Cuba-Memorys

Long-term memory for AI coding agents. An MCP server that gives your agent a knowledge graph it can search, reason over, and be corrected by — so it stops forgetting your codebase between sessions.

Written in Rust. Backed by PostgreSQL + pgvector. 28 MCP tools (29 with CUBA_DOCS=1), 19 CLI commands, and every number below measured on a benchmark that — as of v0.12 — actually measures what it claims to. (The previous one did not. See Measured.)

Install

pip install cuba-memorys        # or: npm install -g cuba-memorys
claude mcp add cuba-memorys -- cuba-memorys

That is the whole setup. On first run it provisions a PostgreSQL 18 + pgvector container via Docker and initializes the schema. Docker must be running.

{
  "mcpServers": {
    "cuba-memorys": {
      "command": "cuba-memorys"
    }
  }
}

{
  "mcpServers": {
    "cuba-memorys": {
      "command": "cuba-memorys",
      "env": { "DATABASE_URL": "postgresql://user:pass@localhost:5432/brain" }
    }
  }
}

cuba-memorys serve                      # 127.0.0.1:8787 by default
cuba-memorys serve 127.0.0.1:9000       # or pick the address

{
  "mcpServers": {
    "cuba-memorys": {
      "type": "http",
      "url": "http://127.0.0.1:8787/mcp",
      "headers": { "Mcp-Client-Id": "editor-window-1" }
    }
  }
}

cuba-memorys models all          # embeddings + NLI + reranker + runtime
cuba-memorys models embed        # just the embeddings model (~113 MB)
cuba-memorys models all --gpu    # GPU runtime, if you have one
cuba-memorys doctor              # confirms what loaded

What it actually does

Most memory servers are a key-value store with an embedding bolted on. This one models four kinds of memory, because the psychology literature says they are four different things and they decay differently:

Procedural memory is a separate table rather than a ninth observation type for a specific reason: ACT-R separates declarative memory (reinforced by access) from procedural (reinforced by success). As an observation, a recipe consulted constantly because it keeps failing would climb in importance. It is ranked by Wilson lower bound, so 1/1 successes scores 0.21 and 47/50 scores 0.84 — a lucky first try does not outrank a track record.

Retrieval

Hybrid RRF fusion (k=60, Cormack 2009) over three signals — full-text, BM25 (ts_rank_cd), and pgvector HNSW — with entropy-routed weighting that shifts from keyword-heavy to semantic as the query's Shannon entropy rises.

Answers arrive in compact by default: abbreviated keys, content truncated at 1200 chars. 30% fewer tokens, and a slightly better nDCG — measured on the 221 id-scored questions, +0.0090 with a paired 95% interval of [+0.0024, +0.0166]. The format genuinely cannot change which documents rank; what it changes is how many of them survive the response token budget before they are scored. Verbose at the default 5000-token budget weighs 5286 tokens and loses its tail; compact weighs 3723 and keeps it. Pass "format": "verbose" for the full per-branch score breakdown.

Verification that actually verifies

cuba_faro mode=verify checks a claim against what is stored. It used to score claims by cosine similarity to the retrieved evidence, and that does not work — similarity measures what a text is about, not what it asserts. "cuba-memorys is written in Rust" and "…in Java" are nearly the same vector. Measured on the live corpus, the false claim scored 0.61 and the true one 0.59.

Entailment is a different question from similarity, and it needs something that reads. A local cross-encoder now judges each piece of evidence — supports / contradicts / unrelated — and confidence is derived from the verdicts, each weighted by that evidence's similarity. Same corpus, after:

Being on-topic is not support, and unrelated counts for neither side.

The judge is mDeBERTa-v3-base-xnli running locally on ONNX: 100 languages, ~50 ms per verdict, no API key, no network, no cost. That matters here — about 75% of this corpus is Spanish, and the English-only NLI models everyone reaches for first would have silently failed on three memories out of four. Install it with cuba-memorys models nli; cuba-memorys doctor will tell you whether it loaded.

Without it, verification falls back to an LLM (your MCP client's own model via sampling, a local claude CLI, or the Anthropic API) — and with none of those, to an honest unknown rather than an invented verdict.

Two things it will not do. It will not confirm a claim on weak evidence: entailment must clear 0.80 while contradiction needs only 0.60, because confirming a false memory and doubting a true one are not errors of equal cost. And when it cannot tell, it says so instead of returning whichever number came out largest — an argmax over a 3-way head will happily publish supports for a claim that is flatly false, and did.

Calibrated abstention

The out-of-distribution gate rejects queries the corpus cannot answer. The threshold is not a magic constant: Ledoit-Wolf covariance shrinkage plus a conformal quantile, calibrated against your own corpus with cuba-memorys calibrate --apply and persisted. (The theoretical χ² threshold rejected 100% of answerable queries. Distribution-free calibration is not a nicety here.)

