/memory-fabric

name: memory-fabric
license: MIT
compatibility: "Claude Code 2.1.251+. 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 Parser │ │Query 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" }
      ]
    }
  ]
}

Entity Extraction

Memory Fabric extracts entities from natural language for graph storage:

Input: "database-engineer uses pgvector for RAG applications"

Extracted:
- Entities:
  - { name: "database-engineer", type: "agent" }
  - { name: "pgvector", type: "technology" }
  - { name: "RAG", type: "pattern" }
- Relations:
  - { from: "database-engineer", relation: "uses", to: "pgvector" }
  - { from: "pgvector", relation: "used_for", to: "RAG" }

Load `Read("references/entity-extraction.md")` for detailed extraction patterns.

Graph Relationship Traversal

Memory Fabric supports multi-hop graph traversal for complex relationship queries.

Example: Multi-Hop Query

Query: "What did database-engineer recommend about pagination?"

1. Search for "database-engineer pagination"
   → Find entity: "database-engineer recommends cursor-pagination"

2. Traverse related entities (depth 2)
   → Traverse: database-engineer → recommends → cursor-pagination
   → Find: "cursor-pagination uses offset-based approach"

3. Return results with relationship context

Integration with Graph Memory

Memory Fabric uses the knowledge graph for entity relationships:

1. **Graph search** via `mcp__memory__search_nodes` finds matching entities 2. **Graph traversal** expands context via entity relationships 3. **Cross-reference** boosts relevance when entities match

Integration Points

With memory Skill

When memory search runs, it can optionally use Memory Fabric for unified results.

With Hooks

• `prompt/memory-fabric-context.sh` - Inject unified context at session start
• `stop/memory-fabric-sync.sh` - Sync entities to graph at session end

Configuration

# Environment variables
MEMORY_FABRIC_DEDUP_THRESHOLD=0.85    # Similarity threshold for merging
MEMORY_FABRIC_BOOST_FACTOR=1.2