memory

Skill: Persistent Memory

Purpose: Provide persistent context across NetClaw sessions through structured facts, semantic search, and entity relationships.

Overview

The Memory skill enables NetClaw to remember information about your network across sessions. Instead of re-explaining your topology, device names, and past issues every time, NetClaw builds a memory that grows smarter with each interaction.

Memory vs. the RAG Knowledge Base: Memory (memory_* tools, ~/.openclaw/memory/) holds NetClaw's OWN experience — facts, session summaries, decisions, entity relationships. Document content lives in the separate RAG knowledge base (rag skill, rag_* tools, ~/.openclaw/rag/). "Remember this PDF / URL / standard" routes to rag_ingest, not memory_record_fact; "what does the vendor guide say" routes to rag_search, not memory_recall. Operational facts ("PE2 is in maintenance until Friday"), past-session questions, and decision history stay here. Neither store writes into the other.

Capabilities

1. Structured Fact Storage

Store precise facts about network entities with temporal validity:

• Device states, configurations, IP addresses
• Maintenance windows, change history
• Performance baselines, thresholds

2. Semantic Recall

Search past sessions using natural language:

• "What was that BGP issue last month?"
• "Show me troubleshooting related to MTU"
• "Find sessions about PE2"

3. Decision Logging

Record operational decisions with rationale:

• Why a route was changed
• Why a device was quarantined
• Links to ServiceNow change requests

4. Entity Relationships

Track network topology and dependencies:

• PE2 peers_with RR1
• Service-X depends_on Device-Y
• Impact analysis for changes

MCP Tools

Tool	Purpose
memory_record_fact	Store a fact with temporal validity
memory_get_facts	Query current facts for an entity
memory_invalidate	Mark a fact as no longer current
memory_timeline	Query historical facts
memory_store_session	Store session summary for semantic search
memory_recall	Semantic search across past sessions
memory_record_decision	Log a decision with rationale
memory_get_decisions	Query past decisions
memory_link_entities	Create relationship between entities
memory_query_graph	Query entity relationships

Usage Examples

Record a Fact

memory_record_fact entity="PE2" key="bgp_state" value="established" metadata={"peer": "10.0.0.1"}

Query Facts

memory_get_facts entity="PE2"

Semantic Search

memory_recall query="BGP flapping problem" top_k=5

Record a Decision

memory_record_decision context="PE2 BGP session flapping" decision="Increased hold timer to 180s" rationale="Reduce flap frequency" entities=["PE2", "RR1"] cr_number="CHG0001234"

Link Entities

memory_link_entities subject="PE2" predicate="peers_with" object="RR1"

Data Storage

All memory data persists in ~/.openclaw/memory/:

• memory.db - SQLite database (facts, decisions, links)
• chroma/ - ChromaDB vector store (session embeddings)

Data is automatically pruned after 1 year.

Integration

With GAIT

All memory write operations log to GAIT for audit trail.

With HEARTBEAT

Memory enables pattern detection in health checks:

"3rd BGP flap on PE2 this week - want me to investigate?"

With SOUL

Memory directly fulfills Principle #9: "Get smarter every session."

Prerequisites

• Memory MCP server enabled (./scripts/memory-enable.sh)
• ~500MB disk space for 1 year of data
• First run downloads embedding model (~80MB)

Troubleshooting

Server Won't Start

uvx --from netclaw-memory-mcp memory-mcp-server --help

Check Data Directory

ls -la ~/.openclaw/memory/

Verify Database

sqlite3 ~/.openclaw/memory/memory.db "SELECT COUNT(*) FROM facts;"