AI Social Media Manager

Overview

This workflow automates social media content creation, scheduling, and engagement across multiple platforms. It combines research, writing, design, and analytics into a cohesive content management system.

Difficulty

Medium - Requires API access to multiple social platforms and design tools.

Tools Required

• CrewAI: Multi-agent orchestration for specialized roles
• Claude / GPT-4: Content generation and engagement
• OpenAI DALL-E 3: AI-generated images and graphics
• Twitter API: Tweet publishing and engagement
• LinkedIn API: Professional content publishing
• Buffer: Social media scheduling and analytics

Workflow Steps

Step 1: Research Agent

Identifies trending topics and content opportunities.

def research_trending_topics(niche: str, platforms: list) -> dict:
    """
    Research trending topics across social platforms.
    
    Args:
        niche: Content niche (e.g., "AI", "marketing", "tech")
        platforms: List of platforms to research
        
    Returns:
        Trending topics with engagement metrics
    """
    # Use Tavily/Brave search for trending topics
    search_results = search(f"{niche} trending topics this week")
    
    # Analyze engagement patterns
    topics = []
    for result in search_results:
        topics.append({
            "title": result["title"],
            "url": result["url"],
            "engagement_potential": estimate_engagement(result),
            "platform_fit": match_platforms(result, platforms),
            "content_angle": suggest_content_angle(result, niche)
        })
    
    return sorted(topics, key=lambda x: x["engagement_potential"], reverse=True)

# Example output
"""
[
  {
    "title": "AI Agents Are Changing Software Development",
    "url": "https://example.com/ai-agents-2025",
    "engagement_potential": 0.85,
    "platform_fit": ["twitter", "linkedin"],
    "content_angle": "Opinion piece on how AI agents are transforming developer workflows"
  },
  {
    "title": "Top 10 MCP Servers for AI Developers",
    "url": "https://example.com/mcp-servers",
    "engagement_potential": 0.78,
    "platform_fit": ["twitter", "linkedin", "reddit"],
    "content_angle": "Listicle with practical recommendations"
  }
]
"""

Step 2: Writer Agent

Creates engaging social media posts.

def create_social_post(topic: dict, platform: str, brand_voice: dict) -> dict:
    """
    Create a social media post for a specific platform.
    
    Args:
        topic: Topic research data
        platform: Target platform (twitter, linkedin, etc.)
        brand_voice: Brand voice guidelines
        
    Returns:
        Complete post with text, hashtags, and media suggestions
    """
    platform_specs = {
        "twitter": {"max_chars": 280, "optimal_length": 100, "hashtag_count": 2},
        "linkedin": {"max_chars": 3000, "optimal_length": 1500, "hashtag_count": 5},
        "instagram": {"max_chars": 2200, "optimal_length": 150, "hashtag_count": 10}
    }
    
    specs = platform_specs[platform]
    
    prompt = f"""
    Create a {platform} post about: {topic['title']}
    
    Content angle: {topic['content_angle']}
    
    Brand voice: {brand_voice['tone']}, {brand_voice['style']}
    
    Requirements:
    - Length: {specs['optimal_length']} characters (max {specs['max_chars']})
    - Include {specs['hashtag_count']} relevant hashtags
    - Include a call-to-action
    - Engaging hook in first sentence
    
    Output JSON with: text, hashtags[], media_suggestion
    """
    
    post = call_claude(prompt)
    
    return {
        "text": post["text"],
        "hashtags": post["hashtags"],
        "media_suggestion": post.get("media_suggestion"),
        "scheduled_for": None,
        "status": "draft"
    }

# Example Twitter post
"""
{
  "text": "🚀 AI agents are no longer a nice-to-have—they're becoming essential for modern development teams.\n\nHere's what I've learned after building with CrewAI, LangGraph, and AutoGen for 6 months:\n\n1/ Multi-agent systems outperform single agents by 3x on complex tasks\n2/ Agent memory is the secret sauce most teams overlook\n3/ The right framework depends on your use case\n\nWhich agent framework are you using? 👇\n\n#AI #AIAgents #SoftwareDevelopment",
  "hashtags": ["#AI", "#AIAgents", "#SoftwareDevelopment"],
  "media_suggestion": "Infographic comparing agent frameworks"
}
"""

Step 3: Designer Agent

Generates accompanying images and graphics.

def create_social_image(post: dict, topic: dict) -> dict:
    """
    Generate an image for the social media post.
    
    Args:
        post: Social media post data
        topic: Topic research data
        
    Returns:
        Generated image URL and description
    """
    prompt = f"""
    Create an engaging social media image for this post:
    
    {post['text'][:200]}...
    
    Topic: {topic['title']}
    
    Style: Modern, clean, professional
    Colors: Brand colors (emerald green, blue)
    Elements: Include relevant icons/illustrations
    Text overlay: Key headline from post
    
    Format: 1200x628px (Twitter/LinkedIn optimal)
    """
    
    response = call_dalle(prompt, size="1792x1024", quality="hd")
    
    return {
        "image_url": response["url"],
        "prompt_used": prompt,
        "alt_text": generate_alt_text(post, topic)
    }

# Example output
"""
{
  "image_url": "https://oaidalleapiprodscus.blob.core.windows.net/...",
  "prompt_used": "Create an engaging social media image...",
  "alt_text": "Infographic showing comparison of AI agent frameworks with icons for CrewAI, LangGraph, and AutoGen"
}
"""

Step 4: Scheduler Agent

Schedules posts for optimal engagement times.

def schedule_posts(posts: list, audience_data: dict) -> list:
    """
    Schedule posts at optimal times for maximum engagement.
    
