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Installation

This guide covers how to install RLM Code, its optional dependencies, and how to verify your installation.


System Requirements

Requirement Minimum Recommended
Python 3.11 3.12+
OS Linux, macOS, Windows macOS (Apple Silicon) or Linux
Memory 2 GB 8 GB+
Disk 200 MB 1 GB+ (for traces and benchmark artifacts)

Install uv

We recommend uv as the primary way to install and manage RLM Code.

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or with Homebrew
brew install uv

Why uv?

uv is 10-100x faster than pip for dependency resolution. uv tool install creates an isolated environment for CLI tools - no virtualenv management needed. If you don't have Python 3.11+ installed, uv can install it for you:

uv python install 3.12

Standard Installation

uv tool install "rlm-code[tui,llm-all]"

This installs rlm-code as a globally available command in its own isolated environment. No virtualenv activation needed - just run rlm-code from anywhere.

If you prefer to install into an existing virtual environment:

uv pip install "rlm-code[tui,llm-all]"
pip install "rlm-code[tui,llm-all]"

This installs the core package, the TUI, and all LLM provider clients:

Dependency Purpose
click >= 8.0 CLI framework
dspy >= 3.0.4 DSPy integration
rich >= 13.7.0 Terminal formatting and panels
requests >= 2.28.0 HTTP client
pyyaml >= 6.0 YAML configuration parsing
mcp >= 1.2.1 Model Context Protocol support
anyio >= 4.5 Async I/O
httpx >= 0.27.1 HTTP/2 client
pydantic >= 2.11.0 Data validation
jsonschema >= 4.20.0 Schema validation
packaging >= 23.0 Version parsing
textual >= 0.86.0 Terminal UI framework (via [tui] extra)
openai >= 2.8.1 OpenAI client (via [llm-all] extra)
anthropic >= 0.39.0 Anthropic client (via [llm-all] extra)
google-genai >= 1.52.0 Gemini client (via [llm-all] extra)

Minimal Installation

If you only need one LLM provider:

# Core + TUI + Anthropic only
uv tool install "rlm-code[tui,anthropic]"

# Core + TUI + OpenAI only
uv tool install "rlm-code[tui,openai]"

# Core + TUI + Gemini only
uv tool install "rlm-code[tui,gemini]"
pip install "rlm-code[tui,anthropic]"
pip install "rlm-code[tui,openai]"
pip install "rlm-code[tui,gemini]"
Extra Package Version
openai openai >= 2.8.1, < 3.0
anthropic anthropic >= 0.39.0, < 1.0
gemini google-genai >= 1.52.0, < 2.0
llm-all All of the above --

Development Installation

For contributors or those who want to run from source:

git clone https://github.com/SuperagenticAI/rlm-code.git
cd rlm-code
uv sync --all-extras
uv run pytest
git clone https://github.com/SuperagenticAI/rlm-code.git
cd rlm-code
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,tui,llm-all]"

The dev extra installs:

Dependency Purpose
pytest >= 8.0 Test framework
pytest-cov >= 4.1 Coverage reporting
pytest-asyncio >= 0.23 Async test support
pytest-xdist >= 3.5 Parallel test execution
hypothesis >= 6.100 Property-based testing
ruff >= 0.8.0 Linting and formatting
mypy >= 1.13 Static type checking
pre-commit >= 4.0 Git hooks

Optional Dependencies

Runtime Backend Requirements

Pick at least one secure backend before running serious experiments.

Backend Install Requirement Typical Use
docker Install Docker Desktop / OrbStack / Colima Recommended secure default
monty pip install pydantic-monty Local secure pure-RLM backend without Docker
apple-container Install Apple's container CLI and verify container system status macOS-only experimental runtime

Observability Integrations

If you installed with uv tool install, use uv tool install --with to add extras, or reinstall with additional extras:

uv tool install "rlm-code[tui,llm-all,mlflow]"
uv tool install "rlm-code[tui,llm-all,mlflow]"
# If using uv tool, reinstall with the extra packages:
uv tool install "rlm-code[tui,llm-all]" --with opentelemetry-api --with opentelemetry-sdk --with opentelemetry-exporter-otlp-proto-grpc
uv tool install "rlm-code[tui,llm-all]" --with langsmith
uv tool install "rlm-code[tui,llm-all]" --with langfuse
uv tool install "rlm-code[tui,llm-all]" --with logfire
Integration Package Environment Variable
MLflow mlflow >= 2.17.0 MLFLOW_TRACKING_URI
OpenTelemetry opentelemetry-sdk OTEL_EXPORTER_OTLP_ENDPOINT
LangSmith langsmith LANGCHAIN_API_KEY
LangFuse langfuse LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY
Logfire logfire LOGFIRE_TOKEN

Framework Adapters

uv tool install "rlm-code[tui,llm-all,frameworks]"
Extra Package Purpose
pydantic pydantic-ai >= 0.4.0 Pydantic AI framework adapter
adk google-adk >= 1.12.0 Google Agent Development Kit adapter
frameworks Both of the above All framework adapters

MCP WebSocket Transport

uv tool install "rlm-code[tui,llm-all,mcp-ws]"

Adds websockets >= 15.0.1 for WebSocket-based MCP server transport.

Docker Runtime

Docker is used as a sandbox runtime for isolated code execution. No pip install is needed, but Docker must be available on the system:

# macOS
brew install --cask docker

# Linux (Ubuntu/Debian)
sudo apt-get install docker.io

# Verify Docker is running
docker info

Monty Backend

Monty is an optional secure backend for pure RLM execution:

pip install pydantic-monty

In TUI:

/sandbox backend monty

Apple Container Runtime (macOS, Experimental)

container --version
container system status

In TUI:

/sandbox apple on
/sandbox use apple-container

Documentation

uv tool install "rlm-code[docs]"

Installs mkdocs, mkdocs-material, mkdocstrings, and mkdocs-minify-plugin for building these docs locally.


Full Installation (Everything)

To install with all optional dependencies at once:

uv tool install "rlm-code[tui,llm-all,mlflow,frameworks,mcp-ws]"
pip install "rlm-code[tui,llm-all,mlflow,frameworks,mcp-ws]"

Verification

After installation, verify that everything works:

Check the version

rlm-code --version

Check sandbox runtimes

Launch the TUI and run the sandbox doctor:

rlm-code

Then in the TUI:

/sandbox doctor

This runs diagnostics on all available sandbox runtimes (local, Docker, Apple Container, Modal, E2B, Daytona) and reports their health status.

Check observability sinks

/rlm observability

This displays the status of all configured observability sinks (Local JSONL, MLflow, OpenTelemetry, LangSmith, LangFuse, Logfire).

Verify Python environment

python -c "import rlm_code; print(rlm_code.__version__)"

Upgrading

uv tool upgrade rlm-code
pip install --upgrade "rlm-code[tui,llm-all]"

Uninstalling

uv tool uninstall rlm-code
pip uninstall rlm-code

Troubleshooting

ModuleNotFoundError: textual

The TUI requires the textual package. Reinstall with the tui extra:

uv tool install "rlm-code[tui,llm-all]"

Docker daemon not running

If /sandbox doctor reports Docker as unavailable, ensure the Docker daemon is running:

# macOS
open -a Docker

# Linux
sudo systemctl start docker

Permission denied on /tmp

Some sandbox operations write to temporary directories. Ensure your user has write access to /tmp or set the TMPDIR environment variable to a writable path.

DSPy not found

RLM Code requires DSPy >= 3.0.4. Reinstall to pick up the latest dependencies:

uv tool install --force "rlm-code[tui,llm-all]"