> For the complete documentation index, see [llms.txt](https://docs.carto.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.carto.com/whats-new.md). # What's new {% hint style="success" %} This page will always reflect the latest changes in the CARTO platform. Older release notes will be archived and are available in the left menu and the search bar. {% endhint %} {% updates %} {% update date="2026-07-06" tags="new,builder" %} ## More export formats and controls in Builder Data exports in Builder are now more flexible and more governed. Beyond CSV, you can export to **GeoJSON**, **Shapefile**, **GeoParquet** and **KML**, so you can take a filtered dataset straight into the tools you already use without any manual conversion. As an Editor, you stay in control of what leaves your map: choose which sources are exportable, whitelist the columns that can be exported so sensitive attributes stay in your warehouse, and set the formats available per source. Every export honors the current map state, so filters, SQL parameters and the viewport are all respected. Learn more in our [Exporting data documentation](/carto-user-manual/maps/exporting-data.md).
{% endupdate %} {% update date="2026-07-03" tags="new,workspace" %} ## Organize your maps and workflows with projects and folders You can now organize your maps and workflows into **projects and folders** instead of one flat list. Group related work together, nest folders as deep as you need, and move or create maps and workflows right where they belong. Projects keep your Workspace tidy and make collaboration easier: share a project or folder once and everything inside inherits those access permissions. You set permissions in one place instead of doing it asset by asset. When an asset belongs in more than one project, add a shortcut instead of moving it. Learn more in the [Projects](/carto-user-manual/projects.md) documentation.
{% endupdate %} {% update date="2026-07-03" tags="new,platform" %} ## More flexible sharing for maps and workflows Sharing maps and workflows is now more flexible. Share assets with specific people or groups, with your whole organization, or publicly, and switch between those modes in a single click. Switching keeps the people and groups you already added with the same access permissions, so you never set anything up twice. Projects and folders share the same flexiblity: share once, and every asset inside a project or folder inherits that access. Learn more in the [map sharing](/carto-user-manual/maps/sharing-and-collaboration.md) and [workflow sharing](/carto-user-manual/workflows/sharing-workflows.md) documentation.
{% endupdate %} {% update date="2026-07-02" tags="builder,improvement" %} ## Single-select mode for text parameters Text parameters in Builder can now be set to single value instead of always allowing multiple selections. Viewers pick exactly one option from a radio list, and you decide which value is selected by default. Single-select fits any map that should answer for one thing at a time: a single product line, region, store, audience segment, or scenario in a what-if comparison. The viewer picks one option and the map returns one clear, correct result. Learn more in the [Text parameter](/carto-user-manual/maps/sql-parameters/text-parameter.md) documentation.
{% endupdate %} {% update date="2026-07-01" tags="builder,improvement" %} ## Pie and Time Series widgets now sync with your layer colors Pie and Time Series widgets in Builder can now inherit the colors of the layer they visualize. When a widget uses the same column your layer is styled by, it automatically picks up the layer's category colors, so the widget, the map, and the legend all match, with no extra setup. A category shown in purple on the map is purple in the widget too. Colors are now stable within a session as well: even without a layer match, each category keeps its color as you zoom, pan, and filter, instead of shifting with the data order. This applies to the Pie widget and to the Time Series widget with Split by. Learn more in the [Pie widget](/carto-user-manual/maps/widgets/pie-widget.md) and [Time Series widget](/carto-user-manual/maps/widgets/time-series-widget.md) documentation.
{% endupdate %} {% update date="2026-06-29" tags="new,builder" %} ## Labels for polygons and lines in Builder You can now add text labels directly to polygon and line layers, not just points. Turn labels on from the **Labels** section of the layer panel and choose the column to display. CARTO places each label automatically, at the center of every polygon and along the middle of every line. For lines, you can also pick a unique ID column so a feature that crosses several tiles, like a long road, shows a single label instead of one per tile. Learn more in our [Layers documentation](/carto-user-manual/maps/layers.md#labels).
{% endupdate %} {% update date="2026-06-29" tags="new,platform" %} ## The latest AI models are now in CARTO CARTO AI now offers the latest generation of models across every supported provider. The CARTO-managed set adds `claude-opus-4.8`, our most capable model for the hardest geospatial reasoning, and `gemini-3.5-flash` for fast, high-volume interactions. If you bring your own provider, the newest models are available too: the GPT-5.5 and GPT-5.4 families on OpenAI, Azure, Snowflake and Databricks, `claude-opus-4.8` on Anthropic, Bedrock, Vertex AI, Snowflake and Databricks, `gemini-3.5-flash` on Vertex AI, Google AI Studio and Databricks, and xAI Grok 4.3 and 4.20 on Oracle. Learn more in the [CARTO AI settings](/carto-user-manual/settings/carto-ai.md) documentation.
{% endupdate %} {% update date="2026-06-25" tags="new,builder" %} ## Organize your layers into groups in Builder You can now organize the layers in your map into named, collapsible **groups**. Related layers fold into tidy sections in the layer panel, so a map with a long list of layers stays easy to read and work with. Group layers however makes sense for your map, collapse the ones you're not using, and turn a whole group's visibility on or off in one click. Learn more in our [Layers documentation](/carto-user-manual/maps/layers.md#layer-groups).
{% endupdate %} {% update date="2026-06-12" tags="improvement,ai-agents" %} ## AI Agents are more visible and accessible in your maps The AI Agent in your published maps now has a more prominent place in the interface. Instead of a button users had to find and click, the Agent is immediately visible alongside the map when it loads — making it easier for your users to start a conversation and get answers right away. Users can also expand the Agent for a more focused conversation when they need it. Learn more about [sharing your AI Agent](/carto-user-manual/ai-agents/sharing-your-agent.md) and [creating AI Agents](/carto-user-manual/ai-agents.md) in our documentation.
{% endupdate %} {% update date="2026-06-05" tags="new,platform" %} ## Bring any custom AI provider, proxy or gateway to CARTO CARTO already supports nine AI providers out of the box — including Google Vertex AI, OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Snowflake, Databricks, and Oracle. Now you can connect any OpenAI-compatible endpoint as well, whether that's an LLM gateway or your own self-hosted models. Connect one or more providers, and their models become available across CARTO's AI features — [AI Agents](/carto-user-manual/ai-agents.md), the [Agent Config Assistant](/carto-user-manual/ai-agents/agent-config-assistant.md), and the [AI Assistant in Data Observatory](/carto-user-manual/data-observatory/accessing-and-browsing-the-spatial-data-catalog.md). Learn more in the [CARTO AI settings](/carto-user-manual/settings/carto-ai.md) documentation.
