🧠 Cuba-Memorys

Persistent memory for AI agents — A Model Context Protocol (MCP) server that gives AI coding assistants long-term memory with a knowledge graph, neuroscience-inspired algorithms, and anti-hallucination grounding.

12 tools with Cuban soul. Sub-millisecond handlers. Mathematically rigorous.

Important

v2.0.0 — Complete Rust rewrite. 5,452 LOC, 36 source files, 0 CVEs, 0 tech debt. Internally audited with GO verdict. The Python version (v1.6.0) remains in src/ as legacy reference.

Why Cuba-Memorys?

AI agents forget everything between conversations. Cuba-Memorys solves this:

🧬 FSRS-6 adaptive decay — Memories strengthen with use and fade realistically (21 parameters, per-entity sigmoid)
🧠 Hebbian + BCM metaplasticity — Self-normalizing importance via Oja's rule with Bienenstock-Cooper-Munro throttle
🔍 4-signal RRF fusion search — TF-IDF + pg_trgm + full-text + pgvector HNSW, with entropy-routed adaptive k
🕸️ Knowledge graph — Entities, observations, typed relations with Leiden community detection
🛡️ Anti-hallucination grounding — Verify claims against stored knowledge with graduated confidence scoring
💤 REM Sleep consolidation — Autonomous background decay + PageRank + neighbor diffusion after idle
📊 Graph intelligence — Personalized PageRank, Leiden communities, Brandes centrality, Shannon entropy
🔐 Dual-Strength memory — Storage + retrieval strengths track independently (Bjork 1992)
⚡ Error memory — Never repeat the same mistake (anti-repetition guard)

Comparison

Feature	Cuba-Memorys	Basic Memory MCPs
Knowledge graph with typed relations	✅	❌
FSRS-6 adaptive spaced repetition	✅	❌
Hebbian learning + BCM metaplasticity	✅	❌
Dual-Strength memory model	✅	❌
4-signal entropy-routed RRF fusion	✅	❌
KG-neighbor query expansion	✅	❌
GraphRAG topological enrichment	✅	❌
Leiden community detection	✅	❌
Brandes betweenness centrality	✅	❌
Shannon entropy analytics	✅	❌
Adaptive prediction error gating	✅	❌
Anti-hallucination verification	✅	❌
Error pattern detection + MTTR	✅	❌
Session-aware search boost	✅	❌
REM Sleep autonomous consolidation	✅	❌
Embedding LRU cache	✅	❌
Optional ONNX BGE embeddings	✅	❌
Write-time dedup gate	✅	❌
Contradiction auto-supersede	✅	❌
Graceful shutdown (SIGTERM/SIGINT)	✅	❌

Quick Start

git clone https://github.com/LeandroPG19/cuba-memorys.git
cd cuba-memorys

# Start PostgreSQL
docker compose up -d

# Build Rust binary
cd rust
cargo build --release

Configure your AI editor (Claude Code, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "cuba-memorys": {
      "command": "/path/to/cuba-memorys/rust/target/release/cuba-memorys",
      "env": {
        "DATABASE_URL": "postgresql://cuba:memorys2026@127.0.0.1:5488/brain"
      }
    }
  }
}

The server auto-creates the brain database and all tables on first run.

Optional: ONNX Embeddings

For real BGE-small-en-v1.5 semantic embeddings instead of hash-based fallback:

export ONNX_MODEL_PATH="$HOME/.cache/cuba-memorys/models"
export ORT_DYLIB_PATH="/path/to/libonnxruntime.so"

Without ONNX, the server uses deterministic hash-based embeddings — functional but without semantic understanding.

🇨🇺 The 12 Tools

Every tool is named after Cuban culture — memorable, professional, meaningful.

Knowledge Graph

Tool	Meaning	What it does
`cuba_alma`	Alma — soul	CRUD entities. Types: `concept`, `project`, `technology`, `person`, `pattern`, `config`. Triggers Hebbian + Dual-Strength boost.
`cuba_cronica`	Crónica — chronicle	Attach observations with dedup gate, contradiction detection, Shannon density gating, and adaptive PE gating V5.1. Supports `batch_add`.
`cuba_puente`	Puente — bridge	Typed relations (`uses`, `causes`, `implements`, `depends_on`, `related_to`). Traverse walks the graph. Infer discovers transitive paths. blake3 dedup.

