diff --git a/app/api/routes.py b/app/api/routes.py
index f4f1e19..ae680a0 100644
--- a/app/api/routes.py
+++ b/app/api/routes.py
@@ -1738,6 +1738,199 @@ async def health_check():
         "version": "1.0.0"
     }
 
+# ============================================================================
+# BATCH PROCESSING ENDPOINTS - For Large Clusters (10,000+ pods)
+# ============================================================================
+
+@api_router.get("/batch/statistics")
+async def get_batch_statistics(
+    k8s_client=Depends(get_k8s_client)
+):
+    """Get batch processing statistics for the cluster"""
+    try:
+        from app.tasks.batch_analysis import get_batch_statistics
+        
+        # Start the task
+        task = get_batch_statistics.delay()
+        
+        return {
+            "task_id": task.id,
+            "status": "started",
+            "message": "Batch statistics calculation started",
+            "timestamp": datetime.now().isoformat()
+        }
+        
+    except Exception as e:
+        logger.error(f"Error starting batch statistics: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@api_router.get("/batch/statistics/{task_id}")
+async def get_batch_statistics_result(task_id: str):
+    """Get batch statistics result"""
+    try:
+        from app.tasks.batch_analysis import get_batch_statistics
+        
+        # Get task result
+        task = get_batch_statistics.AsyncResult(task_id)
+        
+        if task.state == 'PENDING':
+            return {
+                "task_id": task_id,
+                "status": "pending",
+                "message": "Task is still processing..."
+            }
+        elif task.state == 'PROGRESS':
+            return {
+                "task_id": task_id,
+                "status": "progress",
+                "message": "Task is in progress...",
+                "meta": task.info
+            }
+        elif task.state == 'SUCCESS':
+            return {
+                "task_id": task_id,
+                "status": "success",
+                "result": task.result
+            }
+        else:
+            return {
+                "task_id": task_id,
+                "status": "error",
+                "error": str(task.info)
+            }
+            
+    except Exception as e:
+        logger.error(f"Error getting batch statistics result: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@api_router.post("/batch/process")
+async def start_batch_processing(
+    namespace: Optional[str] = None,
+    include_system_namespaces: bool = False,
+    batch_size: int = 100
+):
+    """Start batch processing for large clusters"""
+    try:
+        from app.tasks.batch_analysis import process_cluster_batch
+        
+        # Validate batch size
+        if batch_size < 10 or batch_size > 500:
+            raise HTTPException(
+                status_code=400, 
+                detail="Batch size must be between 10 and 500"
+            )
+        
+        # Start the task
+        task = process_cluster_batch.delay({
+            'namespace': namespace,
+            'include_system_namespaces': include_system_namespaces,
+            'batch_size': batch_size
+        })
+        
+        return {
+            "task_id": task.id,
+            "status": "started",
+            "message": f"Batch processing started with batch size {batch_size}",
+            "namespace": namespace,
+            "include_system_namespaces": include_system_namespaces,
+            "timestamp": datetime.now().isoformat()
+        }
+        
+    except Exception as e:
+        logger.error(f"Error starting batch processing: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@api_router.get("/batch/process/{task_id}")
+async def get_batch_processing_result(task_id: str):
+    """Get batch processing result"""
+    try:
+        from app.tasks.batch_analysis import process_cluster_batch
+        
+        # Get task result
+        task = process_cluster_batch.AsyncResult(task_id)
+        
+        if task.state == 'PENDING':
+            return {
+                "task_id": task_id,
+                "status": "pending",
+                "message": "Task is still processing..."
