Surrealdb Memory

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

Job 3 — Deduplication (daily at 4 AM)

Script: scripts/extract-knowledge.py dedupe --threshold 0.92

• Compares all facts using vector similarity (cosine distance)
• Facts above the threshold (92% similar) are flagged as duplicates
• Keeps the higher-confidence fact, removes the duplicate
• Prevents extraction from creating bloat over time
• Result: a clean, non-redundant knowledge base

Job 4 — Reconciliation (weekly, Sundays at 5 AM)

Script: scripts/extract-knowledge.py reconcile --verbose

• Applies time-based confidence decay to aging facts
• Prunes facts that have decayed below minimum confidence
• Cleans orphaned entities with no linked facts
• Consolidates near-duplicate entities
• Result: the knowledge graph stays healthy, relevant, and pruned of stale information

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:

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)

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.