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memory-search

memory-search

github.com/ruvnetData & databases80.0k750 installsUpdated today
Repository

About memory-search

memory-search is a semantic retrieval skill for Ruflo memory that selects strategies based on query needs. It supports cross-namespace search and ranks results using relevance, diversity, and recency.

Key features

  1. 01Dense semantic retrieval
  2. 02Hybrid search with RRF fusion
  3. 03Graph RAG multi-hop retrieval
  4. 04Smart and cross-namespace search
  5. 05MMR deduplication and recency weighting

Use cases

  1. 01Recall information across sessions
  2. 02Find records with specific keywords
  3. 03Answer questions requiring multi-hop reasoning

MCP support

It can call Ruflo MCP tools for semantic, smart, and cross-namespace memory search, as well as context synthesis.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ruvnet/ruflo --skill memory-search

After installing, type/memory-searchin Claude Code to confirm it loaded.

Details

From the project README

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__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__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

NamespaceBest 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

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