Setup & Deployment Checklist

Step-by-step checklist to get your Content Agent System up and running.

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📋 Phase 1: Initial Setup

Prerequisites

• Python 3.11+ installed

  python --version  # Should show 3.11 or higher
  

• Git installed

  git --version
  

• Text editor or IDE ready
• Terminal/command line access

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🔑 Phase 2: API Key Setup

Get OpenRouter API Key

• Go to OpenRouter
• Sign up for free account
• Navigate to API Keys
• Click "Create Key"
• Copy your API key
• Save it securely (you'll need it multiple times)

💡 Tip: Free tier includes access to Llama 3.1 8B model

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📦 Phase 3: Repository Setup

Clone & Configure

• Clone the repository

  git clone https://github.com/CamelCod/content-agent-system.git
  cd content-agent-system
  

• Run setup script

  chmod +x setup_repo.sh
  ./setup_repo.sh
  

• Configure .env file

  nano .env  # or use your editor
  

  Add:

  OPENROUTER_API_KEY=your_actual_key_here
  

• Configure Streamlit secrets

  nano .streamlit/secrets.toml
  

  Add:

  OPENROUTER_API_KEY = "your_actual_key_here"
  

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🔧 Phase 4: Dependencies Installation

Install Python Packages

• Create virtual environment

  python3 -m venv venv
  

• Activate virtual environment

  # Linux/Mac
  source venv/bin/activate
  
  # Windows
  venv\Scripts\activate
  

• Upgrade pip

  pip install --upgrade pip
  

• Install requirements

  pip install -r requirements.txt
  

• Verify installation

  pip list | grep -E "streamlit|langchain|chromadb"
  

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📚 Phase 5: Knowledge Base Setup (Optional)

Add Content to Knowledge Base

• Create sample content files

  # Voice and style guide
  echo "Write clearly and concisely..." > knowledge_bases/voice_and_style/style_guide.md
  

• Add examples (optional)

  • LinkedIn post examples → knowledge_bases/examples/linkedin_posts/
  • Blog samples → knowledge_bases/examples/blog_samples/
  • Article samples → knowledge_bases/examples/article_samples/

• Add reference materials (optional)

  • Content frameworks → knowledge_bases/content_framework/
  • Reference docs → knowledge_bases/reference/

💡 Tip: System works without knowledge base, but RAG improves quality

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✅ Phase 6: Local Testing

Test Core Components

• Test imports

  python -c "from core.config import OPENROUTER_API_KEY; print('API Key:', 'SET' if OPENROUTER_API_KEY else 'NOT SET')"
  

• Test knowledge base

  python -c "from core.knowledge_base import KnowledgeBase; kb = KnowledgeBase(); print('Knowledge base initialized')"
  

• Test agents

  python -c "from agents.linkedin_agent import LinkedInAgent; agent = LinkedInAgent(); print('Agent initialized')"
  

Run Web Interface

• Start Streamlit

  streamlit run app.py
  

• Open browser to http://localhost:8501

• Test LinkedIn generator

  • Enter a topic
  • Select lens and objective
  • Click "Generate Post"
  • Verify content appears
  • Check validation score

• Test calendar mode

  • Switch to "Calendar Mode"
  • Select a calendar entry
  • Generate post
  • Verify output

• Test content history

  • Navigate to "Content History"
  • Verify generated posts appear
  • Test copy/delete functions

Test Batch Processor

• Run batch processor

  python batch_processor.py
  

• Verify JSON output created

  ls -l week_2_4_results.json
  

• Check results

  cat week_2_4_results.json | head -20
  

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🚀 Phase 7: Deployment Preparation

Choose Deployment Platform

• Review DEPLOYMENT.md
• Select platform:
  • Streamlit Cloud (recommended for beginners)
  • Railway
  • Heroku
  • Hugging Face Spaces
  • Docker
  • Local network

Pre-Deployment Checks

• All tests passing locally
• .env not committed to git

  git status  # Should NOT show .env or secrets.toml
  

• .gitignore properly configured
• Requirements.txt up to date
• README.md reviewed

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🌐 Phase 8: Deploy to Streamlit Cloud

Streamlit Cloud Deployment

• Commit changes to git

  git add .
  git commit -m "Ready for deployment"
  

• Push to GitHub

  git remote add origin https://github.com/yourusername/content-agent-system.git
  git push -u origin main
  

• Go to share.streamlit.io

• Connect GitHub repository

• Configure app

  • Repository: yourusername/content-agent-system
  • Branch: main
  • Main file: app.py

• Add secrets

  • Click "Advanced settings"
  • Add to Secrets:

    OPENROUTER_API_KEY = "your_actual_key_here"
    

• Click "Deploy"

• Wait for deployment (2-5 minutes)

• Test deployed app

  • Open app URL
  • Test all features
  • Generate sample content
  • Verify validation works

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✨ Phase 9: Post-Deployment

Verification

• App loads without errors
• All navigation pages work
• Content generation works
• Validation scores appear
• Content history persists in session

Monitoring

• Check Streamlit Cloud analytics
• Monitor API usage at OpenRouter dashboard
• Set up usage alerts (optional)

Share

• Copy app URL
• Share with team/users
• Document any customizations

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🔧 Phase 10: Customization (Optional)

Customize Content

• Adjust voice guidelines in core/config.py
• Modify signature phrases
• Update content calendar in batch_processor.py
• Add custom lenses/objectives

Customize UI

• Update theme in .streamlit/config.toml
• Modify page title/icon in app.py
• Add custom branding

Optimize Performance

• Pre-build vector store
• Cache knowledge base in session
• Adjust model selection defaults

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📊 Success Criteria

All systems go when:

• ✅ Web UI loads without errors
• ✅ Can generate LinkedIn posts
• ✅ Validation scores 8.0+ for good content
• ✅ Batch processor completes successfully
• ✅ Content history works
• ✅ Calendar mode functional
• ✅ Deployed and accessible online

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🐛 Troubleshooting

Common Issues

Import errors

pip install --upgrade -r requirements.txt

API key not found

• Check .env has correct format
• Check .streamlit/secrets.toml exists
• Verify no spaces around = in .env

Vector store errors

rm -rf vector_stores/
# Restart app to rebuild

Streamlit deployment fails

• Check requirements.txt has all dependencies
• Verify Python version in runtime.txt
• Check logs in Streamlit Cloud dashboard

Port already in use

# Use different port
streamlit run app.py --server.port=8502

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📚 Next Steps

After setup:

1. Read QUICK_START_PRODUCTION.md for production tips
2. Review DEPLOYMENT.md for advanced deployment options
3. Explore calendar customization for your content schedule
4. Add your own knowledge base content for better RAG

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🆘 Getting Help

• Documentation: README.md, DEPLOYMENT.md
• Issues: GitHub Issues
• API: OpenRouter Docs
• Streamlit: Streamlit Docs

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Status Check: If all boxes are checked, you're ready to generate content! 🎉