{% endupdate %} {% update date="2026-06-03" tags="new,ai-agents" %} ## Full visibility into your AI Agent's behavior Get full visibility into your AI Agent's actions with new **tool traceability**. You can now see which tools were executed, the parameters used, and their outputs, all directly in the conversation. Inspect the SQL generated by `execute_query`, the arguments passed to a [Workflows MCP Tool](/carto-user-manual/workflows/workflows-as-mcp-tools.md), or the inputs of any other tool the Agent ran, so you know exactly how the Agent reached its answer. Learn more about the [tools available for AI Agents](/carto-user-manual/ai-agents/working-with-tools.md) in our documentation.
{% endupdate %} {% update date="2026-05-27" tags="new,analytics-toolbox" %} ## New modules in the Analytics Toolbox for Oracle The [Analytics Toolbox for Oracle](/data-and-analysis/analytics-toolbox-for-oracle.md) (v1.1.0) expands its capabilities on Oracle Autonomous Database with three new modules. The new [`data`](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/data.md) module brings **data enrichment** to Oracle, with the [ENRICH\_POINTS](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/data.md#enrich_points), [ENRICH\_POLYGONS](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/data.md#enrich_polygons), [ENRICH\_POLYGONS\_WEIGHTED](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/data.md#enrich_polygons_weighted) and [ENRICH\_GRID](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/data.md#enrich_grid) procedures (plus their `_RAW` variants), so you can augment your spatial data with variables from other datasets directly in SQL. This release also adds the [`h3`](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/h3.md) and [`quadbin`](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/quadbin.md) **spatial indexing** modules. The `h3` module brings the full set of H3 functions to Oracle, covering index conversion ([H3\_FROMGEOGPOINT](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/h3.md#h3_fromgeogpoint), [H3\_BOUNDARY](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/h3.md#h3_boundary), [H3\_CENTER](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/h3.md#h3_center)), hierarchy traversal ([H3\_TOPARENT](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/h3.md#h3_toparent) / [H3\_TOCHILDREN](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/h3.md#h3_tochildren)), neighborhood traversal ([H3\_KRING](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/h3.md#h3_kring)) and polygon-to-grid conversion ([H3\_POLYFILL](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference/h3.md#h3_polyfill)), so you can index, aggregate and analyze your data on hexagonal grids natively in Oracle. The `quadbin` module provides the equivalent set of functions for the Quadbin grid. Learn more in the [Analytics Toolbox for Oracle release notes](/data-and-analysis/analytics-toolbox-for-oracle/release-notes.md) and the [SQL reference](/data-and-analysis/analytics-toolbox-for-oracle/sql-reference.md). {% endupdate %} {% update date="2026-05-27" tags="new,analytics-toolbox" %} ## New capabilities in the Analytics Toolbox for Databricks The [Analytics Toolbox for Databricks](/data-and-analysis/analytics-toolbox-for-databricks.md) (v2.4.0) extends its [`statistics`](/data-and-analysis/analytics-toolbox-for-databricks/reference/statistics.md) module with a new [HOTSPOT\_ANALYSIS](/data-and-analysis/analytics-toolbox-for-databricks/reference/statistics.md#hotspot_analysis) procedure. It locates hotspot areas by combining several variables' [Getis-Ord Gi\*](/data-and-analysis/analytics-toolbox-for-databricks/reference/statistics.md#getis_ord_h3) statistics using Stouffer's method, and works on either H3 or Quadbin grids. The Analytics Toolbox for Databricks also includes a [`data`](/data-and-analysis/analytics-toolbox-for-databricks/reference/data.md) module for **data enrichment**, with the [ENRICH\_POINTS](/data-and-analysis/analytics-toolbox-for-databricks/reference/data.md#enrich_points), [ENRICH\_POLYGONS](/data-and-analysis/analytics-toolbox-for-databricks/reference/data.md#enrich_polygons), [ENRICH\_POLYGONS\_WEIGHTED](/data-and-analysis/analytics-toolbox-for-databricks/reference/data.md#enrich_polygons_weighted) and [ENRICH\_GRID](/data-and-analysis/analytics-toolbox-for-databricks/reference/data.md#enrich_grid) procedures (plus their `_RAW` variants), so you can augment your spatial data with variables from other datasets directly in SQL. Learn more in the [Analytics Toolbox for Databricks release notes](/data-and-analysis/analytics-toolbox-for-databricks/release-notes.md) and the [SQL reference](/data-and-analysis/analytics-toolbox-for-databricks/reference.md). {% endupdate %} {% update date="2026-05-26" tags="new,workflows" %} ## Version history in Workflows Workflows now keep a complete **version history**. CARTO automatically captures versions as you work, and you can also save named versions to mark important milestones. Each time you enable or update an execution method — a schedule, an API endpoint, an MCP Tool, or Viewer mode — that snapshot is recorded and marked as the **published version** for that method, so consumers keep running against a stable state while you keep editing. From the Version History dialog you can browse, search, and filter past versions, preview each one on the canvas, restore the workflow to an earlier state, or duplicate a new workflow from any historical version. Learn more in our [Version history documentation](/carto-user-manual/workflows/version-history.md). {% embed url="" %} {% endupdate %} {% update date="2026-05-26" tags="new,workspace" %} ## Usage attribution by map and workflow Admins can now see which specific **maps and workflows** are consuming their [Usage Quota](/carto-user-manual/settings/understanding-your-organization-quotas.md). The [Activity Data](/carto-user-manual/settings/activity-data.md) export now includes `map_id` and `workflow_id` columns in the API Usage table, making it easy to understand where your Usage Quota is going, identify high-cost maps and workflows, and tie consumption back to specific teams or projects. Learn more in our [Activity Data reference](/carto-user-manual/settings/activity-data/activity-data-reference.md#api-usage).
{% endupdate %} {% update date="2026-05-20" tags="new,workspace" %} ## Wildcard patterns in API Access Token grants API Access Tokens now accept **wildcard patterns** in the **Table, Tileset, Raster source or Pattern** grant. Instead of listing resources one by one, you can use `*` to match multiple resources at once, for example `carto.shared.*` to cover everything under `carto.shared` or `carto.shared.CARTO_*` to cover only resources that share a naming convention. Patterns also match resources created after the token was issued, so you no longer need to re-issue tokens when new tables land. Learn more in our [API Access Tokens documentation](/carto-user-manual/developers/managing-credentials/api-access-tokens.md#wildcard-patterns).
