Building your own memory for Claude MCP

Why Give Claude a Memory?

Imagine having a personal AI assistant that not only understands your queries but also remembers your preferences, past conversations, and evolves its understanding of you over time. This is exactly what the Knowledge Graph Memory Server brings to Claude. By implementing a persistent memory system using a local knowledge graph, we can transform Claude from a stateless chat interface into a truly personalized AI companion that grows with you.

Understanding the Architecture

At its heart, the Knowledge Graph Memory Server is an elegant solution that structures information in a way that mirrors how humans form memories and connections. Let's break down its core components and see how they work together.

The Building Blocks: Entities, Relations, and Observations

Think of the knowledge graph as a sophisticated mental map. Just as our brains connect different pieces of information, this system uses three fundamental elements:

// Example of an entity representing a person
const personEntity = {
    "name": "Sarah_Chen",
    "entityType": "person",
    "observations": [
        "Works remotely from Singapore",
        "Specializes in quantum computing",
        "Prefers technical documentation in markdown"
    ]
}

// Example of a relation showing professional connection
const workRelation = {
    "from": "Sarah_Chen",
    "to": "QuantumTech_Labs",
    "relationType": "leads_research_at"
}        

Each piece serves a specific purpose:

• Entities act as the nodes in our graph, representing distinct people, organizations, or concepts
• Relations create meaningful connections between entities, telling us how they interact
• Observations store individual facts about entities, keeping our knowledge atomic and manageable

Practical Implementation with Claude

Setting Up the Memory Server

First, let's configure Claude to use our memory system. Add this to your claude_desktop_config.json:

{
    "mcpServers": {
        "memory": {
            "command": "npx",
            "args": [
                "-y",
                "@modelcontextprotocol/server-memory"
            ]
        }
    }
}        

Real-World Use Cases

Let's explore some compelling applications of this memory system:

1- Personal Assistant Enhancement

def remember_user_preferences():
    """
    Track and apply user preferences across sessions
    """
    create_entities([{
        "name": "user_preferences",
        "entityType": "settings",
        "observations": [
            "Prefers detailed technical explanations",
            "Uses vim keybindings",
            "Needs examples in Python"
        ]
    }])        

2- Project Management Companion

def track_project_evolution():
    """
    Maintain project context and development history
    """
    create_relations([{
        "from": "Project_Alpha",
        "to": "Architecture_Decision_001",
        "relationType": "implemented_pattern"
    }])        

3- Learning Progress Tracker

def monitor_learning_journey():
    """
    Track topics covered and mastery levels
    """
    add_observations({
        "entityName": "Learning_Progress",
        "contents": [
            "Completed advanced SQL concepts",
            "Needs review on database indexing",
            "Ready for distributed systems"
        ]
    })        

Advanced Memory Management

Optimizing Memory Storage

The system uses efficient strategies to maintain relevant information:

def prune_outdated_knowledge():
    """
    Remove obsolete information while preserving crucial context
    """
    current_time = datetime.now()
    
    # Check for outdated observations
    for entity in entities:
        outdated = [
            obs for obs in entity.observations
            if is_obsolete(obs, current_time)
        ]
        if outdated:
            delete_observations({
                "entityName": entity.name,
                "observations": outdated
            })        

Implementing context aware memory

def enhance_with_context():
    """
    Enrich memories with situational awareness
    """
    def analyze_conversation_context(dialogue):
        entities = extract_key_entities(dialogue)
        relations = identify_connections(entities)
        
        create_entities(entities)
        create_relations(relations)        

Best Practices and Tips

Here's what most developers get wrong about implementing memory systems for AI: they treat it like a simple database problem. But memory, especially for systems like Claude, is more like tending a garden than filling a storage unit. Let me explain.

The Fundamental Truth About Memory Organization

Think about how you remember things. You don't store isolated facts – you weave them into a tapestry of understanding. When building a knowledge graph memory system, the same principle applies. Your observations should be like single threads in this tapestry: atomic, yes, but meaningfully connected to the whole.

The key insight here is that memory organization isn't about storage – it's about retrieval.

When you design your entity naming conventions, you're not just creating labels; you're creating pathways for future understanding. A well-named entity is like a well-worn path in your garden – it leads naturally to related concepts and deeper understanding.

Beyond Single-User Systems: The Next Frontier

Now, let's talk about where this is all heading. The real power of knowledge graph memory systems isn't in personal assistance – it's in collective intelligence. Imagine a research lab where each researcher's Claude instance doesn't just remember individual preferences but builds a shared understanding of the team's collective knowledge.

But here's the challenge: collaborative memory building isn't just a technical problem. It's a human one. How do you balance personal context with shared knowledge? How do you maintain privacy while fostering collaboration? These are the questions that will define the next generation of AI memory systems.

A Framework for Implementation

Your base layer should handle the fundamentals: atomic observations, clear entity relationships, basic security. This is your foundation.

Build your performance optimization layer next. This isn't just about database indices – it's about understanding usage patterns. Which memories are accessed frequently? Which connections are most valuable? Let this understanding guide your optimization strategy.

Finally, add your intelligence layer. This is where you implement pattern recognition, temporal awareness, and contextual understanding. But remember sophistication should serve simplicity.

Every feature should make the system more intuitive, not more complex.

The Future We're Building Toward

The most exciting possibilities aren't in the technical features – they're in the new kinds of human-AI collaboration they enable. A properly implemented knowledge graph memory system doesn't just make Claude smarter; it makes the entire human-AI interaction more meaningful.

Consider the research assistant use case. It's not just about remembering citations and methodologies. It's about understanding the evolution of ideas, spotting patterns in approaches, and suggesting novel connections.

This is where the real power lies – not in remembering facts, but in generating insights.