Blog Writer with Storage System

A powerful blog generation system using CrewAI with integrated storage capabilities for managing and organizing generated blog posts.

Features

• 🤖 AI-Powered Blog Generation: Uses CrewAI with multiple agents (researcher, writer, proofreader)
• 📁 Automatic Storage: Automatically saves generated blogs with proper naming and organization
• 🔍 Blog Management: List, search, retrieve, and delete stored blogs
• 📊 Storage Statistics: Track storage usage and blog metadata
• 🏷️ Smart Naming: Automatic filename generation based on topic and timestamp
• 📝 Metadata Tracking: Comprehensive metadata for each blog post
• 🎨 Workflow Visualization: Visualize agents, tasks, and execution flow
• 🔄 Retry System: Configurable retry logic for both tasks and crew execution

Installation

# Install the package
pip install -e .

# Or using uv
uv sync

Usage

Generate a New Blog

# Run the blog writer
$ blog-writer

This will:

1. Prompt you for a blog topic
2. Generate a comprehensive blog post using AI agents
3. Automatically save the blog to the output/ directory
4. Display the save location

Blog Management Commands

List All Blogs

$ blog-writer list

Show a Specific Blog

$ blog-writer show <blog_id>
# Example: show ai_llms_20241201_143022

Search Blogs

$ blog-writer search <query>
# Example: search AI

Show Latest Blog

$ blog-writer latest

Delete a Blog

$ blog-writer delete <blog_id>
# Example: delete ai_llms_20241201_143022

Storage Statistics

blog-writer stats

Workflow Visualization

Generate All Visualizations

blog-writer visualize

Generate Specific Visualizations

blog-writer viz workflow    # Workflow diagram
blog-writer viz network     # Agent network
blog-writer viz timeline    # Execution timeline

The visualizations will be saved in the visualizations/ directory:

• workflow_diagram.png - Complete workflow showing agents, tasks, and outputs
• agent_network.png - Network diagram of agent interactions
• execution_timeline.png - Timeline of execution phases
• visualization_summary.json - Metadata about the visualizations

Retry Configuration

Default Retries

blog-writer
# Uses: 2 crew retries, 5s delay, task-specific retries

Custom Retries

blog-writer retry
# Prompts for custom retry settings

Storage Structure

The system creates a blogs/ directory with the following structure:

blogs/
├── metadata.json                    # Database of all blogs
├── ai_llms_20241201_143022/        # Individual blog directory
│   ├── draft_blog_post.md          # Draft version
│   └── final_blog_post.md          # Final version
├── machine_learning_20241201_150000/
│   └── final_blog_post.md
└── ...

File Naming Convention

• Directory: {sanitized_topic}_{YYYYMMDD_HHMMSS}
• Files: {stage}_blog_post.md (e.g., draft_blog_post.md, final_blog_post.md)

Metadata Structure

Each blog entry in metadata.json contains:

{
  "topic": "AI and Machine Learning",
  "directory": "ai_and_machine_learning_20241201_143022",
  "stage": "final",
  "filename": "final_blog_post.md",
  "created_at": "2024-12-01T14:30:22.123456",
  "file_path": "blogs/ai_and_machine_learning_20241201_143022/final_blog_post.md"
}

Workflow Visualization

The system provides three types of visualizations:

1. Workflow Diagram

Shows the complete blog generation process:

• Agents: Researcher, Blog Writer, Proofreader
• Tasks: Research Task, Write Blog Task, Proofread Task
• Outputs: Research Findings, Blog Post (Draft), Final Blog Post
• Flow: Sequential execution with dependencies

2. Agent Network

Network diagram showing:

• Agent interactions and relationships
• Task dependencies
• Data flow between components
• Visual representation of the crew structure

3. Execution Timeline

Timeline showing:

• Execution phases and duration
• Agent responsibilities
• Retry configuration
• Sequential processing flow

API Usage

BlogStorage Class

from blog_writer.storage import BlogStorage

# Initialize storage
storage = BlogStorage("custom_blogs_dir")

# Save a blog post
file_path = storage.save_blog_post("My Topic", "Blog content...", "final")

# List all blogs
blogs = storage.get_blog_list()

# Retrieve a blog
content = storage.get_blog_content("blog_directory_name")

# Search blogs
results = storage.search_blogs("AI")

# Get latest blog
latest = storage.get_latest_blog()

# Delete a blog
success = storage.delete_blog("blog_directory_name")

# Get storage statistics
stats = storage.get_storage_stats()

Visualization

from blog_writer.visualization import BlogWriterVisualizer

# Create visualizer
visualizer = BlogWriterVisualizer()

# Generate all visualizations
visualizer.generate_all_visualizations("my_visualizations")

# Generate specific visualization
visualizer.create_workflow_diagram("workflow.png")
visualizer.create_agent_network("network.png")
visualizer.create_execution_timeline("timeline.png")

Configuration

Storage Directory

You can customize the storage directory:

# In your code
blog_writer = BlogWriter(storage_dir="my_blogs")

# Or for storage only
storage = BlogStorage("my_blogs")

Retry Configuration

Default retry settings:

• Crew Retries: 2 attempts
• Crew Delay: 5 seconds between retries
• Research Task: 2 retries
• Writing Task: 3 retries (most complex)
• Proofreading Task: 2 retries

Environment Variables

• BLOG_STORAGE_DIR: Default storage directory (defaults to "blogs")

Development

Project Structure

src/blog_writer/
├── __init__.py
├── main.py              # CLI entry points
├── crew.py              # CrewAI crew definition
├── storage.py           # Storage system
├── visualization.py     # Workflow visualization
├── cli.py               # Command-line interface
├── config/
│   ├── agents.yaml      # Agent configurations
│   └── tasks.yaml       # Task configurations

Adding New Features

1. New Storage Methods: Add methods to BlogStorage class
2. New CLI Commands: Add functions to main.py and update cli.py
3. New Visualizations: Add methods to BlogWriterVisualizer class
4. New Tools: Create tools and integrate with agents

Troubleshooting

Common Issues

1. Permission Errors: Ensure write permissions to the storage directory
2. File Not Found: Check if the blog directory exists in the metadata
3. Encoding Issues: All files are saved with UTF-8 encoding
4. Visualization Errors: Install required packages: pip install matplotlib networkx

Debug Mode

Enable verbose logging by setting the verbose parameter in the CrewAI configuration.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License.