Changelog
Follow up on the latest improvements and updates.
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new
improved
AuraDB Virtual Dedicated Cloud
AuraDB Professional
AuraDB Free
AuraDS Professional
AuraDS Enterprise
Query
AuraDB Business Critical
Query Tabs: A new way to work with your queries
Query now provides tabs, giving you a dedicated space to write, edit, and run Cypher queries you want to persist. The existing stream view is retained for workflows where it is less important to retain your Cypher.
This represents a shift away from editing and invoking saved queries in the stream view, changing a few things about the sidebar, saving, and layout along the way. Here's what's new:
A renewed sidebar
The sidebar is now vertical, and Saved Cypher is now called Queries. Downloading and deleting queries is available via a right-click context menu along with other options such as running the query immediately in the stream view - useful for quick executions of your Cypher.
Save queries from the stream view
Saving a query from the stream view is now triggered via a "Save and open in tab" action. This creates a query straight to a Drafts folder and opens the query in a tab, ready to iterate on. From here you can change the default name and arrange it in your own folder structure.
Create a new query
You can create a new query from scratch by opening a new tab. Newly created tabs start out unsaved, but the moment you rename or edit a query, it's saved automatically to the Drafts folder, so you never lose work.
Editing saved queries
The interaction for editing a query is now as simple as clicking it in the sidebar and iterating on it in the tab. All changes are automatically saved.
Flexible layouts in tabs
Flexible layouts that allow you to place results to the bottom, left, or even have both table and graph views open simultaneously are now reserved for tabs, where you have maximum space to work on your queries.
Quick history, Generate with AI and formatting, wherever you're working
Quick history, Generate with AI and formatting are no longer limited to the main editor. All are now available from anywhere, whether it's a stream frame or a tab.
We want to hear from you!
We hope you enjoy the new tabbed Query experience and we'd love to hear your feedback. Share ideas or report issues at feedback.neo4j.com/query.
improved
new
AuraDB Virtual Dedicated Cloud
AuraDB Professional
AuraDB Free
AuraDS Professional
Query
AuraDB Business Critical
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.
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.
new
AuraDB Virtual Dedicated Cloud
AuraDB Professional
Import
AuraDB Business Critical
Bulk Import for Aura (Public Preview)
We are excited to introduce Bulk Import, a high-performance data loading path for Aura. By bypassing standard transaction overhead and writing directly to disk, Bulk Import enables significantly faster initial loads and unlocks massive datasets that were previously difficult to ingest.
⚠️
IMPORTANT:
Bulk Import is a "destructive" load. It overwrites the entire contents of your database. It is intended for initial migrations and large-scale refreshes.Key Features
- High Performance: Uses the same engine as neo4j-admin import.
- Cloud Native: Supports CSV and Parquet files directly from S3, GCS, or Azure Blob Storage.
- Familiar Workflow: Use your existing graph models and mapping configurations, choosing the Bulk Import option at runtime (see screen grab below).
- Detailed Tracking: Monitor progress through specific import stages in the Aura Console.
Performance Benchmarks
The larger your data, the more time you save. Our initial tests show significant improvements over standard "Online" imports and we're continuing to improve performance
- Small Datasets: ~3x speedup.
- Large Datasets (100GB+): 10x speedup or more.
- Enterprise Scale (TB+): Enables imports that were previously too slow to complete via transactional APIs.
Availability
Bulk Import is available now for Aura Professional, Business Critical, and Virtual Dedicated Cloud customers. This feature is currently in Public Preview as we continue to refine performance and stability.
new
AuraDB Virtual Dedicated Cloud
AuraDB Professional
AuraDB Free
AuraDS Professional
AuraDS Enterprise
Data Importer
Import
Announcing the Aura Import API
We’re excited to introduce the Aura Import API, making it easier to automate and manage your data imports into Aura programmatically — whether you’re scheduling recurring jobs or integrating imports into your CI/CD pipelines.
This release is part of the new v2beta1 API, expanding Aura’s automation capabilities. The existing v1 API remains available for broader instance management.
New endpoints include:
- POST /import/jobs— Run an import job
- GET /import/jobs/{AURA_API_IMPORT_JOB_ID}— Check job status (with optional progress details)
- POST /import/jobs/{AURA_API_IMPORT_JOB_ID}/cancellation— Cancel a running job
Learn more:
- API specification
- Authentication guide
- Blog post walkthrough - featuring a Google Cloud Workflows example to schedule imports
The same functionality is also available via the Aura CLI.
