About NVIDIA NeMo Studio | NVIDIA NeMo Platform

Studio is still in early development. Many features are missing or should be expected to change.

NeMo Studio is the web app for AI development with NVIDIA NeMo Platform. It provides a workspace-oriented UI for managing local platform resources, reviewing agents, running agent optimization workflows, monitoring agent telemetry, and working with filesets, jobs, and secrets.

Getting Started

NeMo Studio is included with the platform. Follow the Setup guide to start NeMo Platform, then access Studio at /studio on your running server.

Features

Agents

Use the Studio Agents area to review platform-managed NeMo Agent Toolkit workflows, inspect agent details, deploy agents, open a chat session against a running deployment, and clean up deployments that are no longer needed.

For the full workflow, see Studio Agents.

Suggestions

Use Agents > Suggestions to review optimizer suggestions for deployed agents. Studio groups suggestions by workspace or agent and lets you filter by type, priority, scope, and agent.

For the full workflow, see Studio Suggestions.

Monitor

Use Agents > Monitor to inspect agent telemetry stored by the platform, including recent inference logs and token usage summaries.

For the full workflow, see Studio Monitor.

Guardrails

Use Guardrails to view NeMo Guardrails configurations for the current workspace. The list shows each config’s name, attached task model count, and rail count. Select a row to open the detail page, which displays the full configuration payload as formatted JSON.

Guardrail configurations are created through the nemo guardrail CLI or Guardrails API.

For the full workflow, see Studio Guardrail Configs.

Virtual Models

Use Virtual Models to inspect inference routes managed by the Inference Gateway. The list shows all user-created virtual models in the workspace — auto-provisioned passthrough routes are hidden. Select a row to open the details panel, which shows the default backend model when configured, and the ordered request, response, and post-response middleware pipelines attached to the route.

Virtual models are created through the nemo inference virtual-models CLI or the Inference Gateway API.

For the full workflow, see Studio Virtual Models.

Models

Use Models > Base Models to browse model entities available in the current workspace. Base models include models registered in the workspace and default platform models.

The Base Models page supports searching by model name, filtering by creation or update date, sorting alphabetically or by creation time, and showing only customizable models. Select a model card to open the model details side panel.

The model details side panel shows model metadata, parameters, and available actions. From the side panel you can open the chat playground for chat-capable models or navigate to model evaluation results.

Workspaces

Keep models and data organized in dedicated workspaces.

Filesets

Use Filesets to organize files by purpose, which determines which metadata fields are available. Purpose is chosen at creation time and can’t be changed afterward.

Purpose	Use for
Generic	Default. Files that don’t fit the Dataset or Model categories. Doesn’t add purpose-specific metadata fields.
Dataset	Training and evaluation data. Enables dataset-specific metadata, including schema information. Use built-in tools to transform data schemas with LLM assistance or create train/test splits.
Model	Model weights and checkpoints. Enables model-specific metadata, including tool-calling and model configuration fields.

Supported file formats: JSON (.json), JSONL (.jsonl), CSV (.csv), and Parquet (.parquet). PDF, image, and other binary formats are not currently supported.

You can upload any file type into a fileset. However, each service supports specific input file types:

Service	Supported File Types
Data Designer	`.json`, `.jsonl`, `.csv`, `.parquet`

Evaluations

Evaluations appears in two separate sidebar groups because it covers two distinct scopes:

Location	What it shows
Agents > Evaluations	Evaluation runs scoped to a specific agent — results tied to agent deployments and versions.
Evaluate > Evaluations	All evaluation results across the workspace, regardless of which agent or model was evaluated.

Use Agents > Evaluations when reviewing how a specific agent performed. Use Evaluate > Evaluations for a broader view of evaluation history across the workspace.

Jobs

Track and manage all platform jobs from a single view. The Jobs page aggregates jobs across enabled capabilities, such as Data Designer, so you can monitor progress, view logs, download artifacts, and cancel jobs in one place.

Navigate to Jobs in the workspace sidebar to see all jobs in the current workspace.

Search and filter — Find jobs by name, filter by status, or narrow results by creation or update date range.

Status tracking — Each job displays its current status:

Status	Description
Created	Job has been created but not yet scheduled
Pending	Job is queued and waiting for resources
Active	Job is currently running
Completed	Job finished successfully
Error	Job encountered an error
Cancelled	Job was cancelled by a user
Cancelling	Cancellation is in progress
Paused	Job execution is paused
Pausing	Job is transitioning to paused
Resuming	Paused job is restarting

Service routing — Clicking a job navigates to its service-specific detail view when one exists. Jobs from services that aren’t enabled fall back to the generic job detail view.

Cancelling a job — Active or pending jobs can be cancelled from the job list or the detail page. Cancelling a job permanently stops it and cannot be undone.

Secrets

Store API keys and credentials to securely connect with external providers. See manage-secrets for details.