How to handle memory in OpenClaw

Memory management is critical for a high-performing AI agent. In OpenClaw, context size limits and token costs must be balanced against conversational continuity. Here's how Clawz.io manages memory across isolated deployments.

Understanding Retained Context

Unlike stateless APIs, OpenClaw agents need dynamic memory access to keep track of user preferences, ongoing tasks, and historical dialogue. Clawz handles persistent storage across the cloud so your VPS instance restarts don't wipe out context.

• Short-Term Memory (Session): Kept in RAM during Active State. This is usually managed by the OpenClaw library itself and is optimal for rapid back-and-forth messaging on Telegram or Discord.
• Long-Term Memory (Embeddings): Offloaded to local SQLite databases that are periodically backed up by Clawz. When the context window gets full, older interactions are embedded and safely stored, prioritizing relevance over recency.

Vector Databases and the Clawz Infrastructure

Currently, on Clawz-managed VPS instances, vector memory functionality runs via lightweight alternatives to reduce DevOps complexity. Future plans involve integrating cloud-hosted vector solutions directly out of the box. Each agent has its own environment variable sets (e.g., `SOUL.md` or `.env` files) populated during provisioning, allowing you to pass down config needed for memory management seamlessly.