We’d love to hear how this API fits into your workflows — share your thoughts or suggestions on our feedback page.
new
AuraDB Virtual Dedicated Cloud
AuraDB Professional
AuraDB Free
AuraDS Professional
AuraDS Enterprise
Data Importer
AuraDB Business Critical
🚀 New: Import from Cloud Object Storage
We’re excited to announce that Neo4j Aura now supports importing data directly from
AWS S3
, Google Cloud Storage
, and Azure Blob & Data Lake Storage
!
With this release, you can ingest data stored in object storage into your graph, using CSV or Parquet files as input. You can map one or more files—using glob patterns if needed—into temporary tables, enabling flexible transformations and modeling. These tables support the same intuitive mapping experience available for RDBMS and Cloud Data Warehouse sources, so you can stay productive no matter where your data lives.
Whether you're loading product catalogs, user activity logs, or complex relationship data, it’s now easier than ever to bring that data into Neo4j using our visual import tools.
- 📁 Supported file types: .csv,.parquet
- 🧩 Supports multiple files with matching schema
- 🛠️ Familiar modeling and mapping workflow
- 🌐 Works across AWS, GCP, and Azure
💬
Have feedback or ideas?
We’d love to hear from you! Visit feedback.neo4j.com to share your thoughts and help shape future improvements.new
AuraDB Virtual Dedicated Cloud
AuraDS Enterprise
Browser.neo4j.io
Query
Upcoming changes to Neo4j Browser
In the last couple of years, we’ve completely rebuilt our Cypher querying capability under the “Query” tab of Aura Console. It brings a large number of improvements over the old Neo4j Browser, including:
- New Cypher editor with more accurate syntax highlighting, completions and linting support
- Richer table visualization with virtualized rendering to improve responsiveness at scale
- More scalable graph visualization
- New history with quick search
- Improved saved Cypher experience
- New parameter setting UI
- New built-in searchable Cypher reference
- Overhauled parameters support
- New styling support
- General overall stability improvements
This same capability, built as a standalone Browser application has been available as an opt-in preview for some time:
Since Neo4j version 2025.05, the new Browser is now the default experience for those running Browser on localhost and is available integrated into the recently released Desktop v2.
From Neo4j
2025.08
, scheduled for release at the end of August, we’ll be making the switch for all remaining browser users including AuraDB Virtual Dedicated Cloud and AuraDS Enterprise customers accessing Browser at <dbid>.databases.neo4j.io/browser. No changes are required to SSO configurations since both versions will be served under the same /browser
path. To ensure a smooth migration, the previous version of the Browser will remain accessible for a limited time.We hope you enjoy the new Browser and if you have any feedback, please don’t hesitate to reach out at feedback.neo4j.com.
new
AuraDB Virtual Dedicated Cloud
AuraDB Professional
AuraDB Free
Data Importer
AuraDB Business Critical
Import from Databricks
We're pleased to announce that the Import service now supports Databricks. You'll find the new option when adding a data source option under Data services > Import.
To get started, ensure you have a Personal Access Token (PAT) configured, then configure the connection form with details for your warehouse, catalog, schema and PAT.
As always, we'd love to hear any feedback. Please head here to share your thoughts.
new
AuraDB Virtual Dedicated Cloud
AuraDB Professional
AuraDB Free
Data Importer
Import from BigQuery and VDC support
BigQuery
We're pleased to announce that the Import service now supports BigQuery. You'll find the new option when adding a data source option under
Data services > Import
To get started, simply provide your Project ID and Dataset Name along with your Service Account Key JSON.
Virtual Dedicated Cloud (VDC) Support
We've also now enabled the import service for VDC instances, so import is now available across all Aura tiers.
Note: At present, instances with private connection enabled are not supported.
As always, we'd love to hear any feedback. Please head here to share your thoughts.
new
AuraDB Professional
AuraDB Free
Data Importer
Import
AuraDB Business Critical
New in Neo4j AuraDB: Direct Import from Cloud Data Warehouses
We’re making it easier to bring your data into Neo4j AuraDB! The Import Service now supports direct connections to Snowflake alongside existing CSV and relational database imports. BigQuery and Databricks support will be coming next.
A new AI-powered schema analysis helps infer primary and foreign keys—even when they’re not explicitly defined—automating graph model generation. This means faster, more seamless imports from your enterprise data ecosystem.
Get started today in the Aura Console under Import Service. Read more in the blog post.
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