Moonshot AI Launches Kimi Claw: Native OpenClaw on Kimi.com with 5,000 Community Skills and 40GB Cloud Storage Now

Moonshot AI has officially brought the power of OpenClaw framework directly to the browser. The newly rebranded Kimi Claw is now native to kimi.com, providing developers and data scientists with a persistent, 24/7 AI agent environment.

This update moves the project from a local setup to a cloud-native powerhouse. This means the infrastructure for complex agents is now fully managed and ready to scale.

ClawHub: A Global Skill Registry

The core of Kimi Claw’s versatility is ClawHub. This library features over 5,000 community-contributed skills.

Modular Architecture: Each ‘skill’ is a functional extension that allows the AI to interact with external tools.
Instant Orchestration: Developers can discover, call, and chain these skills within the kimi.com interface.
No-Code Integration: Instead of writing custom API wrappers, engineers can leverage existing skills to connect their agents to third-party services immediately.

40GB Cloud Storage for Data Workflows

Data scientists often face memory limits in standard chat interfaces. Kimi Claw addresses this by providing 40GB of dedicated cloud storage.

Persistent Context: Store large datasets, technical documentation, and code repositories directly in your tab.
RAG Ready: This space facilitates high-volume Retrieval-Augmented Generation (RAG), allowing the model to ground its responses in your specific files across sessions.
Large-Scale File Management: The 40GB limit enables the AI to handle complex, data-heavy projects that were previously restricted to local environments.

Pro-Grade Search with Real-Time Data

To solve the knowledge cutoff problem, Kimi Claw integrates Pro-Grade Search. This feature allows the agent to fetch live, high-quality data from sources like Yahoo Finance.

Structured Data Fetching: The AI does not just browse the web; it retrieves specific data points to inform its reasoning.
Grounding: By pulling live financial or technical data, the agent significantly reduces hallucinations and provides up-to-the-minute accuracy for time-sensitive tasks.

‘Bring Your Own Claw’ (BYOC) & Multi-App Bridging

For devs who already have a custom setup, Kimi Claw offers a ‘Bring Your Own Claw’ (BYOC) feature.

Hybrid Connectivity: Connect your third-party OpenClaw to kimi.com to maintain control over your local configuration while using the native cloud interface.
Telegram Integration: You can bridge your AI setup to messaging apps like Telegram. This allows your agent to participate in group chats, execute skills, and provide automated updates outside of the browser.
Automation Pipelines: With 24/7 uptime, these bridged agents can monitor workflows and trigger notifications autonomously.

Kimi Claw simplifies the process of building and deploying agents. By combining a massive skill library with significant storage and real-time data access, Moonshot AI is turning the browser tab into a professional-grade development environment.

Key Takeaways

Native Cloud Integration: Kimi Claw is now officially native to kimi.com, providing a persistent, 24/7 environment that lives in your browser tab and eliminates the need for local hardware management.

Extensive Skill Ecosystem: Developers can access ClawHub, a library of 5,000+ community skills, allowing for the instant discovery and chaining of pre-built functions into complex agentic workflows.

High-Capacity Storage: The platform provides 40GB of cloud storage, enabling data scientists to manage large datasets and maintain deep context for RAG (Retrieval-Augmented Generation) operations.

Live Financial Grounding: Through Pro-Grade Search, the AI can fetch real-time, high-quality data from sources like Yahoo Finance, reducing hallucinations and providing accurate market information.

Flexible Connectivity (BYOC): The ‘Bring Your Own Claw’ feature allows engineers to connect third-party OpenClaw setups or bridge their AI agents to external platforms like Telegram group chats.

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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.