Build with OpenAI Agents API on Vercel
Vercel pairs OpenAI-managed agent sessions with isolated, persistent Sandboxes, using signed webhooks and queues for reconnection and scaling workers to zero.
The integration runs long-lived, tool-using agents with an **OpenAI-managed loop and session state**, while Vercel hosts the app and attaches each session to an **isolated Sandbox** with persistent files. Signed webhooks and Vercel Queues handle creation and reconnection.
Builders can separate agent orchestration from execution infrastructure: let OpenAI retain the session while Vercel provides code execution, file access, and a workspace that survives follow-up instructions. The worker architecture can scale to zero.
The integration runs long-lived, tool-using agents with an **OpenAI-managed loop and session state**, while Vercel hosts the app and attaches each session to an **isolated Sandbox** with persistent files. Signed webhooks and Vercel Queues handle creation and reconnection. Builders can separate agent orchestration from execution infrastructure: let OpenAI retain the session while Vercel provides code execution, file access, and a workspace that survives follow-up instructions. The worker architecture can scale to zero. The material does not state persistence duration, sandbox limits, retry semantics, pricing, or recovery behavior when either provider is unavailable, so those boundaries need validation before production use.
This makes the brain-versus-hands split concrete across providers: OpenAI owns the durable loop and session, while Vercel owns isolated execution and persistent files. It reduces the custom machinery needed for long-lived agents and idle workers, but creates a cross-provider reliability boundary. Persistence, retries, recovery, limits, pricing, and partial outages therefore become production prerequisites rather than settled properties.