Follow up on the latest improvements and updates.

RSS

July 31, 2026

new

Enterprise Studio

Neo4j Enterprise Studio 2026.07

This is a minor release which includes the latest tool updates & improvements to Enterprise Studio operations.

Bloom

Added fixes for minor issues with search, scenes, GDS, filters, and perspective behaviour.
Fixed issue with drill down from Dashboards into Bloom.

Dashboards

Dashboard cards can now use parameters in titles, descriptions, and markdown.
Improved experience for concurrent editing on shared dashboards.
Styling editors gained broader support for data types, temporal values, big integers, and error handling.
Fixed bug that caused issues with Standalone mode in Enterprise Studio.

Operational improvements

Helm chart improvement: Enterprise Studio now checks container readiness on initialization.
Enterprise Studio automatically validates licenses on startup.
Connection management: cache the last deployment selected in the connection form, and fixes the login flow when no home database is set.

July 22, 2026

new

AuraDB Professional

AuraDB Free

AuraDB Business Critical

MCP for Aura Now Available

MCP for Aura is a hosted Model Context Protocol service built directly into Aura. Your AI client and agents can now connect straight to your graph and get grounded, accurate answers pulled from your real data.

With MCP for Aura, you get:

No setup:
Connect through the Aura platform using your existing Aura credentials, so there is no server to install and no credentials to manage separately.
Three ready-to-use tools:
Schema, read, and read-write let your AI client explore your graph and start answering questions immediately, even one it has never seen before.
Grounded answers:
Every response comes from a real query against your real data, not a guess.
Broad compatibility:
Works with any MCP-compatible AI client or agent.

Available on AuraDB Free, Professional, and Business Critical tiers, with Virtual Dedicated Cloud (VDC) support coming soon.

Cypher Copilot updates: Smarter Queries, Baseline Edits, and In-Editor Review

The Cypher Copilot in Query just got a major upgrade. Beyond turning your natural-language questions into Cypher, it now validates its own output in the background, works from an existing query, targets specific parts of a query, and lets you review changes right in the editor. Here's what's new:
Background validation
Generated queries now go through linting and EXPLAIN-retry phases in the background. Any errors surfaced in those steps are fed back to the LLM so it can correct itself and produce more accurate, runnable queries. This includes correcting hallucinated paths and inverted relationship directions
Query generation from a baseline
You no longer have to start from scratch. Pass an existing query as a baseline and ask the LLM to edit, update, or improve it — perfect for iterating on a query you already have.
Targeted edits via selection
Highlight a specific part of a query and ask the LLM to focus its changes on just that section.
Diff view with accept/reject
Generated queries are now presented as a diff against your current query, with clear accept and reject flows, so you can review exactly what changed before accepting and executing it.
Support for Virtual Graph
When working with a Virtual Graph, outputs are adjusted to support the constructs supported by Virtual Graph.
We want to hear from you!
We'd love your feedback on the updated copilot experience. Share ideas or report issues at feedback.neo4j.com/query.

