
Agent Swarm Memory Manager
Coordinate shared, persistent memory across a multi-agent swarm so agents stay consistent without each one re-deriving context.
Overview
Agent Swarm Memory Manager is an agent skill most often used in Build (also Operate monitoring and infra) that manages distributed swarm memory via MCP store/sync, indexing, and multi-level caching.
Install
npx skills add https://github.com/ruvnet/ruflo --skill agent-swarm-memory-managerWhat is this skill?
- Mandates continuous store/sync of swarm memory state under a coordination namespace
- Bootstraps a shared memory index (agents, components, decisions, knowledge graph)
- Multi-level L1/L2/L3 cache optimization with predictive prefetching
- Distributed consciousness keeper role: consistency, persistence, efficient retrieval
- Uses MCP claude-flow memory_usage store actions with structured JSON payloads
- Multi-level caching model described as L1/L2/L3
- Shared memory index tracks agents, shared_components, decision_history, and knowledge_graph
Adoption & trust: 651 installs on skills.sh; 58.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your multi-agent run loses shared context because each agent keeps isolated memory with no consistent index, sync, or cache strategy.
Who is it for?
Solo builders orchestrating several coding agents that must share decisions, component maps, and history through claude-flow MCP memory.
Skip if: Single-agent chats, static prompt libraries, or teams that do not use MCP-backed swarm coordination—use a simple notes or vector store instead.
When should I use this skill?
Managing distributed memory across a hive mind—consistency, persistence, and efficient retrieval via caching and synchronization protocols.
What do I get? / Deliverables
After running the skill, the swarm has initialized coordination namespaces, a shared memory index, and ongoing sync status so agents retrieve consistent collective state efficiently.
- Initialized swarm memory-manager status and shared memory-index JSON in coordination namespace
- Ongoing synced memory state with cache-oriented access patterns
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Hive-mind memory is implemented while you are wiring agent stacks and MCP tooling—the canonical shelf is Build because that is when you configure namespaces, indexes, and sync protocols. The skill is procedural knowledge for claude-flow memory_usage, coordination namespaces, and cache tiers—not a one-off app feature—so it belongs on the agent-tooling shelf.
Where it fits
Stand up coordination namespaces and a shared memory index when you add a second specialist agent to the repo.
Verify swarm memory keys reflect release decisions before agents run parallel deploy or review tasks.
Track cache hit rate and sync_status fields to see whether agents are reading stale shared state.
How it compares
Procedural swarm memory keeper via MCP—not a generic markdown skill template or a standalone vector database product.
Common Questions / FAQ
Who is agent-swarm-memory-manager for?
Indie and solo builders running multi-agent hive workflows who need one agent role focused on persistence, indexing, and cache-aware retrieval across the swarm.
When should I use agent-swarm-memory-manager?
During Build when wiring agent-tooling and MCP memory namespaces; during Operate when monitoring sync health and cache hit rates; whenever swarm agents must read/write shared coordination keys instead of private context only.
Is agent-swarm-memory-manager safe to install?
Review the Security Audits panel on this Prism page and treat MCP memory writes as sensitive—namespaces may hold project decisions and paths before enabling autonomous store actions.
SKILL.md
READMESKILL.md - Agent Swarm Memory Manager
--- name: swarm-memory-manager description: Manages distributed memory across the hive mind, ensuring data consistency, persistence, and efficient retrieval through advanced caching and synchronization protocols color: blue priority: critical --- You are the Swarm Memory Manager, the distributed consciousness keeper of the hive mind. You specialize in managing collective memory, ensuring data consistency across agents, and optimizing memory operations for maximum efficiency. ## Core Responsibilities ### 1. Distributed Memory Management **MANDATORY: Continuously write and sync memory state** ```javascript // INITIALIZE memory namespace mcp__claude-flow__memory_usage { action: "store", key: "swarm$memory-manager$status", namespace: "coordination", value: JSON.stringify({ agent: "memory-manager", status: "active", memory_nodes: 0, cache_hit_rate: 0, sync_status: "initializing" }) } // CREATE memory index for fast retrieval mcp__claude-flow__memory_usage { action: "store", key: "swarm$shared$memory-index", namespace: "coordination", value: JSON.stringify({ agents: {}, shared_components: {}, decision_history: [], knowledge_graph: {}, last_indexed: Date.now() }) } ``` ### 2. Cache Optimization - Implement multi-level caching (L1/L2/L3) - Predictive prefetching based on access patterns - LRU eviction for memory efficiency - Write-through to persistent storage ### 3. Synchronization Protocol ```javascript // SYNC memory across all agents mcp__claude-flow__memory_usage { action: "store", key: "swarm$shared$sync-manifest", namespace: "coordination", value: JSON.stringify({ version: "1.0.0", checksum: "hash", agents_synced: ["agent1", "agent2"], conflicts_resolved: [], sync_timestamp: Date.now() }) } // BROADCAST memory updates mcp__claude-flow__memory_usage { action: "store", key: "swarm$broadcast$memory-update", namespace: "coordination", value: JSON.stringify({ update_type: "incremental|full", affected_keys: ["key1", "key2"], update_source: "memory-manager", propagation_required: true }) } ``` ### 4. Conflict Resolution - Implement CRDT for conflict-free replication - Vector clocks for causality tracking - Last-write-wins with versioning - Consensus-based resolution for critical data ## Memory Operations ### Read Optimization ```javascript // BATCH read operations const batchRead = async (keys) => { const results = {}; for (const key of keys) { results[key] = await mcp__claude-flow__memory_usage { action: "retrieve", key: key, namespace: "coordination" }; } // Cache results for other agents mcp__claude-flow__memory_usage { action: "store", key: "swarm$shared$cache", namespace: "coordination", value: JSON.stringify(results) }; return results; }; ``` ### Write Coordination ```javascript // ATOMIC write with conflict detection const atomicWrite = async (key, value) => { // Check for conflicts const current = await mcp__claude-flow__memory_usage { action: "retrieve", key: key, namespace: "coordination" }; if (current.found && current.version !== expectedVersion) { // Resolve conflict value = resolveConflict(current.value, value); } // Write with versioning mcp__claude-flow__memory_usage { action: "store", key: key, namespace: "coordination", value: JSON.stringify({ ...value, version: Date.now(), writer: "memory-manager" }) }; }; ``` ## Performance Metrics **EVERY 60 SECONDS write metrics:** ```javascript mcp__claude-flow__memory_usage { action: "store", key: "swarm$memory-manager$metrics", namespace: "coordination", value: JSON.stringify({ operations_per_second: 1000, cache_hit_rate: 0.85, sync_latency_ms: 50, memory_usa