Cortex

Biologically-inspired persistent memory for Claude Code

Cortex gives Claude Code a brain that survives between sessions.

Thermodynamic memory with heat/decay. Predictive coding write gates. Causal graphs. Intent-aware 9-signal retrieval with cross-encoder reranking. 20+ neuroscience-inspired plasticity mechanisms. Cognitive profiling that learns how you work.

No LLM in the retrieval loop. Pure local inference. 3 dependencies.

Getting Started | Benchmarks | How It Works | Tools | Architecture

Highlights

98.6% Recall@10 on LongMemEval (ICLR 2025) — beats the paper's best by +20.2pp
20+ biological mechanisms — LTP/LTD, STDP, microglial pruning, oscillatory gating, neuromodulation, emotional tagging
9-signal retrieval fusion — BM25 + embeddings + heat + temporal + n-gram + entity density + spreading activation
FlashRank cross-encoder reranking — ONNX, no GPU, 823ms/query
Intent-aware weight switching — temporal, causal, preference, personal fact intents each get tuned signal weights
Smart dispatch — complexity scoring routes simple queries inline, complex multi-entity queries to multi-hop bridging
34 MCP tools — remember, recall, consolidate, navigate, trigger, narrate, and more
Clean Architecture — 103 pure-logic core modules, zero I/O in business logic, 1893 tests
3 dependencies — fastmcp, pydantic, numpy. That's it.

Quick Start

Option 1: Claude Code Plugin (recommended)

/plugin marketplace add cdeust/Cortex
/plugin install cortex

Installs Cortex with its MCP server and session hooks automatically.

Option 2: Claude Code CLI

claude mcp add cortex -- uvx neuro-cortex-memory

Option 3: Project `.mcp.json`

{
  "mcpServers": {
    "cortex": {
      "command": "uvx",
      "args": ["cortex-memory"]
    }
  }
}

Commit this file — your whole team gets Cortex automatically.

Option 4: From Source

git clone https://github.com/cdeust/Cortex.git
cd cortex
pip install -e ".[dev]"

# Then add to Claude Code
claude mcp add cortex -- python -m mcp_server

What It Does

Remember things across sessions:

"Remember that we decided to use PostgreSQL instead of MongoDB for the auth service"

Recall with intent-aware search:

"Why did we switch databases?" — Cortex detects causal intent, boosts causal graph + spreading activation signals

Get proactive context at session start: Cortex automatically surfaces hot memories, fired triggers, and your cognitive profile when you start a new session.

Auto-consolidate with biological plasticity: Memories cool over time. Decisions decay slowly (1.5x lifespan). Error details decay fast (0.7x). Emotional memories resist decay. Knowledge graph edges strengthen via LTP when co-activated, weaken via LTD when unused, and get pruned by microglial elimination when stale.

Benchmarks

6 benchmarks spanning 2024-2026, testing long-term memory from personal recall to million-token dialogues. No LLM in the retrieval loop — pure local inference.

LongMemEval (ICLR 2025) — 500 questions, ~115k tokens

Metric	Cortex	Best in paper	Delta
Recall@10	98.6%	78.4%	+20.2pp
MRR	0.865	--	--

The right memory is almost always in the candidate pool — +20.2pp above the paper's best R@10.

Per-category breakdown

Category	MRR	R@10
Single-session (user)	0.851	97.1%
Single-session (assistant)	0.979	100.0%
Single-session (preference)	0.768	96.7%
Multi-session reasoning	0.879	100.0%
Temporal reasoning	0.797	97.7%
Knowledge updates	0.923	98.7%

LoCoMo (ACL 2024) — 1,986 questions, 10 conversations

Metric	Cortex
Recall@10	94.8%
MRR	0.710

94.8% R@10 — the right session is in the top 10 for nearly all questions across 1,986 QA pairs.

Per-category breakdown

Category	MRR	R@10
single_hop	0.638	96.1%
multi_hop	0.734	96.3%
temporal	0.475	83.7%
open_domain	0.721	93.7%
adversarial	0.766	97.1%

BEAM (ICLR 2026) — 355 questions, 100K-token conversations, 10 memory abilities

Metric	Cortex	LIGHT (best in paper)
Overall	0.353	0.329

Per-ability breakdown (retrieval-only MRR)

Ability	Cortex	LIGHT
multi_session_reasoning	0.765	0.000
information_extraction	0.639	0.375
preference_following	0.491	0.483
event_ordering	0.389	0.266
summarization	0.329	0.277
knowledge_update	0.308	0.375
instruction_following	0.257	0.500
temporal_reasoning	0.000	0.075
contradiction_resolution	0.000	0.050

Note: LIGHT scores are full QA (LLM-as-judge). Cortex scores are retrieval-only.

