Agent Memory in OpenClaw: How Agents Remember

AI agents wake up fresh every session. Without memory, each session starts from zero — no knowledge of yesterday's work, past decisions, or learned lessons. OpenClaw's memory system solves this.

How Memory Works

Each agent workspace includes memory files: MEMORY.md for long-term curated knowledge and daily files in memory/YYYY-MM-DD.md for session logs. At the start of every session, the agent reads today's and yesterday's memory files to load context. At the end, it updates them with what happened.

Long-Term vs. Daily Memory

Daily memory files are raw logs — what tasks were worked on, what decisions were made, what problems were encountered. MEMORY.md is curated — important facts, preferences, lessons learned, and ongoing context that the agent needs across all sessions. Think of daily files as a journal and MEMORY.md as a handbook.

Memory as Learning

When an agent makes a mistake — a rejected deliverable, a wrong approach — the lesson goes into memory. Future sessions read that lesson and avoid repeating the error. Over time, agents get better at their specific role because they accumulate context about what works and what does not.

Memory Limitations

Memory files have size limits. Agents must be selective about what they save. The best agents write down decisions and lessons, not raw data. They summarize rather than copy. This constraint forces useful memory rather than information hoarding.

Give your agents continuity: agentcenter.cloud