newsAWS Machine LearningTrust 88 · LabPublished 1mo agoLive · 1mo ago
Structured memory filtering with metadata in AgentCore Memory
In this post, you will learn how metadata works across configuration, ingestion, and retrieval, explore enterprise use cases including multi-agent and multi-tenant architectures, and discover best practices for implementation.
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownlas7/memharness →
- LinkedLinked via unknownSelective Memory Retention for Long-Horizon LLM Agents →
- LinkedLinked via unknownTraceLab: Characterizing Coding Agent Workloads for LLM Serving →
- LinkedLinked via unknownForensic Trajectory Signatures for Agent Memory Poisoning Detection →
- LinkedLinked via unknownMemSyco-Bench: Benchmarking Sycophancy in Agent Memory →
- PossiblePossibly related (embedding) · 59%joshuaswarren/remnic →
- PossiblePossibly related (embedding) · 48%lotus-data/lotus →
- PossiblePossibly related (embedding) · 54%ruvnet/agenticow →
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Related across the graph
paperTraceLab: Characterizing Coding Agent Workloads for LLM ServingrepoOpenDataBox/MemoryDatarepoOpenDataBox/awesome-agent-memoryrepoagentjido/llmdbreporuvnet/agenticowrepojoshuaswarren/remnicrepoAQ-MedAI/MedMemoryBenchrepoagentjido/llm_dbrepolotus-data/lotuspaperSelective Memory Retention for Long-Horizon LLM Agentsrepolas7/memharnesspaperSupra Cognitive Modes: A Routed Architecture for Agent MemorypaperMemSyco-Bench: Benchmarking Sycophancy in Agent MemorypaperForensic Trajectory Signatures for Agent Memory Poisoning DetectionrepoTencentCloud/TencentDB-Agent-Memory