intermediateCommunityQuiz
Agent Memory Retrieval in Practice
Practical patterns for implementing two-tier memory systems in AI agents — semantic search, working vs long-term storage, context overflow prevention, and OpenClaw memory commands.
Commands
$ openclaw memory search --query 'user preferences' --limit 5
$ openclaw memory update --summarize --session-id SESSION_ID
$ openclaw memory list --category preference
$ openclaw memory add --fact 'User prefers Python for backend'
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Two-Tier Memory in Practice: Retrieval Patterns That Actually Work
Agent Memory Retrieval in Practice# Agent Memory Retrieval in Practice Effective agent memory architecture separates **working memory** (active session context) from **long-term memory** (persistent cross-session storage). Getting retrieval right means knowing when and how to use each tier. ## Working Memory vs Long-Term Memory |
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