Enables LLM agents to process local videos into timestamped, citable text documents and then query them through tools for listing videos, retrieving transcripts, and fetching specific segments, all fully offline.
Turns a YouTube video or allowlisted local video into a timestamped transcript, chronological timeline, and retrievable image resources for transparent media preprocessing.
Enables AI agents to query local video timelines by extracting speech, frame captions, and on-screen text into a SQLite store, exposing search and retrieval tools via MCP.
Let AI agents watch videos: local transcripts, speaker labels, scenes, chapters and exact-moment search from any video URL or file. Fully local, no API keys.
Enables high-performance, offline transcription of videos from 1000+ platforms and local files using whisper.cpp, with support for multiple model sizes, languages, and output formats over stdio or HTTP.
