Context Graphs: Agent Memory with Neo4j
Build an AI agent that records its reasoning, then query the trace to understand what it did and why
In this 2-hour course, you will learn
In this course, you will learn how to give AI agents persistent, explainable memory backed by Neo4j using the neo4j-agent-memory library.
You will learn why most AI agent deployments fail to deliver enterprise value — and how context graphs solve the three critical gaps: no memory, no audit trail, and no shared learning. You will explore the three-layer memory model (short-term, long-term, and reasoning), the POLE+O entity classification system, and the full graph schema that connects them.
By the end of the course, you will have built a Pydantic AI agent that records its complete reasoning trace into Neo4j, and written Cypher queries to traverse that trace and explain exactly what the agent did and why.
- Neo4j Fundamentals — graph database concepts
- Cypher Fundamentals — query language basics
- Neo4j & GenAI Fundamentals — generative AI and GraphRAG concepts
- Basic Python — reading and writing simple Python programs
Context Graphs
Agent Memory
neo4j-agent-memory
Reasoning Traces
POLE+O