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Memory Categories

MemSync automatically tags memories with category labels, making it easier to find relevant context and build comprehensive user profiles. Understanding these categories helps you optimize memory retrieval and user personalization.

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Available Categories

MemSync uses 10 predefined categories that cover the most important aspects of user information:

Identity

Personal information including name, age, background, location, and origin

Career

Work history, profession, studies, and professional goals

Interests

Hobbies, passions, interests, and recreational activities

Relationships

Family, friends, social connections, and relationship patterns

Health

Medical history, fitness goals, wellness information, and health behaviors

Finance

Budget, investments, financial goals, income, and financial background

Learning

Educational background, skills, courses, and knowledge goals

Travel

Past trips, future travel plans, favorite destinations, and travel history

Productivity

Tasks, projects, time management, and organizational systems

Private

Sensitive or confidential personal information

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Category Details

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Identity

Captures who the user is at their core - their fundamental personal information and background. Examples:
  • “Lives in New York City, originally from California”
  • “29 years old, married with two children”
  • “Identifies as a creative problem-solver and lifelong learner”
{
  "memory": "Lives in San Francisco, originally from Chicago",
  "categories": ["identity"],
  "type": "semantic"
}

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Career

Professional life, work experience, and career-related information. Examples:
  • “Works as a Senior Data Scientist at Microsoft”
  • “Has 8 years of experience in machine learning”
  • “Currently pursuing MBA to transition into product management”
{
  "memory": "Recently promoted to Team Lead at their tech startup",
  "categories": ["career"],
  "type": "episodic"
}

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Interests

Hobbies, passions, and activities the user enjoys in their personal time. Examples:
  • “Passionate about photography and hiking”
  • “Enjoys cooking Italian cuisine on weekends”
  • “Avid reader of science fiction novels”
{
  "memory": "Recently started learning guitar and practicing daily",
  "categories": ["interests", "learning"],
  "type": "episodic"
}

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Relationships

Social connections, family relationships, and interpersonal dynamics. Examples:
  • “Married to Sarah, together for 5 years”
  • “Close relationship with parents, calls them weekly”
  • “Mentors junior developers at work”
{
  "memory": "Has a close group of college friends who meet monthly for board games",
  "categories": ["relationships", "interests"],
  "type": "semantic"
}

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Health

Physical and mental health information, fitness goals, and wellness practices. Examples:
  • “Training for a marathon, runs 30 miles per week”
  • “Follows a vegetarian diet for ethical reasons”
  • “Practices meditation for stress management”
{
  "memory": "Recently started therapy to work on work-life balance",
  "categories": ["health", "productivity"],
  "type": "episodic"
}

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Finance

Financial situation, goals, and money-related decisions and preferences. Examples:
  • “Saving for a house down payment, budget of $500K”
  • “Invests in index funds and retirement accounts”
  • “Budgets carefully and tracks all expenses”
{
  "memory": "Recently started investing in individual stocks after research",
  "categories": ["finance", "learning"],
  "type": "episodic"
}

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Learning

Educational pursuits, skill development, and knowledge acquisition. Examples:
  • “Currently learning Spanish through Duolingo”
  • “Taking online courses in data visualization”
  • “Reading books about behavioral psychology”
{
  "memory": "Completed AWS certification and planning to pursue advanced certification",
  "categories": ["learning", "career"],
  "type": "episodic"
}

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Travel

Travel experiences, plans, preferences, and location-related information. Examples:
  • “Loves exploring national parks, visited 15 so far”
  • “Planning a trip to Japan for cherry blossom season”
  • “Prefers sustainable travel and eco-friendly accommodations”
{
  "memory": "Recently returned from a solo backpacking trip through Southeast Asia",
  "categories": ["travel", "interests"],
  "type": "episodic"
}

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Productivity

Work habits, organizational systems, tools, and productivity preferences. Examples:
  • “Uses Notion for project management and note-taking”
  • “Prefers working in focused 2-hour blocks”
  • “Struggles with email management and time blocking”
{
  "memory": "Recently adopted Getting Things Done methodology for task management",
  "categories": ["productivity", "learning"],
  "type": "episodic"
}

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Private

Sensitive information that should be handled with special care. Examples:
  • Social Security numbers, passwords, private addresses
  • Medical diagnoses or sensitive health information
  • Financial account numbers or private financial details
{
  "memory": "Has been dealing with anxiety, takes medication as needed",
  "categories": ["private", "health"],
  "type": "semantic"
}

