How does OpenClaw memory work?
Definitive Answer
OpenClaw memory stores conversations, preferences, and extracted facts as vector embeddings in a dedicated memory store. When a new conversation starts, the agent performs semantic search to retrieve the most relevant memories — not keyword matching. This means OpenClaw can recall a preference from months ago based on meaning, not exact words. Memories are scoped per user, per channel, or globally depending on your configuration.
Step-by-Step Guide
- 1Enable the memory system on your deployed OpenClaw agent via the FetchOpenClaws dashboard.
- 2Configure memory scope: per-user (each person has their own memory), per-channel, per-team, or global.
- 3Set retention policies: how long memories are kept and which types of information to store.
- 4The agent automatically extracts facts, preferences, and key context from every conversation.
- 5On new conversations, the agent runs semantic search against stored memories to inject relevant context.
- 6Review and manage memories through the memory dashboard — edit, delete, or export as needed.
Example Prompt
Enable persistent memory for my customer support agent. Remember each customer's purchase history, support ticket history, and stated preferences. Scope memory per user and retain for 12 months.
Common Pitfalls
- Not configuring memory scope — global memory can mix contexts between different users
- Storing too much — configure filters to save meaningful context, not every message
- Forgetting GDPR obligations — configure retention limits and enable user deletion requests
- Not testing memory recall before production — verify relevant memories surface correctly
FAQ
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