Connect Neo4j

The Schema You Never Wrote Down

A duplicate-node incident is one of the quietest failures in software. Nothing throws. No log line turns red. A `MERGE (p:Person {email: $email})` runs a few hundred thousand times a day and, for eleven months, does exactly what everyone expects. Then a second ingestion path is added — this one merges on `name`, because that is what the upstream file happened to carry — and the graph starts growing two `Person` nodes where there used to be one. Traversals still return results. Dashboards still render. The number of people in the company just slowly stops being true.

Recipe: GraphRAG Grounding for Agents

Four Things That Exist Afterwards

Adoption stories usually get told as a list of features. This one is easier to tell as an inventory: after a team wires CoreModels to their Neo4j instance, four artifacts exist that did not exist before, and almost everything that changes about the week is downstream of those four.

Recipe: CI Drift Gate for Neo4j · First Governed Import for Neo4j

Read the Roles, Not the Brochure

You can predict how a tool will behave inside your systems by reading which of its operations require which permission. Roles are enforced by code paths; brochures are not.

Recipe: Generate Neo4j Constraints from the Governed Model

Count the Homes of One Entity

Pick a single entity in your organization — `Customer`, `Patient`, `Instrument`, whatever your domain calls the thing everything else hangs off — and count where it physically lives. In a serious estate the count is rarely below four. It is a label in Neo4j. It is a table in a warehouse. It is a subject in a schema registry. It is a model in a transformation project, a form in a capture system, a class in an application's JSON Schema.

Six Guesses Before the First Hop

Ask an agent a question about your graph — "which companies do our top account contacts work for?" — and watch what it has to decide before it can write a single line of Cypher.

Recipe: GraphRAG Grounding for Agents

Your First Neo4j Schema Audit: A Complete Worked Example

Take a small graph — three labels, a handful of properties, three relationship types. By the end of this article that graph has a governed model in CoreModels and a recorded baseline audit that tells you, in machine-readable form, exactly what is under governance and where the graph is structurally weak. Total effort: two Cypher statements and two HTTP calls.

Recipe: Blank Neo4j project · First Governed Import for Neo4j · Generate Neo4j Constraints from the Governed Model

The Neo4j Integration API, Route by Route

Ten routes, two surfaces, one rule about who is allowed to write. That is the core HTTP contract for governing a Neo4j estate with CoreModels, and this article documents it exhaustively — payloads, roles, response shapes, and the failure modes you will actually hit.

Four Tools, One Knowledge Graph: Neo4j Governance from the Agent Side

An AI agent connected to CoreModels over MCP sees a small, deliberately shaped set of vendor-integration tools. Three of them cannot write anything at all; the fourth requires Admin membership on the project *and* a different endpoint. That shape is the point of this article: an agent can drive the entire Neo4j governance loop — discover, audit, report, generate the fix — while the only mutating step in the loop stays behind an explicit privilege boundary.

Recipe: GraphRAG Grounding for Agents

Automating Neo4j Drift Control: The Gate, the Trail, the Badge, the Heartbeat

Most CI integrations start with an artifact the build already produces. Neo4j does not produce one — there is no compile step for a property graph, no manifest, no migration file that reviewers can read. So the first job in automating Neo4j governance is not writing the gate. It is deciding where `meta_schema.json` comes from in your pipeline.

Recipe: CI Drift Gate for Neo4j

Projecting a Property Graph onto a Governed Model: Inside the Neo4j Connector

CoreModels has one estate model and every vendor parses into it: **datasets** (table-shaped things) with **fields** and normalized **checks**, plus **lineage** edges and **projections**. A dbt project, a warehouse schema, and a schema registry all land in the same shape.