YouTube Research Assistant

A standalone Streamlit project that turns a topic into a transcript-grounded YouTube research brief.

The research workflow is implemented with LangGraph, uses LangChain's Groq chat model integration, and is also available to other AI clients as MCP tools.

What it does

1. Searches YouTube for the top five results.

2. Fetches available English transcripts.

3. Summarizes each usable source with Groq.

4. Synthesizes a comprehensive brief with [Video N] citations.

5. Answers follow-up questions using retrieved chunks from the combined transcript index.

Videos with captions that are disabled, restricted, or unavailable are shown in the source list but are excluded from the evidence base.

Related MCP server: YouTube MCP Server

Setup and run

cd youtube_research_assistant
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env

Set GROQ_API_KEY in .env, then run:

streamlit run app.py

The default model is openai/gpt-oss-20b. If your .env was created from an older version of this project, replace GROQ_MODEL=llama-3.1-8b-instant with GROQ_MODEL=openai/gpt-oss-20b (or another model enabled for your Groq account).

The project does not download video media. It uses yt-dlp for search and youtube-transcript-api for captions.

MCP server

The repository exposes three MCP tools: search_youtube, get_transcript, and create_research_brief. They are backed by the same LangGraph workflow as the Streamlit app.

This server uses the MCP stdio transport. Do not run python mcp_server.py in a terminal or type into it: the server accepts only JSON-RPC messages from an MCP client. Instead, add this configuration to your MCP client; it will launch the server as a subprocess and connect to it automatically:

{
  "mcpServers": {
    "youtube-research": {
      "command": "python",
      "args": ["/absolute/path/to/youtube_research_assistant/mcp_server.py"]
    }
  }
}