Reducing Multi-Modal Agent Latency by Omitting Screenshot History

Latency Reduction Through Screenshot Omission

A developer building computer agents identified latency as a major pain point, particularly when waiting for agents to perform simple actions like pressing buttons. To address this, they conducted an experiment using Claude to find ways to reduce latency beyond just model selection.

The key finding was that latency can be significantly reduced by omitting previous screenshots from agent requests. Instead of including full base64-encoded image data for historical screenshots, the developer replaced them with the string "[image omitted]". This approach maintains flat latency while reducing overall response times.

The developer noted that focusing on agentic engineering and ReAct patterns had caused them to overlook basic HTTP principles that impact performance. The experiment and findings are documented in a GitHub repository titled "inference-latency-study" created by Emericen.

Technical Implementation

The core technique involves modifying how multi-modal agents handle screenshot history:

• Instead of sending complete base64-encoded images for previous screenshots
• Replace these with placeholder text: "[image omitted]"
• Maintain current screenshot data while omitting historical image data

This approach reduces payload size and transmission time without compromising the agent's ability to understand and interact with the current screen state.

The GitHub repository contains the experimental setup and results, providing a practical reference for developers working with multi-modal agents who are experiencing latency issues.