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rLLM provides two interfaces for working with agents: a CLI for running evaluations and training from the terminal, and a web UI for monitoring runs and exploring results.

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CLI

rLLM CLI showing the banner, available commands including agent, dataset, eval, init, login, model, and train, along with global options
The rllm CLI is the primary way to evaluate and train agents. Datasets are auto-pulled from HuggingFace, agents and evaluators are resolved from the built-in catalog, and a local LiteLLM proxy handles API routing.

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First-time setup

Configure your model provider before running evaluations or training: 
rllm model setup
This walks you through selecting a provider (OpenAI, Anthropic, etc.), entering your API key, and choosing a default model. Configuration is saved to ~/.rllm/config.json.

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Core commands

rllm eval gsm8k                          # Evaluate on a benchmark
rllm train gsm8k --model Qwen/Qwen3-8B  # Train with RL
rllm init my-agent                       # Scaffold a new agent project
rllm dataset list --all                  # Browse available datasets
rllm model setup                         # Configure model providers

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Typical workflow

1

Configure your model

rllm model setup
2

Explore available datasets

rllm dataset list --all
3

Evaluate on a benchmark

rllm eval gsm8k
4

Train with RL

rllm train gsm8k --model Qwen/Qwen3-8B

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Global behavior

  • Auto-pull: Datasets are automatically downloaded from HuggingFace when first referenced by eval or train.
  • LiteLLM proxy: When no --base-url is provided, eval and train start a local LiteLLM proxy automatically, routing requests to your configured provider.
  • Lazy loading: Commands are loaded on-demand, so rllm --help starts instantly regardless of installed extras.

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Web UI

The rLLM web UI at ui.rllm-project.com provides a dashboard for monitoring training runs and exploring evaluation results.

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Logging in

Authenticate with the hosted UI from the CLI: 
rllm login
This opens a browser for authentication and stores your API key locally in ~/.rllm/config.json. You can also set the RLLM_API_KEY environment variable directly.

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Enabling UI logging

Add the --ui flag to eval or train to stream live data to the dashboard: 
rllm eval gsm8k --ui
rllm train gsm8k --model Qwen/Qwen3-8B --ui
When enabled, the CLI sends metrics, episode data, and step-by-step execution traces to the UI backend in real time.

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What you can see

The UI provides:
  • Episode explorer: Browse individual episodes with full trajectory and step-level detail
  • Metrics dashboard: Track rewards, success rates, and custom signals across training runs
  • Trajectory viewer: Inspect agent reasoning chains and tool calls step by step
To use a self-hosted UI instance, set the RLLM_UI_URL environment variable to your instance URL.