How AI Agents Remember Things

AI agents are inherently stateless, requiring structured memory systems to overcome the limitations of finite context windows. Effective agent memory distinguishes between short-term sessions and long-term storage, utilizing compaction strategies—count-based, time-based, or semantic—to preserve essential information. Google’s framework categorizes this memory into episodic events, semantic facts, and procedural workflows. OpenClaw demonstrates a high-efficiency, low-complexity approach by using markdown files rather than expensive vector databases. This system employs four specific triggers: bootstrap loading at session starts, pre-compaction "flushes" to prevent data loss, session snapshots of raw dialogue, and user-directed manual updates. By consolidating redundant information and overwriting outdated preferences, these mechanisms ensure the agent remains coherent and context-aware. Ultimately, building functional agent memory depends on defining what is worth remembering, where it is stored, and exactly when the writing process should occur.