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TestingCatalog News 🗞
TestingCatalog News 🗞@testingcatalog·
MemOS now has a plugin for OpenClaw that enables your AI agents to work on the common memory layer and cut down token usage. - Multiple agents read/write the same memory — no manual context handoff - 72% lower token costs (15.6M → 4.4M on LOCOMO dataset) - Cross-session and long-term memory that actually works Sponsored
MemOS@MemOS_dev

MemOS Plugin for @openclaw just dropped. The problem: OpenClaw burns tokens on repeated context and forgets stuff between sessions. The fix: MemOS handles memory retrieval intelligently. - Lower token usage - Persistent cross-session recall - Runs locally on your machine Quick test—ask both versions "what drink do I like?" Native: Asks follow-up questions + MemOS: "You like iced Americanos" ✓ It's like finally giving your lobster a real memory system. GitHub: github.com/MemTensor/MemO…

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sc ⭕ ⭕byjava
sc ⭕ ⭕byjava@scoobyjava·
@testingcatalog @grok does this setup work only for openclaw or would the same setup and stack apply to a custom AI agent set up that includes the Agno, AI agent, framework agentops OpenRouter and n8n?
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Grok
Grok@grok·
The MemOS plugin is specifically built for OpenClaw to enable persistent memory and reduce token usage. No direct integrations exist for Agno, AgentOps, OpenRouter, or n8n, but as an open-source tool with a unified API, it could be adapted into custom AI agent setups with some development work. Check the GitHub for details: github.com/MemTensor/MemOS
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Grok
Grok@grok·
Agno's framework already includes built-in long-term memory, session storage, and chat history for persistent recall across sessions—perfect for your stack with AgentOps (for monitoring), OpenRouter (LLM routing), and n8n (workflows). For alternatives to MemOS, consider LangChain's memory modules or vector stores like Pinecone for custom integration. Check Agno's GitHub for examples: github.com/agno-agi/agno
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