> This page is for version 0.6.0.
> For other versions, use one of these documentation indexes:
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> - 0.7.0: https://docs.nvidia.com/nemo/gym/v0.7.0/llms.txt
> - 0.6.0: https://docs.nvidia.com/nemo/gym/v0.6.0/llms.txt
> - 0.5.1: https://docs.nvidia.com/nemo/gym/v0.5.1/llms.txt
> - 0.5.0: https://docs.nvidia.com/nemo/gym/v0.5.0/llms.txt
> - 0.4.0: https://docs.nvidia.com/nemo/gym/v0.4.0/llms.txt
> - 0.3.0: https://docs.nvidia.com/nemo/gym/v0.3.0/llms.txt
> - 0.2.1: https://docs.nvidia.com/nemo/gym/v0.2.1/llms.txt

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# 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](/tutorials/training-tutorials)

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

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

Create your own evaluation or training environments.