> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/gym/llms.txt.
> For full documentation content, see https://docs.nvidia.com/nemo/gym/llms-full.txt.

# Ecosystem

We're building NeMo Gym to integrate with a broad set of RL training frameworks and environment libraries.

We would love your contribution! Open a PR to add an integration, or [file an issue](https://github.com/NVIDIA-NeMo/Gym/issues/new/choose) to share what would be valuable for you.

***

## Training Framework Integrations

We have hands-on tutorials with supported training frameworks to help you train with NeMo Gym environments. If you're interested in integrating another training framework, see the [Training Framework Integration Guide](/v0.2/contribute/rl-framework-integration).

* **[NeMo RL](/v0.2/training-tutorials/nemo-rl-grpo)** - GRPO training to improve multi-step tool calling on the Workplace Assistant environment
* **[OpenRLHF](https://github.com/OpenRLHF/OpenRLHF/blob/main/examples/python/agent_func_nemogym_executor.py)** - example agent executor for RL training
* **[Unsloth](/v0.2/training-tutorials/unsloth)** - GRPO training on instruction following and reasoning environments
* **NeMo Customizer** - *(In progress)*
* **VeRL** - *(In progress)*

***

## Environment Library Integrations

NeMo Gym integrates with external environment libraries and benchmarks. See the [README](https://github.com/NVIDIA-NeMo/Gym?tab=readme-ov-file#table-2-resource-servers-for-training) for the full list—here are a few examples:

* **[Aviary](https://github.com/NVIDIA-NeMo/Gym/tree/main/resources_servers/aviary)** - environments spanning math, knowledge, biological sequences, scientific literature search, and protein stability
* **[Harbor](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/harbor_agent)** - popular agentic environments including Terminus2
* **[OpenEnv](https://github.com/NVIDIA-NeMo/Gym/tree/main/resources_servers/openenv)** - open environment interface via MCP
* **[Reasoning Gym](https://github.com/NVIDIA-NeMo/Gym/tree/main/resources_servers/reasoning_gym)** - reasoning environments spanning computation, cognition, logic and more
* **[Verifiers](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/verifiers_agent)** - environments spanning coding, data & ML, science & reasoning, tool use and more
* **[BrowserGym](https://github.com/ServiceNow/BrowserGym)** - *(In progress)* - environments for web task automation

***

## Agent Harness Integrations

Popular agent harnesses are available out of the box for evaluation and training.

* **[Mini SWE Agent](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/mini_swe_agent)** - lightweight software engineering agent harness
* **[OpenHands](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/swe_agents)** - software engineering agent harness
* **[SWE Agent](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/swe_agents)** - software engineering agent harness

***

## Agent Framework Integrations

Use your custom agent harnesses with NeMo Gym environments.

* **[LangGraph](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/langgraph_agent)** - build custom graph-based agent workflows

***

## Related NeMo Libraries

NeMo Gym is a component of NVIDIA NeMo, a GPU-accelerated platform for building and training generative AI models.

Depending on your workflow, you may also find these libraries useful:

| Library                                                                | Purpose                                                                           |
| ---------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
| [NeMo Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) | Pretraining and fine-tuning with Megatron-Core                                    |
| [NeMo AutoModel](https://github.com/NVIDIA-NeMo/Automodel)             | PyTorch native training for Hugging Face models                                   |
| [NeMo RL](https://github.com/NVIDIA-NeMo/RL)                           | Scalable post-training with GRPO, DPO, and SFT                                    |
| **[NeMo Gym](https://github.com/NVIDIA-NeMo/Gym)**                     | RL environment infrastructure and rollout collection *(this project)*             |
| [NeMo Curator](https://github.com/NVIDIA-NeMo/Curator)                 | Data preprocessing and curation                                                   |
| [NeMo Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner)      | High-quality Synthetic data generation from scratch or seed data                  |
| [NeMo Evaluator](https://github.com/NVIDIA-NeMo/Evaluator)             | Model evaluation and benchmarking                                                 |
| [NeMo Guardrails](https://github.com/NVIDIA-NeMo/Guardrails)           | Programmable safety guardrails                                                    |
| [NeMo Skills](https://github.com/NVIDIA-NeMo/NeMo-Skills)              | Convenience pipelines used by LLM researchers across SDG, evaluation and training |