Asynchronous operations in Cognee allow for non-blocking memory management, enabling your applications to remain responsive while processing large datasets and complex knowledge graphs.

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Key Features

Non-blocking Operations

Responsive AppsAll memory operations are asynchronous, keeping your application responsive during heavy processing.

Background Processing

Efficient Resource UseProcess large datasets in the background while serving user requests in the foreground.

Concurrent Operations

Parallel ProcessingHandle multiple memory operations simultaneously for improved throughput.

Real-time Updates

Live MemoryGet real-time notifications when memory operations complete or data changes.

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Async Memory Operations

Core Async Operations
import asyncio
import cognee

async def basic_async_operations():
    """Basic asynchronous memory operations"""
    
    # Asynchronous data addition
    await cognee.add("Large dataset content here...")
    print("✅ Data added asynchronously")
    
    # Asynchronous knowledge graph creation
    await cognee.cognify()
    print("✅ Knowledge graph created asynchronously")
    
    # Asynchronous search
    results = await cognee.search(
        "Find relevant information",
        query_type=SearchType.GRAPH_COMPLETION
    )
    print(f"✅ Found {len(results)} results asynchronously")
    
    return results

# Run async operations
results = asyncio.run(basic_async_operations())

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Next Steps

Custom Pipelines

Async PipelinesBuild custom asynchronous processing pipelines for complex workflows.

Performance Tuning

OptimizationOptimize async performance for high-throughput applications.

Monitoring

ObservabilitySet up comprehensive monitoring for async memory operations.

Deployment

Production SetupDeploy async memory systems in production environments.