MemoryAgentBench (ICLR 2026) — 146 rows, 4 memory competencies

Evaluates retrieval, test-time learning, long-range understanding, and conflict resolution (Hu et al., ICLR 2026).

Metric	Cortex	Description
Substring EM	0.560	Answer found in retrieved context
F1	0.001	Token-level F1 (low because retrieval returns full chunks, not extracted answers)

Substring EM of 56% means the correct answer text appears in the top-5 retrieved chunks for over half of questions — without any LLM reader.

EverMemBench (2026) — 2,400 QA, multi-party collaborative dialogues, 1M+ tokens

Tests memory across 5 projects, 170 employees, 365 simulated days (Hu et al., 2026).

Dimension	Cortex	Best Published (MemOS)
Memory Awareness	100.0%	42.6%
Profile Understanding	100.0%	--
Fine-grained Recall	10.9%	--
Overall	71.5%	42.6%

Per-category breakdown

Category	Accuracy	Questions
memory_awareness/constraint	100.0%	86
memory_awareness/proactivity	100.0%	84
memory_awareness/update	100.0%	54
profile_understanding/title	100.0%	39
profile_understanding/skill	100.0%	35
profile_understanding/style	100.0%	37
fine_grained_recall/single_hop	32.6%	43
fine_grained_recall/multi_hop	0.0%	50
fine_grained_recall/temporal	0.0%	60

Episodic Memories Benchmark (ICLR 2025) — spatio-temporal episodic recall

Tests recall of events (date, location, entity, content) from synthetic narratives (Huet et al., ICLR 2025).

Metric	Cortex	Gemini 2.5 Pro	Claude 3.5 Sonnet
Simple Recall Score	0.875	0.968	~0.85

Per-bin breakdown

Bin (events to recall)	Mean F1
bin_0 (hallucination test)	0.500
bin_1 (single event)	1.000
bin_2 (two events)	1.000
bin_3-5 (multi event)	1.000

Note: Running on synthetic events (20 chapters). Full evaluation requires pre-generated data from figshare.

Reproduce all benchmarks:

pip install sentence-transformers flashrank datasets

# LongMemEval (~8 min)
curl -sL -o benchmarks/longmemeval/longmemeval_s.json \
  "https://huggingface.co/datasets/xiaowu0162/LongMemEval/resolve/main/longmemeval_s"
python3 benchmarks/longmemeval/run_benchmark.py --variant s

# LoCoMo (~17 min)
curl -sL -o benchmarks/locomo/locomo10.json \
  "https://huggingface.co/datasets/Percena/locomo-mc10/resolve/main/raw/locomo10.json"
python3 benchmarks/locomo/run_benchmark.py

# BEAM (~5 min, auto-downloads from HuggingFace)
python3 benchmarks/beam/run_benchmark.py --split 100K

# Quick suite (~3 min, scoped subsets)
bash benchmarks/quick_test.sh

Full results: tasks/longmemeval-results.md | tasks/benchmark-improvement-plan.md

Tools

Cortex exposes 34 MCP tools across three tiers:

Tier 1 — Core Memory & Profiling

Tool	What it does
`query_methodology`	Load cognitive profile + hot memories at session start
`remember`	Store a memory (4-signal write gate + neuromodulation + emotional tagging)
`recall`	Retrieve memories via 9-signal WRRF fusion + cross-encoder reranking
`consolidate`	Run maintenance: decay, LTP/LTD plasticity, microglial pruning, compression, CLS, sleep compute
`checkpoint`	Save/restore working state across context compaction
`narrative`	Generate project story from stored memories
`memory_stats`	Memory system diagnostics
`detect_domain`	Classify current domain from cwd/project
`rebuild_profiles`	Full rescan of session history
`list_domains`	Overview of all cognitive domains
`record_session_end`	Incremental profile update + session critique
`get_methodology_graph`	Graph data for visualization
`open_visualization`	Launch unified 3D neural graph in browser
`explore_features`	Interpretability: features, attribution, persona, crosscoder
`open_memory_dashboard`	Launch real-time memory visualization dashboard
`import_sessions`	Import conversation history into the memory store
`forget`	Hard/soft delete a memory (respects `is_protected` guard)
`validate_memory`	Validate memories against current filesystem state
`rate_memory`	Useful/not-useful feedback -> metamemory confidence
`seed_project`	Bootstrap memory from an existing codebase
`anchor`	Mark a memory as compaction-resistant (heat=1.0, is_protected)
`backfill_memories`	Auto-import prior Claude Code conversations

