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Multi Profile Workflows

Varun Pratap Bhardwaj edited this page May 24, 2026 · 2 revisions

Multi-Profile Workflows

What are profiles, use cases, and best practices - Separate work/personal contexts, multi-client management, and experimentation with complete isolation guarantees.

What Are Profiles?

Profiles are isolated memory contexts in SuperLocalMemory. Each profile has its own:

  • SQLite database
  • Knowledge graph
  • Learned patterns
  • Configuration settings

Think of profiles as completely separate workspaces. Memories in one profile never affect another profile.

Example:

Profile: work
 - 500 memories about work projects
 - Graph: FastAPI, PostgreSQL, microservices
 - Patterns: Python backend, REST APIs
Profile: personal
 - 200 memories about personal projects
 - Graph: React, Firebase, Next.js
 - Patterns: TypeScript frontend, serverless
No context bleeding between them!

Why Use Profiles?

1. Work/Personal Separation

Problem:

Without profiles:
- Work memories mixed with personal projects
- AI gets confused about which project you mean
- Privacy concerns (work info in personal context)

Solution:

# Work context
slm switch-profile work
slm remember "Company API uses OAuth2" --tags work,security
# Personal context
slm switch-profile personal
slm remember "My side project uses simple JWT" --tags personal

2. Multi-Client Management

Scenario: Freelancer working with 5 clients

# Client A
slm switch-profile client-acme
slm remember "Acme uses AWS, PostgreSQL, React"
# Client B
slm switch-profile client-techcorp
slm remember "TechCorp uses GCP, MySQL, Vue"
# Each client's context completely isolated

Benefits:

  • No accidental data leaks between clients
  • Clear context switching
  • Easy to archive old clients

3. Experimentation

Scenario: Testing new patterns without polluting main database

# Create experiment profile
slm switch-profile experiment
# Try new patterns
slm remember "Testing new architecture pattern"
slm build-graph --clustering
# If experiment fails, delete profile
slm switch-profile delete experiment --confirm

4. Team Collaboration

Scenario: Team sharing common knowledge base

# Team profile (shared via git)
slm switch-profile team
slm remember "Team standard: Use TypeScript" --tags team
slm remember "Team uses Jest for testing" --tags team
# Each member switches to team profile for shared context

Profile Operations

List Profiles

slm switch-profile list

Output:

Available Profiles:
* default (active)
 Location: ~/.claude-memory/profiles/default/
 Memories: 247
 Last used: 2026εΉ΄02月07ζ—₯ 14:23
 work
 Location: ~/.claude-memory/profiles/work/
 Memories: 538
 Last used: 2026εΉ΄02月06ζ—₯ 09:15
 personal
 Location: ~/.claude-memory/profiles/personal/
 Memories: 123
 Last used: 2026εΉ΄02月05ζ—₯ 18:42
 client-acme
 Location: ~/.claude-memory/profiles/client-acme/
 Memories: 89
 Last used: 2026εΉ΄01月28ζ—₯ 11:30

Create Profile

# Basic creation
slm switch-profile create work
# With description
slm switch-profile create work --description "Work projects and decisions"
# With custom location (advanced)
slm switch-profile create work --path /custom/path/work

What happens:

  1. Creates new directory: ~/.claude-memory/profiles/work/
  2. Initializes fresh SQLite database
  3. Creates empty graph and pattern tables
  4. Sets up profile metadata

Switch Profile

# Switch to profile
slm switch-profile work
# Verify switch
slm status

Output:

βœ“ Switched to profile: work
Current Profile: work
Total memories: 538
Knowledge graph: 342 nodes, 789 edges

What changes:

  • All subsequent commands use work profile
  • Database path: ~/.claude-memory/profiles/work/memory.db
  • Graph and patterns specific to work

Delete Profile

# Delete profile (requires confirmation)
slm switch-profile delete experiment
# Force delete without confirmation
slm switch-profile delete experiment --confirm

Warning: This permanently deletes all memories in that profile!

Backup first:

# Backup before deletion
cp -r ~/.claude-memory/profiles/experiment/ ~/backups/experiment-$(date +%Y%m%d)/
slm switch-profile delete experiment --confirm

Profile Isolation Guarantees

Complete Data Isolation

Each profile has:

~/.claude-memory/profiles/work/
β”œβ”€β”€ memory.db # Separate SQLite database
β”œβ”€β”€ graph_data.db # Separate graph storage
β”œβ”€β”€ patterns.db # Separate learned patterns
└── config.json # Profile-specific config

Guarantee: Memories in one profile NEVER appear in another profile.

