Run agent tools, evaluations, and training workloads in isolated environments on CoreWeave.
CoreWeave sandboxes provide isolated compute environments for running untrusted code, agent tools, evaluations, and reinforcement learning (RL) workloads. Create an environment, run commands, work with files, and collect results through the Python or TypeScript software development kit (SDK).Start on serverless capacity that CoreWeave operates, with no cluster to provision. When you need control over placement and policy, you can also run sandboxes on your own CoreWeave Kubernetes Service (CKS) clusters.
CoreWeave Serverless sandboxes are in public preview.
Configure execution. Choose your image, CPU, memory, and lifetime with sandbox configuration, or create a multi-container sandbox from a supported Docker Compose file.
Save and reuse files. Capture a working directory on a scratch volume with file system snapshots, then restore it into new sandboxes.
Attach storage. Use volumes for scratch space. On CKS, registered volumes let sandboxes share existing storage.
Expose a service. Give a sandbox application a public address with public endpoints.
Use GPUs. Request GPU sandboxes on serverless or CKS capacity. GPU sandboxes require separate access. Use serverless GPU sandboxes for untrusted code. GPU sandboxes on your own CKS cluster aren’t virtualized and should run only trusted code.
CoreWeave operates serverless capacity and sets its policy. Serverless is the default. Follow the serverless quickstart to choose a credential and start running sandboxes.CKS uses clusters in your organization. Your administrator enables a runner and configures its sandbox policy. For deployment and scheduling details, see Use your own compute, architecture, and CoreWeave SUNK integration.