ZenBrain Memory

ZenBrain The neuroscience-inspired memory system for AI agents. 7 memory layers. Real neuroscience — FSRS, Hebbian, sleep consolidation, emotional tagging, plus 10 advanced research modules (vmPFC-FSRS, two-factor Hebbian, simulation-selection sleep, Fiedler-value KG health, IB budget, Hopfield STM, ...).Pure TypeScript. Zero dependencies. 528 tests. Extracted from a production AI platform.

Status: pre-1.0, semver — the public API can still change before 1.0. 528 tests green on Node 22, 24 and 26 in CI · every release published to npm with build provenance · every change recorded in the CHANGELOG · issues and pull requests get a first response typically within 72 hours.

▶ Try the live playground in your browser — runs this published code, no install required.

📄 Paper & Citation — ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems

• arXiv preprint (cs.AI): arxiv.org/abs/2604.23878
• Open-access archive (Zenodo / CERN): doi.org/10.5281/zenodo.19353663
• Reproducibility artifacts (Zenodo): doi.org/10.5281/zenodo.19481262
• ORCID: 0009-0001-1793-012X
• License: CC BY 4.0 (paper) · Apache-2.0 (code)

@misc{bering2026zenbrain,
  title         = {ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems},
  author        = {Bering, Alexander},
  year          = {2026},
  eprint        = {2604.23878},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  doi           = {10.5281/zenodo.19353663},
  url           = {https://arxiv.org/abs/2604.23878}
}

Feedback, replications, and counter-results are explicitly welcome — please open an issue or reach out via research@zensation.ai.

Your AI forgets everything after every conversation. ZenBrain fixes that — with the same mechanisms your brain uses: spaced repetition, emotional consolidation, Hebbian strengthening, and exponential forgetting curves. Not a vector database with a wrapper. Actual neuroscience.

Architecture vs. this package. ZenBrain's architecture is 15 neuroscience-inspired mechanisms — 9 foundational algorithms + 6 Predictive Memory Architecture (PMA) components (paper). The 6 PMA components are proprietary and run in the production system. This open-source package ships the algorithm library: 10 core algorithms + 10 advanced research modules (20 modules), zero-dependency.

Benchmark: LongMemEval-500

On LongMemEval-500, ZenBrain wins all nine head-to-head answer-quality comparisons against Letta, Mem0 and A-Mem — three competitors x three LLM judges, under Bonferroni-corrected significance (alpha = 0.05/18, p_min = 6.2e-31, d in [0.18, 0.52]). It reaches 91.3% of a full-context oracle's binary-judge accuracy at 1/106th of the per-query token cost (47.7% vs. 52.2%).

The paper prints where ZenBrain loses as well: on LoCoMo, substring-based aggregate F1 favours lexical retrieval (BM25) by metric design, and we do not contest that. The advantage is most pronounced on judge-graded answer quality and cross-session reasoning.

Every ablation table in the paper reproduces in under a minute on a laptop — npm install, no API keys. The material ships with the reproduction record, so the numbers above can be checked rather than believed.

• Method, effect sizes and ablations: arXiv:2604.23878
• Reproduction material: 10.5281/zenodo.19481262

How ZenBrain differs from Mem0, Letta and Zep

ZenBrain implements fifteen mechanisms taken from human memory research. No system among those surveyed in the paper integrates more than two of them. The table below records which of the mechanisms appear in the public source of three widely used memory systems, at pinned versions, on a fixed date.

How this was measured, 27 August 2026. Full-text search over the checked-out public source of mem0ai/mem0 (npm mem0ai 3.1.7, PyPI mem0ai 2.0.19), letta-ai/letta-code (npm @letta-ai/letta-code 0.31.2) and getzep/zep, lockfiles excluded. A dash means the term does not occur in that snapshot — not that the system cannot do something comparable under another name. Dependency counts are declared direct dependencies: @zensation/core resolves to two packages, both our own; mem0ai declares four, @letta-ai/letta-code eighteen. Re-run the whole check yourself with scripts/compare-mechanisms.sh; it prints its own positive and negative controls so you can see the instrument works before you trust the result.

Human memory does not work like a key-value store. The brain keeps specialised systems for different kinds of memory, forgets actively, modulates by emotion and retrieves by context. ZenBrain brings those mechanisms to AI agents.

Advanced algorithms (since v0.3.0, May 2026)

On top of the 10 core algorithms above, @zensation/algorithms ships 10 advanced algorithms grounded in recent neuroscience and ML research. Each is exposed as its own sub-path (@zensation/algorithms/<name>) and remains zero-dependency:

• fsrs-vmPFC — Prediction-Error coupled FSRS
• hebbian-two-factor — Two-Factor synaptic consolidation
• sleep-simulation-selection — RL-based replay selection
• spectral-health — Fiedler-value KG health monitor
• ib-budget — Information-Bottleneck retention budget
• dopamine-routing · hopfield-stm · personalized-pagerank · surprise-gradient-memory · temporal-multi-route

See CHANGELOG.md for details.