{% endupdate %} {% update date="2026-05-21" tags="new,workspace" %} ## Granular controls for CARTO AI features Organization Admins can now control CARTO AI at the feature level from **Settings > CARTO AI**. In addition to the organization-wide **Enable CARTO AI** toggle, each individual AI feature has its own switch and its own default model selector. The granular controls currently cover **AI Agents in Builder maps** and the new **AI Assistant in Data Observatory**, with more features to follow. The per-feature default model overrides the organization-wide default for that specific feature, so different capabilities can run on different models. Newly introduced features are disabled by default, so Admins need to enable them explicitly before they become available to users. Learn more in our [CARTO AI settings documentation](/carto-user-manual/settings/carto-ai.md#ai-features).
{% endupdate %} {% update date="2026-05-21" tags="new,data-observatory" %} ## AI Assistant in Data Observatory Finding the right dataset in the Spatial Data Catalog now takes a sentence instead of a series of filter clicks. The new **AI Assistant in Data Observatory** lets you describe what you need in natural language and applies the matching filters to the catalog for you. Open the assistant with the **Ask AI** button at the top of the Data Observatory catalog, ask something like *"What datasets would help analyze consumer purchasing patterns in the UK?"*, and the sidebar will filter the catalog down to the datasets that fit. You can keep iterating in the same conversation to refine the results or change direction, and manual filters remain available at any time. Learn more in our [Browsing the Spatial Data Catalog documentation](/carto-user-manual/data-observatory/accessing-and-browsing-the-spatial-data-catalog.md#ai-assistant-in-data-observatory).
{% endupdate %} {% update date="2026-05-19" tags="new,builder" %} ## Custom SQL aggregation expressions in Builder Spatial index layers (H3, Quadbin) and aggregated-by-geometry layers in Builder now support **custom SQL aggregation expressions** for styling and interactions. Apart from the predefined `avg`, `sum`, `min`, `max` set, you can write any aggregation expression that runs on your data warehouse. This is useful for derived metrics like rates, ratios and weighted averages. ```sql SUM(female) / NULLIF(SUM(population), 0) ``` Learn more in our [H3 layer documentation](/carto-user-manual/maps/layers/h3.md#custom-aggregation-expressions).
{% endupdate %} {% update date="2026-05-18" tags="improvement,builder" %} ## Scale point radius with zoom in Builder Point layers in Builder now support a new **Scale with zoom level** option for radius. Instead of a fixed pixel size, points grow and shrink with the map zoom, staying visually proportional to context. A **Min / Max bounds** clamp keeps points readable at extreme zooms, and the option applies to both simple points and custom markers. Learn more in our [Point layer documentation](/carto-user-manual/maps/layers/point.md#scale-with-zoom-level).
{% endupdate %} {% update date="2026-05-14" tags="new,carto-for-agents" %} ## CARTO for Agents: bring the platform into your AI workflows AI agents are quickly becoming part of how teams build with spatial data. We're launching **CARTO for Agents**, three new capabilities that put the entire CARTO platform within reach of the AI agents you already use: authoring Builder maps and Workflows, managing connections, browsing the Data Observatory, running imports and exports, and anything else you do day to day. * [**CARTO CLI**](/carto-for-agents/cli.md). A script-friendly command-line for the platform that humans run in a terminal and agents call as a tool. The latest release adds first-class Builder map and Workflow authoring from JSON bundles. * [**CARTO MCP Server**](/carto-for-agents/mcp-server.md). A hosted [Model Context Protocol](https://modelcontextprotocol.io/) server that exposes built-in CARTO tools, plus any workflow you publish, to web and desktop AI clients like Claude.ai, ChatGPT, and Gemini. * [**CARTO Agent Skills**](/carto-for-agents/agent-skills.md). A public catalog of skill playbooks at [`CartoDB/agent-skills`](https://github.com/CartoDB/agent-skills) that teaches coding agents (Claude Code, Codex, Cursor, Gemini CLI) how to drive CARTO without re-discovering the API every session. The three pieces work together depending on the scenario. A chat agent in Claude.ai, ChatGPT, or Gemini connects through the MCP Server. A coding agent in Claude Code, Cursor, or Codex combines the CLI with the Agent Skills, which teach it the right flags and patterns for each task. Learn more in our [CARTO for Agents documentation](/carto-for-agents/carto-for-agents.md).
{% endupdate %} {% update date="2026-05-14" tags="new,workspace" %} ## CARTO AI Analytics for organization Admins Organization Admins now have a dedicated **Analytics** tab in Settings > CARTO AI to see how CARTO AI is being used across the organization. The tab is split into three views: **All activity** with active users and consumption of your AI and agentic usage quotas, **CARTO Agents** with activity from AI Agents created in Builder, and **External Agents** with activity driven from external clients consuming CARTO through the MCP Server and the CARTO CLI. Learn more in our [CARTO AI Analytics documentation](/carto-user-manual/settings/carto-ai/carto-ai-analytics.md).
{% endupdate %} {% update date="2026-05-14" tags="new,workspace" %} ## Quota controls for organization Admins Organization Admins can now cap how much of each quota a specific user or group is allowed to consume, directly from **Settings > Quotas & Activity > Quota Limits**. Limits can be set for the Usage quota, the Location Data Services quota, and the AI quota, and they are hard limits: once a user or group reaches the limit, they are blocked from consuming more of that quota until an Admin raises or removes the limit. Learn more in our [Managing quotas documentation](/carto-user-manual/settings/managing-quotas.md).
{% endupdate %} {% update date="2026-05-08" tags="new,builder" %} ## Google Photorealistic 3D Tiles in Builder Bring your maps to life with our new CARTO Builder basemap option: **Google Photorealistic 3D Tiles**. This high-fidelity representation of the world built from aerial and satellite imagery lets you explore your data on top of detailed 3D buildings and terrain. Your data will automatically cover the surface of buildings and other 3D terrain features, allowing you to understand the data in real-world context. This can be incredibly useful for urban planning, real estate or insurance use cases. Google Photorealistic 3D Tiles span over **2,500 cities across 49 countries.** See Google's [Photorealistic 3D Tiles coverage](https://developers.google.com/maps/documentation/tile/3d-tiles-overview) for the latest list of supported areas. Ready to try it? Learn more in our [Basemaps documentation](/carto-user-manual/maps/basemaps.md).
