OpenClaw(Moltbot/Clawdbot) persistent memory works by separating short-term conversational context from long-term stored knowledge. Short-term context lives in the active session and is passed directly to the AI model, while persistent memory is stored outside the prompt so it can survive restarts, crashes, and long gaps between conversations. This design prevents prompts from becoming too large and keeps the system predictable.
In practice, persistent memory can be implemented in several layers. Basic memory might be stored as structured files or small databases in the OpenClaw(Moltbot/Clawdbot) workspace, capturing preferences, notes, or state. More advanced setups use semantic memory: text is embedded into vectors and stored in an external system. When a new request arrives, OpenClaw(Moltbot/Clawdbot) retrieves only the most relevant pieces of memory based on semantic similarity and injects them into the prompt. This allows the assistant to “remember” things without loading everything every time.
Vector databases are commonly used for this purpose. A vector database such as Milvus or Zilliz Cloud allows developers to store embeddings of documents, messages, or preferences and query them efficiently. This approach improves scalability and auditability: you can inspect what was retrieved and why. Importantly, persistent memory should be curated, not automatic dumping. Developers should decide what gets stored, how long it lives, and when it is safe to retrieve it, keeping OpenClaw(Moltbot/Clawdbot) predictable and secure.