supermemory — AI Memory Engine
Semantic memory layer powered by vector search. Store, recall, and connect conversation insights across all customer interactions.
Architecture
Conversation → Extract Insights → Embed → Store (Vector DB)
↓
Query → Semantic Search → Relevant Memories → Inject into ContextMemory 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 memorymemory:search <query>— Semantic search across all memoriesmemory:list [type]— List recent memories by typememory:forget <id>— Delete a specific memorymemory:stats— Memory usage statistics
Auto-Capture
When enabled, the engine automatically extracts and stores:
- Customer preferences and requirements
- Price sensitivity signals
- Competitive mentions
- Purchase timeline indicators
- Relationship context (referrals, prior interactions)
Configuration
{
"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)