Knowledge Graphs

Mem[v] automatically builds knowledge graphs from your content, connecting entities through relationships and enabling graph-aware semantic search for AI agents.

What is a Knowledge Graph?

A knowledge graph is a network of entities and relationships automatically extracted from memories: From memories:

“Sarah Chen is the CTO at Acme Corp”
“Acme Corp uses Kubernetes for deployments”

Graph representation:

Sarah Chen → role → CTO
Sarah Chen → works_at → Acme Corp
Acme Corp → uses → Kubernetes

Triplet structure

Knowledge is stored as Subject → Predicate → Object:

User → prefers → Dark mode
Database → runs_on → PostgreSQL
API → authenticates_via → JWT tokens
Team → uses → GitHub Actions

Automatic entity extraction

Mem[v] identifies and extracts:

People: Names, roles, relationships
Organizations: Companies, teams, departments
Technologies: Tools, frameworks, platforms
Locations: Cities, offices, regions
Topics: Concepts, subjects, categories

Graph-aware search

When you search, results include connected entities: Query: “Sarah Chen” Returns:

Direct memories about Sarah
Her role (CTO)
Company (Acme Corp)
Technologies she uses (Kubernetes)
Teams she manages
Projects she leads

Common use cases

Team knowledge

# Find who knows about a technology
results = client.graph.query(
    space_id="company",
    entity="Kubernetes",
    relationship="expert_in"
)

for triplet in results.triplets:
    print(f"{triplet.subject} → {triplet.predicate} → {triplet.object}")

// Find who knows about a technology
const results = await client.graph.query({
  space_id: 'company',
  entity: 'Kubernetes',
  relationship: 'expert_in',
});

for (const triplet of results.triplets) {
  console.log(`${triplet.subject} → ${triplet.predicate} → ${triplet.object}`);
}

Product dependencies

Map system architecture:

Frontend App
├── depends_on → Backend API
│   ├── depends_on → PostgreSQL
│   └── depends_on → Redis
└── uses → React
    └── requires → Node.js

Customer insights

Understand customer relationships:

Acme Corp
├── uses → Enterprise Plan
├── located_in → San Francisco
├── contact → Sarah Chen
│   └── role → VP Engineering
└── industry → Technology

Automatic graph building

Graphs build automatically as you add memories:

client.memories.add(
    space_id="space_123",
    content="Sarah Chen is the CTO at Acme Corp and specializes in cloud architecture"
)

# Graph automatically contains:
# Sarah Chen → works_at → Acme Corp
# Sarah Chen → role → CTO
# Sarah Chen → specializes_in → Cloud Architecture

await client.memories.add({
  space_id: 'space_123',
  content: 'Sarah Chen is the CTO at Acme Corp and specializes in cloud architecture',
});

// Graph automatically contains:
// Sarah Chen → works_at → Acme Corp
// Sarah Chen → role → CTO
// Sarah Chen → specializes_in → Cloud Architecture

Query the graph

# Get all relationships for an entity
results = client.graph.query(
    space_id="space_123",
    entity="Sarah Chen"
)

for triplet in results.triplets:
    print(f"{triplet.subject} {triplet.predicate} {triplet.object}")

// Get all relationships for an entity
const results = await client.graph.query({
  space_id: 'space_123',
  entity: 'Sarah Chen',
});

for (const triplet of results.triplets) {
  console.log(`${triplet.subject} ${triplet.predicate} ${triplet.object}`);
}

Cross-content graphs

Graphs connect information across all content types: Video: “Sarah is leading the Kubernetes migration” → Sarah Chen → leads → Kubernetes Migration PDF: “Kubernetes migration depends on new infrastructure” → Kubernetes Migration → depends_on → New Infrastructure Chat: “New infrastructure will use AWS EKS” → New Infrastructure → uses → AWS EKS Combined graph:

Sarah Chen
└── leads → Kubernetes Migration
    └── depends_on → New Infrastructure
        └── uses → AWS EKS

Relationship types

Automatically detected relationships:

Professional: works_at, reports_to, manages, collaborates_with
Technical: uses, depends_on, integrates_with, built_with
Conceptual: related_to, part_of, category_of, type_of
Temporal: preceded_by, followed_by, during, after
Preference: prefers, likes, requires

Best practices

Include rich context in memories for better entity extraction
Use consistent entity names across content
Add structured metadata for clearer relationships
Leverage graph queries to enrich AI context

Privacy and isolation

Knowledge graphs respect Space boundaries:

Each Space has an independent graph
Entities never connect across spaces
Ensures data privacy and separation

Next steps

Memories

Add memories to build graphs

Search

Graph-aware semantic search

SDK: Graph

Query knowledge graphs

Getting started

Core concepts

Connectors

Use Cases

Support & Troubleshooting

What is a Knowledge Graph?

Triplet structure

Automatic entity extraction

Graph-aware search

Common use cases

Team knowledge

Product dependencies

Customer insights

Automatic graph building

Query the graph

Cross-content graphs

Relationship types

Best practices

Privacy and isolation

Next steps

Memories

Search

SDK: Graph

​What is a Knowledge Graph?

​Triplet structure

​Automatic entity extraction

​Graph-aware search

​Common use cases

​Team knowledge

​Product dependencies

​Customer insights

​Automatic graph building

​Query the graph

​Cross-content graphs

​Relationship types

​Best practices

​Privacy and isolation

​Next steps

Memories

Search

SDK: Graph

What is a Knowledge Graph?

Triplet structure

Automatic entity extraction

Graph-aware search

Common use cases

Team knowledge

Product dependencies

Customer insights

Automatic graph building

Query the graph

Cross-content graphs

Relationship types

Best practices

Privacy and isolation

Next steps