# Multi-agent YouTube Video Analyst

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

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.

Website: https://blog.dailydoseofds.com/p/hands-on-multi-agent-youtube-video

## What it is 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](https://xiry.app/topics/development.md)
- 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](https://x.com/akshay_pachaar/status/1882406725313311065): 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 10 October 2026. Page: https://xiry.app/resources/multi-agent-youtube-video-analyst

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