> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/gym/v0.3.0/about/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/gym/_mcp/server.

# NeMo Gym

> NeMo Gym is a library for evaluating and improving models and agents using environments.

NeMo Gym is a library for evaluating and improving models and agents using environments. NeMo Gym provides infrastructure to develop environments, scalably run evaluation and training, and a collection of popular benchmarks and training environments.

## When to Use NeMo Gym

* You need to **evaluate models or agents** in stateful environments (for example, code execution, tool calling, sandboxes).
* You want **reproducible evaluation** across teams using shared environments and verifiers.
* You need to use environments **at scale** — multiple repeats per task, or thousands of concurrent requests for training.
* You want to **seamlessly transition** between evaluation, agent optimization, and training.

If you are scoring model outputs with a stateless check and do not need scale or training, a script is probably sufficient.

## What NeMo Gym Provides

* Modular, extensible interfaces for agents, environments, tasks, and verifiers
* Environment hub of popular benchmarks and training environments
* Use your own agents or choose from built-in harnesses
* Scale to thousands of concurrent environments
* Train with the RL framework of your choice
* Battle-tested in production Nemotron training

![NeMo Gym Product Overview](/nemo/gym/_fern-img/1f21af95c3aa75bf8425dab33fad469b4b724f6cdb434d53cdeaaf53e8c88ec9.webp)

## Integrations

NeMo Gym integrates with the broader agentic ecosystem:

* **Environment libraries**: Seamlessly combine environments and benchmarks from other libraries alongside NeMo Gym environments.
* **Training framework libraries**: Use environments for SFT and RL training.
* **Agent harnesses**: Popular agent harnesses for evaluation and training available out of the box.
* **Agent framework libraries**: Use your custom agent built with agent frameworks in NeMo Gym environments.
* **Sandboxes**: Isolate agent runtime execution.

## Next Steps

#### [Get Started](/get-started/installation)

Install NeMo Gym and run your first evaluation.

#### [Browse Environments](https://github.com/NVIDIA-NeMo/Gym#-available-environments)

Browse available environments for evaluation and training.

#### [Agents](/agent-server)

Explore available agent harnesses and learn how to integrate your own agent.

#### [Training](/training-tutorials)

Improve your agent or model with RL or fine-tuning.

#### [Build Custom Environments](/environment-tutorials)

Create your own evaluation or training environments.

> NeMo Gym is a library for evaluating and improving models and agents using environments.

## Docs

- [Concepts](https://docs.nvidia.com/nemo/gym/v0.3.0/about/concepts.md): Understand what environments are, how they power evaluation, agent optimization, and training, and how the same components serve all three.
- [Environments](https://docs.nvidia.com/nemo/gym/v0.3.0/about/concepts/environments.md): Environments decompose into dataset, agent harness, verifier, and state — unifying evaluation, agent optimization, and training.
- [Evaluation](https://docs.nvidia.com/nemo/gym/v0.3.0/about/concepts/evaluation.md): What evaluation is, how to evaluate agents and models, and what makes a good eval.
- [Training](https://docs.nvidia.com/nemo/gym/v0.3.0/about/concepts/training.md): Post-training techniques and how environments are used for training.
- [Key Terminology](https://docs.nvidia.com/nemo/gym/v0.3.0/about/concepts/key-terminology.md): 
- [Architecture](https://docs.nvidia.com/nemo/gym/v0.3.0/about/architecture.md): Understand how NeMo Gym implements environments as composable server components.
- [Ecosystem](https://docs.nvidia.com/nemo/gym/v0.3.0/about/ecosystem.md): 
- [Release Notes](https://docs.nvidia.com/nemo/gym/v0.3.0/about/release-notes.md):