Production-ready infrastructure with distributed tool execution, granular compute control, sandboxing, and fault tolerance. Built on Ray—run it yourself or on our managed platform.
Whether you're exploring or scaling, we're here to help
Ray's core runtime has scaled to thousands of nodes for training and serving the world's largest language models. We're adapting this proven open-source foundation specifically for Agentic AI workloads.
Bring your agent code or use our adapters for LangGraph, CrewAI, Pydantic-AI, and other frameworks. Your tools, your workflow—Ray handles the infrastructure.
Parallelize tool calls across hundreds of nodes with automatic retries and failure recovery. Ray manages scheduling, resource planning, and distributed execution seamlessly.
Execute untrusted or dynamically generated code—within secure gVisor sandboxes. Leverage kernel-level isolation without sacrificing the high-throughput execution needed for function calling.
Automatic checkpointing, retry logic, and failover handling. Your agents keep running even when individual nodes fail—no manual intervention required.
Mix and match hardware as needed—CPU nodes for logic, GPU nodes for inference, custom accelerators for specialized tasks. Ray handles the orchestration seamlessly.
Deploy to AWS, GCP, Azure, or on-premise infrastructure. Same API, whether self-hosted or on our managed platform.
Built from first principles for AI agents. While others focus on training clusters and model serving, we've reimagined distributed computing for agent orchestration.
Built on Ray open-source for proven distributed computing
Laser-focused on agent orchestration and tool execution
From engineers who contribute to Ray and ship agents
Part of the Ray ecosystem, purpose-built for agents
Join thousands of engineers already building on Ray.