README.md
July 26, 2026 · View on GitHub
SuperLocalMemory V3.8.5
Enterprise-grade, local-first memory for AI agents and teams.
A persistent, auditable long-term brain for your agents that runs on your own infrastructure — with multi-workspace isolation, role-based access, and GDPR + EU AI Act governance controls built in.
v3.8.5 — one control plane: auditable retrieval · multi-scope memory (personal / shared / global) · Cache · Compress · trusted-peer Mesh · 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
3 public research preprints (arXiv + Zenodo archives) · arXiv:2603.02240 · arXiv:2603.14588 · arXiv: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 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.
| If your primary need is… | Product boundary to evaluate |
|---|---|
| Local-first agent memory plus operations, optimization, and IDE-agent surfaces | SuperLocalMemory — Mode A local core; Modes B/C by explicit choice. |
| A memory SDK, self-hosted server, or managed platform | Mem0 |
| A temporal context-graph service or graph engine | Zep / Graphiti |
| A stateful agent runtime with memory blocks and archival memory | Letta |
| LangGraph-native memory primitives and managers | LangMem |
| A context API/app with profiles, connectors, and RAG | Supermemory |
| User profiles and event-timeline memory | Memobase |
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.
| Capability | What ships today | Operator boundary |
|---|---|---|
| Memory types and lifecycle | Atomic facts, episodic scenes, temporal events, canonical entities, profiles/scopes, consolidation, forgetting and retention controls | Lifecycle policies and retention decisions remain operator-configured. |
| Memory boundaries | Profile-isolated workspaces plus personal, shared, and global memory scopes | Personal is the default; shared/global recall requires explicit scope policy or per-call opt-in. |
| Ingestion | Durable raw-to-complete operation state, fact extraction, entity resolution, graph/temporal/provenance derivations, and replay-safe identity | --sync waits for declared stages; dependencies and mode determine which enrichers are available. |
| Retrieval and recall | Semantic, lexical, temporal, Hopfield and spreading-activation candidate channels; RRF fusion, optional reranking and graph score enhancement | Healthy channels participate; response provenance states the evidence used. |
| Brain and learning | Behavioral patterns, feedback/outcome records, rewards, consolidation, LightGBM-related ranking components, soft prompts, and guarded skill-evolution workflows | Learning is evidence-driven; it does not claim autonomous correctness or guaranteed improvement. |
| Knowledge graph and entities | Canonical entities, aliases, entity profiles, graph edges, scenes, timelines, explorer and graph APIs | Stored/derived graph data is evidence, not an instruction authority. |
| Scale Engine | SQLite + sqlite-vec are canonical. CozoDB graph and LanceDB vector projections are managed with prepare → verify → promote → rollback; a structurally detected pre-v3.7 projection can be explicitly adopted. | Promotion is parity-gated and crash-recoverable. Legacy adoption preserves the prior projection as a rollback backup; repeated physical edge rows normalize to one logical edge with the strongest weight. |
| Optimize | Exact cache, tagged invalidation, safe compression, opt-in aggressive prose compression, CCR originals, proxy/MCP/skill surfaces | Only proxy intercepts a primary provider turn. MCP/skill cache results explicitly routed through SLM. |
| Mesh | Authenticated peer messages, inbox/outbox, locks, offline queue, optional discovery and mesh MCP tools | Mesh is coordination, not automatic replicated memory or conflict resolution. |
| Governance and operations | Provenance, audit/retention/policy surfaces, export/erasure controls, diagnostics, health, backups and daemon lifecycle | These are engineering controls, not a legal certification. |
| Integrations | CLI, Python SDK, MCP HTTP/stdio, Claude plugin, Codex add-on, supported IDE configurations, Gmail/Calendar/transcript adapters | Hooks, IDE edits, connectors, and networked adapters require explicit operator activation. |