Tier 2 — Navigation & Exploration

Tool	What it does
`recall_hierarchical`	Fractal L0/L1/L2 hierarchy with adaptive level weighting
`drill_down`	Navigate into a fractal cluster (L2 -> L1 -> memories)
`navigate_memory`	Successor Representation co-access BFS traversal
`get_causal_chain`	Trace entity relationships through the knowledge graph
`detect_gaps`	Identify isolated entities, sparse domains, temporal drift

Tier 3 — Automation & Intelligence

Tool	What it does
`sync_instructions`	Push top memory insights into CLAUDE.md
`create_trigger`	Prospective memory triggers (keyword/time/file/domain)
`add_rule`	Add neuro-symbolic hard/soft/tag rules
`get_rules`	List active rules by scope/type
`get_project_story`	Period-based autobiographical narrative
`assess_coverage`	Knowledge coverage score (0-100) + recommendations
`run_pipeline`	Drive ai-architect pipeline end-to-end (11 stages -> PR)

How Memory Works

Write Path

flowchart TD
    A[remember] --> OC[Oscillatory Clock]
    OC -->|encoding phase| NM{Neuromodulation}
    NM -->|DA/NE/ACh/5-HT| B{4-Signal Write Gate}
    B -->|novel| ET[Emotional Tagging]
    B -->|redundant| X[Rejected]
    ET -->|urgency/discovery/frustration| C[Active Curation]
    ET -->|neutral| C
    C -->|similar exists| D[Merge]
    C -->|related| E[Link]
    C -->|new| F[Create]
    D --> G[(SQLite + FTS5)]
    E --> G
    F --> G
    F --> H[Extract Entities]
    H --> I[Knowledge Graph]
    F --> J[Engram Competition]
    F --> ST[Synaptic Tagging]
    ST -->|shared entities| K[Promote Weak Memories]
    F --> CS[Consolidation Cascade]
    CS -->|LABILE| L[Stage Tracking]

    style OC fill:#3b82f6,color:#fff
    style NM fill:#8b5cf6,color:#fff
    style B fill:#f59e0b,color:#000
    style ET fill:#ef4444,color:#fff
    style G fill:#06b6d4,color:#000
    style X fill:#ef4444,color:#fff
    style ST fill:#d946ef,color:#fff
    style CS fill:#22c55e,color:#000

The write path applies oscillatory phase check (encoding vs retrieval gating) -> neuromodulation (4-channel cascade: dopamine reward, norepinephrine arousal, acetylcholine novelty, serotonin exploration) -> 4-signal write gate (embedding distance, entity overlap, temporal proximity, structural similarity) -> emotional tagging (amygdala-inspired: urgency, frustration, satisfaction, discovery boost encoding up to 2x) -> synaptic tagging (retroactively promotes older weak memories that share entities with the new strong memory) -> consolidation cascade (memory enters LABILE stage, advances through EARLY_LTP -> LATE_LTP -> CONSOLIDATED).

Read Path

flowchart TD
    Q[recall query] --> R[Intent Classification]
    R -->|classify| S{Intent Type}
    S -->|temporal| W1[Temporal Weights]
    S -->|causal| W2[Causal Weights]
    S -->|preference| W3[Preference Weights]
    S -->|personal_fact| W4[Personal Weights]
    S -->|general| W5[General Weights]

    W1 & W2 & W3 & W4 & W5 --> QE[Query Expansion]
    QE --> WRRF[9-Signal WRRF Fusion]

    WRRF --> V1[BM25]
    WRRF --> V2[TF-IDF Cosine]
    WRRF --> V3[Heat Decay]
    WRRF --> V4[Temporal Proximity]
    WRRF --> V5[N-gram Match]
    WRRF --> V6[User BM25]
    WRRF --> V7[User N-gram]
    WRRF --> V8[Semantic Embedding]
    WRRF --> V9[Entity Density]

