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Full-Stack Agent Examples

Explore complete examples of AI agents built with Kubiya. Each example includes the full code and demonstrates different use cases.

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DevOps Assistant Agent

An intelligent agent that helps with DevOps tasks like deployments, monitoring, and troubleshooting.

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Backend Setup

# devops_agent.py
import os
from kubiya_workflow_sdk.mcp import AgentServer

# Initialize with GPT-4 for complex DevOps reasoning
server = AgentServer(
    provider="openai",
    model="gpt-4o",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Custom system prompt for DevOps context
server.system_prompt = """
You are an expert DevOps engineer assistant. You can:
- Deploy applications with zero downtime
- Set up monitoring and alerting
- Debug production issues
- Create CI/CD pipelines
- Manage infrastructure

Always consider security, scalability, and best practices.
Use appropriate Docker images for different tasks.
"""

if __name__ == "__main__":
    server.run(port=8765)

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Frontend (React)

// DevOpsAssistant.tsx
import React from 'react';
import { useChat } from '@ai-sdk/react';

export function DevOpsAssistant() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat({
    api: 'http://localhost:8765/v1/chat/completions',
    initialMessages: [
      {
        id: '1',
        role: 'assistant',
        content: 'Hi! I\'m your DevOps assistant. I can help you deploy apps, set up monitoring, create CI/CD pipelines, and more. What would you like to do today?'
      }
    ]
  });

  return (
    <div className="max-w-4xl mx-auto p-4">
      <h1 className="text-2xl font-bold mb-4">DevOps Assistant</h1>
      
      <div className="bg-gray-100 rounded-lg p-4 h-96 overflow-y-auto mb-4">
        {messages.map(m => (
          <div key={m.id} className={`mb-4 ${m.role === 'user' ? 'text-right' : ''}`}>
            <div className={`inline-block p-3 rounded-lg ${
              m.role === 'user' ? 'bg-blue-500 text-white' : 'bg-white'
            }`}>
              <div className="font-semibold mb-1">
                {m.role === 'user' ? 'You' : 'DevOps Assistant'}
              </div>
              <div className="whitespace-pre-wrap">{m.content}</div>
            </div>
          </div>
        ))}
        {isLoading && (
          <div className="text-center text-gray-500">
            <span className="animate-pulse">Assistant is working...</span>
          </div>
        )}
      </div>
      
      <form onSubmit={handleSubmit} className="flex gap-2">
        <input
          className="flex-1 p-2 border rounded-lg"
          value={input}
          onChange={handleInputChange}
          placeholder="e.g., Deploy my Node.js app with health checks"
          disabled={isLoading}
        />
        <button
          type="submit"
          disabled={isLoading}
          className="px-4 py-2 bg-blue-500 text-white rounded-lg hover:bg-blue-600 disabled:opacity-50"
        >
          Send
        </button>
      </form>
    </div>
  );
}

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Example Interactions

User: Deploy my Node.js app with zero downtime

Agent: I'll create a zero-downtime deployment workflow for your Node.js app. This will include:
1. Building a new Docker image
2. Running health checks
3. Gradually rolling out the update
4. Automatic rollback if issues are detected

[Creates and executes blue-green deployment workflow]

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Data Pipeline Agent

Build data processing pipelines with natural language.

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Configuration

# data_agent.py
from kubiya_workflow_sdk.cli import main
import sys

# Run with specialized data processing context
sys.argv = [
    "kubiya", "mcp", "agent",
    "--provider", "anthropic",
    "--model", "claude-3-5-sonnet-20241022",
    "--port", "8766"
]

# The agent will have access to:
# - Data processing templates
# - ETL workflow patterns
# - Database integrations
# - Analytics tools

if __name__ == "__main__":
    main()

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Example Workflows Created

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CSV Processing Pipeline

User: Process sales data from CSV, calculate monthly totals, and generate a report

Agent: I'll create a data processing workflow that:
1. Reads your CSV file
2. Calculates monthly sales totals
3. Generates visualizations
4. Creates a PDF report

[Workflow uses python:3.11-slim with pandas, matplotlib]

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Real-time Data Stream

User: Set up a pipeline to process streaming data from Kafka

Agent: Creating a streaming data pipeline with:
1. Kafka consumer setup
2. Data transformation
3. Real-time aggregation
4. Output to database

[Workflow with kafka-python and stream processing]

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CI/CD Automation Agent

Automate your entire CI/CD pipeline with AI.

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Docker Compose Setup

# docker-compose.yml
version: '3.8'

services:
  cicd-agent:
    build: .
    ports:
      - "8767:8767"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - KUBIYA_API_KEY=${KUBIYA_API_KEY}
      - GITHUB_TOKEN=${GITHUB_TOKEN}
    volumes:
      - ./workflows:/app/workflows
    command: >
      kubiya mcp agent
      --provider openai
      --model gpt-4o
      --port 8767

  frontend:
    image: node:18-alpine
    working_dir: /app
    volumes:
      - ./frontend:/app
    ports:
      - "3000:3000"
    command: npm run dev

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CI/CD Workflow Examples

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Multi-Language Testing

User: Create a CI pipeline that tests my app in Python, Node.js, and Go

Agent: I'll set up parallel testing for all three languages:

[Creates workflow with parallel steps]
- Python: pytest with coverage
- Node.js: jest with ESLint
- Go: go test with race detection

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Kubernetes Deployment

User: Deploy to Kubernetes with canary releases

Agent: Creating a Kubernetes deployment workflow with:
1. Build and push Docker image
2. Deploy 10% canary
3. Monitor metrics for 5 minutes
4. Gradual rollout to 100%
5. Automatic rollback on errors

[Workflow includes kubectl, helm, and monitoring]

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Security Scanner Agent

Automated security scanning and remediation.