{% endupdate %} {% update date="2026-04-20" tags="improvement,builder" %} ## Reorder categorical legend entries in Builder Categorical and ordinal legend entries can now be reordered in Builder to improve map readability. Categories can be sorted by frequency or alphabetically, in ascending or descending order.\ \ Reordering only affects the legend’s reading order, colors remain fixed to each category, so the map visualization does not change. Learn more in our [Legend documentation](/carto-user-manual/maps/legend.md).
{% endupdate %} {% update date="2026-04-15" tags="improvement,builder" %} ## Click interactions now show every overlapping feature Click interactions in Builder now paginate across every feature at clicked location, in both pop-up and info panel modes. When overlapping polygons, overlapping lines, or multiple records sharing the same geometry sit at the same spot, you can step through all of them within a layer instead of only seeing the top one.\ \ Previously, clicking a stacked location surfaced only a single feature and the rest were unreachable without zooming in or filtering the data. The new prev/next controls let you browse all of them, with the map highlighting updating as you paginate. Learn more in our [Click interactions documentation](/carto-user-manual/maps/interactions.md#click-type-interactions).
{% endupdate %} {% update date="2026-03-30" tags="new,carto-for-developers" %} ## Build AI-first spatial apps with CARTO Agentic Tools for developers Building AI-powered apps with geospatial capabilities just got a lot easier. We're releasing `@carto/agentic-deckgl`, an open-source TypeScript library built on [CARTO + deck.gl](https://carto.com/blog/modern-spatial-app-development-carto/) that lets any AI Agent create and style map layers, run spatial analytics, and interact with the map through natural language — with one npm install. It's framework-agnostic, includes Zod-validated tool definitions, a geospatial system prompt builder, and SDK converters for major AI frameworks. The library gives AI Agents full control over the map experience: creating and styling vector tile, H3, GeoJSON, and raster layers from any CARTO data source; navigating the globe with smooth transitions; switching basemaps; placing and managing markers; applying spatial filters from user-drawn areas or analytical workflows; and managing widgets, visual effects, and layer ordering. Learn more about this new library in our [product announcement](https://carto.com/blog/carto-agentic-tools-for-developers/).
{% endupdate %} {% update date="2026-03-30" tags="new,builder" %} ## Create AI Agents through conversation with the new Configuration Assistant Creating [AI Agents](/carto-user-manual/ai-agents.md) just got significantly easier. The new **Agent Configuration Assistant** lets you design, set up, and iterate on your agents using natural language. Instead of manually defining instructions, selecting tools, and configuring capabilities step by step, you can now **simply describe what your agent should do**. The Assistant generates a complete configuration for you — including the use case, structured instructions, model selection, tool setup, capabilities, and a tailored introduction message. The Assistant is context-aware, meaning its recommendations are grounded in your actual map. It understands your datasets, layers, widgets, and available tools, helping you create more accurate and relevant agents from the start. You can refine any part of the configuration through conversation or combine it with manual edits for full control. [Learn more about the Agent Config Assistant in our documentation.](/carto-user-manual/ai-agents/agent-config-assistant.md)
{% endupdate %} {% update date="2026-03-27" tags="new,workflows" %} ## Viewer Mode for Workflows Workflows can now be shared with **Viewer Mode**, allowing editors to publish interactive workflows that other users can run without editing the canvas. This opens up new possibilities for delivering analytical tools and reports to stakeholders and business users. * **Viewer parameters**: Editors define which variables are exposed to viewers as configurable parameters, with custom display names and helper text. Supported types include Number, String, and the new **Geo** type, which lets viewers draw geographic features on a map. * **Viewer Result Output**: A new component that defines which node's output is displayed to viewers, giving editors full control over what results are visible. * **Viewer settings**: Granular controls over the viewer experience, including toggling the canvas visibility, SQL preview, data export, result caching, and more. [Learn more about Viewer Mode in our documentation](/carto-user-manual/workflows/viewer-mode.md). {% embed url="" %} {% endupdate %} {% update date="2026-03-19" tags="improvement,platform" %} ## Claude 4.6 models now available for AI Agents We've expanded the AI models available for AI Agents with the latest generation of Anthropic models. * **More CARTO-managed models:** Claude Opus 4.6 and Claude Sonnet 4.6 are now available out of the box with no additional configuration. * **Broader bring-your-own-model support:** You can now use Claude Opus 4.6 and Claude Sonnet 4.6 through any of our supported providers, including Vertex AI, AWS Bedrock, Azure OpenAI, Anthropic, Snowflake Cortex, and Databricks Serving Model. The 4.6 models deliver better performance across reasoning, tool usage, and complex geospatial workflows. Configure your models in **Settings > CARTO A**I — see the [CARTO AI documentation](/carto-user-manual/settings/carto-ai.md) for the full list of supported models and providers.
{% endupdate %} {% update date="2026-02-17" tags="improvement,platform" %} ## Additional AI models to power your Agents We've expanded the AI models available for AI Agents with more advanced models from Anthropic, Google, and OpenAI. * **More CARTO-managed models:** Claude Opus 4.5, Claude Sonnet 4.5, Gemini 3 Pro, and Gemini 3 Flash are now available out of the box with no additional configuration. * **Broader bring-your-own-model support:** You can now use Gemini 3, Claude Opus 4.5, and GPT-5.2 through any of our supported providers, including Vertex AI, Google AI Studio, Snowflake Cortex, Databricks Serving Model, AWS Bedrock, Azure OpenAI, OpenAI, and Anthropic. We recommend upgrading to the newest models available — you'll see a significant improvement in agent performance, reasoning, and tool usage. Configure your models in **Settings > CARTO AI** — see the [CARTO AI documentation](/carto-user-manual/settings/carto-ai.md) for the full list of supported models and providers.
{% endupdate %} {% update date="2026-02-09" tags="improvement,builder" %} ## AI Agents now create interactive charts [AI Agents](/carto-user-manual/ai-agents.md) can now generate and render interactive charts directly inside the conversation. Users can ask for data visualizations and see charts rendered inline — no need to leave the chat. Charts expand the way AI Agents can communicate insights, complementing map layers with statistical visualizations like bar charts for comparisons, line charts for trends, or histograms for distributions. Combined with other tools, AI Agents can query your data, analyze it, and present findings in the format that best fits the question. [Learn more about AI Agent tools in our documentation](/carto-user-manual/ai-agents/working-with-tools.md).
