Plugins

There are three recommended methods for adding Neo4j plugins to Neo4j Helm chart deployments. You can use:

an automatic plugin download
a custom container image.
a plugins volume.

Add plugins using an automatic plugin download

You can configure the Neo4j deployment to automatically download and install plugins. If licenses are required for the plugins, you must provide the licenses in a secret.

Install GDS Community Edition (CE)

GDS Community Edition does not require a license. To add the GDS CE, configure the Neo4j values.yaml and set the env to download the plugins:

neo4j:
  name: licenses
  acceptLicenseAgreement: "yes"
  edition: enterprise
volumes:
  data:
    mode: defaultStorageClass
env:
  NEO4J_PLUGINS: '["graph-data-science"]'
config:
  dbms.security.procedures.unrestricted: "gds.*"

Install GDS Enterprise Edition (EE) and Bloom plugins

To install GDS EE and Bloom, you must provide a license for each plugin. You provide the licenses in a Kubernetes secret.

Create a secret containing the licenses:

kubectl create secret  generic --from-file=gds.license,bloom.license gds-bloom-license

Configure the Neo4j values.yaml file using the secret as the /licenses volume mount, and set the env to download the plugins:

neo4j:
  name: licenses
  acceptLicenseAgreement: "yes"
  edition: enterprise
volumes:
  data:
    mode: defaultStorageClass
  licenses:
    disableSubPathExpr: true
    mode: volume
    volume:
      secret:
        secretName: gds-bloom-license
        items:
          - key: gds.license
            path: gds.license
          - key: bloom.license
            path: bloom.license
env:
  NEO4J_PLUGINS: '["graph-data-science", "bloom"]'
config:
  gds.enterprise.license_file: "/licenses/gds.license"
  dbms.security.procedures.unrestricted: "gds.*,apoc.*,bloom.*"
  server.unmanaged_extension_classes: "com.neo4j.bloom.server=/bloom,semantics.extension=/rdf"
  dbms.security.http_auth_allowlist: "/,/browser.*,/bloom.*"
  dbms.bloom.license_file: "/licenses/bloom.license"

Add plugins using a custom container image

The best method for adding plugins to Neo4j running in Kubernetes is to create a new Docker container image that contains both Neo4j and the Neo4j plugins. This way, you can ensure when building the container that the correct plugin version for the Neo4j version of the container is used and that the resulting image encapsulates all Neo4j runtime dependencies.

The Neo4j Bloom plugin requires a license activation key, which needs to be placed in a directory accessible by the Neo4j Docker container, for example, mounted to /licenses (default). To obtain a valid license, reach out to your Neo4j account representative or use the form Contact Neo4j.

Building a Docker container image that is based on the official Neo4j Docker image and does not override the official image’s ENTRYPOINT and COMMAND is the recommended method to use with the Neo4j Helm chart, as shown in this example Dockerfile:

ARG  NEO4J_VERSION
FROM neo4j:${NEO4J_VERSION}

# copy my-plugins into the Docker image
COPY my-plugins/ /var/lib/neo4j/plugins

# install the apoc core plugin that is shipped with Neo4j
RUN cp /var/lib/neo4j/labs/apoc-* /var/lib/neo4j/plugins

Once the Docker image has been built, push it to a container repository that is accessible to your Kubernetes cluster.

CONTAINER_REPOSITORY="my-container-repository.io"
IMAGE_NAME="my-neo4j"

# export this so that it's accessible as a docker build arg
export NEO4J_VERSION=2026.08.1-enterprise

docker build --build-arg NEO4J_VERSION --tag ${CONTAINER_REPOSITORY}/${IMAGE_NAME}:${NEO4J_VERSION} .
docker push ${CONTAINER_REPOSITORY}/${IMAGE_NAME}:${NEO4J_VERSION}

To use the image that you have created, in the Neo4j Helm deployment’s values.yaml file, set image.customImage to use the image.

Many plugins require additional Neo4j configuration to work correctly. Plugin configuration should be set on the config object in the Helm deployment’s values.yaml file. In some cases, plugin configuration can cause Neo4j’s strict config validation to fail. Strict config validation can be disabled by setting server.config.strict_validation.enabled: "false".

Add plugins using a plugins volume

An alternative method for adding Neo4j plugins to a Neo4j Helm deployment is to mount a shared plugins volume. With this method, the plugin jar files are stored on a Persistent Volume that is mounted at /plugins in the Neo4j container.

To configure Neo4j to load plugins from the mounted volume, add server.directories.plugins: /plugins to the config: object of your values.yaml file to ensure Neo4j recognizes the mounted volume. Although the Helm chart mounts the shared volume at /plugins, explicit configuration is required to override the default plugin directory.

To set up a persistent plugins volume, reuse the Persistent Volume that stores Neo4j data. The following example shows how to configure that in the Neo4j Helm deployment values.yaml file:

# neo4j-values.yaml
volumes:
  data:
    # your data volume configuration
    ...

  plugins:
    mode: "share"
    share:
      name: "data"

Details of different ways to configure volume mounts are covered in Mapping volume mounts to persistent volumes.

