MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents
MemLens scores individual agent memories, stores them by value, and exposes quality, latency, and token tradeoffs—an inspectable alternative to retaining every interaction equally.
MemLens treats memory records as first-class data objects and applies **Shapley-style memory evaluation**, **value-aware storage**, and memory-assisted response. Its dashboard exposes record values and hierarchical memory structures.
Builders of long-running agents should consider ranking memories by demonstrated utility instead of retaining every interaction uniformly. The study-copilot implementation compares strategies across **response quality, retrieval latency, and token consumption**.
MemLens treats memory records as first-class data objects and applies **Shapley-style memory evaluation**, **value-aware storage**, and memory-assisted response. Its dashboard exposes record values and hierarchical memory structures. Builders of long-running agents should consider ranking memories by demonstrated utility instead of retaining every interaction uniformly. The study-copilot implementation compares strategies across **response quality, retrieval latency, and token consumption**. The supplied material describes the system and its intended benefits but gives no benchmark results, deployment details, or evidence that its value estimates generalize beyond the study-copilot application.
MemLens makes memory retention an observable optimization problem rather than a default policy of keeping everything. Against prior memory and context systems, it adds per-record utility estimates and explicit quality, latency, and token tradeoffs, but its current evidence is descriptive: builders still need application-specific validation and provenance controls before using its scores to retain, consolidate, or discard operational knowledge.