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Development · Web app

Multi-agent YouTube Video Analyst

End-to-end tutorial building a YouTube analyst using CrewAI, DeepSeek-R1, and Bright Data

From the Xiry library · Updated 10 October 2026

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A hands-on guide and reference architecture for building an AI-powered YouTube video trend analyst. It combines Bright Data for video scraping, a local DeepSeek-R1 via Ollama for LLM inference, and CrewAI to coordinate transcript analysis and response generation. A Streamlit interface provides the frontend for interacting with the multi-agent system.

What it's for

  • Scrape and analyze video transcripts from targeted YouTube channels over specific date ranges
  • Identify emerging topics and trends across competitor YouTube channels
  • Learn how to connect local LLMs via Ollama to CrewAI agent workflows

Features

  • Web scraping integration via Bright Data API specifying channels, dates, and country
  • Local model inference running DeepSeek-R1 through Ollama
  • Two-agent CrewAI orchestration with a Transcript Analyst and a Response Generator
  • Streamlit front-end UI for triggering runs and reviewing output
  • Complete code repository provided via GitHub

Pricing

Free tutorial and open-source code; requires Bright Data API credits and local hardware for Ollama

Details

Type
Web app
Category
Development
Topic
Agent Tutorials
Open source
Yes
Platforms
Web, Command line
For
AI engineers, data scientists, and developers building autonomous research agents
Tags
crewai, deepseek r1, ollama, bright data, streamlit, youtube scraping

Why it was saved

  • @akshay_pachaar, 23 January 2025: The code steps (scrape → LLM setup → analyst agent → response agent → crew) are the tutorial the demo advertises; keeping the thread's build steps with the demo.

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Updated