Independent Graphs

# Build graph in work profile
slm switch-profile work
slm build-graph
# Creates work-specific graph
# Build graph in personal profile
slm switch-profile personal
slm build-graph
# Creates completely separate graph

No shared nodes or edges between profiles.

Separate Pattern Learning

# Work profile learns work patterns
slm switch-profile work
# Learns: Python, FastAPI, microservices
# Personal profile learns different patterns
slm switch-profile personal
# Learns: JavaScript, Next.js, serverless
# No pattern leakage

Use Cases

Use Case 1: Consultant with Multiple Clients

Setup:

# Client profiles
slm switch-profile create client-acme --description "Acme Corp (2026)"
slm switch-profile create client-techcorp --description "TechCorp (2026)"
slm switch-profile create client-startup --description "Startup Inc (2026)"
# Personal profile
slm switch-profile create personal --description "Personal projects"

Daily workflow:

# Morning: Client Acme
slm switch-profile client-acme
slm recall "last meeting notes"
# Work on Acme project
# Afternoon: Client TechCorp
slm switch-profile client-techcorp
slm recall "API architecture"
# Work on TechCorp project
# Evening: Personal
slm switch-profile personal
slm remember "New blog post idea"

Benefits:

  • Clear context separation
  • No accidental leaks
  • Easy billing (track per client)
  • Archive old clients easily

Use Case 2: Work/Personal Split

Setup:

# Work profile
slm switch-profile create work --description "Day job"
# Personal profile
slm switch-profile create personal --description "Side projects"

Work hours:

slm switch-profile work
slm remember "Sprint planning: Implement OAuth2" --tags sprint
slm remember "Code review feedback: Add error handling" --tags review

After hours:

slm switch-profile personal
slm remember "Side project: Build portfolio site with Next.js" --tags project
slm remember "Learn Rust for fun" --tags learning

Benefits:

  • Mental separation
  • Privacy (work info not in personal)
  • Different pattern learning

Use Case 3: Project-Based Organization

Setup:

# One profile per major project
slm switch-profile create project-api --description "REST API rewrite"
slm switch-profile create project-frontend --description "React dashboard"
slm switch-profile create project-mobile --description "React Native app"

Benefits:

  • Clear project boundaries
  • Easy to archive finished projects
  • Project-specific knowledge graphs
  • No cross-project confusion

Use Case 4: Experimentation

Setup:

# Experiment profile
slm switch-profile create experiment --description "Testing new patterns"
# Try new approaches
slm remember "Experimenting with GraphQL"
slm remember "Testing microservices with gRPC"
slm build-graph --clustering

Cleanup:

# Experiment failed, delete profile
slm switch-profile default
slm switch-profile delete experiment --confirm

Benefits:

  • Safe to try new patterns
  • No pollution of main database
  • Easy to discard failed experiments

Use Case 5: Team Shared Knowledge

Setup:

# Create team profile
slm switch-profile create team --description "Team shared knowledge"
# Save team decisions
slm remember "Team standard: TypeScript for all new code" --tags standard
slm remember "Team uses Jest for testing" --tags standard
slm remember "Code review checklist: Security, tests, docs" --tags checklist

Sharing (via git):

# Export team profile
cd ~/.claude-memory/profiles/
tar czf team-profile.tar.gz team/
# Team members import
cd ~/.claude-memory/profiles/
tar xzf team-profile.tar.gz
slm switch-profile team

Benefits:

  • Shared context across team
  • Consistent recommendations
  • Onboarding new members

Best Practices

1. Name Profiles Clearly

Good:

slm switch-profile create work-project-api
slm switch-profile create personal-blog
slm switch-profile create client-acme-2026

Poor:

slm switch-profile create p1
slm switch-profile create test
slm switch-profile create stuff

2. Add Descriptions

slm switch-profile create work \
 --description "Day job at TechCorp - API team (2026)"

Benefits:

  • Easy to remember what profile is for
  • Shows in list output
  • Helps team members understand context

3. Use Default Profile for General Knowledge

# General programming knowledge
slm switch-profile default
slm remember "Python best practices" --tags reference
slm remember "Git cheatsheet" --tags reference
# Switch to project-specific for work
slm switch-profile work

4. Archive Old Profiles

# Archive completed project
tar czf ~/archives/client-acme-$(date +%Y%m%d).tar.gz \
 ~/.claude-memory/profiles/client-acme/
# Delete profile
slm switch-profile delete client-acme --confirm

