Imported from johnalbertini14-glitch/openclaw-skills (skills/maverick-software/surrealdb-knowledge-graph-memory/SKILL.md). Install upstream with npx skills add johnalbertini14-glitch/openclaw-skills --skill surrealdb-knowledge-graph-memory. Copyright stays with the author.

SurrealDB Knowledge Graph Memory v2.2

A comprehensive knowledge graph memory system with semantic search, episodic memory, working memory, automatic context injection, and per-agent isolation — enabling every agent to become a continuously self-improving AI.

Description

Use this skill for:

• Semantic Memory — Store and retrieve facts with confidence-weighted vector search
• Episodic Memory — Record task histories and learn from past experiences
• Working Memory — Track active task state with crash recovery
• Auto-Injection — Automatically inject relevant context into agent prompts
• Outcome Calibration — Facts gain/lose confidence based on task outcomes
• Self-Improvement — Scheduled extraction and relation discovery make every agent smarter over time

Triggers: "remember this", "store fact", "what do you know about", "memory search", "find similar tasks", "learn from history"

Security: This skill reads workspace memory files and sends their content to OpenAI for extraction. It registers two background cron jobs and (optionally) patches OpenClaw source files. All behaviors are opt-in or documented. See SECURITY.md for the full breakdown before enabling.

Required: OPENAI_API_KEY, surreal binary, python3 ≥3.10

🔄 Self-Improving Agent Loop

This is the core concept: every agent equipped with this skill improves itself automatically, with no manual intervention required. Two scheduled cron jobs — knowledge extraction and relationship correlation — run on a fixed schedule and continuously grow the knowledge graph. Combined with auto-injection, the agent gets progressively smarter with each conversation.

The Cycle

[Agent Conversation]
       ↓  stores important facts via knowledge_store_sync
[Memory Files]  ← agent writes to MEMORY.md / daily memory/*.md files
       ↓  every 6 hours — extraction cron fires
[Entity + Fact Extraction]  ← LLM reads files, extracts structured facts + entities
       ↓  facts stored with embeddings + agent_id tag
[Knowledge Graph]  ← SurrealDB: facts, entities, mentions
       ↓  daily at 3 AM — relation discovery cron fires
[Relationship Correlation]  ← AI finds semantic links between facts
       ↓  relates_to edges created between connected facts
[Richer Knowledge Graph]  ← facts are no longer isolated; they form a web
       ↓  on every new message — auto-injection reads the graph
[Context Window]  ← relevant facts + relations + episodes injected automatically
       ↓
[Better Responses]  ← agent uses accumulated knowledge to respond more accurately
       ↑  new insights written back to memory files → cycle repeats

What Each Scheduled Job Does

Job 1 — Knowledge Extraction (every 6 hours)

Script: scripts/extract-knowledge.py extract

• Reads MEMORY.md and all memory/YYYY-MM-DD.md files in the workspace
• Uses an LLM (GPT-4) to extract structured facts, entities, and key concepts
• Hashes file content to skip unchanged files — only processes diffs
• Stores each fact with:
  • A vector embedding (OpenAI text-embedding-3-small) for semantic search
  • A confidence score (defaults to 0.9)
  • An agent_id tag so facts stay isolated to the right agent
  • source metadata pointing back to the originating file
• Result: raw conversational knowledge becomes searchable, structured memory

Job 2 — Relationship Correlation (daily at 3 AM)

Script: scripts/extract-knowledge.py discover-relations

• Queries the graph for facts that have no relationships yet ("isolated facts")
• Batches them and asks an LLM to identify semantic connections between them
• Creates relates_to edges in SurrealDB linking related facts
• Result: isolated facts become a connected knowledge web — the agent can now traverse relationships, not just keyword-match
• Over time, the graph evolves from a flat list into a rich semantic network

Why This Makes Agents Self-Improving

When auto-injection is enabled, every new conversation starts with the most relevant slice of the accumulated knowledge graph. As the agent:

1. Has conversations → writes insights to memory files
2. Extraction job fires → converts those insights into structured facts
3. Relation job fires → connects those facts to existing knowledge
4. Next conversation → auto-injection pulls in richer, more connected context

...the agent effectively gets smarter with every cycle. It learns from its own outputs, grounds future responses in its accumulated history, and avoids repeating mistakes (via episodic memory and outcome calibration).

OpenClaw Cron Jobs (as configured)

These jobs are registered in OpenClaw and run automatically:

Both jobs use sessionTarget: "main" and deliver via systemEvent, so the main agent session receives the command and executes it inline.

To check job status at any time:

# Via OpenClaw cron list (in Koda's chat)
# Or via CLI:
openclaw cron list

Adding Cron Jobs for a New Agent

When spawning a new agent that should self-improve, register its own extraction job:

# OpenClaw cron add (via Koda) — example for a 'scout-monitor' agent
# Schedule: every 6h, extract facts tagged to scout-monitor
python3 scripts/extract-knowledge.py extract --agent-id scout-monitor

The --agent-id flag ensures extracted facts are isolated to that agent's pool and don't pollute the main agent's knowledge. Each agent self-improves independently while still reading shared scope='global' facts.

