VidXP

Search video by what was said, what appeared on screen, and recurring faces.

A local-first video search engine for people, applications, and AI agents.

Dialogue search · Scene search · Action search · Actor grouping

Windows · Apple Silicon macOS · Linux

Find the moment, not the timestamp

VidXP makes one video—or an entire collection—searchable by meaning:

• Dialogue search: type what you remember someone saying and jump to the matching moments.
• Scene search: describe what appeared on screen and find the closest visual matches.
• Action search: describe something that happens over several seconds.
• Actor matching: find recurring faces within a video and export a highlighted video for a selected group.

Use it to search years of family videos, add video search to an editing workflow, or let an AI agent answer questions using evidence from your own video library. Your videos can stay on your machine.

Start here

Choose the setup that fits how you want to use VidXP.

1. Desktop app

Download the installer for Windows, Apple Silicon macOS, or Linux from GitHub Releases.

Connect an existing VidXP installation or let the desktop app manage an isolated runtime for you. See the Desktop installation instructions for supported setup options.

2. CLI and local AI assistants

For commands, scripts, and local AI assistants, install uv, then run:

# Install the CPU edition
uv tool install --python 3.14 --torch-backend cpu "vidxp[local-worker,mcp]"

# Check FFmpeg, download models, and verify the installation
vidxp init
vidxp prepare
vidxp doctor

# Print the settings for a local MCP client
vidxp mcp-config

Add the browser interface with:

uv tool install --python 3.14 --torch-backend cpu \
  "vidxp[local-worker,mcp,frontend]"
vidxp ui

See the installation guide for client-specific MCP configuration, the HTTP API, and remote server setup.

3. Docker for a server

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

For a long-lived server, pin a published version instead of latest. For a Coolify deployment, use the published -control and -worker images with compose.coolify.yaml—no repository build is required. See the Coolify guide for the complete setup.

What you can do today

• 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.

A first search

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.

Run vidxp --help or vidxp <command/> --help for the full command reference.

For applications and AI agents

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.

Codex plugin and skills

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.

Paste this into Codex:

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 its Set up in Codex button.

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.

• Python, HTTP, and MCP installation
• Local HTTP API and MCP server
• ChatGPT and Codex plugin integration
• Optional capability packages
• Coolify server setup

Downloads and storage

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 the vidxp-data volume shown above.

Product roadmap

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.

Help and project links

• Installation and troubleshooting
• Desktop development
• Coolify deployment
• Changelog
• Issue tracker
• MIT license

Contributing

Contributions are welcome. Read the contribution guide before opening a pull request.

Credits

VidXP began as a student research project by:

• Abdullah Mansoor
• Muhammad Haroon
• Sarah Jawaid
• Talha Ahmed

The research was conducted with Dr Shahab Tahzeeb at NED University of Engineering and Technology and Saad Bazaz at Grayhat.

VidXP is now built by Grayhat and maintained by community contributors.

Email: info@grayhat.studio /info@grayhat.studio