qualixar
OrganizationWorld's first local-only AI memory to break 74% retrieval and 60% zero-LLM on LoCoMo. No cloud, no APIs, no data leaves your machine. Additionally, mode C (LLM/Cloud) - 87.7% LoCoMo. Research-backed. arXiv: 2603.14588
Categories
Indexed Skills (13)
superlocalmemory
AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally. Local data root; optional networked features have separate behavior.
slm-cache
KV cache for repeated reads — call slm_cache_get(key) first; on a miss do the expensive operation then slm_cache_set(key, value, ttl_seconds) to store it; on a hit use the returned value directly; always fail-open (hit:false on any error, never raises); saves tokens when the same file, query result, or tool output is read more than once in a session.
slm-compress
Compress large text, tool output, or transcripts to reduce context-window usage while keeping the full 1M window intact — call slm_compress(content, mode, reversible, ttl_seconds) to shrink content; if the result is lossy a ccr_id is returned so you can call slm_retrieve(ccr_id) later to recover the exact original; always fail-open (ok:false → continue with the original).
slm-governance
Enterprise compliance and governed workspace behavior for SuperLocalMemory. Covers role-based access (admin/member/viewer), retention policies, audit trail, GDPR data export/erase, and how agents must behave when operating under workspace governance. Requires power MCP profile for audit/retention tools. Agents must never bypass governance controls.
slm-graph
Index and query a codebase as a structural graph — build the code graph, trace blast radius of a change, find callers/callees/inheritors, semantic code search by meaning, assemble PR review context, and detect what changed since last index. Use when the user asks how code connects, what breaks if X changes, what calls a function, what a class inherits from, how to navigate an unfamiliar codebase, or to understand risk before editing.
slm-loop
Run gate-verified bounded loops with SuperLocalMemory as the durable ledger. Use when a task has a checkable acceptance condition (tests, schema, lint, reconciliation) and you must iterate until an INDEPENDENT gate passes — never stopping just because the agent believes it is done. `slm loop demo` runs a keyless convergence demo; `slm loop history` and `slm loop show <run_id>` inspect past runs whose every lap is persisted as queryable SLM memory (tag `loop:<name>`). Terminal statuses are DONE / HALT / PAUSE / KILLED / ERROR — report them exactly, never converting HALT/PAUSE/ERROR into success.
slm-mesh
Cross-session peer coordination via the SLM mesh network. Lets multiple AI agent sessions on the same machine discover each other, send messages, share lightweight state, and lock files to avoid conflicts. Requires full, power, or mesh MCP profile. All 8 tools are MCP-only — there is no CLI fallback.
slm-profile
Workspace isolation and runtime profile switching for SuperLocalMemory. Each profile is a fully independent memory namespace — separate facts, code graphs, and tool sets. Use switch_profile (MCP, requires code/full/power profile) to change the active workspace without restarting. Check the active profile with slm status. Required when working across multiple projects, clients, or tenants.
slm-recall
Search and retrieve facts, decisions, and past context from SuperLocalMemory. Use when the user asks to recall, find, search, or "what did we decide/say about X". Triggers multi-channel semantic retrieval with reranking; always call before storing anything new.
slm-remember
Capture durable facts, decisions, constraints, and gotchas into SuperLocalMemory. Use when the user says "remember that", "save this decision", "note this constraint", or when a session produces a conclusion worth persisting across sessions. Always recall first to avoid duplicates.
slm-scope
Controls memory visibility across profiles — personal (private, default), shared (selected profiles), or global (all profiles on this machine). Default is always personal. Only change scope when the user explicitly asks to share a memory across workspaces. Works with both remember (write scope) and recall (read scope flags).
slm-session
Manage SuperLocalMemory session lifecycle — call session_init once at the start of every fresh session to load relevant project context and get a session_id; call close_session when work is meaningfully complete to commit temporal summaries. Correct lifecycle hygiene is what makes SLM's learning loop work.
slm-status
Health and optimization stats for SuperLocalMemory — call slm_optimize_stats() for live compression and cache counters (compress_runs, tokens_saved_compress, cache_proxy_hits, cache_proxy_misses, cache_kv_hits, cache_kv_misses); run slm status [--json] for system state (mode, profile, DB size, fact/entity/edge counts) and slm doctor [--json] for preflight including the "Optimize (Surface B)" health line; use together to confirm optimization is actually saving tokens.
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