Real-World Use Cases

1. Automated Code Review in CI/CD

Automatically review pull requests, scan for vulnerabilities, and provide detailed feedback.

Setup

# .github/workflows/lydos-review.yml
name: LyDos Code Review

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
        with:
          fetch-depth: 0

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'

      - name: Clone LYDOS Agent OS
        run: git clone https://github.com/lydianai/AILYDIAN-AGENT-ORCHESTRATOR.git

      - name: Run security scan
        env:
          LYDOS_API_KEY: ${{ secrets.LYDOS_API_KEY }}
        run: |
          python3 << 'EOF'
          from lydos import LyDos
          import json
          import sys

          client = LyDos()
          pr_files = "${{ github.event.pull_request.title }}"

          # Run security-scanner on changed files
          result = client.agents.run(
              "security-scanner",
              task=f"Review PR changes for vulnerabilities",
              params={"path": "./src", "depth": "standard"}
          )

          # Wait for completion
          import time
          while True:
              task = client.tasks.get(result.task_id)
              if task.status in ["completed", "failed"]:
                  break
              time.sleep(2)

          # Output results
          print(f"::notice::LyDos Security Scan Complete")
          print(task.result)
          sys.exit(0 if task.status == "completed" else 1)
          EOF

Expected Outcome

• PRs blocked if HIGH/CRITICAL vulnerabilities found
• Detailed scan report posted as PR comment
• Security metrics tracked over time
• Developers notified immediately of issues

2. AI-Powered Research Newsletter

Generate a weekly market research newsletter using agents to research, analyze, and synthesize content.

Full Example

#!/usr/bin/env python3
"""
Weekly AI Research Newsletter Generator
Uses LyDos agents to research, analyze, and create newsletter content
"""
import time
from datetime import datetime
from lydos import LyDos

client = LyDos()

def generate_newsletter():
    topics = [
        "Latest developments in AI agents (2025)",
        "Emerging cybersecurity threats",
        "Machine learning optimization techniques",
    ]

    print("Starting weekly research newsletter generation...")
    print(f"Topics: {len(topics)} to research")

    # Step 1: Research all topics
    print("\nPhase 1: Deep Research")
    research_tasks = {}
    for topic in topics:
        result = client.agents.run(
            "deep-research",
            task=f"Research {topic} and provide detailed insights",
            params={"depth": "comprehensive", "sources": ["web", "academic-papers", "github"]}
        )
        research_tasks[topic] = result.task_id
        print(f"  • {topic}: {result.task_id}")

    # Wait for research to complete
    print("\nPhase 2: Collecting Results")
    research_results = {}
    for topic, task_id in research_tasks.items():
        while True:
            task = client.tasks.get(task_id)
            if task.status == "completed":
                research_results[topic] = task.result
                print(f"  ✓ {topic}")
                break
            elif task.status == "failed":
                print(f"  ✗ {topic}: {task.error_message}")
                break
            time.sleep(2)

    # Step 2: Synthesize and write content
    print("\nPhase 3: Content Generation")
    synthesis_result = client.agents.run(
        "documentation-writer",
        task=f"Write a professional newsletter using this research: {str(research_results)}",
        params={"format": "markdown", "style": "professional"}
    )

    while True:
        task = client.tasks.get(synthesis_result.task_id)
        if task.status in ["completed", "failed"]:
            newsletter_content = task.result
            break
        time.sleep(2)

    # Step 3: Generate summary for social media
    print("\nPhase 4: Social Media Summary")
    summary_result = client.agents.run(
        "content-marketer",
        task=f"Create a Twitter thread from this newsletter: {newsletter_content}",
        params={"max_results": 5, "format": "twitter_thread"}
    )

    while True:
        task = client.tasks.get(summary_result.task_id)
        if task.status in ["completed", "failed"]:
            social_content = task.result
            break
        time.sleep(2)

    # Step 4: Save and publish
    timestamp = datetime.now().isoformat()
    newsletter = {
        "timestamp": timestamp,
        "topics": topics,
        "newsletter_html": newsletter_content,
        "social_media": social_content,
        "research_tasks": research_tasks
    }

    # Save to file
    import json
    with open(f"newsletters/newsletter_{timestamp}.json", "w") as f:
        json.dump(newsletter, f, indent=2)

    # Publish (example: email, Medium, LinkedIn, etc.)
    print(f"\n✓ Newsletter generated and saved")
    print(f"Content length: {len(newsletter_content)} characters")
    print(f"Social posts: {len(social_content.split('---'))} tweets")

    return newsletter

if __name__ == "__main__":
    newsletter = generate_newsletter()

Expected Outcome

• Professional newsletter generated from 3 topics
• Takes ~5-10 minutes for comprehensive research
• Social media content auto-created for distribution
• Repeatable weekly with new topics

3. Self-Healing Infrastructure Monitoring

Monitor system health and automatically trigger remediation agents when issues are detected.

