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Multi-Agent Memory Management
Three-tier memory with hot context, cold storage, and auto-summarization. 35-45% token reduction at 87% semantic recall.
The problem
Multi-agent systems risk corrupting shared context or leaking secrets across sessions when memory isn't scoped.
- One agent's session context bleeds into another user's conversation.
- A support ticket includes a customer's data that a completely different session generated.
- Nobody's sure how long context should live, so it either vanishes too soon or lingers too long.
What NEO built
NEO built a three-tier memory system (hot context, cold storage, and auto-summarization) with namespaced stores, TTLs, and PII-redaction hooks across agent handoffs.
The result
35-45% token reduction
Cuts token usage 35-45% while holding 87% semantic recall, keeping swarms within context limits without losing state.
From the blog · 8 min
3-Tier Memory for LLM Agents: 35-45% Token Reduction Without Losing Context
NEO built a multi-agent memory management system with hot context, cold storage, and automatic summarization that cuts token usage by 35-45% while maintaining 87% semantic recall accuracy.
Try this in your workspace
Paste this into NEO chat to kick off the same workflow on your own data.
Set up tiered memory for my multi-agent system — hot context, cold storage, auto-summarization — with namespaced stores and PII redaction so sessions don't leak into each other.
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