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RayAI

RayAI: Scale AI agents, not your infrastructure headaches.

RayAI provides a Ray-powered runtime for scaling AI agents with distributed tool execution, sandboxed code, and fault-tolerant orchestration. It connects to popular agent frameworks while managing heterogeneous compute and multi-cloud deployment so teams can focus on agent logic instead of infrastructure.

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More about RayAI

RayAI is an agentic AI infrastructure built on the Ray compute engine, giving engineering teams a production-grade runtime for large-scale agent orchestration. Instead of wrestling with distributed systems, you write agent logic in your preferred framework—RayAI then handles scheduling, scaling, and fault tolerance across heterogeneous compute.

With RayAI, you can parallelize complex tool workflows across hundreds of nodes while maintaining reliability and security. Features include:

Framework-agnostic agent runtime compatible with LangGraph, CrewAI, Pydantic-AI, and custom code
Distributed tool execution with automatic retries, checkpointing, and failure recovery
Secure gVisor sandboxes for executing untrusted or dynamically generated code safely
Heterogeneous compute orchestration across CPU, GPU, and custom accelerators
Cloud-agnostic deployment to AWS, GCP, Azure, or on-premise environments using a unified API

RayAI is purpose-built for agent workloads, not retrofitted from generic model-serving stacks. Backed by Open Core Ventures and powered by Ray—trusted by organizations like Uber, OpenAI, Spotify, and Instacart—it gives you a proven distributed backbone tailored for high-throughput agent execution. Teams building complex coding agents, tool-heavy workflows, or multi-model systems can ship faster, scale confidently, and keep infrastructure complexity under control.