And it tells you when it is broken

$ cuba-memorys doctor
[  ok  ] migrations           33 aplicadas, ninguna dirty
[  ok  ] embedding_dim        runtime 1024-d == columna vector(1024)
[  ok  ] runtime_role         'cuba_app' sin superuser — RLS y audit efectivos
[ warn ] binary_freshness     4 proceso(s) MCP corren un binario más viejo que el de disco

This exists because the failure mode of a hybrid search engine is not a crash — it is a vector branch dying and the search quietly becoming lexical, with no symptom. The server now refuses to start on an embedding-dimension mismatch, and search sets degraded: true in the response when a branch fails.

The CLI: your memory without an LLM in the middle

Nineteen commands. cuba-memorys --help lists them all.

dedupe — because a different string is a different entity

cuba_alma create inserts with ON CONFLICT (name). So one project fragments into Mapupita-Web, Mapupitta-Web (typo), Mapupita Web, mapupita… and searching one finds none of the others. On a real 266-entity graph, 158 of them (59%) had not a single relation — for PageRank and multi-hop retrieval, they did not exist.

What decides a merge is not the embedding centroid. That was the obvious idea and it is wrong: M-Codes Reference Guide and G-Codes Reference Guide sit at 0.811 cosine between centroids. On a corpus about one domain, centroid similarity measures the domain, not the entity — a 0.80 threshold would have merged two different CNC guides, irreversibly.

So --apply merges only what is provable (identical after normalizing case and separators). Typos and near-matches are shown, and judged one at a time with --judge. The old name is written to brain_entity_aliases, so nothing is lost: looking it up still resolves.

The 28 tools

Named after Cuban culture. cuba-memorys advertises all of them, or set CUBA_TOOL_PROFILE=lean to advertise only cuba_tools + cuba_call — 67% smaller tool catalogue, zero functions lost, schemas loaded on demand.

Knowledge graph — cuba_alma (entities) · cuba_cronica (observations, episodes, timeline) · cuba_puente (typed relations, traversal, link prediction) · cuba_ingesta (bulk import)

Search — cuba_faro (hybrid RRF, verification, MMR diversification, OOD abstention)

Error memory — cuba_alarma (report) · cuba_remedio (resolve) · cuba_expediente (search past errors; warns if an approach failed before)

Sessions & decisions — cuba_jornada (session lifecycle, diff) · cuba_decreto (architecture decisions) · cuba_proyecto (per-project isolation) · cuba_pre_compact (survive /compact)

Procedural — cuba_receta (recipes ranked by Wilson lower bound)

Cognition — cuba_reflexion (gap detection) · cuba_hipotesis (abductive inference) · cuba_contradiccion (semantic conflicts) · cuba_juez (LLM judge) · cuba_centinela (prospective triggers) · cuba_calibrar (Bayesian calibration, source credibility)

Maintenance — cuba_zafra (decay, prune, merge, PageRank, Leiden communities) · cuba_eco (RLHF feedback) · cuba_vigia (health, drift, centrality) · cuba_forget (GDPR erasure) · cuba_archivo (CFR-21 hash-chain audit log) · cuba_pizarra (working memory) · cuba_sync (git-friendly export/import)

Meta — cuba_tools (discover) · cuba_call (invoke)

Configuration

Footprint

A memory server is infrastructure: it is running when you are not using it. On the 6 GB laptop GPU this was measured on, it used to hold 5228 MiB of VRAM from boot — 93% of the card — and other GPU programs stopped being able to start. The NVIDIA driver was returning NV_ERR_NO_MEMORY on channel creation, which is what a game or a GPU-accelerated terminal fails on.

Two of the numbers below ship as defaults; two need a line of config, and this table keeps them apart rather than quoting the best one as if it came free.

Everything in the defaults column is code that ships. CUBA_RERANK_CHUNK=4 is one env var. The last column additionally needs the rebuilt reranker described below. None of it removed a feature.

Four things got it there:

Placement per model, not per process. Only the reranker is accelerated by a GPU — the INT8 embedder cannot be, and the FP32 NLI is not worth a gigabyte of VRAM for a judge that runs occasionally and tolerates 150-400 ms. The arena cap is per session, so three sessions asking for CUDA on a 6 GB card is a 3× overcommit waiting to fail.

A CUDA arena that stops doubling. ArenaExtendStrategy::NextPowerOfTwo is the ONNX Runtime default and it reserves in powers of two rather than what the session asked for.

The reranker loads on its first batch. Under socket activation the daemon starts far more often than it reranks, and plenty of those starts only ever answer a save.

A daemon that is not running when nobody is asking. CUBA_IDLE_SHUTDOWN_SECS plus a systemd .socket unit: the socket owns the port, the daemon starts on the first real connection and exits after the idle window. It shuts down through the normal path — serve returns, the background drain flushes in-flight embedding writes, sqlx closes its pool — because exiting the process directly loses those writes silently.

