rlm-go

A Go implementation of Recursive Language Models (RLM) - an inference-time scaling strategy that enables LLMs to handle arbitrarily long contexts by treating prompts as external objects that can be programmatically examined and recursively processed.

Overview

RLM-go provides a Go REPL environment where LLM-generated code can:

• Access context stored as a variable
• Make recursive sub-LLM calls via Query() and QueryBatched()
• Use standard Go operations for text processing
• Signal completion with FINAL() or FINAL_VAR()

Key Design Decisions

Unlike the Python RLM which uses socket IPC, rlm-go uses direct function injection via Yaegi - a Go interpreter. This eliminates:

• Socket server overhead
• Serialization/deserialization
• Process boundaries

The result is ~100x less latency per sub-LLM call compared to socket IPC.

Requirements

• Go 1.23 or later (for building from source)
• An LLM API key:
  • ANTHROPIC_API_KEY for Claude models (default)
  • GEMINI_API_KEY for Gemini models
  • OPENAI_API_KEY for OpenAI models
• (Optional) Podman or Docker for isolated sandbox execution

Supported Models

The provider is auto-detected based on model name. Anthropic is the default.

Installation

Quick Install (Recommended)

# Download and install the latest release
curl -fsSL https://raw.githubusercontent.com/XiaoConstantine/rlm-go/main/install.sh | bash

This installs the rlm binary to ~/.local/bin/rlm.

Go Install

go install github.com/XiaoConstantine/rlm-go/cmd/rlm@latest

From Source

git clone https://github.com/XiaoConstantine/rlm-go.git
cd rlm-go
go build -o rlm ./cmd/rlm

As a Library

go get github.com/XiaoConstantine/rlm-go

Claude Code Integration

RLM includes a skill for Claude Code that provides documentation and usage guidance for large context processing.

Install the Skill

rlm install-claude-code

This creates a skill at ~/.claude/skills/rlm/SKILL.md that teaches Claude Code:

• When to use RLM (contexts >50KB, token efficiency needed)
• Command usage and options