RLMesh documentation

RLMesh connects models to Gymnasium-style environments, even when they run in different processes or on different machines. The Python package serves environments and runs evaluations. The managed platform schedules evaluations on connected clusters and is available through early access.

Start locally

1. Install RLMesh.
2. Serve CartPole and run an evaluation in two local processes.
3. Bring your own environment or your own model.

The quickstart introduces EnvFactory and Model subclasses. These are the recommended starting point for code you will run locally and package for the managed catalog. The repository walkthrough has runnable files.

Run on managed

Use the dashboard to select a catalog pair and run an evaluation. To bring your own model or environment from a local container, follow Upload a Model or Environment.

For other tasks, see Connect a Client, Run an Evaluation, or Adapters. The examples and Python API cover runnable recipes and signatures. For supported versions, see Compatibility.