Intelligent Context Management Optimization Solution
The context management techniques in the Claude Code project provide two actionable solutions to the Token limitation problem:
- Dynamic compression technology: automatically trigger compression when Token utilization ≥ 92%. The implementation method includes 1) extracting dialog summaries using LLM 2) preserving keyword vector index 3) constructing logical relationship mapping. The docs/memory_management.md in the repository details the compression algorithm parameters
- Hierarchical storage strategy: Referring to its CLAUDE.md long-term memory mechanism, the context can be divided into three layers: working memory (real-time interaction), session cache (current conversation), and knowledge base (persistent storage). The context_manager.mjs module in the project chunks/ directory shows the concrete implementation
Practical suggestion: Combined with the analysis script provided by the repository (node scripts/context_optimizer.js), we can test the impact of different compression thresholds (85-95%) on response quality. The technical documentation shows that Claude Code uses a "sliding window + keyframe retention" scheme to reduce the context size by 70% without affecting the core semantics.
This answer comes from the articleanalysis_claude_code: a library for reverse engineering Claude Code.The
May not be reproduced without permission:AI productivity tools " How to optimize the context management mechanism of AI assistants to avoid the Token waste problem?
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