Tech Blog Writer Agent

A sophisticated AI-powered blog writing system using Google's Gemini models and the chain design pattern. This agent breaks down blog creation into sequential, focused steps for higher quality output.

🎯 Features

• Chain Design Pattern: Sequential 5-step workflow with quality gates
• Dual Deployment Options: Local testing with Gemini API or production with Vertex AI
• Easy Local Testing: No cloud setup required - just an API key
• Quality Validation: Automated checks between each step
• SEO Optimization: Built-in search engine optimization
• Flexible Configuration: Customizable audience, style, and length
• Error Recovery: Retry failed steps with feedback

🏗️ Architecture

Chain Steps

1. Topic Research & Analysis 📊

   • Analyzes topic relevance and trending status
   • Identifies target audience and pain points
   • Researches competitive landscape
   • Discovers search intent and keywords

2. Content Outline Generation 📝

   • Creates structured blog outline
   • Develops compelling titles and meta descriptions
   • Plans content strategy and engagement elements
   • Maps internal linking opportunities

3. Content Writing ✍️

   • Writes engaging, well-structured content
   • Includes practical examples and case studies
   • Maintains consistent tone and style
   • Incorporates markdown formatting

4. SEO Optimization 🔍

   • Optimizes keyword placement and density
   • Enhances readability and structure
   • Adds meta tags and schema recommendations
   • Improves heading hierarchy

5. Final Review & Polish ✨

   • Grammar and spell checking
   • Quality assessment and scoring
   • Final formatting and consistency
   • Publication readiness verification

Quality Gates

Each step includes automated validation:

• Content completeness checks
• Word count validation
• Structure verification
• SEO requirement validation

🚀 Installation

Prerequisites

• Python 3.11+
• UV package manager
• Either: Gemini API key (local) OR Google Cloud Project (production)

Quick Setup

1. Clone and setup project:

git clone <repository>
cd tech_blog_agent

2. Install dependencies:

uv sync

3. Choose your deployment mode:

🟢 Option A: Local Testing (Recommended for getting started)

# 1. Get API key from: https://makersuite.google.com/app/apikey
# 2. Configure environment:
cp .env.example .env

# Edit .env and set:
USE_GEMINI_API=true
GEMINI_API_KEY=your_api_key_here

🔵 Option B: Production Deployment (Google Cloud)

# 1. Set up Google Cloud:
export GOOGLE_CLOUD_PROJECT=your-project-id
gcloud auth application-default login
gcloud services enable aiplatform.googleapis.com

# 2. Configure environment:
cp .env.example .env

# Edit .env and set:
USE_GEMINI_API=false
GOOGLE_CLOUD_PROJECT=your-project-id

📖 Usage

Interactive Mode

uv run python main.py

The system will automatically detect your configuration and use the appropriate API. Then provide:

• Blog topic
• Target audience (optional)
• Writing style (optional)
• Target word count (default: 1500)
• SEO keywords (optional)

Demo Mode

uv run python main.py

Select option 2 to run predefined examples.

Programmatic Usage

import asyncio
from agents.client_factory import ClientFactory
from chains.blog_writer_chain import BlogWriterChain

async def generate_blog():
    # Automatically uses correct client based on environment
    client = ClientFactory.create_client()
    chain = BlogWriterChain(client)
    
    result = await chain.generate_blog_post(
        topic="Introduction to Docker Containers",
        target_audience="beginner developers",
        style_guide="friendly and practical",
        word_count_target=1800,
        seo_keywords=["docker", "containers", "containerization"]
    )
    
    if result["success"]:
        print("Blog generated successfully!")
        print(f"Word count: {result['metadata']['word_count']}")
        
        # Save to file
        with open("blog_post.md", "w") as f:
            f.write(result["blog_post"])
    else:
        print(f"Generation failed: {result['error']}")

asyncio.run(generate_blog())

🏗️ Project Structure

tech_blog_agent/
├── agents/                    # Individual AI agents
│   ├── client_factory.py     # Client factory for API switching
│   ├── vertex_ai_client.py   # Google Vertex AI client
│   ├── gemini_api_client.py  # Direct Gemini API client
│   ├── researcher.py         # Topic research agent
│   ├── outliner.py          # Content outline agent
│   ├── writer.py            # Content writing agent
│   ├── seo_optimizer.py     # SEO optimization agent
│   └── reviewer.py          # Final review agent
├── chains/                   # Chain orchestration
│   ├── base.py              # Base classes and models
│   └── blog_writer_chain.py # Main blog writer chain
├── validators/              # Quality validation
│   └── quality_gates.py    # Quality gate implementations
├── main.py                  # Main application entry point
├── pyproject.toml          # UV project configuration
└── README.md               # This file

🔧 Configuration

Environment Variables

For Local Testing:

• USE_GEMINI_API=true (enables Gemini API mode)
• GEMINI_API_KEY=your_api_key (get from https://makersuite.google.com/app/apikey)

For Production:

• USE_GEMINI_API=false (or omit - enables Vertex AI mode)
• GOOGLE_CLOUD_PROJECT=your-project-id (your Google Cloud project)
• VERTEX_AI_LOCATION=us-central1 (optional, default location)

Chain Configuration

You can customize the chain by:

1. Modifying agent prompts in individual agent files
2. Adjusting quality gates in validators/quality_gates.py
3. Changing model parameters in agent constructors
4. Adding new steps by extending BaseAgent class

Model Configuration

Default models and parameters:

• Model: gemini-1.5-pro (works with both APIs)
• Temperature: 0.3-0.7 (varies by step)
• Max tokens: 1500-5000 (varies by step)

🧪 Quality Gates

Each step has validation criteria:

• Research: Completeness, word count, required sections
• Outline: Structure, headings, required elements
• Content: Word count targets, paragraph structure, headings
• SEO: Keyword usage, meta elements, topic mentions
• Final: Publication readiness, quality score

🚨 Troubleshooting

Local Testing Issues

1. API Key Error

   # Get API key from: https://makersuite.google.com/app/apikey
   export GEMINI_API_KEY=your_api_key_here
   export USE_GEMINI_API=true

2. Import Error

   uv sync  # Reinstall dependencies

Production Issues

1. Authentication Error

   gcloud auth application-default login

2. Project ID Not Set

   export GOOGLE_CLOUD_PROJECT=your-project-id
   export USE_GEMINI_API=false

3. Vertex AI API Not Enabled

   gcloud services enable aiplatform.googleapis.com

Switching Between Modes

# Switch to local testing
export USE_GEMINI_API=true
export GEMINI_API_KEY=your_api_key

# Switch to production
export USE_GEMINI_API=false
export GOOGLE_CLOUD_PROJECT=your_project_id

4. Quota Exceeded
   • Check your Vertex AI quotas in Google Cloud Console
   • Consider using different regions

Debug Mode

Set environment variable for detailed logging:

export DEBUG=1
uv run python main.py

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add your changes
4. Ensure quality gates pass
5. Submit a pull request

📄 License

MIT License - see LICENSE file for details.

🎯 Next Steps

• Add support for multiple output formats (HTML, PDF)
• Integrate with CMS platforms (WordPress, Medium)
• Add image generation capabilities
• Implement A/B testing for different approaches
• Add analytics and performance tracking