🎬 mcptube-vision

YouTube video knowledge engine — transcripts, vision, and persistent wiki.

mcptube-vision transforms YouTube videos into a persistent, structured knowledge base using both transcripts and visual frame analysis. Built on the Karpathy LLM Wiki pattern: knowledge compounds with every video you add.

Evolved from mcptube v0.1 — mcptube-vision replaces semantic chunk search with a persistent wiki that gets smarter with every video ingested.

🧠 How It Works

Traditional video tools re-discover knowledge from scratch on every query. mcptube-vision is different:

           mcptube v0.1                    mcptube-vision
    ┌─────────────────────┐         ┌─────────────────────────┐
    │ Query → vector search│         │ Video ingested → LLM     │
    │ → raw chunks → LLM  │         │ extracts knowledge →     │
    │ → answer (from scratch│        │ wiki pages created →     │
    │   every time)        │         │ cross-references built   │
    └─────────────────────┘         │                         │
                                    │ Query → FTS5 + agent    │
                                    │ → reasons over compiled  │
                                    │   knowledge → answer     │
                                    └─────────────────────────┘

🏗️ Technical Architecture

mcptube-vision is built around a core insight: video knowledge should compound, not be re-discovered. Every architectural decision flows from this principle.

System Overview

flowchart TD
    YT[YouTube URL] --> EXT[YouTubeExtractor\ntranscript + metadata]
    EXT --> FRAMES[SceneFrameExtractor\nffmpeg scene-change detection]
    FRAMES --> VISION[VisionDescriber\nLLM vision model]
    VISION --> WIKI_EXT[WikiExtractor\nLLM knowledge extraction]
    EXT --> WIKI_EXT
    WIKI_EXT --> WIKI_ENG[WikiEngine\nmerge + update]
    WIKI_ENG --> FILE[FileWikiRepository\nJSON pages on disk]
    WIKI_ENG --> FTS[SQLite FTS5\nsearch index]
    FILE --> AGENT[Ask Agent\nFTS5 → LLM reasoning]
    FTS --> AGENT
    FILE --> CLI[CLI / MCP Server]
    FTS --> CLI

    subgraph Ingestion Pipeline
        EXT
        FRAMES
        VISION
        WIKI_EXT
    end

    subgraph Knowledge Store
        WIKI_ENG
        FILE
        FTS
    end

    subgraph Retrieval
        AGENT
    end

The system overview shows three distinct subsystems connected by a unidirectional data flow. The Ingestion Pipeline (left) transforms a raw YouTube URL into structured knowledge through four stages: transcript extraction, scene-change frame detection, vision-model description, and LLM-powered knowledge extraction. Each stage enriches the signal — raw video becomes text, text becomes typed knowledge objects.

The Knowledge Store (center) is the persistent layer. The WikiEngine applies merge semantics — deciding whether to create new pages or append to existing ones — then writes JSON files to disk and updates the FTS5 search index in parallel. These two stores serve different access patterns: files for full-page reads

... View full README on GitHub