OpenClaw Advanced Memory
Three-tier AI agent memory system — real-time capture, vector search, and LLM-curated long-term recall.
What It Does
Gives your OpenClaw agent persistent, searchable memory that survives across sessions:
- HOT tier — Redis buffer captures conversation turns in real-time (every 30s)
- WARM tier — Qdrant vector store with chunked, embedded conversations (searchable, 7-day retention)
- COLD tier — LLM-curated "gems" extracted nightly (decisions, lessons, milestones — stored forever)
Requirements
- Qdrant — vector database (Docker recommended)
- Redis — buffer queue (Docker recommended)
- Ollama — local embeddings (
snowflake-arctic-embed2) + curation LLM (qwen2.5:7b) - Python 3.10+ with
qdrant-client,redis,requests
No cloud APIs. No subscriptions. Runs entirely on your own hardware.
Setup
# 1. Start Qdrant + Redis (Docker)
docker compose up -d
# 2. Pull Ollama models
ollama pull snowflake-arctic-embed2
ollama pull qwen2.5:7b
# 3. Run the installer
bash scripts/install.shThe installer sets up Qdrant collections, installs a systemd capture service, and configures cron jobs.
Edit connection hosts at the top of each script if your infra isn't on localhost.
Usage
# Search your memory
./recall "what did we decide about pricing"
./recall "deployment" --project myproject --tier cold -v
# Check system status
./mem-status
# Force a warm flush or curation run
./warm-now
./curate-now 2026-03-01Schedules
| Component | Schedule | What It Does |
|---|---|---|
mem-capture | Always running (systemd) | Watches transcripts → Redis |
mem-warm | Every 30 min (cron) | Redis → Qdrant warm |
mem-curate | Nightly 2 AM (cron) | Warm → LLM curation → Qdrant cold |
How Curation Works
Every night, a local LLM (qwen2.5:7b via Ollama) reads the day's conversations and extracts structured gems:
{
"gem": "Chose DistilBERT over TinyBERT — 99.69% F1, zero false positives",
"context": "A/B tested both architectures on red team suite",
"categories": ["decision", "technical"],
"project": "guardian",
"importance": "high"
}Only decisions, milestones, lessons, and people info make the cut. Casual banter and debugging noise get filtered out.
Links
- GitHub: https://github.com/jtil4201/openclaw-advanced-memory
- Full docs: See README.md in the repo for architecture diagrams, tuning guide, and adaptation notes