Meet Kimi Work - a local AI agent on your desktop that does the work for you. 🔹Native agent swarm: Up to 300 AI agents running in parallel on your local machine. 🔹Browser use: Paired with WebBridge extension, your agent will navigate websites in your browser: search, scroll, click, type and complete tasks. 🔹Built for Finance: Native global market data tool call from Yahoo Finance and World Bank - no complex API setup required. 🔹Memory system: Kimi Desktop keeps a running diary of your preferences, past decisions, and context to know you better. Available for macOS (Apple Silicon) and Windows. 🔗Try it now: https://t.co/yhiai2VWIy
Moonshot AI Launches Kimi Work, a Desktop AI Agent with 300-Agent Swarm and Browser Control
Moonshot AI has launched Kimi Work, a local desktop AI agent for macOS and Windows. It features a native agent swarm of up to 300 parallel agents, browser automation, and integrated finance tools to autonomously perform tasks and learn user preferences. This brings advanced agentic capabilities directly to the user's machine for continuous, personalized workflow automation.
- Platforms
- macOS (Apple Silicon), Windows
- Max Parallel Agents
- 300
- Browser Automation
- WebBridge extension
- Native Finance Data Sources
- Yahoo Finance, World Bank, Binance
- Automation Engine
- Built-in Cron engine
- Output Formats
- PPTX, Word, PDF, Excel
Kimi Work extends the agent swarm paradigm by bringing multi-agent coordination and browser automation directly to the user's machine. This enables continuous, 24/7 automation of repetitive tasks using a built-in Cron engine. Its specialized design for finance includes native access to global market data from sources like Yahoo Finance, World Bank, and Binance.
Kimi Work is available for download on macOS (Apple Silicon) and Windows. It can generate reports, analyze market data, and reconcile spreadsheets, delivering output in formats like PPTX, Word, PDF, and Excel. The system includes a memory to learn user preferences and offers an "Ask before acting" safeguard for local file privacy.
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