memory-fabric

name	memory-fabric
license	MIT
compatibility	Claude Code 2.1.183+. 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" }
      ]
    }
  ]
}

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("${CLAUDE_SKILL_DIR}/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        # Cross-reference boost multiplier
MEMORY_FABRIC_MAX_RESULTS=20          # Max results per source

MCP Requirements

Required: Knowledge graph MCP server:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@anthropic/memory-mcp-server"]
    }
  }
}

Error Handling

Scenario	Behavior
graph unavailable	Error - graph is required
Query empty	Return recent memories from graph

Related Skills

• ork:memory - User-facing memory operations (search, load, sync, viz)
• ork:remember - User-facing memory storage
• caching - Caching layer that can use fabric

Key Decisions

Decision	Choice	Rationale
Dedup threshold	85%	Balances catching duplicates vs. preserving nuance
Parallel queries	Always	Reduces latency, both sources are independent
Cross-ref boost	1.2x	Validated info more trustworthy but not dominant
Ranking weights	0.3/0.5/0.2	Relevance most important, recency secondary