---
title: "Ecosystem"
description: "How NeMo Gym fits within the NVIDIA NeMo platform and the broader agentic ecosystem."
position: 3
---
NeMo Gym is one of several specialized libraries in the NVIDIA NeMo platform. Alongside NeMo RL for
post-training, NeMo Gym focuses on the *environments* agents act in — pairing a dataset, agent harness, and
verifier so the same environment can drive both evaluation and training.

<Tip>
For what NeMo Gym does and when to use it, see the [Overview](/about) and [What NeMo Gym Provides](/about#what-nemo-gym-provides). For how environments are implemented as composable servers, see [Architecture](/about/architecture).
</Tip>

NeMo Gym is also integrated with the broader agentic ecosystem. 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.

## NeMo Gym Within NVIDIA NeMo

NVIDIA NeMo is a GPU-accelerated platform for training generative AI models and optimizing AI agents. It includes specialized libraries that span the model lifecycle end to end:

- **NeMo Curator**: Prepares, filters, and deduplicates training data at scale.
- **NeMo Gym**: Defines the environments — dataset, agent harness, and verifier — that agents are evaluated and trained in *(this library)*.
- **NeMo RL**: Runs scalable post-training (GRPO, DPO, and SFT) against those environments.

**NeMo Gym's role**: NeMo Gym is where a task becomes an interactive environment. It standardizes how an agent interacts with the world (the agent harness), how task completion is scored (the verifier), and how per-task state is isolated — producing the rollouts and reward signals that training frameworks like NeMo RL consume, and the consistent scoring that evaluation depends on.

## Environment Libraries

Seamlessly combine environments and benchmarks from other libraries alongside NeMo Gym environments, with access to over 1,000 community-contributed environments across integrated libraries.

- **[Aviary](https://github.com/NVIDIA-NeMo/Gym/tree/main/resources_servers/aviary)**
- **[Harbor](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/harbor_agent)**
- **[OpenEnv](https://github.com/NVIDIA-NeMo/Gym/tree/main/resources_servers/openenv)**
- **[Reasoning Gym](https://github.com/NVIDIA-NeMo/Gym/tree/main/resources_servers/reasoning_gym)**
- **[Verifiers](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/verifiers_agent)**

## Training Framework Libraries

Use environments for SFT and RL training. If you're interested in integrating another training framework, see the [Training Framework Integration Guide](/contribute/rl-framework-integration).

- **[NeMo RL](/training-tutorials/nemo-rl-grpo)**
- **[Unsloth](/training-tutorials/unsloth)**
- **[VeRL](/training-tutorials/verl)**

## Agent Harnesses

Agent harnesses are available out of the box for evaluation and training. Some examples:

- **[OpenHands](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/swe_agents)** - software engineering agent harness
- **[Mini SWE Agent](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/mini_swe_agent)** - software engineering agent harness
- **[LangGraph](https://github.com/NVIDIA-NeMo/Gym/tree/main/responses_api_agents/langgraph_agent)** - agent patterns built with LangGraph (reflection, orchestration, parallel thinking)

## Related Products in the NVIDIA NeMo Ecosystem

Depending on your workflow, you may also find these NeMo libraries useful across data preparation, evaluation and training, and deployment.

### Data

| Library | Purpose |
|---------|---------|
| [NeMo Curator](https://github.com/NVIDIA-NeMo/Curator) | Scalable data preprocessing and curation |
| [NeMo Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner) | Generate synthetic training data from scratch or seed examples |
| [NeMo Safe Synthesizer](https://github.com/NVIDIA-NeMo/Safe-Synthesizer) | Generate privacy-safe synthetic copies of sensitive datasets |
| [NeMo Anonymizer](https://github.com/NVIDIA-NeMo/Anonymizer) | Detect and replace sensitive data |

### Evaluation & Training

| Library | Purpose |
|---------|---------|
| **[NeMo Gym](https://github.com/NVIDIA-NeMo/Gym)** | Evaluate and improve models and agents using environments *(this project)* |
| [NeMo Evaluator](https://github.com/NVIDIA-NeMo/Evaluator) | Model evaluation and benchmarking |
| [NeMo RL](https://github.com/NVIDIA-NeMo/RL) | Scalable post-training with GRPO, DPO, and SFT |
| [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 |

### Deployment

| Library | Purpose |
|---------|---------|
| [NeMo Agent Toolkit](https://github.com/NVIDIA/NeMo-Agent-Toolkit) | Connect and optimize teams of AI agents |
| [NeMo Guardrails](https://github.com/NVIDIA-NeMo/Guardrails) | Enforce safety and policy rules |
| [NeMo Retriever](https://github.com/NVIDIA/NeMo-Retriever) | Document extraction and retrieval for RAG pipelines |
