QMD Memory Skill for OpenClaw
Local Hybrid Search — Save $50-300/month in API Costs
Author: As Above Technologies Version: 1.0.0 ClawHub: [Coming Soon]
💰 THE VALUE PROPOSITION
API Costs You're Paying Now
| Operation | API Cost | Frequency | Monthly Cost |
|---|---|---|---|
| memory_search (embedding) | $0.02-0.05 | 50-200/day | $30-300 |
| Context retrieval | $0.01-0.03 | 100+/day | $30-90 |
| Semantic queries | $0.03-0.08 | 20-50/day | $18-120 |
| TOTAL | $78-510/month |
With QMD Local
| Operation | Cost | Why |
|---|---|---|
| All searches | $0 | Runs on your machine |
| Embeddings | $0 | Local GGUF models |
| Re-ranking | $0 | Local LLM |
Your savings: $50-300+/month
One-time setup. Forever free searches.
🚀 QUICK START
# Install the skill
clawhub install asabove/qmd-memory
# Run setup (installs QMD, configures collections)
openclaw skill run qmd-memory setup
# That's it. Your memory is now supercharged.
WHAT YOU GET
1. Automatic Collection Setup
Based on your workspace structure, we create optimized collections:
✓ workspace — Core agent files (MEMORY.md, SOUL.md, etc.)
✓ daily-logs — memory/*.md daily logs
✓ intelligence — intelligence/*.md (if exists)
✓ projects — projects/**/*.md (if exists)
✓ documents — Any additional doc folders you specify
2. Smart Context Descriptions
We add context to each collection so QMD understands what's where:
qmd://workspace → "Agent identity and configuration files"
qmd://daily-logs → "Daily work logs and session history"
qmd://intelligence → "Analysis, research, and reference documents"
3. Pre-configured Cron Jobs
# Auto-update index (nightly at 3am)
0 3 * * * qmd update && qmd embed
# Keep your memory fresh without thinking about it
4. OpenClaw Integration
Memory search now uses QMD automatically:
memory_search→ routes to QMD hybrid searchmemory_get→ retrieves from QMD collections- Results include collection context
5. Multi-Agent MCP Server (Optional)
# Start shared memory server
openclaw skill run qmd-memory serve
# All your agents can now query collective memory
# Forge, Thoth, Axis — shared knowledge base
📊 SEARCH MODES
| Mode | Command | Best For |
|---|---|---|
| Keyword | qmd search "query" | Exact matches, fast |
| Semantic | qmd vsearch "query" | Conceptual similarity |
| Hybrid | qmd query "query" | Best quality (recommended) |
Example Queries
# Find exact mentions
qmd search "Charlene" -n 5
# Find conceptually related content
qmd vsearch "how should we handle customer complaints"
# Best quality — expansion + reranking
qmd query "what decisions did we make about pricing strategy"
# Search specific collection
qmd search "API keys" -c workspace
🔧 CONFIGURATION
Add Custom Collections
openclaw skill run qmd-memory add-collection ~/Documents/research --name research
Add Context
openclaw skill run qmd-memory add-context qmd://research "Market research and competitive analysis"
Refresh Index
openclaw skill run qmd-memory refresh
💡 TEMPLATES
Trading/Investing Workspace
openclaw skill run qmd-memory template trading
Creates:
intelligence— Trading systems, dashboards, signalsmarket-data— Price history, analysisresearch— Due diligence, reportsdaily-logs— Trade journal
Content Creator Workspace
openclaw skill run qmd-memory template content
Creates:
articles— Published contentdrafts— Work in progressresearch— Source materialideas— Brainstorms, notes
Developer Workspace
openclaw skill run qmd-memory template developer
Creates:
docs— Documentationnotes— Technical notesdecisions— ADRs, architecture decisionssnippets— Code snippets, examples
📈 COST SAVINGS CALCULATOR
Run this to see your estimated savings:
openclaw skill run qmd-memory calculate-savings
Output:
Your Current API Memory Costs (estimated):
memory_search calls/day: ~75
Average cost per call: $0.03
Monthly API cost: $67.50
With QMD Local:
Monthly cost: $0.00
YOUR MONTHLY SAVINGS: $67.50
YOUR ANNUAL SAVINGS: $810.00
ROI on skill purchase: 40x (if skill was $20)
🛠️ TECHNICAL DETAILS
Models Used (Auto-Downloaded)
| Model | Purpose | Size |
|---|---|---|
| embeddinggemma-300M-Q8_0 | Vector embeddings | ~300MB |
| qwen3-reranker-0.6b-q8_0 | Re-ranking results | ~640MB |
| qmd-query-expansion-1.7B-q4_k_m | Query expansion | ~1.1GB |
Total: ~2GB (one-time download)
System Requirements
- Node.js >= 22
- ~3GB disk space (models + index)
- ~2GB RAM during embedding (then minimal)
Where Data is Stored
~/.cache/qmd/
├── index.sqlite # Search index
├── models/ # GGUF models
└── mcp.pid # MCP server PID (if running)
🤝 SUPPORT
Questions?
- GitHub Issues: github.com/asabove/qmd-memory-skill
- Discord: As Above community
- Email: support@asabove.tech
Found it valuable?
- Star us on ClawHub
- Share with other OpenClaw users
- Subscribe to our newsletter for more agent optimization tips
📜 LICENSE
MIT — Use freely, modify as needed.
QMD itself is created by Tobi Lütke (github.com/tobi/qmd). This skill provides easy OpenClaw integration.
"Stop paying for memory. Start compounding knowledge."
As Above Technologies — Agent Infrastructure for Humans