Strategies for managing AI agent context windows including optimization, summarization, retrieval-augmented generation, progressive disclosure, and pruning. Use when the user is hitting context limits, building long-running agents, implementing RAG, optimizing token usage, or designing systems that need to manage large amounts of information within limited context windows.
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Context Management
Strategies for optimizing AI agent context windows. Covers summarization, RAG, progressive disclosure, pruning, and token budgeting for long-running agents.
When to Use
User is hitting context window limits in agent workflows
User is building long-running agents that accumulate context
User needs retrieval-augmented generation (RAG) patterns
User wants to optimize token usage and reduce costs
User is designing memory systems for agents
Core Patterns
S
[Description truncada. Veja o README completo no GitHub.]
