Core Exports¶

The top-level rlmesh package re-exports the common entry points: environment serving and clients, model running, sandboxing, and the spaces, types, and adapters subpackages. The common imports are listed below.

The top-level client and model classes are dependency-free wrappers around RLMesh-native values. For most user code, prefer the backend-specific modules when you want decoded NumPy arrays or Torch tensors.

Import	Description
`rlmesh.EnvServer`	Serve a Gymnasium-compatible environment endpoint.
`rlmesh.RemoteEnv`	Connect to one environment and preserve RLMesh-native values.
`rlmesh.RemoteVectorEnv`	Connect to a vector endpoint and preserve RLMesh-native values.
`rlmesh.SandboxEnv`	Build an env image and own the container behind a single client.
`rlmesh.SandboxVectorEnv`	Build an env image and own the container behind a vector client.
`rlmesh.Model`	Wrap a Python prediction function as a native-value model worker.
`rlmesh.RemoteModel`	Connect to an already-served model and drive it against an env.
`rlmesh.SandboxModel`	Run a model policy in its own container (experimental).
`rlmesh.ServeOptions`	Native serve lifecycle options.
`rlmesh.Tensor`	Native tensor value used by dependency-free clients.
`rlmesh.adapters`	Observation/action adapters and contract-based resolution.
`rlmesh.spaces`	Space wrappers and Gymnasium conversion helpers.
`rlmesh.types`	Structural protocols and value aliases.

The detailed pages below describe the shared behavior: