OpenClaw Knowledge Base: Build a Searchable Memory System

Building a searchable OpenClaw knowledge base turns your AI agent into a long-term intellectual companion — one that remembers everything you've told it, every document you've processed, and every conversation you've had, and can retrieve relevant information semantically when you need it. Here's how to build and use this system.

How OpenClaw's Memory Works

OpenClaw's memory system stores information in a local database with optional vector embeddings for semantic search. The core distinction:

• Simple memory (default): Keyword-based retrieval. "Remember X" stores X, "recall X" retrieves it.
• Vector memory: Semantic retrieval. You can ask "what did I store about project management tools?" and get relevant memories even if they don't use those exact words.

Setting Up Vector Memory

python -m openclaw skill install memory

In openclaw.json:

{
  "memory": {
    "enabled": true,
    "backend": "sqlite",
    "vector_search": true,
    "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
    "max_memories": 50000,
    "auto_recall_threshold": 0.7
  }
}

The embedding model runs locally, creating vector representations of memories for semantic search.

Building Your Knowledge Base

Explicit Memory Storage

"Remember: For all client proposals, we always include a 30% contingency in the project budget."

"Remember: My personal notes for the Johnson project are stored in ~/Documents/Johnson/"

"Remember: Marcus prefers async communication and responds best to bullet-point summaries."

Document Ingestion

"Read the document at ~/Documents/company-handbook.pdf and store the key policies in memory. Tag as company-policy."

"Ingest all my meeting notes from 2025 in ~/Notes/meetings/ and store them in memory."

Conversation-Based Learning

Each substantive conversation automatically adds context to memory. When you have a detailed conversation about a project, your agent stores the key points and can recall them in future conversations.

Retrieving Information

Natural language retrieval:

• "What do I know about client onboarding best practices?"
• "Recall anything I've stored about the Johnson project."
• "What did I tell you about Marcus's communication preferences?"
• "Search my memory for anything related to pricing strategy."

Using Memory for Context in Work

The memory system is most powerful when it automatically enriches conversations:

"I'm preparing for a call with the Johnson team. Pull any relevant context from memory."

Your agent retrieves project notes, previous meeting notes, client preferences, and open items — providing context that would otherwise require you to manually search through files and notes.

Building a Personal CRM

Use memory as a lightweight personal CRM:

"Create a person record for Sarah Chen. Email: sarah@techcorp.com. Role: Head of Product. Met: TechConf 2026. Relationship: potential collaborator on content. Key info: interested in AI content tools, has 2M+ LinkedIn followers."

Frequently Asked Questions

How much storage does the memory system use?

Basic memories are tiny (kilobytes each). Vector embeddings add about 1–2KB per memory. 10,000 memories with vectors requires approximately 20–30MB. Practical limit is essentially unlimited for personal use.

Can I search my memory from a mobile device?

Yes — through your connected Telegram/Discord channel. Your memory is stored server-side (or on nacre.sh), accessible from any device that can reach your agent.

How do I prevent my agent from storing sensitive information?

Configure memory categories with explicit opt-in: "auto_remember": false requires explicit "remember this" commands. The agent won't store information from conversations unless instructed to.