// 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.
| name | slm-status |
| description | 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. |
| version | 3.4.23 |
| license | AGPL-3.0-or-later |
| compatibility | Requires SuperLocalMemory V2 installed at ~/.claude-memory/ |
| attribution | {"creator":"Varun Pratap Bhardwaj","role":"Solution Architect & Original Creator","project":"SuperLocalMemory V2"} |
Check system status, health metrics, and statistics for your local memory system.
slm status [--verbose] [--check-integrity]
$ slm status
Output:
╔══════════════════════════════════════════════════════╗
║ SuperLocalMemory V2 - System Status ║
╚══════════════════════════════════════════════════════╝
📊 Memory Statistics
Total Memories: 1,247
This Month: 143
This Week: 28
Today: 5
📈 Knowledge Graph
Nodes (Entities): 892
Edges (Relationships): 3,456
Clusters: 47
Avg Cluster Size: 19 memories
🎯 Pattern Learning
Coding Patterns: 34
Framework Preferences: React (72%), Vue (18%), Angular (10%)
Testing Style: TDD (65%), BDD (35%)
Performance Priority: High (78%)
💾 Database Health
Size: 4.2 MB
Integrity: ✅ OK
Last Backup: 2026-02-07 09:15
Backup Count: 12
🔧 Current Profile
Name: default
Created: 2026-01-15
Last Used: 2026-02-07 14:23
⚙️ System Info
Install Path: ~/.claude-memory
Database: memory.db
Python Version: 3.11.7
SQLite Version: 3.43.2
✅ Status: HEALTHY
$ slm status --verbose
Additional information:
$ slm status --check-integrity
Runs full database integrity check:
Running integrity check...
Database Structure: ✅ OK
FTS5 Index: ✅ OK
Graph Consistency: ✅ OK
Orphaned Nodes: 0 found
Duplicate Memories: 0 found
Corrupted Entries: 0 found
✅ All checks passed
Useful for:
Health indicators:
Based on:
Warning signs:
Profiles allow:
| Option | Description | Use Case |
|---|---|---|
--verbose | Show detailed stats | Debugging, analysis |
--check-integrity | Run full DB check | Troubleshooting |
--format json | JSON output | Scripting |
--format text | Human-readable (default) | Terminal use |
slm status --check-integrity
# Ensure DB is healthy before big import
slm status
# "Do I have enough memories for pattern learning?"
# (Need 20+ for basic patterns, 50+ for advanced)
slm status --verbose
# Check graph stats, optimize if needed
slm status | grep "Last Backup"
# Ensure recent backup exists
# Before switching
slm status
# Note: "Current Profile: work"
slm switch-profile personal
slm status
# Note: "Current Profile: personal"
Daily health check (cron job):
#!/bin/bash
# Daily at 9 AM
status=$(slm status --check-integrity)
if echo "$status" | grep -q "NOT OK"; then
echo "SuperLocalMemory: Integrity check FAILED" | mail -s "Alert" you@example.com
fi
Monitoring script:
#!/bin/bash
# Monitor memory growth
count=$(slm status | grep "Total Memories:" | awk '{print $3}' | tr -d ',')
echo "$(date),${count}" >> memory-growth.csv
JSON output for dashboards:
slm status --format json > status.json
# Parse with jq, send to monitoring system
Good indicators:
Warning signs:
Cause: Database locked by another process
Solution:
# Check for locks
lsof ~/.claude-memory/memory.db
# Kill hanging processes
killall python3
# Try again
slm status
Cause: Database corruption
Solution:
# Restore from backup
cp ~/.claude-memory/backups/memory.db.backup.* ~/.claude-memory/memory.db
# Verify
slm status --check-integrity
Cause: Need more memories (minimum 20)
Solution:
# Check memory count
slm status | grep "Total Memories"
# Add more memories
slm remember "Prefer React hooks over classes"
# ... add 20+ memories ...
# Rebuild patterns
slm build-graph
✅ All systems operational
⚠️ Minor issues detected
Action: Review verbose output
❌ Critical issues
Action: Restore from backup immediately
| Command | Typical Time | Notes |
|---|---|---|
slm status | ~200ms | Fast, lightweight |
slm status --verbose | ~500ms | More data fetching |
slm status --check-integrity | ~2s | Full DB scan |
For large databases (10,000+ memories):
slm list - List recent memoriesslm build-graph - Rebuild knowledge graphslm switch-profile - Switch memory profileslm recall - Search memoriesCreated by: Varun Pratap Bhardwaj (Solution Architect) Project: SuperLocalMemory V2 License: MIT (see LICENSE) Repository: https://github.com/varun369/SuperLocalMemoryV2
Open source doesn't mean removing credit. Attribution must be preserved per MIT License terms.
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.
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).
Search SuperLocalMemory for relevant facts, decisions, and past context. Use when the user asks to recall, search, find, or retrieve stored information. Invokes 5-channel retrieval with LightGBM reranking via MCP.
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.
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.
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.