/memory-fabric

name: memory-fabric
license: MIT
compatibility: "Claude Code 2.1.220+. Requires memory MCP server."
description: "Knowledge graph orchestration layer with entity extraction, natural language query parsing, deduplication (>85% similarity), and cross-reference boosting. Unifies search results ranked by recency, relevance, and authority. Use when designing memory retrieval, building entity graphs, or optimizing knowledge graph queries."
context: fork
version: 2.1.0
author: OrchestKit
tags: [memory, orchestration, graph-first, graph, unified-search, deduplication, cross-reference]
user-invocable: false
disable-model-invocation: true
allowed-tools: [Read, Bash, mcp__memory__search_nodes]
complexity: high
persuasion-type: collaborative
effort: high
metadata:
  category: mcp-enhancement
  mcp-server: memory

Memory Fabric - Graph Orchestration

Knowledge graph orchestration via mcp__memory__* for entity extraction, query parsing, deduplication, and cross-reference boosting.

Overview

• Comprehensive memory retrieval from the knowledge graph
• Cross-referencing entities within graph storage
• Ensuring no relevant memories are missed
• Building unified context from graph queries

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    Memory Fabric Layer                      │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│   ┌─────────────┐              ┌─────────────┐              │
│   │   Query     │              │   Query     │              │
│   │   Parser    │              │   Executor  │              │
│   └──────┬──────┘              └──────┬──────┘              │
│          │                            │                     │
│          ▼                            ▼                     │
│   ┌──────────────────────────────────────────────┐          │
│   │            Graph Query Dispatch              │          │
│   └──────────────────────┬───────────────────────┘          │
│                          │                                  │
│                ┌─────────▼──────────┐                       │
│                │  mcp__memory__*    │                       │
│                │  (Knowledge Graph) │                       │
│                └─────────┬──────────┘                       │
│                          │                                  │
│                          ▼                                  │
│        ┌─────────────────────────────────────────┐          │
│        │        Result Normalizer                │          │
│        └─────────────────────┬───────────────────┘          │
│                              │                              │
│                              ▼                              │
│        ┌─────────────────────────────────────────┐          │
│        │     Deduplication Engine (>85% sim)     │          │
│        └─────────────────────┬───────────────────┘          │
│                              │                              │
│                              ▼                              │
│        ┌─────────────────────────────────────────┐          │
│        │  Cross-Reference Booster                │          │
│        └─────────────────────┬───────────────────┘          │
│                              │                              │
│                              ▼                              │
│        ┌─────────────────────────────────────────┐          │
│        │  Final Ranking: recency × relevance     │          │
│        │                 × source_authority      │          │
│        └─────────────────────────────────────────┘          │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Unified Search Workflow

Step 1: Parse Query

Extract search intent and entity hints from natural language:

Input: "What pagination approach did database-engineer recommend?"

Parsed:
- query: "pagination approach recommend"
- entity_hints: ["database-engineer", "pagination"]
- intent: "decision" or "pattern"

Step 2: Execute Graph Query

**Query Graph (entity search):**

mcp__memory__search_nodes({
  query: "pagination database-engineer"
})

Step 3: Normalize Results

Transform results to common format:

{
  "id": "graph:original_id",
  "text": "content text",
  "source": "graph",
  "timestamp": "ISO8601",
  "relevance": 0.0-1.0,
  "entities": ["entity1", "entity2"],
  "metadata": {}
}

Step 4: Deduplicate (>85% Similarity)

When two results have >85% text similarity:

1. Keep the one with higher relevance score 2. Merge metadata 3. Mark as "cross-validated" for authority boost

Step 5: Cross-Reference Boost

If a result mentions an entity that exists elsewhere in the graph:

• Boost relevance score by 1.2x
• Add graph relationships to result metadata

Step 6: Final Ranking

Score = `recency_factor × relevance × source_authority`

| Factor | Weight | Description | | ---------------- | ------ | ------------------------------------------- | | recency | 0.3 | Newer memories rank higher | | relevance | 0.5 | Semantic match quality | | source_authority | 0.2 | Graph entities boost, cross-validated boost |

Result Format

{
  "query": "original query",
  "total_results": 4,
  "sources": {
    "graph": 4
  },
  "results": [
    {
      "id": "graph:cursor-pagination",
      "text": "Use cursor-based pagination for scalability",
      "score": 0.92,
      "source": "graph",
      "timestamp": "2026-01-15T10:00:00Z",
      "entities": ["cursor-pagination", "database-engineer"],
      "graph_relations": [
        { "from": "database-engineer", "relation": "recommends", "to": "cursor-pagination" }
      ]
    }
  ]
}

Enti