Introduction

In this post, I’ll share how I use CrewAI to build and maintain this blog site. CrewAI is a framework for orchestrating role-playing AI agents that work together to complete complex tasks. For this blog, I’ve created a specialized crew of agents that handle different aspects of development.

The Blog Development Crew

I’ve set up a crew with three specialized agents:

1. Content Architect

• Role: Technical writer and content strategist
• Responsibilities:
  • Drafting blog post outlines
  • Researching technical topics
  • Ensuring content quality and consistency
  • Maintaining the blog’s voice and style

2. Code Engineer

• Role: Full-stack developer
• Responsibilities:
  • Implementing Astro components
  • Creating new page layouts
  • Fixing bugs and optimizing performance
  • Managing the content collection schema

3. Quality Assurance

• Role: Reviewer and tester
• Responsibilities:
  • Reviewing content for accuracy
  • Testing components across devices
  • Checking accessibility and SEO
  • Ensuring consistency with existing content

Real-World Example: Creating This Blog Post

Here’s how I used CrewAI to create this very post:

Step 1: Content Planning

# crewai_config.py
from crewai import Agent, Task, Crew
from langchain.tools import Tool

# Define the Content Architect
content_architect = Agent(
    role="Content Architect",
    goal="Create engaging technical content for the blog",
    backstory="Expert in technical writing with 10+ years experience",
    tools=[web_search_tool, code_analysis_tool],
    verbose=True
)

# Define the task
content_task = Task(
    description="Create a blog post about using CrewAI for blog development",
    agent=content_architect,
    expected_output="A detailed blog post with code examples and workflow diagrams"
)

# Create the crew
crew = Crew(
    agents=[content_architect],
    tasks=[content_task],
    verbose=2
)

# Execute
result = crew.kickoff()

Step 2: Code Implementation

The Code Engineer agent then takes the content and:

1. Creates the MDX file in the correct directory
2. Adds appropriate frontmatter (title, description, tags, heroImage)
3. Implements any custom components needed
4. Updates the blog index to include the new post

Step 3: Quality Review

The QA agent reviews:

• Content accuracy and technical correctness
• Code examples compile and work
• SEO meta tags are properly set
• Mobile responsiveness
• Accessibility compliance

Workflow Automation

I’ve automated this workflow using n8n, which triggers on specific events:

# n8n workflow configuration
workflow:
  triggers:
    - on: "git push to main"
    - on: "new issue labeled 'blog-post'"
  
  steps:
    - crewai_analyze_changes
    - crewai_draft_content
    - crewai_review_content
    - crewai_create_pr

Benefits for Blog Development

1. Consistency

• Every post follows the same structure
• Consistent code examples and formatting
• Uniform tone and voice

2. Speed

• Draft posts in minutes instead of hours
• Automated code generation
• Instant review and feedback

3. Quality

• Multiple agents review for different aspects
• Automated testing of code examples
• SEO and accessibility checks

Code Examples

Here’s how I use CrewAI to generate code for this blog:

# generate_blog_post.py
from crewai import Crew, Process
from langchain_openai import ChatOpenAI

# Define agents
agents = [
    Agent(
        role="Astro Developer",
        goal="Create Astro components for the blog",
        backstory="Expert in Astro and TypeScript",
        tools=[file_system_tool, code_generation_tool]
    ),
    Agent(
        role="Content Writer",
        goal="Write technical blog posts",
        backstory="Technical writer with microservices expertise",
        tools=[web_search_tool, markdown_tool]
    )
]

# Define tasks
tasks = [
    Task(
        description="Create a new blog post about microservices patterns",
        agent=agents[1],
        expected_output="MDX file with frontmatter and content"
    ),
    Task(
        description="Update the blog index to include the new post",
        agent=agents[0],
        expected_output="Updated src/pages/blog/index.astro"
    )
]

# Create and run crew
crew = Crew(
    agents=agents,
    tasks=tasks,
    process=Process.sequential,
    verbose=2
)

result = crew.kickoff()

Integration with Git

I use CrewAI to automate git workflows:

# Create a new blog post
$ python create_blog_post.py --topic "CrewAI" --tags "ai,automation"

# CrewAI will:
# 1. Create the MDX file
# 2. Generate content
# 3. Update the blog index
# 4. Create a git branch
# 5. Commit the changes
# 6. Create a pull request

Lessons Learned

What Works Well

• Clear agent roles: Each agent has a specific, well-defined role
• Sequential workflow: Content → Code → Review works perfectly
• Integration with n8n: Automated triggers make it seamless

Challenges

• Context management: Keeping all agents aware of the blog’s structure
• Code quality: Sometimes need manual review for complex components
• Cost: Multiple API calls can add up

Future Improvements

I’m planning to add more agents:

• SEO Specialist: Optimizes content for search engines
• Accessibility Auditor: Ensures WCAG compliance
• Performance Analyst: Checks page load times and optimization opportunities

Conclusion

CrewAI has transformed how I develop this blog. What used to take hours now takes minutes, and the quality is consistently high. The multi-agent approach ensures that every aspect of the blog gets proper attention.

For developers building content-heavy sites, I highly recommend exploring CrewAI. The initial setup takes time, but the long-term productivity gains are substantial.

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Next in this series: I’ll show you how I use Aider for pair programming with AI to write the actual code for this blog.