qualixar/superlocalmemory

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 benchmark evidence carried into V3.8.0 is protocol-scoped evidence from the published V3 research, not a claim of a newly rerun V3.8.0 package benchmark.

SuperLocalMemory V3 combines conventional dense and lexical retrieval with graph, temporal, associative, and statistical relevance scoring. 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 V3.8.0 added. The 3.8.0 capability release introduced the following foundation; 3.8.1 is the existing-install stability patch for it:

• Temporal depth — the time-aware retrieval and lifecycle described above.

• Governance & EU compliance — team roles, workspace isolation, a login gate, multi-scope memory, GDPR access/erasure/portability rights, a hash-chained audit trail, and per-mode EU AI Act self-assessment.

• Framework adapters — drop-in, engine-backed memory for nine agent frameworks.

• Bounded loops — gate-verified agent loops where an independent check, not the agent's own claim, decides when a task is done.

• Stronger cache and compression — exact-match caching with tagged invalidation plus opt-in reversible compression, across proxy, MCP, and skill surfaces.

• Stability — a long defect-and-audit sweep across ingestion, retrieval, mesh, and the dashboard hardens the everyday path.

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

Different products solve different boundaries. SLM is for developers who want one local-first operating control plane—not only an SDK, managed context API, or agent runtime. It combines dated evidence, graph-aware retrieval, cache and compression controls, trusted-peer Mesh, and MCP/CLI/hooks/dashboard/IDE surfaces in one install.

See the source-linked market comparison for current primary sources and protocol-scoped benchmark evidence. A LoCoMo percentage is comparable only when the dataset scope, answer model, judge, retrieval stack, and release artifact match.

The V3.8.0 capability architecture

SuperLocalMemory is one local control plane for persistent agent context. It is not just a vector store: the same runtime can accept evidence, build and govern memory, retrieve bounded evidence for an agent, and expose cache, compression, and peer-coordination controls through a CLI, MCP, dashboard, and supported IDE integrations.

Architecture boundary: SQLite + sqlite-vec remain canonical; CozoDB and LanceDB are parity-gated projections; Mesh coordinates trusted peers rather than replicating a distributed memory database; connectors are opt-in.

Memory boundaries: profiles isolate workspaces by default. Every memory is personal, shared with named profile readers, or global; cross-profile recall is default-deny and must be explicitly enabled. This scoped sharing is local authorization, not SLM Mesh synchronization. See shared-memory.md.

 IDEs, agents, scripts, connectors, and humans
             │  CLI · MCP (HTTP/stdio) · hooks · dashboard
             ▼
 ┌────────────────────────── SLM CONTROL PLANE ──────────────────────────┐
 │  1. Admission       identity, scope, idempotency, raw evidence         │
 │  2. Queryable core  SQLite facts + FTS durable receipt                  │
 │  3. Enrichment      facts, entities, scenes, time, provenance, graph   │
 │  4. Memory brain    feedback, patterns, rewards, consolidation          │
 │  5. Retrieval       semantic · BM25 · temporal · Hopfield · activation │
 │  6. Context safety  policy, trust, provenance, redaction, budgets      │
 │  7. Operations      lifecycle, audit, cache/compress, mesh, backups    │
 └───────────────────────────────────────────────────────────────────────┘
             │
             ▼
 SQLite + sqlite-vec canonical store  ──► optional graph/vector projections

The seven stages are an execution model, not a promise that every optional enricher or retrieval channel runs for every request. The receipt, trace, and health surfaces expose the stages actually completed by the installed runtime.

What the dashboard exposes

slm dashboard opens a local operational view of the same control plane:

Dashboard visibility is not a substitute for runtime proof: use slm doctor, slm health, slm trace, and the relevant CLI/MCP operation to validate a deployment.

Watch the product walkthrough

Watch the SuperLocalMemory demo on YouTube — a five-minute walkthrough of installation, setup, recall, cache, and compression. The video shows a product walkthrough; use the commands and release notes in this README as the current release contract.

Published LoCoMo evidence (V3 architecture, carried into V3.8.0)

The V3 paper evaluates the architecture carried into V3.8.0. Every figure below is protocol-scoped, so a reader can distinguish local retrieval, answer construction, and cloud-assisted evaluation rather than treating unlike runs as one score.

