Context Compression

Techniques for reducing the number of tokens in an AI agent's context window while preserving the essential information. Methods include summarizing long conversation histories, extracting key facts from retrieved documents, pruning irrelevant tool outputs, and using specialized compression models. Context compression enables agents to handle longer conversations and more complex tasks within token budget and context window limits—especially important for cost-sensitive production deployments.