--- title: Memory Management in Long-Running Agents: Short-Term vs.... description: Long-running AI agents face two failure modes: silent amnesia when context windows overflow and drop old turns, or cost/latency collapse when full history is... canonical: https://daily.dev/posts/memory-management-in-long-running-agents-short-term-vs-long-term-vector-memory-lycwr1r8r twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Memory Management in Long-Running Agents: Short-Term vs. Long-Term Vector Memory | daily.dev og:description: Long-running AI agents face two failure modes: silent amnesia when context windows overflow and drop old turns, or cost/latency collapse when full history is... og:url: https://daily.dev/posts/memory-management-in-long-running-agents-short-term-vs-long-term-vector-memory-lycwr1r8r og:image: https://api.daily.dev/og/posts/lYCwR1R8r.png og:image:alt: Memory Management in Long-Running Agents: Short-Term vs. Long-Term Vector Memory og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Memory Management in Long-Running Agents: Short-Term vs. Long-Term Vector Memory **[Medium](https://daily.dev/sources/medium_js)** · 10 min read · 0 upvotes · 1 comments ## Summary Long-running AI agents face two failure modes: silent amnesia when context windows overflow and drop old turns, or cost/latency collapse when full history is retained. A three-tier memory architecture addresses both: Tier 1 is a token-budgeted working memory (deque of recent turns), Tier 2 is an incremental summary buffer with a compression-pass cap, and Tier 3 is a long-term vector store populated by explicit atomic fact extraction rather than raw summary embedding. Key design details include never silently discarding evicted turns, tracking fact supersession to handle contradictions, and a retrieval scoring function that hard-excludes superseded facts while blending semantic similarity with recency decay. The architecture keeps per-turn token costs roughly flat as conversations grow, trading a small extraction overhead for bounded context size and precise retrieval. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How AI Agents Manage Memory and Avoid Forgetfulness](https://daily.dev/posts/how-ai-agents-manage-memory-and-avoid-forgetfulness-wzlg6idul) · ByteByteGo · 3 upvotes · 1 comments - [Memory for AI Agents: A New Paradigm of Context Engineering](https://daily.dev/posts/memory-for-ai-agents-a-new-paradigm-of-context-engineering-ku2rvmlcw) · The New Stack · 2 upvotes · 0 comments - [Your AI Agent Was Great in Week One. 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