Break the context window barrier with Amazon Bedrock AgentCore

This article explains how to implement Recursive Language Models (RLM) using Amazon Bedrock AgentCore to process documents exceeding model context windows.

• RLM treats documents as external environments, not context window inputs
• Root LLM writes code to explore documents; sub-LLMs analyze specific sections
• Amazon Bedrock AgentCore Code Interpreter provides sandboxed Python runtime with persistent state
• Results stored as Python variables, not consumed from context window
• Achieves 100% success rate vs. 46.7-93.3% for baseline approaches
• Claude Opus 4.6 + RLM: 80% accuracy on financial QA (vs. 66.7% Long Context)
• Claude Sonnet 4.5 + RLM: 76% accuracy on code repository QA (vs. 46% Long Context)
• Tradeoffs: higher latency (seconds to minutes), increased costs, requires careful prompt engineering

RLM enables practical analysis of arbitrarily large documents by decoupling document size from context window limits through iterative code execution and delegated sub-LLM calls.