Published category results: Mode A Retrieval scored 72.0% single-hop, 70.3% multi-hop, 80.0% temporal, and 85.0% open-domain. Mode C scored 64.0% single-hop, 100.0% multi-hop, and 86.0% open-domain on its 81-question Conv-30 scope (no temporal category was reported for that run). Across six LoCoMo conversations, the paper reports 71.7% with the information-geometric layers versus 58.9% without them: +12.7pp.

See arXiv:2603.14588 and the official LoCoMo paper for the full protocol, ablation table, and limitations. These are published V3 architecture results carried into V3.8.0—not a substitute for a newly rerun release-artifact benchmark.

Related MCP server: dakera-mcp

Quick Start

# Primary path 1 — npm global CLI (Node 18+)
# Creates a package-owned virtual environment. It does not modify system Python.
npm install -g superlocalmemory
slm setup       # Choose mode (A/B/C)
slm doctor      # Verify everything is working

# Primary path 2 — Python CLI + SDK in an activated virtual environment
python3 -m venv .venv
source .venv/bin/activate  # Windows PowerShell: .venv\Scripts\Activate.ps1
python -m pip install superlocalmemory
slm setup
slm doctor

# First use
slm remember "Alice works at Google as a Staff Engineer" --json
slm recall "What does Alice do?"
slm status

The default daemon write commits raw evidence plus a relational/FTS projection and returns a durable receipt in queryable state. Enrichment then advances the same operation through enriching to complete, or records a retryable failed state. Use slm remember "..." --sync when the caller must wait for all declared derivation and projector stages. JSON output includes the opaque operation_id, current materialization_state, and fact IDs.

# Wrap your agent — starts proxy + sets environment + launches agent
slm wrap claude
# Your first repeat prompt → CACHE HIT → $0.00
# See savings: slm optimize savings --since 1

Upgrading: use the owner of the installation: npm update -g superlocalmemory or, while the Python virtual environment is active, python -m pip install --upgrade superlocalmemory. Then run slm restart && slm doctor. Repository-clone users use the matching upgrade action in scripts/install.sh or scripts/install.ps1. Installers never move or delete memory data.

Three Pillars

Memory

Current recall has five candidate producers—dense semantic, BM25 lexical, temporal, Hopfield associative, and spreading activation—followed by fusion, optional reranking, and entity-graph score enhancement. The entity graph does not create an independent candidate in the current implementation. Core memory is SQLite-backed. SQLite and sqlite-vec remain the canonical source of truth. The packaged Scale Engine can maintain CozoDB graph and LanceDB vector projections, and it remains outside active retrieval paths until a staged parity witness proves it matches the canonical store. New installations remain on Local Core. During upgrade, slm db scale status can identify a positive pre-v3.7 layout candidate; the operator confirms it with slm db scale adopt. SLM then rebuilds from canonical SQLite, verifies it, and promotes it with a durable recovery journal while retaining the prior directories as a rollback backup. adopt reports restart_required: true; run slm restart before checking daemon health. If proof fails, recall remains on SQLite and status retains the rejected manifest for inspection, retires its replaceable derived payload, and allows a corrected retry.

Canonical ingestion is a durable state machine: raw → queryable → enriching → complete, with failed retaining raw evidence, error details, attempt count, and retry timing. SQLite relational facts and FTS are the queryable checkpoint; optional ANN/vector projectors are verified before complete is granted.

Recalled text is treated as untrusted evidence. Hooks, MCP session_init, CLI session context, and chat use one bounded renderer that redacts recognized secrets, neutralizes forged boundary markers, and attaches provenance. Trusted IDE instruction files contain only the static SLM protocol; fresh memory is retrieved at runtime rather than copied into those files.

Score Contract v2: relevance_score is query-relative relevance; ranking_score is internal ranking utility; memory_confidence belongs to the stored assertion; and trust_score is an evidence-policy signal. Legacy score and confidence remain aliases for one compatibility release. V3.8.0 is explicitly uncalibrated: calibration_status is uncalibrated and answer_confidence is null. See the retrieval score contract.

The retrieval/lifecycle implementation includes three mathematical layers that can run without a cloud LLM:

1. Fisher-informed scoring — dense candidate generation uses cosine similarity; Fisher-derived terms can modify later scoring when their state is available.

