メニュー
AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally with zero cloud dependency.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
| name | superlocalmemory |
| description | AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally with zero cloud dependency. |
| version | 3.4.22 |
| author | Varun Pratap Bhardwaj |
| license | AGPL-3.0-or-later |
| homepage | https://superlocalmemory.com |
| repository | https://github.com/qualixar/superlocalmemory |
| triggers | ["remember something","recall memory","search memories","memory status","store fact","agent memory","local memory","memory health"] |
AI agent memory that runs 100% locally. Four-channel retrieval (semantic, graph, BM25, temporal) with mathematical similarity scoring. No cloud, no API keys, EU AI Act compliant.
pip install superlocalmemory
# or
npm install -g superlocalmemory
slm remember "Alice works at Google as a Staff Engineer" --json
slm recall "Who is Alice?" --json
slm status --json
All data-returning commands support --json for structured agent-native output.
slm remember "<content>" --json # Store a memory
slm remember "<content>" --tags "a,b" --json
slm recall "<query>" --json # Semantic search
slm recall "<query>" --limit 5 --json
slm list --json -n 20 # List recent memories
slm forget "<query>" --json # Preview matches (add --yes to delete)
slm forget "<query>" --json --yes # Delete matching memories
slm delete <fact_id> --json --yes # Delete specific memory by ID
slm update <fact_id> "<content>" --json # Update a memory
slm status --json # System status (mode, profile, DB)
slm health --json # Math layer health
slm trace "<query>" --json # Recall with per-channel breakdown
slm mode --json # Get current mode
slm mode a --json # Set mode (a=local, b=ollama, c=cloud)
slm profile list --json # List profiles
slm profile switch <name> --json # Switch profile
slm profile create <name> --json # Create profile
slm connect --json # Auto-configure IDEs
slm connect --list --json # List supported IDEs
slm setup # Interactive setup wizard
slm mcp # Start MCP server (for IDE integration)
slm dashboard # Open web dashboard
slm warmup # Pre-download embedding model
Every --json response follows a consistent envelope:
{
"success": true,
"command": "recall",
"version": "3.0.22",
"data": {
"results": [
{"fact_id": "abc123", "score": 0.87, "content": "Alice works at Google"}
],
"count": 1,
"query_type": "semantic"
},
"next_actions": [
{"command": "slm list --json", "description": "List recent memories"}
]
}
Error responses:
{
"success": false,
"command": "recall",
"version": "3.0.22",
"error": {"code": "ENGINE_ERROR", "message": "Description of what went wrong"}
}
| Mode | Description | Cloud Required |
|---|---|---|
| A | Local Guardian -- zero cloud, zero LLM, EU AI Act compliant | None |
| B | Smart Local -- local Ollama LLM, data stays on your machine | Local only |
| C | Full Power -- cloud LLM for maximum accuracy | Yes |
SuperLocalMemory works via both MCP and CLI:
--json for scripts, CI/CD, agent frameworks (OpenClaw, Codex, Goose)Part of Qualixar | Author: Varun Pratap Bhardwaj (qualixar.com | varunpratap.com)
SOC 職業分類に基づく
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.
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.
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).
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.
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.
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.
