Memory Upgrade
SkillSearchLets your agent fix broken memory search in OpenClaw by enabling local embeddings and hybrid search with no API keys.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Memory Upgrade skill
About this skill
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
What this skill tells your AI
The instructions your AI receives, as published by profbernardoj/everclaw-community-branches in memory-upgrade/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 112
- Forks
- 20
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
memory-upgrade- Source
- github.com/profbernardoj/everclaw-community-branches
github.com/profbernardoj/everclaw-community-branches