Quick Start

Requires Node.js 22 or newer (since 0.4.0). On Node 20 or older, npm silently installs the last compatible release (@zensation/algorithms@0.3.4, @zensation/core@0.2.2) instead of the current one — which looks like a broken package but is a platform mismatch. See CHANGELOG.

npm install @zensation/algorithms

import {
  initFromDecayClass,
  getRetrievability,
  updateAfterRecall,
  tagEmotion,
  computeEmotionalWeight,
  computeHebbianStrengthening,
  propagateForRelation,
} from '@zensation/algorithms';

// 1. Schedule a memory with FSRS
const memory = initFromDecayClass('normal_decay');

// 2. A week later, check recall probability (Ebbinghaus curve)
const aWeekLater = new Date(Date.now() + 7 * 24 * 60 * 60 * 1000);
const retention = getRetrievability(memory, aWeekLater);
console.log(`Recall probability: ${(retention * 100).toFixed(1)}%`);
// ~36.8% — retrievability has decayed over the week

// 3. User recalled it anyway — update scheduling
const updated = updateAfterRecall(memory, 4, retention, aWeekLater);
// stability 7 -> 8.19: recalling at low retrievability gives a bigger boost

// 4. Tag emotional significance
const emotion = tagEmotion('I am absolutely thrilled — I got the promotion!');
const weight = computeEmotionalWeight(emotion);
console.log(`Decay multiplier: ${weight.decayMultiplier}x`);
// 2.7x — emotional memories decay nearly 3x slower

// 5. Strengthen knowledge connections (Hebbian)
const stronger = computeHebbianStrengthening(1.0);
// 1.09 — "neurons that fire together wire together"

// 6. Propagate confidence through your knowledge graph
const confidence = propagateForRelation(0.5, 0.8, 1.0, 'supports');
// 0.9 — supporting evidence increases confidence

Want the advanced algorithms?

import {
  computeKGPredictionError,
  computeAdaptiveFSRSInterval,
} from '@zensation/algorithms/fsrs-vmPFC';

// Couple FSRS scheduling with the prediction-error signal from your
// knowledge graph: when the embedding has shifted a lot since the last
// review (high cosine distance), shrink the next interval; otherwise push
// it out. Both arrays must have the same length.
const lastEmbedding = [0.1, 0.2, 0.3, 0.4];
const currentEmbedding = [0.5, 0.4, 0.1, 0.2];
const pe = computeKGPredictionError(lastEmbedding, currentEmbedding);
const nextInterval = computeAdaptiveFSRSInterval(14, pe);

Each advanced algorithm has its own sub-path (@zensation/algorithms/spectral-health, @zensation/algorithms/ib-budget, …). All zero dependencies.

Runnable examples

Five self-contained examples live in examples/:

npx tsx examples/basic-chatbot.ts

The integration examples additionally need their respective SDK installed. Want a LlamaIndex.TS or Mastra example? Those are open as good first issues.

The Science Behind It

7-Layer Memory Architecture

Layer 7: Cross-Context Memory    ← Shared knowledge across domains
Layer 6: Core Memory             ← Pinned facts (Letta-style)
Layer 5: Procedural Memory       ← "How to do X" (skills & workflows)
Layer 4: Long-Term Semantic      ← Facts with FSRS scheduling
Layer 3: Episodic Memory         ← Concrete experiences & events
Layer 2: Short-Term / Session    ← Current conversation context
Layer 1: Working Memory          ← Active task focus (7±2 items)

Each layer has different retention characteristics, consolidation rules, and retrieval mechanisms — just like the human brain.

FSRS Spaced Repetition

FSRS (Free Spaced Repetition Scheduler) outperforms SM-2 by 30%. It uses the desirable difficulty principle: reviewing when retention is low gives a bigger stability boost. Your AI reviews important facts at optimal intervals — never too early (wasteful), never too late (forgotten).

Emotional Memory

The amygdala modulates memory consolidation — emotional events are remembered more vividly (flashbulb memory). ZenBrain's emotional tagger assigns arousal, valence, and significance scores using a 400+ keyword lexicon (English & German). Emotional memories get up to 3x longer decay half-lives.

Hebbian Learning

"Neurons that fire together wire together" (Hebb, 1949). Knowledge graph edges that are frequently co-activated grow stronger. Unused edges decay and eventually get pruned. The result: a self-organizing knowledge structure that reflects actual usage patterns, with homeostatic normalization to prevent runaway growth.

Ebbinghaus Forgetting Curves

Ebbinghaus (1885) showed that memory decays exponentially: R = e^(-t/S). ZenBrain implements personalized decay profiles that adapt to individual learning patterns, with SM-2 compatibility for existing spaced repetition systems.

