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SentrySearch

github.com

WindowsmacOSLinuxSelf-hosted
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Spec score 7.2 #20 of 24 in AI Video Search Tools

What it is

SentrySearch searches video footage by text or reference image and can return trimmed matching clips. It splits videos into overlapping chunks, embeds footage and queries into a shared vector space, and stores video vectors in a local ChromaDB database. Search options include text, image, anomaly highlights, and optional reranking of candidate clips. The project documents embedding backends including Gemini, Alibaba DashScope Qwen Cloud, LiteLLM, local Qwen3-VL, and MLX for Apple Silicon. Its local backend runs on the user's machine without an API key and is described as private and offline-capable. With Qwen Cloud, local video chunks are uploaded by the official SDK to DashScope-managed temporary object storage for processing. The scanner recursively finds MP4 and MOV files, including footage not from Tesla Sentry Mode. An optional Tesla overlay can use supported footage metadata for speed, GPS, and time information. The project requires Python 3.11 or later and FFmpeg or imageio-ffmpeg. It is open source under Apache-2.0 and supports Linux, macOS, Windows, and self-hosting.

Spec sheet

Purpose
SentrySearch performs semantic search over video footage and returns trimmed clips.Read 8 Oct 2026 · github.com
Integrations
SentrySearch can hand search results to the maker's SentryMerge tool and saved clips to SentryBlur.Read 8 Oct 2026 · github.com
Privacy
The README describes the local backend as private and says it runs entirely on the user's machine.Read 8 Oct 2026 · github.com
License
The GitHub repository identifies the project as Apache-2.0 licensed.Read 8 Oct 2026 · github.com
Maker
The maintainer's GitHub profile names Soham Rajadhyaksha and lists Fremont, California.Read 8 Oct 2026 · github.com
Cloud data handling
For the Qwen Cloud backend, local video chunk files are uploaded to DashScope-managed temporary object storage before API processing.Read 8 Oct 2026 · github.com
Compatibility
The directory scanner recursively finds MP4 and MOV footage, including footage not recorded in Tesla Sentry Mode.Read 8 Oct 2026 · github.com
Embedding backends
It supports Gemini Embedding, Alibaba DashScope Qwen Cloud, LiteLLM gateways, and local Qwen3-VL models.Read 8 Oct 2026 · github.com
Highlights
The highlights command ranks anomalous clips in an index and can trim them automatically.Read 8 Oct 2026 · github.com
How it works
It embeds video chunks and text or image queries into a shared vector space, stores video vectors in a local ChromaDB database, and matches queries against them.Read 7 Oct 2026 · github.com
Limits
Still-frame detection is heuristic, and search quality can be affected when an event crosses chunk boundaries.Read 8 Oct 2026 · github.com
Local processing
The local backend runs without an API key and processes footage on the user's machine.Read 8 Oct 2026 · github.com
Privacy option
The local backend runs on the user's machine without an API key, which the README describes as private and offline-capable.Read 7 Oct 2026 · github.com
Requirements
The project requires Python 3.11 or later and FFmpeg, with bundled FFmpeg available through imageio-ffmpeg for the default setup.Read 8 Oct 2026 · github.com
Search methods
Users can search with text queries or reference images.Read 8 Oct 2026 · github.com
Search modes
It supports text search, image search, anomaly highlights, and optional reranking of candidate clips.Read 7 Oct 2026 · github.com
Supported footage
The directory scanner recursively finds MP4 and MOV files, including footage that is not from Tesla Sentry Mode.Read 7 Oct 2026 · github.com
Tesla overlay
An optional overlay can display Tesla dashcam speed, date, time, city, and road name when supported metadata is available.Read 8 Oct 2026 · github.com
Tesla support
An optional overlay extracts speed, GPS, and time metadata from supported Tesla driving footage and can add location labels through optional OpenStreetMap reverse geocoding.Read 7 Oct 2026 · github.com
Usage costs
The README estimates Gemini indexing at about $2.84 per hour of footage with its default settings and says local-backend calls use no API quota.Read 7 Oct 2026 · github.com
Video handling
It splits videos into overlapping chunks, stores embeddings in a local ChromaDB database, and can trim matching clips.Read 8 Oct 2026 · github.com

Strong points and limits

Strong points

  • Searches by text and reference images.
  • Can rank anomalous clips and trim them.
  • Local backend runs without an API key.
  • Scans MP4 and MOV files recursively.
  • Apache-2.0 license.

Limits

  • Requires Python 3.11 or later and FFmpeg or imageio-ffmpeg.
  • Local inference requires CUDA or Apple Metal.
  • Still-frame detection can miss subtle motion.
  • Events across chunk boundaries may not match perfectly.

Who it suits

SentrySearch may suit people who need to find moments in video using text or images, including users working with Tesla footage. Its local backend is relevant to those seeking machine-side processing without an API key.

Verdict

SentrySearch offers several search backends and a local processing option, with clear runtime requirements. Account for its motion-detection and chunk-boundary limits when searching for brief or subtle events.

Where Specifiction places it

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