Nemo

API Interface

Memory System APIs

The Nemo API layer represents the critical interface between AI applications and memory systems, enabling seamless integration of mnemonic capabilities into artificial intelligence architectures. Modern memory APIs provide standardized access patterns for storing, retrieving, and manipulating cognitive data structures.

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Quick Start Guide API Reference View Examples

Core API Components

Memory allocation, storage operations, associative functions, and consolidation interfaces work together to provide comprehensive memory management capabilities for enterprise-scale AI deployments.

// Memory Allocation Interface
nemo.memory.allocate(size, type='episodic', persistence='session')
nemo.memory.deallocate(memory_id)
nemo.memory.resize(memory_id, new_size)

// Storage and Retrieval Operations
nemo.memory.store(data, context, associations=[])
nemo.memory.retrieve(query, similarity_threshold=0.8)
nemo.memory.update(memory_id, new_data, merge_strategy='append')

// Associative Memory Functions
nemo.memory.associate(item_a, item_b, strength=1.0)
nemo.memory.find_associations(item, depth=3)
nemo.memory.strengthen_path(association_chain)

// Memory Consolidation APIs
nemo.memory.consolidate(memory_set, strategy='temporal')
nemo.memory.optimize_retrieval_paths()
nemo.memory.compress_redundant_data()
                    

Real-time Processing

Sub-millisecond response times for memory operations with concurrent access support and lock-free data structures optimized for high-throughput applications.

Scalable Architecture

Distributed memory systems that scale horizontally across multiple compute nodes with automatic load balancing and fault tolerance.

Type Safety

Strongly typed memory objects with compile-time validation and runtime integrity checking to prevent memory corruption and ensure data consistency.

Security Framework

End-to-end encryption for memory operations with fine-grained access controls and audit logging for compliance requirements.

The API design prioritizes both performance and developer experience, providing intuitive interfaces while maintaining the flexibility needed for complex AI memory architectures. Integration with existing machine learning frameworks is seamless, with native support for popular libraries and cloud platforms.