    Args:
        posts: List of drafted posts
        audience_data: Audience activity patterns
        
    Returns:
        Scheduled posts with timestamps
    """
    # Analyze best posting times
    optimal_times = analyze_best_times(audience_data)
    
    scheduled = []
    for i, post in enumerate(posts):
        # Spread posts across optimal times
        scheduled_time = optimal_times[i % len(optimal_times)]
        
        # Add to Buffer scheduling queue
        buffer_response = buffer_api.create_post({
            "text": post["text"],
            "image": post.get("image_url"),
            "profile_ids": get_profile_ids(post["platform"]),
            "scheduled_at": scheduled_time.isoformat(),
            "shorten_links": True
        })
        
        scheduled.append({
            **post,
            "scheduled_for": scheduled_time,
            "buffer_id": buffer_response["id"],
            "status": "scheduled"
        })
    
    return scheduled

# Example output
"""
[
  {
    "text": "🚀 AI agents are no longer a nice-to-have...",
    "scheduled_for": "2025-06-10T09:00:00Z",
    "platform": "twitter",
    "status": "scheduled",
    "buffer_id": "65a1b2c3d4e5f6g7h8i9j0k1"
  }
]
"""

Step 5: Engagement Agent

Monitors and responds to comments and messages.

def manage_engagement(posts: list, hours: int = 24) -> dict:
    """
    Monitor and respond to engagement on published posts.
    
    Args:
        posts: Published posts to monitor
        hours: Hours to monitor
        
    Returns:
        Engagement report with responses sent
    """
    engagement_report = {
        "total_engagements": 0,
        "responses_sent": 0,
        "sentiment_breakdown": {"positive": 0, "neutral": 0, "negative": 0},
        "responses": []
    }
    
    for post in posts:
        # Fetch comments and mentions
        comments = fetch_comments(post["platform"], post["post_id"])
        
        for comment in comments:
            engagement_report["total_engagements"] += 1
            
            # Analyze sentiment
            sentiment = analyze_sentiment(comment["text"])
            engagement_report["sentiment_breakdown"][sentiment] += 1
            
            # Determine if response needed
            if needs_response(comment, sentiment):
                response = generate_response(comment, post)
                send_response(post["platform"], post["post_id"], response)
                
                engagement_report["responses_sent"] += 1
                engagement_report["responses"].append({
                    "comment": comment["text"][:100],
                    "response": response[:100],
                    "platform": post["platform"]
                })
    
    return engagement_report

# Response generation
"""
def generate_response(comment: dict, post: dict) -> str:
    prompt = f"""
    Respond to this comment on our social post:
    
    Comment: "{comment['text']}"
    Original post: "{post['text'][:200]}..."
    
    Guidelines:
    - Be helpful and engaging
    - Match the commenter's tone
    - Keep it under 280 characters
    - Include a question to continue conversation
    
    Response:
    """
    return call_claude(prompt)
"""

Step 6: Analytics Agent

Tracks performance and optimizes strategy.

def analyze_performance(posts: list, period: str = "7d") -> dict:
    """
    Analyze social media performance metrics.
    
    Args:
        posts: Posts to analyze
        period: Time period ("7d", "30d", "90d")
        
    Returns:
        Performance report with insights
    """
    metrics = {
        "impressions": 0,
        "engagements": 0,
        "engagement_rate": 0,
        "clicks": 0,
        "shares": 0,
        "saves": 0,
        "top_performing": [],
        "insights": []
    }
    
    for post in posts:
        post_metrics = buffer_api.get_analytics(post["buffer_id"])
        
        metrics["impressions"] += post_metrics["impressions"]
        metrics["engagements"] += post_metrics["engagements"]
        metrics["clicks"] += post_metrics["clicks"]
        metrics["shares"] += post_metrics["shares"]
        
        # Track top performers
        if post_metrics["engagement_rate"] > 0.05:
            metrics["top_performing"].append({
                "post_id": post["buffer_id"],
                "engagement_rate": post_metrics["engagement_rate"],
                "platform": post["platform"]
            })
    
    # Calculate overall engagement rate
    if metrics["impressions"] > 0:
        metrics["engagement_rate"] = metrics["engagements"] / metrics["impressions"]
    
    # Generate insights
    metrics["insights"] = generate_insights(metrics, posts)
    
    return metrics

# Example insights
"""
[
  "Posts with images get 2.3x more engagement than text-only posts",
  "Tuesday and Thursday mornings (9-11am) have highest engagement",
  "Posts with questions in the first sentence get 40% more comments",
  "LinkedIn posts perform best for technical content, Twitter for quick tips"
]
"""

Example Usage

# Weekly content automation
1. Research Agent identifies 15 trending topics
2. Writer Agent creates 10 posts (5 Twitter, 5 LinkedIn)
3. Designer Agent generates 5 images
4. Scheduler Agent schedules posts for next 7 days
5. Engagement Agent monitors and responds (runs daily)
6. Analytics Agent generates weekly report (runs Monday)

Pros

• ✅ Saves 10+ hours per week on social media management
• ✅ Consistent posting schedule
• ✅ Data-driven content strategy
• ✅ Automated engagement responses
• ✅ Multi-platform coordination

Cons

• ❌ Requires API access to multiple platforms
• ❌ AI-generated content may lack authentic voice
• ❌ Engagement responses need human oversight
• ❌ Image generation costs add up
• ❌ Platform algorithm changes affect performance

When to Use

Use this workflow when:

• You manage social media for a company or brand
• You need consistent posting across multiple platforms
• You want to scale content creation
• You need data-driven optimization

Consider alternatives when:

• You have a very small audience (< 1000 followers)
• Your brand voice requires highly personalized content
• You're in a regulated industry with content restrictions

Resources

• CrewAI Documentation
• Buffer API
• Twitter API
• LinkedIn API
• OpenAI DALL-E