Self-improving Agent Memory Upgrade (SurrealDB)

A comprehensive knowledge graph memory system with semantic search, episodic memory, working memory, automatic context injection, and per-agent isolation.

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Install

openclaw skills install surrealdb-knowledge-graph-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:

Has conversations → writes insights to memory files
Extraction job fires → converts those insights into structured facts
Relation job fires → connects those facts to existing knowledge
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

📊 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

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

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

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

Tool	Description
`knowledge_search`	Semantic search for facts
`knowledge_recall`	Get a fact with full context (relations, entities)
`knowledge_store`	Store a new fact
`knowledge_stats`	Get database statistics

v2 Tools

Tool	Description
`knowledge_store_sync`	Store with importance routing (high importance = immediate write)
`episode_search`	Find similar past tasks
`episode_learnings`	Get actionable learnings from history
`episode_store`	Record a completed task episode
`working_memory_status`	Get current task state
`context_aware_search`	Search with task context boosting
`memory_inject`	Intelligent context injection for prompts

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:

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)
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

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 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)

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

File	Purpose
`mcp-server-v2.py`	MCP server with all 11 tools
`mcp-server.py`	Legacy v1 MCP server
`episodes.py`	Episodic memory module
`working_memory.py`	Working memory module
`memory-cli.py`	CLI for manual operations
`extract-knowledge.py`	Bulk extraction from files (supports `--agent-id`)
`knowledge-tools.py`	Higher-level extraction
`schema-v2.sql`	v2 database schema
`migrate-v2.py`	Migration script

Integration

File	Purpose
`openclaw-integration/gateway/memory.ts`	Gateway server methods
`openclaw-integration/ui/memory-view.ts`	Memory dashboard UI
`openclaw-integration/ui/memory-controller.ts`	UI controller

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