July 15, 2026

new

AuraDB Virtual Dedicated Cloud

AuraDB Professional

AuraDB Business Critical

Neo4j Fleet Manager

Fleet Manager: Migrate a Community Edition database to Aura

Fleet manager now includes the ability to help you migrate your self-managed Community Edition databases to Aura via a simple user interface. In order to use this, you will need to have your Community Edition Neo4j database(s) registered with Fleet Manager. Please see the documentation for details on how to do this.
The Aura migration works for Neo4j Community Edition version 5.26 with Fleet Manager plugin 1.20, or version 2026.05 or later. If your Community Edition is running an earlier version of Neo4j, please upgrade to a supported version to use the migration utility.
There are two migration paths which are available, one which is automatic, and one which provides more control over the options and process.
Migration with one click
NOTE: The automatic migration will perform actions on your Community Edition database including stopping the Neo4j database whilst a dump is taken. Please review the documentation to ensure you understand what will happen.
To migrate your Community Edition database to Aura automatically, choose the “migrate with one click” option from the ellipsis menu.
This will bring up a confirmation window of the new Aura instance which will be created to migrate the database onto. The cheapest AuraDB instance is chosen, and the costs are displayed for you to review. If you are happy with the suggestion, click “Migrate” and the migration process will start. If you wish to have more control over the target, click “Cancel” and choose the “Migrate to Aura" (with options) approach. 
Once the migration starts, the credentials for the neo4j user on the newly created AuraDB instance are shown. Please save these details to connect to the instance later. Once done, click next and the status of the steps is displayed. If you navigate away from this page, it can be displayed again by choosing “Migration status” from the ellipsis menu.
Once all four steps in the migration have completed, you may review your migrated Community Edition database in the new AuraDB instance. Should you need to review progress or investigate any issues, the “standard output” shows more information about each step in the process.
Migrate to Aura (with options)
Choose this option if you require more control over your migration—such as choosing a specific instance type or size, selecting a region, or reusing an existing Aura instance. To launch the option, press the “Migrate to Aura” button on the instance card, or choose “Migrate to Aura” from the ellipsis menu.
The first step is displayed, which provides details of the need to confirm that your application is compatible with Aura. This includes reviewing the driver and application versions in use, and checking for any plugins or custom code which may not be supported in Aura. NOTE: The automatic migration bypasses this step and assumes the application is compatible.
The manual option provides guidance on what is needed and links to the documentation. The automatic option can be used to enable collection of workload (queries) for a 24 hour period to assist with this analysis. See documentation for details on this feature and requirements.
To proceed to the next step, check the box to confirm you are aware of the need for application compatibility and click “Next”.
The second step is to choose where you want the Community Edition database to migrate to in Aura. You may choose to migrate to a new AuraDB instance, or reuse an existing one. Choose the options for your target AuraDB instance for the migration, review the costs and choose next to continue. If creating a new instance, the credentials for the neo4j user on the newly created AuraDB instance are shown. Please save these details to connect to the instance later. 
The third step is to run the migration. You may choose to run the steps yourself (manually) and follow the commands, or Fleet Manager can run the steps automatically for you. The progress can be followed on the Migration status window.
For full details of the feature, including additional content such as managing migration history, please refer to the documentation.

July 15, 2026

new

AuraDB Virtual Dedicated Cloud

AuraDS Enterprise

AuraDB Business Critical

🎉 New in Aura: Self-Service Single Sign-On (SSO)

Securing your Neo4j Aura environment just got a whole lot easier! We are excited to announce that Single Sign-On (SSO) configuration is now fully self-service, most importantly no support tickets are required
!
What’s new?
Organization Owners and Admins could already integrate Aura with Identity Providers (IdPs) like Okta and Microsoft Entra ID using the OpenID Connect (OIDC) protocol. Now we have enhanced this experience to provide complete, granular control to configure SSO exactly how your team needs it:
Organization-Level SSO
: Centralize authentication and manage how users log in to the Aura Console.
Instance SSO
: Configure login methods specifically for instances within your projects. This includes role mapping which allows per instance configuration to map existing IdP groups to specific database roles, ensuring users automatically get the exact access they need.  Importantly -- this configuration can apply to both new and existing instances!
Take control of your organization's security today. Head over to Organization > Security > Single Sign-On in the Aura console to set up your configuration!
Read the Aura SSO Documentation for more details.

July 7, 2026

new

AuraDB Professional

AuraDB Free

AuraDB Business Critical

Document Intelligence Preview now available

Your unstructured documents are full of useful data. Getting it into a graph has always been the hard part.
Document Intelligence handles that. Upload your files or connect your cloud storage, and an AI assistant walks you through building a graph model: identifying node labels, relationship types, and properties automatically. You can iterate on the model in conversation, fine-tune it, and trigger the import when you're ready. Once your data is in the graph, the same assistant lets you query it in plain language, no Cypher required.
Supported formats: PDF, MD, DOCX, TXT, EPUB. Supported storage: AWS S3, Azure Blobs & Data Lake Storage, Google Cloud Storage, and local files.
Read the documentation  and try the preview in Aura today.