Architecture

Health Monitor (WebSocket) → Issue Detected → Remediation Agent
                                                ↓
                                          Restart Service
                                          Rebalance Load
                                          Scale Deployment
                                          Rollback Changes

Implementation

#!/usr/bin/env python3
import asyncio
import websockets
import json
from lydos import LyDos

client = LyDos()

async def monitor_infrastructure():
    """Monitor health and auto-remediate issues"""
    async with websockets.connect(
        'wss://lydos.ailydian.com/v1/stream',
        extra_headers={'Authorization': f'Bearer {client.api_key}'}
    ) as ws:
        while True:
            msg = await ws.recv()
            event = json.loads(msg)

            if event['type'] == 'health':
                health_score = event['score']

                if health_score < 70:
                    print(f"⚠️  Health degraded: {health_score}/100")
                    await remediate(event)

async def remediate(event):
    """Automatically fix infrastructure issues"""
    k8s_result = client.agents.run(
        "k8s-specialist",
        task="Diagnose cluster health and recommend fixes",
        params={"cluster": "production", "depth": "comprehensive"}
    )

    # Wait for diagnosis
    while True:
        task = client.tasks.get(k8s_result.task_id)
        if task.status == "completed":
            diagnosis = task.result
            break
        await asyncio.sleep(2)

    # Parse and apply fixes
    if "restart" in diagnosis.lower():
        print("  → Restarting unhealthy pods...")
        # kubectl rollout restart...

    if "scale" in diagnosis.lower():
        print("  → Scaling deployment...")
        # kubectl scale deployment...

    print("✓ Remediation complete")

Expected Outcome

• Real-time health monitoring with WebSocket streaming
• Automatic remediation when issues detected
• Reduced MTTR (Mean Time To Recovery)
• Fewer manual interventions and pages

4. Multi-Model AI Application (RAG Chatbot)

Build a RAG (Retrieval-Augmented Generation) chatbot using LyDos as the intelligent routing layer.

Architecture

User Query → LyDos Router Agent → Select Best Model/Tool
                                        ↓
                                   Retrieve Context
                                   Generate Response
                                   Format Output
                                        ↓
                                    User Response

Full Implementation

#!/usr/bin/env python3
from lydos import LyDos
from typing import Optional

client = LyDos()

class RAGChatbot:
    def __init__(self):
        self.conversation_history = []
        self.knowledge_base = self.load_knowledge_base()

    def load_knowledge_base(self):
        """Load documents for RAG"""
        # Load from database, documents, or web
        return {
            "products": "...",
            "pricing": "...",
            "docs": "..."
        }

    def query(self, user_input: str) -> str:
        """Process user query and return response"""
        # Step 1: Determine intent
        intent = self.determine_intent(user_input)
        print(f"Detected intent: {intent}")

        # Step 2: Retrieve relevant context
        context = self.retrieve_context(user_input)

        # Step 3: Route to appropriate agent based on intent
        if intent == "technical_question":
            response = client.agents.run(
                "deep-research",
                task=f"Answer: {user_input}\nContext: {context}",
                params={"depth": "standard"}
            )
        elif intent == "product_help":
            response = client.agents.run(
                "documentation-writer",
                task=f"Help with: {user_input}\nDocs: {context}",
                params={"depth": "fast"}
            )
        else:
            response = client.chat(
                f"User question: {user_input}\nContext: {context}"
            )
            return response.text

        # Wait for agent response
        import time
        while True:
            task = client.tasks.get(response.task_id)
            if task.status == "completed":
                return task.result
            elif task.status == "failed":
                return f"Sorry, I couldn't process that: {task.error_message}"
            time.sleep(1)

    def determine_intent(self, user_input: str) -> str:
        """Classify user intent"""
        keywords = {
            "technical": ["API", "code", "error", "bug", "debug"],
            "product": ["feature", "pricing", "plan", "purchase"],
            "general": ["hello", "help", "info"]
        }

        for intent, terms in keywords.items():
            if any(term.lower() in user_input.lower() for term in terms):
                return intent
        return "general"

    def retrieve_context(self, query: str) -> str:
        """Retrieve relevant documents (simplified)"""
        # In production, use vector DB for semantic search
        return self.knowledge_base.get("docs", "")

