---
title: "Ecosystem"
description: ""
position: 3
---
NeMo Gym is 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.

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## 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)**

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## 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)**


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## 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 NeMo Libraries

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

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

### 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 |
