Developer Builds SQLite Memory Sidecar to Give AI Agents Persistent Session Recall

A developer working with the OpenClaw AI agent framework built a custom SQLite-based memory sidecar to solve the problem of agents losing context between sessions. Standard workarounds suggested by OpenClaw's documentation rely on the agent itself deciding what to log, which proved inconsistent and error-prone in practice. The solution shifts logging responsibility to the system level, automatically capturing tool calls, session events, cron triggers, errors, and human messages without agent intervention. The sidecar stores structured records in a local SQLite database that persists across restarts and remains queryable for future context retrieval. The developer shared the core code publicly, highlighting that reliable agent memory requires infrastructure-level logging rather than depending on in-session agent judgment.