July 3, 2026

new

AuraDB Virtual Dedicated Cloud

AuraDB Professional

AuraDB Free

AuraDS Professional

AuraDS Enterprise

AuraDB Business Critical

New Aura User Management APIs

Neo4j is thrilled to introduce the new Aura User Management REST APIs, bringing comprehensive programmatic control to user lifecycle management across both Organization and Project levels!  Previously driven primarily through the Aura Console UI, this new programmatic capability eliminates operational friction for enterprise teams looking to scale efficiently.  We've built this feature specifically to support automated deployments and modern DevOps workflows,
These new APIs allow you to seamlessly integrate Neo4j Aura into your existing Identity and Access Management (IAM) workflows, CI/CD pipelines, and Internal Developer Platforms (IDPs). This unlocks "Zero-Touch" user onboarding, where automated API calls handle provisioning and align with Aura's unified organization-level identity model. 
Refer to the Aura API Specification documentation  for information on using each endpoint and the Aura API Overview documentation for how to access the Aura API.
The Organization-level endpoints, admins can programmatically manage invitations (GET, POST, DELETE /organizations/{org_id}/invites) and oversee the entire user directory: including fetching metadata, adjusting global organization roles, or offboarding users entirely (GET, PATCH, DELETE /organizations/{org_id}/users). 
To support granular access control, Project-level endpoints empower development teams to manage memberships natively within their specific project workloads. You can programmatically list project users, grant access, update project roles, or revoke specific project memberships (GET, POST, PATCH, DELETE /organizations/{org_id}/projects/{project_id}/users). 
This dual-layer framework ensures that project-level access can be granted instantly or cleanly terminated during onboarding and offboarding workflows, eradicating manual entry errors and significantly elevating your security posture. 

July 3, 2026

new

AuraDB Virtual Dedicated Cloud

AuraDB Professional

AuraDB Free

Data Importer

Import

New Console

Import Service: Support for List and Vector Datatypes

We are pleased to announce that the Import Service now supports List and Vector datatypes. Whether you are bringing in graph embeddings for GenAI applications or working with list attributes, you can now easily map and import these complex datatypes into Neo4j.
This support is fully integrated across both local file workflows and remote data sources. Here is what you can expect:
1. Local CSV Flow
For your local CSV workflow you can manage your formatting configurations directly within the files panel. Simply click the ...
 menu next to your Data source and select Set delimiters
.
This opens a dedicated configuration modal where you can define the exact characters separating your array and vector values.
2. CSV Files in Cloud Buckets
When importing CSV files hosted in cloud storage buckets, you can configure array and vector delimiters during your data source setup flow. Providing these delimiters triggers the automatic detection of list and vector types, ensuring your cloud-hosted file properties are parsed automatically.
3. Strongly-Typed Data Sources
 (Parquet, RDBMS, & Cloud Data Warehouses)
If you are connecting to remote data sources that natively support structured or typed data—such as Apache Parquet files, relational databases, or cloud data warehouses—the Data Importer does the heavy lifting for you. Compatible source types are automatically recognized and offered as options mappable directly to their equivalent Neo4j types (such as vector<float32>
, string[]
, or float[]
).
Once selected, you can easily fine-tune your configuration (such as specifying vector dimensions) right inside the property settings UI.
We Want to Hear From You!
We are continuously evolving the data loading experience. Please try out the new list and vector support and share your thoughts, feature requests, or bug reports with us at feedback.neo4j.com/data-importer.

June 30, 2026

new

AuraDB Virtual Dedicated Cloud

AuraDB Professional

AuraDB Free

AuraDS Professional

AuraDS Enterprise

AuraDB Business Critical

Neo4j Aura June Database Release

The Neo4j Aura June release has started to roll out, commencing on AuraDB Free and progressing to higher tiers later.