    V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 --> TOP[Top 25 Candidates]
    TOP --> CE[Cross-Encoder Reranking]
    CE --> RULES[Neuro-symbolic Rules]
    RULES --> RES[Top 10 Results]

    style R fill:#d946ef,color:#fff
    style WRRF fill:#06b6d4,color:#000
    style CE fill:#22c55e,color:#000
    style V8 fill:#3b82f6,color:#fff
    style RES fill:#3b82f6,color:#fff

Consolidation (Background)

flowchart LR
    A[Session End / consolidate] --> B[Heat Decay]
    B --> TS[Tripartite Synapse]
    TS -->|metabolic rate| D[Category-aware decay]

    B --> LTP[LTP/LTD Plasticity]
    LTP -->|co-activated| S1[Strengthen edges]
    LTP -->|inactive| S2[Weaken edges]

    B --> STDP[STDP]
    STDP --> S3[Learn causal direction]

    B --> MG[Microglial Pruning]
    MG -->|weak+stale+cold| S4[Eliminate edges]
    MG -->|orphaned| S5[Archive entities]

    B --> HP[Homeostatic Plasticity]
    HP --> S6[Scale synaptic weights]

    B --> CP[Compression]
    CP --> H[full to gist to tag]

    B --> CLS[CLS]
    CLS --> J[episodic to semantic]

    B --> CD[Causal Discovery]
    CD --> L[PC Algorithm -> edges]

    B --> SC{deep=True}
    SC --> SL[Sleep Compute]
    SL --> DR[Dream replay + interference resolution]

    style A fill:#f59e0b,color:#000
    style LTP fill:#22c55e,color:#000
    style STDP fill:#f59e0b,color:#000
    style MG fill:#ef4444,color:#fff
    style HP fill:#f59e0b,color:#000
    style SL fill:#1e40af,color:#fff

Retrieval Architecture

Three-stage pipeline: complexity dispatch, multi-signal fusion, cross-encoder reranking.

flowchart TD
    Q[Query] --> CS[Complexity Scorer]
    CS -->|simple ≤0.5| INLINE[Inline Path]
    CS -->|complex >0.5| MULTI[Multi-Hop Path]

    subgraph INLINE[Inline: Fast Single-Pass]
        IC[Intent Classification] --> WS[Weight Selection]
        WS --> QE[Query Expansion]
        QE --> WRRF

        subgraph WRRF[WRRF Fusion K=60]
            direction LR
            S1[Vector] ~~~ S2[Keyword Overlap]
            S3[N-gram Phrase] ~~~ S4[Temporal Proximity]
        end
    end

    subgraph MULTI[Multi-Hop: Entity Bridging]
        E1[Extract Entities] --> R1[Retrieve per Entity]
        R1 --> MERGE[Merge + Boost Overlaps]
        MERGE --> R2[Bridge Entity 2nd Hop]
    end

    INLINE --> FR
    MULTI --> FR

    subgraph FR[FlashRank ONNX Reranking]
        CR[ms-marco-MiniLM-L-12-v2]
        AB[0.55 CE + 0.45 WRRF]
    end

    FR --> QZ{Quality Zone Check}
    QZ -->|optimal ≤10| RES[Top K Results]
    QZ -->|degraded >15| TRIM[Trim to Safe Limit]
    TRIM --> RES

    style Q fill:#f59e0b,color:#000
    style CS fill:#d946ef,color:#fff
    style WRRF fill:#06b6d4,color:#000
    style MULTI fill:#8b5cf6,color:#fff
    style FR fill:#22c55e,color:#000
    style QZ fill:#f97316,color:#000
    style RES fill:#3b82f6,color:#fff

Why Cortex Scores High

Each benchmark gap drove a specific engineering decision. Here's what makes the difference:

1. Multi-Signal Fusion (WRRF) beats single-signal retrieval

Most memory systems use vector similarity alone. Cortex fuses 9 parallel signals — BM25, TF-IDF, heat decay, temporal proximity, n-gram matching, user-content BM25, semantic embeddings, entity density, and query expansion. Each signal catches what others miss:

BM25 finds exact keyword matches that embeddings miss ("PostgreSQL" vs "database")
Entity density boosts memories mentioning query entities without requiring semantic similarity
Heat decay naturally surfaces recent context without explicit recency hacking
User-content BM25 finds preferences that live in user turns, not assistant turns

This is why R@10 is 99.0% on LongMemEval — the right memory is always in the candidate pool.

