CoPaw: Alibaba's Python OpenClaw Worker Uses 80% Less Memory (150MB vs 500MB)

CoPaw is a lightweight Python-based OpenClaw-compatible worker by Alibaba's AgentScope team. Uses 150MB vs OpenClaw's ~500MB. Integrated into HiClaw's multi-agent system. No Node.js dependency.

Product Idea from this Signal

A memory system that gives OpenClaw agents persistent recall that survives session crashes and context window degradation

49.8k ▲

OpenClaw agents lose their entire conversation history between sessions even when the files exist on disk. Silent daily session resets wipe agent memory without warning. Meanwhile, every frontier LLM degrades past 50K tokens (proven by Chroma across 18 models), meaning even within a session, agents progressively forget earlier context. ByteDance's OpenViking (19K stars in two weeks) proves massive demand for agent memory infrastructure. This tool gives OpenClaw agents a persistent, queryable memory layer that survives crashes, session boundaries, and context window limits by storing structured knowledge externally and injecting only relevant memories per turn.

DEVTOOLCLIAI-AGENTOPEN-SOURCE

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