Features (v2.2)

Agent Isolation (v2.2)

Each agent in OpenClaw has its own scoped memory pool. Facts are tagged with agent_id on write; all read queries filter to (agent_id = $agent_id OR scope = 'global').

How it works

Agent A (main)          Agent B (scout-monitor)
   ┌──────────┐              ┌──────────┐
   │ 391 facts│              │   0 facts│   ← isolated pools
   └──────────┘              └──────────┘
         ↑                         ↑
         └──── scope='global' ─────┘   ← shared facts visible to both

Storing facts

All knowledge_store / knowledge_store_sync calls accept agent_id:

# Stored to scout-monitor's pool only
mcporter call surrealdb-memory.knowledge_store \
    content="API is healthy at /ping" \
    agent_id='scout-monitor'

# Stored globally (visible to all agents)
mcporter call surrealdb-memory.knowledge_store \
    content="Project uses Python 3.12" \
    agent_id='main' scope='global'

Auto-injection (agent-aware)

With references/enhanced-loop-hook-agent-isolation.md applied to src/agents/enhanced-loop-hook.ts, the enhanced loop automatically extracts the agent ID from the session key and passes it to memory_inject. No manual configuration needed — each agent's auto-injection is silently scoped to its own facts.

Extraction (agent-aware)

Pass --agent-id to extract-knowledge.py so cron-extracted facts are correctly tagged:

python3 scripts/extract-knowledge.py extract --agent-id scout-monitor

Default is "main". Update cron jobs accordingly for non-main agents.

Backward compatibility

Existing facts without an explicit agent_id are treated as owned by "main". Nothing is lost on upgrade to v2.2.

Dashboard UI

The Memory tab in the Control dashboard provides a two-column layout:

Left Column: Dashboard

• 📊 Statistics — Live counts of facts, entities, relations, and archived items
• Confidence Bar — Visual display of average confidence score
• Sources Breakdown — Facts grouped by source file
• 🏥 System Health — Status of SurrealDB, schema, and Python dependencies
• 🔗 DB Studio — Quick link to SurrealDB's web interface

Right Column: Operations

• 📥 Knowledge Extraction

  • Extract Changes — Incrementally extract facts from modified files
  • Find Relations — Discover semantic relationships between existing facts
  • Full Sync — Complete extraction + relation discovery
  • Progress bar with real-time status updates

• 🔧 Maintenance

  • Apply Decay — Reduce confidence of stale facts
  • Prune Stale — Archive facts below threshold
  • Full Sweep — Complete maintenance cycle

• 💡 Tips — Quick reference for operations

When the system needs setup, an Installation section appears with manual controls.

Prerequisites

1. SurrealDB installed and running:

   # Install (one-time)
   ./scripts/install.sh
   
   # Start server
   surreal start --bind 127.0.0.1:8000 --user root --pass root file:~/.openclaw/memory/knowledge.db

2. Python dependencies (use the skill's venv):

   cd /path/to/surrealdb-memory
   python3 -m venv .venv
   source .venv/bin/activate
   pip install surrealdb openai pyyaml

3. OpenAI API key for embeddings (set in OpenClaw config or environment)

4. mcporter configured with this skill's MCP server

MCP Server Setup

Add to your config/mcporter.json:

{
  "servers": {
    "surrealdb-memory": {
      "command": ["python3", "/path/to/surrealdb-memory/scripts/mcp-server-v2.py"],
      "env": {
        "OPENAI_API_KEY": "${OPENAI_API_KEY}",
        "SURREAL_URL": "http://localhost:8000",
        "SURREAL_USER": "root",
        "SURREAL_PASS": "root"
      }
    }
  }
}

MCP Tools (11 total)

Core Tools

v2 Tools

memory_inject Tool

The memory_inject tool returns formatted context ready for prompt injection:

# Scoped to a specific agent (returns only that agent's facts + global facts)
mcporter call surrealdb-memory.memory_inject \
    query="user message" \
    max_facts:7 \
    max_episodes:3 \
    confidence_threshold:0.9 \
    include_relations:true \
    agent_id='scout-monitor'

Output:

## Semantic Memory (Relevant Facts)
📌 [60% relevant, 100% confidence] Relevant fact here...

## Related Entities
• Entity Name (type)

## Episodic Memory (Past Experiences)
✅ Task: Previous task goal [similarity]
   → Key learning from that task

Auto-Injection (Enhanced Loop Integration)

When enabled, memory is automatically injected into every agent turn:

1. Enable in Mode UI:

   • Open Control dashboard → Mode tab
   • Scroll to "🧠 Memory & Knowledge Graph" section
   • Toggle "Auto-Inject Context"
   • Configure limits (max facts, max episodes, confidence threshold)