The Neo4j container now has an empty /plugins directory backed by a persistent volume. Plugin jar files can be copied onto the volume using kubectl cp. Because it is backed by a persistent volume, plugin files will persist even if the Neo4j pod is restarted or moved.

Neo4j loads plugins only on startup. Therefore, you must restart the Neo4j pod to load them once all plugins are in place.

For example:

# Copy plugin files into the Neo4j container
kubectl cp my-plugins/* <namespace>/<neo4j-pod-name>:/plugins/

# Restart Neo4j
kubectl rollout restart statefulset/<neo4j-statefulset-name>

# Verify plugins are still present after restart
kubectl exec <neo4j-pod-name> -- ls /plugins

Configure and install APOC core only

APOC core library is shipped with Neo4j and is located in the labs folder.

If APOC core is the only plugin that you want to add to Neo4j, it is not necessary to perform plugin installation as described above. Instead, you can configure the helm deployment to use APOC core by upgrading the deployment with these additional settings in the values.yaml file:

Configure APOC core by directly pointing to the location of the APOC core library in the labs folder and by loading and unrestricting the functions and procedures you need (for more details see APOC installation guide). For example:
```
config:
  server.directories.plugins: "/var/lib/neo4j/labs"
  dbms.security.procedures.unrestricted: "apoc.*"
  server.config.strict_validation.enabled: "false"
  dbms.security.procedures.allowlist: "apoc.math.*,apoc.cypher.*"
```

Under apoc_config, configure the APOC settings that you want, for example:

apoc_config:
  apoc.trigger.enabled: "true"
  apoc.jdbc.neo4j.url: "jdbc:foo:bar"
  apoc.import.file.enabled: "true"

Run helm upgrade to apply the changes:

helm upgrade <release-name> neo4j/neo4j -f values.yaml

After the Helm upgrade rollout is complete, verify that APOC core has been configured by running the following Cypher query using cypher-shell or Neo4j Browser:
```
RETURN apoc.version()
```

Configure credentials for the plugin’s aliases using APOC-extended

The Neo4j Helm chart supports configuring credentials for the plugin’s aliases using a Kubernetes secret mounted on the provided path. This feature is available apoc.jdbc.<aliasname>.url and apoc.es.<aliasname>.url via APOC-extended.

The secret must be created beforehand and must contain the key-named URL, otherwise, the Helm chart throws an error. For example: kubectl create secret generic jdbcsecret --from-literal=URL="jdbc:mysql://30.0.0.0:3306/Northwind?user=root&password=password"

Under apoc_credentials, configure aliasName, secretName, and secretMountPath. For example:

apoc_credentials: {}
#   jdbc:
#    aliasName: "jdbc"
#    secretName: "jdbcsecret"
#    secretMountPath: "/secret/jdbcCred"
#
#   elasticsearch:
#     aliasName: "es"
#     secretName: "essecret"
#     secretMountPath: "/secret/esCred"

