Your Own RLM in 5 Minutes (Claude Code)

Introduction to Recursive Language Models

Overview of RLM Implementation

• The speaker introduces an open-source implementation of Recursive Language Models (RLMs) using Claude code primitives, encouraging viewers to experiment with it.

• The setup utilizes existing Claude code primitives, allowing the main instance to act as the root LLM call while leveraging sub-agent capabilities for processing.

Repository and Setup Instructions

• A publicly available repository will be accessible by the video's release, requiring only cloning for setup; no additional configurations are necessary.

• The repository includes a "claude" file familiar to users, along with agent and skill setups that guide the recursive language model's operations.

Core Components of RLM

• The Ripple script serves as the core of the RLM setup, functioning as a Read-Evaluate-Print Loop (REPL), implemented in approximately 400 lines of Python code.

• Procedural instructions are provided in "skill.md," which directs how the sub-agent executes tasks related to running the Ripple script.

Understanding Claude's Role

High-Level Abstraction in Execution

• The "claude.md" file is designed to provide high-level instructions without excessive detail, akin to an executive summary for efficient task execution.

• It outlines available skills and delegates tasks effectively while maintaining a focus on abstract workflows rather than granular details.

Testing Contextual Complexity

• The demonstration involves analyzing public merger agreements, specifically between Amazon and Whole Foods, emphasizing that legal validation is not within the speaker's expertise.

• The context being processed is characterized by its length and complexity due to its nature as a merger agreement contract.

Executing RLM Flow

Setting Up Execution Environment

• The speaker prepares their Claude code instance in "dangerously skip permissions mode" for streamlined operation without constant approvals during execution.

Agent Configuration Insights

• Within agents, there exists an RLM subcore agent utilizing Claude Haiku for memory object searches instead of processing lengthy contexts all at once.

Processing Methodology

• By virtualizing long contexts into Python objects and employing a Ripple loop approach, various operations like slicing can be performed programmatically on complex data sets.

Initiating Queries

Starting Query Process

• To initiate processing through RLM flow, a file path must be provided alongside user queries; sample queries from ChatGPT are referenced for testing purposes.

Conditions Precedent to Closing: Analyzing Legal Contracts

Context and Initial Setup

• The discussion begins with identifying the conditions precedent to closing for each party involved in a legal contract.

• The speaker mentions using tags in their query, indicating a personal preference despite considering them somewhat outdated.

Workflow Initialization

• The RLM workflow is initiated, which is seen as a positive step towards processing the legal document.

• A clarification is made regarding the specific document being analyzed; it’s the Nvidia share purchase agreement rather than an Amazon one.

Processing Methodology

• Basic Python string operations are employed to search through the contract, showcasing a methodical approach to data handling.

• The system processes context programmatically by performing operations on virtualized memory instead of reasoning over large contexts, avoiding potential issues like context rot.

Content Analysis and Extraction

• The focus shifts to locating relevant sections within Article 7 of the contract for further analysis by sub-agents.

• It’s noted that the system successfully finds answers from the contract without needing to engage sub-agents, highlighting efficiency in its operation.

Conclusion and Observations

• Although there was an expectation for sub-agent involvement, the system's ability to extract information directly demonstrates its sophistication. The speaker refrains from validating legal content due to lack of expertise but encourages others to explore further.