SkillsUpdated Feb 26, 2026openclaw memory system
OpenClaw Memory System for Persistent AI Context
An AI agent without memory forgets everything between conversations. FetchOpenClaws Memory System gives your OpenClaw agent long-term recall with semantic search, user profile building, conversation summarization, and fact extraction. Your agent remembers preferences, past interactions, and learned context — making every conversation smarter than the last.
Audience
AI developers, product teams, and businesses building personalized agent experiences
Use Case
Build agents that remember user preferences, past conversations, and learned context to deliver increasingly personalized interactions
Workflow
4 steps · 5 checks
Workflow
- 1Enable the memory system on your deployed agent with a single configuration toggle.
- 2Configure memory scoping rules: what to remember, for whom, and for how long.
- 3The agent automatically extracts facts, builds profiles, and indexes conversations.
- 4Review and manage stored memories through the memory dashboard — edit, delete, or export.
What You Get
- Personalized interactions that improve with every conversation
- No repeated questions — the agent recalls user preferences and history
- Semantic recall that surfaces relevant context even from months-old conversations
- Full control over stored data with GDPR-compliant memory management tools
Key Features
- Long-term memory storage with semantic search and vector-based recall
- Automatic user profile building from conversation history and preferences
- Conversation summarization that compresses long histories into retrievable context
- Fact extraction and knowledge graph construction from user interactions
- Memory scoping: per-user, per-channel, per-team, or global agent memory
Common Questions
User Feedback
Feedback from teams using this tool in production.