What the dashboard exposes
slm dashboard opens a local operational view of the same control plane:
| Workspace | Use it to inspect or control |
|---|---|
| Dashboard and Health | daemon identity, storage/runtime health, diagnostics and recent activity |
| Brain | consolidation, behavioral patterns, outcomes/rewards, learning state and soft prompts |
| Knowledge Graph and Memories | graph neighborhoods, entities, scenes, temporal evidence, memory inspection and mutation |
| Operations | ingestion-operation state, traces, maintenance and lifecycle work |
| Entity Explorer and Skill Evolution | compiled entity summaries/timelines; opt-in skill lineage, budgets and verification outcomes |
| Multi-Agent Memory | per-agent write activity and attribution; memories stamped by SLM_AGENT_ID, agent write counts, and trust signals |
| Mesh Peers | configured peers, inbox/outbox, pending coordination and locks |
| Settings and Optimize | mode/provider/configuration; cache, compression and savings telemetry |
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 configuration | LoCoMo aggregate | Protocol scope | What the result establishes |
|---|---|---|---|
| Mode A Raw | 60.4% | 10 conversations; 1,276 scored questions; local embeddings, local retrieval, and zero-LLM answer construction | End-to-end local answer construction under the published V3 protocol. |
| Mode A Retrieval | 74.8% | 10 conversations; 1,276 scored questions; local retrieval, then GPT-4.1-mini answer synthesis | Retrieval evidence: local retrieval contributes the evidence, while the disclosed external model constructs the final answer. |
| Mode C | 87.7% | Conv-30 only; 81 scored questions; text-embedding-3-large plus GPT-4.1-mini answer generation and judge | Cloud-assisted configuration on one fully disclosed conversation; not a full-dataset result. |
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.
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:
- Fisher-informed scoring — dense candidate generation uses cosine similarity; Fisher-derived terms can modify later scoring when their state is available.
- Sheaf Cohomology for Consistency — algebraic topology detects contradictions via coboundary norms on the knowledge graph.
- 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:
| Surface | How you use it | Requires proxy? | Window effect | Cache scope |
|---|---|---|---|---|
| A — Proxy | slm wrap claude or ANTHROPIC_BASE_URL=http://127.0.0.1:8765 | Yes | Shrinks | Full-turn cache — every call |
| B — MCP tools | Add 5 tools to MCP config; call slm_compress, slm_cache_set/get | No | Preserved (1M) | Results you explicitly route through SLM |
| C — Skill | Copy skills/slm-optimize/SKILL.md → ~/.claude/skills/ | No | Preserved (1M) | Auto-applied by the agent per skill rules |
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
| Path | Command | When |
|---|---|---|
| npm global CLI (primary) | npm install -g superlocalmemory | Node 18+; package-owned virtual environment; system Python is not modified; run slm setup explicitly afterward |
| Python CLI + SDK (primary) | Activate a Python virtual environment, then python -m pip install superlocalmemory | Python 3.11+; the slm CLI and importable SDK stay inside that environment |
| Repository clone — macOS/Linux | ./scripts/install.sh install | Research/contributor path; delegates to an existing uv or pipx installation |
| Repository clone — Windows | .\scripts\install.ps1 -Action Install | Research/contributor path; delegates to an existing uv or pipx installation |
| Claude Code Plugin (WP-06) | /plugin marketplace add qualixar/superlocalmemory then /plugin install superlocalmemory@qualixar | Self-bootstraps venv, isolated SLM_DATA_DIR, additive — 14-tool core. Ships the skills/agents/hooks/commands |
| Portable / IDE connect (WP-08) | slm connect <ide> [--here] | Wire any IDE without reinstalling; slm connect claude-code → plugin pointer |
After any install path: slm setup → slm doctor → slm warmup (optional, pre-downloads ~500MB embedding model).