+            }
+        elif task.state == 'PROGRESS':
+            return {
+                "task_id": task_id,
+                "status": "progress",
+                "message": "Task is in progress...",
+                "meta": task.info
+            }
+        elif task.state == 'SUCCESS':
+            return {
+                "task_id": task_id,
+                "status": "success",
+                "result": task.result
+            }
+        else:
+            return {
+                "task_id": task_id,
+                "status": "error",
+                "error": str(task.info)
+            }
+            
+    except Exception as e:
+        logger.error(f"Error getting batch processing result: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@api_router.get("/batch/validations")
+async def get_batch_validations(
+    namespace: Optional[str] = None,
+    severity: Optional[str] = None,
+    page: int = 1,
+    page_size: int = 50,
+    include_system_namespaces: bool = False,
+    k8s_client=Depends(get_k8s_client)
+):
+    """Get validations using batch processing for large clusters"""
+    try:
+        from app.services.batch_processing import batch_processing_service
+        
+        # Get all validations using batch processing
+        all_validations = []
+        
+        async for batch_result in batch_processing_service.process_cluster_in_batches(
+            k8s_client,
+            namespace=namespace,
+            include_system_namespaces=include_system_namespaces
+        ):
+            all_validations.extend(batch_result.validations)
+        
+        # Filter by severity if specified
+        if severity:
+            all_validations = [
+                v for v in all_validations if v.get('severity') == severity
+            ]
+        
+        # Pagination
+        total = len(all_validations)
+        start = (page - 1) * page_size
+        end = start + page_size
+        paginated_validations = all_validations[start:end]
+        
+        return {
+            "validations": paginated_validations,
+            "pagination": {
+                "page": page,
+                "page_size": page_size,
+                "total": total,
+                "total_pages": (total + page_size - 1) // page_size
+            },
+            "processing_method": "batch",
+            "batch_size": batch_processing_service.batch_size,
+            "timestamp": datetime.now().isoformat()
+        }
+        
+    except Exception as e:
+        logger.error(f"Error getting batch validations: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
 # ============================================================================
 # OPTIMIZED ENDPOINTS - 10x Performance Improvement
 # ============================================================================
diff --git a/app/core/config.py b/app/core/config.py
index 6c3cb01..7b14ed2 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -57,6 +57,11 @@ class Settings(BaseSettings):
     enable_rbac: bool = True
     service_account_name: str = "resource-governance-sa"
     
+    # Batch processing settings
+    batch_size: int = Field(default=100, alias="BATCH_SIZE")
+    max_batch_size: int = Field(default=500, alias="MAX_BATCH_SIZE")
+    min_batch_size: int = Field(default=10, alias="MIN_BATCH_SIZE")
+    
     class Config:
         env_file = ".env"
         case_sensitive = False
diff --git a/app/services/batch_processing.py b/app/services/batch_processing.py
new file mode 100644
index 0000000..199cb92
--- /dev/null
+++ b/app/services/batch_processing.py
@@ -0,0 +1,284 @@
+"""
+Batch Processing Service for Large Clusters
+
+This service implements intelligent batch processing to handle large clusters
+efficiently by processing pods in batches of 100, reducing memory usage and
+improving performance for clusters with 10,000+ pods.
+"""
+
+import asyncio
+import logging
+from typing import List, Dict, Any, Optional, AsyncGenerator, Tuple
+from dataclasses import dataclass
+from datetime import datetime
+import gc
+
+from app.core.kubernetes_client import K8sClient, PodResource
+from app.services.validation_service import ValidationService
+from app.services.smart_recommendations import SmartRecommendationsService
+from app.services.historical_analysis import HistoricalAnalysisService
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class BatchResult:
+    """Result of a batch processing operation"""
+    batch_number: int
+    total_batches: int
+    pods_processed: int
+    validations: List[Dict[str, Any]]
+    recommendations: List[Dict[str, Any]]
+    processing_time: float
+    memory_usage: float
+    errors: List[str]
+
+@dataclass
+class BatchProgress:
+    """Progress tracking for batch processing"""
+    current_batch: int
+    total_batches: int
+    pods_processed: int
+    total_pods: int
+    validations_found: int
+    recommendations_generated: int
+    processing_time: float
+    estimated_completion: Optional[datetime]
+    status: str  # 'running', 'completed', 'error', 'paused'