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Specialized Security Agent

# security_agent.py
import os
from fastapi import FastAPI
from kubiya_workflow_sdk.cli import run_agent_server

app = FastAPI()

# Security-focused configuration
config = {
    "provider": "anthropic",
    "model": "claude-3-5-sonnet-20241022",
    "system_message": """
    You are a security expert. Create workflows for:
    - Vulnerability scanning
    - Dependency checking
    - Security compliance
    - Incident response
    
    Always use security best practices and latest scanning tools.
    """
}

@app.on_event("startup")
async def startup():
    await run_agent_server(**config)

# Run with: uvicorn security_agent:app --port 8768

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Security Workflow Examples

User: Scan my Docker images for vulnerabilities

Agent: I'll create a comprehensive security scanning workflow:
1. Scan with Trivy for vulnerabilities
2. Check with Snyk for dependency issues
3. Run CIS Docker Benchmark
4. Generate security report

[Creates workflow with multiple security tools]

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Custom Chatbot with Memory

Build a chatbot that remembers context and learns from interactions.

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Stateful Agent Implementation

# stateful_agent.py
from typing import Dict, List
import json
from datetime import datetime

class StatefulAgent:
    def __init__(self):
        self.conversation_memory: Dict[str, List] = {}
        self.workflow_history: List[Dict] = []
    
    def remember_workflow(self, user_id: str, workflow: Dict):
        """Store successful workflows for reuse"""
        self.workflow_history.append({
            "user_id": user_id,
            "workflow": workflow,
            "timestamp": datetime.now().isoformat(),
            "description": workflow.get("description", "")
        })
    
    def suggest_workflows(self, query: str) -> List[Dict]:
        """Suggest relevant past workflows"""
        # Implement similarity matching
        relevant = []
        for item in self.workflow_history:
            if query.lower() in item["description"].lower():
                relevant.append(item["workflow"])
        return relevant[:3]  # Top 3 suggestions

# Integration with agent server
agent = StatefulAgent()

# Use in your agent responses to provide contextual suggestions

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Frontend with Workflow History

// ChatWithHistory.tsx
import React, { useState, useEffect } from 'react';
import { useChat } from '@ai-sdk/react';

export function ChatWithHistory() {
  const [workflowHistory, setWorkflowHistory] = useState([]);
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: 'http://localhost:8765/v1/chat/completions',
    onFinish: (message) => {
      // Extract and save workflows from responses
      if (message.content.includes('workflow')) {
        const workflow = extractWorkflow(message.content);
        if (workflow) {
          setWorkflowHistory(prev => [...prev, {
            id: Date.now(),
            name: workflow.name,
            description: workflow.description,
            timestamp: new Date()
          }]);
        }
      }
    }
  });

  return (
    <div className="flex h-screen">
      {/* Chat Interface */}
      <div className="flex-1 flex flex-col">
        {/* Messages */}
        <div className="flex-1 overflow-y-auto p-4">
          {messages.map(m => (
            <ChatMessage key={m.id} message={m} />
          ))}
        </div>
        
        {/* Input */}
        <ChatInput
          input={input}
          onChange={handleInputChange}
          onSubmit={handleSubmit}
        />
      </div>
      
      {/* Workflow History Sidebar */}
      <div className="w-80 bg-gray-100 p-4 overflow-y-auto">
        <h2 className="text-lg font-bold mb-4">Workflow History</h2>
        {workflowHistory.map(wf => (
          <WorkflowCard
            key={wf.id}
            workflow={wf}
            onRerun={() => {
              handleSubmit({
                preventDefault: () => {},
                target: {
                  elements: {
                    prompt: { value: `Run the ${wf.name} workflow again` }
                  }
                }
              });
            }}
          />
        ))}
      </div>
    </div>
  );
}

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Production Deployment Example

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Kubernetes Deployment

# k8s-deployment.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: agent-config
data:
  AGENT_CONFIG: |
    {
      "provider": "openai",
      "model": "gpt-4o",
      "port": 8765,
      "features": {
        "streaming": true,
        "authentication": true,
        "rate_limiting": {
          "enabled": true,
          "requests_per_minute": 60
        }
      }
    }

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: kubiya-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: kubiya-agent
  template:
    metadata:
      labels:
        app: kubiya-agent
    spec:
      containers:
      - name: agent
        image: kubiya/agent:latest
        ports:
        - containerPort: 8765
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-keys
              key: openai
        - name: KUBIYA_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-keys
              key: kubiya
        volumeMounts:
        - name: config
          mountPath: /app/config
        livenessProbe:
          httpGet:
            path: /health
            port: 8765
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /health
            port: 8765
          initialDelaySeconds: 5
          periodSeconds: 5
      volumes:
      - name: config
        configMap:
          name: agent-config

---
apiVersion: v1
kind: Service
metadata:
  name: kubiya-agent
spec:
  selector:
    app: kubiya-agent
  ports:
  - port: 80
    targetPort: 8765
  type: LoadBalancer

---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: kubiya-agent-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: kubiya-agent
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

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Monitoring Setup

# prometheus-monitoring.yaml
apiVersion: v1
kind: ServiceMonitor
metadata:
  name: kubiya-agent-metrics
spec:
  selector:
    matchLabels:
      app: kubiya-agent
  endpoints:
  - port: metrics
    interval: 30s
    path: /metrics

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Next Steps