PrivateClawd is a platform for deploying and managing private OpenClaw AI agents in the cloud.
Instead of running AI agents locally or managing complex infrastructure, PrivateClawd lets users launch autonomous agents in under a minute. Each agent runs on its own dedicated virtual machine and operates independently with full browser access, code execution capabilities, and messaging integrations.
The platform is designed for founders, developers, marketers, and operators who want AI agents that can perform real tasks continuously without manual supervision.
Users can deploy agents through a simple step-by-step wizard. The system handles all infrastructure automatically, including containers, networking, storage, and runtime management.
Each agent can be configured with multiple capabilities such as:
• Web browsing and automation
• Code execution and scripting
• Proxy configuration
• File workspace and storage
• AI model selection
PrivateClawd supports multiple leading AI models through OpenRouter, including Claude, GPT, Gemini, DeepSeek, Groq and others. Users can either use platform tokens or bring their own API keys from supported providers.
Agents can also connect to communication channels such as Telegram and WhatsApp, allowing users to interact with their AI agents through messaging apps. This makes it possible to run AI assistants, research agents, automation bots, and operational AI workers directly inside chat environments.
The platform also includes a skills system with built-in capabilities for tasks such as content creation, SEO, analytics, marketing automation, strategy, and growth experimentation. Users can enable skills during agent creation or add custom ones later.
Every agent runs 24/7 on dedicated infrastructure and remains isolated from other users, ensuring privacy and security. Files, configurations, and logs are preserved even when agents are stopped or redeployed.
PrivateClawd is built to make autonomous AI agents accessible without requiring DevOps knowledge or infrastructure management. Users can focus on defining tasks and workflows while the platform handles deployment and runtime operations.
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