{% endupdate %} {% update date="2026-01-29" tags="new,platform" %} ## Introducing our new Command-line Interface We're excited to announce the [CARTO CLI](https://carto.com/blog/carto-cli-automateed-carto-management-build-for-ai-agents), which brings command line power to your CARTO organization. Manage Maps, Workflows, connections and credentials; transfer assets between organizations, and query your organization's activity data; all from the terminal! The CLI supports structured JSON output, non-interactive execution, and headless authentication, making it a natural interface to script and automate. To get started, head over to our [CARTO CLI documentation](/carto-for-agents/cli.md).
{% endupdate %} {% update date="2026-01-29" tags="improvement,workspace" %} ## Tracking activity data from public maps Public maps are the way to distribute geospatial data and insights across wider audiences outside your organization. From coverage maps to deforestation storytelling, many geospatial dashboards are making an impact on public websites thanks to CARTO. Starting now, CARTO administrators can measure that impact, and answer questions like: * **How many times my public maps have been viewed** * **Which are my most active public maps** * **How many exports from public maps last month...** We've automatically added to our [Activity Data](/carto-user-manual/settings/activity-data.md) the data coming from your public maps thanks to a robust, secure, event pipeline that can track millions of events coming from unauthenticated users. To get started, simply [export your Activity Data](/carto-user-manual/settings/activity-data.md) or [integrate it via API](/carto-user-manual/settings/activity-data.md#access-via-api).
{% endupdate %} {% update date="2026-01-20" tags="new,platform" %} ## New AI provider and LLM integrations to power your AI Agents CARTO now supports seven additional AI providers, expanding the AI and LLM integrations available to power **AI Agents**. Previously limited to **OpenAI** and **Google AI Studio**, you can now connect AI Agents to models hosted on your preferred cloud or data platform: * **Google Vertex AI**: Enterprise GCP deployments with service account authentication. * **Amazon Bedrock**: Claude models through AWS infrastructure. * **Snowflake Cortex**: AI models within your Snowflake environment. * **Databricks Model Serving**: Models through Databricks endpoints. * **Oracle Generative AI**: Access to models via OCI. * **Anthropic**: Direct access to Claude models. * **Azure OpenAI Service**: OpenAI models through Azure. These new integrations allow AI Agents to run on your preferred cloud or data platform, leverage existing cloud contracts, meet data residency requirements, and access the latest large language models available from each provider. Configure providers in **Settings > CARTO AI**. See the [CARTO AI documentation](/carto-user-manual/settings/carto-ai.md) for setup instructions.
{% endupdate %} {% update date="2026-01-14" tags="improvement,builder" %} ## CARTO Basemap labels now stay on top of your layers When using CARTO Basemaps, labels (like city and street names) now automatically appear on top of your map layers instead of being hidden underneath them. This makes it easier to read your maps, especially when working with multiple overlapping layers. You can still turn labels off in the basemap settings if you prefer a cleaner look.
{% endupdate %} {% update date="2026-01-12" tags="new,workflows,analytics-toolbox" %} ## H3-based isochrones powered by TravelTime A new capability is now available for generating **H3-based isochrones** using [TravelTime](https://docs.traveltime.com/api/reference/h3), expanding how accessibility and travel-time analysis can be performed in CARTO. This release introduces a new endpoint in the **Location Data Services (LDS) API** that leverages TravelTime’s H3 isochrone support. In addition, corresponding functions are available in the **Analytics Toolbox** (for [BigQuery](/data-and-analysis/analytics-toolbox-for-bigquery/sql-reference/lds.md#create_h3_isolines), [Snowflake](/data-and-analysis/analytics-toolbox-for-snowflake/sql-reference/lds.md#create_h3_isolines), [Databricks](/data-and-analysis/analytics-toolbox-for-databricks/reference/lds.md#create_h3_isolines) and [Redshift](/data-and-analysis/analytics-toolbox-for-redshift/sql-reference/lds.md#create_h3_isolines)), along with a new [Create H3 Isolines](/carto-user-manual/workflows/components/spatial-constructors.md#create-h3-isolines) component in **Workflows**, enabling low-code and programmatic access to this functionality. Customers can now generate H3-indexed isochrones directly, with support for the same configuration options provided by the underlying TravelTime API, including **departure time** and **transport mode**. Using H3 as the output format simplifies downstream analysis, aggregation, and visualization, particularly for workflows that already rely on hexagonal indexing. {% embed url="" %} {% endupdate %} {% update date="2026-01-07" tags="new,platform" %} ## Full support for new Databricks Spatial SQL functions and data types A new [Databricks connection](/carto-user-manual/connections/databricks.md) type is now generally available across all CARTO accounts, delivering deeper and more modern support for Databricks as a data warehouse and compute platform. This integration adopts **Databricks SQL Warehouses** as the sole compute resource, providing a serverless, cloud-native experience without the need to manage traditional compute clusters. It also leverages Databricks’ [**native spatial capabilities**](https://www.databricks.com/blog/introducing-spatial-sql-databricks-80-functions-high-performance-geospatial-analytics?utm_source=chatgpt.com), including the GEOMETRY data type and Spatial SQL functions documented by Databricks, enabling efficient storage and processing of spatial data directly in SQL without external libraries. Connectivity options include **Personal Access Tokens (PAT), M2M, and U2M integrations**, offering flexibility in how authentication and access are managed. Builder and Workflows fully support Databricks tables with geometry types out of the box, including query sources, SQL parameters, Location Data Services, and Create Builder Map workflows — no additional data preparation is required to work with spatial columns. The [**Analytics Toolbox**](/data-and-analysis/analytics-toolbox-for-databricks.md) now installs directly into the Databricks Unity Catalog with no external dependencies, simplifying governance and deployment. Older Databricks connection types remain available for existing accounts that used them previously. This release represents a significant step in CARTO’s support for major cloud data warehouse providers and extends CARTO’s capabilities for spatial analytics on modern data platforms.
