> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nebuly.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Product changelog
> Product updates and announcements
## MCP Server (Beta)
The Nebuly MCP Server is now available in beta. It allows you to connect Claude and other MCP-compatible AI assistants directly to your Nebuly data, enabling natural language queries over your interactions, conversations, and analytics.
Get started at [MCP Server](/guides/mcp-server)
## Manual labels for interactions and conversations
You can now manually label interactions and conversations directly in the UI. These labels can then be exported.
Labels are project-specific, and interaction labels are separate from conversation labels, so the two do not share the same label assignments.
There are two ways to add, remove, or create labels.
The first is from the details view of an interaction or conversation: scroll down to **More properties**, click **Add label**, then type to search for an existing label or create a new one.
The second is from the table view: select one or more interactions or conversations using the checkboxes, then click **Add label** and follow the same flow to add, remove, or create labels in bulk.
## Conversation health
We introduced a new entity to track agent performance at the conversation level. Built on top of the existing error rate metric, Conversation Health classifies each conversation into one of three statuses:
* Successful — No errors detected during the conversation
* Problematic — One or more errors occurred within the conversation
* Abandoned — The user dropped off following an agent error
Conversation Health is now available as a filter across the entire platform and as a group-by dimension in all charts.
## Interaction events on homepage charts
You can now view interaction events directly on homepage charts, just like in reports charts.
This includes events already available in your project, and events can be added or removed directly from the chart.
When you click a datapoint in the chart, a context menu opens with quick actions, including **Add event** or **Remove event**.
Selecting **Add event** opens a modal form where you can enter the event name and date.
## Chart Targets
You can now overlay goal lines on line charts using the new **Target** section in the chart sidebar. Up to 5 targets can be added simultaneously, each displayed with a distinct color matching its line on the chart.
Two target types are supported:
* **Growth target** — projects a trend line starting from a chosen date, based on a growth rate (absolute or percentage) applied per a configurable time granularity (minute / hour / day / week / month / year).
* **Fixed target** — draws a constant horizontal reference line at a specified value across the entire chart.
To add a target, open the sidebar and click the **Add target** button in the Target section. A popover will appear where you can configure the target type and its parameters.
For growth targets, choose a start date using the calendar picker, set the growth rate, the desired granularity, and select whether it is absolute or percentage-based.
For fixed targets, simply enter the constant value to display as a horizontal reference line.
## Group by time in table charts
You can now aggregate **table data** by **time** granularity, enabling time-series data to be visualized in tabular format. This allows you to track how multiple variables change over time across different time intervals:
- Month-over-month
- Week-over-week
- Day-over-day
To get started, navigate to your table settings and select the desired time granularity from the aggregation options.
## Input and Output Token Variables
You can now track input and output token consumption directly in the report section and interaction table. Add the "Input Token" and "Output Token" variables to your charts to monitor LLM trace data.
Note: these values come from what's passed through the Interaction API, so if they aren't provided there, they'll appear as 0.
## Output Length Metric to Chart Variables
A new chart variable is now available to measure output length in characters, allowing users to track how verbose model responses are over time.
## New Failure Category: Thumbs Down
A new failure category has been added that automatically groups all interactions receiving a thumbs down into a dedicated category for easier analysis and reporting.
## Custom Variables for Analytics and Table Charts
Users can now define custom variables when creating or editing a chart. Custom variables support mathematical formulas, allowing customers to express calculations that were not possible with built-in variables. Formulas can reference both built-in and custom variables, and filters can be applied as usual, enabling businesses to track their metrics that matter most to their usage.
## AI Summary available in multiple Languages
AI summaries can be generated in different languages now.
In order to change the language you need to open the project settings and click on “Summary Language”.
The button will open a dialog where users can chose the language to be used for all AI summaries in that project.
For example, after changing the language to Italian, the summary for a topic called “Weather in Venice” looks like this:
## Total Users variable
Added new variable Total Users which computes the total number of users without applying the time range / filters. The variable is usable inside custom variables, but only tags are available as breakdown. If this variable is used among another group ( ex. topic ) the visualized value will always be total users for that project.
## Nebuly-Langfuse native integration
We've launched a native integration that lets teams ingest Langfuse observability data directly into Nebuly for user analytics.
What's new:
* Full integration option: Nebuly automatically pulls traces from your Langfuse account daily (interval configurable). API keys are securely stored in encrypted vaults.
* Local integration option: Use open-source Python scripts to extract and transform data in your own infrastructure. Full control over data residency and enrichment.
* Seamless data flow: No changes to your existing Langfuse instrumentation. Traces flow automatically to Nebuly for user intent, sentiment, and adoption analysis.
* Tag-based segmentation: Use Langfuse tags to add user context (segment, geography, cohort, role). Analyze adoption by any dimension that matters.
Get started at [Langfuse integration](/integrations/langfuse)