RLM PRO

Analyze any corpus of unstructured data using Recursive Language Models - enables LLMs to handle near-infinite context through recursive decomposition.

Works with codebases, document collections, research papers, logs, and any text-based data corpus.

Based on the RLM research from MIT OASYS lab.

Installation

``bash


Using npx (recommended - auto-installs Python package from GitHub)

npx @superadnim/rlm-pro ./my-project -q "Explain the architecture"

Or install globally

npm install -g @superadnim/rlm-pro
`

$3

- Node.js 18+
- uv (Python package manager) - will be prompted to install if missing
- OpenAI API key (set as environment variable)

`bash


Install uv if not already installed

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

Install the Python package (auto-installed on first run, or install manually)

uv pip install git+https://github.com/CG-Labs/RLM-PRO.git

Set your API key

export OPENAI_API_KEY="your-key"
`

Usage

$3

`bash


Basic usage

npx @superadnim/rlm-pro ./my-project -q "Explain the architecture"

Get JSON output (for programmatic use)

npx @superadnim/rlm-pro ./my-project -q "List all API endpoints" --json

Use a specific model

npx @superadnim/rlm-pro ./my-project -q "Find potential bugs" -m gpt-5.2

Use Anthropic backend

npx @superadnim/rlm-pro ./my-project -q "Review this code" -b anthropic

Verbose output for debugging

npx @superadnim/rlm-pro ./my-project -q "How does authentication work?" -v

Only build context (no LLM call)

npx @superadnim/rlm-pro ./my-project -q "" --context-only
`

$3

`javascript
const { analyzeCodebase } = require('@superadnim/rlm-pro');

async function main() {
const result = await analyzeCodebase('./my-project', {
query: 'Summarize the codebase structure',
backend: 'openai',
model: 'gpt-5.2',
});

console.log(result.response);
console.log('Execution time:', result.execution_time, 'seconds');
}

main();
`

Options

| Option | Description | Default |
|--------|-------------|---------|
| -q, --query | Question or task to perform (required) | - |
| -b, --backend | LLM backend (openai, anthropic, etc.) | openai |
| -m, --model | Model name | gpt-5.2 |
| -e, --env | Execution environment (local, docker) | local |
| --max-depth | Maximum recursion depth | 1 |
| --max-iterations | Maximum iterations | 30 |
| --max-file-size | Max size per file | 100000 |
| --max-total-size | Max total context size | 500000 |
| --no-tree | Exclude directory tree from context | - |
| --json | Output as JSON | - |
| -v, --verbose | Enable verbose output | - |
| --context-only | Only output built context | - |

Environment Variables

| Variable | Description | Required |
|----------|-------------|----------|
| OPENAI_API_KEY | OpenAI API key | Yes (for OpenAI backend) |
| ANTHROPIC_API_KEY | Anthropic API key | For Anthropic backend |

How It Works

RLM (Recursive Language Models) enables LLMs to handle near-infinite context by:

1. Context Building: Intelligently reads and formats your codebase
2. Recursive Decomposition: Breaks complex queries into manageable sub-tasks
3. Code Execution: Runs Python code in a sandboxed environment to explore and analyze
4. Iterative Refinement: Continues until a complete answer is found

This allows answering complex questions about large codebases that would exceed normal context limits.

Examples

$3


`bash
npx @superadnim/rlm-pro ./backend -q "Describe the system architecture and key design patterns"
`

$3

`bash
npx @superadnim/rlm-pro ./src -q "Find potential security vulnerabilities" --json
`

$3

`bash
npx @superadnim/rlm-pro ./api -q "Generate API documentation for all endpoints"
`

$3

`bash
npx @superadnim/rlm-pro ./feature-branch -q "Review this code for best practices"
``

License

MIT

Links

- RLM Research Paper
- GitHub Repository