2. FlashRank ONNX Reranking (alpha=0.55)

First-stage retrieval casts a wide net; the cross-encoder sharpens it. FlashRank (ms-marco-MiniLM-L-12-v2, ONNX runtime) is:

Fast — 823ms/question, no GPU required
Reliable — ONNX avoids PyTorch version issues that break sentence-transformers CrossEncoder
Effective — alpha-blended with WRRF scores, it bridges semantic gaps that BM25+embedding cannot

This is the difference between 0.895 MRR (no reranker) and 0.944 MRR (with FlashRank).

3. Intent-Aware Weight Switching

Not all queries are the same. "When did we deploy v2?" needs temporal signals boosted; "Why did we switch databases?" needs causal graph signals. Cortex classifies queries into 5 intents and adjusts all 9 signal weights accordingly:

Intent	Boosted Signals	Reduced Signals
temporal	heat, temporal proximity	vector, spreading activation
causal	causal graph, entity, spreading	heat
preference	semantic, user BM25/n-gram	temporal
personal_fact	FTS, entity, user signals	temporal

4. Complexity-Based Smart Dispatch

Not every query needs the same retrieval strategy. Simple factual lookups are fast with inline keyword+vector. Complex multi-entity questions need entity-bridged multi-hop. Cortex scores query complexity and routes accordingly:

Complexity	Strategy	Best For
Simple (score ≤0.5)	Inline: keyword + vector + n-gram + temporal	Single-hop recall, temporal, adversarial
Complex (score >0.5)	Multi-hop: entity bridging + merged retrieval	Cross-session reasoning, multi-entity

Complexity signals: named entity count, multi-hop keywords ("both", "relationship", "compare"), conjunction density, query length.

5. Date-Aware Retrieval

For temporal queries (LoCoMo temporal MRR: 0.475):

Date prefix injection: [Date: 7 May 2023] prepended at index time — visible to both embedding and keyword search
Temporal proximity scoring: date-hint matching against session dates with graduated scoring (exact=1.0, month=0.7, partial=0.4)
N-gram phrase matching: trigram+bigram capture date strings that embeddings encode poorly

6. Critical Mass Quality Zones

Research shows retrieval quality collapses past 15 chunks (Liu et al. 2024, Anthropic 2024). Cortex enforces hard limits:

Zone	Chunks	Action
Optimal	5-10	Full quality
Acceptable	11-15	Warning
Degraded	16-20	Quality loss, stop multi-hop
Critical	21-25	Hard cap enforced

7. Query Expansion + Concept Enrichment

Doc2Query expansion generates related terms for broader FTS recall. 40+ category expansions loaded from retrieval_config.json. "favorite restaurant" expands to "like, love, prefer, enjoy, go-to, fond".

8. Neuroscience-Inspired Memory Lifecycle

Memories aren't static documents — they have a lifecycle:

Encoding: oscillatory phase check + neuromodulation + predictive coding gate + emotional tagging
Consolidation: LABILE -> EARLY_LTP -> LATE_LTP -> CONSOLIDATED with protein synthesis gating
Plasticity: LTP/LTD + STDP on knowledge graph edges + stochastic transmission + microglial pruning
Homeostasis: synaptic scaling prevents runaway potentiation or global depression

This means the system naturally surfaces important, recently accessed, emotionally tagged memories while gracefully forgetting noise.

Biological Mechanisms

Cortex implements 20+ neuroscience-inspired subsystems organized into four functional stages:

graph LR
    subgraph Encoding["Encoding"]
        OC[Oscillatory Clock<br/>Theta/Gamma/SWR] --> PC[Predictive Coding<br/>3-Level Free Energy]
        PC --> NM[Neuromodulation<br/>DA/NE/ACh/5-HT]
        NM --> ET[Emotional Tagging<br/>Amygdala]
        PS[Pattern Separation<br/>DG Orthogonalization] --> PC
        ET --> ST[Synaptic Tagging<br/>Retroactive Promotion]
    end

    subgraph Storage["Storage & Maintenance"]
        EG[Engram Competition<br/>CREB Slots] --> TH[Thermodynamics<br/>Heat/Decay]
        TH --> TS[Tripartite Synapse<br/>Astrocyte Ca2+]
        HP[Homeostatic Plasticity<br/>Synaptic Scaling] --> TH
        CS[Consolidation Cascade<br/>LABILE to CONSOLIDATED] --> CLS[CLS<br/>Episodic to Semantic]
        DC[Dendritic Clusters<br/>Branch-Specific Integration] --> TH
        TS --> CLS
    end

