LocalREPL

LocalREPL is the default execution environment for RLM. It runs Python code in the same process as the RLM host application, using Python's built-in exec() function with a sandboxed namespace. The REPL shares the same virtual environment and memory space as the host process, but restricts access to dangerous builtins like eval,exec, andcompile. This provides fast execution with minimal overhead, making it ideal for development and trusted code execution, but offers no process-level isolation from the host system.

rlm = RLM(
    backend="openai",
    backend_kwargs={"model_name": "gpt-5-mini"},
    environment="local",  # Default
    environment_kwargs={
        "setup_code": "import json",  # Optional
    },
)

Arguments

Argument	Type	Default	Description
`setup_code`	`str`	`None`	Code to run at initialization
`context_payload`	`str \| dict \| list`	Auto	Initial context (set by RLM)
`lm_handler_address`	`tuple`	Auto	Socket address (set by RLM)

How It Works

Creates sandboxed globals with restricted __builtins__
Injects context, llm_query(), llm_query_batched(), FINAL_VAR()
Executes each code block via exec()
llm_query() sends TCP requests to LM Handler
Variables persist across code blocks in locals

Sandboxed Builtins

Allowed: print, len, range, str, int, float, list, dict, set, tuple, open, min, max, sum, sorted, enumerate, zip, map, filter, standard exceptions

Blocked: eval, exec, compile, input, globals, locals

Limitations

Shares process memory with host
No network isolation
Dependencies must be installed in host virtualenv