memory-upgrade
Skill112 starsChanged 3 months agoMITView on GitHub
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.
In this repository14 other files it gives its agents.
- AGENTS.mdAGENTS.md
- cron-packsSkill
- mission-controlSkill
- everclawSkill
- agent-chatSkill
- bagmanSkill
- night-shiftSkill
- pii-guardSkill
- prompt-guardSkill
- relationshipsSkill
- skillguardSkill
- three-shiftsSkill
- xmtp-comms-guardSkill
- three-shiftsSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 55
No external dependencies required
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.shOptional Enhancements
# Organize memory files into clean directory structure
bash scripts/organize.sh
# Add YAML frontmatter tags to untagged files
bash scripts/tag.shWhat 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
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.