Nemo

Workbench

Development Environment

The Nemo Workbench provides a comprehensive development environment for creating, testing, and deploying memory-enhanced AI systems. This integrated platform combines visual memory modeling tools with powerful debugging capabilities and enterprise-grade deployment infrastructure.

Start Building with the Workbench

Learn how to use the Nemo Workbench to create and deploy memory-enhanced applications with our comprehensive guides and tutorials.

Setup Guide Workbench Tutorials API Reference

50+ Memory Components

99.9% Uptime SLA

10ms Deploy Time

1M+ API Calls/Sec

Visual Memory Designer

Drag-and-drop interface for constructing complex memory architectures with real-time validation, optimization suggestions, and automated testing. The visual designer supports collaborative development with version control integration.

Visual Designer

Intuitive drag-and-drop interface for constructing complex memory architectures with real-time validation and optimization suggestions.

Debug Console

Advanced debugging tools with memory trace visualization, bottleneck detection, and performance profiling for optimal system tuning.

Cloud Integration

Seamless deployment to cloud platforms with auto-scaling memory clusters, global distribution, and disaster recovery capabilities.

from nemo.workbench import MemoryTestSuite, VisualDesigner, CloudDeployer

# Initialize development environment
designer = VisualDesigner()
test_suite = MemoryTestSuite()
deployer = CloudDeployer()

# Create memory architecture
memory_arch = designer.create_architecture()
memory_arch.add_component('episodic_store', capacity='10GB')
memory_arch.add_component('associative_network', nodes=1000000)
memory_arch.add_component('working_memory', slots=256)

# Configure optimization parameters
memory_arch.set_optimization_strategy('adaptive_learning')
memory_arch.enable_auto_scaling(min_nodes=2, max_nodes=100)

# Run comprehensive tests
test_suite.add_scenario('episodic_recall', episodes=1000)
test_suite.add_scenario('stress_test', duration='24h')
results = test_suite.run_benchmark()

# Deploy to production
deployer.deploy(memory_arch, environment='production')
                    