superlocalmemory

Agent Building

AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally with zero cloud dependency.

QUICK START

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SuperLocalMemory

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.

Installation

pip install superlocalmemory
# or
npm install -g superlocalmemory

Quick Start

slm remember "Alice works at Google as a Staff Engineer" --json
slm recall "Who is Alice?" --json
slm status --json

Commands

All data-returning commands support --json for structured agent-native output.

Memory Operations

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

Diagnostics

slm status --json                         # System status (mode, profile, DB)
slm health --json                         # Math layer health
slm trace "<query>" --json                # Recall with per-channel breakdown

Configuration

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

Services (no --json)

slm setup                                 # Interactive setup wizard
slm mcp                                   # Start MCP server (for IDE integration)
slm dashboard                             # Open web dashboard
slm warmup                                # Pre-download embedding model

JSON Envelope

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"}
}

Operating Modes

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

Dual Interface

SuperLocalMemory works via both MCP and CLI:

MCP: 24 tools for IDE integration (Claude Code, Cursor, Windsurf, VS Code, JetBrains, Zed)
CLI: 18 commands with --json for scripts, CI/CD, agent frameworks (OpenClaw, Codex, Goose)

Part of Qualixar | Author: Varun Pratap Bhardwaj (qualixar.com | varunpratap.com)