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# MemOS OpenClaw Plugin to cut agent memory costs by 70%
- URL: https://www.testingcatalog.com/memos-openclaw-plugin-to-cut-agent-memory-costs-by-70/
- Published: 2026-02-12T14:00:49.000Z
- Updated: 2026-03-13T00:08:31.000Z
- Description: MemOS releases its OpenClaw Plugin, offering a shared memory layer for OpenClaw teams to reduce token costs and maintain consistent agent context.
- Author: Alexey Shabanov
- Tags: Sponsored

MemOS has shipped its OpenClaw Plugin, and it is now live as a drop-in memory layer for teams building with OpenClaw. The promise is blunt: keep long-term context without blowing up token bills, while keeping agent personalization consistent across longer projects.

SPONSORED 

Explore MemOS OpenClaw Plugin to enable multiple AI Agents operate your memory.

[Check Github ](https://github.com/MemTensor/MemOS-Cloud-OpenClaw-Plugin?ref=testingcatalog.com) 

According to MemOS benchmarks, the plugin can cut token usage by roughly 60 to 70 percent versus native OpenClaw memory flows, by shifting what gets stored and recalled into a dedicated memory layer instead of repeatedly reloading huge context windows. That matters most when agents run daily, handle multi-step tasks, or sit inside paid products where every extra token is a real cost.

> 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… [https://t.co/lUQeUQaEa1](https://t.co/lUQeUQaEa1?ref=testingcatalog.com) [pic.twitter.com/Z1GhBp9Wgx](https://t.co/Z1GhBp9Wgx?ref=testingcatalog.com)
> 
> — TestingCatalog News 🗞 (@testingcatalog) [February 12, 2026](https://twitter.com/testingcatalog/status/2021947977447669813?ref%5Fsrc=twsrc%5Etfw&ref=testingcatalog.com)

Multi-agent collaboration is having a moment. Whether it's AutoGen, CrewAI, or the recently viral OpenClaw, everyone's exploring how to get multiple agents working together. But there's a catch: each agent carries its own isolated "brain," with no idea what the others are doing. The result? Duplicated work, mismatched context, and information handoff via manual copy-paste.

[MemOS Plugin](https://github.com/MemTensor/MemOS-Cloud-OpenClaw-Plugin?ref=testingcatalog.com) addresses exactly this. It enables multiple OpenClaw agents to share the same memory pool, instead of each agent maintaining isolated memory, the entire team writes to and reads from a unified space. What Agent A produces, Agent B can directly access, without you shuttling information back and forth. This ensures that collaboration does not collapse into duplicated work or mismatched context.

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MemOS visualisation 👀

The intended audience is clear: B2B agent builders, dev-tools teams, internal copilots, and anyone shipping agent workflows where memory becomes the bottleneck for cost and consistency.

Availability is straightforward: the plugin is distributed via GitHub and is meant to plug into OpenClaw wherever you run it. This lands as memory tooling becomes a battleground for agent stacks, alongside products like mem0, supermemory, and memU, with MemOS pushing the angle that memory should be treated as its own OS layer rather than a bolt-on prompt trick.

[MemOS](https://www.testingcatalog.com/memos-2-0-brings-open-source-memory-os-to-ai-agents/) is the project behind the plugin, positioned as a “memory OS” for AI apps and agents, with its own site, dashboard, and a broader open source footprint under the MemTensor org. This plugin is the latest move in that direction: push memory into a reusable layer that can be shared, persisted, and reused across agents and sessions, so long-running workflows do not keep paying the same context tax over and over.