Cypher 25 Features

CALL { ... } IN CONCURRENT TRANSACTIONS
now supports
DISJOINT BY
, a deadlock prevention mechanism that eliminates lock contention in parallel write workloads natively. High concurrency ingestion, particularly when merging nodes with unique constraints or creating relationships, has always been vulnerable to deadlock, as parallel threads compete for the same locks simultaneously.
DISJOINT BY
resolves contention at scheduling time, before any transaction is attempted. See the Cypher Manual → Batch scheduling and deadlock prevention with DISJOINT BY.
GRAPH TYPE
moves from public preview to GA in this release. You can now define, enforce, and validate your graph schema at the database level in production workloads with the stability guarantees that come with a fully supported feature. See the Cypher Manual → Graph types.
Extend
SHOW CURRENT GRAPH TYPE
to be able to return lists of virtual nodes and relationships representing the graph type (similar in format to the output of db.schema.visualization()) instead of the current string representation. To get the graph version of the command, use
SHOW CURRENT GRAPH TYPE AS GRAPH
. See the Cypher Manual → SHOW CURRENT GRAPH TYPE AS GRAPH.
For customers using Change Data Capture (CDC), we have added a
txCommitTime
column to the
db.cdc.current()
procedure. This update allows clients to conveniently identify the commit time of their most recent transaction alongside the transaction identifier.
Vector
SEARCH
queries now support
IN
for filtering, allowing queries like:

MATCH (movie:Movie)
  SEARCH movie IN (
    VECTOR INDEX moviePlots
    FOR $queryVector
    WHERE movie.genre IN ["Horror", "SciFi"]
    LIMIT $topK
  )
RETURN movie.title AS title, movie.rating AS rating

See the Cypher Manual → Predicates for vector search with filters.

Other relevant updates

Preview feature

: an improved vector search that uses our new Hi-Fidelity Quantized Vector Search method. HFQ performs an initial expanded search on the quantized vectors, then re-ranks the results using the full precision vectors. This should make queries faster, reduce memory requirements, and increase recall accuracy when compared to the previous default quantized vector index. HFQ adds the following options to

CREATE VECTOR INDEX

, vector.quantization.type and vector.default_search_expansion_factor.

To try the preview of HFQ:

CREATE VECTOR INDEX moviePlots IF NOT EXISTS
FOR (m:Movie)
ON m.embedding
OPTIONS { indexConfig: {
  `vector.quantization.type`: 'binary',
  `vector.default_search_expansion_factor`: 2.0,
  `vector.dimensions`: 1536,
  `vector.similarity_function`: 'cosine'
}}

HFQ requires an index re-build and is expected to be GA in the next release.

Aura Graph Analytics (AGA)

Native projections are now available in AGA via both Cypher and the GDS Python client.
Project-level Machine Learning model publishing is now supported, automatically granting model access to all members of a project.

Security

Upgraded netty to version
4.2.15.Final
in order to resolve several CVEs.

Bug fixes

Fixed a bug that allowed cached pages to exceed the configured limit.
Fixed a bug where outbound network chunks for secondaries were not flushed properly. This could lead to a buffer overflow in the outbound.
Fixed a bug in the pipelined runtime where an
OR EXISTS
subquery containing ordering could silently drop a result row:

MATCH (c:Customer)
WHERE c.vipStatus = true OR EXISTS { MATCH (c)-[:PLACED]->(o:Order) RETURN o ORDER BY o.total DESC }
RETURN c.name

Fixed a record format bug where undirected scans of multiple relationship types omitted sibling relationships, resulting in a ~50% undercount in the output.
Fixed an issue that let a User Defined Function (UDF) be defined as part of a Property-Based Access Control (PBAC) privilege. This isn't supported and wouldn't have worked as a user might have expected.

For full details of all updates and fixes in this release, as well as links to relevant documentation please visit: Release Notes: Neo4j Aura Database – June 2026.

June 29, 2026

new

AuraDB Professional

AuraDB Free

AuraDS Professional

AuraDS Enterprise

AuraDB Business Critical

Aura Graph Analytics

Neo4j Fleet Manager

Aura Project Deletion now available

We are happy to announce that project deletion is now available within the Aura Console for all self serve users.

Required roles:

Project Administrator, Organization Administrator (with Project Administrator role), Organization Owner

Delete a project

Go to Project settings → Delete Project
Select Delete Project
A warning modal appears.
Type the name of the project to confirm, then select Confirm Deletion.

Recover a deleted project

A project can be restored within the 30 day recovery window that begins when the project is deleted. Aura instances will automatically resume. Compute billing will resume once the resources are active.

Go to Organization settings → Recover deleted projects
Locate the project you wish to recover.
Select Restore Project.

See documentation for details: https://neo4j.com/docs/aura/managing-projects/delete-and-restore-projects/

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