+
+class BatchProcessingService:
+    """Service for processing large clusters in batches"""
+    
+    def __init__(self, batch_size: int = 100):
+        self.batch_size = batch_size
+        self.validation_service = ValidationService()
+        self.smart_recommendations_service = SmartRecommendationsService()
+        self.historical_service = HistoricalAnalysisService()
+        
+    async def process_cluster_in_batches(
+        self, 
+        k8s_client: K8sClient,
+        namespace: Optional[str] = None,
+        include_system_namespaces: bool = False,
+        progress_callback: Optional[callable] = None
+    ) -> AsyncGenerator[BatchResult, None]:
+        """
+        Process cluster pods in batches with progress tracking
+        
+        Args:
+            k8s_client: Kubernetes client instance
+            namespace: Optional namespace filter
+            include_system_namespaces: Whether to include system namespaces
+            progress_callback: Optional callback for progress updates
+            
+        Yields:
+            BatchResult: Results for each batch processed
+        """
+        try:
+            # Get all pods
+            if namespace:
+                namespace_resources = await k8s_client.get_namespace_resources(namespace)
+                all_pods = namespace_resources.pods
+            else:
+                all_pods = await k8s_client.get_all_pods(include_system_namespaces=include_system_namespaces)
+            
+            total_pods = len(all_pods)
+            total_batches = (total_pods + self.batch_size - 1) // self.batch_size
+            
+            logger.info(f"Starting batch processing: {total_pods} pods in {total_batches} batches of {self.batch_size}")
+            
+            # Process pods in batches
+            for batch_num in range(total_batches):
+                start_idx = batch_num * self.batch_size
+                end_idx = min(start_idx + self.batch_size, total_pods)
+                batch_pods = all_pods[start_idx:end_idx]
+                
+                # Process this batch
+                batch_result = await self._process_batch(
+                    batch_num + 1, 
+                    total_batches, 
+                    batch_pods,
+                    start_idx,
+                    total_pods
+                )
+                
+                # Update progress
+                if progress_callback:
+                    progress = BatchProgress(
+                        current_batch=batch_num + 1,
+                        total_batches=total_batches,
+                        pods_processed=end_idx,
+                        total_pods=total_pods,
+                        validations_found=sum(len(r.validations) for r in batch_result),
+                        recommendations_generated=sum(len(r.recommendations) for r in batch_result),
+                        processing_time=batch_result.processing_time,
+                        estimated_completion=None,  # Could calculate based on avg time
+                        status='running'
+                    )
+                    progress_callback(progress)
+                
+                yield batch_result
+                
+                # Memory cleanup after each batch
+                await self._cleanup_memory()
+                
+                # Small delay to prevent overwhelming the system
+                await asyncio.sleep(0.1)
+                
+        except Exception as e:
+            logger.error(f"Error in batch processing: {e}", exc_info=True)
+            raise
+    
+    async def _process_batch(
+        self, 
+        batch_number: int, 
+        total_batches: int, 
+        pods: List[PodResource],
+        start_idx: int,
+        total_pods: int
+    ) -> BatchResult:
+        """Process a single batch of pods"""
+        start_time = datetime.now()
+        errors = []
+        validations = []
+        recommendations = []
+        
+        try:
+            logger.info(f"Processing batch {batch_number}/{total_batches}: {len(pods)} pods")
+            
+            # Process validations for this batch
+            for pod in pods:
+                try:
+                    pod_validations = self.validation_service.validate_pod_resources(pod)
+                    for validation in pod_validations:
+                        validations.append({
+                            'pod_name': validation.pod_name,
+                            'namespace': validation.namespace,
+                            'container_name': validation.container_name,
+                            'validation_type': validation.validation_type,
+                            'severity': validation.severity,
+                            'message': validation.message,
+                            'recommendation': validation.recommendation,
+                            'priority_score': validation.priority_score,
+                            'workload_category': validation.workload_category,
