yt-mem-ai — Claude Code MCP server for YouTube memory
A local memory for YouTube: transcripts, search across them, summaries and timestamped highlights. Everything runs on your machine.
claude mcp add yt-mem-ai -- uvx yt-mem-aiWhat yt-mem-ai does
The server lets your assistant work with videos you care about. It pulls YouTube captions when they exist and falls back to offline Whisper when they don't. Every transcript is kept on disk and indexed, so the library stays searchable. Ask what a video said about a topic and you get an answer pointing at the moment.
Writing is left to the model you already have connected: summaries, highlights, Q&A, slide decks, in the language of the video itself. Subscriptions are handled too — new uploads land in a single daily digest. Mark videos you liked or didn't, and recommendations come from your own library.
Two ways in. An interactive wizard asks what you want and which apps to wire up. Or add one entry to your MCP config: uvx fetches the package on the host's first launch and caches it after that. That first start takes longer than the host's startup check allows, so the tools may not appear. Run uvx --from 'yt-mem-ai[mcp]' yt-ai-mcp --help once and reopen the app. GUI apps often miss ~/.local/bin on PATH; absolute paths fix that.
What it helps with
- transcribe a video that has no captions
- jump to the moment a topic came up in a talk
- turn a day of new uploads into one digest
- build slides or a PDF from a video's content
- search your own watch history without a cloud service
Example prompts for the agent
- Summarize this video:
https://youtu.be/… - Find where my library covers transcript indexing
- Build a digest from my subscriptions' new uploads
How to install yt-mem-ai
Platform CLI
claude mcp add yt-mem-ai -- uvx yt-mem-aiuvx runs the command named after the package; if the README names another one, use uvx --from yt-mem-ai <command>
Platform CLI
codex mcp add yt-mem-ai -- uvx yt-mem-aiuvx runs the command named after the package; if the README names another one, use uvx --from yt-mem-ai <command>
Config file
{
"mcpServers": {
"yt-mem-ai": {
"command": "uvx",
"args": [
"yt-mem-ai"
]
}
}
}uvx runs the command named after the package; if the README names another one, use uvx --from yt-mem-ai <command>
Config file
{
"servers": {
"yt-mem-ai": {
"type": "stdio",
"command": "uvx",
"args": [
"yt-mem-ai"
]
}
}
}uvx runs the command named after the package; if the README names another one, use uvx --from yt-mem-ai <command>
Platform CLI
gemini mcp add yt-mem-ai uvx yt-mem-aiuvx runs the command named after the package; if the README names another one, use uvx --from yt-mem-ai <command>
Step by step, with commands for each platform: How to add an MCP server to Claude Code, Codex, Cursor, VS Code, Gemini CLI and more
Questions about yt-mem-ai
How do I install yt-mem-ai in Claude Code?
Run claude mcp add yt-mem-ai -- uvx yt-mem-ai and restart the app. The package downloads itself the first time the server starts.
What does yt-mem-ai do?
It transcribes YouTube videos, stores and indexes the transcripts locally, then answers questions about them with timestamps. Summaries, highlights and decks are written by your connected model.
Does it need an API key or a cloud service?
No. It runs on your machine: captions come from YouTube, and offline Whisper handles videos without them.
