This article provides an in-depth exploration of context compression techniques in large model application development, detailing three core methods: ClaudeCode’s prompt compression technology, Gemini’s chain-of-thought compression strategy, and tool message pruning algorithms. Through reverse engineering analysis of specific code implementations, the article deciphers key design principles of context compression, including the selection logic for middle and oldest strategies. The content covers adaptive methods for different models (such as Claude, Gemini) and conversation features, providing practical code examples and decision frameworks to help developers optimize context window management and improve AI agent execution efficiency and response quality. These technical practices offer valuable guidance for building efficient large model applications and serve as an important practical guide to context engineering.
