---
name: vector-memory
description: Smart memory search with automatic vector fallback. Uses semantic embeddings when available, falls back to built-in search otherwise. Zero configuration - works immediately after ClawHub install. No setup required - just install and memory_search works immediately, gets better after optional sync.
---
# Vector Memory
Smart memory search that **automatically selects the best method**:
- **Vector search** (semantic, high quality) when synced
- **Built-in search** (keyword, fast) as fallback
**Zero configuration required.** Works immediately after install.
## Quick Start
### Install from ClawHub
```bash
npx clawhub install vector-memory
```
Done! `memory_search` now works with automatic method selection.
### Optional: Sync for Better Results
```bash
node vector-memory/smart_memory.js --sync
```
After sync, searches use neural embeddings for semantic understanding.
## How It Works
### Smart Selection
```javascript
// Same call, automatic best method
memory_search("James principles values")
// If vector ready: finds "autonomy, competence, creation" (semantic match)
// If not ready: uses keyword search (fallback)
```
### Behavior Flow
1. **Check**: Is vector index ready?
2. **Yes**: Use semantic search (synonyms, concepts)
3. **No**: Use built-in search (keywords)
4. **Vector fails**: Automatically fall back
## Tools
### memory_search
**Auto-selects best method**
Parameters:
- `query` (string): Search query
- `max_results` (number): Max results (default: 5)
Returns: Matches with path, lines, score, snippet
### memory_get
Get full content from file.
### memory_sync
Index memory files for vector search. Run after edits.
### memory_status
Check which method is active.
## Comparison
| Feature | Built-in | Vector | Smart Wrapper |
|---------|----------|--------|---------------|
| Synonyms | ❌ | ✅ | ✅ (when ready) |
| Setup | Built-in | Requires sync | ✅ Zero config |
| Fallback | N/A | Manual | ✅ Automatic |
## Usage
**Immediate (no action needed):**
```bash
node vector-memory/smart_memory.js --search "query"
```
**Better quality (after sync):**
```bash
# One-time setup
node vector-memory/smart_memory.js --sync
# Now all searches use vector
node vector-memory/smart_memory.js --search "query"
```
## Files
| File | Purpose |
|------|---------|
| `smart_memory.js` | Main entry - auto-selects method |
| `vector_memory_local.js` | Vector implementation |
| `memory.js` | OpenClaw wrapper |
## Configuration
**None required.**
Optional environment variables:
```bash
export MEMORY_DIR=/path/to/memory
export MEMORY_FILE=/path/to/MEMORY.md
```
## Scaling
- **< 1000 chunks**: Built-in + JSON (current)
- **> 1000 chunks**: Use pgvector (see references/pgvector.md)
## References
- [Integration](references/integration.md) - Detailed setup
- [pgvector](references/pgvector.md) - Large-scale deploymentAI advertising agents that automates ad campaigns across Google Ads, Meta Ads, LinkedIn Ads, and TikTok Ads. Creates campaigns, reads live performance data, researches keywords with real CPC data, optimizes budgets, and manages ads through natural language via the Adspirer MCP server. 103 tools across 4 ad platforms.
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