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Multiple Categories

Memories can belong to multiple categories when they span different aspects of a user’s life:

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Common Combinations

Professional development activities
{
  "memory": "Taking a course in advanced React to improve frontend skills at work",
  "categories": ["career", "learning"],
  "type": "episodic"
}
Hobbies that also benefit physical or mental health
{
  "memory": "Practices yoga 3 times a week for both fitness and stress relief",
  "categories": ["interests", "health"],
  "type": "semantic"
}
Social activities involving travel
{
  "memory": "Planning annual ski trip with college friends to Colorado",
  "categories": ["relationships", "travel"],
  "type": "episodic"
}
Work-related organizational systems
{
  "memory": "Uses Slack and Asana to coordinate with remote team members",
  "categories": ["productivity", "career"],
  "type": "semantic"
}
Use categories to find specific types of information about users: 
# Find career-related memories
career_memories = memsync.search_memories(
    query="What does the user do professionally?",
    categories=["career"],
    limit=10
)

# Find current interests
interest_memories = memsync.search_memories(
    query="What are the user's hobbies?",
    categories=["interests"],
    limit=8
)

# Find learning and career overlap
professional_development = memsync.search_memories(
    query="What skills is the user developing?",
    categories=["career", "learning"],
    limit=15
)

# Find health and productivity intersection
wellness_habits = memsync.search_memories(
    query="How does the user maintain work-life balance?",
    categories=["health", "productivity"],
    limit=10
)

# Comprehensive user overview
all_memories = memsync.search_memories(
    query="Tell me about this user",
    limit=20,
    rerank=True
)

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Category Distribution Insights

Understanding how memories are distributed across categories can provide insights:

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Typical Distribution

  • Career: 25-30% of memories
  • Interests: 20-25% of memories
  • Learning: 15-20% of memories
  • Relationships: 10-15% of memories
  • Health: 8-12% of memories
  • Productivity: 6-10% of memories
  • Identity: 5-8% of memories
  • Travel: 3-7% of memories
  • Finance: 2-5% of memories
  • Private: 1-3% of memories
Distribution varies significantly based on user conversation patterns, application type, and personal disclosure preferences.

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Best Practices

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For Memory Extraction

Provide detailed conversation context to help MemSync accurately categorize memories.
# Good: Provides context for categorization
messages = [
    {"role": "user", "content": "I've been learning Python at work to automate our data pipeline tasks"},
    {"role": "assistant", "content": "That's great! Automation can save a lot of time in data workflows"}
]
# Result: Likely categorized as ["career", "learning"]
Include specific details that help with accurate categorization.
# Better than: "I like to exercise"
# Use: "I go rock climbing twice a week at the local gym for fitness and stress relief"
# Result: Categories ["interests", "health"]
Don’t avoid mentioning when activities span multiple life areas.
# Good: Shows intersection of categories
"I'm taking evening MBA classes while working full-time to prepare for a career transition"
# Result: Categories ["learning", "career"]

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For Search Optimization

Tailor your search queries to specific categories for better results.
# Instead of generic queries, be category-specific
career_query = "What is the user's professional background and current role?"
interest_query = "What activities does the user enjoy in their free time?"
Look for patterns across categories to understand user holistically.
# Find connections between work and personal life
work_life_balance = memsync.search_memories(
    query="How does the user balance professional and personal priorities?",
    categories=["career", "health", "relationships"],
    limit=15
)
Start broad, then narrow down based on initial results.
# Step 1: Broad search
general_memories = memsync.search_memories(query="user overview", limit=20)

# Step 2: Focus on most relevant categories found
specific_memories = memsync.search_memories(
    query="user career goals", 
    categories=["career", "learning"], 
    limit=10
)

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Category Evolution

Categories can provide insights into how users change over time:

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Tracking Changes

  • New categories appearing: User exploring new areas of life
  • Category frequency changes: Shifting priorities or life phases
  • Cross-category connections: Growing integration of different life aspects

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Example Evolution

# Early conversations: Heavy focus on career
early_categories = ["career", "learning", "productivity"]

# Later conversations: More balanced life interests
later_categories = ["career", "interests", "relationships", "health", "travel"]

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Next Steps

Semantic Search

Learn how to search across categories effectively

User Profiles

Understand how categories combine to create user profiles

Memory Types

Learn about semantic vs episodic memory classification

API Reference

Explore API endpoints for category-based operations
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