Context-Dependent Retrieval

Tulving's Encoding Specificity Principle (1973): memories are recalled better when the retrieval context matches the encoding context. ZenBrain captures temporal context (time of day, day of week) and task type at encoding time, providing up to a 30% retrieval boost when contexts match.

Bayesian Confidence Propagation

Knowledge isn't isolated — facts support or contradict each other. ZenBrain propagates confidence through your knowledge graph using Bayesian belief updates: supporting evidence increases confidence, contradictions decrease it, with damping for numerical stability.

Sleep Consolidation

During sleep, the hippocampus replays recent experiences, strengthening important memories and pruning weak connections (Stickgold & Walker, 2013). ZenBrain simulates this process: selectForReplay() prioritizes emotional and recently-accessed memories, simulateReplay() boosts their stability by 50%, and pruneWeakConnections() removes weak Hebbian edges — implementing the Synaptic Homeostasis Hypothesis (Tononi & Cirelli, 2006).

import { selectForReplay, simulateReplay } from '@zensation/algorithms/sleep-consolidation';

// Select memories for overnight consolidation
const toReplay = selectForReplay(allMemories);
// Simulate sleep replay — stability ↑, weak edges pruned
const result = simulateReplay(toReplay);
console.log(`Replayed ${result.summary.totalReplayed} memories, avg stability +${result.summary.avgStabilityIncrease.toFixed(1)} days`);

Memory Coordinator

The MemoryCoordinator orchestrates all 7 layers into a single cohesive system — inspired by Global Workspace Theory (Baars, 1988):

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

const memory = new MemoryCoordinator({ storage: adapter, embedding: embedder });

// 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();

Packages

Tree-Shakeable Imports

Every algorithm is available as a subpath export:

// Import everything
import { tagEmotion, updateAfterRecall } from '@zensation/algorithms';

// Or just what you need (better tree-shaking)
import { updateAfterRecall } from '@zensation/algorithms/fsrs';
import { tagEmotion } from '@zensation/algorithms/emotional';
import { computeHebbianStrengthening } from '@zensation/algorithms/hebbian';
import { propagateForRelation } from '@zensation/algorithms/bayesian';
import { selectForReplay } from '@zensation/algorithms/sleep-consolidation';
import { getRetrievabilityWithCI } from '@zensation/algorithms/intervals';
import { generateRetentionCurve } from '@zensation/algorithms/visualization';

Use Cases

AI Chatbots with Long-Term Memory

import { updateAfterRecall, getRetrievability, scheduleNextReview } from '@zensation/algorithms/fsrs';
import { tagEmotion, computeEmotionalWeight } from '@zensation/algorithms/emotional';

// When your AI learns a fact about the user:
function rememberFact(fact: string) {
  const memory = initFromDecayClass('normal_decay');
  const emotion = tagEmotion(fact);
  const weight = computeEmotionalWeight(emotion);

  // Emotional facts get longer retention
  return {
    ...memory,
    emotionalWeight: weight.consolidationWeight,
    decayMultiplier: weight.decayMultiplier,
  };
}

// Before each conversation, check what needs reinforcement:
function getFactsDueForReview(facts: MemoryState[]) {
  return facts.filter(f => getRetrievability(f) < 0.7);
}

Knowledge Graph with Self-Organizing Edges

import { computeHebbianStrengthening, computeHebbianDecay } from '@zensation/algorithms/hebbian';
import { propagateForRelation } from '@zensation/algorithms/bayesian';

// When two concepts are mentioned together:
function coActivate(edge: { weight: number }) {
  edge.weight = computeHebbianStrengthening(edge.weight);
}

// Periodic maintenance — decay unused edges:
function decayEdges(edges: { weight: number; lastUsed: Date }[]) {
  for (const edge of edges) {
    edge.weight = computeHebbianDecay(edge.weight);
    // Edges below MIN_WEIGHT (0.1) can be pruned
  }
}

RAG with Confidence Scoring

import { propagateForRelation, isSignificantChange } from '@zensation/algorithms/bayesian';

// After retrieval, propagate confidence through related facts:
function updateConfidenceGraph(facts: Fact[], relations: Relation[]) {
  for (const rel of relations) {
    const newConf = propagateForRelation(
      rel.target.confidence,
      rel.source.confidence,
      rel.weight,
      rel.type // 'supports' | 'contradicts' | 'related_to'
    );
    if (isSignificantChange(newConf, rel.target.confidence)) {
      rel.target.confidence = newConf;
    }
  }
}

Extracted From Production

These aren't toy implementations — ZenBrain's algorithms are extracted from ZenAI, a production AI platform. Everything claimed here is verifiable in this repository:

• 528 tests