Glossary

allocator: A component in the cluster that allocates databases to servers according to the topology constraints specified and an allocation strategy.
asynchronous replication: Asynchronous replication is used by secondary copies to poll for new transactions, which means they cannot be guaranteed to have received the most recent transactions. This enables efficient scale-out of read-performance.
Aura instance: A fully-managed DBMS represented by a single instance ID, that is running in the Neo4j Aura cloud.
auto-commit transaction: An automatically committed transaction that contains a single query.
Bolt protocol: Bolt is a protocol used for interaction between Neo4j instances and drivers.
bookmark: A marker the client can request from the cluster to ensure that it is able to read its own writes so that the application’s state is consistent and only databases that have a copy of the bookmark are permitted to respond.
category (Bloom): A category is based on a node label and is defined in a Perspective as a way of visually distinguishing nodes with the same label(s).
causal consistency: All servers in a cluster agree on the order in which transactions take place. The position of a server on the causal chain can be guaranteed using a bookmark.
cluster: A Neo4j DBMS that spans multiple servers working together to increase fault tolerance and/or read scalability. Databases on a cluster may be configured to replicate across servers in the cluster thus achieving read scalability or high availability.
client application: Software that interacts with a Neo4j server.
commit: A commit is the successful completion of a transaction, which ensures durability of any changes made. For more details, visit Operations Manual → Transaction management.
composite database: Composite databases are the means to access partitioned graph data with a single Cypher query.
constraint: Constraints are sets of data modeling rules that ensure the data is consistent and reliable.
Cypher®: Neo4j’s graph query language.
data model: A data model defines how information is organized in a database. A good data model will make querying and understanding your data easier. In Neo4j, the data models have a graph structure.
database: A database is a container used by the DBMS to manage and store graph data. The physical structure of data is controlled by the database.
database vs graph: Databases are the physical containers of graph data. Graphs are the logical structure of data in Neo4j.
Database Management System: Database Management System, or DBMS, capable of managing multiple databases. A DBMS may run on a single server, or span several servers configured as a cluster.
database schema: The prescribed property existence and datatypes for nodes and relationships.
deallocate: An act of removing a database from a server or a server from a cluster without loss of data or reduced fault tolerance.
degree (of a node): The number of relationships of a specific node; loops are counted twice.
disaster recovery: A manual intervention to restore availability of a cluster, or databases within a cluster.
driver: A software library that provides access to Neo4j from a particular programming language.
election: In the event that the Raft leader becomes unresponsive, followers automatically trigger an election and vote for a new leader.
entity: A node or a relationship.
expression (Cypher): A component of a Cypher query which produces values. It may be used in projections, as a predicate, or when setting properties on graph elements.
fabric: Fabric is the architectural design of a unified system that provides a single access point to local or distributed graph data.
fault tolerance: A guarantee that a cluster can maintain a database’s persistence and availability in the event of one or more servers failing.
follower: A primary copy of a database acting as a follower, receives and acknowledges synchronous writes from the leader.
Generative AI (GenAI): A type of artificial intelligence (AI) system that generates text, images, or other media in response to prompts.
graph: A logical representation of a set of nodes where some pairs are connected by relationships.
index: Data structure that improves read performance of a database.
knowledge graph: A specific type of graph that has an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle provides an additional layer of structure that adds context to support knowledge discovery.
label: Marks a node as a member of a named and indexed subset. A node may be assigned zero or more labels.
leader: A single primary copy of a database is designated as the leader. It receives all write transactions from clients and replicates writes synchronously to followers and asynchronously to secondary copies of the database.
main database: In terms of Neo4j Enterprise Studio, the database(s) containing the user’s data. Can exist in the same Neo4j deployment as the tool asset database.
motif: A description of a specific pattern within a graph.
node: A node represents an entity or discrete object in your graph data model. Nodes can be connected by relationships, hold data in properties, and are classified by labels.
operator: A symbol representing a mathematical or logical operation.
parameter: Named value provided when running a Cypher statement.
path: A sequence of nodes and the relationships connecting them, that does not contain duplicate relationships. Several paths can match a pattern.
pattern: A specific arrangement of nodes and relationships that can be matched in a graph. A pattern follows a motif.
perspective (Bloom): A Perspective defines a certain business view or domain that can be found in the target Neo4j graph. A single Neo4j graph can be viewed through different Perspectives, each tailored for a different business purpose.
primary: A copy of the database that is able to process write transactions and is eligible to be elected as a leader. It participates in fault tolerant writes as it is part of the majority required to acknowledge and commit write transactions.
primary vs secondary: In a cluster, databases can operate in either primary or secondary mode. Primary databases are able to process write and read transactions, ensuring fault tolerance. Secondary databases are replicated asynchronously from primaries, and their main purpose is to provide read scaling within the cluster.
project (Aura): An isolated environment in the unified Aura console that contains its own database instances, configurations, and resources. Preceded by tenant in the classic Aura console.
property: Properties are key-value pairs that are used for storing data on nodes and relationships.
query (Cypher): A statement that retrieves or writes information to a database.
Raft group: A group of servers that are participating in hosting a particular database in primary mode.
Raft group member: A server that is participating in a Raft group. A server can be a member of one or more groups.
Raft log: A shared log between all Raft group members that is guaranteed to be consistently updated and viewed by those members. The log contains both database data and operational state of the Raft group.
Raft protocol: The networking mechanism that enables a database to replicate its data across multiple servers to give high availability for accessing the data and high durability to the data stored.
read scaling: Distributing query load by creating additional database copies hosted in secondary mode (read-only).
relationship: A relationship represents a connection between nodes in your graph data model. Relationships connect a source node to a target node, hold data in properties, and are classified by type.
secondary: An asynchronously replicated copy of the database that provides read scaling within the cluster.
seed: A seed is a database dump or a full backup used to create a database on a cluster. This is sometimes called seeding.
server: A physical machine, a virtual machine, or a container running an instance of Neo4j. Servers can be standalone or part of a cluster.
session: A causally linked sequence of transactions.
session consistency: An alternative name for Neo4j’s causal consistency.
standalone: A single server running Neo4j and not part of a cluster.
synchronous replication: Synchronous replication requires the leader primary to replicate a transaction and block the commit until a quorum of the follower primaries acknowledges that the transaction is successfully replicated. Once the transaction is replicated, the commit is allowed to proceed. This ensures data durability and consistency within the cluster.
system database: A database used by Neo4j to store system information.
tenant (Aura): An isolated environment in the classic Aura console that contains its own database instances, configurations, and resources. Replaced by project in the unified Aura console.
tool asset database: In terms of Neo4j Enterprise Studio, the database where tools' assets are stored. This can be in the same Neo4j deployment as the main database(s) or in a separate deployment.
topology: A configuration that describes how the copies of a database should be spread across the servers in a cluster, see primary mode and secondary mode.
transaction: A transaction comprises a unit of work performed against a database. It is treated in a coherent and reliable way, independent of other transactions. Transactions comply with the ACID consistency model (atomic, consistent, isolated, and durable).