Context window management
What is Context window management?
Context window management refers to techniques and tools for making better use of the limited memory available to AI models during conversations—compressing information, tracking what the model sees, and caching repeated content to fit more interactions into the same space without losing capability.
As AI agents tackle longer tasks and handle more complex interactions, managing context efficiently directly impacts both operating costs and the quality of work the model can do, making this a practical lever for improving agent performance at scale.
References
- Why a Large Context Window Doesn't Mean an AI Will Remember Everything — Medium: Large Language Models
- Why Many AI Agents Still Fail Despite Having Tools, Memory And Planning? | Context Window — YouTube
- Better prompt caching for GPT-6 — Hacker News
- Jilazem/Czip — Infinite context without the infinite context window. Compresses agent sessions (Claude Code, Codex, Grok, Hermes) into a searchable HKP1 pack — full-history RAG, ~60x lower context cost, 0 deps. — GitHub
- Part 7: RAG in Python — retrieve, rank, then fill the context window (before LangChain) — Medium: Large Language Models
- Part 6 — RAG, the Idea (what makes the cut in the context window) — Medium: Large Language Models
- Your AI assistant is spending your context window before you type a word — Medium: AI Agents
- Your Context Window Is Lying to You: Agentic AI Design Patterns 12–13 — Medium: LLM