{% endupdate %} {% update date="2025-12-29" tags="new,builder" %} ## Track map changes over time with Version history We've introduced **Version history** in Builder, giving you the ability to track and manage different versions of your maps over time. CARTO automatically saves versions as you work, and you can also manually save named versions to mark important milestones. You can view the full history of changes, restore any previous version to undo unwanted changes, or duplicate from a historical version to create variations without affecting the current map. Version history works seamlessly with collaborative maps—all changes are tracked with the collaborator's name and timestamp, providing a complete audit trail. When you publish a map, the published version is marked with a badge so you always know which version is live. [Learn more in our documentation](/carto-user-manual/maps/version-history.md).
{% endupdate %} {% update date="2025-12-12" tags="new,workflows" %} ## **New components to run spatial analytics techniques on embeddings from Geospatial Foundation Models** We’re introducing the **Analytics on Embeddings** extension package for **CARTO Workflows**, a new set of components that bring high-dimensional vector embedding analytics into spatial workflows. This extension enables users to analyze, cluster, compare, and visualize embedding representations (whether derived from geospatial foundation models, satellite data, or other spatial sources) directly within their Workflows pipelines. Key capabilities in this package include: * [**Change Detection**](/carto-user-manual/workflows/components/embedding-analytics.md#change-detection): Quantifies temporal changes in embedding vectors to monitor dynamics over time. * [**Clustering**](/carto-user-manual/workflows/components/embedding-analytics.md#clustering): Groups locations based on similarity in embedding space, with optional dimensionality reduction to improve performance. * [**Similarity Search**](/carto-user-manual/workflows/components/embedding-analytics.md#similarity-search): Identifies regions with similar spatial or contextual characteristics relative to one or more reference locations. * [**Visualization**](/carto-user-manual/workflows/components/embedding-analytics.md#visualization): Converts high-dimensional embeddings into RGB colors for intuitive mapping and pattern discovery. These components work seamlessly with embedding vectors stored as table columns and support integration with the [**Geospatial Foundation Models**](/carto-user-manual/workflows/components/google-pdfm-embeddings.md) extension, enabling richer insights from learned representations without leaving the low-code Workflows environment. {% endupdate %} {% update date="2025-12-09" tags="improvement,workspace" %} ## Manage developer credentials from the asset management table Superadmin users can now view and manage all developer credentials in their organization, including **API Access Tokens**, **SPA OAuth Clients**, and **M2M OAuth Clients**. From the Asset Management table of the settings, Superadmins now can: * Find credentials by type, name and owner * Transfer credentials to another user (only available for API Access Tokens and SPA OAuth Clients) * Delete credentials This improvement simplifies team collaboration by allowing credentials to be transferred between users seamlessly, preventing disruptions if the credential owner is unavailable or leaves. For more information, see our section on the [Superadmin role](/carto-user-manual/settings/users-and-groups/managing-user-roles.md#superadmin).
{% endupdate %} {% update date="2025-11-18" tags="improvement,workspace" %} ## Unified access to Data Observatory subscriptions and improved subscription management With this release, we’re making it simpler and more consistent for users to access and work with data from their Data Observatory subscriptions. Access to data has now been fully unified to always be via your own data warehouse connections. Additionally we've also improved the way Admin users can manage the organization's Data Observatory subscriptions from the Settings section in the CARTO Workspace. * We’ve unified access to the data from Data Observatory subscriptions to always be rom the end-user data warehouse connections. As announced earlier this year, we have **deprecated the Data Observatory tab in Data Explorer, Builder, and Workflows**. This tab previously exposed subscriptions only through a small set of connections (i.e. CARTO Data Warehouse and BigQuery US multi-region). Since all subscriptions are now available directly via data warehouse connections, the tab has been removed to avoid confusion. * The [Data Observatory section](/carto-user-manual/settings/data-observatory.md) in Settings has been significantly improved. It now serves as the central place to manage your organization’s subscriptions, showing to which data warehouse each subscription has been transferred, and allowing users to request new transfers so the data is available directly in their data warehouses.
{% endupdate %} {% update date="2025-11-18" tags="improvement,workspace" %} ## Starring data assets for quicker access Users are now able to star items at any level in the Data Explorer, including connections, projects/databases/schemas, and the data tables themselves. Simply click on the star icon next to any item in the Data Explorer and then use the *Starred only* filter to show just your starred items. This is especially helpful to users that have connections or data assets that are recurrently used in their maps and workflows. No more browsing the data tree until you find what you need! Your starred items are now also easily accessible from the "Add data source" flow in CARTO Builder and from the data sources panel in CARTO Workflows. To learn more about starring items and the Data Explorer in general, check out our [documentation](/carto-user-manual/data-explorer.md).
{% endupdate %} {% update date="2025-11-07" tags="improvement,builder" %} ## Integrate CARTO maps anywhere with the new authenticated embeds and reactive map events Embedding maps from CARTO in other webpages and applications just became exponentially easier and more powerful thanks to two additions to our platform: * **New methods for seamless and secure private embedding:** We added two new strategies to embed private maps securely, without having to publish the map or forcing the users to login in a different tab or browser. Developers can also re-use existing authorization in their applications. [Learn more about private embedding strategies](/carto-user-manual/maps/sharing-and-collaboration/embedding-maps.md#private-embedding). * **Build bi-directional interactive experiences with our embedded events:** Embedded maps from CARTO now send `postMessage` events every time something changes in the map. This allows the parent application to react, creating bi-directional interactive experiences when combined with our embed URL parameters. [Learn more about embedding events](/carto-user-manual/maps/sharing-and-collaboration/embedding-maps.md#listening-to-events-from-embedded-maps). We're excited to see where you will embed your next CARTO map!
{% endupdate %} {% update date="2025-11-06" tags="new,builder" %} ## New map interaction tools for AI Agents AI Agents can now interact directly with your maps through two new tools: **Dynamic marker placement**: Ask the AI Agent to mark specific locations, and it will instantly place markers on your map. Simply provide an address, place name, or coordinates—the agent handles geocoding and placement automatically. **Spatial filtering by area**: The AI Agent can define custom areas of interest to filter your data dynamically. When an area is set, all map widgets and layers update automatically to show only data within that region. These tools enable your AI Agent to provide immediate visual context and perform focused analysis on specific geographic areas without manual configuration. [Learn more in our documentation](/carto-user-manual/ai-agents/working-with-tools.md#map-tools).
