For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
DocumentationAPI Reference
DocumentationAPI Reference
  • Documentation
    • Home
  • About
    • Concepts
      • Training Approaches
      • Environment Components
      • Configuration System
      • Architecture
      • Task Verification
      • Key Terminology
    • Ecosystem
  • Get Started
    • Quickstart
    • Detailed Setup Guide
    • Install from PyPI
    • Rollout Collection
  • Agent Server
  • Model Server
    • vLLM
  • Resources Server
  • Data
    • Prepare and Validate
    • Download from Hugging Face
    • Prompt Config
  • Environment Tutorials
    • Single-Step Environment
    • Multi-Step Environment
    • Stateful Environment
    • Real-World Environment
    • Integrate external libraries
    • Aggregate Metrics
    • LLM-as-Judge Verification
  • Benchmarks
    • Run benchmarks
    • Add a benchmark
    • Design a customer evaluation
  • Training Tutorials
    • NeMo RL
    • Unsloth
    • Multi-Environment Training
    • Offline Training (SFT/DPO)
  • Model Recipes
    • Nemotron 3 Nano
    • Nemotron 3 Super
  • Infrastructure
    • Deployment Topology
    • Engineering Notes
  • Reference
    • Configuration
    • RL Framework Compatibility
    • CLI Commands
    • FAQ
  • Troubleshooting
    • Configuration Errors
  • Contribute
    • Development Setup
    • Environments
    • Integrate RL Frameworks
NVIDIANVIDIA
Developer-friendly docs for your API
Privacy Policy | Manage My Privacy | Do Not Sell or Share My Data | Terms of Service | Accessibility | Corporate Policies | Product Security | Contact

Copyright © 2026, NVIDIA Corporation.

LogoLogoNeMo Gym
On this page
  • How Servers Connect
  • Config File Locations
  • Server Block Structure
AboutConcepts

Configuration System

||View as Markdown|
Previous

Environment Components

Next

Architecture

NeMo Gym uses YAML configuration files to define Model, Resources, and Agent servers. Each server gets its own configuration block, providing modular control over the entire training environment.

How Servers Connect

A training environment typically includes all three server types working together. The Agent server config specifies which Model and Resources servers to use by referencing their server IDs. These references connect each training environment together — the Agent knows which Model to call and which Resources to use.

Config File Locations

Each server type has a dedicated directory with its implementations and their configs:

# Model Server Config
responses_api_models/
└── openai_model/
└── configs/openai_model.yaml
# Resources Server Config
resources_servers/
└── example_single_tool_call/
└── configs/example_single_tool_call.yaml
# Agent Server Config
responses_api_agents/
└── simple_agent/
└── configs/simple_agent.yaml

Server Block Structure

Each config file defines a server using this structure:

1server_id: # Your unique name for this server
2 server_type: # responses_api_models | resources_servers | responses_api_agents
3 implementation: # Directory name inside the server type directory
4 entrypoint: app.py # Python file to run
5 # ... additional fields vary by server type

Different server types have additional required fields (e.g., domain for resources servers, resources_server and model_server for agents). See Configuration for complete field specifications.

Config files in NeMo Gym often use the same name for both server ID and implementation:

1example_single_tool_call: # ← Server ID
2 resources_servers:
3 example_single_tool_call: # ← Implementation

These serve different purposes:

  • Server ID (example_single_tool_call on line 1): Your chosen identifier for this server instance. Used in API requests and when other servers reference it. You could name it my_weather or weather_prod instead.

  • Implementation (example_single_tool_call on line 3): Must match the folder resources_servers/example_single_tool_call/. This tells NeMo Gym which code to run.

Examples often use matching names for simplicity, but the two values are independent choices.


  • Configuration for complete syntax and field specifications
  • Configuration for troubleshooting configuration related errors