Learn

Graph databases, without the jargon.

Short, honest guides to the ideas behind CognoDB: what a graph database is, how Cypher works, where GraphRAG helps, and when a graph is the wrong tool. Every code example runs on the free tier.

Concepts

Start here: the data model and why it's different.

What is a graph database?

A graph database stores data as nodes and relationships instead of tables and joins. Here's how the model works, why traversals beat joins for connected data, and when to use one.

6 min read

Vector database vs graph database

Vector databases retrieve by semantic similarity; graph databases retrieve by relationships. What each is for, where each wins in AI applications, and why serious stacks usually run both.

6 min read

Context graphs

The working-memory layer for AI agents, and what it saves in tokens.

What is a context graph?

A context graph is the connected, queryable record of what an AI system currently knows: entities, facts, sources and time. How it differs from a knowledge graph, and how agents read and write one.

6 min read

How to build AI agent memory

Persistent memory for AI agents, built as a graph: why transcripts don't scale, the entity–fact–source schema that does, the write and read paths in Cypher, and how multi-agent memory stays sane.

7 min read

How a graph cuts LLM token costs

Context-stuffing costs grow with your knowledge base; graph retrieval stays bounded by the neighbourhood you traverse. The measured numbers: 202,285 tokens per query down to 2,668, at 3,700 entities.

5 min read

GraphRAG

Grounding LLMs and agents in a graph, honestly.

What is GraphRAG?

GraphRAG grounds an LLM by traversing a context graph instead of retrieving text chunks by similarity alone. Here's how it works, why it improves multi-hop answers, and how to build it.

7 min read

Graph RAG vs vector RAG

Vector RAG retrieves by similarity; graph RAG retrieves by traversing relationships. A side-by-side comparison, where each wins, and why the 2026 answer is usually a hybrid of the two.

6 min read

Cypher

The query language, and why it reads like the question.

What is Cypher?

Cypher is a declarative query language for graphs where the query looks like the pattern you're matching. A quick tour of MATCH, MERGE, variable-length paths and full-text search, all runnable on CognoDB.

6 min read

Guidance

Straight talk on where a graph fits and where it doesn't.

When not to use a graph database

Graph databases are powerful for connected data, but they're the wrong tool for several common workloads. Here's when to reach for Postgres, a columnar store, a queue or a vector index instead.

5 min read

Start now

~98.7%

token efficiency at 3,700 entities (see the footnotes above)

Read one, then run it.

The fastest way to understand a graph database is to query one. A free instance takes about a minute and no card.

Start free Read the quickstart

First-graph path

Live

  1. Create a free instance

    No card. Ready in about a minute.

  2. Connect your driver

    bolt+s:// URI into the driver you already use.

  3. Write two MERGEs

    That's the entire shape of agent memory.

  4. Point an agent at it

    One MCP config block. No integration code.