{% endupdate %} {% update date="2025-10-08" tags="new,builder" %} ## Introducing Agentic GIS in CARTO: making spatial insights available to everyone We are incredibly excited to announce new features that bring enterprise-grade geospatial agentic experiences to CARTO. * **Introducing AI Agents in Builder:** [CARTO AI Agents](/carto-user-manual/ai-agents.md) (now in General Availability) provide a conversational interface in your maps where your end users can get instant and actionable geospatial insights through natural language. * **AI Agents can now query sources, generate layers and more:** We've added a ton of exciting capabilities that allow agents to reason and perform geospatial analysis autonomously. * **Integrate Workflows as tools for your AI Agents:** From building operational dashboards to running complex analyses, your AI Agent can be supercharged with your own custom workflows [enabled as MCP tools](/carto-user-manual/workflows/workflows-as-mcp-tools.md). * **Evolved experience to tailor your Agent:** You can now reference tools, sources, and other context available in the map when customizing your agent. * **Use your own AI models:** [Configure your own AI models](/carto-user-manual/settings/carto-ai.md) and maintain total control over the AI technology used. Supported providers include Google Gemini and Open AI, with others coming soon. With CARTO you can now create and share access to powerful geospatial AI Agents tailored to your specific needs. Combine your custom prompt instructions with CARTO's built-in geospatial intelligence and your own workflows, and **build trustworthy AI solutions that make complex geospatial analysis accessible to any user within your organization**. Get started today by [enabling CARTO AI in your organization](/carto-user-manual/settings/carto-ai.md). And learn more about Agentic GIS in our [announcement blog post](https://carto.com/blog/agentic-gis-bringing-ai-driven-spatial-analysis-to-everyone)!
{% endupdate %} {% update date="2025-10-08" tags="new,workflows" %} ## Turn your AI Agents into geospatial experts with CARTO MCP Server CARTO now supports the **Model Context Protocol (MCP)**, a standard that enables AI Agents to interact with external tools and data sources. With the new **CARTO MCP Server**, organizations can now expose their own geospatial [Workflows as MCP Tools](/carto-user-manual/workflows/workflows-as-mcp-tools.md) that any MCP-compliant agent can use. This release allows GIS teams to design custom workflows in CARTO—defining inputs, outputs, and logic specific to their spatial problems—and make them available to AI Agents through the MCP Server. Each tool includes detailed metadata following the MCP specification, ensuring interoperability across agentic AI environments. By combining Workflows and the MCP Server, organizations can empower AI Agents to perform advanced spatial analysis, automate geospatial decision-making, and connect AI-driven applications to their cloud data infrastructure. {% embed url="" %} {% endupdate %} {% update date="2025-09-30" tags="new,workflows" %} ## Territory Balancing and Location Allocation components in Workflows CARTO's new [Territory Planning Extension Package](/carto-user-manual/workflows/components/territory-planning.md) for Workflows has been built to power location allocation and territory balancing directly within CARTO and your data warehouse, this extension helps analysts and planners create fair, efficient, and data-driven territory strategies. * [Territory Balancing](https://docs.carto.com/carto-user-manual/workflows/components/territory-planning#territory-balancing) – Divide an area into continuous, optimized territories that are balanced according to a chosen metric (e.g. consumer demand or other business KPIs), while keeping each territory internally cohesive. Learn more about this new capability following this [tutorial](https://academy.carto.com/creating-workflows/step-by-step-tutorials/optimizing-workload-distribution-through-territory-balancing). * [Location Allocation](https://docs.carto.com/carto-user-manual/workflows/components/territory-planning#location-allocation) – Find the optimal locations to open facilities (stores, warehouses, service hubs) and efficiently assign demand points (retail stores, populated regions) to them, minimizing costs or maximizing coverage. Take a look at this [tutorial](https://academy.carto.com/creating-workflows/step-by-step-tutorials/transforming-telco-network-management-decisions-with-location-allocation) to learn more!. This extension package is currently available for Google BigQuery and Snowflake.
{% endupdate %} {% update date="2025-08-28" tags="improvement,workspace" %} ## Improved flow for deleting connections We've added a new option for users deleting connections so that all maps, workflows, tokens, etc. using the connection are updated to use another connection. Previously, users had to either update each asset individually or delete the connection along with all assets using it. This new option vastly facilitates migrating from one connection to another, which is a common case when upgrading authentication types (changing from username/password to key pair or OAuth, for example). Alternatively, users can still choose to delete the connection along with all assets that use it. For more information, see our article on [deleting connections](/carto-user-manual/connections/deleting-a-connection.md).
{% endupdate %} {% update date="2025-08-28" tags="improvement,workspace" %} ## Improvements to importing geospatial data into cloud data warehouses Recent updates have enhanced the experience of importing geospatial data into cloud data warehouses, with improvements in performance, scalability, and raster support. Import operations now run faster thanks to a new, optimized process. The maximum supported file size has also been raised from **1GB to 5GB**, addressing a very frequent need when working with large geospatial datasets. Raster-processing capabilities have been extended in **BigQuery and Snowflake**, supporting the import of non-COG GeoTIFF rasters into warehouse tables following the [Raquet specification](https://github.com/CartoDB/raquet?utm_source=chatgpt.com). This removes the strict preparation steps previously required for Cloud Optimized GeoTIFFs, making the process considerably simpler. Combined with the higher size limit, these updates provide a more efficient way for customers to bring raster data into their cloud environment. {% embed url="" %} {% endupdate %} {% update date="2025-08-27" tags="improvement,builder" %} ## **Drag and drop reordering of properties in Table and Interactions** We've introduced the ability to reorder the properties shown in the Table widget and Tooltip via simple drag and drop functionality. Until now, users could configure which properties to show, but changing the order they are presented often meant clearing the setup and starting over again. With this enhancement, it’s easier than ever to customize how data is displayed, improving readability and enabling tailored views for different audiences.