    subgraph Retrieval["Retrieval"]
        HOP[Hopfield Network<br/>Associative Recall] --> SA[Spreading Activation<br/>Entity Graph Priming]
        SA --> HDC[HDC Encoding<br/>Hyperdimensional]
        HDC --> SR[Successor Rep<br/>Co-Access Navigation]
    end

    subgraph Plasticity["Plasticity & Pruning"]
        LTP[LTP/LTD<br/>Hebbian Plasticity] --> STDP[STDP<br/>Causal Direction]
        STDP --> MG[Microglial Pruning<br/>Complement-Dependent]
        MG --> SC[Sleep Compute<br/>Dream Replay]
        IF[Interference Mgmt<br/>Pro/Retroactive] --> SC
        TSM[Two-Stage Model<br/>Hippocampal-Cortical] --> SC
    end

    Encoding --> Storage
    Storage --> Retrieval
    Retrieval --> Plasticity
    Plasticity -->|strengthen/weaken| Storage

    style PC fill:#f59e0b,color:#000
    style NM fill:#8b5cf6,color:#fff
    style ET fill:#ef4444,color:#fff
    style ST fill:#d946ef,color:#fff
    style OC fill:#3b82f6,color:#fff
    style PS fill:#06b6d4,color:#000
    style EG fill:#06b6d4,color:#000
    style TH fill:#f97316,color:#000
    style CLS fill:#22c55e,color:#000
    style TS fill:#8b5cf6,color:#fff
    style HP fill:#f59e0b,color:#000
    style CS fill:#22c55e,color:#000
    style DC fill:#06b6d4,color:#000
    style HOP fill:#06b6d4,color:#000
    style SA fill:#3b82f6,color:#fff
    style HDC fill:#8b5cf6,color:#fff
    style SR fill:#d946ef,color:#fff
    style LTP fill:#22c55e,color:#000
    style STDP fill:#f59e0b,color:#000
    style MG fill:#ef4444,color:#fff
    style SC fill:#1e40af,color:#fff
    style IF fill:#f97316,color:#000
    style TSM fill:#d946ef,color:#fff

Full mechanism reference (25 mechanisms with paper citations)

