memory-fabric

# 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):**

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

### Step 3: Normalize Results

Transform results to common format:

```json
{
  "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

```json
{
  "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"