vector-memory

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.

7 stars

Best use case

vector-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using vector-memory should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/vector-memory/SKILL.md --create-dirs "https://raw.githubusercontent.com/Demerzels-lab/elsamultiskillagent/main/public/skills/bluepointdigital/vector-memory/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/vector-memory/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How vector-memory Compares

Feature / Agentvector-memoryStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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 deployment

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