YoMo is a serverless AI agent framework with geo-distributed edge AI infrastructure. It enables real-time, low-latency automation for operations teams. Connects to OpenAI, Claude, and other AI models for distributed cloud workflows.
git clone https://github.com/yomorun/yomo.githttps://yomo.run/
Automate real-time weather information retrieval for user queries.
Build AI agents that provide personalized recommendations based on user preferences.
Create chatbots that can handle customer inquiries with low latency responses.
Implement serverless AI functions for processing streaming data in real-time.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/yomorun/yomoCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Deploy a serverless AI agent using Yomo for [APPLICATION]. Configure it to process data from [DATA_SOURCE] and provide real-time insights based on [SPECIFIC_CRITERIA]. Ensure it utilizes geo-distributed edge AI infrastructure to optimize performance.
After deploying the serverless AI agent for the smart city traffic management system, the Yomo framework processed data from real-time traffic cameras located at various intersections. The agent analyzed traffic flow patterns and identified peak congestion times at Main St. and 5th Ave. It recommended adjusting traffic light timings to reduce wait times by 30% during rush hours. Additionally, the agent provided insights on alternative routes for drivers, helping to alleviate congestion and improve overall traffic efficiency. The geo-distributed edge AI infrastructure ensured that data processing occurred locally, minimizing latency and enhancing responsiveness to real-time conditions.
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