Understanding when to use vector databases versus graph databases is crucial for building effective AI memory systems. MemoryGraph leverages graph databases to capture the relationships that make memory useful.

The Core Difference

Vector databases store high-dimensional numerical representations (embeddings) and excel at similarity search—finding items that are semantically "close" to a query.

Graph databases store nodes (entities) connected by edges (relationships), excelling at traversing and analyzing how things connect.

Why MemoryGraph Uses Graphs

AI assistant memory isn't just about finding "similar" content—it's about understanding how knowledge connects:

  • Solution → Problem: "What fixed that Redis timeout issue?"
  • Error → Cause → Fix: "What caused this error and how did we resolve it?"
  • Pattern → Projects: "Where have we used this authentication pattern?"
  • Decision → Rationale: "Why did we choose PostgreSQL over MongoDB?"

These are relationship queries, not similarity searches. Graphs answer them naturally through traversal.

Technical Comparison

Feature Vector Database Graph Database
Data Model High-dimensional vectors Nodes and edges (relationships)
Query Type Similarity search (ANN) Relationship traversal (Cypher)
Best For "Find things like X" "How does X connect to Y?"
Interpretability Low (black box embeddings) High (visible relationships)
Memory Use Case Semantic document search Knowledge relationships

When Vector Search Falls Short

Consider this scenario: You fixed a Redis connection timeout last month. With a vector database: 

# Vector search: "Redis timeout"
# Returns: Documents containing "Redis" and "timeout"
# Problem: May return unrelated discussions, miss the actual solution

With a graph database:

# Graph query: Find solutions that SOLVE problems tagged "redis"
MATCH (s:Memory)-[:SOLVES]->(p:Memory)
WHERE 'redis' IN p.tags AND p.type = 'problem'
RETURN s

# Returns: The exact solution linked to that specific problem

The graph query follows the relationship between problem and solution, not just text similarity.

MemoryGraph's Relationship Types

MemoryGraph supports rich relationship semantics for AI memory: 

# Causal relationships
problem --CAUSES--> error
change --TRIGGERS--> bug

# Solution relationships
solution --SOLVES--> problem
fix --ADDRESSES--> error
pattern --IMPROVES--> code

# Context relationships
pattern --APPLIES_TO--> project
solution --REQUIRES--> dependency
pattern --WORKS_WITH--> technology

# Learning relationships
new_approach --BUILDS_ON--> old_approach
finding --CONTRADICTS--> assumption

Real-World Example

Debugging workflow captured in MemoryGraph: 

# Day 1: You encounter an error
store_memory type="error" title="ConnectionTimeout in payment flow"

# Day 1: You find the root cause
store_memory type="problem" title="Redis pool exhaustion under load"
create_relationship error --TRIGGERS--> problem

# Day 1: You implement a fix
store_memory type="solution" title="Increase Redis pool size, add connection timeout"
create_relationship solution --SOLVES--> problem

# Day 30: Same error appears in another service
recall_memories query="ConnectionTimeout"
# → Returns the error, with path to problem AND solution
# → You immediately know the fix without re-debugging

When to Consider Vector Search

Vector databases shine when you need:

  • Semantic search across large document collections
  • Finding "similar" content without explicit relationships
  • Processing images, audio, or multilingual content
  • RAG (Retrieval Augmented Generation) pipelines

MemoryGraph focuses on structured knowledge—the relationships between problems, solutions, decisions, and patterns that make AI assistants genuinely helpful over time.

FalkorDB: The Best of Both

MemoryGraph's optional FalkorDB backend supports both graph traversal and vector operations in a single database:

  • Native Cypher queries for relationship traversal
  • Vector similarity search when needed
  • Hybrid queries combining both approaches
  • High performance for AI workloads

For most MemoryGraph users, the SQLite default provides excellent graph capabilities. FalkorDB adds vector support and production-scale performance when you need it.

Next Steps