Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
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.
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.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
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.
npx clawhub install vector-memory Done! memory_search now works with automatic method selection.
node vector-memory/smart_memory.js --sync After sync, searches use neural embeddings for semantic understanding.
// 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)
Check: Is vector index ready? Yes: Use semantic search (synonyms, concepts) No: Use built-in search (keywords) Vector fails: Automatically fall back
Auto-selects best method Parameters: query (string): Search query max_results (number): Max results (default: 5) Returns: Matches with path, lines, score, snippet
Get full content from file.
Index memory files for vector search. Run after edits.
Check which method is active.
FeatureBuilt-inVectorSmart WrapperSynonymsββ β (when ready)SetupBuilt-inRequires syncβ Zero configFallbackN/AManualβ Automatic
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"
FilePurposesmart_memory.jsMain entry - auto-selects methodvector_memory_local.jsVector implementationmemory.jsOpenClaw wrapper
None required. Optional environment variables: export MEMORY_DIR=/path/to/memory export MEMORY_FILE=/path/to/MEMORY.md
< 1000 chunks: Built-in + JSON (current) > 1000 chunks: Use pgvector (see references/pgvector.md)
Integration - Detailed setup pgvector - Large-scale deployment
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.