When you analyze documents that span millions of characters, you hit the context window barrier and even the largest context windows fall short. Your model either rejects the input or produces answers based on incomplete information. How do you reason over documents that don’t fit?

In this post, you will learn how to implement Recursive Language Models (RLM) using Amazon Bedrock AgentCore Code Interpreter and the Strands Agents SDK. By the end, you will know how to:

Consider a typical financial analysis task of comparing metrics across two years of annual reports from a single company. Each report runs 300–500 pages. Add analyst reports, SEC filings, and supplementary materials, and the total reaches millions of characters.

When you send these documents directly to a model, either the input exceeds the model’s context window limit and the request fails, or the input fits but the model has difficulty attending to information in the middle of long inputs, often referred to as the “lost in the middle” problem.

Both failure modes exist because context window size is a hard limit that prompt engineering alone can’t solve. You need an approach that decouples document size from the model’s context window.

RLMs, introduced by Zhang et al. in arXiv:2512.24601, reframe the problem. Instead of feeding an entire document into the model’s context window, an RLM treats the input as an external environment that the model interacts with programmatically.