Kimi Work is Moonshot AI's new desktop application that turns your local machine into a multi-agent command center. Rather than a chat window that hands you text to act on, Kimi Work is designed to execute tasks end-to-end: browsing the web, reading and writing local files, running scheduled jobs overnight, and delivering finished documents straight to your desktop.

What's actually shipping

The announcement bundles four distinct capabilities into one desktop app:

• Agent Swarm: Up to 300 specialized sub-agents running in parallel on your local machine, each with its own tools and context. Think of it as spawning a small team of AI workers that divide a complex task and execute their pieces simultaneously, rather than one agent plodding through steps sequentially.
• WebBridge: A Chrome/Edge browser extension that lets the agent click, scroll, type, and extract data from real websites inside your existing browser session. Everything runs locally, so your login sessions and page content never leave your device.
• Finance data tool calls: Native integrations with Yahoo Finance, World Bank, and Binance , no API keys to configure. You can ask for earnings reports, macro data, or crypto prices in plain language and get structured results back.
• Memory system: A persistent diary of your preferences, past decisions, and context that accumulates across sessions so the agent doesn't start from scratch every time.

On top of those, a built-in Cron scheduler lets you set tasks to run on a timer , daily briefings at 6 AM, overnight data processing, recurring reports , with an option to keep the machine awake so nothing is missed.

The engine underneath

Kimi Work runs on Kimi K2.6, Moonshot AI's latest open-weight model. It's a 1-trillion-parameter Mixture-of-Experts model with only 32B parameters activated per token, released fully open-source under a Modified MIT License. The MoE design (Mixture-of-Experts is an architecture where only a fraction of the model's parameters are activated for any given input, keeping inference cost low while maintaining a large knowledge capacity) is what makes running 300 agents locally even plausible , each agent call is cheap because the model activates only ~32B parameters at a time.