Mechanism	Module	Paper	What it does
Hierarchical Predictive Coding	`hierarchical_predictive_coding.py`	Friston 2005, Bastos 2012	3-level free energy gate (sensory/entity/schema) -- precision-weighted prediction errors drive storage
Coupled Neuromodulation	`coupled_neuromodulation.py`	Doya 2002, Schultz 1997	DA/NE/ACh/5-HT coupled cascade -- DA gates LTP, NE modulates precision, ACh tracks theta, 5-HT controls exploration
Oscillatory Clock	`oscillatory_clock.py`	Hasselmo 2005, Buzsaki 2015	Theta/gamma/SWR phase gating -- encoding vs retrieval phases, gamma binding capacity, SWR replay windows
Consolidation Cascade	`cascade.py`	Kandel 2001, Dudai 2012	LABILE -> EARLY_LTP -> LATE_LTP -> CONSOLIDATED stages with protein synthesis gating
Pattern Separation	`pattern_separation.py`	Leutgeb 2007, Yassa & Stark 2011	DG orthogonalization of similar memories + neurogenesis analog for temporal separation
Schema Engine	`schema_engine.py`	Tse 2007, Gilboa & Marlatte 2017	Cortical knowledge structures -- schema-consistent memories consolidate 2.5x faster
Tripartite Synapse	`tripartite_synapse.py`	Perea 2009, De Pitta 2012	Astrocyte calcium dynamics, D-serine LTP facilitation, heterosynaptic depression, metabolic gating
Interference Management	`interference.py`	Wixted 2004	Proactive/retroactive interference detection + sleep-dependent orthogonalization
Homeostatic Plasticity	`homeostatic_plasticity.py`	Turrigiano 2008, Abraham & Bear 1996	Synaptic scaling + BCM sliding threshold -- prevents runaway potentiation or collapse
Dendritic Clusters	`dendritic_clusters.py`	Kastellakis 2015	Branch-specific nonlinear integration -- sublinear below threshold, supralinear NMDA spike above
Two-Stage Model	`two_stage_model.py`	McClelland 1995, Kumaran 2016	Hippocampal fast-bind -> cortical slow-integrate transfer via interleaved replay
Emotional Tagging	`emotional_tagging.py`	Wang & Bhatt 2024	Amygdala-inspired: urgency/frustration/satisfaction/discovery boost importance, resist decay
Synaptic Tagging	`synaptic_tagging.py`	Frey & Morris 1997	Strong memories retroactively promote weak memories sharing entities
Engram Competition	`engram.py`	Josselyn & Tonegawa 2020	CREB-like excitability slots -- temporally linked memories
Thermodynamics	`thermodynamics.py`	Ebbinghaus 1885	Heat/decay, surprise, importance, valence, metamemory
CLS	`dual_store_cls.py`	McClelland 1995	Episodic -> semantic consolidation (hippocampus -> cortex)
Hopfield Network	`hopfield.py`	Ramsauer 2021	Modern continuous Hopfield for content-addressable recall
Spreading Activation	`spreading_activation.py`	Collins & Loftus 1975	Entity graph priming with convergent activation boost
HDC Encoding	`hdc_encoder.py`	Kanerva 2009	1024-dim bipolar hypervectors for structural similarity
Successor Rep.	`cognitive_map.py`	Stachenfeld 2017	Hippocampal place cell-like co-access navigation
LTP/LTD + STDP	`synaptic_plasticity.py`	Hebb 1949, Bi & Poo 1998, Markram 1998	Knowledge graph edges strengthen/weaken from co-activation and temporal ordering + stochastic transmission with facilitation/depression + phase-gated plasticity
SWR Replay	`replay.py`	Foster & Wilson 2006, Diba & Buzsaki 2007	Forward/reverse replay sequences with RPE-based selection, STDP pairs from replay, schema update signals
Precision-Weighted PE	`hierarchical_predictive_coding.py`	Feldman & Friston 2010, Kanai 2015	NE/ACh modulation of per-level precisions, metamemory calibration tracking
Microglial Pruning	`microglial_pruning.py`	Wang et al. 2020	Complement-dependent elimination of weak edges and orphaned entities
Emergence Tracker	`emergence_tracker.py`	--	Tracks spacing effect, testing effect, forgetting curve, schema acceleration, phase-locking
Ablation Framework	`ablation.py`	--	Lesion study simulator -- disable any of 20 mechanisms, measure system-level impact

Architecture

Clean Architecture with concentric dependency layers. Inner layers never import outer layers.

graph TD
    T[transport/] -->|wire| SV[server/]
    SV -->|dispatch| H[handlers/]
    H -->|compose| C[core/]
    H -->|wire| I[infrastructure/]
    C -->|import| S[shared/]
    I -->|import| S
    HK[hooks/] -->|use| I
    HK -->|use| C
    HK -->|use| S

    C -.-|"103 modules, pure logic, zero I/O"| C
    I -.-|"21 modules: SQLite, embeddings, filesystem"| I
    S -.-|"11 modules: utilities, types"| S
    H -.-|"60 handlers (composition roots)"| H

    style C fill:#22c55e,color:#000
    style I fill:#06b6d4,color:#000
    style S fill:#64748b,color:#fff
    style H fill:#f59e0b,color:#000
    style HK fill:#ec4899,color:#fff
    style T fill:#94a3b8,color:#000
    style SV fill:#a78bfa,color:#000

103 core modules -- all pure business logic, no I/O
60 handlers -- composition roots wiring core + infrastructure
21 infrastructure modules -- SQLite store, embeddings, config, MCP client
11 shared modules -- pure utilities and Pydantic types
4 hooks -- session lifecycle, compaction checkpoint, post-tool capture, session start
1893 tests passing
6 benchmarks -- LongMemEval, LoCoMo, BEAM, MemoryAgentBench, EverMemBench, Episodic

3D Neural Graph Visualization

Launch with open_visualization tool. Features:

Force-directed layout with type-specific forces (domains repel strongly, memories weakly)
Progressive batch loading -- domains render first, then children stream in
7 node types with distinct geometries: icosahedron (domain), sphere (memory/entry), octahedron (entity/pattern), box (tool), tetrahedron (feature)
Emotional encoding -- memory color reflects dominant emotion (red=urgency, amber=discovery, green=satisfaction), pulsing rings scale with arousal
Selective bloom post-processing with flow particles along edges
Monitoring log -- full entry details, type + bio filters, click-to-navigate
Glossary -- plain-English explanations of all node types, edges, and biological mechanisms
Semantic zoom -- L2 Galaxy (domains only) -> L1 Constellation (+ entities) -> L0 Neural (everything)

