Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Solving Context Window Overflow in AI Agents

Anton Bulle Labate, Valesca Moura de Sousa, Sandro Rama Fiorini, Leonardo Guerreiro Azevedo, Raphael Melo Thiago, Viviane Torres da Silva

TL;DR

The paper addresses context-window overflow when LLM-powered agents must process large tool outputs. It proposes a pointer-based memory framework with mirrored tools that store large results externally and let the model interact via memory paths. This preserves full tool functionality without modifying tools or architecture, while reducing token usage and increasing speed. The approach is validated in materials science tasks, including electronic-grid-based molecule retrieval and SDS ingredient extraction, showing successful end-to-end workflows and substantial token savings compared to conventional methods. The work broadens the applicability of agent systems to domains with routinely large tool outputs.

Abstract

Large Language Models (LLMs) have become increasingly capable of interacting with external tools, granting access to specialized knowledge beyond their training data - critical in dynamic, knowledge-intensive domains such as Chemistry and Materials Science. However, large tool outputs can overflow the LLMs' context window, preventing task completion. Existing solutions such as truncation or summarization fail to preserve complete outputs, making them unsuitable for workflows requiring the full data. This work introduces a method that enables LLMs to process and utilize tool responses of arbitrary length without loss of information. By shifting the model's interaction from raw data to memory pointers, the method preserves tool functionality, allows seamless integration into agentic workflows, and reduces token usage and execution time. The proposed method is validated on a real-world Materials Science application that cannot be executed with conventional workflows, and its effectiveness is demonstrated via a comparative analysis where both methods succeed. In this experiment, the proposed approach consumed approximately seven times fewer tokens than the traditional workflow.

Solving Context Window Overflow in AI Agents

TL;DR

The paper addresses context-window overflow when LLM-powered agents must process large tool outputs. It proposes a pointer-based memory framework with mirrored tools that store large results externally and let the model interact via memory paths. This preserves full tool functionality without modifying tools or architecture, while reducing token usage and increasing speed. The approach is validated in materials science tasks, including electronic-grid-based molecule retrieval and SDS ingredient extraction, showing successful end-to-end workflows and substantial token savings compared to conventional methods. The work broadens the applicability of agent systems to domains with routinely large tool outputs.

Abstract

Large Language Models (LLMs) have become increasingly capable of interacting with external tools, granting access to specialized knowledge beyond their training data - critical in dynamic, knowledge-intensive domains such as Chemistry and Materials Science. However, large tool outputs can overflow the LLMs' context window, preventing task completion. Existing solutions such as truncation or summarization fail to preserve complete outputs, making them unsuitable for workflows requiring the full data. This work introduces a method that enables LLMs to process and utilize tool responses of arbitrary length without loss of information. By shifting the model's interaction from raw data to memory pointers, the method preserves tool functionality, allows seamless integration into agentic workflows, and reduces token usage and execution time. The proposed method is validated on a real-world Materials Science application that cannot be executed with conventional workflows, and its effectiveness is demonstrated via a comparative analysis where both methods succeed. In this experiment, the proposed approach consumed approximately seven times fewer tokens than the traditional workflow.

Paper Structure

This paper contains 8 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Handling large tool responses
  3. Experiments
  4. Retrieval of molecules with similar electronic grid structures
  5. Extracting ingredients from a safety data sheet
  6. Conclusion
  7. Workflow execution of the retrieval of molecules with similar electronic grid structures experiment
  8. Workflow execution of the extracting ingredients from a safety data sheet experiment

Figures (1)

  • Figure 1: Overview of the approach: general architecture of the proposed method and workflow illustration of a real-world use case.