The ability of an AI agent to remember information across conversations and sessions, building knowledge over time.
By default, LLMs have no memory between conversations. Agent memory solves this by persisting important information — user preferences, past decisions, learned facts — across sessions.
OpenClaw supports agent memory through its session and workspace features. The agent can store and retrieve information from its workspace, maintaining context across interactions.
Memory comes in different forms: short-term (within a conversation), long-term (across sessions), and working memory (the current context window).
Without memory, every conversation starts from zero. The agent forgets your preferences, past issues, and context. Memory makes agents feel like persistent assistants, not stateless chatbots.
Clawctl manages agent workspace and session data securely. Memory is stored encrypted. Audit trails track what the agent remembers and accesses. Memory is isolated per tenant on multi-agent plans.
Try Clawctl — 60 Second DeployConfigurable. Workspace memory persists across sessions indefinitely. Session memory resets per conversation.
Yes. Clawctl encrypts agent workspace data at rest. Access is logged in the audit trail.
Yes. You can clear agent memory through the Clawctl dashboard or CLI.
Context Window
The maximum amount of text an LLM can process in a single request — including the conversation history, system prompt, and tool results.
RAG (Retrieval-Augmented Generation)
A technique where an AI agent retrieves relevant documents or data before generating a response, grounding its answers in real information rather than relying solely on training data.
Agent Isolation
The separation of AI agents into isolated environments so that one compromised agent cannot affect others.
Human-in-the-Loop
A design pattern where an AI agent pauses before taking risky actions and waits for a human to approve or reject the action.