Openclaw Memory: How Live Data Compaction Works In AI

Reliable long-term OpenClaw deployments require active memory management because missing context, irrelevant retained information, and inconsistent behavior often result from memory limits and retrieval settings rather than model quality. Operators should inspect live context with `/context list`, which can reveal per-file truncation at 20,000 characters, a 150,000-character aggregate bootstrap limit, and automatic loading of only the current and previous day’s notes. Compaction summarizes sessions approaching the model context limit after prompting the agent to save important facts, but this preservation step is lossy and can cause older knowledge to decay over repeated events, making concise, structured `MEMORY.md` files preferable to append-only logs. The Mem0 plugin offers turn-level capture, store statistics, direct memory search, and visibility into recalled memories, while `topK` and `searchThreshold` can be tuned to balance recall against irrelevant context. Custom extraction instructions help limit stored information to durable, domain-relevant facts, and regular deletion of stale memories prevents outdated details from competing with current ones. User, agent, and organization scopes isolate or share memories appropriately, while self-hosted SQLite indexes require capacity monitoring and occasional vacuuming after deletions. Common warning signs include repeated questions, oversized memory files, stalled memory counts, outdated recalled facts, and knowledge that disappears between sessions.

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