2. Sheaf Cohomology for Consistency — algebraic topology detects contradictions via coboundary norms on the knowledge graph.

3. Riemannian Langevin Lifecycle — memory positions evolve on the Poincare ball; neglected memories self-archive, no hardcoded thresholds.

Auto-capture hooks are installed explicitly with slm hooks install (Claude Code) or slm hooks install --agent codex (Codex). Hook latency and capture quality must be evaluated for the target client and workload; V3.8.0 publishes no universal p99 claim.

Multi-scope memory (v3.6.15, opt-in): keep memories personal (default), shared with named profiles, or global across the machine. Off by default — recall only ever returns your own facts until you turn sharing on, per call or in config. See docs/shared-memory.md.

Multilingual models: configure an OpenAI-compatible embedding endpoint such as Ollama, vLLM, LiteLLM, bge-m3, multilingual-e5, or Qwen3-Embedding. Language coverage and retrieval quality depend on the selected model and should be evaluated for the deployment corpus.

Cache + Compress

One engine, three ways in — choose the surface that fits your setup:

The hard constraint: The primary Claude conversation turn cannot be cached without a proxy. The MCP/skill path caches results you explicitly route through SLM (tool outputs, file reads, sub-model calls) — without a proxy the main conversation turn is not intercepted.

How to choose:

• Metered API (pay-per-token), want every call cached → Proxy (A)

• Pro/Max/Team subscription or any plan where you won't run a proxy → MCP tools (B) or Skill (C)

• Zero configuration → Skill (C): install once, auto-compresses CLAUDE.md and large outputs

• Agent-controlled caching of repeated file reads → MCP tools (B)

Cache: exact-match SQLite lookup is the stable cache path. Semantic cache controls are experimental until release-linked precision, invalidation, and tenant-isolation evidence exists. A cache hit can avoid a provider request, but actual cost and latency savings depend on the intercepted surface and provider.

Compress: safe mode uses conservative normalization and preserves JSON and code; measured reduction varies by content and can be zero. Aggressive prose compression is opt-in and lossy. CCR can retain an original for later byte-exact retrieval when reversible storage is enabled.

Savings dashboard: slm optimize savings --since 7 — live USD/INR/tokens saved. Hot-reload config, fail-open.

Mesh

Mesh provides authenticated coordination messages between configured peers, with an offline queue and optional mDNS discovery (SLM_MESH_DISCOVERY=on). It is not a replicated or conflict-resolving distributed-memory database.

# Machine A (broker)
export SLM_MESH_HOST=192.168.1.100
export SLM_MESH_SHARED_SECRET=my-secret-key
slm init

# Machine B (client)
export SLM_MESH_PEER_URL=http://192.168.1.100:8765
export SLM_MESH_SHARED_SECRET=my-secret-key
slm init

8 mesh MCP tools: mesh_summary, mesh_peers, mesh_send, mesh_inbox, mesh_state, mesh_lock, mesh_events, mesh_status.

Full docs: docs/multi-machine.md · docs/distributed-deployment.md

Install Paths

After any install path: slm setup → slm doctor → slm warmup (optional, pre-downloads ~500MB embedding model).

MCP + Profiles

SLM supports two MCP transports:

HTTP (recommended, v3.6.7+):

{ "mcpServers": { "superlocalmemory": { "type": "http", "url": "http://127.0.0.1:8765/mcp/" } } }

Or: claude mcp add --transport http superlocalmemory http://127.0.0.1:8765/mcp/

stdio (universal fallback):

{ "mcpServers": { "superlocalmemory": { "command": "slm", "args": ["mcp"] } } }

MCP Profiles (WP-01)

Control tool surface via SLM_MCP_PROFILE:

Precedence: ALL > TOOLS > PROFILE > default

export SLM_MCP_PROFILE=full   # or core / code / mesh / power / whole
slm mcp

For a predictable small surface, set core explicitly. Leaving the variable unset retains the compatibility default, whose mesh tools follow the local mesh setting. Count-suffixed aliases remain for backward compatibility and emit a migration warning: core14, code20, code21, code24, mesh8, full38, full39, full42, power50, power51, power54, whole81, whole84. Unknown names stop startup instead of silently selecting another tool set.

