npm install ainest-sdk

import { AINest } from 'ainest-sdk';

// Initialize the SDK
const nest = new AINest({
  apiKey: 'your-api-key',
  region: 'us-west-2'
});

// Create your first agent
const agent = await nest.createAgent({
  name: 'my-first-agent',
  model: 'gpt-4',
  maxTokens: 1000
});

// Deploy the agent
await agent.deploy();

AINEST_API_KEY=your-api-key
AINEST_REGION=us-west-2
AINEST_ENV=production
AINEST_LOG_LEVEL=info

// Configure cloud deployment
await agent.deploy({
  provider: 'ainest-cloud',
  region: 'us-west-2',
  resources: {
    cpu: '2',
    memory: '4Gi',
    storage: '100Gi'
  },
  scaling: {
    minReplicas: 2,
    maxReplicas: 10,
    targetCPUUtilization: 70
  },
  networking: {
    ingressEnabled: true,
    loadBalancer: 'application'
  }
});

# Install AINest operator
kubectl apply -f https://ainest.io/operator.yaml

# Deploy agent using custom resource
apiVersion: ainest.io/v1
kind: AIAgent
metadata:
  name: my-agent
spec:
  model: gpt-4
  replicas: 3
  resources:
    requests:
      cpu: "2"
      memory: "4Gi"
    limits:
      cpu: "4"
      memory: "8Gi"
  storage:
    size: 100Gi
    storageClass: standard
  monitoring:
    prometheus: true
    grafana: true

const chatAgent = await nest.createAgent({
  name: 'customer-support',
  type: 'conversational',
  model: 'gpt-4',
  context: {
    role: 'customer-support',
    knowledgeBase: 'support-docs',
    tone: 'professional'
  }
});

// Handle user messages
chatAgent.on('message', async (message, context) => {
  // Access conversation history
  const history = await context.getHistory();
  
  // Generate response
  const response = await chatAgent.respond(message, {
    temperature: 0.7,
    maxTokens: 500
  });

  // Store interaction
  await context.logInteraction({
    message,
    response,
    metadata: {
      userId: context.userId,
      timestamp: Date.now()
    }
  });

  return response;
});

const processingAgent = await nest.createAgent({
  name: 'data-processor',
  type: 'worker',
  schedule: '*/15 * * * *', // Run every 15 minutes
  
  async process(data, context) {
    // Process batch of data
    const results = await Promise.all(
      data.map(async (item) => {
        const processed = await processItem(item);
        return {
          id: item.id,
          result: processed,
          timestamp: Date.now()
        };
      })
    );

    // Store results
    await context.storage.saveResults(results);

    // Trigger notifications
    await context.notify('processing.complete', {
      batchId: context.batchId,
      itemsProcessed: results.length
    });

    return results;
  }
});

// Create API-enabled agent
const apiAgent = await nest.createAgent({
  name: 'api-service',
  type: 'service',
  endpoints: {
    '/analyze': {
      method: 'POST',
      handler: async (req, context) => {
        const { text } = req.body;
        const analysis = await analyzeText(text);
        return {
          result: analysis,
          metadata: {
            processingTime: context.duration,
            modelUsed: context.model
          }
        };
      }
    }
  },
  middleware: [
    authenticate,
    validateInput,
    rateLimit
  ]
});

// Deploy as API service
await apiAgent.deployService({
  port: 3000,
  cors: true,
  rateLimit: {
    windowMs: 15 * 60 * 1000,
    max: 100
  }
});

const agent = await nest.createAgent({
  name: 'advanced-agent',
  behaviors: {
    reasoning: {
      type: 'chain-of-thought',
      steps: ['analyze', 'plan', 'execute'],
      maxDepth: 3
    },
    learning: {
      type: 'reinforcement',
      model: 'dqn',
      parameters: {
        learningRate: 0.001,
        discountFactor: 0.95
      }
    },
    memory: {
      type: 'hierarchical',
      storage: {
        shortTerm: 'redis',
        longTerm: 'postgresql'
      }
    }
  }
});

// Input validation middleware
const validateInput = next => async (input, context) => {
  const validationResult = await validator.validate(input);
  
  if (!validationResult.valid) {
    throw new ValidationError(validationResult.errors);
  }
  
  return next(input, context);
};

// Rate limiting middleware
const rateLimit = next => async (input, context) => {
  const { userId } = context;
  const key = `rate_limit:${userId}`;
  
  const count = await redis.incr(key);
  if (count === 1) {
    await redis.expire(key, 60); // 1 minute window
  }
  
  if (count > 100) {
    throw new RateLimitError('Too many requests');
  }
  
  return next(input, context);
};

// Apply middleware
agent.use(validateInput);
agent.use(rateLimit);

// Set up event handlers
agent.on('processing.start', async (context) => {
  // Initialize processing context
  await context.initialize();
  
  // Start performance monitoring
  const span = tracer.startSpan('agent.processing');
  context.setSpan(span);
});

agent.on('processing.complete', async (context, result) => {
  // Record metrics
  metrics.recordLatency('processing_time', context.duration);
  metrics.incrementCounter('processed_items');
  
  // Store results
  await context.storage.save(result);
  
  // Clean up
  context.getSpan().end();
});

agent.on('error', async (error, context) => {
  // Log error details
  logger.error('Processing error', {
    error: error.message,
    context: context.toJSON()
  });
  
  // Handle specific error types
  if (error instanceof ValidationError) {
    await handleValidationError(error, context);
  } else if (error instanceof RateLimitError) {
    await handleRateLimitError(error, context);
  }
  
  // Notify monitoring systems
  await alerting.notify('agent.error', {
    error,
    context,
    severity: 'high'
  });
});