# NeMo Data Designer

> Open-source NVIDIA orchestration framework for generating synthetic datasets from scratch or seed data using LLM endpoints.

NeMo Data Designer is an open-source Python framework from NVIDIA for generating synthetic datasets from scratch or from seed data. You connect LLM endpoints such as NVIDIA Build, OpenAI, OpenRouter or vLLM, and the library handles batching, parallelism and validation. The latest release listed on GitHub is v0.9.4.

## What It Is

Data Designer is a synthetic data generation library. You define a dataset as a set of columns, using samplers, LLM text columns, image models or seed datasets. Columns can depend on one another, so fields in a record can be correlated. The documentation contrasts this with raw LLM calls, saying it provides statistical diversity, field correlations, automated validation and reproducible workflows.

## Workflow

The documented loop is Configure, Preview, Create. You configure columns and models through a Python config builder, preview sample records to iterate, then create the full dataset at scale. Models and providers can be set up with the `data-designer config` CLI commands. Local Python config modules can also accept workflow-specific command-line arguments.

## Capabilities

- Generate text, structured data and images, with image, audio and video context for multimodal workflows.
- Connect generation to local or remote MCP servers and capture interaction traces.
- Validate and score outputs with Python, SQL, custom validators and LLM judges.
- Sample realistic person data with demographic attributes.
- Chain workflows, and pause, inspect and resume runs.
- Extend it with plugins for custom columns, seed readers and processors.

The documentation includes recipes for text-to-SQL, text-to-Python, multi-turn chat, image generation, document QA and agent trace distillation.

## Agent Skill and Telemetry

A `data-designer` skill for coding agents lets you describe the dataset you want while the agent handles schema design, validation and generation. It is tested with Claude Code and Codex and installs via `npx skills add NVIDIA-NeMo/DataDesigner`. The library includes optional anonymous telemetry covering model names and token counts, which can be disabled with `NEMO_TELEMETRY_ENABLED=false`. The README notes that NVIDIA Build is intended for evaluation and testing only.

## Features
- Column-based synthetic dataset configuration
- Samplers, LLM text columns and image generation
- Dependency-aware field generation
- Seed dataset support
- Validators for Python, SQL, custom and remote checks, plus LLM judges
- Multimodal workflows with image, audio and video context
- MCP tool use with trace capture
- Person sampling with demographic attributes
- Workflow chaining
- Preview, resume and monitor generation
- Plugin system for custom columns, seed readers and processors
- CLI for configuring model providers and models
- Agent skill for coding agents

## Integrations
NVIDIA Build API, OpenAI, OpenRouter, vLLM, MCP servers, Hugging Face Hub, Claude Code, Codex

## Platforms
LINUX, WEB, API, DEVELOPER_SDK, CLI

## Pricing
Open Source

## Version
v0.9.4

## Links
- Website: https://docs.nvidia.com/nemo/datadesigner
- Documentation: https://docs.nvidia.com/nemo/datadesigner
- Repository: https://github.com/NVIDIA-NeMo/DataDesigner
- EveryDev.ai: https://www.everydev.ai/tools/nemo-data-designer