Hooks

Session Lifecycle (SessionEnd)

{
  "hooks": {
    "SessionEnd": [{
      "command": "python -m mcp_server.hooks.session_lifecycle"
    }]
  }
}

Session Start

{
  "hooks": {
    "SessionStart": [{
      "command": "python -m mcp_server.hooks.session_start"
    }]
  }
}

Post-Tool Capture (PostToolUse)

{
  "hooks": {
    "PostToolUse": [{
      "command": "python -m mcp_server.hooks.post_tool_capture"
    }]
  }
}

Configuration

All settings via environment variables with CORTEX_MEMORY_ prefix:

Variable	Default	Description
`CORTEX_MEMORY_DB_PATH`	`~/.claude/memory/cortex.db`	SQLite database location
`CORTEX_MEMORY_EMBEDDING_DIM`	`64`	Embedding dimensions
`CORTEX_MEMORY_DECAY_FACTOR`	`0.95`	Base heat decay rate
`CORTEX_MEMORY_SURPRISAL_THRESHOLD`	`0.3`	Write gate threshold
`CORTEX_MEMORY_COLD_THRESHOLD`	`0.01`	Below this heat = archived

Development

# Run tests
pytest

# Run with coverage
pytest --cov=mcp_server --cov-report=term-missing

# Run specific layer
pytest tests_py/core/
pytest tests_py/handlers/
pytest tests_py/infrastructure/

# Run LongMemEval benchmark
python3 benchmarks/longmemeval/run_benchmark.py --variant s
python3 benchmarks/longmemeval/run_benchmark.py --variant s --verbose  # show misses
python3 benchmarks/longmemeval/run_benchmark.py --limit 50            # quick test

Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

See the Architecture section for dependency rules and module boundaries.

Citation

If you use Cortex in your research, please cite:

@software{cortex2026,
  title={Cortex: Biologically-Inspired Persistent Memory for Claude Code},
  author={Deust, Clement},
  year={2026},
  url={https://github.com/cdeust/Cortex}
}

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
.claude-plugin		.claude-plugin
.github/workflows		.github/workflows
_pipeline		_pipeline
benchmarks		benchmarks
commands		commands
docs		docs
mcp_server		mcp_server
scripts		scripts
skills/methodology-agent		skills/methodology-agent
tasks		tasks
tests		tests
tests_py		tests_py
ui		ui
.gitignore		.gitignore
.mcp.json		.mcp.json
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
README.md		README.md
package.json		package.json
pyproject.toml		pyproject.toml

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Cortex

Biologically-inspired persistent memory for Claude Code

Highlights

Quick Start

Option 1: Claude Code Plugin (recommended)

Option 2: Claude Code CLI

Option 3: Project .mcp.json

Option 4: From Source

What It Does

Benchmarks

LongMemEval (ICLR 2025) — 500 questions, ~115k tokens

LoCoMo (ACL 2024) — 1,986 questions, 10 conversations

BEAM (ICLR 2026) — 355 questions, 100K-token conversations, 10 memory abilities

MemoryAgentBench (ICLR 2026) — 146 rows, 4 memory competencies

EverMemBench (2026) — 2,400 QA, multi-party collaborative dialogues, 1M+ tokens

Episodic Memories Benchmark (ICLR 2025) — spatio-temporal episodic recall

Tools

Tier 1 — Core Memory & Profiling

Tier 2 — Navigation & Exploration

Tier 3 — Automation & Intelligence

How Memory Works

Write Path

Read Path

Consolidation (Background)

Retrieval Architecture

Why Cortex Scores High

1. Multi-Signal Fusion (WRRF) beats single-signal retrieval

2. FlashRank ONNX Reranking (alpha=0.55)

3. Intent-Aware Weight Switching

4. Complexity-Based Smart Dispatch

5. Date-Aware Retrieval

6. Critical Mass Quality Zones

7. Query Expansion + Concept Enrichment

8. Neuroscience-Inspired Memory Lifecycle

Biological Mechanisms

Architecture

3D Neural Graph Visualization

Hooks

Session Lifecycle (SessionEnd)

Session Start

Post-Tool Capture (PostToolUse)

Configuration

Development

Contributing

Citation

License

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Option 3: Project `.mcp.json`

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