Supermemory

Store, recall, and semantically search conversation insights using a vector memory engine to enhance customer interactions and context.

supermemory — AI Memory Engine

Semantic memory layer powered by vector search. Store, recall, and connect conversation insights across all customer interactions.

Architecture

text

Conversation → Extract Insights → Embed → Store (Vector DB)
                                            ↓
Query → Semantic Search → Relevant Memories → Inject into Context

Memory Types

Type	TTL	Example
Customer Fact	Permanent	"Ahmed from Dubai, buys 50 units/quarter"
Conversation Insight	90 days	"Interested in bulk pricing for Model X"
Market Signal	30 days	"East Africa demand spike for product Y"
Effective Script	Permanent	"Opening with local market data → 3x reply rate"

Commands

• memory:add <text> — Manually add a memory
• memory:search <query> — Semantic search across all memories
• memory:list [type] — List recent memories by type
• memory:forget <id> — Delete a specific memory
• memory:stats — Memory usage statistics

Auto-Capture

When enabled, the engine automatically extracts and stores:

1. Customer preferences and requirements
2. Price sensitivity signals
3. Competitive mentions
4. Purchase timeline indicators
5. Relationship context (referrals, prior interactions)

Configuration

json

{
  "provider": "lancedb",
  "embedding_model": "{{embedding_model}}",
  "auto_capture": true,
  "capture_strategy": "last_turn",
  "recall_top_k": 5,
  "ttl_days": {
    "customer_fact": null,
    "conversation_insight": 90,
    "market_signal": 30,
    "effective_script": null
  }
}

Integration

Works with:

• LanceDB (local, no external dependency)
• Supermemory Cloud (hosted, API key required)
• Memos (self-hosted note-taking)