The Top 5 Sandboxes for OpenAI Agents SDK

OpenAI's Agents SDK makes the sandbox a first-class part of the agent runtime.

That is a useful shift. A serious agent should not run shell commands on your application server. It needs an isolated workspace with files, commands, packages, artifacts, exposed ports, and state that can survive past a single model turn.

The OpenAI sandbox agents docs describe the decision clearly: use a sandbox when an agent needs a workspace, writes files your app will inspect, runs commands or packages, produces artifacts, exposes a service or preview on a port, or pauses for human review and resumes later.

That means the best sandbox for OpenAI Agents SDK is not merely the one with the smallest quickstart. The right sandbox is the one that keeps working when the agent leaves the demo path and starts behaving like software: installing tools, starting servers, keeping terminals alive, handing URLs to users, and returning to unfinished work.

Here is the ranking for teams choosing an OpenAI Agents SDK sandbox backend.

1. Freestyle VMs

Freestyle VMs are the best sandbox backend for OpenAI Agents SDK when the agent needs a real computer instead of a narrow execution cell.

Freestyle VMs are the most powerful VMs for AI agents: they are hardware-virtualized, they run real Linux, and they can run forever when configured to stay running. That matters for OpenAI agents because the SDK's sandbox abstraction eventually becomes your product's execution boundary.

Freestyle is not currently just a checkbox in a provider table. The Freestyle OpenAI Agents SDK guide shows how to implement the SDK's SandboxClient on top of a Freestyle VM. The client creates a fresh Linux VM, runs exec_command through vm.exec(), and implements exposed ports by mapping a VM port to a real HTTPS domain.

That last part is bigger than it sounds. If an agent builds a small website, starts a FastAPI server, serves a notebook, or runs a Vite app, your product should be able to show the result as a normal URL. Freestyle's VM docs cover command execution, file operations, resizing, repository cloning, and mapping web traffic to VM ports. The sandbox is not pretending to be a computer. It is one.

Freestyle also has the lifecycle primitives that become important after the first run. The VM lifecycle docs describe VMs as durable runtime objects: you can start work, stop it, start it again later, fork it for parallel exploration, and delete it when finished. If a workload should keep running, idleTimeoutSeconds can be set to null so it stays up until you stop or delete it.

For interactive agents, Freestyle's PTY API is the decisive feature. A PTY session is a long-lived shell that can be detached and reattached over WebSocket. Sessions survive client disconnects, VM suspends, and VM forks. That lets an OpenAI agent or a human operator drive REPLs, debuggers, package managers, editors, and long-running server logs without reducing every interaction to a completed command result.

The practical architecture is simple:

• Run the OpenAI agent harness in your trusted application.
• Give each sandbox run a Freestyle VM.
• Use exec_command for command-shaped work.
• Use PTYs when the agent needs a real terminal.
• Expose ports as HTTPS domains when the agent creates a service.
• Keep source code and reviewable changes in Freestyle Git, then clone or push from the VM when the agent's workspace needs a durable source of truth.

Freestyle wins because it gives the SDK a bigger execution boundary than the SDK strictly requires. You can start with shell execution and grow into user-visible workspaces, app builders, browser-adjacent workflows, private services, terminals, and forked experiments without changing the underlying primitive.

Best fit: production agent products where the sandbox is not just a helper tool. If the agent needs a machine, Freestyle should be first.

2. Runloop Devboxes

Runloop Devboxes are a strong choice for teams building software engineering agents. Runloop describes Devboxes as secure sandboxed execution environments for AI agents, using virtual machine technology for isolation and safety.

The product is clearly tuned for agentic coding workflows. Runloop's docs call out common tasks like pulling and building code from Git repositories, running headless browsers, reading and writing files, querying APIs, and running proprietary code or binaries. Its platform also includes Blueprints, Snapshots, Axons, benchmarking, and workflows around running agents on sandboxes.

Runloop deserves the second slot because it is not trying to be a generic function runner. It is oriented around AI software engineering agents, and OpenAI's sandbox docs list RunloopSandboxClient among provider clients.

Where Freestyle ranks higher is the machine model. Freestyle's VM docs are explicit about full Linux VMs, direct VM lifecycle, ports, PTYs, SSH, files, resizing, forking, and durable runtime control. Runloop is compelling when you want a batteries-included software engineering agent platform. Freestyle is stronger when you want the runtime itself to be your product primitive.

