Context Eviction

Budgets are hard. Token limits are real. So when you've got a finite context window and new information keeps arriving, something's gotta go. Context eviction is the brutal act of deciding what gets kicked out. Unlike context management (which is holistic and strategic), eviction is tactical and reactive. It's the FIFO queue, the LRU cache, the garbage collector of your AI system. The naive approach: just drop the oldest stuff. Simple, cheap, obviously suboptimal. Better approaches assign eviction priorities: maybe system instructions stay forever, recent messages stay, and old background info gets dropped first. Some systems use weighted scoring. Others implement time-decay functions where relevance naturally diminishes over time. There's also the question of what 'eviction' really means. Do you permanently delete it? Archive it? Compress it into a summary? Replace it with metadata pointers? I've seen implementations that evict full messages but keep their semantic embeddings, so the model can still find them if explicitly retrieved. The cost-performance tradeoff is interesting. Aggressively evicting old context saves tokens and speeds inference (good for latency and cost), but sometimes that old context was actually important (bad for quality). Maximem Vity manages this intelligently, using temporal and semantic signals to keep memories you're likely to need while gracefully archiving older ones. Maximem Synap's infrastructure layer lets developers tune eviction strategies to match their specific application requirements and quality targets.