DockerREPL

DockerREPL executes Python code in a Docker containerrunning on the same host machine as the RLM process. Each code execution runs in an isolated container environment with its own filesystem, network namespace, and process tree, providing better security and reproducibility than LocalREPL. The container requests LM calls from the host's LM Handler when code executes llm_query(). This environment is ideal for CI/CD pipelines, reproducible execution environments, and scenarios requiring stronger isolation than LocalREPL while maintaining the convenience of local execution. For more information on Docker, see the Docker documentation.

Prerequisite: Docker must be installed and running.

rlm = RLM(
    backend="openai",
    backend_kwargs={"model_name": "gpt-5-mini"},
    environment="docker",
    environment_kwargs={
        "image": "python:3.11-slim",
    },
)

Arguments

Argument	Type	Default	Description
`image`	`str`	`"python:3.11-slim"`	Docker image to use
`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

Starts Docker container with volume mount to temp directory
Installs dill and requests in container
Host runs HTTP proxy server on random port
Container calls proxy via host.docker.internal
Proxy forwards llm_query() to LM Handler via socket
State persisted via dill to /workspace/state.dill

┌────────────────────────────────────────┐
│ Host                                   │
│  ┌────────────┐ Socket ┌────────────┐ │
│  │ HTTP Proxy │◄──────►│ LM Handler │ │
│  └─────┬──────┘        └────────────┘ │
└────────┼───────────────────────────────┘
         │ HTTP
┌────────┼───────────────────────────────┐
│ Docker │ Container                     │
│  ┌─────▼──────┐                        │
│  │   Python   │ llm_query() → proxy    │
│  │   exec()   │                        │
│  └────────────┘                        │
└────────────────────────────────────────┘

Custom Image

You can use your own custom Docker images or update the given image. Pre-install dependencies:

FROM python:3.11-slim
RUN pip install numpy pandas dill requests

environment_kwargs={"image": "my-rlm-image"}