+                            'estimated_impact': validation.estimated_impact
+                        })
+                except Exception as e:
+                    error_msg = f"Error validating pod {pod.name}: {str(e)}"
+                    logger.warning(error_msg)
+                    errors.append(error_msg)
+            
+            # Generate smart recommendations for this batch
+            try:
+                batch_recommendations = await self.smart_recommendations_service.generate_smart_recommendations(pods, [])
+                for rec in batch_recommendations:
+                    recommendations.append({
+                        'workload_name': rec.workload_name,
+                        'namespace': rec.namespace,
+                        'recommendation_type': rec.recommendation_type,
+                        'priority_score': rec.priority_score,
+                        'title': rec.title,
+                        'description': rec.description,
+                        'estimated_impact': rec.estimated_impact,
+                        'implementation_effort': rec.implementation_effort
+                    })
+            except Exception as e:
+                error_msg = f"Error generating recommendations for batch {batch_number}: {str(e)}"
+                logger.warning(error_msg)
+                errors.append(error_msg)
+            
+            processing_time = (datetime.now() - start_time).total_seconds()
+            
+            return BatchResult(
+                batch_number=batch_number,
+                total_batches=total_batches,
+                pods_processed=len(pods),
+                validations=validations,
+                recommendations=recommendations,
+                processing_time=processing_time,
+                memory_usage=self._get_memory_usage(),
+                errors=errors
+            )
+            
+        except Exception as e:
+            processing_time = (datetime.now() - start_time).total_seconds()
+            error_msg = f"Error processing batch {batch_number}: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            
+            return BatchResult(
+                batch_number=batch_number,
+                total_batches=total_batches,
+                pods_processed=len(pods),
+                validations=[],
+                recommendations=[],
+                processing_time=processing_time,
+                memory_usage=self._get_memory_usage(),
+                errors=[error_msg]
+            )
+    
+    async def _cleanup_memory(self):
+        """Clean up memory after each batch"""
+        try:
+            # Force garbage collection
+            gc.collect()
+            
+            # Small delay to allow memory cleanup
+            await asyncio.sleep(0.01)
+            
+        except Exception as e:
+            logger.warning(f"Error during memory cleanup: {e}")
+    
+    def _get_memory_usage(self) -> float:
+        """Get current memory usage in MB"""
+        try:
+            import psutil
+            process = psutil.Process()
+            return process.memory_info().rss / 1024 / 1024  # Convert to MB
+        except ImportError:
+            return 0.0
+        except Exception:
+            return 0.0
+    
+    async def get_batch_statistics(self, k8s_client: K8sClient) -> Dict[str, Any]:
+        """Get statistics about batch processing for the cluster"""
+        try:
+            all_pods = await k8s_client.get_all_pods(include_system_namespaces=False)
+            total_pods = len(all_pods)
+            total_batches = (total_pods + self.batch_size - 1) // self.batch_size
+            
+            # Group by namespace
+            namespace_counts = {}
+            for pod in all_pods:
+                namespace_counts[pod.namespace] = namespace_counts.get(pod.namespace, 0) + 1
+            
+            return {
+                'total_pods': total_pods,
+                'total_namespaces': len(namespace_counts),
+                'batch_size': self.batch_size,
+                'total_batches': total_batches,
+                'estimated_processing_time': total_batches * 2.0,  # 2 seconds per batch estimate
+                'namespace_distribution': namespace_counts,
+                'memory_efficiency': 'High' if total_batches > 10 else 'Standard',
+                'recommended_batch_size': self._recommend_batch_size(total_pods)
+            }
+            
+        except Exception as e:
+            logger.error(f"Error getting batch statistics: {e}", exc_info=True)
+            return {
+                'error': str(e),
+                'total_pods': 0,
+                'total_batches': 0
+            }
+    
+    def _recommend_batch_size(self, total_pods: int) -> int:
+        """Recommend optimal batch size based on cluster size"""
+        if total_pods < 1000:
+            return 50
+        elif total_pods < 5000:
+            return 100
+        elif total_pods < 10000:
+            return 150
+        else:
+            return 200
+
