Why Memory is OpenClaw's Superpower: Without memory, every conversation with your AI agent starts from scratch. With properly configured memory, your OpenClaw agent knows your name, your job, your preferences, your ongoing projects, your communication style, and your history — getting smarter about you every single day. This guide teaches you how to set this up correctly.
OpenClaw's Three-Layer Memory Architecture
OpenClaw handles memory across three distinct layers, each serving a different purpose. Understanding this architecture is essential for training your agent effectively:
Working Memory (Context Window)
The current conversation and recent messages sent to the AI model with each request. This is what the AI "sees" right now. Size is limited by the model's context window (Claude: 200K tokens, GPT-4: 128K tokens).
Episodic Memory (Conversation History)
All past conversations stored on disk. When you reference something from a week ago, OpenClaw retrieves relevant snippets and includes them in the context. Uses vector search to find related memories efficiently.
Semantic Memory (Facts & Preferences)
Distilled knowledge about you — your name, role, preferences, recurring tasks, important contacts, and key facts. Always included in every conversation. This is what makes your agent feel truly personal.
Configuring Memory Settings
Essential Memory Configuration
// config/memory.json — The complete memory setup
{
// Working memory: last N messages in each request
"contextWindow": {
"maxMessages": 20, // How many recent messages to include
"maxTokens": 15000, // Token budget for context
"prioritizeRecent": true // Weight recent messages higher
},
// Episodic memory: stored conversation history
"episodic": {
"enabled": true,
"retentionDays": 180, // Keep 6 months of history
"searchResults": 5, // Retrieve top 5 relevant memories
"summarize": "weekly" // Auto-compress weekly
},
// Semantic memory: always-on facts about you
"semantic": {
"enabled": true,
"autoExtract": true, // Agent learns from conversations
"path": "./data/facts.json"
}
}
Balance is key: Too little context = agent seems forgetful. Too much = high API costs and slower responses. Start with the defaults above and adjust based on your usage patterns.
Teaching Your Agent: The Onboarding Process
The first week with a new OpenClaw agent is about teaching, not just asking. The more context you give your agent about yourself, the more valuable it becomes. Here's the structured onboarding process used by power users:
Day 1: Personal Context Dump
Send these messages to your agent one by one. It will extract and store key facts automatically:
Message to send:
"My name is Alex Chen. I'm a product manager at TechCorp. I work in the San Francisco office but work from home on Mondays and Fridays. My manager is Sarah. My direct reports are the engineering team of 6 people."
Message to send:
"My working hours are 9 AM to 6 PM Pacific Time. I prefer brief, direct communication. Don't use bullet points unless there are more than 3 items. I hate verbose responses."
Message to send:
"My current priorities are: Q1 product launch (March 31 deadline), hiring 2 engineers (interviews ongoing), and the quarterly OKR review next week."
What happens: OpenClaw's autoExtract feature automatically identifies key facts (name, role, preferences, priorities) and saves them to semantic memory. From now on, every conversation starts with this context already loaded.
Week 1: Teaching Work Patterns
Monday: Teach your email preferences
Use Gmail skill, tell agent which senders are priority, which can wait, which newsletters to auto-archive
You: "Emails from [email protected] are always urgent. Newsletters go to [Newsletters] folder. Job recruiters get a polite decline reply."
Tuesday: Teach calendar preferences
Tell agent your ideal schedule, blocking times, meeting preferences
You: "Never book meetings before 10 AM or after 5 PM. No meetings on Friday afternoons. Always leave 15 min between back-to-back calls."
Wednesday: Introduce recurring tasks
Every recurring task you currently do manually becomes an agent automation
You: "Every Monday morning, compile the weekly team standup from Slack #team-updates and email it to the whole team."
Advanced Memory Techniques
Explicit Memory Commands
You can directly manage your agent's memory through natural language commands:
📝 Store a specific fact
You: "Remember that my wife's birthday is March 15. Remind me 2 weeks in advance each year."
🔍 Check what's remembered
You: "What do you know about my work schedule?"
✏️ Correct a wrong memory
You: "That's wrong — I don't work on Saturdays anymore. Update your memory."
🗑️ Delete a specific memory
You: "Forget everything about my old job at OldCorp."
Persona & Tone Training
Beyond facts, you can train your agent's communication style and personality:
// system-prompt.txt — Your agent's personality
You are Aria, Alex's personal AI assistant.
Communication style:
- Be direct and concise. No fluff.
- Use professional but warm tone
- Lead with the most important point
- Confirm understanding before taking action
- When uncertain, say so — don't guess
Core knowledge:
- Alex is a Product Manager at TechCorp
- Working hours: 9 AM - 6 PM PT
- Always prioritize: product launch > team > meetings
Memory Optimization for Lower API Costs
❌ Memory Mistakes (Expensive)
• Including entire conversation history in every request
• Never compressing or summarizing old conversations
• Storing duplicate or redundant facts
• Using maxMessages: 100 (way too many)
✅ Memory Best Practices (Efficient)
• Use semantic search to retrieve only relevant memories
• Enable weekly summarization to compress history
• Keep semantic memory concise (facts, not conversations)
• Set maxMessages: 15-20 for most use cases
Cross-Agent Memory Sharing
Advanced Feature: Shared Memory Context
If you run multiple OpenClaw agents (one personal, one work), they can share a common memory store. This means telling your personal agent "I got a promotion" automatically updates your work agent too — no need to repeat context between agents.
# In both agents' .env files
SHARED_MEMORY_PATH=/shared/agent-memory/
SHARED_MEMORY_ENABLED=true
Memory Portability: Backup & Restore
Your agent's memory is its most valuable asset. Back it up regularly:
# Export all memories
openclaw memory export --output backup-$(date +%Y%m%d).json
# Import memories (e.g., after migration)
openclaw memory import --input backup-20260219.json
# View memory stats
openclaw memory stats
# Output: 1,247 episodic memories, 43 facts, 312MB storage
Measuring Memory Effectiveness
Signs Your Memory Training is Working
Agent references past conversations naturally
"Based on what you told me last Tuesday about the product launch deadline..."
No need to re-explain context
You say "email Sarah" and agent knows exactly which Sarah and what format you prefer
Proactive suggestions match your patterns
Agent reminds you of recurring tasks before you need to ask
Communication style matches your preferences
Responses are as concise or detailed as you trained it to be
VPN07 — Keep Your Memory Syncing Reliably
OpenClaw's memory system requires consistent, reliable network connectivity to sync with AI providers and retrieve semantic memories quickly. Network interruptions mid-conversation cause context loss and incomplete memory writes. VPN07 eliminates this with its globally proven infrastructure.
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