Option A: Clone and setup (recommended)

# 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

# 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):

{
  "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:

export OPENROUTER_API_KEY="your-key-here"

5. Verify Installation

# 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:

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

First N primes:

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

Data analysis:

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

Generate code:

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

Complex task (decomposed):

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:

mcporter call 'rlm.rlm_status()'

When to Use RLM

Use RLM for:

Mathematical calculations requiring precision
Statistical analysis (mean, std dev, correlations)
Financial calculations (compound interest, NPV, IRR)
Algorithm execution (primes, sorting, searching)
Data transformations and aggregations
Code generation with verification

Don't use RLM for:

Simple factual questions (use direct response)
Creative writing or brainstorming
Tasks requiring web search or real-time data
Very simple calculations (2+2)

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:

Root: grok-code-fast-1 (fast code execution)
Subtasks: gpt-4o-mini (cheap sub-queries)

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'":

# Reinstall dependencies
cd $HOME/.claude/mcp-servers/rlm
python3 -m venv venv
venv/bin/pip install mcp litellm

"mcporter: command not found":

npm install -g mcporter

"rlm not in mcporter list":

Check $HOME/.mcp.json exists and has rlm config
Verify run_server.sh is executable: chmod +x $HOME/.claude/mcp-servers/rlm/run_server.sh

Slow response:

RLM executes real code, typically 10-30 seconds
Complex tasks with decomposition take longer

References

Paper: Recursive Language Models (Zhang, Kraska, Khattab 2025)
RLM Library: github.com/alexzhang13/rlm
MCP Server: github.com/eesb99/rlm-mcp
MCP SDK: modelcontextprotocol.io
mcporter: mcporter.dev

rlm

RLM - Recursive Language Models

Prerequisites

1. Install mcporter (MCP bridge)

2. Install RLM MCP Server