+# Global instance
+batch_processing_service = BatchProcessingService()
diff --git a/app/tasks/batch_analysis.py b/app/tasks/batch_analysis.py
new file mode 100644
index 0000000..d58c843
--- /dev/null
+++ b/app/tasks/batch_analysis.py
@@ -0,0 +1,226 @@
+"""
+Celery tasks for batch processing of large clusters
+"""
+
+import asyncio
+import logging
+from typing import Dict, Any, List
+from datetime import datetime
+import os
+
+from app.celery_app import celery_app
+from app.services.batch_processing import batch_processing_service, BatchProgress
+from app.core.kubernetes_client import K8sClient
+
+logger = logging.getLogger(__name__)
+
+@celery_app.task(bind=True, name='app.tasks.batch_analysis.process_cluster_batch')
+def process_cluster_batch(self, cluster_config: Dict[str, Any] = None):
+    """
+    Process cluster analysis in batches for large clusters
+    
+    Args:
+        cluster_config: Cluster configuration dict
+        
+    Returns:
+        dict: Batch processing results
+    """
+    try:
+        # Update task state
+        self.update_state(
+            state='PROGRESS',
+            meta={
+                'current': 0, 
+                'total': 1, 
+                'status': 'Starting batch processing...',
+                'batch_number': 0,
+                'total_batches': 0,
+                'pods_processed': 0,
+                'total_pods': 0
+            }
+        )
+        
+        # Initialize clients
+        k8s_client = K8sClient()
+        
+        # Run async processing
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        
+        try:
+            result = loop.run_until_complete(_process_cluster_async(self, k8s_client, cluster_config))
+            return result
+        finally:
+            loop.close()
+            
+    except Exception as exc:
+        logger.error(f"Batch processing failed: {str(exc)}", exc_info=True)
+        return {
+            'error': str(exc),
+            'status': 'failed',
+            'timestamp': datetime.now().isoformat()
+        }
+
+async def _process_cluster_async(task, k8s_client: K8sClient, cluster_config: Dict[str, Any]):
+    """Async processing function"""
+    try:
+        # Initialize K8s client
+        await k8s_client.initialize()
+        
+        # Get batch statistics
+        batch_stats = await batch_processing_service.get_batch_statistics(k8s_client)
+        
+        # Update task with statistics
+        task.update_state(
+            state='PROGRESS',
+            meta={
+                'current': 1,
+                'total': batch_stats.get('total_batches', 1),
+                'status': f"Processing {batch_stats.get('total_pods', 0)} pods in {batch_stats.get('total_batches', 0)} batches...",
+                'batch_number': 0,
+                'total_batches': batch_stats.get('total_batches', 0),
+                'pods_processed': 0,
+                'total_pods': batch_stats.get('total_pods', 0),
+                'statistics': batch_stats
+            }
+        )
+        
+        # Process in batches
+        all_validations = []
+        all_recommendations = []
+        total_errors = []
+        total_processing_time = 0
+        
+        batch_count = 0
+        
+        async for batch_result in batch_processing_service.process_cluster_in_batches(
+            k8s_client,
+            namespace=cluster_config.get('namespace') if cluster_config else None,
+            include_system_namespaces=cluster_config.get('include_system_namespaces', False) if cluster_config else False,
+            progress_callback=lambda progress: _update_task_progress(task, progress)
+        ):
+            batch_count += 1
+            
+            # Collect results
+            all_validations.extend(batch_result.validations)
+            all_recommendations.extend(batch_result.recommendations)
+            total_errors.extend(batch_result.errors)
+            total_processing_time += batch_result.processing_time
+            
+            # Update task progress
+            task.update_state(
+                state='PROGRESS',
+                meta={
+                    'current': batch_count,
+                    'total': batch_result.total_batches,
+                    'status': f"Completed batch {batch_count}/{batch_result.total_batches} - {len(all_validations)} validations found",
+                    'batch_number': batch_count,
+                    'total_batches': batch_result.total_batches,
+                    'pods_processed': batch_count * batch_processing_service.batch_size,
+                    'total_pods': batch_stats.get('total_pods', 0),
+                    'validations_found': len(all_validations),
+                    'recommendations_generated': len(all_recommendations),
+                    'processing_time': total_processing_time,
+                    'memory_usage': batch_result.memory_usage,
+                    'errors': len(total_errors)
+                }
+            )
+        
+        # Final results
+        results = {
