SuperLocalMemory V4.1.14

Rent the LLM. Own the memory.

Rent an LLM — but own the memory, for your company and for your industry.

The governed memory layer for AI agents: local-first, auditable, and built for the compliance obligations teams now actually carry.
Models are interchangeable and rented by the token. What your agents remember is yours — it is your customers' data, your retention obligations, and your audit trail. SLM keeps that layer on infrastructure you control, with multi-workspace isolation, role-based access, and GDPR + EU AI Act governance controls built in.

The boundary. SuperLocalMemory starts with a local runtime; provider-backed enrichment, cloud backup, connectors, and proxy use are explicit choices. Different products solve different boundaries. Published benchmark evidence carried into V4 comes from the published V3 research architecture; it is not a claim of a newly rerun V4 package benchmark.

How to check that, rather than believe it. Every reliability guarantee here is stated as a falsifiable invariant, tested under an adversarial condition with a negative control, and shipped with the harness that regenerates the evidence: python benchmark/run_all.py --trials 200 --output-dir results/. What each experiment does not exercise is stated too.

v4.1.14 — one control plane: SLM-Mesh peer coordination · multi-scope memory (personal / shared / global) · profiles · Cache · Compress · 7-layer retrieval · code graph · Entity Explorer · skill evolution · Modes A/B/C · GDPR retention & audit chain · bounded loops — across CLI, MCP, dashboard, the Claude plugin, the Codex add-on, and documented IDE integrations.
Proxy: slm wrap claude  ·  MCP: add slm_compress to your config  ·  Skill: zero-config

Four public arXiv preprints · V4: arXiv:2608.08253 · companion archive: Zenodo 21853302 (DOI 10.5281/zenodo.21853302) · prior preprints: 2603.02240 · 2603.14588 · 2604.04514.

Why SuperLocalMemory?

SuperLocalMemory is an enterprise-grade, local-first memory control plane for AI agents. Your team's agent memory lives on infrastructure you control, with per-workspace isolation, role-based access, and GDPR / EU AI Act governance controls — built for organizations, and for EU data-residency obligations where agent context must not leave your environment by default.

Agent-memory systems make different storage, model-provider, and deployment trade-offs. SuperLocalMemory starts with a local runtime and makes provider-backed enrichment, cloud backup, connectors, and proxy use explicit choices.

Different products solve different boundaries. The published LoCoMo benchmark evidence in this README is protocol-scoped evidence from the published V3 research architecture; it is carried forward for continuity and is not a claim of a newly rerun V4 package benchmark.

SuperLocalMemory V4 combines conventional dense and lexical retrieval with graph, temporal, associative, and statistical relevance scoring in a 7-layer control plane (admission → queryable core → enrichment → brain → multi-channel retrieval → context safety → operations). The default local runtime does not require Docker, a separately operated graph database, or an API key.

Memory with a sense of time. SLM does not only store what an agent learned — it records when. Every fact carries ingestion timing and provenance; recall runs a dedicated temporal candidate channel alongside semantic, lexical, and associative retrieval; scenes and entity timelines reconstruct sequence; and the lifecycle lets neglected memory decay and self-archive instead of growing without bound. Time is a first-class ranking and lifecycle signal rather than a timestamp column an agent never reads — which is what lets a long-lived agent reason about how its context changed, not only what it currently holds.

What changed in this release. See the CHANGELOG — every release is written up there, in plain language, newest first.

• SLM-Mesh — authenticated cross-session and cross-machine peer coordination (messages, locks, shared state, inbox/outbox, optional discovery). Coordination only — not automatic replicated memory.
• Multi-scope memory & profiles — workspaces (profiles) plus personal / shared / global scopes; cross-profile recall is default-deny.
• Cache & compression (context optimization) — exact-match cache with tagged invalidation, safe compression, and opt-in reversible/aggressive paths across proxy, MCP, and skill surfaces.
• Entity Explorer & skill evolution — compiled entity summaries/timelines; opt-in skill lineage, budgets, and verification outcomes.
• Modes A / B / C — local-only (A), on-device LLM enrichment (B), provider-assisted (C). An operating mode records technical locality facts; it does not determine EU AI Act legal compliance (that is deployment-context assessment — see Privacy controls).
• GDPR posture, retention & audit chain — export, fail-closed cross-store erasure, retention policies, and a hash-chained audit trail. Engineering controls for compliance programs, not a legal certification.
• 7-layer retrieval/recall stack & code graph — multi-channel candidates (semantic, BM25, temporal, Hopfield, spreading activation) plus optional code-graph tools for blast radius and review context.
• MCP profiles — code exposes 31 tools for installed coding agents; full 49; power 61; whole 94 (all registered). Also core (16), mesh (8), and the unrestricted default surface (49 with mesh enabled).
• Governed write path & verifiable transactions — admission + policy control, a per-owner obligation ledger, and a hash-sealed completion manifest with a reconciler that redrives unmet obligations.
• Self-healing lifecycle & admin remediation — stale locks cleared on restart; list/resolve stuck operations from CLI, MCP, or the dashboard.

SLM is one strand of Qualixar's work on AI reliability engineering: making agent behavior observable, bounded, and reproducible instead of best-effort.

The architecture evaluated in the V3 paper remains the foundation of this release. The figures below keep their original LoCoMo protocol, answer-construction, model, and sample scope.

How SLM fits beside other memory systems