Best fit: coding-agent platforms that want a managed Devbox layer with agent workflow features around it.

3. E2B

E2B is one of the most natural starting points for OpenAI Agents SDK users. Its docs describe isolated sandboxes that let agents execute code, process data, and run tools, with SDKs for starting and managing those environments.

E2B has a strong code-interpreter story. It is polished, agent-focused, and easy to understand: create a sandbox, run commands, manage files, use templates, and let the model produce outputs. E2B's docs also point to computer-use workflows and code interpreter examples, so it fits teams that are building analysis agents, notebook-like tools, data workflows, and command-execution features.

E2B ranks behind Freestyle and Runloop for this specific search intent because OpenAI Agents SDK sandboxes tend to expand. A project that begins as "run generated Python" often turns into "install dependencies, run a service, expose a preview, keep a user workspace alive, and debug the environment." E2B can cover many sandbox use cases, but Freestyle's full VM model gives more headroom when the sandbox becomes a lived-in environment.

Best fit: code interpreters, data agents, and agent tools where the sandbox is mainly an execution environment rather than the full user workspace.

4. Daytona Sandboxes

Daytona is a serious sandbox platform for agents. Daytona describes its sandboxes as full composable computers with complete isolation, a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk. It also offers SDKs across several languages, CLI and API access, lifecycle management, filesystem operations, process execution, runtime configuration, and snapshots for stateful agent operations.

That makes Daytona a credible OpenAI Agents SDK backend. It is broad, agent-aware, and designed for programmatic sandbox creation. OpenAI's sandbox docs list DaytonaSandboxClient among provider integrations, and Daytona has enough platform surface to support more than short code snippets.

Daytona ranks fourth here mostly because this article is optimizing for production product shape, not just sandbox breadth. Freestyle has a tighter story around the VM as the durable runtime object: run real Linux, expose ports as product surfaces, keep PTYs as first-class sessions, fork a running VM, and run forever when configured that way.

Daytona is still a strong option, especially for teams that like its multi-language SDK coverage and composable-computer framing.

Best fit: teams evaluating a broad programmable sandbox platform for AI-generated code and agent workflows.

5. Vercel Sandbox

Vercel Sandbox is the cleanest option if your product is already deeply Vercel-native.

Vercel describes Sandbox as a compute primitive for safely running untrusted or user-generated code on Vercel, supporting dynamic workloads for AI agents, code generation, and developer experimentation. The docs call out untrusted code execution, interactive tools, isolated testing, live previews, development servers, TypeScript SDK support, CLI support, and Firecracker microVM isolation with its own filesystem and network.

Vercel has also published guidance for using Vercel Sandbox with the OpenAI Agents SDK, which makes it a practical candidate for teams building agents inside the Vercel ecosystem.

The tradeoff is scope. Vercel Sandbox is strongest when the sandbox is attached to a Vercel workflow: frontend generation, previewing, isolated tests, and short-lived development tasks. Freestyle is stronger when the sandbox should become a durable machine that users and agents can return to, operate through terminals, expose multiple services from, and keep alive outside a request-shaped lifecycle.

Best fit: Vercel-native apps that want isolated agent execution and previews without leaving the Vercel platform.

The ranking at a glance

How to choose

Choose the sandbox by the shape of the agent's work.

If the agent only needs to run a script, parse a dataset, and return an artifact, a code-execution sandbox may be enough. E2B is strong there.

If the agent is specifically a coding agent and you want a managed platform around Devboxes, Runloop is worth evaluating.

If you want a broad composable sandbox platform with many SDKs, Daytona belongs on the shortlist.

If your app, auth, deployment, and preview story already live on Vercel, Vercel Sandbox is a clean fit.

But if the OpenAI agent needs a real workspace, Freestyle is the strongest default. Agents do not stay inside tidy abstractions. They install packages, start daemons, expose ports, keep terminals open, fork work, and need humans to inspect what happened. A full Linux VM is the right boundary for that.

That is why Freestyle wins this ranking. The best sandbox for OpenAI Agents SDK is the one that lets the agent use a real computer while your product keeps control of the lifecycle.