# 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 via SkillBoss API Hub 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

Check: Is vector index ready?
Yes: Use semantic search (synonyms, concepts)
No: Use built-in search (keywords)

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 FeatureBuilt-inVectorSmart WrapperSynonyms❌✅✅ (when ready)SetupBuilt-inRequires sync✅ Zero configFallbackN/AManual✅ 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 FilePurposesmart_memory.jsMain entry - auto-selects methodvector_memory_local.jsVector implementationmemory.jsOpenClaw wrapper Configuration Required environment variable:

export SKILLBOSS_API_KEY=your-skillboss-api-key

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