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Welcome to **RLM Code** -- the Research Playground and Evaluation OS for Recursive Language Model (RLM) agentic systems. RLM Code provides an interactive TUI-based development environment for building, benchmarking, and optimizing agent workflows through slash commands and natural language.
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Welcome to **RLM Code**, the Research Playground and Evaluation OS for Recursive Language Model (RLM) agentic systems. RLM Code provides a unified TUI-based development environment for building, benchmarking, and optimizing agent workflows through slash commands and natural language.
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---
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## What is RLM Code?
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## 🧪 What is RLM Code?
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RLM Code implements the **Recursive Language Model** paradigm from the research paper *"Recursive Language Models"* (Zhang, Kraska, Khattab, 2025). It extends the paper's concepts with:
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-**Context-as-variable**: Context is stored as a REPL variable rather than in the token window, enabling unbounded output and token-efficient processing.
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-**Deep recursion**: Support for recursion depth > 1, exceeding the paper's original limitation.
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-**Multi-paradigm execution**: Pure RLM, CodeAct, and Traditional paradigms with side-by-side comparison.
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-**Pluggable observability**: MLflow, OpenTelemetry, LangSmith, LangFuse, and Logfire integrations.
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-**Sandbox runtimes**: Local, Docker, Apple Container, Modal, E2B, and Daytona execution environments.
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-🧠 **Context-as-variable**: Context is stored as a REPL variable rather than in the token window, enabling unbounded output and token-efficient processing
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-🔁 **Deep recursion**: Support for recursion depth > 1, exceeding the paper's original limitation
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-🔀 **Multi-paradigm execution**: Pure RLM, CodeAct, and Traditional paradigms with side-by-side comparison
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-📊 **Pluggable observability**: MLflow, OpenTelemetry, LangSmith, LangFuse, and Logfire integrations
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-📦 **Sandbox runtimes**: Local, Docker, Apple Container, Modal, E2B, and Daytona execution environments
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---
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## Where to Go Next
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## 📚 Where to Go Next
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| Guide | Description |
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|-------|-------------|
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|[Installation](installation.md)| System requirements, package installation, optional dependencies, and verification |
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|[Quick Start](quickstart.md)| Launch the TUI, connect a model, run your first benchmark, and explore session replay|
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|[CLI Reference](cli.md)| Complete reference for both entry points and all 50+ slash commands |
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|[Configuration](configuration.md)| Full `rlm_config.yaml` schema, environment variables, and ConfigManager API |
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|[📦 Installation](installation.md)| System requirements, package installation, optional dependencies, and verification |
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|[⚡ Quick Start](quickstart.md)| Launch the TUI, connect a model, run your first benchmark, explore the Research tab|
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|[💻 CLI Reference](cli.md)| Complete reference for the entry point and all 50+ slash commands |
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|[⚙️ Configuration](configuration.md)| Full `rlm_config.yaml` schema, environment variables, and ConfigManager API |
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---
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## Quick Overview
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## ⚡ Quick Overview
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```bash
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# Install
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pip install rlm-code
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# Launch the standard TUI
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# Launch the unified TUI
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rlm-code
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# Launch the Research TUI directly
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rlm-research
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# Connect to a model and run a benchmark
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/connect anthropic claude-sonnet-4-20250514
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/rlm bench preset=dspy_quick
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/leaderboard
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```
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!!! tip "First Time?"
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Start with the [Installation](installation.md) guide to set up your environment, then follow the [Quick Start](quickstart.md) for a hands-on walkthrough.
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!!! tip "🆕 First Time?"
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Start with the [📦 Installation](installation.md) guide to set up your environment, then follow the [⚡ Quick Start](quickstart.md) for a hands-on walkthrough.
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!!! info "Two TUI Modes"
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RLM Code ships with two TUI modes: the **Standard TUI**(multi-pane workspace with chat, files, details, and shell panels) and the **Research TUI** (dark-themed research lab interface with file browser, code viewer, and metrics bar). Use `rlm-code`for the standard mode or `rlm-research` (or `rlm-code --research`) for the research mode.
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!!! info "🖥️ Unified TUI"
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RLM Code ships a **single TUI**with 5 tabs: **💬 Chat**, **📁 Files**, **📋 Details**, **⚡ Shell**, and **🔬 Research**. Use `rlm-code`to launch, and press `Ctrl+5` to access the Research tab for experiment tracking, benchmarks, and session replay.
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# Installation
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# 📦 Installation
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This guide covers how to install RLM Code, its optional dependencies, and how to verify your installation.
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pip install rlm-code[tui]
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```
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!!! note "TUI Required for Interactive Mode"
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The `textual` package (>= 0.86.0) is required for both the Standard TUI and the Research TUI. Without it, only headless/scripting usage is available.
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!!! note "🖥️ TUI Required for Interactive Mode"
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The `textual` package (>= 0.86.0) is required for the TUI with all 5 tabs (Chat, Files, Details, Shell, Research). Without it, only headless/scripting usage is available.
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# Quick Start
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# ⚡ Quick Start
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This guide walks you through launching RLM Code, connecting to an LLM, running your first benchmark, viewing the leaderboard, and exploring session replay -- all in under 10 minutes.
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This guide walks you through launching RLM Code, connecting to an LLM, running your first benchmark, viewing the leaderboard, and exploring the Research tab, all in under 10 minutes.
-[x]🔑 At least one LLM API key (OpenAI, Anthropic, or Gemini) or a local Ollama instance
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!!! tip "Local Models"
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!!! tip "🏠 Local Models"
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You can use RLM Code entirely with local models via [Ollama](https://ollama.com/). No API keys needed:
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```bash
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---
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## Step 1: Launch the TUI
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## Step 1: 🚀 Launch the TUI
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Navigate to a project directory (not your home directory) and launch:
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rlm-code
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```
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!!! warning "Directory Safety Check"
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!!! warning "⚠️ Directory Safety Check"
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RLM Code performs a safety check on startup. It will warn you if you are running from your home directory, Desktop, Documents, or a system directory. Always run from a dedicated project directory.
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You should see the RLM Code TUI with a multi-pane layout: a chat panel, file browser, details panel, and shell.
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### Alternative: Research TUI
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For a dark-themed research lab interface with file browser, code viewer, and metrics bar:
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```bash
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rlm-research
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```
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Or use the flag:
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```bash
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rlm-code --research
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```
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You should see the **RLM Research Lab** TUI with 5 tabs: 💬 Chat, 📁 Files, 📋 Details, ⚡ Shell, and 🔬 Research. The Chat tab is active by default.
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## Step 2: Initialize Your Project
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## Step 2: 📁 Initialize Your Project
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Initialize a project configuration file:
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## Step 3: Connect to a Model
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## Step 3: 🔗 Connect to a Model
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Use the `/connect` command to connect to an LLM provider:
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## Step 4: Run a Benchmark
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## Step 4: 🏆 Run a Benchmark
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RLM Code ships with 10+ built-in benchmark presets. Start with the quick DSPy smoke test:
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