Self-improving Agent Memory Upgrade (SurrealDB)

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:

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

#### Job 2 — Relationship Correlation (daily at 3 AM) Script: scripts/extract-knowledge.py discover-relations

#### Job 3 — Deduplication (daily at 4 AM) Script: scripts/extract-knowledge.py dedupe --threshold 0.92

#### Job 4 — Reconciliation (weekly, Sundays at 5 AM) Script: scripts/extract-knowledge.py reconcile --verbose

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 (Required)

The skill requires 5 cron jobs for full self-improving operation. All run as isolated background sessions with no delivery:

| Job Name | Schedule | What it runs | |----------|----------|--------------| | Memory Knowledge Extraction | Every 6 hours (0 */6 * * *) | extract-knowledge.py extract — extracts facts from memory files | | Memory Relation Discovery | Daily at 3 AM (0 3 * * *) | extract-knowledge.py discover-relations — AI-powered relationship finding | | Memory Deduplication | Daily at 4 AM (0 4 * * *) | extract-knowledge.py dedupe --threshold 0.92 — removes duplicate/near-duplicate facts | | Memory Reconciliation | Weekly Sun 5 AM (0 5 * * 0) | extract-knowledge.py reconcile --verbose — prunes stale facts, applies confidence decay, cleans orphans |

> All jobs use sessionTarget: "isolated" with delivery: none. They run in fully isolated background sessions and never fire into the main agent session. A bottom-right corner toast notification appears in the Control UI when each job starts and completes.

Setup commands (run after installation):

# 1. Knowledge Extraction — every 6 hours
openclaw cron add \
  --name "Memory Knowledge Extraction" \
  --cron "0 */6 * * *" \
  --agent main --session isolated --no-deliver \
  --timeout-seconds 300 \
  --message "Run memory knowledge extraction. Execute: cd SKILL_DIR && source .venv/bin/activate && python3 scripts/extract-knowledge.py extract"
2. Relation Discovery — daily at 3 AM
openclaw cron add \
  --name "Memory Relation Discovery" \
  --cron "0 3 * * *" --exact \
  --agent main --session isolated --no-deliver \
  --timeout-seconds 300 \
  --message "Run memory relation discovery. Execute: cd SKILL_DIR && source .venv/bin/activate && python3 scripts/extract-knowledge.py discover-relations"
3. Deduplication — daily at 4 AM
openclaw cron add \
  --name "Memory Deduplication" \
  --cron "0 4 * * *" --exact \
  --agent main --session isolated --no-deliver \
  --timeout-seconds 120 \
  --message "Run knowledge graph deduplication. Execute: cd SKILL_DIR && source .venv/bin/activate && python3 scripts/extract-knowledge.py dedupe --threshold 0.92"
4. Reconciliation — weekly on Sundays at 5 AM
openclaw cron add \
  --name "Memory Reconciliation" \
  --cron "0 5 * * 0" --exact \
  --agent main --session isolated --no-deliver \
  --timeout-seconds 180 \
  --message "Run knowledge graph reconciliation. Execute: cd SKILL_DIR && source .venv/bin/activate && python3 scripts/extract-knowledge.py reconcile --verbose"

> Replace SKILL_DIR with your actual skill path.

To check job status:

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)

| Feature | Description | |---------|-------------| | Semantic Facts | Vector-indexed facts with confidence scoring | | Episodic Memory | Task histories with decisions, problems, solutions, learnings | | Working Memory | YAML-based task state that survives crashes | | Outcome Calibration | Facts used in successful tasks gain confidence | | Auto-Injection | Relevant facts/episodes injected into prompts automatically | | Entity Extraction | Automatic entity linking and relationship discovery | | Confidence Decay | Stale facts naturally decay over time | | Agent Isolation | Each agent has its own scoped memory pool; scope='global' facts are shared across all agents | | Self-Improving Loop | Scheduled extraction + relation discovery automatically grow the graph |

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

Right Column: 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

Key Fields (fact)

Key Fields (episode)

Confidence Scoring

Effective confidence is calculated from:

Maintenance

Automated — OpenClaw Cron (as deployed)

The self-improving loop runs via 4 registered OpenClaw cron jobs:

Every 6h     → extract-knowledge.py extract            (extract facts from memory files)
Daily 3 AM   → extract-knowledge.py discover-relations  (find relationships between facts)
Daily 4 AM   → extract-knowledge.py dedupe              (remove duplicate facts)
Weekly Sun   → extract-knowledge.py reconcile            (prune stale, decay, clean orphans)

See the "OpenClaw Cron Jobs (Required)" section above for setup commands.

To verify they're active:

openclaw cron list

To manually trigger any job:

cd SKILL_DIR && source .venv/bin/activate
python3 scripts/extract-knowledge.py extract
python3 scripts/extract-knowledge.py discover-relations
python3 scripts/extract-knowledge.py dedupe --threshold 0.92
python3 scripts/extract-knowledge.py reconcile --verbose

Manual (UI)

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*

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

"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