Search & Verification

Tool	Meaning	What it does
`cuba_faro`	Faro — lighthouse	4-signal RRF fusion with entropy routing and adaptive k. KG-neighbor expansion for low recall. `verify` mode with source triangulation. Session-aware boost.

Error Memory

Tool	Meaning	What it does
`cuba_alarma`	Alarma — alarm	Report errors. Auto-detects patterns (≥3 similar = warning).
`cuba_remedio`	Remedio — remedy	Resolve errors with cross-reference to similar unresolved issues.
`cuba_expediente`	Expediente — case file	Search past errors. Anti-repetition guard: warns if similar approach failed before.

Sessions & Decisions

Tool	Meaning	What it does
`cuba_jornada`	Jornada — workday	Session tracking with goals and outcomes. Goals used for decay exemption and search boost.
`cuba_decreto`	Decreto — decree	Record architecture decisions with context, alternatives, rationale.

Memory Maintenance

Tool	Meaning	What it does
`cuba_zafra`	Zafra — sugar harvest	Consolidation: FSRS-6 decay, prune, merge, summarize, pagerank, find_duplicates, export, stats, backfill.
`cuba_eco`	Eco — echo	RLHF feedback: positive (Oja boost), negative (decrease), correct (update with versioning).
`cuba_vigia`	Vigía — watchman	Analytics: summary, health (Shannon entropy, MTTR), drift (χ²), Leiden communities, Brandes bridges.

Architecture

cuba-memorys/
├── docker-compose.yml           # Dedicated PostgreSQL 18 (port 5488)
├── rust/                        # ← PRIMARY (v2.0.0, 5452 LOC)
│   ├── src/
│   │   ├── main.rs              # mimalloc + graceful shutdown
│   │   ├── protocol.rs          # JSON-RPC 2.0 + REM daemon (4h cycle)
│   │   ├── db.rs                # sqlx PgPool (10 max, 600s idle, 1800s lifetime)
│   │   ├── schema.sql           # 5 tables, 15+ indexes, HNSW
│   │   ├── constants.rs         # Tool definitions, thresholds, enums
│   │   ├── handlers/            # 12 MCP tool handlers (1 file each)
│   │   ├── cognitive/           # FSRS-6, Hebbian/BCM, Dual-Strength, PE gating
│   │   ├── search/              # RRF fusion, BM25+, confidence, LRU cache
│   │   ├── graph/               # Brandes centrality, Leiden, PageRank
│   │   └── embeddings/          # ONNX BGE-small (optional, spawn_blocking)
│   └── tests/
└── src/cuba_memorys/            # Python legacy (v1.6.0)

Performance: Rust vs Python

Metric	Python v1.6.0	Rust v2.0.0
Binary size	~50MB (venv)	7.6MB
Entity create	~2ms	498µs
Hybrid search	<5ms	2.52ms
Analytics	<2.5ms	958µs
Memory usage	~120MB	~15MB
Startup time	~2s	<100ms
Dependencies	12 Python packages	0 runtime deps

Database Schema

Table	Purpose	Key Features
`brain_entities`	KG nodes	tsvector + pg_trgm + GIN indexes, importance, FSRS stability/difficulty
`brain_observations`	Facts with provenance	9 types, versioning, temporal validity, Dual-Strength, `vector(384)` (pgvector)
`brain_relations`	Typed edges	5 types, bidirectional, Hebbian strength, blake3 dedup
`brain_errors`	Error memory	JSONB context, synapse weight, MTTR, pattern detection
`brain_sessions`	Working sessions	Goals (JSONB), outcome tracking, duration

Search Pipeline

4-signal Reciprocal Rank Fusion (RRF, k=60) with entropy-routed weighting:

#	Signal	Source	Condition
1	Entities (ts_rank + trigrams + importance + freshness)	`brain_entities`	Always
2	Observations (ts_rank + trigrams + BM25+ + importance)	`brain_observations`	Always
3	Errors (ts_rank + trigrams + synapse_weight)	`brain_errors`	Always
4	Vector cosine distance (HNSW)	`brain_observations.embedding`	pgvector installed

Post-fusion pipeline: Dedup → KG-neighbor expansion → Session boost → GraphRAG enrichment → Token-budget truncation → Batch access tracking

Mathematical Foundations

Built on peer-reviewed algorithms, not ad-hoc heuristics:

FSRS-6 — Ye (2024) / Expertium (2025)

R(t, S) = (1 + FACTOR · t/S)^(-decay_rate)
S_new = S · (1 + e^0.1 · (11 - D) · S^(-0.2) · (e^((1-R)·0.9) - 1))

21 parameters. Per-entity adaptive decay via sigmoid of access_count — frequently accessed memories decay slower.

