RLM - Recursive Language Models

Execute tasks with **verified code execution** via mcporter MCP bridge.

RLM writes and executes Python code iteratively until it produces a verified answer. Unlike direct LLM responses, RLM computations are **100% accurate** for calculations.

Prerequisites

1. Install mcporter (MCP bridge)

```bash

npm install -g mcporter

```

2. Install RLM MCP Server

**Option A: Clone and setup (recommended)**

```bash

Clone RLM project

git clone https://github.com/alexzhang13/rlm.git $HOME/rlm

cd $HOME/rlm

pip install -e .

Create MCP server directory

mkdir -p $HOME/.claude/mcp-servers/rlm/src

Download MCP server files

curl -o $HOME/.claude/mcp-servers/rlm/src/server.py \

https://raw.githubusercontent.com/eesb99/rlm-mcp/main/src/server.py

curl -o $HOME/.claude/mcp-servers/rlm/run_server.sh \

https://raw.githubusercontent.com/eesb99/rlm-mcp/main/run_server.sh

curl -o $HOME/.claude/mcp-servers/rlm/setup.sh \

https://raw.githubusercontent.com/eesb99/rlm-mcp/main/setup.sh

curl -o $HOME/.claude/mcp-servers/rlm/requirements.txt \

https://raw.githubusercontent.com/eesb99/rlm-mcp/main/requirements.txt

Setup venv and install dependencies

chmod +x $HOME/.claude/mcp-servers/rlm/*.sh

cd $HOME/.claude/mcp-servers/rlm

python3 -m venv venv

venv/bin/pip install -r requirements.txt

```

**Option B: Manual setup**

```bash

Create server directory

mkdir -p $HOME/.claude/mcp-servers/rlm/src

Create venv and install dependencies

cd $HOME/.claude/mcp-servers/rlm

python3 -m venv venv

venv/bin/pip install mcp litellm

Create run_server.sh

cat > $HOME/.claude/mcp-servers/rlm/run_server.sh << 'EOF'

#!/bin/bash

SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"

cd "$SCRIPT_DIR"

export PYTHONPATH="$HOME/rlm:$PYTHONPATH"

export RLM_MODEL="${RLM_MODEL:-openrouter/x-ai/grok-code-fast-1}"

export RLM_SUBTASK_MODEL="${RLM_SUBTASK_MODEL:-openrouter/openai/gpt-4o-mini}"

export RLM_MAX_DEPTH="${RLM_MAX_DEPTH:-2}"

export RLM_MAX_ITERATIONS="${RLM_MAX_ITERATIONS:-20}"

exec "$SCRIPT_DIR/venv/bin/python" -m src.server

EOF

chmod +x $HOME/.claude/mcp-servers/rlm/run_server.sh

```

3. Configure MCP (for Claude Code)

Add to `~/.mcp.json` (replace `YOUR_HOME` with your actual home path, e.g., `/Users/john` or `/home/john`):

```json

{

"mcpServers": {

"rlm": {

"command": "bash",

"args": ["YOUR_HOME/.claude/mcp-servers/rlm/run_server.sh"]

}

}

}

```

**Get your home path:** `echo $HOME`

4. Set API Key

RLM requires an OpenRouter API key:

```bash

export OPENROUTER_API_KEY="your-key-here"

```

5. Verify Installation

```bash

Check mcporter sees RLM

mcporter list | grep rlm

Test RLM

mcporter call 'rlm.rlm_status()'

```

Available Tools

| Tool | Use For | Parameters |

|------|---------|------------|

| `rlm_execute` | General tasks, calculations | `task` (required), `context` (optional) |

| `rlm_analyze` | Data analysis | `data`, `question` (both required) |

| `rlm_code` | Generate tested code | `description` (required), `language` (optional, default: python) |

| `rlm_decompose` | Complex multi-step tasks | `complex_task`, `num_subtasks` (default: 5) |

| `rlm_status` | Check system status | (none) |

Quick Commands

**Simple calculation:**

```bash

mcporter call 'rlm.rlm_execute(task: "calculate 127 * 389")'

```

**First N primes:**

```bash

mcporter call 'rlm.rlm_execute(task: "calculate the first 100 prime numbers")'

```

**Data analysis:**

```bash

mcporter call 'rlm.rlm_analyze(data: "[23, 45, 67, 89, 12, 34]", question: "what is the mean, median, and standard deviation?")'

```

**Generate code:**

```bash

mcporter call 'rlm.rlm_code(description: "function to check if a number is prime")'

```

**Complex task (decomposed):**

```bash

mcporter call 'rlm.rlm_decompose(complex_task: "analyze a $500K portfolio with 60/30/10 allocation, calculate risk metrics and 10-year projection", num_subtasks: 5)'

```

**Check status:**

```bash

mcporter call 'rlm.rlm_status()'

```

When to Use RLM

**Use RLM for:**

**Don't use RLM for:**

How It Works

```

1. You give RLM a task

2. RLM writes Python code to solve it

3. Code executes in sandbox

4. If not complete, RLM iterates

5. Returns verified final answer

```

**Models used:**

Configuration

**Environment variables:**

| Variable | Default | Description |

|----------|---------|-------------|

| `RLM_MODEL` | `openrouter/x-ai/grok-code-fast-1` | Root execution model |

| `RLM_SUBTASK_MODEL` | `openrouter/openai/gpt-4o-mini` | Subtask model |

| `RLM_MAX_DEPTH` | `2` | Max recursion depth |

| `RLM_MAX_ITERATIONS` | `20` | Max iterations per task |

| `OPENROUTER_API_KEY` | (required) | OpenRouter API key |

**Server location:** `$HOME/.claude/mcp-servers/rlm/`

Troubleshooting

**"Server offline" or "No module named 'mcp'":**

```bash

Reinstall dependencies

cd $HOME/.claude/mcp-servers/rlm

python3 -m venv venv

venv/bin/pip install mcp litellm

```

**"mcporter: command not found":**

```bash

npm install -g mcporter

```

**"rlm not in mcporter list":**

**Slow response:**

References