Core interfaces

When you wire a new env or primitive into RPent, you implement the interfaces below. Walkthroughs: Add a New Robot, Add an Action Primitive. Repo layout: System Internals.

Environment entry

After you add robots/<env>/, main.py calls two functions in __init__.py:

def get_env_spec() -> EnvSpec: ...
def get_toolkit(*, primitives_kwargs, video_path=None, dashboard=None): ...

get_env_spec returns an EnvSpec. You supply:

Field / hook	What you provide
name	Env name for --env.
prompts	A PromptBundle with system and user prompt factories (see robots/<env>/prompt_bundle.py).
add_cli_args	Register this env’s CLI flags (e.g. --suite, --env-endpoint).
parse_config	Validate args and return RunConfig; set at least recipe_tag, output_dir, and prompt_vars for prompt templating.
init_runtime	Start or attach to env / VLA subprocesses; build primitives_kwargs (env client, model client, etc.) for the toolkit’s primitive driver.

get_toolkit usually just passes primitives_kwargs into your env subclass; video_path and dashboard are passed by main.py — you rarely touch them.

Reference: robots/libero/__init__.py.

Planner

Most users pick a built-in api, claude_code, or codex planner — see Agentic Planner. Only custom planners need rpent.planner.base.Planner:

def solve(
    self,
    *,
    system_prompt: str,
    user_message: str,
    toolkit: Toolkit,
    max_turns: int,
    input_queue=None,
) -> PlannerResult: ...

Contract: pass toolkit.get_tools_spec() to the model; dispatch each call via toolkit.execute_tool(name, input_dict); feed results back to the model; return PlannerResult on the finish tool or when turns are exhausted.

Toolkit

Subclass Toolkit in robots/<env>/toolkit.py and register env tools with add_tool:

def add_tool(self, name: str, spec: dict, handler) -> None: ...

Argument	Meaning
name	Tool name the LLM sees.
spec	Tool description and parameter schema (name, description, input_schema).
handler	Implementation; must return a ``dict``. Set _finish when the task ends; optional _image_bytes (etc.) to return camera images.

The base class already registers common file tools; call super().__init__() then add_tool for env tools. Per-step state and view_driver_state are in Add an Action Primitive.

Inter-process communication

Relevant when attaching to existing servers or writing env_server / vla_server.

Client endpoints — expose in add_cli_args or parse in init_runtime:

[protocol://]host:port    # defaults to http when protocol is omitted

Common flags: --env-endpoint, --vla-endpoint. The default http sends JSON over POST /call, encoding NumPy arrays as {"__ndarray__": <base64>, "dtype": ..., "shape": ...}; switch to socket for large or history-stacked nested-NumPy observations to move length-prefixed pickle frames and skip repeated JSON encoding. Pickle is unsafe on untrusted input, so only point socket at trusted endpoints.

Server: subclass rpent.utils.rpc.RpcFacade and implement _dispatch for business RPCs (e.g. reset, step, predict). Do not implement healthz or shutdown in the subclass.

Details are in the env_server / vla_server sections of Add a New Robot.