memory-search

name	memory-search
description	SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
allowed-tools	Bash Read mcp__claude-flow__memory_search mcp__claude-flow__memory_store mcp__claude-flow__memory_list mcp__claude-flow__memory_retrieve mcp__claude-flow__memory_search_unified mcp__claude-flow__agentdb_pattern-search mcp__claude-flow__agentdb_context-synthesize
argument-hint	<query> [--hybrid] [--graph-rag] [--namespace NAME]

Memory Search (SOTA)

State-of-the-art semantic search across Ruflo memory with multiple retrieval strategies.

Strategy Selection

Choose based on query type:

• Default (dense): fast single-hop semantic match
• --hybrid: sparse + dense with RRF fusion (20-49% better for keyword+semantic queries)
• --graph-rag: multi-hop knowledge retrieval (30-60% better for reasoning queries)

Steps

1. Parse query and flags — extract search text and strategy flags from arguments

2. Select retrieval strategy:

   Dense search (default):

   npx @claude-flow/cli@latest memory search --query "QUERY" --namespace NAMESPACE --limit 10
   

   Or via MCP: mcp__claude-flow__memory_search({ query: "QUERY", namespace: "NAMESPACE", limit: 10 })

   Hybrid search (when --hybrid or query has specific keywords):

   npx ruvector search "QUERY" --hybrid --limit 10
   

   Graph RAG (when --graph-rag or multi-hop reasoning needed):

   npx ruvector search "QUERY" --graph-rag --limit 10
   

   Smart retrieval (when --smart or complex recall needed):

   npx @claude-flow/cli@latest memory search --query "QUERY" --smart --limit 10
   

   Or via MCP: mcp__claude-flow__memory_search({ query: "QUERY", smart: true, limit: 10 })

   Applies 5-phase pipeline: query expansion, RRF fusion, recency boost, MMR diversity, session round-robin. Best for: multi-session recall, temporal queries, diverse result sets.

   Unified cross-namespace: mcp__claude-flow__memory_search_unified({ query: "QUERY", limit: 10 })

3. Apply MMR reranking — for diverse results, filter near-duplicates (cosine > 0.92) while maximizing relevance

4. Apply recency weighting — boost recent entries with exponential decay (0.95/day)

5. Synthesize context (for complex queries): mcp__claude-flow__agentdb_context-synthesize({ query: "QUERY", sources: ["patterns", "tasks", "solutions"] })

6. Present results — ranked by composite score (relevance * diversity * recency), with source namespace attribution

Namespace Guide

Namespace	Best For
patterns	"How did we handle X?"
tasks	"What was the context for Y?"
solutions	"How did we fix Z?"
feedback	"What did the user prefer?"
security	"Known vulnerabilities in..."
(omit)	Search all namespaces