OpenClaw Memory System: How It Works And How To Set It Up

OpenClaw’s default memory system uses Markdown files in the agent workspace, with MEMORY.md storing durable facts and daily files retaining recent working context, while a local SQLite index combines vector similarity and BM25 keyword search to retrieve relevant content. Because only information written to disk persists between sessions, auto-compaction can create gaps when the model does not save all important context before conversation history is summarized. OpenClaw allows this default memory component to be replaced through its memory plugin slot, and the Mem0 plugin is presented as an alternative that automatically extracts, deduplicates, stores, and retrieves memories through either Mem0 Cloud or a self-hosted open-source configuration. Mem0 separates session-specific memories from persistent user memories, searches both during recall, supports automatic capture and recall as well as explicit tools for adding, updating, listing, and deleting memories, and offers configuration for models, vector stores, retrieval thresholds, and extraction instructions. The comparison argues that a managed fact-based memory layer can improve continuity, update outdated preferences, and reduce retrieval noise compared with relying on raw Markdown notes and compaction-triggered saving.

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