OpenClaw community adds external memory runtimes as MEMORY.md stops scaling

• OpenClaw users are converging on the same bottleneck: once MEMORY.md grows into a long hand-maintained startup file, recall across sessions gets unreliable and expensive, according to the memory thread.
• Two new community projects push memory out of that file and into external systems: a Reddit user’s memory runtime that normalizes context from Slack, email, docs, model chats, and API events, and Astro for OpenClaw, a self-hosted workspace for notes, prompts, tasks, feeds, and agent output.
• The shared idea is persistence with retrieval, not just accumulation: the runtime post says reads are reordered by agent, context, and intent, while the Astro post focuses on keeping outputs organized, searchable, and reusable after the chat window closes.

The failure mode is concrete. In the OpenClaw discussion, one user says a 200-line MEMORY.md still misses decisions from a few days earlier, daily log files are only useful if the agent knows to search them, and bigger prompts raise token cost while degrading quality. Their wishlist is automatic memory capture, semantic recall, compression, and shared context across multiple agents.

The two projects posted today map directly to that gap. The memory runtime post describes a layer between context sources and agents that converts incoming data into a canonical record with claims, entities, relationships, and temporal state; on retrieval, it keeps the source data intact but changes relevance ordering based on role and intent. Astro takes the more creator-workspace route: the Astro build connects to OpenClaw over IRC, exposes MCP tools, and gives agents a persistent home for markdown, docs, bookmarks, prompts, and task output, with code on GitHub and a quick-start video.