+            'timestamp': datetime.now().isoformat(),
+            'total_pods': batch_stats.get('total_pods', 0),
+            'total_batches': batch_count,
+            'batch_size': batch_processing_service.batch_size,
+            'total_validations': len(all_validations),
+            'total_recommendations': len(all_recommendations),
+            'total_errors': len(total_errors),
+            'processing_time': total_processing_time,
+            'statistics': batch_stats,
+            'validations': all_validations,
+            'recommendations': all_recommendations,
+            'errors': total_errors,
+            'status': 'completed'
+        }
+        
+        logger.info(f"Batch processing completed: {len(all_validations)} validations, {len(all_recommendations)} recommendations in {total_processing_time:.2f}s")
+        
+        return results
+        
+    except Exception as e:
+        logger.error(f"Error in async batch processing: {e}", exc_info=True)
+        raise
+
+def _update_task_progress(task, progress: BatchProgress):
+    """Update Celery task progress"""
+    try:
+        task.update_state(
+            state='PROGRESS',
+            meta={
+                'current': progress.current_batch,
+                'total': progress.total_batches,
+                'status': f"Processing batch {progress.current_batch}/{progress.total_batches} - {progress.pods_processed}/{progress.total_pods} pods",
+                'batch_number': progress.current_batch,
+                'total_batches': progress.total_batches,
+                'pods_processed': progress.pods_processed,
+                'total_pods': progress.total_pods,
+                'validations_found': progress.validations_found,
+                'recommendations_generated': progress.recommendations_generated,
+                'processing_time': progress.processing_time,
+                'estimated_completion': progress.estimated_completion.isoformat() if progress.estimated_completion else None
+            }
+        )
+    except Exception as e:
+        logger.warning(f"Error updating task progress: {e}")
+
+@celery_app.task(bind=True, name='app.tasks.batch_analysis.get_batch_statistics')
+def get_batch_statistics(self, cluster_config: Dict[str, Any] = None):
+    """
+    Get batch processing statistics for the cluster
+    
+    Args:
+        cluster_config: Cluster configuration dict
+        
+    Returns:
+        dict: Batch statistics
+    """
+    try:
+        # Initialize clients
+        k8s_client = K8sClient()
+        
+        # Run async processing
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        
+        try:
+            result = loop.run_until_complete(_get_statistics_async(k8s_client, cluster_config))
+            return result
+        finally:
+            loop.close()
+            
+    except Exception as exc:
+        logger.error(f"Error getting batch statistics: {str(exc)}", exc_info=True)
+        return {
+            'error': str(exc),
+            'status': 'failed',
+            'timestamp': datetime.now().isoformat()
+        }
+
+async def _get_statistics_async(k8s_client: K8sClient, cluster_config: Dict[str, Any]):
+    """Async function to get batch statistics"""
+    try:
+        # Initialize K8s client
+        await k8s_client.initialize()
+        
+        # Get batch statistics
+        batch_stats = await batch_processing_service.get_batch_statistics(k8s_client)
+        
+        return {
+            'timestamp': datetime.now().isoformat(),
+            'statistics': batch_stats,
+            'status': 'completed'
+        }
+        
+    except Exception as e:
+        logger.error(f"Error in async statistics: {e}", exc_info=True)
+        raise
diff --git a/app/workers/celery_worker.py b/app/workers/celery_worker.py
index 2816fe1..c86a78c 100644
--- a/app/workers/celery_worker.py
+++ b/app/workers/celery_worker.py
@@ -11,6 +11,10 @@ sys.path.insert(0, '/app')
 
 from app.celery_app import celery_app
 
+# Import tasks to register them
+from app.tasks.cluster_analysis import analyze_cluster
+from app.tasks.batch_analysis import process_cluster_batch, get_batch_statistics
+
 if __name__ == '__main__':
     # Start Celery worker
     celery_app.worker_main([
diff --git a/k8s/configmap.yaml b/k8s/configmap.yaml
index 53a0d12..5892548 100644
--- a/k8s/configmap.yaml
+++ b/k8s/configmap.yaml
@@ -20,6 +20,11 @@ data:
   INCLUDE_SYSTEM_NAMESPACES: "false"
   SYSTEM_NAMESPACE_PREFIXES: '["kube-", "openshift-", "knative-", "default", "kube-system", "kube-public", "kube-node-lease"]'
   
+  # Configurações de batch processing
+  BATCH_SIZE: "100"
+  MAX_BATCH_SIZE: "500"
+  MIN_BATCH_SIZE: "10"
+  
   # URL do Prometheus
   PROMETHEUS_URL: "https://prometheus-k8s.openshift-monitoring.svc.cluster.local:9091"
   
diff --git a/requirements.txt b/requirements.txt
index f3cc122..610dd3d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -17,3 +17,4 @@ aiohttp==3.9.1
 celery==5.3.4
 redis==5.0.1
 flower==2.0.1
+psutil==5.9.6