Hebbian + BCM — Oja (1982), Bienenstock-Cooper-Munro (1982)

Positive: Δw = η · (1 - w²)    → self-normalizing, converges to 1.0
Negative: Δw = η · (1 + w²)    → floor at 0.01
BCM: θ_M slides based on recent activity → prevents winner-take-all

Dual-Strength — Bjork (1992)

Storage:   S_s = S_s + α·(1 - S_s)     (grows fast, never decreases)
Retrieval: S_r = S_r + β·(1 - S_r)·S_s  (grows proportional to storage)
Effective: importance = 0.4·S_s + 0.6·S_r

RRF Fusion — Cormack (2009)

RRF(d) = Σ w_i/(k + rank_i(d))   where k = 60

Entropy-routed weighting: keyword-dominant vs mixed vs semantic queries get different signal weights.

Other Algorithms

Algorithm	Reference	Used in
BM25+ (δ=1)	Lv & Zhai (SIGIR 2011)	`tfidf.rs`
Leiden communities	Traag et al. (Nature 2019)	`community.rs` → `vigia.rs`
Personalized PageRank	Brin & Page (1998)	`pagerank.rs` → `zafra.rs`
Brandes centrality	Brandes (2001)	`centrality.rs` → `vigia.rs`
Neighbor diffusion	Collins & Loftus (1975)	`spreading.rs` → REM daemon
Adaptive PE gating	Friston (Nature 2023)	`prediction_error.rs` → `cronica.rs`
Shannon entropy	Shannon (1948)	`density.rs` → information gating
Chi-squared drift	Pearson (1900)	Error distribution change detection

Configuration

Environment Variables

Variable	Default	Description
`DATABASE_URL`	—	PostgreSQL connection string (required)
`ONNX_MODEL_PATH`	—	Path to BGE model directory (optional)
`ORT_DYLIB_PATH`	—	Path to libonnxruntime.so (optional)
`RUST_LOG`	`cuba_memorys=info`	Log level filter

Docker Compose

Dedicated PostgreSQL 18 Alpine:

Port: 5488 (avoids conflicts with 5432/5433)
Resources: 256MB RAM, 0.5 CPU
Restart: always
Healthcheck: pg_isready every 10s

How It Works

1. The agent learns from your project

Agent: FastAPI requires async def with response_model.
→ cuba_alma(create, "FastAPI", technology)
→ cuba_cronica(add, "FastAPI", "All endpoints must be async def with response_model")

2. Error memory prevents repeated mistakes

Agent: IntegrityError: duplicate key on numero_parte.
→ cuba_alarma("IntegrityError", "duplicate key on numero_parte")
→ cuba_expediente: Similar error found! Solution: "Add SELECT EXISTS before INSERT"

3. Anti-hallucination grounding

Agent: Let me verify before responding...
→ cuba_faro("FastAPI uses Django ORM", mode="verify")
→ confidence: 0.0, level: "unknown" — "No evidence. High hallucination risk."

4. Memories adapt with FSRS-6

Initial stability:    S = 1.0  (decays in ~9 days)
After 5 reviews:      S = 8.2  (decays in ~74 days)
After 20 reviews:     S = 45.0 (survives ~13 months)

5. Community intelligence

→ cuba_vigia(metric="communities")
→ Community 0 (4 members): [FastAPI, Pydantic, SQLAlchemy, PostgreSQL]
  Summary: "Backend stack: async endpoints, V2 validation, 2.0 ORM..."
→ Community 1 (3 members): [React, Next.js, TypeScript]
  Summary: "Frontend stack: React 19, App Router, strict types..."