5. Regular Backups

# Backup all profiles (cron)
#!/bin/bash
backup_dir=~/backups/slm-$(date +%Y%m%d)
mkdir -p "$backup_dir"
cp -r ~/.claude-memory/profiles/ "$backup_dir/"

6. Switch Profile at Start of Day

# Add to .bashrc or .zshrc
# Auto-prompt for profile on terminal start
if command -v slm &> /dev/null; then
 echo "Current profile: $(slm status | grep 'Current Profile' | cut -d: -f2)"
 echo "Switch profile? (work/personal/default): "
fi

Profile Configuration

Per-Profile Settings

Each profile can have different settings:

Example: ~/.claude-memory/profiles/work/config.json

{
 "profile_name": "work",
 "description": "Work projects",
 "created_at": "2026εΉ΄02月07ζ—₯T14:23:00",
 "settings": {
 "default_importance": 5,
 "auto_build_graph": true,
 "pattern_learning_threshold": 0.5
 }
}

Shared Global Settings

Global config: ~/.claude-memory/config.json

{
 "default_profile": "work",
 "mcp_server_enabled": true,
 "shell_integration": true
}

Advanced Features

Switch with Environment Variable

# Set profile via env var
export SLM_PROFILE=work
slm status # Uses work profile
# Temporary switch
SLM_PROFILE=personal slm recall "blog ideas"

Profile Auto-Switch by Directory

Setup (zsh/bash):

# Add to .bashrc or .zshrc
cd() {
 builtin cd "$@"
 # Auto-switch profile based on directory
 if [[ $PWD == ~/work/* ]]; then
 export SLM_PROFILE=work
 elif [[ $PWD == ~/personal/* ]]; then
 export SLM_PROFILE=personal
 else
 export SLM_PROFILE=default
 fi
}

Profile-Specific Aliases

# Add to .bashrc or .zshrc
# Work aliases
alias work-slm='SLM_PROFILE=work slm'
alias work-recall='SLM_PROFILE=work slm recall'
# Personal aliases
alias personal-slm='SLM_PROFILE=personal slm'
alias personal-recall='SLM_PROFILE=personal slm recall'
# Usage
work-recall "API architecture"
personal-remember "Blog post idea"

Troubleshooting

"Profile not found"

Cause: Profile doesn't exist

Solution:

# List available profiles
slm switch-profile list
# Create if needed
slm switch-profile create missing-profile

"Cannot switch to active profile"

Cause: Already using that profile

Solution:

# Check current profile
slm status | grep "Current Profile"
# Switch to different profile
slm switch-profile default

"Profile database corrupted"

Cause: Database file damaged

Solution:

# Restore from backup
cp ~/backups/slm-20260207/profiles/work/memory.db \
 ~/.claude-memory/profiles/work/memory.db
# Or rebuild from scratch
slm switch-profile delete work --confirm
slm switch-profile create work

"Memories not showing after switch"

Cause: Switched to wrong profile or profile empty

Solution:

# Verify current profile
slm status
# List all profiles and memory counts
slm switch-profile list
# Switch to correct profile
slm switch-profile work
# Verify memories exist
slm list

Performance

Profile Overhead

Disk space per profile:

  • Empty: ~100 KB
  • 1,000 memories: ~5 MB
  • 10,000 memories: ~50 MB

Switch time:

  • ~10ms (instant)

No performance degradation with multiple profiles.

Migration Between Profiles

Export from One Profile

# Switch to source profile
slm switch-profile work
# Export memories
sqlite3 ~/.claude-memory/profiles/work/memory.db \
 "SELECT json_group_array(json_object(
 'content', content,
 'tags', tags,
 'project_name', project_name,
 'importance', importance
 )) FROM memories;" > work_export.json

Import to Another Profile

# Switch to destination profile
slm switch-profile personal
# Import memories (Python script)
python3 << 'EOF'
import json, sqlite3
conn = sqlite3.connect("/Users/$(whoami)/.claude-memory/profiles/personal/memory.db")
with open('work_export.json') as f:
 data = json.load(f)
 for mem in data:
 conn.execute(
 "INSERT INTO memories (content, tags, project_name, importance) VALUES (?, ?, ?, ?)",
 (mem['content'], mem['tags'], mem['project_name'], mem['importance'])
 )
conn.commit()
EOF
# Rebuild graph in destination
slm build-graph

Related Pages

Created by Varun Pratap Bhardwaj Solution Architect β€’ SuperLocalMemory

GitHub β€’ Issues β€’ Wiki

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