Getting Started with SKGraph

Overview

SKGraph provides graph-based knowledge storage for AI agents using FalkorDB. It maps semantic relationships between memories, concepts, agents, and events.

Installation


pip install "skmemory[skgraph]"

Or standalone:


pip install skgraph

Requirements

Quick Start


from skgraph import KnowledgeGraph

kg = KnowledgeGraph(url="redis://localhost:6379")

# Add entities
kg.add_entity("lumina", type="agent", properties={"role": "queen"})
kg.add_entity("chef", type="human", properties={"name": "David"})

# Add relationships
kg.add_relation("lumina", "chef", "partners_with", weight=0.97)

# Query
results = kg.query("MATCH (a)-[r]->(b) WHERE a.name = 'lumina' RETURN b, r")

Integration with SKMemory

SKGraph works as a backend layer for SKMemory, automatically building a knowledge graph from stored memories:


from skmemory import Memory
from skgraph import KnowledgeGraph

# Memories automatically create graph nodes and edges
mem = Memory(backend="skgraph")
mem.snapshot(title="Project meeting", content="Discussed capauth integration...")
# Creates: (meeting) -[discusses]-> (capauth), (meeting) -[attended_by]-> (agent)