vector-memory

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

npx clawhub install vector-memory

Done! memory_search now works with automatic method selection.

Optional: Sync for Better Results

node vector-memory/smart_memory.js --sync

After sync, searches use neural embeddings for semantic understanding.

How It Works

Smart Selection

// 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):

node vector-memory/smart_memory.js --search "query"

Better quality (after sync):

# 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:

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 - Detailed setup
• pgvector - Large-scale deployment