Security & Audit

Internal Audit Verdict: ✅ GO (2026-03-14)

Check	Result
SQL injection	✅ All queries parameterized (sqlx bind)
CVEs in dependencies	✅ 0 active (sqlx 0.8.6, tokio 1.50.0)
UTF-8 safety	✅ `safe_truncate` on all string slicing
Secrets	✅ All via environment variables
Division by zero	✅ Protected with `.max(1e-9)`
Error handling	✅ All `?` propagated with `anyhow::Context`
Clippy pedantic	✅ 276 cosmetic warnings only
Tests	✅ 11/11 passing
Licenses	✅ All MIT/Apache-2.0 (0 GPL/AGPL)

Dependencies

Crate	Purpose	License
`tokio`	Async runtime	MIT
`sqlx`	PostgreSQL (async)	MIT/Apache-2.0
`serde` / `serde_json`	Serialization	MIT/Apache-2.0
`pgvector`	Vector similarity	MIT
`petgraph`	Graph algorithms	MIT/Apache-2.0
`ort`	ONNX Runtime (optional)	MIT/Apache-2.0
`tokenizers`	HuggingFace tokenizers	Apache-2.0
`blake3`	Cryptographic hashing	Apache-2.0/CC0
`mimalloc`	Global allocator	MIT
`tracing`	Structured JSON logging	MIT
`lru`	O(1) LRU cache	MIT
`chrono`	Timezone-aware timestamps	MIT/Apache-2.0

Version History

Version	Key Changes
2.0.0	🦀 Complete Rust rewrite. FSRS-6 (21 params), BCM metaplasticity, Dual-Strength memory, Brandes centrality, Leiden communities, Shannon entropy, BM25+, adaptive RRF, neighbor diffusion, blake3 dedup. Internal audit: GO verdict.
1.6.0	KG-neighbor expansion, embedding LRU cache, async embed rebuild, community summaries, batch access tracking
1.5.0	Token-budget truncation, post-fusion dedup, source triangulation, adaptive confidence, session-aware decay
1.3.0	Modular architecture (CC avg D→A), 87% CC reduction
1.1.0	GraphRAG, REM Sleep, conditional pgvector, 4-signal RRF
1.0.0	Initial release: 12 tools, Hebbian learning, FSRS

License

CC BY-NC 4.0 — Free to use and modify, not for commercial use.

Author

Leandro Pérez G.

GitHub: @LeandroPG19
Email: leandropatodo@gmail.com

Credits

Mathematical foundations: Wozniak (1987), Ye (2024, FSRS-6), Expertium (2025), Oja (1982), Bienenstock, Cooper & Munro (1982, BCM), Bjork (1992, Dual-Strength), Salton (1975, TF-IDF), Lv & Zhai (2011, BM25+), Cormack (2009, RRF), Brin & Page (1998, PageRank), Collins & Loftus (1975), Traag et al. (2019, Leiden), Brandes (2001), Shannon (1948), Pearson (1900, χ²), Friston (2023, PE gating), BAAI (2023, BGE), Malkov & Yashunin (2018, HNSW), O'Connor et al. (2020, blake3).

Name		Name	Last commit message	Last commit date
Latest commit History 28 Commits
rust		rust
src/cuba_memorys		src/cuba_memorys
tests		tests
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
docker-compose.yml		docker-compose.yml
pyproject.toml		pyproject.toml
setup_db.sh		setup_db.sh

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🧠 Cuba-Memorys

Why Cuba-Memorys?

Comparison

Quick Start

Optional: ONNX Embeddings

🇨🇺 The 12 Tools

Knowledge Graph

Search & Verification

Error Memory

Sessions & Decisions

Memory Maintenance

Architecture

Performance: Rust vs Python

Database Schema

Search Pipeline

Mathematical Foundations

FSRS-6 — Ye (2024) / Expertium (2025)

Hebbian + BCM — Oja (1982), Bienenstock-Cooper-Munro (1982)

Dual-Strength — Bjork (1992)

RRF Fusion — Cormack (2009)

Other Algorithms

Configuration

Environment Variables

Docker Compose

How It Works

1. The agent learns from your project

2. Error memory prevents repeated mistakes

3. Anti-hallucination grounding

4. Memories adapt with FSRS-6

5. Community intelligence

Security & Audit

Dependencies

Version History

License

Author

Credits

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