# Usage
bot = RAGChatbot()
while True:
    user_input = input("You: ")
    if user_input.lower() == "quit":
        break
    response = bot.query(user_input)
    print(f"Bot: {response}\n")

Expected Outcome

• Intelligent chatbot with context-aware responses
• Automatic intent detection and routing
• Answers based on knowledge base + LLM
• Customizable for any domain (support, sales, tech)

5. Security Operations Center (SOC)

Real-time threat detection combining CTI feeds with agent-based analysis and automated response.

Workflow

# Continuous monitoring
1. Fetch CTI feeds (ransomware, CVEs, exploits)
2. Match against asset inventory
3. Run vulnerability-hunter agent for affected systems
4. If HIGH/CRITICAL: trigger incident response workflow
5. Notify security team and create incident ticket

Example: CVE Detection

#!/usr/bin/env python3
from lydos import LyDos
import schedule
import time

client = LyDos()

def check_vulnerabilities():
    """Daily vulnerability check against known CVEs"""
    # Check for new CVEs and affected packages
    result = client.agents.run(
        "dependency-auditor",
        task="Check all dependencies for known CVEs",
        params={"path": "./", "check_cves": True}
    )

    # Wait for scan
    while True:
        task = client.tasks.get(result.task_id)
        if task.status == "completed":
            vulnerabilities = parse_vulnerabilities(task.result)

            if vulnerabilities:
                print(f"Found {len(vulnerabilities)} vulnerabilities!")

                for vuln in vulnerabilities:
                    if vuln['severity'] in ['HIGH', 'CRITICAL']:
                        trigger_incident_response(vuln)

            break
        time.sleep(2)

def trigger_incident_response(vulnerability):
    """Auto-create incident and notify team"""
    # Create incident in ticketing system
    print(f"🚨 CRITICAL: {vulnerability['id']} detected!")
    # Send alerts, create Jira ticket, etc.

# Schedule daily
schedule.every().day.at("01:00").do(check_vulnerabilities)

while True:
    schedule.run_pending()
    time.sleep(60)

Expected Outcome

• Automated threat detection 24/7
• Rapid identification of affected systems
• Incident tickets auto-created for critical issues
• Security team notified in real-time

6. Autonomous SEO Optimization

Automatically run SEO audits and optimize content across your website.

Implementation

#!/usr/bin/env python3
from lydos import LyDos
import schedule

client = LyDos()

def weekly_seo_audit():
    """Run comprehensive SEO audit"""
    result = client.agents.run(
        "seo-specialist",
        task="Perform technical SEO audit of website",
        params={"domain": "example.com", "depth": "comprehensive"}
    )

    # Wait for audit
    while True:
        task = client.tasks.get(result.task_id)
        if task.status == "completed":
            audit_results = task.result
            optimize_content(audit_results)
            break
        time.sleep(2)

def optimize_content(audit_results):
    """Update content based on SEO recommendations"""
    # Parse audit findings
    issues = parse_issues(audit_results)

    for issue in issues:
        if issue['type'] == 'title_too_short':
            # Run content agent to improve titles
            result = client.agents.run(
                "content-marketer",
                task=f"Improve page title for SEO: {issue['page']}",
                params={"format": "seo", "max_length": 60}
            )

        elif issue['type'] == 'missing_meta':
            # Generate meta descriptions
            result = client.agents.run(
                "content-marketer",
                task=f"Write SEO meta description for: {issue['page']}",
                params={"format": "meta", "max_length": 160}
            )

# Schedule weekly
schedule.every().monday.at("02:00").do(weekly_seo_audit)

while True:
    schedule.run_pending()

Expected Outcome

• Weekly automated SEO audits
• Content recommendations prioritized by impact
• Improved search rankings over time
• Reduced manual SEO workload

Common Patterns

try:
    while elapsed < timeout:
        task = client.tasks.get(task_id)
        if task.status == "completed":
            return task.result
        elif task.status == "failed":
            raise Exception(task.error_message)
        time.sleep(poll_interval)
except Exception as e:
    log_error(f"Task failed: {e}")
    fallback_action()

# Queue tasks instead of sequential execution
task_ids = []
for item in items:
    result = client.agents.run(agent_id, task=item)
    task_ids.append(result.task_id)

# Poll all tasks in parallel
results = poll_many(task_ids)