Per-IDE configs available for Claude Code, Cursor, Windsurf, VS Code Copilot, Continue, Gemini CLI, JetBrains, Zed, and more (15 configs in ide/configs/). See docs/ide-setup.md.

Claude Code Plugin

Install directly in Claude Code — no system-level npm/pip needed. This is how you get the skills, agents, hooks, commands, and rules (the MCP server is bootstrapped automatically). It is a two-step flow — add the marketplace once, then install:

# 1. Add the Qualixar marketplace (one-time — the repo IS the marketplace)
/plugin marketplace add qualixar/superlocalmemory

# 2. Install the plugin
/plugin install superlocalmemory@qualixar

• Self-bootstraps a Python venv, installs all deps in an isolated SLM_DATA_DIR

• Registers the 14-tool core MCP surface (core14 profile by default)

• Ships the SLM skills / agents / hooks / commands / rules

• Additive — does not replace an existing SLM install

• slm connect claude-code detects an existing plugin install and links them

Plugin vs Python/npm: python -m pip install superlocalmemory inside an activated virtual environment, or npm i -g superlocalmemory, give you the slm CLI + the MCP server (the tools). The skills/agents/hooks/ commands come only through the plugin above. Use the plugin for Claude Code; use pip/npm for the CLI or other IDEs.

To update later: /plugin marketplace update qualixar then /plugin install superlocalmemory@qualixar.

Codex add-on

For Codex, install the SLM-owned skills, two focused subagents, and four lifecycle hooks explicitly:

slm codex install

This adds only SLM-owned files under ~/.agents/skills, ~/.codex/agents, and ~/.codex/hooks.json; it does not replace another agent's hooks or rewrite ~/.codex/config.toml. Codex requires review and trust for new command hooks: open /hooks after installation. MCP wiring remains a separate explicit step:

slm connect codex

slm connect codex semantically merges the superlocalmemory MCP server into ~/.codex/config.toml, preserving unrelated configuration keys and writing atomically. TOML serializers can normalize whitespace and comments, so it is not a byte-preserving operation; use it only when you want the MCP server configured. Check the result with slm codex status; undo SLM-owned add-ons with slm codex remove.

GitHub Copilot integration

The shipped installer configures the SuperLocalMemory MCP server and additive agent instructions for VS Code with GitHub Copilot:

slm connect vscode-copilot --here

Run it from the project root. It semantically merges the SLM server into .vscode/mcp.json and adds SLM-owned guidance inside .github/copilot-instructions.md, preserving unrelated servers and existing instructions. The generated copilot-plugin/ source bundle is maintained for parity checks, but v3.8.1 does not claim that slm connect installs its prompt, agent, or hook files.

Privacy controls and operating modes

slm mode a   # Zero-cloud (default)
slm mode b   # Local Ollama
slm mode c   # Cloud LLM

Mode A can run core memory operations without sending memory content to a cloud model provider. This does not disable optional connectors, cloud backup, proxy providers, dependency acquisition, or model downloads; review configuration and network policy for the deployment.

SuperLocalMemory provides local storage, export/erasure commands, provenance, policy, and audit features that can support a compliance program. The software is not a legal certification, and compliance depends on the use case, operator, configuration, and surrounding systems.

Available controls include local export and erasure commands, hash-chained audit records, provenance tracking, and ABAC policy enforcement. Verify their behavior and retention boundaries for your deployment; see docs/compliance.md.

Teams and Enterprise Memory (v3.8.0)

V3.8.0 adds multi-user, multi-workspace controls for teams and organizations. These are opt-in — personal single-user installs work exactly as before with no required login.

Users and roles

SLM supports three role tiers within a workspace: admin, member, and viewer.

Roles are scoped per workspace (profile). A user may have different roles in different workspaces.

Workspace isolation

Each workspace (profile) is a fully isolated memory namespace. One workspace cannot read another's personal memories. Shared and global scopes are opt-in and still profile-bounded at the authorization layer.

Login gate

Enterprise deployments set require_login = true in configuration. With login enabled:

• Every dashboard and API request requires an authenticated session.

• First-run creates an admin account with a user-chosen password (no default credentials are shipped).

• Session cookies use HttpOnly with optional Secure enforcement.

