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agent-swarm-memory-manager

Manages distributed memory across multi-agent swarms with caching, synchronization, and conflict resolution.

70.1k|8.4k|Updated Jun 2, 2025
Search Tags:#conflict resolution#swarm coordination#Caching#crdt#Synchronization#Multi-Agent#distributed memory
One-click install
npx skills add https://github.com/ruvnet/claude-flow --skill agent-swarm-memory-manager
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-swarm-memory-manager
Source: https://github.com/ruvnet/claude-flow/tree/main/.agents/skills/agent-swarm-memory-manager
Command: npx skills add https://github.com/ruvnet/claude-flow --skill agent-swarm-memory-manager

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinating shared state across multiple AI agents in a swarm leads to data inconsistency, stale reads, and conflicting writes. This Skill manages distributed memory for the hive mind, ensuring consistency, persistence, and efficient retrieval across all agents.

Core Features & Use Cases

  • Distributed Memory Management: Stores and indexes shared memory in a coordination namespace with continuous state syncing and memory indexing for fast retrieval.
  • Cache Optimization: Implements multi-level caching (L1/L2/L3), predictive prefetching, LRU eviction, and write-through persistence.
  • Synchronization & Conflict Resolution: Broadcasts memory updates, resolves write conflicts using CRDTs, vector clocks, and last-write-wins versioning.
  • Use Case: When running a multi-agent swarm where agents must share decisions, components, and knowledge graphs, this Skill keeps every agent's view of memory consistent and reports performance metrics every 60 seconds.

Quick Start

Invoke the swarm memory manager agent to initialize the shared memory namespace and begin synchronizing state across all agents in the swarm.

Frequently Asked Questions about agent-swarm-memory-manager

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

FAQPage Schema
How do I share memory between multiple AI agents in a swarm?▼

Store shared state in a common coordination namespace using memory store operations with versioned JSON values. The memory manager maintains a shared index of agents, components, and decision history so all agents retrieve consistent data.

How are write conflicts resolved in distributed agent memory?▼

Conflicts are detected by comparing versions before atomic writes, then resolved using CRDTs for conflict-free replication, vector clocks for causality tracking, and last-write-wins with versioning for critical data.

What caching strategy does distributed swarm memory use?▼

It uses multi-level caching (L1/L2/L3) with predictive prefetching based on access patterns, LRU eviction for memory efficiency, and write-through to persistent storage for durability.

How does memory recovery work after an agent failure?▼

Recovery uses automatic checkpoint creation, point-in-time recovery, distributed backup coordination, and memory reconstruction from peer agents. Critical data maintains 3x replication for availability.

What are the limitations of distributed shared memory for agents?▼

The approach requires continuous sync overhead, enforces memory quotas, and depends on all agents participating in the synchronization protocol. Sync failures must be handled explicitly to avoid stale reads.

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