VidXP

Run the published all-in-one image on a home server or another single machine:

# Download the search models into the persistent volume docker run --rm -it \ -v vidxp-data:/var/lib/vidxp \ ghcr.io/grayhatdevelopers/vidxp:latest \ vidxp prepare # Start the browser interface docker run --rm --init \ -p 8501:8501 \ -v vidxp-data:/var/lib/vidxp \ ghcr.io/grayhatdevelopers/vidxp:latest

- Build searchable libraries from individual videos or whole collections.
- Find dialogue by meaning and visual moments by describing the scene.
- Ask grounded questions and inspect the supporting boards, frames, or clips.
- Group recurring faces and render highlighted actor overlays.
- Keep personal, client, or project libraries separate.
- Use VidXP through the desktop app, browser, CLI, MCP, or HTTP API.

The browser app guides you through importing and indexing. The same flow from the command line is:

# Add a video vidxp media import samplevideo.mp4 --json # Index the returned media ID vidxp index create <media-id> # Find a visual moment vidxp search scene "a yellow taxi on a city street" # Find an action or event vidxp search videoprism "a person opens a door and walks outside" # Find something that was said vidxp search dialogue "the bread just came out of the oven"

Results include the source video, timestamps, match score, and the evidence used to find the moment. Add--media-id <media-id>to search only one video.

Runvidxp --helporvidxp <command> --helpfor the full command reference.

Use the Python package to add selected VidXP capabilities directly to an application, or use the HTTP API when VidXP runs as a service.

MCP lets AI clients add and index videos, search a library, ask grounded questions, and return inspectable evidence such as boards, frames, and clips. A local client can start VidXP as a program on the same computer. A hosted client connects to a deployed VidXP server.

VidXP is distributed as a Codex plugin through a Git marketplace hosted in this GitHub repository. It includes three reusable workflows:

- install Desktop or the CLI and connect Codex;
- ingest and index videos; and
- find moments and return inspectable evidence.

Add https://github.com/grayhatdevelopers/vidxp as a Git plugin marketplace, install the VidXP plugin, then use its $vidxp-install skill to set up VidXP on this computer.

VidXP Desktop can perform the same setup from itsSet up in Codexbutton.

Compatible AI clients can show an interactive upload and evidence-review view. Clients without that interface still receive the same workflow results through ordinary MCP tools.

First setup downloads only the models needed for the capabilities you select. VidXP shows the download size and destination before it starts.

The Desktop-managed Python runtime and its selected dependencies can use approximately 3 GiB.

A full local Desktop setup with every search capability uses approximately 8.1 GiB. Leave additional temporary space during installation and for indexes, source videos, and exported results.

By default, the CLI and desktop app share the same VidXP data directory:

Docker keeps the same data in thevidxp-datavolume shown above.

The next product improvements are focused on:

- labeling actor groups and matching the same person across different videos;
- more reliable face tracking across angle, lighting, motion, and occlusion;
- connecting visible people with the dialogue they are speaking;
- better search ranking, time ranges, and natural-language questions across a whole library;
- richer previews, timelines, filters, saved searches, and result playback;
- easier organization for large personal and project video collections;
- faster indexing and supported GPU acceleration; and
- smoother desktop updates, repair, and model management.

VidXP is in beta. Feedback about search quality, actor workflows, and real video-library use cases is especially useful.

VidXP began as a student research project by:

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