• Personal installs run with require_login = false (loopback owner is trusted).

slm config set security.require_login true   # Enable for team/enterprise use

Memory scopes

Recall is default-deny: shared and global facts are never returned unless the caller explicitly opts in (--include-shared, --include-global) or the scope policy allows it. See docs/shared-memory.md.

GDPR and data governance

SLM ships built-in controls that support GDPR compliance programs:

• Export — full profile data export as a structured JSONL bundle

• Erasure — profile deletion removes data from 30+ scoped tables; erasure is logged to the tamper-proof audit chain before any data is deleted

• Retention rules — time-based policies (indefinite, gdpr-30d, hipaa-7y, custom) applied per profile

• Audit trail — every store, recall, mutation, and erasure produces a hash-chained audit record

• PII redaction — configurable automatic redaction before memory content crosses trust boundaries

These are engineering controls. Compliance depends on deployment configuration, use case, and operator responsibility. See docs/compliance.md.

EU AI Act mode verification

SLM includes a per-mode EU AI Act self-assessment. The EUAIActChecker produces a compliance report for the active operating mode — risk category, whether data stays local, whether generative AI is used, and transparency / human-oversight signals:

• Mode A (Local Guardian) and Mode B (Smart Local) — assessed as compliant: memory processing stays local and uses no generative AI.

• Mode C (Provider-assisted) — flagged non-compliant, because query or enrichment content is sent to a cloud model provider.

This is operator self-assessment tooling, not a legal certification or conformity assessment; actual EU AI Act obligations depend on your system, deployment, and role. See docs/compliance.md.

Deployment tiers

SLM ships one binary and is configured for the appropriate tier at install or post-install time.

The installer or slm reconfigure sets the tier. Each setting is independently overridable at runtime. Full tier documentation: docs/deployment-tiers.md.

RBAC and teams docs

Full reference: docs/rbac-teams.md · docs/deployment-tiers.md

Bounded Loops (v3.8.0)

A bounded loop terminates only when an independent gate passes — a test suite exit code, a linter, a JSON-schema check, or an SLM-recall condition. The agent's own "I finished" message is recorded as advisory context and never used as the termination signal. Every lap is persisted to SLM memory under the tag loop:<name>, so runs are auditable and resumable across sessions.

Three surfaces ship together:

# Run the built-in convergence demo (no API key needed)
slm loop demo

# Inspect recorded runs
slm loop history --name convergence-demo
slm loop show <run_id>

Loop laps are stored as ordinary SLM memories and are visible in the dashboard under Knowledge Graph and Memories (filter by tag loop:<name>) and in the Multi-Agent Memory workspace.

Framework Adapters (v3.8.0)

SLM ships nine framework adapters under ide/integrations/. Each adapter wires SLM as the memory and history provider for the respective framework without replacing the framework's own agent runtime.

Pydantic AI is not included — it does not expose a formal memory interface for external providers. Each adapter's README.md covers installation and configuration for that framework.

Advanced

Open the web dashboard with slm dashboard; workspaces appear only when their runtime capability is enabled and healthy. See CHANGELOG.md for the complete release history.

Research Papers

SuperLocalMemory is backed by three preprints by Varun Pratap Bhardwaj (2026):

• The Living Brain (V3.3): arXiv:2604.04514 · Zenodo 19435120

• Information-Geometric Foundations (V3): arXiv:2603.14588 · Zenodo 19038659

• Trust & Behavioral Foundations (V2): arXiv:2603.02240 · Zenodo 18709670

Use the citation metadata on the linked arXiv or Zenodo records.

Support / License / Qualixar

See CONTRIBUTING.md for guidelines. Wiki for detailed documentation.

GNU Affero General Public License v3.0 (AGPL-3.0). See LICENSE.

For commercial licensing (closed-source, proprietary, or hosted use), see COMMERCIAL-LICENSE.md or contact varun.pratap.bhardwaj@gmail.com.

Copyright (c) 2026 Varun Pratap Bhardwaj / Qualixar.

Part of Qualixar · Author: Varun Pratap Bhardwaj

Acknowledgments

• Everything Claude Code (ECC) inspired SLM's skill-observation patterns; SLM can ingest ECC observations with slm ingest --source ecc.

• HKUDS/OpenSpace informed the skill-evolution verification design (arXiv:2604.01687).

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