                    "finish_reason": finish_reason,
-                },
-            )
-
-        except Exception as e:
-            logger.warning("Azure OpenAI streaming returned an error: %s", str(e))
-            yield (
-                "message",
-                {
-                    "content": None,
-                    "tool_calls": None,
-                    "finish_reason": "error",
-                },
-            )

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
@@ -108,11 +108,16 @@ 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/__init__.py

@@ -0,0 +1 @@
+"""GenAI provider plugins."""

frigate/genai/plugins/azure-openai.py

@@ -0,0 +1,53 @@
+"""Azure OpenAI Provider for Frigate AI.
+
+Azure OpenAI exposes the same chat completions API as OpenAI once the
+client is constructed, so this provider inherits all transport, streaming,
+reasoning, and tool-calling logic from :class:`OpenAIClient` and only
+overrides what is genuinely Azure-specific:
+
+- Client construction: parses ``api-version`` out of the configured
+  ``base_url`` query string and instantiates :class:`openai.AzureOpenAI`
+  with ``azure_endpoint`` instead of ``base_url``. Raises if the URL is
+  malformed; :class:`GenAIClientManager` catches the exception and
+  disables the provider.
+- Context size: Azure does not expose a per-model ``max_model_len`` field
+  reliably, so we keep the historical 128K default rather than the
+  model-name heuristic used by OpenAI.
+"""
+
+import logging
+from urllib.parse import parse_qs, urlparse
+
+from openai import AzureOpenAI
+
+from frigate.config import GenAIProviderEnum
+from frigate.genai import register_genai_provider
+from frigate.genai.plugins.openai import OpenAIClient
+
+logger = logging.getLogger(__name__)
+
+
+@register_genai_provider(GenAIProviderEnum.azure_openai)
+class AzureOpenAIClient(OpenAIClient):
+    """Generative AI client for Frigate using Azure OpenAI."""
+
+    def _init_provider(self) -> AzureOpenAI:
+        """Initialize the AzureOpenAI client from the configured base_url."""
+        parsed_url = urlparse(self.genai_config.base_url or "")
+        query_params = parse_qs(parsed_url.query)
+        api_version = query_params.get("api-version", [None])[0]
+
+        if not api_version:
+            raise ValueError("Azure OpenAI base_url is missing api-version.")
+
+        azure_endpoint = f"{parsed_url.scheme}://{parsed_url.netloc}/"
+
+        return AzureOpenAI(
+            api_key=self.genai_config.api_key,
+            api_version=api_version,
+            azure_endpoint=azure_endpoint,
+        )
+
+    def get_context_size(self) -> int:
+        """Azure does not reliably surface per-model context size; use 128K."""
+        return 128000