{% endupdate %} {% update date="2025-08-12" tags="new,workflows" %} ## **Control Components in Workflows: Conditional Split & Success/Error Split** We’ve added two new [control components](/carto-user-manual/workflows/components.md#control) to CARTO Workflows that make it easier to control how your workflows execute and respond to different scenarios. * [**Conditional Split**](/carto-user-manual/workflows/components/control.md#conditional-split) – Direct your workflow into **If** and **Else** branches based on a condition you define. Build the condition with a simple UI (column + aggregation + operator + value) or use a custom SQL expression for more complex logic.\ Some usage examples: * “If the **count of underserved households** in a service area is greater than 500, trigger a fiber expansion workflow; otherwise plan for wireless coverage.” * “If the **average property value** in a high-risk flood zone is above $1M, apply the high-risk pricing model; otherwise, use the standard pricing model.” * [**Success/Error Split**](/carto-user-manual/workflows/components/control.md#success-error-split) – Branch execution depending on whether the previous step ran successfully or failed.\ Some usage examples: * “If network quality metrics fail to load, send an alert; otherwise continue with churn prediction.” * “If address geocoding fails, switch to a backup geocoder; otherwise proceed with claims analysis.” These components let you build workflows that adapt to your data, add robust error-handling, and reduce the need for manual monitoring — helping teams act faster on reliable insights. {% embed url="" %} {% endupdate %} {% update date="2025-08-05" tags="improvement,workspace" %} ## **Separation of working locations for improved data governance** We have introduced a clearer separation of datasets/schemas that CARTO creates and manages in connected data warehouses. This change improves data governance and prevents persistent objects from being stored alongside temporary workflow tables. **New locations per connection:** * **CARTO temp location** – stores only temporary tables created during workflow execution. * **CARTO Workspace location** – stores persistent objects related to workflows, such as API stored procedures and imported files. * **CARTO Extensions location** – stores Extension Package resources, including shared stored procedures and metadata. Only for BigQuery and Snowflake. **Additional notes:** * For connections shared requiring Viewer Credentials, `carto_temp_` and `carto_workspace_` are created per user. * The Extensions location is always shared across all users in a connection, ensuring consistent access to installed packages. * Default names can be overridden in the connection’s advanced options. * Locations are automatically created as needed (`CREATE IF NOT EXISTS`). This update applies to all supported warehouses. Find specific documentation on the *Advanced settings* section for each warehouse in the [Connections](/carto-user-manual/connections.md) section of the documentation.
{% endupdate %} {% update date="2025-07-29" tags="new,builder" %} ## Support for adding sources without associated layer in Builder Editor users can now add data sources to a Builder map **without displaying associated layers**. These sources can be used to power widgets, SQL parameters, and even be used by AI Agents to generate insights. This is especially useful when a dataset is needed for interactivity or calculations, but not for visualization. It helps keep your maps cleaner, more focused, and easier to maintain.
{% endupdate %} {% update date="2025-07-17" tags="new,workspace" %} ## Organization Governance Settings for Admins Admins can now set up custom governance policies through the new **Governance** section in Settings. These controls give you the tools to manage data access, sharing, and feature usage across your organization with precision. Control who can create new Data Warehouse connections with granular settings for providers and authentication methods. Manage connection sharing, disable the CARTO Data Warehouse, and fine-tune Builder features like Download PDF report, export viewport data, and more! To see all the new settings, check our section on [Organization Governance](/carto-user-manual/settings/organization-governance.md).
{% endupdate %} {% update date="2025-07-15" tags="improvement,builder" %} ## Support for Widgets linked to Raster sources in Builder You can now use Widgets with raster sources in Builder — just like you already can with vector sources. This improvement allows for richer exploration and analysis of raster sources stored in your data warehouse directly from the map. Use the Formula Widget to calculate metrics like tree coverage in your current view. Leverage Category and Pie Widgets to list distinct values in your raster layer, or use the Histogram Widget to explore data distributions such as precipitation. These widgets can also be used for filtering, letting you interactively refine what’s shown on the map and extract insights more effectively. [Learn more in our documentation](/carto-user-manual/maps/widgets.md).
{% endupdate %} {% update date="2025-07-08" tags="new,workspace" %} ## Audit all queries with the CARTO SQL audit trails When a map or workflow is opened, CARTO launches a set of SQL queries to your data warehouse to visualize your data and run your analysis. And from now on, each of those SQL queries will contain a **rich audit trail** in the form of SQL comments at the beginning or the end of the query. This audit information allows data warehouse administrators to monitor CARTO and answer questions such as: *How many queries did CARTO run in a period of time? Which workflows or maps have processed more data? What are some common performance or cost patterns?* To start using this information in your audits, check our [Auditing SQL queries documentation](/carto-user-manual/connections/auditing-sql-queries-from-carto.md).
{% endupdate %} {% update date="2025-06-30" tags="improvement,workflows" %} ## Collaborative edition in Workflows Workflows now supports [**collaborative editing**](/carto-user-manual/workflows/sharing-workflows.md#editor-collaboration) for teams. Editors can share workflows with their entire organization, SSO groups, or specific users to enable collaborative development. This feature eliminates the need to duplicate workflows for minor changes, ensuring teams work from a single, consistent source of truth. Asynchronous editing with a request/approval model reduces conflicts while supporting smooth, coordinated teamwork. Editor collaboration makes it easier for organizations to use Workflows at scale and promotes more frequent, effective use across teams. {% embed url="" %} {% endupdate %} {% update date="2025-06-25" tags="new,builder" %} ## Cross-filtering multiple data sources from map widgets You can now use a **single widget to filter multiple sources** in your Builder map as long as they share the same field. Previously, widgets could only filter a single source. Now, widgets like Category or Time Series will update multiple sources and their related elements (like other widgets or layers) when the filtering property matches. This is especially useful when working with complementary datasets. For example, filtering both sales and demographic data by region to uncover richer insights. Learn more in our [Widget Behavior](/carto-user-manual/maps/widgets.md#widget-behavior) section of the documentation.
{% endupdate %} {% update date="2025-06-19" tags="improvement,builder,carto-for-developers" %} ## Custom aggregation support on Category, Pie and Time Series Widget You can now define **custom aggregation** operations directly in [**Category**](/carto-user-manual/maps/widgets/category-widget.md), [**Pie**](/carto-user-manual/maps/widgets/pie-widget.md), and [**Time Series**](/carto-user-manual/maps/widgets/time-series-widget.md) widgets, previously only available in Formula widgets. This enhancement enables more advanced use cases by allowing tailored **SQL expressions** within the widget configuration, giving users greater control over how insights are calculated and displayed. Custom aggregations are supported in both **CARTO Builder** and the **CARTO Developer framework** for programmatically creating widgets.\ \ Learn more in the [Widget*s*](/carto-user-manual/maps/widgets.md) section of Builder or the CARTO for Developers [technical reference](/carto-for-developers/reference/carto-widgets-reference.md).
{% endupdate %} {% endupdates %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.carto.com/whats-new.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.