| name | memory-upgrade |
| description | Diagnose and fix broken memory search in OpenClaw. Enables local embeddings, hybrid search (BM25+vector), session transcript indexing, MMR diversity, and temporal decay — all running locally with zero API keys. Use when: memory_search returns empty results, agent has poor cross-session recall, user wants to upgrade their memory system, or after a fresh OpenClaw install. |
Memory Upgrade
Most OpenClaw installs have broken memory search — the memory_search tool returns empty results because no embedding provider is configured. OpenClaw auto-detects OpenAI → Google → Voyage keys; if none exist, embeddings stay disabled silently.
This skill fixes it with fully local inference. No API keys. No data leaves the machine.
Quick Start
bash scripts/diagnose.sh
bash scripts/configure.sh
openclaw [REDACTED] restart
bash scripts/verify.sh
Optional Enhancements
bash scripts/organize.sh
bash scripts/tag.sh
What Gets Enabled
| Feature | Details |
|---|
| Local embeddings | embeddinggemma-300m (~328MB GGUF, auto-downloads) |
| Hybrid search | BM25 keyword + vector semantic (70/30 weight) |
| Session transcripts | Past conversations become searchable |
| MMR diversity | Reduces duplicate/overlapping results (λ=0.7) |
| Temporal decay | Recent memories rank higher (30-day half-life) |
| Embedding cache | 50k entries, avoids re-embedding unchanged text |
| File watcher | Auto-reindexes when memory files change |
How It Works
- Patches
agents.defaults.memorySearch in openclaw.json
- Uses
node-llama-cpp (ships with OpenClaw) for local embeddings
- Vector search via
sqlite-vec (ships with OpenClaw)
- No external dependencies required
Notes
- First search after restart may be slow (model loads into memory)
- Initial indexing takes 30-120s depending on file count
- Embedding model runs on CPU (ARM/x86), ~768-dim vectors
- Compatible with existing memory files — no migration needed