# ~/.config/systemd/user/cuba-memorys.socket
[Socket]
ListenStream=127.0.0.1:8787
Accept=no

[Install]
WantedBy=default.target

# ~/.config/systemd/user/cuba-memorys.service — no [Install]; the socket starts it
[Unit]
Requires=cuba-memorys.socket

[Service]
Type=exec
ExecStart=%h/.local/bin/cuba-memorys serve 127.0.0.1:8787
# An idle shutdown exits 0 — Restart=always would bounce it straight back up.
Restart=on-failure
Environment=CUBA_IDLE_SHUTDOWN_SECS=1200
Environment=CUBA_EMBED_DEVICE=cpu
Environment=CUBA_RERANK_DEVICE=gpu
Environment=CUBA_NLI_DEVICE=cpu

The reranker artifact

The published bge-reranker-v2-m3 ONNX is converted to FP16 before any graph fusion, which leaves 785 Cast nodes threaded through it. ONNX Runtime claws some of that back at load time (2023 → 897 nodes, 49 SkipLayerNormalization), but it cannot fuse Gelu and it repeats the work on every cold start. Rebuilding from the FP32 export and fusing first:

python -m onnxruntime.transformers.optimizer \
  --input model.onnx --output model.onnx \
  --model_type bert --num_heads 16 --hidden_size 1024 \
  --opt_level 1 --use_gpu --float16

Identical top-10 order on a real search, fused_score differing by at most 0,0029; on synthetic logits at the real batch shapes, Pearson ≥ 0,9997 with the same ranking in every batch.

Attention does not fuse, and that is not fixable here. is_fully_optimized: Attention (or MultiHeadAttention) not fused, at opt_level 0, 1, 2 and 99, on both the FP16 artifact and the clean FP32 one. The export builds its Q/K/V reshapes from dynamic shape subgraphs (Shape → Gather → Unsqueeze → Concat → Reshape) and AttentionFusion needs a Reshape with a constant shape to read num_heads and head_size off it. So flash/efficient attention stays unavailable without a re-export using static shapes — worth knowing before anyone spends an afternoon on it.

Measured — and the benchmark that was lying

Until v0.12 this section carried a line reading "every number here is measured rather than assumed", and every number in it was wrong. The benchmark was broken in three ways, and finding out cost two published conclusions.

It had ten queries. A 95% interval of roughly ±0.12; the smallest effect it could detect was ~0.25 nDCG. Any claim about a smaller difference was noise wearing a decimal point.

Relevance was judged by substring match. A result counted as correct if its text merely contained a marker word — so every observation mentioning "postgres" scored as a right answer to any question about postgres, whether it answered anything or not. That measures keyword presence, not retrieval, and it tilts the whole benchmark toward the lexical branch and against the vector one.

nDCG normalized against what was retrieved, not what exists. With 5 relevant documents in the corpus and 2 found, the "ideal" ranking was taken to be those 2 — so a system that missed 60% of the answer scored a perfect 1.0. (And R@10 = 3.125 shipped in this file. Recall is a proportion.)

The real number is not 0.894. On 221 id-scored queries it is nDCG@10 = 0.50 [95% CI 0.44–0.56]. The system did not get worse. It was never 0.894.

What that cost

• "The cross-encoder reranker earns nothing" — it had never run. Three bugs in series: faro wrapped the call in if let Ok(..) and dropped the error; it fed token_type_ids to a model that is XLM-RoBERTa and has none; it read f16 logits as f32. The output was "bit for bit identical" to no reranking not because reranking changed nothing, but because it never happened. Fixed; being measured properly now.

• Associative retrieval does degrade — but the old evidence (−0.03 at n=10) could not have shown it. On the new dataset with a paired bootstrap (the correct test: same queries in both arms), the interval is [−0.051, −0.018] and never touches zero. It improves 0 queries and hurts 23. The decision was right; the reasoning was not. The power was never in more data — it was in using the right test.

What survives, re-measured honestly

Foundations

Development

git clone https://github.com/LeandroPG19/cuba-memorys.git
cd cuba-memorys/rust && cargo build --release

# On an NVIDIA machine, build this way instead — without it the reranker spends
# its whole budget for a ranking that gets discarded. It accelerates the
# reranker only; see Footprint for why the other two models stay on the CPU.
cargo build --release --features docs,cuda

./scripts/demo.sh                  # runs on a throwaway Postgres it removes on exit
./scripts/merge-gate.sh            # fmt · clippy -D warnings · 316 tests · audit · integration
cargo run --release --example rerank_bench   # does the reranker fit its budget here?

Publishing is tag-driven: v* triggers GitHub Release binaries (5 platforms), PyPI wheels, npm, and the MCP Registry. A test pins all four files that hold a version number to the same value, because they used to drift and nothing caught it.

License

Apache-2.0 — use it, modify it, ship it, sell it, embed it in a closed product. No copyleft obligation. The licence also grants patent rights explicitly, which is the part legal departments care about.

Author

Leandro Perez G. — @LeandroPG19