In this article, you will learn how to implement a context pruning pipeline for long-running AI agents, enabling them to manage conversational memory efficiently through semantic similarity. Topics we will cover include: Why unbounded conversation history is a problem for agents built on top of large language models, and what a context pruning strategy looks like. How to use sentence transformer embedding models to compute semantic similarity between a current prompt and archived conversation turns. How to assemble a pruned context window from the most recent turn, the top-K semantically relevant past turns, and the current prompt. Building a Context Pruning Pipeline for Long-Running Agents Introduction Modern AI agents built on top of large language models (LLMs) are designed to run continuously. As a result, their conversation history keeps growing indefinitely.…