@zensation/core

@zensation/core

7-layer memory system for AI agents. Pluggable storage, embeddings, and LLM providers.

What's Inside

The orchestration layer that turns @zensation/algorithms into a complete memory system:

• MemoryCoordinator — orchestrates all 7 layers: auto-routing store(), cross-layer recall(), consolidate(), decay(), FSRS review queue
• 7 Memory Layers — Working, Short-Term, Episodic, Semantic, Procedural, Core, Cross-Context
• Sleep Consolidation — memory replay simulation (Stickgold & Walker, 2013)
• Pluggable Adapters — bring your own storage (@zensation/adapter-postgres, @zensation/adapter-sqlite)

Quick Start

npm install @zensation/core @zensation/adapter-sqlite

import { MemoryCoordinator } from '@zensation/core';
import { SqliteAdapter } from '@zensation/adapter-sqlite';

// Storage is pluggable; SQLite needs no server. Without an embedding provider recall ranks
// by wording; pass `embedding: yourProvider` for semantic matching.
const memory = new MemoryCoordinator({ storage: new SqliteAdapter({ filename: './memory.db' }) });

// Auto-routes to the right layer (semantic, episodic, procedural, or core)
await memory.store('User prefers TypeScript', { type: 'auto' });

// Cross-layer search with ranked, deduplicated results
const results = await memory.recall('programming preferences');

// Consolidate: promote episodic -> semantic, apply decay
await memory.consolidate();

// FSRS review queue across all layers
const dueItems = await memory.getReviewQueue();

Tree-Shakeable Imports

// Full coordinator
import { MemoryCoordinator } from '@zensation/core';

// Individual layers
import { WorkingMemory } from '@zensation/core/layers/working';
import { EpisodicMemory } from '@zensation/core/layers/episodic';
import { SemanticMemory } from '@zensation/core/layers/semantic';
import { ProceduralMemory } from '@zensation/core/layers/procedural';
import { CoreMemory } from '@zensation/core/layers/core';
import { CrossContextMemory } from '@zensation/core/layers/cross-context';

// Adapter interfaces
import type { StorageAdapter, EmbeddingProvider } from '@zensation/core/interfaces';

Research

These algorithms are documented in an open-access technical disclosure: ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture (Zenodo). See also: HuggingFace Model Card.

Part of ZenBrain

This package is part of the ZenBrain monorepo — the neuroscience-inspired memory system for AI agents.

Package	Description
@zensation/algorithms	Pure algorithms (FSRS, Hebbian, emotional, Bayesian)
@zensation/core	Memory layers + coordinator (this package)
@zensation/adapter-postgres	PostgreSQL + pgvector storage
@zensation/adapter-sqlite	SQLite storage (better-sqlite3)
@zensation/mcp	MCP server: store, recall, consolidate, health
@zensation/ai-sdk	Vercel AI SDK memory middleware

License

Apache 2.0 — see LICENSE.

About ZenBrain

ZenBrain is a seven-layer, neuroscience-derived memory architecture for LLM agents, built as zero-dependency TypeScript and published under Apache-2.0. The benchmark results, and the configuration they were measured in, are reported in the paper; the reproduction packages are on Zenodo.

Works out of the box without an embedding provider — lexical ranking, zero dependencies. The paper's measurements used nomic-embed-text as the embedding provider.

• Source and issues: github.com/zensation-ai/zenbrain
• Paper: arXiv:2604.23878 · Open-access archive: 10.5281/zenodo.19353663
• Try it in the browser: zensation.ai/en/playground
• Model card: huggingface.co/zensation-ai/zenbrain
• Packages: @zensation/algorithms · @zensation/core · @zensation/adapter-postgres · @zensation/adapter-sqlite · @zensation/mcp · @zensation/ai-sdk

License: Apache-2.0