Context Window Compaction for AI Agents: Managing LLM Memory

Large language models have strict memory limits, and managing the context window is critical for building reliable AI agents. Unmanaged chat histories lead to high token costs, slow response times, and lost context. This text-only course guides you through the foundational concepts of context window compaction, showing you how to design systems that summarize, filter, and compress conversational state. You will learn to build efficient memory architectures that keep your AI agents working smoothly without exceeding token thresholds. What you'll learn: Understand the core mechanics of LLM context windows and token limitations; Design summarization prompts to condense long conversations without losing critical details; Filter and convert complex message structures into concise text formats; Implement sliding window memory and compaction strategies for AI agents; Apply token-counting techniques to dynamically manage context size; Explore modern agentic memory patterns and state management workflows. You will start with the fundamental terminology of context limits and tokenization before moving on to practical text-based exercises. Through step-by-step written explanations and code snippets, you will learn how to implement robust compaction algorithms that optimize your agent's performance. This course is designed for beginner developers, prompt engineers, and AI enthusiasts who want to build smarter agents. No advanced machine learning background is required, though a basic understanding of programming concepts is helpful. Start reading today to master context management and build highly efficient AI systems.