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name: memclawz description: AI agent fleet memory system — Qdrant + Mem0 + Neo4j/Graphiti. Composite scoring, compaction engine, temporal knowledge graph, multi-claw federation, sleep-time reflection, routing engine, MCP server. Use when you need to install, configure, manage, search, route, compact, or upgrade the agent memory system. metadata: openclaw: requires: bins: ["python3", "pip3"]

MemClawz v6 🧠

Fleet memory system for OpenClaw agents with composite scoring, compaction engine, Graphiti temporal knowledge graph, multi-claw federation, and sleep-time reflection.

What's New in v6

• Composite Scoring — Weighted blend of semantic similarity + recency decay + importance + access frequency
• Compaction Engine — Session/daily/weekly compaction with LLM extraction
• Graphiti Integration — Neo4j temporal knowledge graph for entity relationships and contradiction detection
• Multi-Claw Federation — HTTP push/pull protocol for sharing memories across fleet
• Sleep-Time Reflection — LLM-driven pattern detection, insight generation, and MEMORY.md update proposals
• Enhanced MCP Server — New tools: compact_session, reflect, memory_stats

Quick Install

Prerequisites

• Python 3.10+
• Qdrant running (Docker or binary)
• Neo4j running (for Graphiti; optional but recommended)
• OpenAI API key (for embeddings)
• Anthropic API key (for classification)

Install Qdrant

# Docker (preferred)
docker run -d --name qdrant -p 6333:6333 -p 6334:6334 \
  -v ~/.openclaw/qdrant-storage:/qdrant/storage \
  --restart unless-stopped qdrant/qdrant

# Or binary (no Docker)
curl -sL https://github.com/qdrant/qdrant/releases/latest/download/qdrant-x86_64-unknown-linux-musl.tar.gz | tar xz
./qdrant --storage-path ~/.openclaw/qdrant-storage &

Install MemClawz

cd ~
git clone https://github.com/yoniassia/memclawz.git
cd memclawz
pip3 install -r requirements.txt

Configure

cat > ~/memclawz/.env << EOF
OPENAI_API_KEY=<your-key>
ANTHROPIC_API_KEY=<your-key>
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_COLLECTION=yoniclaw_memories
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=
GRAPHITI_ENABLED=true
FEDERATION_ENABLED=true
FEDERATION_ROLE=master
WORKSPACE_DIR=/home/yoniclaw/.openclaw/workspace
EOF

Deploy Services

cp ~/memclawz/systemd/*.service ~/.config/systemd/user/
systemctl --user daemon-reload
systemctl --user enable --now neo4j memclawz-api memclawz-watcher memclawz-cron

Verify

curl http://localhost:3500/health
# {"status":"ok","version":"6.0.0","qdrant":"ok","neo4j":"ok","graphiti":"ok","federation":"ok",...}

API Reference

Core (v5 compatible)

# Search with composite scoring
curl "http://localhost:3500/api/v1/search?q=eToro+SuperApp&limit=10"
# Use raw cosine: &use_composite=false

# Add memory (feeds both Qdrant AND Graphiti)
curl -X POST "http://localhost:3500/api/v1/add" \
  -H "Content-Type: application/json" \
  -d '{"content":"BTC hit 100K on March 1","agent_id":"tradeclaw","memory_type":"event"}'

# List by agent
curl "http://localhost:3500/api/v1/memories?agent_id=tradeclaw&limit=20"

# Stats / Agents
curl http://localhost:3500/api/v1/stats
curl http://localhost:3500/api/v1/agents

Graph Search (v6)

# Search temporal knowledge graph
curl "http://localhost:3500/api/v1/graph/search?q=eToro+deployment"

# Get entity relationships
curl "http://localhost:3500/api/v1/graph/entity/YoniClaw"

Compaction (v6)

# Trigger session compaction
curl -X POST "http://localhost:3500/api/v1/compact/session" \
  -H "Content-Type: application/json" \
  -d '{"session_id":"main:whatsapp:direct:+35794329522","agent_id":"main"}'

# Generate daily digest
curl -X POST "http://localhost:3500/api/v1/compact/daily"

# Run weekly merge
curl -X POST "http://localhost:3500/api/v1/compact/weekly"

# Check compaction status
curl "http://localhost:3500/api/v1/compact/status"

Reflection (v6)

# Trigger reflection (analyzes last 24h of memories)
curl -X POST "http://localhost:3500/api/v1/reflect" \
  -H "Content-Type: application/json" \
  -d '{"hours":24,"max_memories":100}'

Federation (v6)

# Register a remote node
curl -X POST "http://localhost:3500/api/v1/federation/register" \
  -H "Content-Type: application/json" \
  -d '{"node_id":"clawdet","node_url":"http://188.34.197.212:3500","node_key":"shared-secret"}'

# Push memories from remote
curl -X POST "http://localhost:3500/api/v1/federation/push" \
  -H "Content-Type: application/json" \
  -d '{"node_id":"clawdet","node_key":"shared-secret","memories":[{"content":"...","type":"fact","agent":"main"}]}'

# Pull memories to remote
curl -X POST "http://localhost:3500/api/v1/federation/pull" \
  -H "Content-Type: application/json" \
  -d '{"node_id":"clawdet","node_key":"shared-secret","since":"2026-03-13T00:00:00Z","limit":100}'

# Federation status
curl "http://localhost:3500/api/v1/federation/status"

Composite Scoring

score = (w_semantic × similarity + w_recency × decay + w_importance × weight) × access_boost

• Semantic: 50% weight (cosine from Qdrant)
• Recency: 30% weight (exponential, 90-day half-life)
• Importance: 20% weight (type-based: decisions > preferences > facts > events)
• Access boost: up to 1.5× for frequently accessed memories
• Persistent types (decisions, preferences, relationships): 40% recency floor

Memory Types

• fact — factual statement about a person, project, system
• decision — a choice that was made
• preference — user preference or style choice
• procedure — steps to accomplish something
• relationship — info about a person or org relationship
• event — something that happened at a specific time
• insight — learned lesson, pattern, or strategic insight

Canonical Memory Order

1. Local canonical files first — MEMORY.md, memory/*.md, memory/people/*, memory/sessions/*, knowledge/*.md
2. MemClawz second — Qdrant + Mem0 + Neo4j/Graphiti + API + MCP
3. LCM/transcripts third — raw capture and extraction layer

Services

Service	Port	Description
memclawz-api	3500	REST API (v6)
memclawz-watcher	—	LCM auto-extract (+ Graphiti feed)
memclawz-cron	—	Compaction scheduler (30-min cycle)
memclawz-mcp	stdio	MCP server (v6 tools)
Neo4j	7474/7687	Graph database (Graphiti)
Qdrant	6333	Vector database

MCP Integration

{
  "mcpServers": {
    "memclawz": {
      "command": "python3",
      "args": ["/path/to/memclawz/memclawz/mcp_server.py"],
      "env": {"OPENAI_API_KEY": "<key>", "ANTHROPIC_API_KEY": "<key>"}
    }
  }
}

MCP tools: search_memory, add_memory, get_agent_memories, compact_session, reflect, memory_stats

Architecture

LCM → Watcher → Classify → Mem0 → Qdrant + Graphiti/Neo4j
                                    ↑↓            ↑↓
Fleet Agents ←→ REST API :3500  ←→ Qdrant    Neo4j
MCP Clients  ←→ MCP Server     ←→ Qdrant
Remote Claws ←→ Federation API ←→ Qdrant
Cron         →  Compactor/Reflection → Files + Qdrant + Graphiti