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memory-management

Design tiered memory systems with semantic retrieval and consolidation for AI agents.

364|53|Updated May 9, 2026
Search Tags:#summarization#rag#agent orchestration#vector search#context window#garbage collection#memory architecture
One-click install
npx skills add https://github.com/cosmicstack-labs/mercury-agent-skills --skill memory-management-cosmicstack-labs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-management
Source: https://github.com/cosmicstack-labs/mercury-agent-skills/tree/main/categories/ai-ml/memory-management
Command: npx skills add https://github.com/cosmicstack-labs/mercury-agent-skills --skill memory-management-cosmicstack-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents long-running AI agents from forgetting crucial information, growing costly context windows, and polluting decisions with stale or irrelevant memories.

Core Features & Use Cases

  • Tiered memory architecture: Build working, episodic, and semantic memory layers that match different access needs and performance/cost trade-offs.
  • Context window optimization: Select high-importance working memories and retrieve relevant semantic items, adding summaries when the budget forces drops.
  • Memory consolidation and garbage collection: Deduplicate, merge related entries, prune low-value/old memories, and keep retrieval accurate over time.

Use case: You run a customer-support agent continuously for weeks; this Skill helps it retain user preferences and prior resolutions, retrieve the right past episodes via semantic search, and periodically consolidate/trim memory so responses stay coherent without runaway token costs.

Quick Start

Ask your agent to “design and implement a tiered memory system with context budgeting, summarization when needed, and scheduled consolidation that prunes stale low-importance memories.”

Frequently Asked Questions about memory-management

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent context window overflow in long-running AI agents?▼

Prevent context window overflow by applying token-budgeted context construction that selects high-importance working memories, retrieves relevant semantic items, and adds summaries when the budget forces drops.

What is tiered memory architecture for AI agent orchestration?▼

Tiered memory architecture builds working, episodic, and semantic memory layers that match different access needs and performance or cost trade-offs for long-running agents.

How do I implement garbage collection and pruning for stale agent memories?▼

Implement memory garbage collection by periodically deduplicating, merging related entries, and pruning low-value or old memories to keep vector search retrieval accurate over time.

Does importance-weighted summarization work with vector search retrieval?▼

Importance-weighted summarization works with vector search by generating summaries of lower-priority memories, allowing the retrieval system to rerank and fetch relevant semantic items within the context budget.

Can I use this memory architecture for a customer-support agent running continuously for weeks?▼

You can use this memory architecture for continuous customer-support agents by retaining user preferences, retrieving past resolutions via semantic search, and scheduling consolidation to trim stale memories without runaway token costs.

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