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 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)