sarvamai/Gym

sarvamai/Gym

Description: Evaluate and improve models and agents using environments

License: Apache-2.0

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Created: 2026-05-28T10:59:24Z

Pushed: 2026-06-05T05:08:13Z

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Parent repository: NVIDIA-NeMo/Gym

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README:

NeMo Gym

[Requirements](#-requirements) • [Quick Start](#-quick-start) • [Available Environments](#-available-environments) • [Documentation & Resources](#-documentation--resources) • [Community & Support](#-community--support) • [Citations](#-citations)

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.

An environment is the complete system an agent interacts with to complete a task. It consists of a dataset (tasks to solve), an agent harness (how the model interacts with the world), a verifier (task completion scoring), and state (per-task execution context).

🎯 When to Use NeMo Gym

• You need to evaluate models or agents in stateful environments (e.g. 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're scoring model outputs with a stateless check and don't 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


🌎 Ecosystem

NeMo Gym is a component of NVIDIA NeMo, a GPU-accelerated platform for training generative AI models and optimizing AI agents. NeMo Gym is integrated with the broader agentic ecosystem - see the Ecosystem page for more details.

Environment Libraries: Seamlessly combine environments and benchmarks from other libraries alongside NeMo Gym environments. Examples: Aviary • Harbor • OpenEnv • Reasoning Gym • Verifiers

Training Framework Libraries: Use environments for SFT and RL training. NeMo RL • Unsloth • VeRL

Agent Harnesses: Agent harnesses for evaluation and training available out of the box. Examples: OpenHands • Mini SWE Agent • LangGraph

> [!IMPORTANT] > NeMo Gym is currently in early development. You should expect evolving APIs, incomplete documentation, and occasional bugs. We welcome contributions and feedback - for any changes, please open an issue first to kick off discussion!

📋 Requirements

NeMo Gym is designed to run on standard development machines:

| Hardware Requirements | Software Requirements | | --------------------- | --------------------- | | GPU: Not required for NeMo Gym library operation • GPU may be needed for specific resources servers or model inference (see individual server documentation) | Operating System: • Linux (Ubuntu 20.04+, or equivalent) • macOS (11.0+ for x86_64, 12.0+ for Apple Silicon) • Windows (via WSL2) | | CPU: Any modern x86_64 or ARM64 processor (e.g., Intel, AMD, Apple Silicon) | Python: 3.12 or higher | | RAM: Minimum 8 GB (16 GB+ recommended for larger environments) | Git: For cloning the repository | | Storage: Minimum 5 GB free disk space for installation and basic usage | Internet Connection: Required for downloading dependencies and API access |

Additional Requirements

• API Keys: OpenAI API key with available credits (for the quickstart examples)
• Other model providers supported (Azure OpenAI, self-hosted models via vLLM)
• Ray: Automatically installed as a dependency (no separate setup required)

🚀 Quick Start

Requires Python 3.12+ on x86_64 or ARM64 (Linux, macOS, Windows via WSL2). No GPU required. See the Getting Started docs for a more comprehensive walkthrough.

Install NeMo Gym:

Requires uv and Python 3.12+.

git clone git@github.com:NVIDIA-NeMo/Gym.git
cd Gym
uv venv --python 3.12 && source .venv/bin/activate
uv sync

Configure your model:

This quickstart uses OpenAI. NeMo Gym supports local and hosted inference — see Configure Model for vLLM, Fireworks, OpenRouter, and others.

Create env.yaml in the project root:

policy_base_url: https://api.openai.com/v1
policy_api_key:
policy_model_name: gpt-4.1-2025-04-14

Run Evaluation

Run your agent on a set of tasks and score the results. This example uses a simple tool calling agent [simple_agent](responses_api_agents/simple_agent/README.md) with the [mcqa](resources_servers/mcqa/README.md) (multiple-choice Q&A) environment and its included example data.

1. Start servers

NeMo Gym uses local servers to coordinate your model, agent, and task verification. Start them first: