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Agent memory

LLM-driven agents need to remember prior plans so they're consistent on similar tasks. Mneme provides task-description embedding → plan lookup with confidence-gated reuse. Two agents (alice, bob) get separate namespaces so their memories don't bleed.

LLM endpoint: {{ spark_url }} · model: {{ model }}

Re-run the same task on the same agent → exact-match cache hit. Try a paraphrase → semantic match. Switch agents → miss (separate memory).

Plan

no plan yet

History (this session)

#AgentLayerLatencyTask
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