| Component | Size | When |
|---|---|---|
| Core libraries (numpy, scipy, networkx) | ~50MB | During install |
| Dashboard & MCP server (fastapi, uvicorn) | ~20MB | During install |
| Learning engine (lightgbm) | ~10MB | During install |
| Search engine (sentence-transformers, torch) | ~200MB | During install |
| Embedding model (nomic-embed-text-v1.5, 768d) | ~500MB | First use or slm warmup |
Mode B requires Ollama + a model (ollama pull llama3.2) | ~2GB | Manual |
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:
| Profile | Tools | Use case |
|---|---|---|
core | 14 | Memory, session, and optimize core |
code | 24 | Core + code-graph tools + profile switching + bounded loops |
mesh | 8 | Mesh-only — multi-machine coordination |
full | 42 | Memory + optimize + evolution + mesh + bounded loops |
power | 54 | Full + administration, lifecycle, and diagnostics |
whole | all registered | Every registered MCP tool |
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 (
core14profile by default) - Ships the SLM skills / agents / hooks / commands / rules
- Additive — does not replace an existing SLM install
slm connect claude-codedetects an existing plugin install and links them
Plugin vs Python/npm:
python -m pip install superlocalmemoryinside an activated virtual environment, ornpm i -g superlocalmemory, give you theslmCLI + 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
| Mode | What | Core memory path | Optional network behavior |
|---|---|---|---|
| A | Local Guardian | Local processing | Model/dependency downloads, connectors, backup, and other enabled integrations may use the network |
| B | Smart Local | Local Ollama enrichment | Same optional integrations as Mode A |
| C | Provider-assisted | Local storage with provider calls | Query or enrichment content is sent to the configured provider |
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.
| Role | Can read memory | Can write memory | Can manage users/config |
|---|---|---|---|
| admin | yes | yes | yes |
| member | yes | yes | no |
| viewer | yes | no | no |
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
HttpOnlywith optionalSecureenforcement. - 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
| Scope | Who can recall | Set with |
|---|---|---|
personal | Owner profile only (default) | slm remember "..." --scope personal |
shared | Named profiles the owner grants | slm remember "..." --scope shared --shared-with profile-a,profile-b |
global | Any authorized user on this machine | slm remember "..." --scope global |
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.
| Tier | Login gate | PII redaction | Retention | Audit |
|---|---|---|---|---|
| Personal | off | off | off | on |
| Enterprise | on | on | on | on |
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:
| Surface | How you use it |
|---|---|
| CLI | slm loop demo · slm loop history [--name <n>] · slm loop show <run_id> |
| Skill + agent | /slm-loop skill with the slm-loop-runner agent — delegate a task that has a checkable acceptance condition |
| MCP tools | slm_loop_run · slm_loop_history · slm_loop_show — call from any IDE or agent (available in the code and full MCP profiles) |
# 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.
| Framework | Directory |
|---|---|
| LangGraph | ide/integrations/langgraph/ |
| Semantic Kernel | ide/integrations/semantic-kernel/ |
| Microsoft Agent Framework | ide/integrations/agent-framework/ |
| LangChain | ide/integrations/langchain/ |
| LlamaIndex | ide/integrations/llamaindex/ |
| CrewAI | ide/integrations/crewai/ |
| AutoGen | ide/integrations/autogen/ |
| Google ADK | ide/integrations/google-adk/ |
| OpenAI Agents | ide/integrations/openai-agents/ |
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
| Topic | Link |
|---|---|
| Full optimize docs | docs/optimize-overview.md · docs/optimize-cli.md · docs/optimize-config.md |
| Distributed deployment | docs/distributed-deployment.md |
| Multi-machine mesh | docs/multi-machine.md |
| Auto-memory hooks | docs/auto-memory.md |
| Architecture + math | docs/ARCHITECTURE.md |
| Published benchmark evidence | docs/benchmarks.md |
| CLI reference | docs/cli-reference.md |
| MCP tools reference | docs/mcp-tools.md |
| Getting started | docs/getting-started.md |
| IDE setup (15 configs) | docs/ide-setup.md |
| Teams, users, and RBAC | docs/rbac-teams.md |
| Deployment tiers | docs/deployment-tiers.md |
| pi.dev integration | docs/pi-dev-integration.md |
| Skill evolution | docs/skill-evolution.md |
| V2 migration | docs/migration-from-v2.md |
| Configuration | docs/configuration.md |
| Retrieval score contract | docs/retrieval-score-contract.md |
| Wiki | github.com/qualixar/superlocalmemory/wiki |
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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