superlocalmemory
AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally with zero cloud dependency.
Best use case
superlocalmemory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally with zero cloud dependency.
Teams using superlocalmemory should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
How superlocalmemory Compares
| Feature / Agent | superlocalmemory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally with zero cloud dependency.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```bash
pip install superlocalmemory
# or
npm install -g superlocalmemory
```
## Quick Start
```bash
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
```bash
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
```bash
slm status --json # System status (mode, profile, DB)
slm health --json # Math layer health
slm trace "<query>" --json # Recall with per-channel breakdown
```
### Configuration
```bash
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)
```bash
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:
```json
{
"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:
```json
{
"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)Related Skills
slm-switch-profile
Switch between memory profiles for context isolation and management. Use when the user wants to change profile context, separate work/personal memories, or manage multiple independent memory spaces. Each profile has its own database, graph, and patterns.
slm-status
Check SuperLocalMemory system status, health, and statistics. Use when the user wants to know memory count, graph stats, patterns learned, database health, or system diagnostics. Shows comprehensive system health dashboard.
slm-show-patterns
Show what SuperLocalMemory has learned about your preferences, workflow patterns, and project context. Use when the user asks "what have you learned about me?" or wants to see their coding identity patterns. Shows tech preferences, workflow sequences, and engagement health.
slm-remember
Save content to SuperLocalMemory with intelligent indexing and knowledge graph integration. Use when the user wants to remember information, save context, store coding decisions, or persist knowledge for future sessions. Automatically indexes, graphs, and learns patterns.
slm-recall
Search and retrieve memories using semantic similarity, knowledge graph relationships, and full-text search. Use when the user asks to recall information, search memories, find past decisions, or query stored knowledge. Returns ranked results with relevance scores.
slm-list-recent
List most recent memories in chronological order. Use when the user wants to see what was recently saved, review recent conversations, check what they worked on today, or browse memory history. Shows memories sorted by creation time (newest first).
slm-build-graph
Build or rebuild the knowledge graph from existing memories using TF-IDF entity extraction and Leiden clustering. Use when search results seem poor, after bulk imports, or to optimize performance. Automatically discovers relationships between memories and creates topic clusters.
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Record polished UI demo videos using Playwright. Use when the user asks to create a demo, walkthrough, screen recording, or tutorial video of a web application. Produces WebM videos with visible cursor, natural pacing, and professional feel.
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Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.
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