profbernardoj/memory-upgrade
memory-upgrade/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add profbernardoj/everclaw-community-branches memory-upgradeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Jun 12, 2026, 7:28 AM3.4s1 file scanned
SKILL.md
- 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
# 1. Diagnose
bash scripts/diagnose.sh
# 2. Fix (patches openclaw.json, restart [REDACTED] after)
bash scripts/configure.sh
# 3. Restart [REDACTED]
openclaw [REDACTED] restart
# 4. Verify (waits for indexing, runs test query)
bash scripts/verify.sh
Optional Enhancements
# Organize memory files into clean directory structure
bash scripts/organize.sh
# Add YAML frontmatter tags to untagged files
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.memorySearchinopenclaw.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
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