2. How it works:

   • On each user message, memory_inject is called automatically
   • Relevant facts are searched based on the user's query
   • If average fact confidence < threshold, episodic memories are included
   • Formatted context is injected into the agent's system prompt
   • v2.2: With references/enhanced-loop-hook-agent-isolation.md applied, the active agent's ID is automatically extracted from the session key and passed as agent_id — each agent's injection is silently scoped to its own facts

3. Configuration (in Mode settings):

   Setting	Default	Description
   Auto-Inject Context	Off	Master toggle
   Max Facts	7	Maximum semantic facts to inject
   Max Episodes	3	Maximum episodic memories
   Confidence Threshold	90%	Include episodes when below this
   Include Relations	On	Include entity relationships

CLI Commands

# Activate venv
source .venv/bin/activate

# Store a fact
python scripts/memory-cli.py store "Important fact" --confidence 0.9

# Search
python scripts/memory-cli.py search "query"

# Get stats
python scripts/knowledge-tool.py stats

# Run maintenance
python scripts/memory-cli.py maintain

# Extract from files (incremental)
python scripts/extract-knowledge.py extract

# Extract for a specific agent
python scripts/extract-knowledge.py extract --agent-id scout-monitor

# Force full extraction (all files, not just changed)
python scripts/extract-knowledge.py extract --full

# Discover semantic relationships
python scripts/extract-knowledge.py discover-relations

Database Schema (v2)

Tables

• fact — Semantic facts with embeddings and confidence
• entity — Extracted entities (people, places, concepts)
• relates_to — Relationships between facts
• mentions — Fact-to-entity links
• episode — Task histories with outcomes
• working_memory — Active task snapshots

Key Fields (fact)

• content — The fact text
• embedding — Vector for semantic search
• confidence — Base confidence (0-1)
• success_count / failure_count — Outcome tracking
• scope — global, client, or agent
• agent_id — Which agent owns this fact (v2.2)

Key Fields (episode)

• goal — What was attempted
• outcome — success, failure, abandoned
• decisions — Key decisions made
• problems — Problems encountered (structured)
• solutions — Solutions applied (structured)
• key_learnings — Extracted lessons

Confidence Scoring

Effective confidence is calculated from:

• Base confidence (0.0–1.0)
• + Inherited boost from supporting facts
• + Entity boost from well-established entities
• + Outcome adjustment based on success/failure history
• - Contradiction drain from conflicting facts
• - Time decay (configurable, ~5% per month)

Maintenance

Automated — OpenClaw Cron (as deployed)

The self-improving loop runs via two registered OpenClaw cron jobs:

Every 6h  → python3 scripts/extract-knowledge.py extract
             (reads memory files, extracts facts into the graph)

Daily 3AM → python3 scripts/extract-knowledge.py discover-relations
             (finds semantic relationships between existing facts)

These jobs are pre-registered in OpenClaw. To verify they're active:

openclaw cron list
# or ask Koda: "list cron jobs"

To manually trigger extraction:

# From the Memory dashboard UI: click "Extract Changes" or "Find Relations"
# Or via CLI:
cd ~/openclaw/skills/surrealdb-memory && source .venv/bin/activate
python3 scripts/extract-knowledge.py extract
python3 scripts/extract-knowledge.py discover-relations

Manual (UI)

Use the Maintenance section in the Memory tab:

• Apply Decay — Reduce confidence of stale facts
• Prune Stale — Archive facts below 0.3 confidence
• Full Sweep — Run complete maintenance cycle

Files

Scripts

Integration

Troubleshooting

"Connection refused" → Start SurrealDB: surreal start --bind 127.0.0.1:8000 --user root --pass root file:~/.openclaw/memory/knowledge.db

"No MCP servers configured" → Ensure mcporter is run from a directory containing config/mcporter.json with the surrealdb-memory server defined

Memory injection returning null → Check that OPENAI_API_KEY is set in the environment → Verify SurrealDB is running and schema is initialized

Empty search results → Run extraction from the UI or via CLI: python3 scripts/extract-knowledge.py extract

"No facts to analyze" on relation discovery → This is normal if all facts are already related — the graph is well-connected. Run extraction first if the graph is empty.

Progress bar not updating → Ensure the gateway has been restarted after UI updates → Check browser console for polling errors

Facts from wrong agent appearing → Check that agent_id is being passed correctly to all store/search calls → Verify references/enhanced-loop-hook-agent-isolation.md is applied for auto-injection scoping

Migration from v1 / v2.1

# Apply v2 schema (additive, won't delete existing data)
./scripts/migrate-v2.sh

# Or manually:
source .venv/bin/activate
python scripts/migrate-v2.py

All existing facts without an agent_id are treated as owned by "main" — backward compatible.

Stats

Check your knowledge graph via UI (Dashboard section) or CLI:

mcporter call surrealdb-memory.knowledge_stats

Example output:

{
  "facts": 379,
  "entities": 485,
  "relations": 106,
  "episodes": 3,
  "avg_confidence": 0.99
}

v2.2 — Agent isolation, self-improving loop, cron-based extraction & relationship correlation