The two "last mile" gaps in memory-core
The built-in memory-core has two gaps that cause your agent to "forget":
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Recall is unreliable.
memory_searchis a tool the LLM must decide to call. Even though it's marked as "mandatory," the LLM doesn't always follow through โ your memories are right there in the database, but the LLM just doesn't look. -
Capture is delayed. Memories only get saved when you run
/newor when tokens are nearly exhausted. Close your terminal too early? Your preferences and decisions are gone.
So I fixed both
memory-core-plus is a drop-in superset of memory-core that adds two automated hooks:
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Auto-Recall โ Before each LLM turn, automatically runs a semantic vector search using your message as the query and injects relevant memories into context. No tool call needed โ past context just appears.
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Auto-Capture โ After each agent run, an LLM subtask extracts durable facts, preferences, and decisions from the conversation and appends them to
memory/YYYY-MM-DD.md. Every turn is captured instantly.
End result: a closed-loop memory
What you say today is automatically recalled in future sessions โ no manual steps, no forgotten context. One command to install, both features enabled by default:
openclaw plugins install memory-core-plus
openclaw gateway restart
๐ ClawHub: https://clawhub.ai/zhoulf1006/memory-core-plus
๐ฆ npm: https://www.npmjs.com/package/memory-core-plus
โญ GitHub: https://github.com/aloong-planet/openclaw-memory-core-plus