Building a SQLite Persistent Memory Sidecar for OpenClaw AI Agents

Short summary

The author built a SQLite-based persistent memory sidecar for OpenClaw AI agents to solve the problem of agents losing context between sessions. The key insight is to log at the system level by hooking into tool calls, session events, and errors rather than relying on the agent to decide what to remember. The approach provides a queryable, indexed audit trail that stays under 1MB for six months of logs and eliminates hallucinated memories.

• •SQLite sidecar logs all agent events automatically at the system level, not the agent level
• •Hooks into tool calls, session boundaries, cron triggers, and errors for complete audit trails
• •Agent queries recent events at startup for exact, structured context instead of fuzzy summaries

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