YouTube Channel Growth AI Suite

An AI-powered suite of tools to optimize and automate your YouTube channel growth. This suite includes specialized agents for content planning, SEO optimization, analytics tracking, engagement management, and scheduling.

Features

• 📊 Analytics Agent: Track KPIs and generate performance reports
• 📝 Content Agent: Generate video ideas and content calendars
• 🔍 SEO Agent: Optimize titles, descriptions, and tags
• 📅 Scheduler Agent: Optimize publishing times based on analytics
• 💬 Engagement Agent: Auto-generate community posts and comment replies

Prerequisites

• Python 3.12 or higher
• A YouTube channel with API access
• OpenAI API key (for content generation)
• RapidAPI key (for trend analysis)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/youtube-growth-ai-suite.git
cd youtube-growth-ai-suite

2. Create and activate a virtual environment:

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Create a .env file in the project root and add your API keys:

# YouTube API credentials
YOUTUBE_API_KEY=your_youtube_api_key_here

# OpenAI API credentials (for content generation)
OPENAI_API_KEY=your_openai_api_key_here

# RapidAPI credentials (for trend analysis)
RAPIDAPI_KEY=your_rapidapi_key_here

Getting Your API Keys

1. YouTube API Key:

   • Go to Google Cloud Console
   • Create a new project or select an existing one
   • Enable the YouTube Data API v3
   • Create credentials (API key)
   • Add your YouTube channel to the OAuth consent screen

2. OpenAI API Key:

   • Visit OpenAI API
   • Create an account or log in
   • Generate a new API key

3. RapidAPI Key:

   • Visit RapidAPI
   • Create an account or log in
   • Subscribe to required APIs
   • Copy your API key

Usage

1. Quick Start:

# Activate virtual environment (if not already activated)
venv\Scripts\activate  # Windows
source venv/bin/activate  # Linux/Mac

# Run the example workflow
python examples/channel_growth_workflow.py

2. Individual Agent Usage:

from agents import (
    ContentAgent,
    SEOAgent,
    AnalyticsAgent,
    EngagementAgent,
    SchedulerAgent
)

# Analytics
analytics = AnalyticsAgent()
kpi_analysis = analytics.analyze_kpi_progress()
performance_report = analytics.generate_performance_report()

# Content Planning
content = ContentAgent()
video_ideas = content.generate_video_ideas(count=5)
calendar = content.generate_content_calendar(weeks=4)

# SEO Optimization
seo = SEOAgent()
title_analysis = seo.analyze_title("Your Video Title")
tags = seo.generate_tags("Your Video Title", count=10)

# Scheduling
scheduler = SchedulerAgent()
schedule = scheduler.generate_monthly_schedule(posts_per_week=2)

# Engagement
engagement = EngagementAgent()
community_posts = engagement.generate_community_posts(count=2)
replies = engagement.generate_comment_replies(your_comments, "your topic")

3. Video Analysis Tool:

The suite includes a powerful command-line tool for analyzing individual YouTube videos. You can analyze any video using its URL or ID:

# Basic usage (analyzes everything)
python examples/analyze_video.py https://www.youtube.com/watch?v=VIDEO_ID

# Or just use the video ID
python examples/analyze_video.py VIDEO_ID

SEO Analysis Options:

# Generate more tag suggestions
python examples/analyze_video.py VIDEO_ID --tag-count 20

# Include competitor analysis
python examples/analyze_video.py VIDEO_ID --competitors --competitor-count 10

Engagement Analysis Options:

# Generate community posts
python examples/analyze_video.py VIDEO_ID --post-count 5 --post-type question

# Generate video response ideas and comments
python examples/analyze_video.py VIDEO_ID \
    --response-ideas \
    --response-count 5 \
    --comment-suggestions \
    --comment-count 10

Performance Analysis Options:

# Get detailed analytics and retention data
python examples/analyze_video.py VIDEO_ID \
    --detailed-analytics \
    --analytics-days 90 \
    --retention

Combining Analyses:

# Run specific types of analysis
python examples/analyze_video.py VIDEO_ID -a seo
python examples/analyze_video.py VIDEO_ID -a engagement
python examples/analyze_video.py VIDEO_ID -a performance

# Comprehensive analysis with all features
python examples/analyze_video.py VIDEO_ID \
    --competitors \
    --tag-count 15 \
    --response-ideas \
    --comment-suggestions \
    --detailed-analytics \
    --retention

For a complete list of options:

python examples/analyze_video.py --help

Directory Structure

youtube-growth-ai-suite/
├── agents/                 # AI agent modules
│   ├── __init__.py
│   ├── analytics_agent.py
│   ├── base_agent.py
│   ├── content_agent.py
│   ├── engagement_agent.py
│   ├── scheduler_agent.py
│   └── seo_agent.py
├── examples/              # Usage examples
│   └── channel_growth_workflow.py
├── results/              # Generated results and reports
├── tests/               # Test suite
├── .env                # API keys and configuration
├── requirements.txt    # Python dependencies
└── README.md          # This file

Troubleshooting

1. YouTube API Issues:

   • Ensure your API key is correct in .env
   • Check if you've enabled YouTube Data API v3 in Google Cloud Console
   • Verify your channel has the necessary permissions
   • Check your API quota usage

2. OpenAI API Issues:

   • Verify your API key is valid and has sufficient credits
   • Check if you're using a supported model
   • Ensure your requests are properly formatted

3. Common Errors:

   • KeyError: 'original_analysis': Make sure to run the full analysis before accessing results
   • YouTube API client not initialized: Check your YouTube API key and permissions
   • No video data available: Ensure your channel has uploaded videos
   • ImportError: Run pip install -r requirements.txt again

4. Performance Issues:

   • Consider reducing the number of API calls
   • Use caching for frequently accessed data
   • Optimize your requests to stay within API limits

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support, please:

1. Check the troubleshooting section above
2. Review existing GitHub issues
3. Open a new issue with:
   • Detailed description of the problem
   • Steps to reproduce
   • Error messages
   • Your environment details