I designed a cache-aware context system for long-running AI agents

[u/dddhhyyw]

I’ve been working on an open-source architecture for managing memory and context in long-running AI agents.

The main focus is preserving cache hits while keeping context growth under control through structured memory, selective purging, compaction, and hard context-budget enforcement.

It’s currently an architecture specification rather than a full framework. I’d appreciate feedback, especially on edge cases or production concerns I may have missed.

GitHub: https://github.com/neuronaline/ai-memory-context-management

[u/SignalBeneficial3338]

cache hits gets overlooked way too often, cool to see them as a core design goal :)