Turn long videos into a queryable knowledge base.

The demo is one application. The platform underneath is what you build on: a persistent index, agent tools over it, your choice of models, and every integration surface an agent framework expects.

1. Index once

   A Rust pipeline decodes video, runs speech recognition, OCR, shot detection and frame embeddings, and stores everything with timestamps at one timebase. An optional fine pass adds scene descriptions and entity and event extraction. The index is a directory you keep.

2. Agents that cite

   Search, view and describe tools let an agent read transcripts and on-screen text, look at frames when text does not settle it, and answer with hh:mm:ss citations. Retrieval-only and custom policies when you need cheaper or deterministic behaviour.

3. Any VLM, any LLM

   Model roles, not model names: speech, OCR, embeddings, description, extraction and the agent are each bound to a provider you choose. OpenAI-compatible servers, Anthropic, Gemini or local ONNX models, swapped in one config file, priced and rate-limited per role.

4. Integrate anywhere

   A library from Python or Node, an HTTP API with streaming answers, or an MCP server that Claude Code, LangGraph and other agent frameworks call as a tool. One binary, one index directory, no services to run for the local case.