aibot88 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:
- Keep the one with higher relevance score
- Merge metadata
- 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:
- Graph search via
mcp__memory__search_nodesfinds matching entities - Graph traversal expands context via entity relationships
- 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 startstop/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 storagecaching- 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 |